From 7adf78c65330a8d04e2bad4021eee89bc4ae516b Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Thu, 6 May 2021 11:42:51 +0200 Subject: [PATCH 01/19] MPX --- XCS_Experiments/TODOs.ipynb | 41 + XCS_Experiments/XCS_compared_Maze.ipynb | 1013 +++++++++++------------ XCS_Experiments/XCS_multiplexer.ipynb | 247 ++++-- environment.yml | 1 + 4 files changed, 681 insertions(+), 621 deletions(-) create mode 100644 XCS_Experiments/TODOs.ipynb diff --git a/XCS_Experiments/TODOs.ipynb b/XCS_Experiments/TODOs.ipynb new file mode 100644 index 0000000..d8b4f43 --- /dev/null +++ b/XCS_Experiments/TODOs.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "applied-topic", + "metadata": {}, + "source": [ + "# TODOs\n", + "\n", + "## XCS\n", + "\n", + "### Bugs\n", + "- There are duplicate classifiers in population. Please ensure that classes implement `__hash__` and `__eq__` methods. It should be unit tested as well\n", + "\n", + "### Enhancements\n", + "- Perform `assert` statements to ensure perception is in valid type" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/XCS_Experiments/XCS_compared_Maze.ipynb b/XCS_Experiments/XCS_compared_Maze.ipynb index 8130440..e323ea5 100644 --- a/XCS_Experiments/XCS_compared_Maze.ipynb +++ b/XCS_Experiments/XCS_compared_Maze.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 87, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 88, + 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[0.39836]\n", + "###0#### => 0 [0.32995]\n", + "0##10001 => 2 [0.66394]\n", + "#111#010 => 0 [0.48421]\n", + "#####1## => 0 [0.32151]\n", + "1####### => 6 [0.06619]\n", + "1####### => 7 [0.09550]\n", + "1####### => 3 [0.09507]\n", + "1##01101 => 2 [0.69134]\n", + "#######0 => 2 [0.47853]\n", + "###1#### => 4 [0.08260]\n", + "##0##### => 2 [0.29018]\n", + "##0##### => 4 [0.08970]\n", + "#0####00 => 2 [0.05478]\n", + "#10##0## => 2 [0.07788]\n", + "0##1000# => 2 [0.09964]\n", + "###0###1 => 0 [0.13767]\n", + "#01##### => 2 [0.08614]\n", + "#100#### => 2 [0.06815]\n", + "#####0## => 5 [0.08743]\n" ] } ], @@ -633,22 +562,22 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 96, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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my3jYYs1fE1GInuFB+IJZFh0i6u7h5RXto7LC1y1Mx7Y1+PMmpfmLzwd91qKXzE12zoR2V5tCtZhPR5OvUjrelPwS80m14UMizLeKJN6KsF7z8uT2MUfPjaoX/jZhTlDb0ajcOfiz0NlcGijcw1cn1PPhjx/iW8Y3PQBRYAF/wWHTcMfZ+3Gv+UX7dDTV4Wun7OPip7SM8w+fjgWTXv7//IkUz+7D2S9aTFvvKyaielbFI4720YTUYDcgxHUGPs8sYvL9UeHJLfj8hbDzq8BtQ13Sdy/coIt5ZLipr0uitUEtmW22v22NdXzBisJ7Jt7Y3czT9XXIW5LC5QpnzZj7qntm4Vp9kOl3rPlrwmbkQpDFWe6HSkXnTSFLyYTDV+V6iT069wXijTCiTIjE/Mus8FUN4ySJMjrwGidez0fV/6ISiRaFSLUowKTTOfu9bQNVL/xlYGLIBqGh+5ltC/w9fMl1nPlVFWZ+E1vQfXBl+EmQx0Ixn3z+sgI3iFKgC3czKUsO33KBKDo8yfBhcjOX2OYfAFH9XBQ6Li3ahb3gdnZyzQouU08+sZtau1Fw2iW8NH9BHXFIJ/GPi9rjnbcKAw3xo0/ch9F6r8J6z8N2+MbbOFYAAoV6FtAxavXXqsVbJJQ9w8DUTVzZXz9fghJVPXiZRPwcviXmLMGxqD1Tz5Zv8yfucVD6wt3KPBQE8xB/rUURJkMzM5p/bPaxBhMDK9Dm1BEY2O7xxcsN4z6VN/uo+zk8eVCixqPvXyZB4lBVmVBPL7r58gKB6U/GCPy0dm26wUlYQ5R5kwH/kWWkynBs9rGHICsPece266q2oe7w5dFy2/x9DThG+LCBBIn5kFnkJXLgCo8tiCVRBkhPKG+pKUlXhQdNZGz+ERg8kjCqqMdmnxg60H1dvLZxBPS3LLT9+soIiETCS/MX0JXS/P3PmxKO9s1nfArlCkyQaSssxcKew5d/jhCbfSIP1bFnWztUhd8K3wJnXy76hU+r+HyUNtf2TAstCvXMe7uFTk+RYCw8H07/jZgxfRQAbh2L/YvA0JGG6RW+thALfwTQkCVWdVplwI9sLipHrQFuYjcXjdxqX01TQjmRifbhX1PN7SMz+dkw73HnZoPKhJQ5K0RpLNNUFMZWHoY2c0FW8w/OEQ+x8AcMOcMCOHyDNx+YbuEKXzmHrzQfIdn85YREkfrugjBKgwp+uCi8Zyr+EfOoJC250hB2ztDMNo4VbPYhoiQR/ZOI7nf+7ySi3xLRC85vRxh8mEYgh6/AZGAS2kKXZ3/k0B1W3PPWD0E0HNmqXpq/COLdrFxlJGz+psA3ybjHU0D6IrqCJ7tk+ij87ZOH23P4Rkyv94NRs49hem6EpflfCGCl6/9LATzEGNsdwEPO/2VDpQ2uciDIJ3/U7q6If9FLJhPaKnLslicNsl1lohjtTWmMkcjaaqPt/PWQ/CkSZXSEtdDhSxUs/IloPIClAG53nT4ewB3O8R0ATrDNh20oDz1yaeammck1oUfZbxGRn82/HGYH2SbJQ/MXxVOrhnS6yScEE0EQ+Nr8jTh81a9bG8dR0x58YHaRV2Wbfb4K4BIA7v1oRjPG1gCA8zsqBD6E0Blco0bUB3aA5RK7WXP4ltKf0NEk4iZfz4MWkBdoIrOPV11beNvYVumyoknR7yXz6obY+cufFEzD5GKyMNagqEJmo59qQ3aRV0Vq/kR0LID1jLEnNOsvJ6IVRLSit7fXMHfBUJwTPsjYk9HQv/7euQFayOOA3UfiKyftrVzPz779/ffzc+SrQX+UX7hkD6kJmOCRKsAnvQPvSu5ItMLXlyN16GjlptvVWmim264dslqIN3ORx2IAxxHRKwB+DOAwIvoBgHVE1AMAzu96XmXG2K2MsfmMsfnd3d3WmNQZXC31qVAjfPbqGRGgrcLW9p7Q5l1eZIAsoucek+2NaWF7YcBr68Ji6K7Gzi664dERJXATZfsMAj+Hb2D6OZ3TXhuqsL+wzRyMLvBFhZp9GGOfZIyNZ4xNAnAygIcZY6cBuA/AGU6xMwDca5MPP4RhIw1aV4e8Ck9u7YJr9tGkK6ZoHlKtkE5WT3/KIprlWMxnq51ymYOisDhQBaYcvlm/YKVq/iJcB+AIInoBwBHO/2XDxM5m5TqiVazy9cOxBQtaV6+hYGqI+rsq4m9Y4PGl3C8Jn5uo/zY0fx7CdvgKagVvOHTK5mE8q6cxaoUITfgzxh5hjB3rHG9kjB3OGNvd+X0zLD54OG7OWHSPqFeuJ1jXow5B3ZP3neBqS0NgO4RLJioOKeZznZe1UlYjkWXdlobjBiFYPn8vuvnjcAS+DC9a9QWBDKaGuxVEKNRTjy7PzOaYfeLcPvZABOw3uVOxDhX9H6B9wZCa0CmKzJGkK4pA0eAn0i++g+L+9ghiz4X+Xr98/iWTKN/JK+LJ5n0r/AqxZfbxntTsftlEddRxYNrha45cAWLhD30bqQVlP7JQMSsUh5iWq69cMyqRUJDI5PMXTYIyZp+yPnSFtosd2xokjMKWT8wWzGX1VPvCVkUs/DVR/KwCTQSWHb4lvPo06DUQAbdA8976MQwzjhvFk7iMNu6GamI3N4QO34JjizZxv8lZ81mIONbJ+qkLfuxReSAX6mlu4MfbOFqG7hdlkEVeMgph0C9d0SvDlQ0KAzbLl5+ZJF8+Kq9u1o6qWEcgWGWeYXiJ3dy2+WBtitZCROgxlkCXtxveszfGdzQa5cXwXi7WtKhY+COYVh2ERp6Wf22dwV1sfvGC3/DyE3RBBM60US3adb0g6rfQ4euXz1+yLZljmzDZThAFxyQPtppeOKVLSbba+7rhnyPE2zjWJMKI15a9Xrh3geJXjuT1oApOMVui1NRCs0+A9kSbtlRiegep9rg8lO/TQN9vZ55nsyt8K3SRV6VAP4zSO/rBv75eXfk2/NuWvc4TLgULw0SeUAnkEsUZHuTCaCeh5u9Nh6jQ+iyT3tlGYjc/BG6GJHgNUc4TomU69IOew1egqCB2+EYetlM96NDPj6diG7wyKaFwU+ODj3QqMwwHh4KN8uJm+Nqpl+bPb1/Gbi92+Iak+buPDQpKFUqVI571EbbDlygW/laha/M3Zc8Va6j6NFUh1OI550wn9WqsSwIA+gaH9IlwIBKCYpu/gE7Rb8kFlN/ObxJSLIcYySUVDad5n6P/fOJon8gh8Jd1AQE7Dt8sXXWTFO9cqZlKGBev1hzq6zLDsG9g2KekN2Sc8F5hg1IrfCUmaqHN36KgMe1byE14CkxHX5AGh0wXtcw+vHOU1fxjm781RHXQmp1g/M/L0vKbHFTbq09lNP9+w5q/iCV1zZ8/ibr/FdFMCCYF01DUJQw3aLkpmWgrXdqa9TxheBtHW4iFPzRtpBTQDh6gblDw+uvWLnQWgfnR90Jj2hH+ATX/Ej545zxs/qK3VmzS8dfwgzjCdRF0bQERhSrcqw06gQuiCLvY5h8GgoYZWnb4BqGrSp8vNN1mhcyxn5+guLwIDY7DN7jNv9ixLXDCihy+PmPAu4+iYzNPd79J3rmnbIV6qjl87YxkGaphKVBhb+aSiEM9o4eS9L6BHL6WXhphBEopfBd5BeZGjIa68DT/zHlFs4/EfZQxsQV6zGGaWWQKhZy6wxoi/oUTa/5hIOxBoBjBECQ8UzUDqV+0j1dbxcf8E4Voa6wDAIxu5WfhlEWJTV5o8+ef93Ngy+aYEU4EEnWFNH2vm3Usi/wcnnXKKEjD2yjHH3qbuZRSzljfSHqvbFWkrFCtAchElkjTCsSJTnt+Nn3OQOT8Y2pI9rQ34BunzsX+U7oAAA9ccABef2snln9fa+vnHPghqR5ZPQUdEk0WYmEnMjcFMA2qCOEwRlTENeZywqiZhux9ZMXCPwAKP/vV3gYZTS1wgi6DL2iCIwFFNn/ViZFAOGZWT+7/t41t09pcp4SusuYvopPXgsUmILvS0H/C5h9rteXpFK8uRD69A2BN+sdmH00UD5lgmr9/7UA7eZXQ4hT2c3YKjk1AFOmgTEeyjG56h1Jaag7lQGNESfM3ifLPAlKyr/xsWkEmt48dxMLfQVdzWrmOrRW+WV6a6+18mOk4fHko/LwV3wDf0FGNtmWgOmH62/yL6QuOOTTaGuusrAIPXphTXaZQlTh8bSyiYhB/Xaogk0vK3iKv2Ozj4JNHT8e2/kH87B+vS5UPGqHjVf1/jtoL00a14PDpo4B7nPIB2ihpS8d5LIhp55ZVJ89pT6dOkWNbla7EEl+dvt1y2ly8bWxbwHBgBbOPdiulNKLg8JURfmEp/rKhnmQoTIcs2vxjzd9BYzqJUxdMVKojWsbPw7yJHUq8nLZwYqS+ZHUihHLXAtLWhSpdoexXPV/U8FEzezChsyk0zT+4zV+CQJQGZ+TAjN2ejOZviFgRYuGvCdmHK2P/Fb5rtoSilj2dU0cxLl6Ftq2vB6++izdz8aaZocs/Dgui/QT06QXjwSRkhJ9u26q1ZMpnNH8dbkrbihd5VQG8xoKcw1ejzazZp4g+j5bfp7WfWSHQWDckM0qtW2qEf/7kGwK6AvtZiFK+XAnWYgVfD8aiiMxYj7iIbf6akA2HI2QUZL+y9563GG/t2FVUN6hfQRCNokMrEB/2aHuB53QLMomWnBeE68qs9s2ipT6Fbf2D/jwEvK6CqDl8ZZrS7b+NrxWZ910G2UVeZRP+RLQV/PvvOKJZq3Guqggix09xaoi9J7R704m4DhYkpTB/daOGaapE9Tdzz8j1K/WVJgoB5Zw/auYY3PXEan+aKjZ/+aIeNHRMg3ZgS/jpQHYzF1Nzio1opCx8hT9jbISVliscqi9HkJTHuhBFbHCzevrR4mi2coGeEpEqPm3rwpR5SrhLl4KGHxQqTvPA7cvQClEXsWXztgUGc8pa1nJgA8pmHyIaBSCXhIUx9qpRjioFAT1FhTZ0NeEi3aSEc9IKFBvgO2YNsGEy5ALepr5UIuM+a3XyFJnmxz+81v0lWS6Hb+BmtaHbtqpwDdfhG5GUzkR0HBG9AGAVgD8AeAXAr+ywVT2gol9tOjovo8KkEnSAeYZ6cq6dMGdssAYl2uFq/lr30f/8mLYGXPGOGfjOmfOV6QThwTaiYG6MktlHFub0jmhE+3wOwEIA/2GMTQZwOIA/W+GqAhBG/pPyv3Z58PL4BLFFunP5cDV/Iw4zQ5/eDp1Sl0LhmTMXT0ZPW6OHOUifH5V1FoG/GGVGHufRR2GiUIWNdzizwtfM11ckNH8AA4yxjQASRJRgjP0ewBw7bFUPRKtsiVPGk06AtktpmRnxhTZ/4h5n/ufwYDguXbZdnTsZDZEmz4WROP/AFKoTsg5fY5GeFA2b/yYiagHwRwA/JKL1APxj1KoUBDVNrhxakcjhq8OKjI9CBQV729pyeBt8ATO/xD1fWt7UtOPfFo+2SV9RFBy+lQhzt8deqKeK5n88gJ0ALgbwawAvAXiHDaZsI50Kb21bEGeuvR2+Ss/p2BVlUzrzboG/MDOhvZbSsOZEL5gc1epKtWuprJiIxhdSGScEXQGpHrUnWc6E2QdZJanMNn/G2HbG2BBjbJAxdgdj7CbHDCQEETUQ0eNE9BQR/ZuIrnTOdxLRb4noBee3I2hHVDC1uwVn7K+Wx6cYyikMAnt89evI8Kry8piKZMjTM6Wi+/yvSzZ7HwOSt7qZi6UvqXIK9CxsxbnbAi/aR3d/CiJguFyaPxH9yfndSkRbXH9biWiLT/V+AIcxxvZGxj9wFBEtBHApgIcYY7sDeMj5PzSY88QrlDUYZSJf35z5Icj7x+XDT1M2MsFYIStoyz2Z2aWvc10FUs+a6/CtQFhwqA4zVuLw1fXZZVb4lm+R1wHOr/JiL5bhepvzb53zx5AxIR3inL8DwCMA/keVvi7MaK5qBUtfTtXPTYNWREPSSTULphvul8OW4m8i4iJDN0Mn+zLmzlt2qBfQVCBZbQ7fytL7Hc3fEC2bDl8psw8RJYjoGZ0GiChJRE8CWA/gt4yxxwCMZoytAQDnd5Sg7nIiWkFEK3p7e3WaF/AUHp1yvkSmzBVuWrrXS8pr8JDF2Ysny7XB9TWYfCLhPd0wQz2lEOZnlQSiYJ7KgsFUeofsZi4maJVCSvgzxoYBPEVEu6k24PgJ5gAYD2A/IpqpUPdWxth8xtj87u5u1aaFKEfK29IUC/lj1e0DpdtUKGtyfHn1lXdO1TF7nGCBmG0HeXbFpVeZ4uNKRZT6YNPkr9pP2c1cit8+7RXIVEazjws9AP5NRI8D2J49yRg7TqYyY2wTET0C4CgA64iohzG2hoh6kPkqCA3mzAzyhGyuoBXXyZsrgtLiwT0obWxgL64rWc7b1RAYYrOPeajkR7I1CcaQg6msnm56NqAi/K9UJU5E3cgsDttERI0AlgD4AoD7AJwB4Drn915V2kFg4rnICn65kM5gbahCh25hWKeqoC9FQZy/ZJ18+7LtRscsE7wBK0X1EeIKX5uJ3VQ5linPGCtJJ66VJZWccRWBfP7HMMYKnLJE9AVk8vyI0APgDiJKImNiupMxdj8R/RXAnUT0fgCvAjhJke9gCFEzMhVxo2WnF53nXQj6ZVJwrOYMUdf89e+pSVOakJSF4eVHsjhFePD2ovP1UGGRno7D11ywQRQ0/yNQGpFzNOdcDoyxpwHswzm/EZncQGWBEc2fQp1DtOC2VZuipVeXY9P3EaNeXxfSmr8xxz7fcy5eyWsj2keeZijD0sePYxLydnZd+mbFq7HNXJD5Qi6bzZ+IPgTgwwCmENHTrksjUKGJ3aIgtKO2SbbKC5BlqyC3j6Imb8NBGsTEJt+Gv8C3E+cvf71cKZ0rEar3Snozl5J2lJopqFdOzf9HyKRuvhaFi7G2MsbezP5DRB2MsbcM8xdphGFmyNPRsBm64tMLaZngCNKjktdeQZy/IXZso5x8hhnqqasJ27o/ts0+pr/UMpp/UbSPJi0iwrClG+Ab6skY28wYe4Uxdgpj7L+uvzeLij5khcOIIgzNyFZKiOAO3+A0C4UVzywkV9e7DT9zkyQdqbbUytvgIVRUmB1ehIw/VdyZY2f3FPwv8xyGTW3mQnbj/E1u4B658SlCmIwKna6G6JQDWQEqqyGqRvPIti973hQyK3zd7YnKhW/zL0yprYbW+gQ+sqADE9vrQCA0pQmp5E7cdlwPGtNJrFy5Mlf2tuMywnBEwwDmO8fNaWDlypUYGma56zw0pQnHTBBfF6G5fhgrV670pN2ZegsrV27F8tn1OH26fBuvvPQfXHtYF4YECXTGtjfg+El5eg11CZy2lzf9dIowPNyEweH23LlUgjDoasN9T7Mo7l/XYC8OHTOMfZeO5pYvRkNDA8aPH4+6OvFucm6YFP4VowuYybhH0ZLIHAhX+HL49tMujK/w9dGUvejJh3rKnvShY9BxrgullhUfxkcWdGDu1LFINY0AEaGjKY10KoF1W/rQ1liHiV3NubIDqzcByCQq693aDwDoaEpjQmcTBoeGMbxGnO6roymNt3bsUuINADqb0xjf0ZRrm4cJHU3oaE7j5d5t2NYvn2l+r7FtSKzbioGhYe716WPbMPTG5tz/IxrqsLVvwJNmYzqJoWGGXYN5mnXJREEb08e3l9Qr7t+krmZs3zWIDdt2Yfq4Ns82GWPYuHEjVq9ejcmTJ3uWzSK83MY1CqGDMAR/r8pXh82Z2+bEEYSODmQWdllhQ8Hjq9r+xPa6nODXpRHDhTIkvSMidHV1oa+vT7qOSeFfMeNFxOjs8W1YMp2bZqiUBoWrCUYx+qLADyBR/rtn7uf6Tz86JsgCO53bKHKcq7QbFCpjTed+xquCzcLrbo7vaFKjIx1YofYMlYS/k6RtLBHtlv1zXS5b3L4qRPeoLpnAjSfvY7Yt59fmKkVh24rx6fJ0lWsAAGaN53+6cnP7eLw+UZRTtnlSivapHD2siiH2S3U2p0PmhQ9p4U9EHwGwDsBvATzg/N2fvc6J/qlqBDfJ+FMIus+tKWezamMy0QkFwkxZU5U7z/claNxHyv+664tTWltw+Cpcj+LkqIMf3P5N7Ny5w6pNknerXn/tVfzynp/mCtx7549wzeWfkKbpx+4FF1yAz33uc7n/r776apx33nkAgD/9/rc45ZhDccKhCzB/zkxcefknATA8//zzOOSQQzBnzhxMnz4dy5cvl+ZHBBWH74UA9vTbvasSYEqrVCpbBm1M5KjkO3yDbeOoCj/Zb8LhWzbYMPsojbXqwA+//U0sPXEZgPZQ231j9av45b134Zh3msk6MzQ0BCTzevbnP/95zJkzB6eeeiqICLfffjv++c9/4qEVz+Layy/BzXf8BJOn7YHxbfW49dZbwZCZMC6++GIcf/zxAIB//etfgflSEf6vAdjsW6pGIK1BBnBORvUl5odu6k+o6tq4hNdVQNdXg6bSSY1yv8Q9L0c5GHyzegbM7dNSn1KKkjGJHTu245IPnYV1a97A0NAQll/4CWzc0Iv169binGXvQHd3N/7y6B/wlz88jG9efx127erHhImTcdVXbkZTcwuO3n823n3SMvz1T39E/+Awrr7xVuw2eQoevP/nuOWGLyCZTKKjvR233nl/QbuMMVxyySdw7/2/BAB84IKP46jjTsSN116JVS8+j2VvPxDL338WgAb0rluLD532brz+6is49O3H4OLLrgIALk8N7W04bN+ZOP49p+Kvf3wYJ5/xARz/rvxE0traiquvvhrnn38+AOCqq65Ce3s7vnfLTTjnIx/D5Gl7AABSqRTO/MC5WL+lD2vWrMH48eNzNGbNmhX4vqsI/5cBPEJEDyCzPSMAgDF2fWAuIgQlx5oC3cA2f50QRZHdMfKqsxmUq5dWHL4WvzIJQENdEtv6B3Hboy/jtTd3IkHArsFhpJKE+lQyV3a7M0HUpRIYcEIZU8kE6lMJMAA7OBPI5O5mfODAKcL2//LIQ+ge3YOb77gTALB1y2aMaG3DD277Om6/8xeYMr4HGzZswG03fRnf+r970NTUjO9846v439u+gQ9edAkAYMSIVjz++OP48s234otXfhI3f+8n+NZXv4hv/uBujO4Zi666QdQ3NeKNzTtz7T70q1/gySefwj2/+zN6e3vx3mMPw7wFi/Dxy67A7d+8CTd/7yeYNa4Nn7/hm3j+2X/hJ7/6A7paW3DYwjk45azlqG9o5PJ00Sc+CQJQX1+PO372a26fTznlFNx0001IJpN43/veBwB48fmVOH35eSXPBgAuuugiHHbYYVi0aBGOPPJInHXWWWhvbxfeUxmoOHxfRcben0Ymr0/2r/Jg4OWUJVFOMeu2VftBZ2pyT2ieZhofOsp6f4Cb6vsV4lFH1hRl45kr8V1hc/u0vWbgb396BDdc81n847G/YERraWDA3/72N7z8wvM4851HYdnbD8Qv7vox1qx+LXf9XSctAwC848ST8PQTfwcAzNl3AT7zsfNw94/uyJheivDPx/+Gk08+GclkEl3dozBv4WL8+6l/cHlcsPhgjGhtQ31DA6bsvifeWP0anv7H3z15evs73ins8+rVq7F27Vq88cYb2LZtm7BcFmeddRZWrlyJk046CY888ggWLlyI/v5+33pekNb8GWNXBmqpQqBm81f5StB3NBYflxt+vKh+5aiHJorO+/s2/Hnh2H0ECDd+y84XaTE+cOAUdDSlUV+XwNrNpYu8nvZa5DU8jGffEC/yEmHSlGn48QOP4NHfP4gbv3AV9j/osJxGnwVjDAsPPARf+Pq3uTQKzV6ZkfDpa2/A0/9cgUcfehCL9puPh//8GJDMh1mqjNO6dD5CJ5FMYmhoSMhTdvg0NjVDhAsvvBBXXHEFVq5ciSuvvBJf+tKXMHWPvfDs009hzxl8k87YsWNx9tln4+yzz8bMmTPxzDPPYN68edJ9KIav5k9EX3V+f0FE9xX/abdcRhCAczw+Q6VocN6wE+eO45TLFOQNtOveNQvzJnZgUpd4kJiAjDAIvNOY1zWF1AT5Ovr0VNuSqaVU2oO/pbN7cOaiSQXnzj90mgTNYNejjPVr16ChsRHHnvgenLH8I3juX08BAJqaW7Dd0YoXLlyIJ1c8hldXvQwA2LlzB155+cUcjXvuvgsA8MDP78bsefsCILz2yirM3mc+zvv4p9A1sguvv766oN25CxbhzjvvxNDQEN7cuAH/eOwvmDlnHppbWrDDQxuvTyWRJMLsuft68iTCr371K6xfvx6nn346Pv3pT+Oee+7Bs88+izM/+BF8++brczSG2TBuuflGAMCvf/1rDAxkVhavXbsWGzduxLhxpfJGBTKa//ed3y8HailimNApv9BCFtcvm4O/vrQRazbnV9l5rUadN7ETd39okZBe0AghewLBmdAMqb7GNP+SC+bCY/3Kykaufv29c/Hka5vwvb+8kjv38bfviZt/7y00/E1nhZpvOaDb6gvPPYsbrv4MEokEUqk6XHbNVwAA7zr1TJx3+kno6enBXx79A666/hu49PxzsGtX5ovj/E9chklTMhNnf38/FixYgJ27BnHNTbcBAK6/+jN4ddVLYIzhyCOWYOas2QXv5uFHHYs3nn8K71yyGABw0aeuxMhRo9HdPRLJVAonHXkAzj3nbAANBfzWpxLobE6js2skl6c999hT2Nf+vj5cdNFFuOuuu0BEaG5uxhe/+EWcf/75+Or//gyfuOIaXHr+OejbuRPpVAKHHXEUAODBBx/ERRddhIaGDC9f+tKXMGbMGM07noGv8GeMPeH8/sGrHBHdzRh7VyBuQoLJjHthIUqKXfb+Febz94j28aPHPWcmHNdE3Sho1dUc6rn4kMOx+JDSNaLvPWs53nvWcnQ0ZUwuCxYfhB898DCXxjnLP4jrrr4qn9uHgBtu+37u+rRRLdixq9DuT0T44pe+hLM/9hkMuvLu1NXV4bYf3wsAmD2+HU+v3oTjl703d/3+++/Hm9t3YfVbO4Q8/X7FM9g5UOpnqG9owPPPP19w7sQTT8SJJ56Ip1dvwsFLjsLBSzICf9LIZvTtGsLaLX34yvXX44YbbuD2XRcm0zsEs6OEiFpZAZlPSxDx/ipr/nIVwhT0KqGWeoYnv1BP/nGMaEDrkVh2KpkU/hWT1dML7heHZ8MXlVVuR2qFr/s4mMO4GPMmdijT82zLdVwSJ++6+OWT9saCyZ1FdVVt/v48COs6v8V2d09ecu1KTjo2VvgqBSLkj0e31ktUUOcnSvjVX59G18iR5WbDBYOiMPtsLEjXmszqKfsiXb9sjjJt0QKhcoLHg5evgQeefV/H5v/ueePxk3P3V6+oAa97f8Vxb7PXrgXnrJovIlN6UlcT7pS414zJr/Ae09bgXygC0HnvRo2QmCglYExOK3ZCdZV+TWb1DBPlNLnYatkrFp5fXt5kIdW+0IkuEeqp4YDI0vWMaPIhGxRKYcWKzLy6eQDbt2wysFF4tEWAxLe2ubYMBB+44fdksvn8sw5hGWht5kJEHQAmMMbcG7r/jw6tqEFthW+AkENDES7ebdgczOagI6NtIYx2wzQLybT1zb+/hfEdjaDhARAIW9NJ1CUJm3cOYks6iR3r8zHuW/sGsHnnIHY0pLC1L7Oad1s6iW3r0hhmDOs2ifPJb00nS5yuMtjq0F/31k5hmYGNdWhKp7BhWz/6BoaRoMx2ilmwt+qxa2gYm3YUbsSS2tKAtVv6MTTMsLMhhS19gwW7bq3c2ljQ7ua6BPp667Fj1yDe3M7f1CWVICQSVLSZC2FgKLOxe2prI7decf+G3kxjYGgYm3cOIrmloWDfax6yO3nJQlr4E9EjAI5z6jwJoJeI/sAY+ygAMMYelG61zAhTGb/smOm49lcrsVtnE/6McHPi5W3VtlpwrfANNBGaiaMvPq2aKlrYnoC+qO2yp3dQZGBLP8PUKVOw5PpMQN8Jc8Ziek8rrv3Vczhm1hh849S9c2W/+chL+MKvn8O5B0/Bt/6QWc367nnj8eWTpmN7/yCWfvY3wnbeuc843PPPNUq8Zevd8J7pOPrSB4Rlrl+2N06cPh7v+/ZjePSFDWhOJ7HdNdH84vwD8NzqTfj0fc8U1Hvh6qNx+rUPY8O2flxw2DTc9PBrmNTVhFc27gAAvHLd0oJ2D96jG3ecPQd3PbEaH7/vKS4v49ob0T2iHk++til3bsrIZry8YTtSCcKL1xzDrVfcv++etS9eWLcV1/zyOTxz5dvRUm9y40U1s08bY2wLgBMBfJcxNg/AEqPcRABK741E2UP27MaKy48oyI8i205UIzj8hEvxJ6qODTxI6GgQhHGf9Wz+PqYzd5y/RlvTRrXgs++YoVwvDOiYo2ST+hGAYYd+ImEuOs7U/cvtl21hF3cV4Z8ioh4Ay+DK41+JqJlQT6ebJvrrO/YCRT4plpeswJ9UFBuD+z5Kli9DtI+RtSsqhUOM7ZNpyi/Qwuv+ZDdvTyXMPDfGWOm91CTNW1NjCirC/yoAvwHwEmPs70Q0BcALFniqaBSncTCVikAvN5DdSa58K3wFZh8TvChQKVjkVrDC1gAjRVAhmbUNhx17bWu4aY0zyYmfiEo0/6AYZiixzwelbEHxlxf+jLGfMsZmM8Y+5Pz/cqWs6C2GSgbKy46ZrkXHl4cK+/pw93Xubu0AgA8cNIV7XYO6xBk+L8Fbkqcd5cVyOqaeEhqkoGxE7FaU+ns4ZQRMDzuaf1IQ1TWuvREH7i6/joCBlfCjK7vJouqvso3jFCe5Wy8RrSeie4losnmWooUPHDQFS2f3aNePxDtimImulnq8ct1SLJzSJde84fZF5MKWzSKB6xeVYXvlcfkipey0rKX4e9yw4/YeW/B/NiooKdD8/3zpYTj7AHlRx1jpvdC9M9l6NvYAVzH7/AjAnQB6AIwF8FMAPzbOUZmhlqY5SDtqZSKsdAIovBeqn6jKtvkgmr9vhtFSZPsjb/M3D1++BekldFcGR328+UF2TBGAIecB54R/wL4zAzSyyLJUVrMPAGKMfZ8xNuj8/QBVktIhhj+C7derLnCD0Cs3/HP7aPhvNHmxhjAdvjrRPgplc2YfCZu/rPO51OGs8QRZvt5wmaN9fk9ElxLRJCKaSESXAHiAiDqJqNO3doVA5RHxHqhXXhubvHjSCUFyBHJs88LytOj51/GPmuE8U8m6XjT8cOqC3XyIqlx2fwWof8nq26c1K1oAz/TG9QNQXvPP1gnunGXGHL5RifZ5D4BzAfwewCMAPgTgbABPAFjBq0BEE4jo90S0koj+TUQXOuc7iei3RPSC89sRqBeKiILjrvwcqEHllpVGPPnQtsSL7Rw6uuDxdf5h3hu6hJLV04Jj4bFPlaZqVoWezV/uHJBX2GQ0f5luM4+2lECuCdmC9FfZxlHHuTsI4GOMsX8Q0QgATxDRbwGcCeAhxth1RHQpgEsRYnqIMAVvFEwUoQi0IHUVKwdx+Nq6F7b1CV25XO7RV7721VtOGnqIjJVG+2gjF7ZbRrMPETUR0eVEdKvz/+5EdKxXHcbYGsbYP5zjrQBWAhgH4HgAdzjF7gBwggbvVhBWVIWcw5fvxKs0mLaBW/1yC8VExms2mF+keA9bQMPxLqAXCCbISPRDxtzKTx2eP5c0tMKXCdrSQY5KmR2+3wWwC0A2F/BqAJ+XrUxEkwDsA+AxAKMZY2uAzAQBYJSgznIiWkFEK3p7exVY9ePFGKmagZ8g8bqnQepyy6sVD9QW4HY4llGP1dX8bbHMeaZReq90WMkJf48y0g7fIiJa98ZFp9w2/6mMsS8CGAAAxthOSN5jImoBcDeAi5z8QFJgjN3KGJvPGJvf3d2twKo+gqbOLTlnyOFbrElce+IseToB38rzDp0aqL5piLpjyMyqV0/JscrTQNXriHmRLmqknidNDt+Lp8mtD1FqR0bY+gjSXBqPgPdhmLGS8aDy/Gb0tJbUK3eo5y4iakQ2jJVoKoB+v0pEVIeM4P8hY+xnzul1Tp4gOL/rlbgOiGgoKNHgQhZKwslgxJNvWxq1Pa8aiPbRgb9ZR4GWAT59SYQ4fHXs3V47somQSviLQ6luc0I9VZB7fuTW/Msb6nkFgF8DmEBEPwTwEHyctJR5k74NYCVj7HrXpfsAnOEcnwHgXgU+AsOmzbhkZV8EZHwEWPCE8j2KYIdUWLIdhaSbEyqonVrni0YGOlqvTrsy0T4yMBbtA7uLvFSifR4koicALERmLF7IGNvgU20xgPcB+BcRPemc+xSA6wDcSUTvB/AqgJNUGY8Cggz2qlvhG8i8perwzR/vMbpFSmPj1ZW9rrrCVwtGVf9AnCg3pwpVQaZS/uR9d8OjL2zAXmNGYM3mwo1l/JQ+Y8Kfk9VTe6V11uwTnK0SqGzm8hBj7HAAD3DOccEY+xPEQzF4AHAEUQ4Z/ZljZ+Cq+5+12oYNzSOLIA7fBy8+2CgvYUBnjOj7ItTLSj1rSYdv2O/D0tk9WDp7KT75s38p1006OoTuF9AZ+0/EHX/9r6P5m42WGh42/wL6Cn8iagDQBGCksxgr26tWZHL81Cz4Dt9iR08Q+u7P9yB0AlQOAWHy56tgc89mXjwvPoP2weQ+x9oThWY9T5rc1dtqNLRs/h4TkShdRPYZ6DvMMxWHefn8VejAzHvvBxnN/1wAFyEj6J9w+GEAtgK42RpnFhEFWRgFHlSgYi5RfVWjFOcfxupvldWnuetK0T7BhYeOwzdKY5ob7KM38yujb2DYmEKju2ZDBr7GUsbYjc7q3qsBzHGOvwvgZQB/Nc+SefS0NWBiV1Pu/1A1zQio3VFYZeyFcOP85WsfumcmvDhv8xfXtX2PQ9H8LXSBR3LOhHbfelO6m3PHMoJPapFXLnKGD5PdN/Xe575Wyhzt827G2BYiOgDAEQC+B+CbxjmygL9+8nA88vFDQmlL3uEbHW3XBAJt4K5a3uKtcCfk+u5Z++GV65ZaaIVjClGu4VHWXTigCcMGpna34Byf/Ph3nrs/bjltnnYbvPGY8JH+QcVroaO2+JrKl1vpcbnj/Iec36UAbmGM3QsgbZ6lMGDTbBBWS+HCpsNXFTYnGl7ARxhx/n5Q0/z1GFWqx3X4BrfvSzbl246XP86GFu3LT0A65V7h+zoRfQuZDdx/SUT1ivXLCht5cnRytGi3FaRuCEIrUKinqtknJM1fBUEXYals1mKSFx6iMM8TAo4pzrnsxD487FNXdwxorq8opVNKU2dPAz+oCO9lyGzgfhRjbBOATgCfMM5RBHH0zDHGaZqWXyfOHWeYYiFU3gf1gRqd7yOu5p+1+RddG9fRaJ8hDfCEh2pFHeco18nqnE0FiKHXW+QlNvsUa/7HzJJ/v71YMb4uJyq5fRhjOxhjP2OMveD8v4Yx9qAFnqxD9bkcO5sf0SqzyCssU8FXTtobT19xZDiNceDVTeOJ3Qzf0/vOX5w7TigIqZEt9bljJZu85LmC6xoNhGHe8IXDS11SzUhAZP4bOuc8LbotN58yFy9efbQx+oD+F2SGkNtKYS/aR3qRVww5yD5y0wKPiAJpV+WEssM3yCc1p6p7ZSfvpc0KUb4TMb8BeBAYDfV0mx80o4R07jCvrezipLpkkLFpKM4/p/kXIpEgJCRj6z2vedxArbmA3BNWec0+VQOb2niQbH5Rhr/2Lu6nzMSlAumIKsl77y4XxvypY1OOXLCXpCwacAzs6ZSi5o9gEUe8STyfJ8eb+aCLvABetI8CHU69cjt8qw5TRjb7F/JAmMvZg+2Ra5ARC4gSe9qf6wE7YXKTe6+QQ+965p9EczpjXFg2f0LB+cXTRhpvyw2+wzdz1kKmhNL2jcX52zP71LTw/83FB+H5zx9VlrYr7YvA3yyRh7K7V9UEpkhfibaHw9dYGzp1PCo997nCMax7fwrqqTzwXJXSk831Kay86ih8/Mg9C84futco3P+RA6SaMJXVM5v/L4gJxdPhKzhWRY73AodvGXL7VCOyglfVCVVKx/9k1LXuSoRpDdVNzlPz97wUWPX3uSwu0FCXLCwb8P6Yti83ppPc802C80DmmQQxeXDXHEBO89e+fQXjiN+2LslY868A2HL4BkEYXxnBYrIVbf76TfmC7/A1C63cPio2Y7fZR3dlqXxz2pDlTWcy4lE2YT/3dgbnrxa3oWXzd0+AsfA3gzAdvjH8Ue5QzwLN38PjG8Uny8tBH0U+VUGgYM/ZK9pHIEkndWV8gIftyd1S3L9JV5uDxhwL2Qil2OxjBOZW+FbGixfOCt/wem6zLf4iL/8XL2j6Bb/qvD6vvIrvr9LV4POLoKIFHX68o334dSaPbMY/P30EhhnDTQ+/qNymu8WhITN3cf+pXXjgggMwtbvFCD03alL420Ss+Kt/opb7nhWGenpo/mVklNdysS2dKHvvNe3LHEejTXg2QcHMlVyzT87mLx6gHc1pvLl9l16brkaHAthp3OOsrbEObY1t2rS8UJtmH0P6uQyVcgu2SkCUTGW6cf66oZj5cz6hnhIN8NJQqNzafChktHR/HXZ0NH+TKN55KzojPI+aFP5BsLdPLnLphUUWRoNoL9soDjw3rPHHE7JFJ3vaGgqve2n+RpjSg9LkotmGW/jbCBJYOKUTADBtVMaE4RlYRcBuzh4cB0isCZDJ55/tkq3JzX3PSjR/Fce7KYZ8UJtmnwB39yfLF6JvYEh4vSS3T4giI51KYMXlSzD/878LrU0ebMf5m8JTnz0S6WQCr765I3fOK7GbTejY/OXoqkf72OrvsvkTcMieozC6tcG/MDJ5/x//1OHoHlHvX7gI3ondfOoqt5ZtM38csY8nLmpT+AdAQ10yF1cdIWtFDu5kY1mEbVYpdpBG9UVoa6wrOedt8xfTCnqPzYZ66vGSM4to1fYHEUkL/ixGSZYvVbo4ZZxfG3lyitssCfVUoRPS6xqbfQxD9sWLkp3bNGzv4WsTvFBP02F2Oo9ex7+k7njPhkKG5fC1Z2Lj2vwT+f55QX+Rl7hiFPWfmhT+psa1jACvYhkvhoS50/0pX+575BbuXg5fLzaDdsF3AtS1GWs4fG1pxmHCK+9WORzaaj6bcF6ImhT+NiH72MKUd2HLVhlNeXxHU+44SvMjd4Wv6dw+ZZ7tnvrMkXjqM6V7P7giPUNa4et1LRgHvEncxArf4rq7dcqNY63uWJ6jatLmb/Plk7E9Vjv8tsnLoimdxI5dQ5G6SeWK89fIoyZFi0e3ranU1wGYSXwWFfD3E5Y0+yjc7Zb6vAgtdPgGuIexzd8ebN7bcpsweAibJ1UbeZRs/rpZPbVyt0QMsonPwkBgM5qH2SeIYC4m646uNu7DsDxQalL420Q54/xtYc8xrUrlZd8t0d64YcPNr6fmHwIvwrY1G9f5YmAB2osK+LuuyYV6qtw03V3TDDUfCDUp/EPNqFmhb9GKy5fgicuX4MGLD8Jxe/P3MC7G5UunA9CJ9jGLaaNa8OdLD9Oqq7/CVz2e3u+cLrTTB0fI4at6P4pZ9rL5m3T4hp0J1SRq0uZvE7KDNkxTh84ElF0v0MVZNyDCBMf5pRtiaArN9SmMa2/UqsvLkhkF6E4uSimdNdvThd3suqXnErI2f00TnikHdhznbxFWbf4hthU15E0mctLfRppaHbiFAe8lNZ7Pn5vVU26k2JycopTbR3mPh6LiXuY7oz6NCv2yB2pU+FtFBQ+GoMg71PTqhQG/x+MpWw0F+uuYfbLXZfLcqGLh1K6CNmSjtSoNNtYxuMeLW3EoSe1jrEVzsCr8ieg7RLSeiJ5xneskot8S0QvOb4dNHsJGieYveOrVOEeoxlFHxeHrBj/O36wmXJ9Sf+1SCcLvPnoQbjltnlI9mVt7y2lzC8oyMKmKFy/ZQ4mXYtjdl4EX6pn59Q/1lIeNHlTLIq/vASjeceJSAA8xxnYH8JDzf6ioVCds1KHrUItSqKd3tI/XGgD5Nor33M3Q9se0USOE++EGQVM6VcCE7OPraVfL06OCoK+op83foCHPLUsqTaxYFf6MsT8CeLPo9PEA7nCO7wBwgk0ewkalDQCTyArHYuEhEia50xG6Z9w4f8Nt6Gj+Kgiazz8si7/Nx+6l+ftv4B7cSV48wagl5pMvGwTlsPmPZoytAQDnV2/DzAAI1eEreJIRknfmoGj2yVUrd5x/QW4fM9q9F7xWn4oQhkB228QrfXzyNf/Mr0kzXhSj+2QRaYcvES0nohVEtKK3t9cgYXOkSkiXW5KVEXmHr6TWk7X5W+JH5yXnRtPIrPBVbskbD33s4IL/UwpRPu4vsCjl8w8TQdI76LZjTjkwQ8cP5RD+64ioBwCc3/WigoyxWxlj8xlj87u7u0NjMAikn1sVzhHaG45YGu1DGjF9nrHaAXjxbbfo/+INu1NJ+6+qO1C33EpMYJu/xzk/n5Suw7fSXulyCP/7AJzhHJ8B4N6wGYjiJ1g1QDfU0xZ04rm50T4Sqr/tzVzqVDR/TVZsrIAtF/g2f/M+DZF/JVhetyqI9iGi/wPwVwB7EtFqIno/gOsAHEFELwA4wvm/ahCGwjS6VX1buzCQD/VUW+Rl65YVb6ItA56Mze4+Nbm7OShL2tBd3KXmaDRvFtFFUAFY3O/Whjpp5URthS9xjzkFIwfb0T6nMMZ6GGN1jLHxjLFvM8Y2MsYOZ4zt7vwWRwNZw5mLJgGwK6ClE7sFGA2/vOBA7bo2Ibt8vhi2nkfJJtoSqOOYVw7ZcxR+eM4CnHvQVBNsAQA+eHAhLb8vBx5fIgRNAhdWPn+byM6V8yd24M5z98duXU1WQj0TgscSZJEXbzc5G6ip3D4zxqplp9RCCM9NJd9OmFA1++QWeVm6abKKv5tfkZBd7LOyVrUHUxS/IsLIOURV5PHNjql0KoH9JndmzmXNWj4rmL3GY+nevBYcvqLGDCPS0T7GEcKYln3+1RwUpJzP39K90DH72I7B14VKtI8usk0Mh7SHrxcCt8+Zx2z0qdz3KQiiOdItwbaNGQCmFEVp1BKKbcbTRmfuRXOa/4Fpey7WifZRMa+4YVsIqGj+bmenjv26mhy+bkWk0VlZPW2U/jsaqqy33FhNCf8sbL6on33HDHvEI47i3D5fevds/OD9C3Kpnv3qeaE47l0Gpmz+UUAooZ7VY/XhmiC7Wurxow8swNfeu493XQ0nefFxEJphoaZs/mEMal7eFh4iOBYCo9hW2ZRO4YDd/bNQytj8i+PeZaCzyCtdBWafoGOrGnbyyu1HXHR+0VSzWVELsnq6zpekd4jgGx/NkW4ZUXwQ1QBSjKawvWOUntlHM6TS8phKafKlAhspj8uF3POw3JWCRV4VJlZqSvhX/pCONnTNBtYcvhoPPLoOX3++AvtIXc+v3AqSsb5YfusLzD5WWzKPaI50S8g6ssKKo601yC6fzyJbSudp3P2hRXj0kkM9y/D48F1JGyGH748+sAAj6jOWWRmHb+5+BlzhW+4d1j77jhmBfRxBFqyp3D/3GPPczCWCIqembP7Z0L+I+vQqHqqbueTrqb8Z8yZ2+JaRNfu4X9Qo2fwXTR2JjuY0tvYPapujVJDfxtF6U54wsVtZIhyrj/S9iqLwj85IDwFZYeCVtjdGEKhpW/lFXnagI8SiFu0zOJRZkaS2yEvXb5EBYyySwkoF+cymdsU/K9D8rTZlHNEa6ZaRFQax8LcDVc1/yki7uXJ0FnnJRmuFhfEdmTDZRgW+3MM7O9Z36/IOt83Uyzrs5dDWWAcA6B5hdsW5jrhubagr+D+I5i/j78j2fWxbo6teHpXgX6wtsw/Lmn1i4a8KGQUqH+opN/R/fO5CPPP6Zms+GJ04/8XTuixwoo9b3jcPf3/lTe2UHnXJBL59xnzMHt/uW1bJYc+AI2eMxvXL9saxs8eWXL7nw4tCm0hHttTj8OmFe0LZXrOwZPooXL9sbyyd3YOfrHitsNEKQU0J/8HhWPjbhOo2gKNGNOCwveztAyur+WcdnDPHteb3s1WErfe+szmNt79tTCAah08fLVXObfaRKk+EE+eO517bZzd/n4wpnDh3XInfKEj6Zpln6e57S30K2/oHC78XKkD1rzGzT2zz14XcC5H5jUqYeCVvJK8DKvpVhdLkbfFWmRg/qhNZEPBMTOWOmJJBbQn/nMO3zIwYxoiGaHzA5ZxsERn4OmafSka2t1mn9Zg2ta8q92Yu5d7JKyh004vrIHuv3F+apSmdM2XGdTQiKoiG1AgJ2WdTbWafdxV9ev9k+UI014f/aCOn+fuk7s3CjKZZOKY+f8JMXP7zZ7Ro/fSD+wfiZXRrA248eY5vyOTvPnoQ1mzuy/1vQmB+58z5mDIyWHJDE8pDkDULqtIhK04GhsQDLpkgfOPUuZgbojnMDzUl/Ksx1PPkfSeUOEwXTCmv0zIywt/FyF5jRuC5tVtDa3vWuDbtuvtO6gzc/vFzxvmWmTZqBKaNGlFyPrPCVw+H7SXnX7CNMDX/bFsDQy7Nn1PumFk99plRQE2ZfYZih69VRG0PWJ3cPrqIkj4RdIVvVSDAV6iXyYtHLlt+12Be8zfia4g3czGHSg/1DGOVZxBEzWE6UjI8Mruqt6slbaztLM3OZnM0/RD07qeT5u+DDlTl5giOiVM18oyHMa1yPpMxbZlx5vYxRUP98UZtmX2ch6Oj4dz9oUXoHxySKvvj5Qtx8q1/U2/EBw9efDD+/cZm43RNwbTN//bT5+Oc/12hXf+7Z+2LBdc8VHCON0FN72nF1e+ciWNmmvsst0HTNka1NuCL75qNQ/bsxuOvhLa1diCcst9uOPfg0r2Vg0T7JBOEG96zN/abXGo+5YmO75yxLx75Ty+29Q0qt+UJy7pUTQn/XG4fS7lkslhoyeY+eWQzJlteFRsEphODLZkRzH48WlJzA4BTF0wM1BZvRAWlqYsgMmPZvhOM8aELFXl9zoGTufmYgvr13rkPf/0CD6NaG7Bs/gTc9seXc+ciYvn0RE2ZfbLO+Eo1+0Qd+XwqZWakRmHytkfNhCeCiMtyRJ4NDleW2aemhH+8yMsuwsqkKEI2vr2Vs+6hvSmTi8XWpihRiouPEi+2Ieqr6a/Qemdsea2pGXSHegaYdVqdvEFpy0kGa8vsI+nw/fp752LSSP9EWABw48lzML2nteT87z56MJ5evUmZx0pGXtsyJ/7v/tAivLV9l1TZt41txWeOnYHj55TmmvnaKXNx/9NvYPcAm3dHHYTK0Dj9oCKwhZp/bjN6AwwB2H9qFy47ZjqWzRebxQYDNva9s/ZFUzqFPceMwKxxrVg01W7Idk0J/yHJFb5LZ8s76UTx1NNGtWBaFQsaPoJHWBRDxddCRDj7gMnca90j6nHWYv61GJUL0UdOwrAiQkT4wEFTPMsMulYV6rR6yJ755HTLDyp1YptGbZp9Ypu/FeRexGpQP2OUDSryWmTCDZLYTRdBNf+wUVPCfyhAtE+tIxtR4ZWmtxZlf9Y+G4VNz8PgJbvHcX3KXrpmEwEZWRLZvPthYNC9wrfoETSlo7VPBFBzZp/Mb6z5q+Mds8filQ07PD99cwmuIiAIg+C7Z+0rHetyz4cX49EXegPvOWsCYfBy2sKJ2LRzAB8+xI5Z4n+O2gt7jSlNOSGCSI9729g2nLloEk7Yxz/NhSkMFUT7FL4D17xzVmh8yKKmhH9WI4o1f3WkkglcfMQenmXyC2vs82MTh+45yr+QgyitvQiDl3QqgY/6jIMg+JDipCKK9mlMJ3HFcW8zwZI0Cmz+Re9AR4grvWVRfnUlRAxVeHqHqMN0eF2MGG7wXtsovclus08loKaEfyqR6W45Numu56xCDIKU8yZEacPxrPOtwaI9uFqQzdMUpefnRnYit2nbV11vw0tTHqU1O26l0vT7bgM1Zfb59LHT0T2iHm9/W/hpZz9y+O7o3daP7f2DmD2+XXpP1utOnMUNGT1hn3F4sXcbzjt0mhSdz50wE3uPL0wz/K33zUOSCGu29JVc08H4jkZ87Ig9QrWz+uGBCw7A31eZzVNzw3v2VkodwcOxs8fiubVb8eFD8s/v8yfMDJQK2iT2ndSJk+aNx/sPtBceO7q1HsvmjweB8O7547Fqw3bP8vd8eBHuf3oNXntzJ7b1D2Dm2DaMbtXb2/iuD+6Pl3u921PFJUfthZaGFFIJwqkLJuI3/16LrX2DSj6MMEHlilIgoqMA3AggCeB2xth1XuXnz5/PVqzQT/IVI0aMGLUIInqCMTa/+HxZvk2IKAng6wCOBjADwClENKMcvMSIESNGLaJchqn9ALzIGHuZMbYLwI8BHF8mXmLEiBGj5lAu4T8OwGuu/1c75wpARMuJaAURrejt7Q2NuRgxYsSodpRL+PNc9CXOB8bYrYyx+Yyx+d3d3SGwFSNGjBi1gXIJ/9UA3OnxxgN4o0y8xIgRI0bNoVzC/+8AdieiyUSUBnAygPvKxEuMGDFi1BzKEufPGBskovMB/AaZUM/vMMb+XQ5eYsSIEaMWUbZFXoyxXwL4ZbnajxEjRoxaRtkWeamCiHoB/Fez+kgAGwyyUwmI+1wbiPtc/Qja34mMsZKImYoR/kFARCt4K9yqGXGfawNxn6sftvob/exDMWLEiBHDOGLhHyNGjBg1iFoR/reWm4EyIO5zbSDuc/XDSn9rwuYfI0aMGDEKUSuaf4wYMWLEcCEW/jFixIhRg6h64U9ERxHR80T0IhFdWm5+TICIJhDR74loJRH9m4gudM53EtFviegF57fDVeeTzj14nojeXj7ug4GIkkT0TyK63/m/qvtMRO1EdBcRPec87/1roM8XO+P6GSL6PyJqqLY+E9F3iGg9ET3jOqfcRyKaR0T/cq7dRKId7XlgjFXtHzKpI14CMAVAGsBTAGaUmy8D/eoBMNc5HgHgP8hsivNFAJc65y8F8AXneIbT93oAk517kix3PzT7/lEAPwJwv/N/VfcZwB0AznGO0wDaq7nPyKR2XwWg0fn/TgBnVlufARwEYC6AZ1znlPsI4HEA+yOTKflXAI6W5aHaNf+q3DSGMbaGMfYP53grgJXIvDTHIyMs4Pye4BwfD+DHjLF+xtgqAC8ic28qCkQ0HsBSALe7Tldtn4moFRkh8W0AYIztYoxtQhX32UEKQCMRpQA0IZPxt6r6zBj7I4DizaWV+khEPQBaGWN/ZZmZ4H9ddXxR7cJfatOYSgYRTQKwD4DHAIxmjK0BMhMEgFFOsWq5D18FcAmAYde5au7zFAC9AL7rmLpuJ6JmVHGfGWOvA/gygFcBrAGwmTH2IKq4zy6o9nGcc1x8XgrVLvylNo2pVBBRC4C7AVzEGNviVZRzrqLuAxEdC2A9Y+wJ2SqccxXVZ2Q04LkAvskY2wfAdmTMASJUfJ8dO/fxyJg3xgJoJqLTvKpwzlVUnyUg6mOgvle78K/aTWOIqA4Zwf9DxtjPnNPrnE9BOL/rnfPVcB8WAziOiF5Bxnx3GBH9ANXd59UAVjPGHnP+vwuZyaCa+7wEwCrGWC9jbADAzwAsQnX3OQvVPq52jovPS6HahX9VbhrjePS/DWAlY+x616X7AJzhHJ8B4F7X+ZOJqJ6IJgPYHRlHUcWAMfZJxth4xtgkZJ7jw4yx01DdfV4L4DUi2tM5dTiAZ1HFfUbG3LOQiJqccX44Mj6tau5zFkp9dExDW4looXOvTnfV8Ue5vd4heNWPQSYa5iUAl5WbH0N9OgCZz7unATzp/B0DoAvAQwBecH47XXUuc+7B81CICIjiH4BDkI/2qeo+A5gDYIXzrH8OoKMG+nwlgOcAPAPg+8hEuVRVnwH8HzI+jQFkNPj36/QRwHznPr0E4GY4WRtk/uL0DjFixIhRg6h2s0+MGDFixOAgFv4xYsSIUYOIhX+MGDFi1CBi4R8jRowYNYhY+MeIESNGDSIW/jFieMDJqvlhj+t/kaCxzSxXMWIERyz8Y8TwRjuAEuFPREkAYIwtCpuhGDFMIFVuBmLEiDiuAzCViJ5EZkHONmQW58wBMIOItjHGWpw8S/ciswirDsDljDH51ZYxYoSMeJFXjBgecLKm3s8Ym0lEhwB4AMBMlkmtC5fwTwFoYoxtIaKRAP4GYHfGGMuWKVMXYsTgItb8Y8RQw+NZwV8EAnANER2ETMrpcQBGA1gbJnMxYsgiFv4xYqhhu+D8qQC6AcxjjA042UcbQuMqRgxFxA7fGDG8sRWZrTL90IbMfgMDRHQogIl22YoRIxhizT9GDA8wxjYS0Z+djbZ3AlgnKPpDAL8gohXIZFl9LiQWY8TQQuzwjREjRowaRGz2iREjRowaRCz8Y8SIEaMGEQv/GDFixKhBxMI/RowYMWoQsfCPESNGjBpELPxjxIgRowYRC/8YMWLEqEH8P0cFFSAaD7o0AAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -671,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -684,23 +613,23 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [3.5458819443170502e-40, 2.7966545038025775e-40, 1.5492520166586155e-40, 1.903331325803806e-40, 3.959521862898773e-40, 2.9618140894520446e-40, 4.89543647546747e-40, 2.971438723585207e-40], 'perf_time': 0.011805800000047384}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [9.88415481322027, 11.250242979256113, 17.97076378051483, 24.665175577055436, 11.641579763570055, 13.317150449980259, 14.972595645496263, 11.116344929060189], 'perf_time': 0.0195018000000573}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': [28.238993444742675, 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"text": [ - "Cond:#11#0#1# - Act:0 - Num:1 [fit: 0.000, exp: 811.00, pred: 139.989]\n", - "Cond:#00##00# - Act:1 - Num:1 [fit: 0.000, exp: 68.00, pred: 138.394]\n", - "Cond:##001##0 - Act:3 - Num:1 [fit: 0.062, exp: 255.00, pred: 146.085]\n", - "Cond:#01001## - Act:0 - Num:1 [fit: 0.000, exp: 509.00, pred: 153.848]\n", - "Cond:00#001#1 - Act:4 - Num:1 [fit: 0.000, exp: 513.00, pred: 152.691]\n", - "Cond:01#1###1 - Act:0 - Num:1 [fit: 0.042, exp: 1437.00, pred: 149.846]\n", - "Cond:#101#111 - Act:3 - Num:1 [fit: 0.003, exp: 490.00, pred: 145.712]\n", - "Cond:#1###### - Act:4 - Num:1 [fit: 0.096, exp: 3964.00, pred: 142.491]\n", - "Cond:#10#1#01 - Act:6 - Num:1 [fit: 0.001, exp: 824.00, pred: 153.839]\n", - "Cond:#0###111 - Act:7 - Num:1 [fit: 0.090, exp: 710.00, pred: 138.327]\n", - "Cond:###10##1 - Act:2 - Num:1 [fit: 0.045, exp: 1425.00, pred: 138.645]\n", - "Cond:00#10#11 - Act:3 - Num:1 [fit: 0.017, exp: 232.00, pred: 138.210]\n", - "Cond:#10#1111 - Act:1 - Num:1 [fit: 0.000, exp: 147.00, pred: 127.779]\n", - "Cond:#00#11#0 - Act:4 - Num:1 [fit: 0.003, exp: 32.00, pred: 144.546]\n", - "Cond:#0011100 - Act:6 - Num:1 [fit: 0.007, exp: 68.00, pred: 156.836]\n", - "Cond:#0#1110# - Act:7 - Num:1 [fit: 0.000, exp: 233.00, pred: 154.185]\n", - "Cond:0010#000 - Act:5 - Num:1 [fit: 0.036, exp: 120.00, pred: 142.111]\n", - "Cond:11000111 - Act:6 - Num:1 [fit: 0.088, exp: 212.00, pred: 153.015]\n", - "Cond:1101#0#1 - Act:0 - Num:1 [fit: 0.000, exp: 1783.00, pred: 153.756]\n", - "Cond:1#0100#1 - Act:5 - Num:1 [fit: 0.005, exp: 240.00, pred: 143.441]\n", - "Cond:##00#0#1 - Act:1 - Num:1 [fit: 0.000, exp: 263.00, pred: 153.671]\n", - "Cond:#10010#1 - Act:5 - Num:2 [fit: 0.469, exp: 123.00, pred: 135.083]\n", - "Cond:10010##0 - Act:0 - Num:1 [fit: 0.000, exp: 321.00, pred: 150.656]\n", - "Cond:00##0101 - Act:3 - Num:1 [fit: 0.000, exp: 183.00, pred: 133.087]\n", - "Cond:0####1#1 - Act:4 - Num:1 [fit: 0.013, exp: 2263.00, pred: 135.550]\n", - "Cond:10100000 - Act:0 - Num:2 [fit: 0.000, exp: 324.00, pred: 195.702]\n", - "Cond:##10#0## - Act:1 - Num:1 [fit: 0.035, exp: 351.00, pred: 139.632]\n", - "Cond:10##000# - Act:4 - Num:1 [fit: 0.000, exp: 60.00, pred: 155.095]\n", - "Cond:##1#0000 - Act:5 - Num:1 [fit: 0.041, exp: 75.00, pred: 148.521]\n", - "Cond:101#000# - Act:7 - Num:1 [fit: 0.142, exp: 156.00, pred: 156.630]\n", - "Cond:000#110# - Act:0 - Num:1 [fit: 0.000, exp: 236.00, pred: 187.137]\n", - "Cond:0001#101 - Act:1 - Num:1 [fit: 0.000, exp: 68.00, pred: 148.106]\n", - "Cond:0001#101 - Act:5 - Num:1 [fit: 0.003, exp: 213.00, pred: 139.894]\n", - "Cond:0001#1#1 - Act:6 - Num:2 [fit: 0.000, exp: 170.00, pred: 140.544]\n", - "Cond:0#000011 - Act:3 - Num:1 [fit: 0.000, exp: 82.00, pred: 127.368]\n", - "Cond:#01111#0 - Act:6 - Num:1 [fit: 0.002, exp: 33.00, pred: 124.208]\n", - "Cond:##0#10## - Act:0 - Num:1 [fit: 0.006, exp: 2656.00, pred: 145.682]\n", - "Cond:1#0#1##1 - Act:3 - Num:1 [fit: 0.000, exp: 444.00, pred: 149.539]\n", - "Cond:1#1101#0 - Act:0 - Num:1 [fit: 0.138, exp: 238.00, pred: 203.704]\n", - "Cond:1##10### - Act:5 - Num:1 [fit: 0.000, exp: 981.00, pred: 151.767]\n", - "Cond:111##10# - Act:6 - Num:1 [fit: 0.195, exp: 111.00, pred: 148.669]\n", - "Cond:00##0010 - Act:5 - Num:2 [fit: 0.236, exp: 131.00, pred: 137.254]\n", - "Cond:#1#0#100 - Act:5 - Num:1 [fit: 0.000, exp: 91.00, pred: 122.773]\n", - "Cond:11#10#1# - Act:2 - Num:2 [fit: 0.000, exp: 92.00, pred: 142.923]\n", - "Cond:#1111110 - Act:1 - Num:1 [fit: 0.003, exp: 116.00, pred: 148.356]\n", - "Cond:0#11#110 - Act:3 - Num:1 [fit: 0.002, exp: 97.00, pred: 133.410]\n", - "Cond:0111100# - Act:3 - Num:2 [fit: 0.002, exp: 33.00, pred: 143.959]\n", - "Cond:#11#1#00 - Act:5 - Num:1 [fit: 0.004, exp: 40.00, pred: 129.222]\n", - "Cond:##111#00 - Act:6 - Num:1 [fit: 0.000, exp: 81.00, pred: 133.005]\n", - "Cond:0##100#0 - Act:0 - Num:2 [fit: 0.000, exp: 186.00, pred: 160.796]\n", - "Cond:0001##10 - Act:1 - Num:3 [fit: 0.003, exp: 27.00, pred: 135.641]\n", - "Cond:#00#0#10 - Act:3 - Num:1 [fit: 0.000, exp: 226.00, pred: 143.033]\n", - "Cond:1000010# - Act:2 - Num:2 [fit: 0.029, exp: 231.00, pred: 158.061]\n", - "Cond:1000010# - Act:4 - Num:1 [fit: 0.030, exp: 75.00, pred: 153.435]\n", - "Cond:#1110001 - Act:1 - Num:3 [fit: 0.001, exp: 61.00, pred: 146.766]\n", - "Cond:#101#10# - Act:0 - Num:1 [fit: 0.000, exp: 1150.00, pred: 166.885]\n", - "Cond:10000#01 - Act:0 - Num:1 [fit: 0.054, exp: 355.00, pred: 160.554]\n", - "Cond:0#100110 - Act:1 - Num:1 [fit: 0.001, exp: 53.00, pred: 129.107]\n", - "Cond:01100### - Act:5 - Num:1 [fit: 0.004, exp: 217.00, pred: 143.436]\n", - "Cond:0##000#0 - Act:4 - Num:1 [fit: 0.031, exp: 151.00, pred: 145.709]\n", - "Cond:0#1#111# - Act:0 - Num:1 [fit: 0.000, exp: 248.00, pred: 186.077]\n", - "Cond:0##01#00 - Act:6 - Num:1 [fit: 0.004, exp: 105.00, pred: 134.247]\n", - "Cond:11##1### - Act:0 - Num:1 [fit: 0.225, exp: 1882.00, pred: 152.249]\n", - "Cond:001#0101 - Act:1 - Num:1 [fit: 0.000, exp: 156.00, pred: 139.954]\n", - "Cond:01##0### - Act:6 - Num:1 [fit: 0.023, exp: 518.00, pred: 148.850]\n", - "Cond:1000#011 - Act:7 - Num:3 [fit: 0.002, exp: 37.00, pred: 128.991]\n", - "Cond:0#011001 - Act:2 - Num:1 [fit: 0.000, exp: 124.00, pred: 158.634]\n", - "Cond:#1101101 - Act:3 - Num:1 [fit: 0.013, exp: 25.00, pred: 142.107]\n", - "Cond:####0#1# - Act:7 - Num:1 [fit: 0.231, exp: 2026.00, pred: 140.293]\n", - "Cond:10111100 - Act:0 - Num:1 [fit: 0.138, exp: 14.00, pred: 125.167]\n", - "Cond:110##100 - Act:2 - Num:1 [fit: 0.000, exp: 145.00, pred: 157.774]\n", - "Cond:0#0##101 - Act:3 - Num:1 [fit: 0.000, exp: 123.00, pred: 142.436]\n", - "Cond:#10#0001 - Act:1 - Num:1 [fit: 0.000, exp: 163.00, pred: 142.399]\n", - "Cond:01011##1 - Act:3 - Num:1 [fit: 0.029, exp: 487.00, pred: 158.312]\n", - "Cond:0#11111# - Act:5 - Num:2 [fit: 0.012, exp: 157.00, pred: 148.665]\n", - "Cond:#0001011 - Act:2 - Num:1 [fit: 0.000, exp: 85.00, pred: 147.251]\n", - "Cond:1#0#0101 - Act:1 - Num:1 [fit: 0.000, exp: 362.00, pred: 153.721]\n", - "Cond:10111100 - Act:1 - Num:3 [fit: 0.035, exp: 38.00, pred: 129.521]\n", - "Cond:010#101# - Act:2 - Num:2 [fit: 0.007, exp: 53.00, pred: 147.623]\n", - "Cond:1001##00 - Act:2 - Num:1 [fit: 0.044, exp: 24.00, pred: 153.096]\n", - "Cond:10000101 - Act:6 - Num:1 [fit: 0.433, exp: 100.00, pred: 142.800]\n", - "Cond:11001#00 - Act:7 - Num:3 [fit: 0.043, exp: 63.00, pred: 137.177]\n", - "Cond:01###0## - Act:7 - Num:1 [fit: 0.203, exp: 351.00, pred: 151.178]\n", - "Cond:101##0#0 - Act:3 - Num:2 [fit: 0.098, exp: 55.00, pred: 137.325]\n", - "Cond:#00#1### - Act:5 - Num:2 [fit: 0.000, exp: 559.00, pred: 149.802]\n", - "Cond:##0#01## - Act:7 - Num:1 [fit: 0.159, exp: 1322.00, pred: 141.836]\n", - "Cond:#01#10#0 - Act:0 - Num:1 [fit: 0.000, exp: 210.00, pred: 148.283]\n", - "Cond:00#010#0 - Act:4 - Num:1 [fit: 0.000, exp: 80.00, pred: 139.735]\n", - "Cond:#01101#1 - Act:5 - Num:1 [fit: 0.002, exp: 338.00, pred: 137.305]\n", - "Cond:01#1###1 - Act:5 - Num:1 [fit: 0.281, exp: 938.00, pred: 146.290]\n", - "Cond:###01101 - Act:7 - Num:3 [fit: 0.066, exp: 18.00, pred: 129.625]\n", - "Cond:#1#1100# - Act:1 - Num:1 [fit: 0.029, exp: 276.00, pred: 124.448]\n", - "Cond:#1##0100 - Act:1 - Num:3 [fit: 0.001, exp: 85.00, pred: 141.218]\n", - "Cond:111#0100 - Act:7 - Num:1 [fit: 0.349, exp: 114.00, pred: 149.267]\n", - "Cond:1101##0# - Act:6 - Num:1 [fit: 0.309, exp: 853.00, pred: 156.076]\n", - "Cond:1001##00 - Act:6 - Num:1 [fit: 0.009, exp: 66.00, pred: 156.883]\n", - "Cond:1#00###1 - Act:2 - Num:2 [fit: 0.021, exp: 884.00, pred: 145.775]\n", - "Cond:#0###000 - Act:2 - Num:1 [fit: 0.000, exp: 314.00, pred: 145.961]\n", - "Cond:10001#00 - Act:6 - Num:1 [fit: 0.000, exp: 60.00, pred: 134.800]\n", - "Cond:1#0#1100 - Act:6 - Num:1 [fit: 0.167, exp: 191.00, pred: 144.507]\n", - "Cond:#10####0 - Act:1 - Num:1 [fit: 0.064, exp: 124.00, pred: 149.402]\n", - "Cond:0#0#11#0 - Act:3 - Num:1 [fit: 0.054, exp: 58.00, pred: 121.352]\n", - "Cond:010#1#00 - Act:5 - Num:1 [fit: 0.005, exp: 45.00, pred: 126.357]\n", - "Cond:#10##100 - Act:6 - Num:1 [fit: 0.188, exp: 211.00, pred: 143.687]\n", - "Cond:#1#11### - Act:6 - Num:1 [fit: 0.001, exp: 1090.00, pred: 167.675]\n", - "Cond:0#1001## - Act:6 - Num:1 [fit: 0.152, exp: 346.00, pred: 144.433]\n", - "Cond:#00#0##0 - Act:2 - Num:3 [fit: 0.030, exp: 331.00, pred: 147.927]\n", - "Cond:#00010## - Act:4 - Num:1 [fit: 0.000, exp: 105.00, pred: 128.043]\n", - "Cond:0##00### - Act:3 - Num:1 [fit: 0.000, exp: 417.00, pred: 140.090]\n", - "Cond:##000011 - Act:2 - Num:1 [fit: 0.000, exp: 127.00, pred: 171.751]\n", - "Cond:0#01#111 - Act:0 - Num:1 [fit: 0.000, exp: 1214.00, pred: 147.911]\n", - "Cond:###11#10 - Act:2 - Num:2 [fit: 0.005, exp: 89.00, pred: 158.222]\n", - "Cond:01001010 - Act:5 - Num:3 [fit: 0.049, exp: 30.00, pred: 139.815]\n", - "Cond:1#11#010 - Act:1 - Num:1 [fit: 0.004, exp: 65.00, pred: 148.904]\n", - "Cond:1#101101 - Act:1 - Num:1 [fit: 0.073, exp: 5.00, pred: 132.906]\n", - "Cond:101##100 - Act:2 - Num:1 [fit: 0.125, exp: 19.00, pred: 141.180]\n", - "Cond:1#11#1## - Act:2 - Num:1 [fit: 0.236, exp: 106.00, pred: 149.866]\n", - "Cond:101#0#00 - Act:6 - Num:2 [fit: 0.001, exp: 62.00, pred: 153.728]\n", - "Cond:0#11#10# - Act:6 - Num:2 [fit: 0.002, exp: 59.00, pred: 123.461]\n", - "Cond:010#1### - Act:6 - Num:1 [fit: 0.029, exp: 477.00, pred: 157.228]\n", - "Cond:0#0#0#10 - Act:6 - Num:1 [fit: 0.026, exp: 22.00, pred: 136.795]\n", - "Cond:#110110# - Act:2 - Num:3 [fit: 0.408, exp: 30.00, pred: 134.008]\n", - "Cond:00#01000 - Act:3 - Num:2 [fit: 0.001, exp: 113.00, pred: 139.155]\n", - "Cond:1###0##1 - Act:0 - Num:2 [fit: 0.549, exp: 2484.00, pred: 148.672]\n", - "Cond:#0####00 - Act:7 - Num:1 [fit: 0.000, exp: 470.00, pred: 148.165]\n", - "Cond:001#00#0 - Act:4 - Num:2 [fit: 0.062, exp: 95.00, pred: 145.710]\n", - "Cond:10#100#0 - Act:6 - Num:3 [fit: 0.090, exp: 66.00, pred: 131.258]\n", - "Cond:0#11##01 - Act:7 - Num:3 [fit: 0.007, exp: 275.00, pred: 139.854]\n", - "Cond:01111##0 - Act:0 - Num:2 [fit: 0.000, exp: 271.00, pred: 182.022]\n", - "Cond:00100101 - Act:2 - Num:1 [fit: 0.008, exp: 72.00, pred: 136.253]\n", - "Cond:1##10### - Act:3 - Num:2 [fit: 0.127, exp: 758.00, pred: 153.261]\n", - "Cond:000#0010 - Act:4 - Num:3 [fit: 0.003, exp: 29.00, pred: 140.880]\n", - "Cond:#0###010 - Act:2 - Num:2 [fit: 0.075, exp: 379.00, pred: 147.720]\n", - "Cond:00###01# - Act:0 - Num:3 [fit: 0.256, exp: 302.00, pred: 153.985]\n", - "Cond:0#1001#1 - Act:1 - Num:1 [fit: 0.019, exp: 166.00, pred: 146.632]\n", - "Cond:#0110111 - Act:1 - Num:1 [fit: 0.003, exp: 72.00, pred: 127.564]\n", - "Cond:#11#00#0 - Act:6 - Num:3 [fit: 0.041, exp: 60.00, pred: 143.154]\n", - "Cond:#1#01101 - Act:5 - Num:3 [fit: 0.052, exp: 10.00, pred: 127.234]\n", - "Cond:001##11# - Act:0 - Num:3 [fit: 0.000, exp: 304.00, pred: 141.520]\n", - "Cond:0##101## - Act:0 - Num:1 [fit: 0.000, exp: 709.00, pred: 136.837]\n", - "Cond:11#0#11# - Act:1 - Num:3 [fit: 0.373, exp: 214.00, pred: 183.288]\n", - "Cond:11#0#1#1 - Act:3 - Num:1 [fit: 0.427, exp: 176.00, pred: 140.034]\n" + "Cond:11##0111 - Act:3 - Num:1 [fit: 0.070, exp: 2.00, pred: 156.215]\n", + "Cond:#11##11# - Act:1 - Num:1 [fit: 0.039, exp: 8.00, pred: 150.209]\n", + "Cond:100#1011 - Act:4 - Num:2 [fit: 0.069, exp: 11.00, pred: 135.360]\n", + "Cond:#0##0#1# - Act:7 - Num:2 [fit: 0.088, exp: 10.00, pred: 138.533]\n", + "Cond:#1110#01 - Act:3 - Num:1 [fit: 0.120, exp: 2.00, pred: 125.072]\n", + "Cond:010##11# - Act:1 - Num:1 [fit: 0.181, exp: 3.00, pred: 148.108]\n", + "Cond:#0##0101 - Act:6 - Num:3 [fit: 0.041, exp: 3.00, pred: 139.931]\n", + "Cond:#0##1101 - Act:1 - Num:2 [fit: 0.136, exp: 3.00, pred: 130.907]\n", + "Cond:1##111#0 - Act:2 - Num:3 [fit: 0.148, exp: 19.00, pred: 132.051]\n", + "Cond:100#0101 - Act:1 - Num:1 [fit: 0.095, exp: 2.00, pred: 150.491]\n", + "Cond:0011#101 - Act:1 - Num:3 [fit: 0.036, exp: 4.00, pred: 114.361]\n", + "Cond:00#00000 - Act:5 - Num:1 [fit: 0.050, exp: 1.00, pred: 149.138]\n", + "Cond:0111#001 - Act:7 - Num:2 [fit: 0.288, exp: 5.00, pred: 122.981]\n", + "Cond:0##10001 - Act:2 - Num:1 [fit: 0.498, exp: 10.00, pred: 133.522]\n", + "Cond:0#1#001# - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 179.370]\n", + "Cond:#####010 - Act:2 - Num:3 [fit: 0.235, exp: 6.00, pred: 137.644]\n", + "Cond:101##### - Act:3 - Num:1 [fit: 0.041, exp: 14.00, pred: 115.773]\n", + "Cond:00##1### - Act:5 - Num:1 [fit: 0.050, exp: 1.00, pred: 127.144]\n", + "Cond:01#1#00# - Act:5 - Num:3 [fit: 0.073, exp: 2.00, pred: 143.144]\n", + "Cond:1#0#1##0 - Act:3 - Num:5 [fit: 0.197, exp: 7.00, pred: 153.728]\n", + "Cond:0##11101 - Act:3 - Num:1 [fit: 0.161, exp: 3.00, pred: 128.375]\n", + "Cond:101####0 - Act:6 - Num:1 [fit: 0.157, exp: 4.00, pred: 128.399]\n", + "Cond:110101## - Act:4 - Num:1 [fit: 0.020, exp: 1.00, pred: 196.108]\n", + "Cond:11##1001 - Act:0 - Num:3 [fit: 0.086, exp: 3.00, pred: 141.271]\n", + "Cond:000#1000 - Act:0 - Num:1 [fit: 0.050, exp: 1.00, pred: 50.074]\n", + "Cond:10010010 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:##10#1## - Act:1 - Num:1 [fit: 0.114, exp: 5.00, pred: 146.331]\n", + "Cond:0##0##11 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:001#0#11 - Act:7 - Num:1 [fit: 0.065, exp: 2.00, pred: 129.698]\n", + "Cond:#000#000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:01##011# - Act:2 - Num:1 [fit: 0.025, exp: 1.00, pred: 172.448]\n", + "Cond:0#0#0111 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0#0##10# - Act:0 - Num:1 [fit: 0.095, exp: 2.00, pred: 136.544]\n", + "Cond:01101### - Act:1 - Num:3 [fit: 0.095, exp: 2.00, pred: 142.154]\n", + "Cond:#0100### - Act:5 - Num:1 [fit: 0.175, exp: 3.00, pred: 223.242]\n", + "Cond:1000#1#1 - Act:2 - Num:2 [fit: 0.095, exp: 2.00, pred: 138.118]\n", + "Cond:1#00#101 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:111#0010 - Act:1 - Num:2 [fit: 0.156, exp: 4.00, pred: 122.694]\n", + "Cond:##110#01 - Act:0 - Num:1 [fit: 0.474, exp: 11.00, pred: 149.869]\n", + "Cond:0#111000 - Act:0 - Num:3 [fit: 0.364, exp: 11.00, pred: 156.779]\n", + "Cond:0010010# - Act:4 - Num:1 [fit: 0.020, exp: 1.00, pred: 190.474]\n", + "Cond:#1##010# - Act:1 - Num:2 [fit: 0.120, exp: 2.00, pred: 133.674]\n", + "Cond:010##01# - Act:0 - Num:3 [fit: 0.024, exp: 8.00, pred: 279.797]\n", + "Cond:##001010 - Act:6 - Num:1 [fit: 0.095, exp: 2.00, pred: 127.124]\n", + "Cond:###11##1 - Act:2 - Num:4 [fit: 0.086, exp: 3.00, pred: 137.766]\n", + "Cond:1#0#1#01 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#1#011#1 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:11010#0# - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#00##100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#00#1101 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#0##110# - Act:5 - Num:1 [fit: 0.100, exp: 3.00, pred: 122.149]\n", + "Cond:#0#01011 - Act:1 - Num:3 [fit: 0.050, exp: 1.00, pred: 118.451]\n", + "Cond:0010###1 - Act:2 - Num:3 [fit: 0.095, exp: 2.00, pred: 136.334]\n", + "Cond:0#11100# - Act:1 - Num:1 [fit: 0.183, exp: 3.00, pred: 128.867]\n", + "Cond:110011## - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:11#01100 - Act:4 - Num:3 [fit: 0.095, exp: 2.00, pred: 124.623]\n", + "Cond:0#1#1000 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1#1100## - Act:6 - Num:2 [fit: 0.050, exp: 1.00, pred: 116.222]\n", + "Cond:01#1#1## - Act:5 - Num:3 [fit: 0.050, exp: 1.00, pred: 147.504]\n", + "Cond:#101111# - Act:6 - Num:2 [fit: 0.050, exp: 1.00, pred: 150.285]\n", + "Cond:010111#1 - Act:7 - Num:2 [fit: 0.095, exp: 2.00, pred: 144.944]\n", + "Cond:#####1#1 - Act:4 - Num:3 [fit: 0.150, exp: 27.00, pred: 190.286]\n", + "Cond:1001##00 - Act:6 - Num:2 [fit: 0.095, exp: 2.00, pred: 147.755]\n", + "Cond:0#000#11 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#1000#1# - Act:3 - Num:1 [fit: 0.025, exp: 1.00, pred: 142.140]\n", + "Cond:###000#1 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:01#0#110 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0011#10# - Act:2 - Num:1 [fit: 0.025, exp: 1.00, pred: 164.592]\n", + "Cond:#01111## - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 183.577]\n", + "Cond:0#011100 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0##1#1#0 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0####100 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0#011#0# - Act:7 - Num:3 [fit: 0.050, exp: 1.00, pred: 123.069]\n", + "Cond:#00##1#0 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#0#1###1 - Act:2 - Num:3 [fit: 0.070, exp: 2.00, pred: 153.792]\n", + "Cond:100#10#0 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:100#100# - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#0001#00 - Act:7 - Num:1 [fit: 0.050, exp: 1.00, pred: 163.201]\n", + "Cond:1##1##01 - Act:2 - Num:3 [fit: 0.050, exp: 1.00, pred: 146.759]\n", + "Cond:##0#0#01 - Act:5 - Num:3 [fit: 0.172, exp: 4.00, pred: 146.619]\n", + "Cond:#10001## - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:00#01000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:00##001# - Act:0 - Num:3 [fit: 0.115, exp: 11.00, pred: 239.312]\n", + "Cond:0#010010 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1#00##00 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1#00##00 - Act:7 - Num:3 [fit: 0.120, exp: 2.00, pred: 193.141]\n", + "Cond:#1111000 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0#111#00 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#11#0#01 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 119.347]\n", + "Cond:#11#0010 - Act:0 - Num:3 [fit: 0.041, exp: 3.00, pred: 176.338]\n", + "Cond:##11#010 - Act:3 - Num:2 [fit: 0.050, exp: 1.00, pred: 153.687]\n", + "Cond:1111001# - Act:4 - Num:1 [fit: 0.261, exp: 7.00, pred: 123.654]\n", + "Cond:0#001010 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 194.372]\n", + "Cond:0##01#1# - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#1#01#10 - Act:4 - Num:1 [fit: 0.050, exp: 1.00, pred: 169.302]\n", + "Cond:01####10 - Act:5 - Num:1 [fit: 0.050, exp: 1.00, pred: 117.463]\n", + "Cond:##001010 - Act:7 - Num:3 [fit: 0.050, exp: 1.00, pred: 175.028]\n", + "Cond:##10##01 - Act:0 - Num:1 [fit: 0.130, exp: 5.00, pred: 288.732]\n", + "Cond:00#0##01 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0#010010 - Act:4 - Num:1 [fit: 0.025, exp: 1.00, pred: 230.361]\n", + "Cond:1000#011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:10#01##1 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:10001011 - Act:7 - Num:2 [fit: 0.050, exp: 1.00, pred: 119.982]\n", + "Cond:#0###010 - Act:5 - Num:3 [fit: 0.114, exp: 2.00, pred: 122.392]\n", + "Cond:1000#101 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1100#1## - Act:5 - Num:2 [fit: 0.112, exp: 2.00, pred: 145.300]\n", + "Cond:##0100## - Act:3 - Num:1 [fit: 0.120, exp: 2.00, pred: 201.216]\n", + "Cond:#####1#1 - Act:6 - Num:1 [fit: 0.041, exp: 2.00, pred: 151.792]\n", + "Cond:1#1#11## - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:10##11#0 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#01111## - Act:6 - Num:1 [fit: 0.005, exp: 0.00, pred: 183.577]\n", + "Cond:##010#10 - Act:6 - Num:2 [fit: 0.050, exp: 1.00, pred: 204.957]\n", + "Cond:100#1#11 - Act:3 - Num:1 [fit: 0.136, exp: 3.00, pred: 214.778]\n", + "Cond:1##01011 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:11##1#00 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0#10#01# - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:00100##0 - Act:4 - Num:1 [fit: 0.050, exp: 1.00, pred: 116.998]\n", + "Cond:11#1##01 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 141.746]\n", + "Cond:11011001 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:110##001 - Act:6 - Num:1 [fit: 0.050, exp: 1.00, pred: 158.489]\n", + "Cond:110#100# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:01#110#1 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0101#00# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:010#1##1 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:01011001 - Act:4 - Num:1 [fit: 0.095, exp: 2.00, pred: 125.368]\n", + "Cond:##01#001 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:0##1###1 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:#1#0110# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:##00##01 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1##010## - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1#0010## - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", + "Cond:1100#001 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n" ] } ], @@ -872,14 +791,14 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "142\n" + "132\n" ] } ], @@ -889,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -898,45 +817,75 @@ "name": "stdout", "output_type": "stream", "text": [ - "##001##0 => 3 [0.06193]\n", - "#1###### => 4 [0.09629]\n", - "#0###111 => 7 [0.09041]\n", - "11000111 => 6 [0.08771]\n", - "#10010#1 => 5 [0.46941]\n", - "101#000# => 7 [0.14190]\n", - "1#1101#0 => 0 [0.13834]\n", - "111##10# => 6 [0.19484]\n", - "00##0010 => 5 [0.23633]\n", - "10000#01 => 0 [0.05420]\n", - "11##1### => 0 [0.22465]\n", - "####0#1# => 7 [0.23132]\n", - "10111100 => 0 [0.13786]\n", - "10000101 => 6 [0.43257]\n", - "01###0## => 7 [0.20321]\n", - "101##0#0 => 3 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[0.05000]\n", + "0111#001 => 7 [0.28848]\n", + "0##10001 => 2 [0.49789]\n", + "0#1#001# => 1 [0.05000]\n", + "#####010 => 2 [0.23541]\n", + "00##1### => 5 [0.05000]\n", + "01#1#00# => 5 [0.07250]\n", + "1#0#1##0 => 3 [0.19702]\n", + "0##11101 => 3 [0.16050]\n", + "101####0 => 6 [0.15695]\n", + "11##1001 => 0 [0.08550]\n", + "000#1000 => 0 [0.05000]\n", + "##10#1## => 1 [0.11360]\n", + "001#0#11 => 7 [0.06500]\n", + "0#0##10# => 0 [0.09500]\n", + "01101### => 1 [0.09500]\n", + "#0100### => 5 [0.17467]\n", + "1000#1#1 => 2 [0.09500]\n", + "111#0010 => 1 [0.15581]\n", + "##110#01 => 0 [0.47364]\n", + "0#111000 => 0 [0.36407]\n", + "#1##010# => 1 [0.12000]\n", + "##001010 => 6 [0.09500]\n", + "###11##1 => 2 [0.08550]\n", + "#0##110# => 5 [0.10025]\n", + "#0#01011 => 1 [0.05000]\n", + "0010###1 => 2 [0.09500]\n", + "0#11100# => 1 [0.18300]\n", + "11#01100 => 4 [0.09500]\n", + "1#1100## => 6 [0.05000]\n", + "01#1#1## => 5 [0.05000]\n", + "#101111# => 6 [0.05000]\n", + "010111#1 => 7 [0.09500]\n", + "#####1#1 => 4 [0.14954]\n", + "1001##00 => 6 [0.09500]\n", + "#01111## => 1 [0.05000]\n", + "0#011#0# => 7 [0.05000]\n", + "#0#1###1 => 2 [0.07000]\n", + "#0001#00 => 7 [0.05000]\n", + "1##1##01 => 2 [0.05000]\n", + "##0#0#01 => 5 [0.17195]\n", + "00##001# => 0 [0.11465]\n", + "1#00##00 => 7 [0.12000]\n", + "#11#0#01 => 1 [0.05000]\n", + "##11#010 => 3 [0.05000]\n", + "1111001# => 4 [0.26085]\n", + "0#001010 => 1 [0.05000]\n", + "#1#01#10 => 4 [0.05000]\n", + "01####10 => 5 [0.05000]\n", + "##001010 => 7 [0.05000]\n", + "##10##01 => 0 [0.13001]\n", + "10001011 => 7 [0.05000]\n", + "#0###010 => 5 [0.11405]\n", + "1100#1## => 5 [0.11167]\n", + "##0100## => 3 [0.12000]\n", + "##010#10 => 6 [0.05000]\n", + "100#1#11 => 3 [0.13550]\n", + "00100##0 => 4 [0.05000]\n", + "11#1##01 => 3 [0.05000]\n", + "110##001 => 6 [0.05000]\n", + "01011001 => 4 [0.09500]\n" ] } ], @@ -948,22 +897,22 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 102, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/XCS_Experiments/XCS_multiplexer.ipynb b/XCS_Experiments/XCS_multiplexer.ipynb index 7efba3b..efc19b8 100644 --- a/XCS_Experiments/XCS_multiplexer.ipynb +++ b/XCS_Experiments/XCS_multiplexer.ipynb @@ -4,164 +4,233 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### XCS in Woods 14\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Wood14." + "# XCS in Multiplexer" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# logging \n", "import logging\n", "logging.basicConfig(level=logging.INFO)\n", "logger = logging.getLogger(__name__)\n", "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", + "# import pandas as pd\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", "\n", - "# environment setup\n", "import gym\n", - "# noinspection PyUnresolvedReferences\n", "import gym_multiplexer \n", "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" + "from lcs.agents import EnvironmentAdapter\n", + "from lcs.agents.xcs import XCS, Configuration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mpx = gym.make('boolean-multiplexer-6bit-v0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of return value" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0]\n", - "[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0]\n" - ] + "data": { + "text/plain": [ + "[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "maze = gym.make('boolean-multiplexer-20bit-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()" + "mpx.reset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last bit is changed after executing an action. It if was valid it's set to `1`, otherwise `0`. This behavior is not needed for the XCS, therefore signal can becompacted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agent" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "metadata": { "scrolled": true }, + "outputs": [], + "source": [ + "class MultiplexerAdapter(EnvironmentAdapter):\n", + " @classmethod\n", + " def to_genotype(cls, env_state):\n", + " return list(map(str, map(int, env_state[:-1]))) # skip last bit\n", + "\n", + "def mpx_metrics(xcs: XCS, environment):\n", + " return {\n", + " 'population': len(xcs.population),\n", + " 'numerosity': sum(cl.numerosity for cl in xcs.population)\n", + " }\n", + " \n", + "cfg = Configuration(number_of_actions=2,\n", + " max_population=1000,\n", + " gamma=0.9,\n", + " metrics_trial_frequency=100,\n", + " environment_adapter=MultiplexerAdapter(),\n", + " user_metrics_collector_fcn=mpx_metrics)\n", + "\n", + "agent = XCS(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Executing 0 experiment\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': [570.5508815523286, 530.0908629315612], 'perf_time': 0.0009067079954547808, 'population': 15, 'numerosity': 372}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 1, 'reward': [517.6587436649028, 369.43516701389626], 'perf_time': 0.00020945801225025207, 'population': 22, 'numerosity': 833}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 1, 'reward': [554.7412290238851, 364.03865588843746], 'perf_time': 0.0002216589928139001, 'population': 28, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 3000, 'steps_in_trial': 1, 'reward': [563.2684490900601, 580.2952947713835], 'perf_time': 0.00021999901218805462, 'population': 31, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 4000, 'steps_in_trial': 1, 'reward': [679.0461013929137, 443.2434086252577], 'perf_time': 0.00022068100224714726, 'population': 32, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': [652.4201243655311, 372.368657015231], 'perf_time': 0.00020892500469926745, 'population': 30, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 6000, 'steps_in_trial': 1, 'reward': [340.3967433715899, 213.5879698145288], 'perf_time': 0.00021586401271633804, 'population': 30, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 7000, 'steps_in_trial': 1, 'reward': [619.0533085256677, 577.8538634747493], 'perf_time': 0.00046395001118071377, 'population': 35, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 8000, 'steps_in_trial': 1, 'reward': [255.10991554049463, 443.8515888568786], 'perf_time': 0.0005984580056974664, 'population': 35, 'numerosity': 1000}\n", + "INFO:lcs.agents.Agent:{'trial': 9000, 'steps_in_trial': 1, 'reward': [688.5152682593864, 538.1776283400435], 'perf_time': 0.00039033699431456625, 'population': 35, 'numerosity': 1000}\n" ] }, { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mnumber_of_tests\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m exploit_metrics=1000)\n\u001b[0m", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\xcs_utils.py\u001b[0m in \u001b[0;36mavg_experiment\u001b[1;34m(maze, cfg, number_of_tests, explore_trials, exploit_metrics)\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber_of_tests\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'Executing {i} experiment'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[0mtest_metrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart_single_experiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'trial'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\xcs_utils.py\u001b[0m in \u001b[0;36mstart_single_experiment\u001b[1;34m(maze, cfg, explore_trials, exploit_metrics)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstart_single_experiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[0magent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mXCS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m \u001b[0mexplore_population\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\Agent.py\u001b[0m in \u001b[0;36mexplore\u001b[1;34m(self, env, trials, decay)\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[0mpopulation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mclassifiers\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \"\"\"\n\u001b[1;32m---> 47\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_explore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecay\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;32mdef\u001b[0m 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\u001b[0mcl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_covering_and_insert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msituation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime_stamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 86\u001b[0m \u001b[0mmatching_ls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mClassifiersList\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmatching_ls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\ClassifiersList.py\u001b[0m in \u001b[0;36m_generate_covering_and_insert\u001b[1;34m(self, situation, action, time_stamp)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_generate_covering_and_insert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msituation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime_stamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m \u001b[0mcl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate_covering_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msituation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mgeneralized\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msituation\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m cl = Classifier(cfg=self.cfg,\n\u001b[1;32m---> 37\u001b[1;33m \u001b[0mcondition\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mCondition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgeneralized\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m time_stamp=time_stamp)\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\ImmutableSequence.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m 11\u001b[0m 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\u001b[1;33m=\u001b[0m \u001b[0mobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAssertionError\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.06 s, sys: 0 ns, total: 3.06 s\n", + "Wall time: 3.05 s\n" ] } ], "source": [ - "cfg = Configuration(number_of_actions=8,\n", - " max_population=2000,\n", - " gamma=0.9,\n", - " metrics_trial_frequency=100,\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=10,\n", - " explore_trials=4000,\n", - " exploit_metrics=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(df)" + "%%time\n", + "explore_population, explore_metrics = agent.explore(mpx, 10_000, decay=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explore population count: 33\n", + "\n", + "Top 15 classifiers:\n", + "###00#\t0\n", + "0#01##\t0\n", + "#00#10\t1\n", + "1#####\t0\n", + "01#00#\t1\n", + "000##1\t1\n", + "#1##1#\t0\n", + "#1####\t0\n", + "#1##1#\t1\n", + "10#11#\t0\n", + "#1##1#\t0\n", + "11####\t1\n", + "#11101\t1\n", + "#1##1#\t1\n", + "111###\t1\n", + "###001\t1\n", + "####0#\t0\n", + "####0#\t1\n" + ] + } + ], "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", + "print(f'Explore population count: {len(explore_population)}\\n')\n", "\n", - "plt.show()" + "print('Top 15 classifiers:')\n", + "for cl in sorted(explore_population, key=lambda cl: -cl.fitness)[15:]:\n", + " print(f'{cl.condition}\\t{cl.action}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "It is hard to say but oking at amount of times algorithm reaches top steps (50) the steps might actually go down over trials. need to somehow smooth it to see it better" + "See if there are some duplicate classifiers." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" + "rules = [(cl.condition, cl.action) for cl in explore_population]\n", + "len(set(rules))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusion\n", - "XCS in Woods14 with wildcards has similar issues as with Maze5." + "There are duplicate classifiers found." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/environment.yml b/environment.yml index 1b804cc..6d238f4 100644 --- a/environment.yml +++ b/environment.yml @@ -15,5 +15,6 @@ dependencies: - dill - pip: - gym==0.11 + - xcs==1.0.0 - "-e git+https://github.com/ParrotPrediction/pyalcs.git@1d6b794e730f22c5c93a78b7aa4d43d75350e41c#egg=parrotprediction-pyalcs" - "-e git+https://github.com/ParrotPrediction/openai-envs.git@d20d24d2a52c0cc01e24613ba5a49b1d781da413#egg=parrotprediction-openai-envs" From 6ecd96b6723f3bbe1d4972b4ef64865335fb6c3b Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Fri, 7 May 2021 13:29:53 +0200 Subject: [PATCH 02/19] Clean Up + Tests --- XCS_Experiments/Untitled.ipynb | 21 +- XCS_Experiments/XCS_Woods14.ipynb | 896 +++---- XCS_Experiments/XCS_compared.ipynb | 2334 ----------------- XCS_Experiments/XCS_compared_Maze.ipynb | 1006 ------- XCS_Experiments/XCS_compared_Maze5.ipynb | 1027 ++------ .../XCS_compared_Maze5_without_#.ipynb | 1358 ---------- ...CS_compared_Maze5_without_#_high_pop.ipynb | 717 ----- XCS_Experiments/XCS_compared_crossover.ipynb | 1354 ---------- XCS_Experiments/XCS_compared_no_GA.ipynb | 774 ------ .../XCS_compared_only_exploit.ipynb | 1225 --------- XCS_Experiments/XCS_maze5.ipynb | 827 ------ XCS_Experiments/XCS_maze5_without_#.ipynb | 678 +++-- XCS_Experiments/XCS_multiplexer.ipynb | 313 ++- XCS_Experiments/XCS_woods14-without_#.ipynb | 988 ------- XCS_Experiments/XCS_woods2.ipynb | 412 --- 15 files changed, 1351 insertions(+), 12579 deletions(-) delete mode 100644 XCS_Experiments/XCS_compared.ipynb delete mode 100644 XCS_Experiments/XCS_compared_Maze.ipynb delete mode 100644 XCS_Experiments/XCS_compared_Maze5_without_#.ipynb delete mode 100644 XCS_Experiments/XCS_compared_Maze5_without_#_high_pop.ipynb delete mode 100644 XCS_Experiments/XCS_compared_crossover.ipynb delete mode 100644 XCS_Experiments/XCS_compared_no_GA.ipynb delete mode 100644 XCS_Experiments/XCS_compared_only_exploit.ipynb delete mode 100644 XCS_Experiments/XCS_maze5.ipynb delete mode 100644 XCS_Experiments/XCS_woods14-without_#.ipynb delete mode 100644 XCS_Experiments/XCS_woods2.ipynb diff --git a/XCS_Experiments/Untitled.ipynb b/XCS_Experiments/Untitled.ipynb index c8a631a..303f39d 100644 --- a/XCS_Experiments/Untitled.ipynb +++ b/XCS_Experiments/Untitled.ipynb @@ -228,9 +228,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "theoretical-projector", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "unique_actions = {1,2,3}\n", + "print(type(unique_actions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "patient-circle", + "metadata": {}, "outputs": [], "source": [] } diff --git a/XCS_Experiments/XCS_Woods14.ipynb b/XCS_Experiments/XCS_Woods14.ipynb index 828b6ed..7b5493e 100644 --- a/XCS_Experiments/XCS_Woods14.ipynb +++ b/XCS_Experiments/XCS_Woods14.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "### XCS in Woods 14\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Wood14." + "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Wood14 version without generalization" ] }, { @@ -43,12 +43,12 @@ "text": [ "This is how maze looks like\n", "\n", - "['O', '.', 'O', 'O', 'O', 'O', 'O', '.']\n", + "['O', 'O', '.', 'O', 'O', 'O', '.', 'O']\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m 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60}\n" ] } ], "source": [ "cfg = Configuration(number_of_actions=8,\n", - " max_population=2000,\n", + " max_population=60,\n", " gamma=0.9,\n", + " chi=1, # crossover\n", " metrics_trial_frequency=100,\n", " user_metrics_collector_fcn=xcs_metrics)\n", "\n", @@ -464,353 +465,353 @@ " \n", " \n", " 0\n", - " 46.7\n", - " 0.008068\n", - " 22.1\n", - " 33.3\n", + " 43.3\n", + " 0.011393\n", + " 21.8\n", + " 32.3\n", " \n", " \n", " 100\n", - " 47.0\n", - " 0.008143\n", - " 33.1\n", - " 73.9\n", + " 45.6\n", + " 0.012572\n", + " 28.4\n", + " 58.6\n", " \n", " \n", " 200\n", " 50.0\n", - " 0.008513\n", - " 33.1\n", - " 74.5\n", + " 0.011686\n", + " 28.3\n", + " 58.6\n", " \n", " \n", " 300\n", - " 36.3\n", - " 0.006216\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.009733\n", + " 28.3\n", + " 58.6\n", " \n", " \n", " 400\n", - " 45.8\n", - " 0.007831\n", - " 33.1\n", - " 74.5\n", + " 29.7\n", + " 0.005734\n", + " 28.3\n", + " 58.6\n", " \n", " \n", " 500\n", - " 40.0\n", - " 0.006924\n", 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\n", " \n", " 2000\n", - " 50.0\n", - " 0.008777\n", - " 33.1\n", - " 74.5\n", + " 44.9\n", + " 0.008856\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2100\n", - " 39.7\n", - " 0.006766\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.009515\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2200\n", - " 46.6\n", - " 0.008109\n", - " 33.1\n", - " 74.5\n", + " 45.2\n", + " 0.008577\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2300\n", " 50.0\n", - " 0.008803\n", - " 33.1\n", - " 74.5\n", + " 0.009396\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2400\n", - " 38.2\n", - " 0.006628\n", - " 33.1\n", - " 74.5\n", + " 45.8\n", + " 0.010249\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2500\n", - " 46.1\n", - " 0.008580\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.009702\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2600\n", - " 47.3\n", - " 0.008332\n", - " 33.1\n", - " 74.5\n", + " 47.8\n", + " 0.008501\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2700\n", - " 50.0\n", - " 0.008688\n", - " 33.1\n", - " 74.5\n", + " 45.9\n", + " 0.008606\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2800\n", " 50.0\n", - " 0.008715\n", - " 33.1\n", - " 74.5\n", + " 0.009561\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 2900\n", - " 45.1\n", - " 0.008021\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.010615\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3000\n", " 50.0\n", - " 0.008901\n", - " 33.1\n", - " 74.5\n", + " 0.009460\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3100\n", - " 47.3\n", - " 0.008356\n", - " 33.1\n", - " 74.5\n", + " 37.5\n", + " 0.007142\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3200\n", - " 46.6\n", - " 0.008195\n", - " 33.1\n", - " 74.5\n", + " 40.1\n", + " 0.008242\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3300\n", - " 48.6\n", - " 0.008402\n", - " 33.1\n", - " 74.5\n", + " 42.7\n", + " 0.008645\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3400\n", - " 45.2\n", - " 0.008063\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.009655\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3500\n", - " 46.6\n", - " 0.008420\n", - " 33.1\n", - " 74.5\n", + " 46.1\n", + " 0.008730\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3600\n", - " 50.0\n", - " 0.009021\n", - " 33.1\n", - " 74.5\n", + " 49.5\n", + " 0.009814\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3700\n", - " 41.7\n", - " 0.007624\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.009495\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3800\n", - " 43.5\n", - " 0.007638\n", - " 33.1\n", - " 74.5\n", + " 45.2\n", + " 0.009641\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 3900\n", - " 50.0\n", - " 0.009107\n", - " 33.1\n", - " 74.5\n", + " 49.7\n", + " 0.012436\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4000\n", - " 50.0\n", - " 0.008558\n", - " 33.1\n", - " 74.5\n", + " 45.1\n", + " 0.008167\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4100\n", " 50.0\n", - " 0.008958\n", - " 33.1\n", - " 74.5\n", + " 0.009736\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4200\n", " 50.0\n", - " 0.008608\n", - " 33.1\n", - " 74.5\n", + " 0.009518\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4300\n", " 50.0\n", - " 0.008925\n", - " 33.1\n", - " 74.5\n", + " 0.009858\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4400\n", " 50.0\n", - " 0.009211\n", - " 33.1\n", - " 74.5\n", + " 0.010168\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4500\n", " 50.0\n", - " 0.008472\n", - " 33.1\n", - " 74.5\n", + " 0.009693\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4600\n", " 50.0\n", - " 0.008443\n", - " 33.1\n", - " 74.5\n", + " 0.009910\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4700\n", " 50.0\n", - " 0.008585\n", - " 33.1\n", - " 74.5\n", + " 0.010870\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4800\n", - " 40.3\n", - " 0.006811\n", - " 33.1\n", - " 74.5\n", + " 50.0\n", + " 0.009473\n", + " 28.3\n", + " 58.8\n", " \n", " \n", " 4900\n", " 50.0\n", - " 0.008398\n", - " 33.1\n", - " 74.5\n", + " 0.009381\n", + " 28.3\n", + " 58.8\n", " \n", " \n", "\n", @@ -819,56 +820,56 @@ "text/plain": [ " steps_in_trial perf_time population numerosity\n", "trial \n", - "0 46.7 0.008068 22.1 33.3\n", - "100 47.0 0.008143 33.1 73.9\n", - "200 50.0 0.008513 33.1 74.5\n", - "300 36.3 0.006216 33.1 74.5\n", - "400 45.8 0.007831 33.1 74.5\n", - "500 40.0 0.006924 33.1 74.5\n", - "600 47.2 0.008196 33.1 74.5\n", - "700 38.4 0.007025 33.1 74.5\n", - "800 43.1 0.007658 33.1 74.5\n", - "900 50.0 0.009596 33.1 74.5\n", - "1000 50.0 0.009200 33.1 74.5\n", - "1100 50.0 0.009144 33.1 74.5\n", - "1200 48.9 0.008629 33.1 74.5\n", - "1300 49.6 0.008684 33.1 74.5\n", - "1400 45.1 0.008003 33.1 74.5\n", - "1500 50.0 0.008947 33.1 74.5\n", - "1600 46.1 0.007970 33.1 74.5\n", - "1700 45.7 0.008075 33.1 74.5\n", - "1800 50.0 0.008826 33.1 74.5\n", - "1900 48.4 0.008319 33.1 74.5\n", - "2000 50.0 0.008777 33.1 74.5\n", - "2100 39.7 0.006766 33.1 74.5\n", - "2200 46.6 0.008109 33.1 74.5\n", - "2300 50.0 0.008803 33.1 74.5\n", - "2400 38.2 0.006628 33.1 74.5\n", - "2500 46.1 0.008580 33.1 74.5\n", - "2600 47.3 0.008332 33.1 74.5\n", - "2700 50.0 0.008688 33.1 74.5\n", - "2800 50.0 0.008715 33.1 74.5\n", - "2900 45.1 0.008021 33.1 74.5\n", - "3000 50.0 0.008901 33.1 74.5\n", - "3100 47.3 0.008356 33.1 74.5\n", - "3200 46.6 0.008195 33.1 74.5\n", - "3300 48.6 0.008402 33.1 74.5\n", - "3400 45.2 0.008063 33.1 74.5\n", - "3500 46.6 0.008420 33.1 74.5\n", - "3600 50.0 0.009021 33.1 74.5\n", - "3700 41.7 0.007624 33.1 74.5\n", - "3800 43.5 0.007638 33.1 74.5\n", - "3900 50.0 0.009107 33.1 74.5\n", - "4000 50.0 0.008558 33.1 74.5\n", - "4100 50.0 0.008958 33.1 74.5\n", - "4200 50.0 0.008608 33.1 74.5\n", - "4300 50.0 0.008925 33.1 74.5\n", - "4400 50.0 0.009211 33.1 74.5\n", - "4500 50.0 0.008472 33.1 74.5\n", - "4600 50.0 0.008443 33.1 74.5\n", - "4700 50.0 0.008585 33.1 74.5\n", - "4800 40.3 0.006811 33.1 74.5\n", - "4900 50.0 0.008398 33.1 74.5" + "0 43.3 0.011393 21.8 32.3\n", + "100 45.6 0.012572 28.4 58.6\n", + "200 50.0 0.011686 28.3 58.6\n", + "300 50.0 0.009733 28.3 58.6\n", + "400 29.7 0.005734 28.3 58.6\n", + "500 50.0 0.011261 28.3 58.6\n", + "600 50.0 0.010247 28.3 58.6\n", + "700 50.0 0.011082 28.3 58.6\n", + "800 50.0 0.009933 28.3 58.6\n", + "900 42.8 0.008121 28.3 58.6\n", + "1000 44.7 0.008949 28.3 58.8\n", + "1100 45.2 0.009078 28.3 58.8\n", + "1200 49.9 0.009888 28.3 58.8\n", + "1300 50.0 0.009424 28.3 58.8\n", + "1400 46.2 0.008729 28.3 58.8\n", + "1500 40.3 0.008758 28.3 58.8\n", + "1600 50.0 0.012403 28.3 58.8\n", + "1700 47.2 0.009315 28.3 58.8\n", + "1800 49.3 0.009231 28.3 58.8\n", + "1900 45.9 0.009067 28.3 58.8\n", + "2000 44.9 0.008856 28.3 58.8\n", + "2100 50.0 0.009515 28.3 58.8\n", + "2200 45.2 0.008577 28.3 58.8\n", + "2300 50.0 0.009396 28.3 58.8\n", + "2400 45.8 0.010249 28.3 58.8\n", + "2500 50.0 0.009702 28.3 58.8\n", + "2600 47.8 0.008501 28.3 58.8\n", + "2700 45.9 0.008606 28.3 58.8\n", + "2800 50.0 0.009561 28.3 58.8\n", + "2900 50.0 0.010615 28.3 58.8\n", + "3000 50.0 0.009460 28.3 58.8\n", + "3100 37.5 0.007142 28.3 58.8\n", + "3200 40.1 0.008242 28.3 58.8\n", + "3300 42.7 0.008645 28.3 58.8\n", + "3400 50.0 0.009655 28.3 58.8\n", + "3500 46.1 0.008730 28.3 58.8\n", + "3600 49.5 0.009814 28.3 58.8\n", + "3700 50.0 0.009495 28.3 58.8\n", + "3800 45.2 0.009641 28.3 58.8\n", + "3900 49.7 0.012436 28.3 58.8\n", + "4000 45.1 0.008167 28.3 58.8\n", + "4100 50.0 0.009736 28.3 58.8\n", + "4200 50.0 0.009518 28.3 58.8\n", + "4300 50.0 0.009858 28.3 58.8\n", + "4400 50.0 0.010168 28.3 58.8\n", + "4500 50.0 0.009693 28.3 58.8\n", + "4600 50.0 0.009910 28.3 58.8\n", + "4700 50.0 0.010870 28.3 58.8\n", + "4800 50.0 0.009473 28.3 58.8\n", + "4900 50.0 0.009381 28.3 58.8" ] }, "metadata": {}, @@ -886,7 +887,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -952,8 +953,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusion\n", - "XCS in Woods14 with wildcards has similar issues as with Maze5." + "### Conclusion\n" ] }, { diff --git a/XCS_Experiments/XCS_compared.ipynb b/XCS_Experiments/XCS_compared.ipynb deleted file mode 100644 index 5d0bab3..0000000 --- a/XCS_Experiments/XCS_compared.ipynb +++ /dev/null @@ -1,2334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs.scenarios import Scenario\n", - "from xcs.bitstrings import BitString\n", - "\n", - "class HaystackProblem(Scenario):\n", - " \n", - " def __init__(self, training_cycles=1000, input_size=50):\n", - " self.input_size = input_size\n", - " self.possible_actions = (True, False)\n", - " self.initial_training_cycles = training_cycles\n", - " self.remaining_cycles = training_cycles\n", - " self.needle_index = random.randrange(input_size)\n", - " self.needle_value = None\n", - "\n", - " def reset(self):\n", - " self.remaining_cycles = self.initial_training_cycles\n", - " haystack = BitString.random(self.input_size)\n", - " self.needle_value = haystack[self.needle_index]\n", - " \n", - " sense_string = str(self.sense())\n", - " raw_state = [str(s) for s in sense_string]\n", - " return raw_state\n", - " \n", - " # XCS Hosford42 functions\n", - " @property\n", - " def is_dynamic(self):\n", - " return False\n", - " \n", - " def get_possible_actions(self):\n", - " return self.possible_actions\n", - " \n", - " def more(self):\n", - " return self.remaining_cycles > 0\n", - " \n", - " def sense(self):\n", - " haystack = BitString.random(self.input_size)\n", - " self.needle_value = haystack[self.needle_index]\n", - " return haystack\n", - " \n", - " def execute(self, action):\n", - " self.remaining_cycles -= 1\n", - " return action == self.needle_value\n", - "\n", - " # XCS Pyalcs functions\n", - " def step(self, action):\n", - " done = not self.execute(action)\n", - " \n", - " haystack = self.sense()\n", - " sense_string = str(haystack)\n", - " raw_state = [str(s) for s in sense_string]\n", - " \n", - " self.needle_value = haystack[self.needle_index]\n", - " reward = action == self.needle_value\n", - " return raw_state, reward, done, _" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "training_cycles = 5000\n", - "input_size = 50\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = HaystackProblem(training_cycles, input_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "algorithm = XCSAlgorithm()\n", - "algorithm.exploration_probability = .1\n", - "algorithm.discount_factor = 0\n", - "algorithm.do_ga_subsumption = True\n", - "algorithm.do_action_set_subsumption = True" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "model = algorithm.new_model(scenario)\n", - "model.run(scenario, learn=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "01#1#1###0##011010001###00##0#0011##0011#0#1##0110 => False\n", - " Time Stamp: 4608\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 8.5e-07\n", - " Experience: 0\n", - " Action Set Size: 1\n", - " Numerosity: 1\n", - "1##1#1011#10010#0#011#0#1##0###01#0#100###1##1#001 => False\n", - " Time Stamp: 3721\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - 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" Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "1#1#1#01###11001#1110101#0#0##1###1#10#1#1#0010011 => False\n", - " Time Stamp: 4987\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "##100111#110#00##11#0##0#00100#0#1011#00##1#00#10# => True\n", - " Time Stamp: 4988\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "#0110#0100#1010##0010#011#0#10#100###101#1#1#10100 => True\n", - " Time Stamp: 4989\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "11111111#1101##1001#10#000##010000#10##01#11###00# => False\n", - " Time Stamp: 4992\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "#1100#10##00##1#0#000010#001#1##0#00000#11#1100##1 => True\n", - " Time Stamp: 4993\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "#101101#0##0#01010#01#0#01#10001#0##0#111#1##1###0 => True\n", - " Time Stamp: 4994\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "0101#0101#1#0000#1#0#10011100##00000#11#1000#0#010 => False\n", - " Time Stamp: 4996\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n", - "11#10#10#0001111110110####11001001110#0##0#0000#0# => True\n", - " Time Stamp: 4998\n", - " Average Reward: 1.0\n", - " Error: 0.0\n", - " Fitness: 0.15000850000000002\n", - " Experience: 1\n", - " Action Set Size: 1.0\n", - " Numerosity: 1\n" - ] - } - ], - "source": [ - "print(model)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200\n" - ] - } - ], - "source": [ - "print(len(model))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#11##1010110##0##01111000011#1001#10##10#0110001#1 => True [0.15001]\n", - "01111100#011#0000101000101#01#0#0110##100#01#0#000 => False [0.15001]\n", - "101#0##0##1111#0#01#1111##100110011101#0#1#1#01001 => False [0.15001]\n", - "###00#0#011###100#010##011110000#0#0##100#1#011111 => True [0.15001]\n", - "1110#0#00010##011#1##001011#011#10#01#00##000####1 => False [0.15001]\n", - "##1000011#100111####11###00#010100#0##0000#01####0 => False [0.15001]\n", - "00011#1#1#01###100#0#1101#001#1101100#1#0111101#01 => True [0.15001]\n", - 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"11111111#1101##1001#10#000##010000#10##01#11###00# => False [0.15001]\n", - "#1100#10##00##1#0#000010#001#1##0#00000#11#1100##1 => True [0.15001]\n", - "#101101#0##0#01010#01#0#01#10001#0##0#111#1##1###0 => True [0.15001]\n", - "010##0#110#0#0##1011#1#011##0###10#1##0#0#0#0#110# => False [0.15001]\n", - "0101#0101#1#0000#1#0#10011100##00000#11#1000#0#010 => False [0.15001]\n", - "##01##01#11###0###0##011#00#10101#1110#11##101010# => False [0.15001]\n", - "11#10#10#0001111110110####11001001110#0##0#0000#0# => True [0.15001]\n", - "000#0#101111#1#10##001001#0#1##0001##01010000#0100 => False [0.15001]\n" - ] - } - ], - "source": [ - "for rule in model:\n", - " if rule.fitness > .05 and rule.experience >= 1:\n", - " print(rule.condition, '=>', rule.action, ' [%.5f]' % rule.fitness)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import XCS, Configuration\n", - "\n", - "cfg = Configuration(number_of_actions=4,\n", - " gamma=0,\n", - " metrics_trial_frequency=5,\n", - " covering_wildcard_chance=0.9\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "agent = XCS(cfg)\n", - "explore_population, explore_metrics = agent.explore(scenario, training_cycles, False)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cond:#1#1111000#100111011111110110111100010#10101010001 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01010001111010###01#00#10#0100000000011000#0011110 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.529]\n", - "Cond:1111010110111#11110#001000###1#00#10##1#0001011011 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:111#0100101111001#00111101100010#011#1001010111000 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#11#11000##0101#00100111001#110000101010010111000 - 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Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:##000111110001100001#0101001001001011#001110000110 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1010001111#01101#011110101011#0##101011#1#10001011 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1010001#1100#101001#110#010110#11101011###100010#1 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:101000111100110100111#01#1011#01110#01111#1#001011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0010011011100001#001#1111101100#011#100101110#1101 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00100#10111000011001011111011#0001111001011#001101 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:010#10000000010#11111#11101#01011110110100#1011101 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0011110#001111101#1#01011010000100101001#000110100 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:10100110100011#1000010110#1000110101110010#1111#10 - Act:2 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:1010011010001111000010#101100#110101110010##111110 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10#001#0100#01100000#1001110100101001101#111100011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:100##1001000011000#0#10011101#010100#1#111#11#0011 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:0001#00#111100#10100111#010#100#11100#100010101100 - Act:0 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.516]\n", - "Cond:0101111010010001#00#0001100##11010111#0#0000010011 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01011#1010010001100#0001#0##111010#1#0010000010011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010111101#01##0#100#0001100111#010#11#010000010011 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:101101000111100#1010#010101001001001#101#101#11010 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10#10#0001#1#0011#101#10101#0#001##11#010101011#10 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1011010001111##11#10#010##1#0100100111010101011010 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:110110##0011001#000#01101#001111#01110#00001001101 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:11#100#01101#01001010000000001100011#00101#0100110 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:111100##1101001001010000#0000110001110010100100110 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010001#011##1#0#0100#11110110#111#10111001010111#1 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#100011#11101000010001111011#0111010111#0101011#01 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:11101110110010001#10111010#0101001000000#010#0#100 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11101110110010001110111010101010010000001010000100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:000111110010100#01#111010111#001100010101110011#00 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#0111110#101000#10111#101111001#000101#111001#1#0 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:1100111100101#0#10101##1010111#0011111110#001#0000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11001#110#101101101011110101110#01111111000#110000 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00011100001110100100010011111010110111#111#00#1001 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#001000010001#0#10100#0001#0110011101001101100000# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1001#000100011#01#1001000110110##110100#1011000000 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00#00100010101100111001110110000010111011111011100 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:001001000101#1100111001110110##001011#01#111011#00 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:11001011101011001101101111#11##11##10110010#011100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1#0010000#00011011100111#1#00000#11100110110101100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:100010#00#000#1011100111110000001111001#0110101100 - Act:2 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:01110010#010001111000#0001010111010011000#11111110 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0111001010#0001#1100010#0101011101001#000011111110 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:1001100110101101100000101100101#01000011000010000# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#101#1001000010100101111#0#01101111010011100100111 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010111001000###10010#1#1101011##111010011#0010011# - Act:2 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:#00101011100#101101#111101011100010#01011100101000 - Act:0 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.564]\n", - "Cond:1001010111001101101111110#011100010001#1110010100# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10#101011100110110#11111010111000#0001011100101000 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0101111110#0010101000#001110010#010001111001011#00 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010111111000#1#10100000011#0#10101000111100101#00# - Act:2 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:0#0001001000#01001001011101011111011111#1110110000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010001#0#0001010010010##1010111110111#1#1110110000 - Act:2 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:01000100100010100100101110##1111101#1111111#110000 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10#11010#000##1101000#0110100000101111001101010110 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1011101010001011010#010110100000#01111001101010110 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.518]\n", - "Cond:10111010100#1011#10#010110100000#01111001101#10110 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#01110101000101##10#01011010000#1#1111##1101010110 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01100110110000#11000111001000001100#10#10000111000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:011001101100001110001110010000011001101100001110#0 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.466]\n", - "Cond:011001101#0#00111000111001000001100#101#0#00111000 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#001#00000#00#01#100101001#01010101100001111111010 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10110100100110111001000#100000101#0010101101100011 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1#11#1001001101110010#00100#0010110#10101101100011 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:#0010111#00#0111100010#11011#0000#1000110100011101 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1001011100010111#00010011011#000011000#1#100011101 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10010111000101111#00100110111000##1000110100011101 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:0110#001101001101000110000#011010111010#100010011# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:011010011010011010001100001#110101110#011000100110 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:#101101101011#001110001100010110#101#10101010#1000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11#11011010##0001110#01100010##00101010101010#1#00 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:11110010011#10101001100110000101101001001101110111 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:111101010#00#100100#1101010101101011111100000010#1 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1111010#0100010#10001#010101011010#1111100000#1001 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:11##0011001111101000#11010000#101010101100#1110011 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:111000110011#110100#01101000011010101011#011110011 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11100#11001111#0100001101000011#101010##0011110011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:111000#10011111###000#1##0000110101010110011#10011 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:110111100#001010111010#0100101110101101000#1100011 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11011#10010010101110100010010#1#01#110100011#00011 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11011#1001001010111#1000100#01110101101000111000#1 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:110111100##010101110#00010010111010110#0001#100#11 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:10#0#10001101#1#010#1101#0010#1100#111#0101101011# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:101011000#10111001011#010001001100011100101101#1## - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010000011110100#0100100000100100#0000100#001#00100 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#0000011110100101001000#0100100##000#00000#10010# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0100000#1110100101001000001001#0000001000001100100 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#01011110101111100101000100010110111111001#101011 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01010#111#1011111001010#0100010110#111110010#01011 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.520]\n", - "Cond:010#01#11010111#1001010001000#01#01111110010101011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01010111#010#111#0010##001000#01#011111100#010#011 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11110100001111#011001#0#00100110000010010111111100 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1111#1000011110011#011000#100#100000100#0#111##100 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11110#000#11110#11#01#00001001100#0010#10111111100 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:00000#00111001#0#10#1111010000100000010100010#1001 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#0000001110010#01001#110100#0#000#001010001001001 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00000000111#01000100#1110100001000000101#00100#001 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0110100001101110101#00010100101100000#111011011010 - Act:0 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.508]\n", - "Cond:01101000##101110101##00101001011000#0111101101#010 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01101#00011011#010#000##0#001011000001111011011010 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#101000011#11#0#01#0001010#1#1100#001#110#1011#1# - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1011000010000000001#11111#1111101#111111#01001#1#0 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1011#00#100000000011111110111110101111111010010110 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.468]\n", - "Cond:011101#000011010101100##11#01101111011#0000#110001 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01110110000110101011#0111##01101111011000001110001 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:##11011000#11#10101100111#1011011110110#0#01110001 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:011101#0000110#01011001111101101111011000001#10001 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:0#000011111100#111010010011##01#001001010#11101111 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010#00111#1100##11010010011100#10#1001010011101#11 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.521]\n", - "Cond:#1000##111110011#10100100111#01100100101001#1#1#11 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01#000#1111100111101001001110011#01001010011101111 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#0110010000000000011101000111010010111101000100100 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#01100100000#0#000111010##111#1#010111101000100100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0011001#0000#0000011#01#001#1010010#1110#0001#0100 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0011001000000000001110#0#01110100101##101000100100 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:100#11#1000011011111010111#1##111010111#1011101111 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10001111000011011111010111011111101011101011101111 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:100011110000#10111#101011#01111#101011101011101111 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:100011110000110#111101011101111110#011101011101111 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:0#010110010#1001010#0000010010111##0100#00#11110#1 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#00#0110010#10010#0100#001001011101010010#1111100# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:000101100101100101010000010010111010#0010011111001 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0001011#010110010101#0#001001011101#100100111110#1 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:#100011#001101101010001#11#01001001#10111101001010 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1100#1110##1#110101000111#0#1001001110111101001010 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11000#1100#10#10101000111#001001001110111101001010 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1100011100110110#010001111001001#0111011110#0#1#10 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:01#1#0111101000001#10000#101101001000#0#1001101110 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010110111101000001#100000101101#0100000110#11011#0 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0101101111010#0001010000010110100100#0011001101110 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01011#11110#000001010000#1011010#1000001100110111# - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n", - "Cond:011100110011100000100011#1101111001100110#0##00101 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:011#0##1001110000#10#01101101111001#0#110001100101 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#1110011#0111#00001000110110111100110#1100011##101 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01110011001#1000001000110110111100#100110001100101 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 0.000]\n" - ] - } - ], - "source": [ - "for rule in explore_population:\n", - " print(rule)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200\n" - ] - } - ], - "source": [ - "print(len(explore_population))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "01010001111010###01#00#10#0100000000011000#0011110 => 1 [0.05000]\n", - "0#11#11000##0101#00100111001#110000101010010111000 => 0 [0.05000]\n", - "1#11001011011100#01#0100#1#010100011010#01#11#01#1 => 3 [0.05000]\n", - "0001#101011001001101010001##011101110111#110000111 => 3 [0.05000]\n", - "111#011110001111010010011#101011101#01100011111010 => 2 [0.05000]\n", - "00#00#1000110111#110001011010#0010101100110001011# => 3 [0.05000]\n", - "01010111001#10##00101000#0101111111111111011100#01 => 3 [0.05000]\n", - "0#010#1#0##110#01011#011#10000101010011111##101110 => 2 [0.05000]\n", - "0101010#110001000000111000000101#00000001010000101 => 0 [0.05000]\n", - "01110101101011001101#00010111100001010110001010110 => 3 [0.05000]\n", - "10#1001010001000001010110#100#0101001#11011000100# => 0 [0.05000]\n", - "010001#0000#10110#101#11000#11000##1#0000011100111 => 3 [0.05000]\n", - "10110#000010001110011#01000101#11001111101000#11#1 => 3 [0.05000]\n", - "111#0111100000011011#00#11001011000001001000111101 => 2 [0.05000]\n", - "00100#10111000011001011111011#0001111001011#001101 => 3 [0.05000]\n", - "0011110#001111101#1#01011010000100101001#000110100 => 3 [0.05000]\n", - "10100110100011#1000010110#1000110101110010#1111#10 => 2 [0.05000]\n", - "100##1001000011000#0#10011101#010100#1#111#11#0011 => 3 [0.05000]\n", - "0001#00#111100#10100111#010#100#11100#100010101100 => 0 [0.05000]\n", - "010111101#01##0#100#0001100111#010#11#010000010011 => 3 [0.05000]\n", - "1011010001111##11#10#010##1#0100100111010101011010 => 3 [0.05000]\n", - "110110##0011001#000#01101#001111#01110#00001001101 => 3 [0.05000]\n", - "#100011#11101000010001111011#0111010111#0101011#01 => 3 [0.05000]\n", - "0#0111110#101000#10111#101111001#000101#111001#1#0 => 3 [0.05000]\n", - "001001000101#1100111001110110##001011#01#111011#00 => 3 [0.05000]\n", - "100010#00#000#1011100111110000001111001#0110101100 => 2 [0.05000]\n", - "0111001010#0001#1100010#0101011101001#000011111110 => 3 [0.05000]\n", - "010111001000###10010#1#1101011##111010011#0010011# => 2 [0.05000]\n", - "#00101011100#101101#111101011100010#01011100101000 => 0 [0.05000]\n", - "010111111000#1#10100000011#0#10101000111100101#00# => 2 [0.05000]\n", - "010001#0#0001010010010##1010111110111#1#1110110000 => 2 [0.05000]\n", - "1011101010001011010#010110100000#01111001101010110 => 1 [0.05000]\n", - "011001101100001110001110010000011001101100001110#0 => 1 [0.05000]\n", - "1#11#1001001101110010#00100#0010110#10101101100011 => 3 [0.05000]\n", - "10010111000101111#00100110111000##1000110100011101 => 3 [0.05000]\n", - "011010011010011010001100001#110101110#011000100110 => 3 [0.05000]\n", - "11#11011010##0001110#01100010##00101010101010#1#00 => 3 [0.05000]\n", - "11110010011#10101001100110000101101001001101110111 => 3 [0.05000]\n", - "1111010#0100010#10001#010101011010#1111100000#1001 => 3 [0.05000]\n", - "111000#10011111###000#1##0000110101010110011#10011 => 3 [0.05000]\n", - "110111100##010101110#00010010111010110#0001#100#11 => 3 [0.05000]\n", - "01010#111#1011111001010#0100010110#111110010#01011 => 1 [0.05000]\n", - "11110#000#11110#11#01#00001001100#0010#10111111100 => 3 [0.05000]\n", - "0110100001101110101#00010100101100000#111011011010 => 0 [0.05000]\n", - "1011#00#100000000011111110111110101111111010010110 => 1 [0.05000]\n", - "011101#0000110#01011001111101101111011000001#10001 => 3 [0.05000]\n", - "010#00111#1100##11010010011100#10#1001010011101#11 => 1 [0.05000]\n", - "0011001000000000001110#0#01110100101##101000100100 => 3 [0.05000]\n", - "100011110000110#111101011101111110#011101011101111 => 3 [0.05000]\n", - "0001011#010110010101#0#001001011101#100100111110#1 => 3 [0.05000]\n", - "1100011100110110#010001111001001#0111011110#0#1#10 => 3 [0.05000]\n", - "01011#11110#000001010000#1011010#1000001100110111# => 3 [0.05000]\n", - "01110011001#1000001000110110111100#100110001100101 => 3 [0.05000]\n" - ] - } - ], - "source": [ - "for rule in explore_population:\n", - " if rule.fitness >= .05 and rule.experience >= 1:\n", - " print(rule.condition, '=>', rule.action, ' [%.5f]' % rule.fitness)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "Both XCS implementations provide similar looking population. Both has population size of 200, and both provide similarly looking classifiers.\n", - "Main difference being differenty amount of classifiers with fitness above 0.05, which can easily be explained by algorithms using different RP." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_compared_Maze.ipynb b/XCS_Experiments/XCS_compared_Maze.ipynb deleted file mode 100644 index e323ea5..0000000 --- a/XCS_Experiments/XCS_compared_Maze.ipynb +++ /dev/null @@ -1,1006 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs.scenarios import Scenario\n", - "from xcs.bitstrings import BitString\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_maze\n", - "\n", - "\n", - "class MazeScenario(Scenario):\n", - " \n", - " def __init__(self, training_cycles=5000, input_size=8):\n", - " self.input_size = input_size\n", - " self.maze = gym.make('Maze5-v0')\n", - " self.possible_actions = (0, 1, 2, 3, 4, 5, 6, 7)\n", - " self.done = False\n", - " self.state = None\n", - " self.reward = 0\n", - " self.state = self.maze.reset()\n", - " self.remaining_cycles = training_cycles\n", - " \n", - " self.steps_array = []\n", - " self.steps = 0\n", - "\n", - " def reset(self):\n", - " self.done = False\n", - " self.state = self.maze.reset()\n", - " no_reward_state=[]\n", - " for char in self.state:\n", - " if char == '1' or char == '0':\n", - " no_reward_state.append(char)\n", - " else:\n", - " no_reward_state.append('1')\n", - " return no_reward_state\n", - " \n", - " # XCS Hosford42 functions\n", - " @property\n", - " def is_dynamic(self):\n", - " return False\n", - " \n", - " def get_possible_actions(self):\n", - " return self.possible_actions\n", - " \n", - " def more(self):\n", - " if self.done:\n", - " self.reset()\n", - " self.remaining_cycles -= 1\n", - " self.steps_array.append(self.steps)\n", - " self.steps = 0\n", - " self.reset()\n", - " return self.remaining_cycles >=0\n", - " \n", - " def sense(self):\n", - " no_reward_state=[]\n", - " for char in self.state:\n", - " if char == '1' or char == '0':\n", - " no_reward_state.append(char)\n", - " else:\n", - " no_reward_state.append('1')\n", - " return BitString(''.join(no_reward_state))\n", - " \n", - " def execute(self, action):\n", - " self.steps += 1\n", - " raw_state, step_reward, done, _ = self.maze.step(action)\n", - " self.state = raw_state\n", - " self.reward = step_reward\n", - " self.done = done\n", - " return self.reward\n", - "\n", - " # XCS Pyalcs functions\n", - " def step(self, action):\n", - " raw_state, step_reward, done, _ = self.maze.step(action)\n", - " self.state = raw_state\n", - " self.reward = step_reward\n", - " self.done = done\n", - " no_reward_state=[]\n", - " for char in self.state:\n", - " if char == '1' or char == '0':\n", - " no_reward_state.append(char)\n", - " else:\n", - " no_reward_state.append('1')\n", - " return no_reward_state, self.reward, self.done, _" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "training_cycles = 1000\n", - "input_size = 8\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(training_cycles, input_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "algorithm = XCSAlgorithm()\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 25\n", - "algorithm.crossover_probability = 0.5\n", - "algorithm.mutation_probability = 0.01\n", - "algorithm.initial_prediction = float(np.finfo(np.float32).tiny) # p_I\n", - "algorithm.initial_error = float(np.finfo(np.float32).tiny) # epsilon_I\n", - "algorithm.initial_fitness = float(np.finfo(np.float32).tiny) # F_I\n", - "algorithm.minimum_actions = 8\n", - "algorithm.wildcard_probability = 0.33" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "model = algorithm.new_model(scenario)\n", - "model.run(scenario, learn=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "######## => 0\n", - " Time Stamp: 38575\n", - " Average Reward: 0.37571021261363624\n", - " Error: 2.329403318204546\n", - " Fitness: 0.0012771595077374358\n", - " Experience: 82\n", - " Action Set Size: 18.08588594887638\n", - " Numerosity: 1\n", - "00####00 => 2\n", - " Time Stamp: 38142\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.020077409540929916\n", - " Experience: 0\n", - " Action Set Size: 1\n", - " Numerosity: 1\n", - "#1###1#0 => 2\n", - " Time Stamp: 38388\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.045376778458758114\n", - " Experience: 2\n", - " Action Set Size: 14.0\n", - " Numerosity: 1\n", - "#00##### => 2\n", - " Time Stamp: 38420\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.04831281665249265\n", - " Experience: 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" Experience: 910\n", - " Action Set Size: 12.419732638069542\n", - " Numerosity: 7\n", - "#10##### => 2\n", - " Time Stamp: 38534\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.6161713675896947\n", - " Experience: 478\n", - " Action Set Size: 14.613383335496076\n", - " Numerosity: 9\n", - "######## => 4\n", - " Time Stamp: 38573\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.8922866441885594\n", - " Experience: 1138\n", - " Action Set Size: 11.918772356074529\n", - " Numerosity: 9\n", - "######## => 3\n", - " Time Stamp: 38565\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.985992009124211\n", - " Experience: 1925\n", - " Action Set Size: 19.40869898304755\n", - " Numerosity: 15\n", - "######## => 5\n", - " Time Stamp: 38541\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.9376603444744054\n", - " Experience: 2128\n", - " Action Set Size: 16.856122739848885\n", - " Numerosity: 16\n", - "######## => 6\n", - " Time Stamp: 38566\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.9562941353572544\n", - " Experience: 945\n", - " Action Set Size: 16.98891996464845\n", - " Numerosity: 16\n", - "######## => 7\n", - " Time Stamp: 38571\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.9938675450489314\n", - " Experience: 1857\n", - " Action Set Size: 17.734318572420353\n", - " Numerosity: 18\n", - "######## => 1\n", - " Time Stamp: 38545\n", - " Average Reward: 0.0\n", - " Error: 0.0\n", - " Fitness: 0.9962950729848306\n", - " Experience: 969\n", - " Action Set Size: 20.274552347801173\n", - " Numerosity: 24\n" - ] - } - ], - "source": [ - "print(model)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "39\n" - ] - } - ], - "source": [ - "print(len(model))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "######## => 5 [0.93766]\n", - "######## => 7 [0.99387]\n", - "######## => 3 [0.98599]\n", - "######## => 4 [0.89229]\n", - "######## => 1 [0.99630]\n", - "######## => 6 [0.95629]\n", - "######## => 2 [0.08427]\n", - "#######1 => 0 [0.47858]\n", - "#######1 => 4 [0.10565]\n", - "#######1 => 5 [0.05780]\n", - "#0###### => 2 [0.70505]\n", - "#00#00## => 0 [0.72414]\n", - "#1#####0 => 2 [0.40239]\n", - "######00 => 0 [0.43793]\n", - "#10##### => 2 [0.61617]\n", - "#11##010 => 0 [0.39836]\n", - "###0#### => 0 [0.32995]\n", - "0##10001 => 2 [0.66394]\n", - "#111#010 => 0 [0.48421]\n", - "#####1## => 0 [0.32151]\n", - "1####### => 6 [0.06619]\n", - "1####### => 7 [0.09550]\n", - "1####### => 3 [0.09507]\n", - "1##01101 => 2 [0.69134]\n", - "#######0 => 2 [0.47853]\n", - "###1#### => 4 [0.08260]\n", - "##0##### => 2 [0.29018]\n", - "##0##### => 4 [0.08970]\n", - "#0####00 => 2 [0.05478]\n", - "#10##0## => 2 [0.07788]\n", - "0##1000# => 2 [0.09964]\n", - "###0###1 => 0 [0.13767]\n", - "#01##### => 2 [0.08614]\n", - "#100#### => 2 [0.06815]\n", - "#####0## => 5 [0.08743]\n" - ] - } - ], - "source": [ - "for rule in model:\n", - " if rule.fitness > .05 and rule.experience >= 1:\n", - " print(rule.condition, '=>', rule.action, ' [%.5f]' % rule.fitness)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "df = pd.DataFrame(scenario.steps_array)\n", - "ax = df.plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps other XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import XCS, Configuration\n", - "\n", - "cfg = Configuration(number_of_actions=algorithm.minimum_actions,\n", - " covering_wildcard_chance=1-algorithm.wildcard_probability)\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [8.53719943350225e-09, 1.8442453025562415e-08, 1.319404340407088e-08, 2.2664404308493155e-08, 1.4908241538230018e-08, 8.982408068035901e-09, 2.7684581271275094e-08, 2.0375159104558157e-08], 'perf_time': 0.028630417000385933}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [34.36346062489276, 34.63300194918697, 27.31210728043027, 35.020699445355866, 33.811886737952946, 35.2579007844482, 29.68979785647382, 31.271719439510314], 'perf_time': 0.02399798299302347}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': [59.07287502145834, 59.02354518946073, 56.34594469293716, 56.69713390680292, 60.11027818341582, 58.862021488304734, 52.342963153730686, 57.01518959594554], 'perf_time': 0.04025520100549329}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 50, 'reward': [24.16169046261253, 21.874793072264627, 70.74385397135241, 21.673993044301483, 24.638060131299344, 18.14046481742258, 22.030523191178716, 31.76363310610115], 'perf_time': 0.027764628001023084}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [45.093870045199544, 52.866120608696065, 56.420059412849795, 42.82793302702784, 56.04630879702996, 46.96238885926114, 39.693535259879404, 63.628745995280156], 'perf_time': 0.030359967990079895}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 50, 'reward': [22.500009246966847, 27.64554618543944, 27.532619157505156, 29.33601346425891, 33.854429699640065, 29.338559773989463, 22.61453103086879, 25.69952991089208], 'perf_time': 0.027180076009244658}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 50, 'reward': [44.31284735272126, 33.157041769396095, 35.56317167207863, 37.01628847538881, 34.51128775798506, 28.82024172566522, 39.51469714983317, 30.305076939290885], 'perf_time': 0.03445921900856774}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 28, 'reward': [31.595548794016636, 45.20986186150161, 139.44043806132748, 29.79833075733197, 44.696142670515286, 57.39147669162783, 36.39692520724961, 34.43299123124266], 'perf_time': 0.01654974299890455}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 50, 'reward': [49.55983793408471, 45.56018606096986, 29.339991555545975, 36.91159370064437, 29.622899631511377, 48.71049533527782, 35.49557816392569, 17.331750848036688], 'perf_time': 0.029363702997216024}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 50, 'reward': [80.09437401667537, 46.16209477653514, 53.56841560848711, 52.83578965207059, 56.08465013520741, 49.70633141336452, 52.34871497087183, 37.56181634240022], 'perf_time': 0.028434281004592776}\n" - ] - } - ], - "source": [ - "agent = XCS(cfg)\n", - "explore_population, explore_metrics = agent.explore(scenario, training_cycles, False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cond:11##0111 - Act:3 - Num:1 [fit: 0.070, exp: 2.00, pred: 156.215]\n", - "Cond:#11##11# - Act:1 - Num:1 [fit: 0.039, exp: 8.00, pred: 150.209]\n", - "Cond:100#1011 - Act:4 - Num:2 [fit: 0.069, exp: 11.00, pred: 135.360]\n", - "Cond:#0##0#1# - Act:7 - Num:2 [fit: 0.088, exp: 10.00, pred: 138.533]\n", - "Cond:#1110#01 - Act:3 - Num:1 [fit: 0.120, exp: 2.00, pred: 125.072]\n", - "Cond:010##11# - Act:1 - Num:1 [fit: 0.181, exp: 3.00, pred: 148.108]\n", - "Cond:#0##0101 - Act:6 - Num:3 [fit: 0.041, exp: 3.00, pred: 139.931]\n", - "Cond:#0##1101 - Act:1 - Num:2 [fit: 0.136, exp: 3.00, pred: 130.907]\n", - "Cond:1##111#0 - Act:2 - Num:3 [fit: 0.148, exp: 19.00, pred: 132.051]\n", - "Cond:100#0101 - Act:1 - Num:1 [fit: 0.095, exp: 2.00, pred: 150.491]\n", - "Cond:0011#101 - Act:1 - Num:3 [fit: 0.036, exp: 4.00, pred: 114.361]\n", - "Cond:00#00000 - Act:5 - Num:1 [fit: 0.050, exp: 1.00, pred: 149.138]\n", - "Cond:0111#001 - Act:7 - Num:2 [fit: 0.288, exp: 5.00, pred: 122.981]\n", - "Cond:0##10001 - Act:2 - Num:1 [fit: 0.498, exp: 10.00, pred: 133.522]\n", - "Cond:0#1#001# - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 179.370]\n", - "Cond:#####010 - Act:2 - Num:3 [fit: 0.235, exp: 6.00, pred: 137.644]\n", - "Cond:101##### - Act:3 - Num:1 [fit: 0.041, exp: 14.00, pred: 115.773]\n", - "Cond:00##1### - Act:5 - Num:1 [fit: 0.050, exp: 1.00, pred: 127.144]\n", - "Cond:01#1#00# - Act:5 - Num:3 [fit: 0.073, exp: 2.00, pred: 143.144]\n", - "Cond:1#0#1##0 - Act:3 - Num:5 [fit: 0.197, exp: 7.00, pred: 153.728]\n", - "Cond:0##11101 - Act:3 - Num:1 [fit: 0.161, exp: 3.00, pred: 128.375]\n", - "Cond:101####0 - Act:6 - Num:1 [fit: 0.157, exp: 4.00, pred: 128.399]\n", - "Cond:110101## - Act:4 - Num:1 [fit: 0.020, exp: 1.00, pred: 196.108]\n", - "Cond:11##1001 - Act:0 - Num:3 [fit: 0.086, exp: 3.00, pred: 141.271]\n", - "Cond:000#1000 - Act:0 - Num:1 [fit: 0.050, exp: 1.00, pred: 50.074]\n", - "Cond:10010010 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:##10#1## - Act:1 - Num:1 [fit: 0.114, exp: 5.00, pred: 146.331]\n", - "Cond:0##0##11 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:001#0#11 - Act:7 - Num:1 [fit: 0.065, exp: 2.00, pred: 129.698]\n", - "Cond:#000#000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01##011# - Act:2 - Num:1 [fit: 0.025, exp: 1.00, pred: 172.448]\n", - "Cond:0#0#0111 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#0##10# - Act:0 - Num:1 [fit: 0.095, exp: 2.00, pred: 136.544]\n", - "Cond:01101### - Act:1 - Num:3 [fit: 0.095, exp: 2.00, pred: 142.154]\n", - "Cond:#0100### - Act:5 - Num:1 [fit: 0.175, exp: 3.00, pred: 223.242]\n", - "Cond:1000#1#1 - Act:2 - Num:2 [fit: 0.095, exp: 2.00, pred: 138.118]\n", - "Cond:1#00#101 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:111#0010 - Act:1 - Num:2 [fit: 0.156, exp: 4.00, pred: 122.694]\n", - "Cond:##110#01 - Act:0 - Num:1 [fit: 0.474, exp: 11.00, pred: 149.869]\n", - "Cond:0#111000 - Act:0 - Num:3 [fit: 0.364, exp: 11.00, pred: 156.779]\n", - "Cond:0010010# - Act:4 - Num:1 [fit: 0.020, exp: 1.00, pred: 190.474]\n", - "Cond:#1##010# - Act:1 - Num:2 [fit: 0.120, exp: 2.00, pred: 133.674]\n", - "Cond:010##01# - Act:0 - Num:3 [fit: 0.024, exp: 8.00, pred: 279.797]\n", - "Cond:##001010 - Act:6 - Num:1 [fit: 0.095, exp: 2.00, pred: 127.124]\n", - "Cond:###11##1 - Act:2 - Num:4 [fit: 0.086, exp: 3.00, pred: 137.766]\n", - "Cond:1#0#1#01 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#1#011#1 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11010#0# - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#00##100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#00#1101 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#0##110# - Act:5 - Num:1 [fit: 0.100, exp: 3.00, pred: 122.149]\n", - "Cond:#0#01011 - Act:1 - Num:3 [fit: 0.050, exp: 1.00, pred: 118.451]\n", - "Cond:0010###1 - Act:2 - Num:3 [fit: 0.095, exp: 2.00, pred: 136.334]\n", - "Cond:0#11100# - Act:1 - Num:1 [fit: 0.183, exp: 3.00, pred: 128.867]\n", - "Cond:110011## - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11#01100 - Act:4 - Num:3 [fit: 0.095, exp: 2.00, pred: 124.623]\n", - "Cond:0#1#1000 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1#1100## - Act:6 - Num:2 [fit: 0.050, exp: 1.00, pred: 116.222]\n", - "Cond:01#1#1## - Act:5 - Num:3 [fit: 0.050, exp: 1.00, pred: 147.504]\n", - "Cond:#101111# - Act:6 - Num:2 [fit: 0.050, exp: 1.00, pred: 150.285]\n", - "Cond:010111#1 - Act:7 - Num:2 [fit: 0.095, exp: 2.00, pred: 144.944]\n", - "Cond:#####1#1 - Act:4 - Num:3 [fit: 0.150, exp: 27.00, pred: 190.286]\n", - "Cond:1001##00 - Act:6 - Num:2 [fit: 0.095, exp: 2.00, pred: 147.755]\n", - "Cond:0#000#11 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#1000#1# - Act:3 - Num:1 [fit: 0.025, exp: 1.00, pred: 142.140]\n", - "Cond:###000#1 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01#0#110 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0011#10# - Act:2 - Num:1 [fit: 0.025, exp: 1.00, pred: 164.592]\n", - "Cond:#01111## - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 183.577]\n", - "Cond:0#011100 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0##1#1#0 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0####100 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#011#0# - Act:7 - Num:3 [fit: 0.050, exp: 1.00, pred: 123.069]\n", - "Cond:#00##1#0 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#0#1###1 - Act:2 - Num:3 [fit: 0.070, exp: 2.00, pred: 153.792]\n", - "Cond:100#10#0 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:100#100# - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#0001#00 - Act:7 - Num:1 [fit: 0.050, exp: 1.00, pred: 163.201]\n", - "Cond:1##1##01 - Act:2 - Num:3 [fit: 0.050, exp: 1.00, pred: 146.759]\n", - "Cond:##0#0#01 - Act:5 - Num:3 [fit: 0.172, exp: 4.00, pred: 146.619]\n", - "Cond:#10001## - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00#01000 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00##001# - Act:0 - Num:3 [fit: 0.115, exp: 11.00, pred: 239.312]\n", - "Cond:0#010010 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1#00##00 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1#00##00 - Act:7 - Num:3 [fit: 0.120, exp: 2.00, pred: 193.141]\n", - "Cond:#1111000 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#111#00 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#11#0#01 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 119.347]\n", - "Cond:#11#0010 - Act:0 - Num:3 [fit: 0.041, exp: 3.00, pred: 176.338]\n", - "Cond:##11#010 - Act:3 - Num:2 [fit: 0.050, exp: 1.00, pred: 153.687]\n", - "Cond:1111001# - Act:4 - Num:1 [fit: 0.261, exp: 7.00, pred: 123.654]\n", - "Cond:0#001010 - Act:1 - Num:1 [fit: 0.050, exp: 1.00, pred: 194.372]\n", - "Cond:0##01#1# - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#1#01#10 - Act:4 - Num:1 [fit: 0.050, exp: 1.00, pred: 169.302]\n", - "Cond:01####10 - Act:5 - Num:1 [fit: 0.050, exp: 1.00, pred: 117.463]\n", - "Cond:##001010 - Act:7 - Num:3 [fit: 0.050, exp: 1.00, pred: 175.028]\n", - "Cond:##10##01 - Act:0 - Num:1 [fit: 0.130, exp: 5.00, pred: 288.732]\n", - "Cond:00#0##01 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#010010 - Act:4 - Num:1 [fit: 0.025, exp: 1.00, pred: 230.361]\n", - "Cond:1000#011 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10#01##1 - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10001011 - Act:7 - Num:2 [fit: 0.050, exp: 1.00, pred: 119.982]\n", - "Cond:#0###010 - Act:5 - Num:3 [fit: 0.114, exp: 2.00, pred: 122.392]\n", - "Cond:1000#101 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1100#1## - Act:5 - Num:2 [fit: 0.112, exp: 2.00, pred: 145.300]\n", - "Cond:##0100## - Act:3 - Num:1 [fit: 0.120, exp: 2.00, pred: 201.216]\n", - "Cond:#####1#1 - Act:6 - Num:1 [fit: 0.041, exp: 2.00, pred: 151.792]\n", - "Cond:1#1#11## - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:10##11#0 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#01111## - Act:6 - Num:1 [fit: 0.005, exp: 0.00, pred: 183.577]\n", - "Cond:##010#10 - Act:6 - Num:2 [fit: 0.050, exp: 1.00, pred: 204.957]\n", - "Cond:100#1#11 - Act:3 - Num:1 [fit: 0.136, exp: 3.00, pred: 214.778]\n", - "Cond:1##01011 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:11##1#00 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0#10#01# - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:00100##0 - Act:4 - Num:1 [fit: 0.050, exp: 1.00, pred: 116.998]\n", - "Cond:11#1##01 - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 141.746]\n", - "Cond:11011001 - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:110##001 - Act:6 - Num:1 [fit: 0.050, exp: 1.00, pred: 158.489]\n", - "Cond:110#100# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01#110#1 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0101#00# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:010#1##1 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:01011001 - Act:4 - Num:1 [fit: 0.095, exp: 2.00, pred: 125.368]\n", - "Cond:##01#001 - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:0##1###1 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:#1#0110# - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:##00##01 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1##010## - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1#0010## - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n", - "Cond:1100#001 - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000]\n" - ] - } - ], - "source": [ - "for rule in explore_population:\n", - " print(rule)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "132\n" - ] - } - ], - "source": [ - "print(len(explore_population))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11##0111 => 3 [0.07000]\n", - "100#1011 => 4 [0.06909]\n", - "#0##0#1# => 7 [0.08753]\n", - "#1110#01 => 3 [0.12000]\n", - "010##11# => 1 [0.18050]\n", - "#0##1101 => 1 [0.13550]\n", - "1##111#0 => 2 [0.14818]\n", - "100#0101 => 1 [0.09500]\n", - "00#00000 => 5 [0.05000]\n", - "0111#001 => 7 [0.28848]\n", - "0##10001 => 2 [0.49789]\n", - "0#1#001# => 1 [0.05000]\n", - "#####010 => 2 [0.23541]\n", - "00##1### => 5 [0.05000]\n", - "01#1#00# => 5 [0.07250]\n", - "1#0#1##0 => 3 [0.19702]\n", - "0##11101 => 3 [0.16050]\n", - "101####0 => 6 [0.15695]\n", - "11##1001 => 0 [0.08550]\n", - "000#1000 => 0 [0.05000]\n", - "##10#1## => 1 [0.11360]\n", - "001#0#11 => 7 [0.06500]\n", - "0#0##10# => 0 [0.09500]\n", - "01101### => 1 [0.09500]\n", - "#0100### => 5 [0.17467]\n", - "1000#1#1 => 2 [0.09500]\n", - "111#0010 => 1 [0.15581]\n", - "##110#01 => 0 [0.47364]\n", - "0#111000 => 0 [0.36407]\n", - "#1##010# => 1 [0.12000]\n", - "##001010 => 6 [0.09500]\n", - "###11##1 => 2 [0.08550]\n", - "#0##110# => 5 [0.10025]\n", - "#0#01011 => 1 [0.05000]\n", - "0010###1 => 2 [0.09500]\n", - "0#11100# => 1 [0.18300]\n", - "11#01100 => 4 [0.09500]\n", - "1#1100## => 6 [0.05000]\n", - "01#1#1## => 5 [0.05000]\n", - "#101111# => 6 [0.05000]\n", - "010111#1 => 7 [0.09500]\n", - "#####1#1 => 4 [0.14954]\n", - "1001##00 => 6 [0.09500]\n", - "#01111## => 1 [0.05000]\n", - "0#011#0# => 7 [0.05000]\n", - "#0#1###1 => 2 [0.07000]\n", - "#0001#00 => 7 [0.05000]\n", - "1##1##01 => 2 [0.05000]\n", - "##0#0#01 => 5 [0.17195]\n", - "00##001# => 0 [0.11465]\n", - "1#00##00 => 7 [0.12000]\n", - "#11#0#01 => 1 [0.05000]\n", - "##11#010 => 3 [0.05000]\n", - "1111001# => 4 [0.26085]\n", - "0#001010 => 1 [0.05000]\n", - "#1#01#10 => 4 [0.05000]\n", - "01####10 => 5 [0.05000]\n", - "##001010 => 7 [0.05000]\n", - "##10##01 => 0 [0.13001]\n", - "10001011 => 7 [0.05000]\n", - "#0###010 => 5 [0.11405]\n", - "1100#1## => 5 [0.11167]\n", - "##0100## => 3 [0.12000]\n", - "##010#10 => 6 [0.05000]\n", - "100#1#11 => 3 [0.13550]\n", - "00100##0 => 4 [0.05000]\n", - "11#1##01 => 3 [0.05000]\n", - "110##001 => 6 [0.05000]\n", - "01011001 => 4 [0.09500]\n" - ] - } - ], - "source": [ - "for rule in explore_population:\n", - " if rule.fitness >= .05 and rule.experience >= 1:\n", - " print(rule.condition, '=>', rule.action, ' [%.5f]' % rule.fitness)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "df = pd.DataFrame(metric[\"steps_in_trial\"] for metric in explore_metrics)\n", - "df['trial'] = df.index * cfg.metrics_trial_frequency\n", - "df.set_index('trial', inplace=True)\n", - "\n", - "ax = df.plot()\n", - "ax.set_xlabel(\"trial\") \n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps my XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "After code review of both classifiers and Algorithmic Description of XCS I found that implementation of Generate Match Set of Hosford42 creates only one classifier if match_set lenght is lower than theta_mna. \n", - "
\n", - "Meanwhile in my implementation and Algorithmic Description of XCS the Generate Match Set creates multiple covering classifiers until match set is equal to theta_mna." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_compared_Maze5.ipynb b/XCS_Experiments/XCS_compared_Maze5.ipynb index 81a8466..6a5d761 100644 --- a/XCS_Experiments/XCS_compared_Maze5.ipynb +++ b/XCS_Experiments/XCS_compared_Maze5.ipynb @@ -30,16 +30,6 @@ "metadata": {}, "outputs": [], "source": [ - "import random\n", - "\n", - "from xcs.scenarios import Scenario\n", - "from xcs.bitstrings import BitString\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_maze\n", - "\n", "from utils.xcs_utils import *" ] }, @@ -47,14 +37,31 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], "source": [ - "exploration_cycles = 4000\n", - "exploitation_cycles = 1000\n", - "\n", + "exploration_cycles = 1000\n", + "exploitation_cycles = 500\n", "input_size = 8\n", "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(input_size)" + "scenario = MazeScenario(input_size)\n", + "scenario.maze.reset()\n", + "scenario.maze.render()" ] }, { @@ -65,26 +72,16 @@ "source": [ "import numpy as np\n", "algorithm = XCSAlgorithm()\n", - "algorithm.max_population_size = 1600 # N\n", - "algorithm.learning_rate = .2 # beta\n", - "algorithm.accuracy_coefficient = .1 # alpha\n", + "algorithm.max_population_size = 1600\n", + "algorithm.learning_rate = .1\n", "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.accuracy_power = 5 # nu\n", - "algorithm.discount_factor = .71 # gamma\n", - "algorithm.ga_threshold = 35 # theta_GA\n", - "algorithm.crossover_probability = .8 # chi\n", - "algorithm.mutation_probability = .01 # mu\n", - "algorithm.deletion_threshold = 20 # theta_del\n", - "algorithm.fitness_threshold = .1 # delta\n", - "algorithm.subsumption_threshold = 20 # theta_sub\n", - "algorithm.wildcard_probability = .3 # P_#\n", - "algorithm.initial_prediction = 10 # p_I\n", - "algorithm.initial_error = .00001 # epsilon_I\n", - "algorithm.initial_fitness = 10 # F_I\n", - "algorithm.exploration_probability = .5 # p_exp\n", - "algorithm.minimum_actions = 8 # theta_mna\n", - "algorithm.do_ga_subsumption = False # doGASubsumption\n", - "algorithm.do_action_set_subsumption = False # doActionSetSubsumption" + "algorithm.ga_threshold = 25\n", + "algorithm.crossover_probability = 1\n", + "algorithm.mutation_probability = 0.01\n", + "algorithm.initial_prediction = 0.000001 # p_I\n", + "algorithm.initial_error = 0.000001 # epsilon_I\n", + "algorithm.initial_fitness = 0.000001 # F_I\n", + "algorithm.wildcard_probability = 0.0" ] }, { @@ -96,8 +93,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Executing 0 experiment\n", - "Executing 1 experiment\n" + "Executing 0 experiment\n" ] } ], @@ -105,7 +101,7 @@ "other_metrics = other_avg_experiment(\n", " maze=scenario,\n", " algorithm=algorithm,\n", - " number_of_tests=2,\n", + " number_of_tests=1,\n", " explore_trials=exploration_cycles,\n", " exploit_trials=exploitation_cycles\n", " )\n" @@ -151,303 +147,93 @@ " \n", " \n", " 0\n", - " 37.5\n", - " 36.0\n", - " 37.0\n", + " 50\n", + " 48\n", + " 48\n", " \n", " \n", " 100\n", - " 36.5\n", - " 436.0\n", - " 1600.0\n", + " 11\n", + " 356\n", + " 1600\n", " \n", " \n", " 200\n", - " 23.0\n", - " 537.5\n", - " 1600.0\n", + " 50\n", + " 406\n", + " 1600\n", " \n", " \n", " 300\n", - " 26.5\n", - " 534.0\n", - " 1600.0\n", + " 21\n", + " 463\n", + " 1600\n", " \n", " \n", " 400\n", - " 12.0\n", - " 541.5\n", - " 1600.0\n", + " 50\n", + " 459\n", + " 1600\n", " \n", " \n", " 500\n", - " 5.5\n", - " 525.5\n", - " 1600.0\n", + " 50\n", + " 430\n", + " 1600\n", " \n", " \n", " 600\n", - " 50.0\n", - " 487.0\n", - " 1600.0\n", + " 48\n", + " 487\n", + " 1600\n", " \n", " \n", " 700\n", - " 18.5\n", - " 459.5\n", - " 1600.0\n", + " 50\n", + " 495\n", + " 1600\n", " \n", " \n", " 800\n", - " 28.0\n", - " 428.5\n", - " 1600.0\n", + " 50\n", + " 501\n", + " 1600\n", " \n", " \n", " 900\n", - " 4.0\n", - " 413.0\n", - " 1600.0\n", + " 50\n", + " 472\n", + " 1600\n", " \n", " \n", " 1000\n", - " 5.5\n", - " 397.5\n", - " 1600.0\n", + " 50\n", + " 478\n", + " 1600\n", " \n", " \n", " 1100\n", - " 27.0\n", - " 370.5\n", - " 1600.0\n", + " 50\n", + " 548\n", + " 1600\n", " \n", " \n", " 1200\n", - " 28.0\n", - " 346.0\n", - " 1600.0\n", + " 5\n", + " 552\n", + " 1600\n", " \n", " \n", " 1300\n", - " 23.0\n", - " 329.5\n", - " 1600.0\n", + " 3\n", + " 538\n", + " 1600\n", " \n", " \n", " 1400\n", - " 31.5\n", - " 310.0\n", - " 1600.0\n", - " \n", - " \n", - " 1500\n", - " 15.0\n", - " 283.5\n", - " 1600.0\n", - " \n", - " \n", - " 1600\n", - " 28.0\n", - " 273.5\n", - " 1600.0\n", - " \n", - " \n", - " 1700\n", - " 18.0\n", - " 264.0\n", - " 1600.0\n", - " \n", - " \n", - " 1800\n", - " 27.0\n", - " 260.0\n", - " 1600.0\n", - " \n", - " \n", - " 1900\n", - " 27.5\n", - " 263.5\n", - " 1600.0\n", - " \n", - " \n", - " 2000\n", - " 50.0\n", - " 268.0\n", - " 1600.0\n", - " \n", - " \n", - " 2100\n", - " 28.5\n", - " 257.0\n", - " 1600.0\n", - " \n", - " \n", - " 2200\n", - " 5.5\n", - " 261.5\n", - " 1600.0\n", - " \n", - " \n", - " 2300\n", - " 10.5\n", - " 257.5\n", - " 1600.0\n", - " \n", - " \n", - " 2400\n", - " 34.5\n", - " 253.5\n", - " 1600.0\n", - " \n", - " \n", - " 2500\n", - " 30.5\n", - " 247.0\n", - " 1600.0\n", - " \n", - " \n", - " 2600\n", - " 8.5\n", - " 248.5\n", - " 1600.0\n", - " \n", - " \n", - " 2700\n", - " 4.5\n", - " 246.5\n", - " 1600.0\n", - " \n", - " \n", - " 2800\n", - " 29.0\n", - " 238.0\n", - " 1600.0\n", - " \n", - " \n", - " 2900\n", - " 30.5\n", - " 237.0\n", - " 1600.0\n", - " \n", - " \n", - " 3000\n", - " 45.5\n", - " 235.5\n", - " 1600.0\n", - " \n", - " \n", - " 3100\n", - " 26.5\n", - " 234.0\n", - " 1600.0\n", - " \n", - " \n", - " 3200\n", - " 28.0\n", - " 242.5\n", - " 1600.0\n", - " \n", - " \n", - " 3300\n", - " 10.5\n", - " 244.5\n", - " 1600.0\n", - " \n", - " \n", - " 3400\n", - " 27.5\n", - " 243.0\n", - " 1600.0\n", - " \n", - " \n", - " 3500\n", - " 19.0\n", - " 239.0\n", - " 1600.0\n", - " \n", - " \n", - " 3600\n", - " 41.5\n", - " 229.5\n", - " 1600.0\n", - " \n", - " \n", - " 3700\n", - " 10.5\n", - " 241.5\n", - " 1600.0\n", - " \n", - " \n", - " 3800\n", - " 26.0\n", - " 253.5\n", - " 1600.0\n", - " \n", - " \n", - " 3900\n", - " 35.5\n", - " 243.5\n", - " 1600.0\n", - " \n", - " \n", - " 4000\n", - " 29.5\n", - " 238.0\n", - " 1600.0\n", - " \n", - " \n", - " 4100\n", - " 26.5\n", - " 231.0\n", - " 1600.0\n", - " \n", - " \n", - " 4200\n", - " 26.5\n", - " 235.5\n", - " 1600.0\n", - " \n", - " \n", - " 4300\n", - " 50.0\n", - " 229.0\n", - " 1600.0\n", - " \n", - " \n", - " 4400\n", - " 50.0\n", - " 219.5\n", - " 1600.0\n", - " \n", - " \n", - " 4500\n", - " 50.0\n", - " 221.5\n", - " 1600.0\n", - " \n", - " \n", - " 4600\n", - " 50.0\n", - " 214.5\n", - " 1600.0\n", - " \n", - " \n", - " 4700\n", - " 50.0\n", - " 215.5\n", - " 1600.0\n", - " \n", - " \n", - " 4800\n", - " 50.0\n", - " 203.0\n", - " 1600.0\n", - " \n", - " \n", - " 4900\n", - " 50.0\n", - " 201.0\n", - " 1600.0\n", + " 21\n", + " 504\n", + " 1600\n", " \n", " \n", "\n", @@ -456,56 +242,21 @@ "text/plain": [ " steps_in_trial population numerosity\n", "trial \n", - "0 37.5 36.0 37.0\n", - "100 36.5 436.0 1600.0\n", - "200 23.0 537.5 1600.0\n", - "300 26.5 534.0 1600.0\n", - "400 12.0 541.5 1600.0\n", - "500 5.5 525.5 1600.0\n", - "600 50.0 487.0 1600.0\n", - "700 18.5 459.5 1600.0\n", - "800 28.0 428.5 1600.0\n", - "900 4.0 413.0 1600.0\n", - "1000 5.5 397.5 1600.0\n", - "1100 27.0 370.5 1600.0\n", - "1200 28.0 346.0 1600.0\n", - "1300 23.0 329.5 1600.0\n", - "1400 31.5 310.0 1600.0\n", - "1500 15.0 283.5 1600.0\n", - "1600 28.0 273.5 1600.0\n", - "1700 18.0 264.0 1600.0\n", - "1800 27.0 260.0 1600.0\n", - "1900 27.5 263.5 1600.0\n", - "2000 50.0 268.0 1600.0\n", - "2100 28.5 257.0 1600.0\n", - "2200 5.5 261.5 1600.0\n", - "2300 10.5 257.5 1600.0\n", - "2400 34.5 253.5 1600.0\n", - "2500 30.5 247.0 1600.0\n", - "2600 8.5 248.5 1600.0\n", - "2700 4.5 246.5 1600.0\n", - "2800 29.0 238.0 1600.0\n", - "2900 30.5 237.0 1600.0\n", - "3000 45.5 235.5 1600.0\n", - "3100 26.5 234.0 1600.0\n", - "3200 28.0 242.5 1600.0\n", - "3300 10.5 244.5 1600.0\n", - "3400 27.5 243.0 1600.0\n", - "3500 19.0 239.0 1600.0\n", - "3600 41.5 229.5 1600.0\n", - "3700 10.5 241.5 1600.0\n", - "3800 26.0 253.5 1600.0\n", - "3900 35.5 243.5 1600.0\n", - "4000 29.5 238.0 1600.0\n", - "4100 26.5 231.0 1600.0\n", - "4200 26.5 235.5 1600.0\n", - "4300 50.0 229.0 1600.0\n", - "4400 50.0 219.5 1600.0\n", - "4500 50.0 221.5 1600.0\n", - "4600 50.0 214.5 1600.0\n", - "4700 50.0 215.5 1600.0\n", - "4800 50.0 203.0 1600.0\n", - "4900 50.0 201.0 1600.0" + "0 50 48 48\n", + "100 11 356 1600\n", + "200 50 406 1600\n", + "300 21 463 1600\n", + "400 50 459 1600\n", + "500 50 430 1600\n", + "600 48 487 1600\n", + "700 50 495 1600\n", + "800 50 501 1600\n", + "900 50 472 1600\n", + "1000 50 478 1600\n", + "1100 50 548 1600\n", + "1200 5 552 1600\n", + "1300 3 538 1600\n", + "1400 21 504 1600" ] }, "metadata": {}, @@ -524,7 +275,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -533,7 +284,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -595,41 +346,41 @@ "outputs": [], "source": [ "from lcs.agents.xcs import Configuration\n", + "from utils.xcs_utils import *\n", + " \n", "\n", - "cfg = Configuration(\n", - " number_of_actions=algorithm.minimum_actions,\n", - " metrics_trial_frequency=100,\n", - " covering_wildcard_chance=1-algorithm.wildcard_probability,\n", - " max_population=algorithm.max_population_size,\n", - " learning_rate=algorithm.learning_rate,\n", - " alpha=algorithm.accuracy_coefficient,\n", - " epsilon_0=algorithm.error_threshold,\n", - " v=algorithm.accuracy_power,\n", - " gamma=algorithm.discount_factor,\n", - " ga_threshold=algorithm.ga_threshold,\n", - " chi=algorithm.crossover_probability,\n", - " mutation_chance=algorithm.mutation_probability,\n", - " deletion_threshold=algorithm.deletion_threshold,\n", - " delta=algorithm.fitness_threshold,\n", - " subsumption_threshold=algorithm.subsumption_threshold,\n", - " initial_prediction=algorithm.initial_prediction,\n", - " initial_error=algorithm.initial_error,\n", - " initial_fitness=algorithm.initial_fitness,\n", - " epsilon=algorithm.exploration_probability,\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n" + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " covering_wildcard_chance = 1,\n", + " ga_threshold = 25,\n", + " metrics_trial_frequency=100,\n", + " mutation_chance=0.03,\n", + " chi=1, # crossover\n", + " initial_prediction = 0.000001, # p_I\n", + " initial_error = 0.000001, # epsilon_I\n", + " initial_fitness = 0.000001, # F_I\n", + " user_metrics_collector_fcn=xcs_metrics)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [1631.9760602461931, 184.63689514930772, 908.0574351818735, 63.345367277620994, 221.79639072107003, 2039.5832761574534, 1707.1241019799998, 5412.924779436832], 'perf_time': 0.015201000000047316, 'population': 70, 'numerosity': 70}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [4.853293400638485e-09, 1.5513008505456238e-08, 4.853293400638485e-09, 1.0666607281520895e-08, 1.1330331416714414e-08, 2.25638247058798e-08, 2.3519964529236892e-08, 1.4966103277479953e-08], 'perf_time': 0.014919000000006122, 'population': 80, 'numerosity': 80}\n" ] }, { @@ -643,68 +394,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [5.015948264654275e+140, 1.6686500256505748e+140, 9.092811131373057e+140, 4.014581644828709e+140, 4.5254441889353626e+140, 1.1612715754276093e+141, 9.793244228443746e+139, 2.5211991980682765e+140], 'perf_time': 0.048124499999971704, 'population': 225, 'numerosity': 1600}\n", - 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"INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 50, 'reward': [0, 0, 0, 0, 0, 0, 0, 0], 'perf_time': 0.03585259999999835, 'population': 252, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 50, 'reward': [0, 0, 0, 0, 0, 0, 0, 0], 'perf_time': 0.037798899999984314, 'population': 252, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [3156.346990540088, 14.200000000000001, 2276.367805801234, 315.47387122232476, 241.0406372460797, 236.92856309367804, 909.5716423422446, 12402.832328982819], 'perf_time': 0.013974099999927603, 'population': 74, 'numerosity': 74}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [1.6088481061371442e+135, 4.7136464506352835e+135, 5.907279459274385e+135, 3.968936163131029e+135, 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'steps_in_trial': 38, 'reward': [46.063869274041984, 36.958552041707506, 138.06561230587332, 37.644000727333044, 37.77768317111649, 49.66088298933521, 39.68959259593951, 36.24199566945813], 'perf_time': 0.10360219999998321, 'population': 400, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [46.43657369975104, 42.95672701923821, 14.003551747776038, 14.003551747776038, 38.294279550416356, 0, 43.36722020471757, 68.89584633487222], 'perf_time': 0.04701449999998886, 'population': 399, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [138.6474988625682, 59.615932636793964, 76.91973299838686, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.04701449999998886, 'population': 399, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 132.49920370722705, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.046287799999987556, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 112.76022215037044, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.046287799999987556, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 91.96968631413897, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.04602849999997716, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 91.19673389012252, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.04602849999997716, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 79.171949950431, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.04738980000001902, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 120.75807051487772, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.04738980000001902, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 77.4303309496462, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.045242999999999256, 'population': 402, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [138.64749886257488, 59.615932636793964, 105.32423005298685, 86.25510974476694, 75.43735989735508, 68.64661051540335, 89.52060883903539, 73.69981570965375], 'perf_time': 0.045242999999999256, 'population': 402, 'numerosity': 1600}\n" ] } ], "source": [ + "from lcs.agents.xcs import XCS\n", + "\n", + "\n", + "agent = XCS(cfg)\n", "my_metrics = avg_experiment(scenario,\n", " cfg,\n", - " number_of_tests=2,\n", + " number_of_tests=1,\n", " explore_trials=exploration_cycles,\n", - " exploit_metrics=exploitation_cycles,\n", - " )\n" + " exploit_metrics=exploitation_cycles)\n" ] }, { @@ -749,353 +470,108 @@ " \n", " \n", " 0\n", - " 50.0\n", - " 0.014588\n", - " 72.0\n", - " 72.0\n", + " 50\n", + " 0.014919\n", + " 80\n", + " 80\n", " \n", " \n", " 100\n", - " 50.0\n", - " 0.043244\n", - " 173.5\n", - " 1600.0\n", + " 35\n", + " 0.060601\n", + " 329\n", + " 1600\n", " \n", " \n", " 200\n", - " 50.0\n", - " 0.048492\n", - " 196.5\n", - " 1600.0\n", + " 36\n", + " 0.080957\n", + " 361\n", + " 1600\n", " \n", " \n", " 300\n", - " 32.0\n", - " 0.043457\n", - " 218.0\n", - " 1600.0\n", + " 19\n", + " 0.051162\n", + " 384\n", + " 1600\n", " \n", " \n", " 400\n", - " 50.0\n", - " 0.059071\n", - " 231.0\n", - " 1600.0\n", + " 14\n", + " 0.030656\n", + " 397\n", + " 1600\n", " \n", " \n", " 500\n", - " 50.0\n", - " 0.057391\n", - " 240.5\n", - " 1600.0\n", + " 50\n", + " 0.110061\n", + " 409\n", + " 1600\n", " \n", " \n", " 600\n", - " 24.5\n", - " 0.032069\n", - " 257.5\n", - " 1600.0\n", + " 50\n", + " 0.117091\n", + " 418\n", + " 1600\n", " \n", " \n", " 700\n", - " 50.0\n", - " 0.068175\n", - " 270.5\n", - " 1600.0\n", + " 19\n", + " 0.044011\n", + " 415\n", + " 1600\n", " \n", " \n", " 800\n", - " 50.0\n", - " 0.068388\n", - " 278.0\n", - " 1600.0\n", + " 6\n", + " 0.013663\n", + " 408\n", + " 1600\n", " \n", " \n", " 900\n", - " 30.5\n", - " 0.041533\n", - " 286.0\n", - " 1600.0\n", + " 38\n", + " 0.103602\n", + " 400\n", + " 1600\n", " \n", " \n", " 1000\n", - " 16.5\n", - " 0.024139\n", - " 280.0\n", - " 1600.0\n", + " 50\n", + " 0.047014\n", + " 399\n", + " 1600\n", " \n", " \n", " 1100\n", - " 38.5\n", - " 0.050869\n", - " 281.0\n", - " 1600.0\n", + " 50\n", + " 0.046288\n", + " 402\n", + " 1600\n", " \n", " \n", " 1200\n", - " 24.5\n", - " 0.028866\n", - " 279.0\n", - " 1600.0\n", + " 50\n", + " 0.046028\n", + " 402\n", + " 1600\n", " \n", " \n", " 1300\n", - " 16.5\n", - " 0.022388\n", - " 268.5\n", - " 1600.0\n", + " 50\n", + " 0.047390\n", + " 402\n", + " 1600\n", " \n", " \n", " 1400\n", - " 41.5\n", - " 0.061662\n", - " 264.0\n", - " 1600.0\n", - " \n", - " \n", - " 1500\n", - " 7.0\n", - " 0.012961\n", - " 263.5\n", - " 1600.0\n", - " \n", - " \n", - " 1600\n", - " 29.5\n", - " 0.070150\n", - " 264.0\n", - " 1600.0\n", - " \n", - " \n", - " 1700\n", - " 31.0\n", - " 0.033161\n", - " 265.5\n", - " 1600.0\n", - " \n", - " \n", - " 1800\n", - " 33.0\n", - " 0.038297\n", - " 262.0\n", - " 1600.0\n", - " \n", - " \n", - " 1900\n", - " 40.5\n", - " 0.043452\n", - " 255.0\n", - " 1600.0\n", - " \n", - " \n", - " 2000\n", - " 36.0\n", - " 0.038903\n", - " 249.5\n", - " 1600.0\n", - " \n", - " \n", - " 2100\n", - " 50.0\n", - " 0.057827\n", - " 248.0\n", - " 1600.0\n", - " \n", - " \n", - " 2200\n", - " 30.0\n", - " 0.039046\n", - " 251.5\n", - " 1600.0\n", - " \n", - " \n", - " 2300\n", - " 39.0\n", - " 0.042940\n", - " 260.0\n", - " 1600.0\n", - " \n", - " \n", - " 2400\n", - " 23.0\n", - " 0.027091\n", - " 256.0\n", - " 1600.0\n", - " \n", - " \n", - " 2500\n", - " 37.0\n", - " 0.053047\n", - " 250.0\n", - " 1600.0\n", - " \n", - " \n", - " 2600\n", - " 32.0\n", - " 0.038229\n", - " 248.5\n", - " 1600.0\n", - " \n", - " \n", - " 2700\n", - " 35.5\n", - " 0.042225\n", - " 249.0\n", - " 1600.0\n", - " \n", - " \n", - " 2800\n", - " 46.0\n", - " 0.058555\n", - " 262.5\n", - " 1600.0\n", - " \n", - " \n", - " 2900\n", - " 24.0\n", - " 0.033518\n", - " 258.5\n", - " 1600.0\n", - " \n", - " \n", - " 3000\n", - " 20.0\n", - " 0.032154\n", - " 258.0\n", - " 1600.0\n", - " \n", - " \n", - " 3100\n", - " 28.5\n", - " 0.030484\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 3200\n", - " 37.5\n", - " 0.045396\n", - " 267.0\n", - " 1600.0\n", - " \n", - " \n", - " 3300\n", - " 23.5\n", - " 0.032998\n", - " 262.0\n", - " 1600.0\n", - " \n", - " \n", - " 3400\n", - " 9.5\n", - " 0.015328\n", - " 253.5\n", - " 1600.0\n", - " \n", - " \n", - " 3500\n", - " 20.0\n", - " 0.025490\n", - " 254.0\n", - " 1600.0\n", - " \n", - " \n", - " 3600\n", - " 50.0\n", - " 0.060601\n", - " 254.0\n", - " 1600.0\n", - " \n", - " \n", - " 3700\n", - " 22.0\n", - " 0.034700\n", - " 256.5\n", - " 1600.0\n", - " \n", - " \n", - " 3800\n", - " 27.0\n", - " 0.032408\n", - " 259.5\n", - " 1600.0\n", - " \n", - " \n", - " 3900\n", - " 40.5\n", - " 0.061574\n", - " 257.0\n", - " 1600.0\n", - " \n", - " \n", - " 4000\n", - " 50.0\n", - " 0.043773\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4100\n", - " 50.0\n", - " 0.035172\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4200\n", - " 50.0\n", - " 0.045444\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4300\n", - " 50.0\n", - " 0.037541\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4400\n", - " 50.0\n", - " 0.036748\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4500\n", - " 50.0\n", - " 0.036683\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4600\n", - " 50.0\n", - " 0.037951\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4700\n", - " 50.0\n", - " 0.034766\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4800\n", - " 50.0\n", - " 0.036899\n", - " 255.5\n", - " 1600.0\n", - " \n", - " \n", - " 4900\n", - " 50.0\n", - " 0.036354\n", - " 255.5\n", - " 1600.0\n", + " 50\n", + " 0.045243\n", + " 402\n", + " 1600\n", " \n", " \n", "\n", @@ -1104,56 +580,21 @@ "text/plain": [ " steps_in_trial perf_time population numerosity\n", "trial \n", - "0 50.0 0.014588 72.0 72.0\n", - "100 50.0 0.043244 173.5 1600.0\n", - "200 50.0 0.048492 196.5 1600.0\n", - "300 32.0 0.043457 218.0 1600.0\n", - "400 50.0 0.059071 231.0 1600.0\n", - "500 50.0 0.057391 240.5 1600.0\n", - "600 24.5 0.032069 257.5 1600.0\n", - "700 50.0 0.068175 270.5 1600.0\n", - "800 50.0 0.068388 278.0 1600.0\n", - "900 30.5 0.041533 286.0 1600.0\n", - "1000 16.5 0.024139 280.0 1600.0\n", - "1100 38.5 0.050869 281.0 1600.0\n", - "1200 24.5 0.028866 279.0 1600.0\n", - "1300 16.5 0.022388 268.5 1600.0\n", - "1400 41.5 0.061662 264.0 1600.0\n", - "1500 7.0 0.012961 263.5 1600.0\n", - "1600 29.5 0.070150 264.0 1600.0\n", - "1700 31.0 0.033161 265.5 1600.0\n", - "1800 33.0 0.038297 262.0 1600.0\n", - "1900 40.5 0.043452 255.0 1600.0\n", - "2000 36.0 0.038903 249.5 1600.0\n", - "2100 50.0 0.057827 248.0 1600.0\n", - "2200 30.0 0.039046 251.5 1600.0\n", - "2300 39.0 0.042940 260.0 1600.0\n", - "2400 23.0 0.027091 256.0 1600.0\n", - "2500 37.0 0.053047 250.0 1600.0\n", - "2600 32.0 0.038229 248.5 1600.0\n", - "2700 35.5 0.042225 249.0 1600.0\n", - "2800 46.0 0.058555 262.5 1600.0\n", - "2900 24.0 0.033518 258.5 1600.0\n", - "3000 20.0 0.032154 258.0 1600.0\n", - "3100 28.5 0.030484 255.5 1600.0\n", - "3200 37.5 0.045396 267.0 1600.0\n", - "3300 23.5 0.032998 262.0 1600.0\n", - "3400 9.5 0.015328 253.5 1600.0\n", - "3500 20.0 0.025490 254.0 1600.0\n", - "3600 50.0 0.060601 254.0 1600.0\n", - "3700 22.0 0.034700 256.5 1600.0\n", - "3800 27.0 0.032408 259.5 1600.0\n", - "3900 40.5 0.061574 257.0 1600.0\n", - "4000 50.0 0.043773 255.5 1600.0\n", - "4100 50.0 0.035172 255.5 1600.0\n", - "4200 50.0 0.045444 255.5 1600.0\n", - "4300 50.0 0.037541 255.5 1600.0\n", - "4400 50.0 0.036748 255.5 1600.0\n", - "4500 50.0 0.036683 255.5 1600.0\n", - "4600 50.0 0.037951 255.5 1600.0\n", - "4700 50.0 0.034766 255.5 1600.0\n", - "4800 50.0 0.036899 255.5 1600.0\n", - "4900 50.0 0.036354 255.5 1600.0" + "0 50 0.014919 80 80\n", + "100 35 0.060601 329 1600\n", + "200 36 0.080957 361 1600\n", + "300 19 0.051162 384 1600\n", + "400 14 0.030656 397 1600\n", + "500 50 0.110061 409 1600\n", + "600 50 0.117091 418 1600\n", + "700 19 0.044011 415 1600\n", + "800 6 0.013663 408 1600\n", + "900 38 0.103602 400 1600\n", + "1000 50 0.047014 399 1600\n", + "1100 50 0.046288 402 1600\n", + "1200 50 0.046028 402 1600\n", + "1300 50 0.047390 402 1600\n", + "1400 50 0.045243 402 1600" ] }, "metadata": {}, @@ -1172,7 +613,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1181,7 +622,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1198,7 +639,7 @@ "ax = my_metrics['steps_in_trial'].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" + "ax.legend([\"steps\"])\n" ] }, { @@ -1209,7 +650,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1218,7 +659,7 @@ }, { "data": { - "image/png": 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CbrrpJlaubHQCDgC+/vWvc+ONN3LyySczfvx4BgwY0Ow5OpLmSsVFhL6Nd6jf1/GvtONDmXJFzy8X6TxKS0tZujS0tP/hD39o0bF9+vRh2LBhPProo0C4ie6NN95osF/fvn3p378/L7/8MgD3338/J5xwAtXV1Xz88cecdNJJ/OxnP2Pbtm3s2lV/CsDevXuzc+fO2vfFxcWcfvrpXHbZZfzLv3S+Ox+aLBnd/UN3X+jux7j7X5OWv7l7vL0ymSuxeIJiPb9cpFO45ppruPvuu5k8eTJbtmxp8fEPPvggv/vd7/jCF77AqFGjePLJJ9Pud++993LttdcyZswYli9fzg033EAikeCCCy7gyCOPZNy4cVx11VX069ev3nHTp0/n5z//OePGjWPNmjUAnH/++ZgZp512Wovzm2vm3vxjL6JZbe8ARhCmZM8Hdrt7n+xmr/UmTJjgNSMvWuvaR9/g1dVbWHR92um4RLqdt99+mxEjRuQ6G13Grbfeyvbt27nppptycv50f08zW+ruE5o7NtMBy3cSnjH+KDCBMDX6YS3MZ6cTi1dTpJqHiGTBOeecw5o1a2pHdHU2Gd/t4u6rzSw/mir9HjNblMV8dQixeEJ9HiKSFY8//nius7BXMg0ee8ysB7DczH4GbAC6way66jAXEUkn05LxW4R+jiuA3YRHxn49W5nqKGJV1ZqaREQkjUwnRvwwWv0MuDF72elYYvEEvYoyn8dGRKS7aO4mwbdIMyFiDXcf05qTmtlVwKXRZ79FmN69J/AIUAqsA86NZvPFzK4HLgESwA/d/c+tOW9LxeLV7NdLzVYiIqmaKxnPBM5qYmkxMxtCmN59gruPJjSHTQdmAi+4+3Dgheg9ZjYy2j6KcGPiXWbWLm1Joc9DzVYi3UHyxIrZdPvttzNixAjOP//8Vn9Ge+W1Kc1NjPhhU9v38rz7mFkVocaxHrgeODHafi+wELgOmAo87O4xYK2ZrQYmAq9lKW+1NNpKRDLRkqna77rrLp5++ukmH//a0qnfcyGjktHMdprZjmipMLOEmbVqOk13/zthivePCKO2trv7s8AB7r4h2mcDsH90yBDg46SPKI/S0uVzhpmVmVnZ5s2bW5O9emJV1ZpRV6QDaWw6dmCvp2QHeOCBB5g8eTKjR49myZIlAOzevZuLL76Yo48+mnHjxtXeeT5v3jymTZvGWWedlfYO8dmzZzN69GhGjx7NbbfdBsD3vvc9PvjgA6ZMmcIvfvGLevunft7ChQs588wza7dfccUVzJs3r8F5nn32WY455hiOOuoopk2bVjstysyZM2uniL/mmmta8W03LdMO897J783sbMKv/xYzs/6E2sQwYBvwqJld0NQh6bLUSD7nAHMg3GHemvwlU7OVSBOengn/eKttP/PAI+Gfbm5yl3TTsV9wQVNFSGZTskMIFIsWLeKll17i4osvZsWKFcyaNYuTTz6ZuXPnsm3bNiZOnMiXv/xlAF577TXefPNN9ttvv3rnW7p0Kffccw+LFy/G3fniF7/ICSecwK9+9SueeeYZXnzxRQYOHNggn8mft3Dhwma/ri1btvDTn/6U559/nl69enHLLbcwe/ZsrrjiCh5//HHeeecdzKzBFPFtoVX1Ind/wsxmtvKcXwbWuvtmADP7IzAZ2Ghmg919g5kNBjZF+5cThgbXKCE0c2Wdmq1EOp6WTscOdVOy9+7du8GU7G+++Wbtfueddx4Axx9/PDt27GDbtm08++yzLFiwgFtvvRUIM/Z+9NFHAJx66qkNAgfAK6+8wjnnnFM7G+/XvvY1Xn75ZcaNG9dkPhv7vMa8/vrrrFq1imOPPRaAyspKjjnmGPr06UNxcTGXXnopX/3qV+vVYNpKRsHDzL6W9DaPMEVJa3/ZfwRMMrOehKG/pwBlhPtHLgRujl5rZiVbAPzezGYDBwHDgSWtPHfG3F03CYo0pZkaQrakm44d2Ksp2WuY1W/oMDPcnccee4wjjjii3rbFixc3OlV7JnMGppP8ecnXAw2vqeY8p556Kg899FCDbUuWLOGFF17g4Ycf5s4772zzaVAyLRmTR1idDuwkND21mLsvBv4A/I0wTDeP0NR0M3Cqmb0PnBq9x91XAvOBVcAzwOXRFClZVZVw3NHcViKdxN5MyV7jkUceAULNoW/fvvTt25fTTz+dO+64ozYgLFu2rNnPOf7443niiSfYs2cPu3fv5vHHH+e4445rUV4OOeQQVq1aRSwWY/v27bzwwgsN9pk0aRKvvvoqq1evBmDPnj2899577Nq1i+3bt/OVr3yF2267rdHnlOyNTPs82nSyeXf/CfCTlOQYoRaSbv9ZwKy2zENzKvT8cpFO5ZprruHcc8/l/vvv5+STT27VZ/Tv35/JkyezY8cO5s6dC8B//ud/cuWVVzJmzBjcndLSUp566qkmP+eoo47ioosuYuLE0DV86aWXNttklWro0KGce+65jBkzhuHDh6c9ftCgQcybN4/zzjuPWCwGwE9/+lN69+7N1KlTqaiowN0bdM63hUynZD8U+CUwidBc9Rpwlbt/0OY5aiN7OyX75p0xjp71PDdNHcW3jiltu4yJdGKakr1r2Zsp2TP9Wf17QtPRYEK/w6NAw0a2LiRWW/NQs5WISKpMg4e5+/3uHo+WB2h9h3mnEIuHjird5yEi0lCmQ3VfjIbmPkwIGv8M/MnM9gNw90+aOrgzilVFwUN9HiIiDWQaPP45ev1uSvrFhGByaJvlqINQs5VIeu7eYEirdD6tHU5cI9PRVo1PwtJF1TZbqeYhUqu4uJitW7cyYMAABZBOzN3ZunUrxcXFrf6MTG8SLAQuA46PkhYCv3b3qlafuYNTn4dIQyUlJZSXl9MWc8dJbhUXF1NSUtLq4zNttrobKATuit5/K0q7tNVn7uBiVWq2EklVWFjY5Gyw0n1kGjyOdvcvJL3/i5m9kY0MdRQ1NY9i1TxERBrItGRMmNnnat5ENw1mfYqQXKrr81DNQ0QkVaY1j2sJw3Vr7igvJTw6tsuKaXoSEZFGZVoyvgr8GqiOll/TDk/yy6W6+zxU8xARSZVpzeM+YAdwU/T+POB+YFo2MtURaLSViEjjMg0eR6R0mL/Y9TvMQ7NVj3wFDxGRVJmWjMvMbFLNGzP7IqEpq8uKxavpkZ9HXp5uhBIRSZVpzeOLwLfN7KPo/cHA22b2FuDuPiYrucuhWJWeIigi0phMg8cZbXlSM+sH/BYYTZgb62LgXeARwkiudcC57v5ptP/1wCWE4cE/dPc/t2V+0onFE+rvEBFpRKZzW33Yxuf9JfCMu3/DzHoAPYF/B15w95ujGXxnAteZ2UhgOjCK8CyR583s8Gw/ijY8v1wjrURE0mn3n9Zm1ocwR9bvANy90t23EZ6Jfm+0273A2dH6VOBhd4+5+1pgNTAx2/kMwUM1DxGRdHJROh4KbAbuMbNlZvZbM+sFHODuGwCi1/2j/YcAHycdXx6lZVWsKkEPBQ8RkbRyUToWAEcBd7v7OGA3oYmqMemGO6WdiN7MZphZmZmV7e2sn7F4NUWFarYSEUknF8GjHCh398XR+z8QgslGMxsMEL1uStp/aNLxJcD6dB/s7nPcfYK7Txg0aNBeZTIWT6jZSkSkEe1eOrr7P4CPzeyIKOkUYBWwALgwSrsQeDJaXwBMN7MiMxsGDAeWZDufFRqqKyLSqEyH6ra1HwAPRiOtPiBMspgHzDezS4CPiKY+cfeVZjafEGDiwOXZHmkFodlqoEZbiYiklZPg4e7LgQlpNp3SyP6zgFnZzFMq3echItI4lY6N0B3mIiKNU+nYCN0kKCLSOAWPRmi0lYhI41Q6NiLc56GvR0QkHZWOabg7lWq2EhFplIJHGrVPEVSzlYhIWiod06gJHsWankREJC0FjzRqHkGrmoeISHoqHdOIVanZSkSkKSod06jt81CzlYhIWgoeaajZSkSkaSod09BoKxGRpql0TKOuz0PNViIi6Sh4pFHbbKU7zEVE0lLpmIaarUREmqbSMY264KFmKxGRdBQ80ohVabSViEhTclY6mlm+mS0zs6ei9/uZ2XNm9n702j9p3+vNbLWZvWtmp2c7b3X3eSh4iIikk8vS8UfA20nvZwIvuPtw4IXoPWY2EpgOjALOAO4ys6y2J6nZSkSkaTkJHmZWAnwV+G1S8lTg3mj9XuDspPSH3T3m7muB1cDEbOZPNwmKiDQtV6XjbcC/AdVJaQe4+waA6HX/KH0I8HHSfuVRWgNmNsPMysysbPPmza3OXIXmthIRaVK7l45mdiawyd2XZnpImjRPt6O7z3H3Ce4+YdCgQa3OYyyeoEdBHmbpTi0iIgU5OOexwBQz+wpQDPQxsweAjWY22N03mNlgYFO0fzkwNOn4EmB9NjMYq6pWrUNEpAntXkK6+/XuXuLupYSO8L+4+wXAAuDCaLcLgSej9QXAdDMrMrNhwHBgSTbzGNMjaEVEmpSLmkdjbgbmm9klwEfANAB3X2lm84FVQBy43N0T2cxILJ5QzUNEpAk5DR7uvhBYGK1vBU5pZL9ZwKz2ylcsXq17PEREmqASMo3Q56FmKxGRxih4pKFmKxGRpqmETCN0mOurERFpjErINEKfh5qtREQao+CRRqwqQbFqHiIijVIJmUalah4iIk1S8EhDfR4iIk1TCZmGRluJiDStI91h3mF0ufs8qhPw6TrY9DZseQ8KiqDfwXVLcT/QJJAi0gIKHml02jvM45XwyQchQGx5Dza/A5veCeuJWOPHFfWJgkhfSFRBohKq43XrOBT2DEuPnnXrxX1g8Beg5GgYNALy9c9JpLvQ//YU1dVOZaIT9HnEdsK6V+DDRbDl/RAgPl0HydN+9R0Kgz4PnzsxvA4aAYMOD0Fh20cpy4cQ2wU9ekF+P8jvAXkFkF8YPqvqM6jaE14/Wx/W92yFpfPC9sJeMOQoKJkAQyZA/1LY9wDouR/kdaFaXHcWj0FeIeR18P8b0i4UPFJUJjroI2gTcVi/DD54Eda8COVLQu0gvwcMOAwOHA2jvwYDD4eBw0NaUe/GP6/nfnDQ2L3Lk3sIOuVlUP6/YVl0J1RX1e1j+dBrEOy7f1h69ALLC+mWFwJLzVOFvToEv+pE/fV4DOIVSa8VIQAW94V9+odr2ac/7LNfWO87NFx//1IoLN67a2zJd7FjfbimngOgoEf7nBfgk7Xh30W8EnoNDEvPmtcBdT8AWmrXZvjotfAD5aNF8I+3wt+q94HQezD0GRxeew+G/Q6FA0aF77y9fixUJyC2I6z36K2abzvTt50i1pGeIpiIwwcL4a358N4zULEdsNBUNPkHcOhJMPSL7VdApjILhUX/UjjyGyGtqgI2roAdf4ddm2DXxmjZBDv/Ads+ThMkqkPhmxcFlOSgklcQ+mgKikPg6TkgvM8rCAXHZ5+GmtNnn8Bn26j/nDCLAsnnwtK/FPoMgb4l4bX3gS0v6NzDOTe/G65z0yrYuCq81hRkEAJbz4EhcPYaGM6V3M/U75BwLa3pa6rcE2qdq58Pyydrmt6/YJ+679Osbj2/R/hOi/YNPzR67Fv3g6O8DLa+Hx1fHJomv3RV+Fvt2AA7N4Q+tDUv1r/uwp6hlnvASDhgdPiePfo7Vyf93dM/zy3sV7k71IIrd4YadmwXVO6Cih1QsS38P6jYXv+8NddZtG/ddfTYt+7fTkFR3ZJf1PX7+E7/r9b/aMiQgkeK2ueX56rPwz38x31rPqz4I+zZEgqiz58Jh50Cw06EXgNyk7dMFBaHpismtP+5q6ujYLIOtn4QCtWtq2HrGnjr0Sj4JrF86HNQKNwL9qlf0BTuE4LYZ5+GZc8nUYD6NNT4ahT1DQXlmHNh/xGAwe4t4e+2ewvs3hz6oda9Egq+ZIU9Q9NeYcq5C4rDf3z3qNBN1BXAlXtCDTQRC3kedhx88bvwuVNC7Wv35vrn3rM1FLw1BXfy5yWqQuFcuSsU0Ls2hddEZfiBMu4COGQyDB7bdE0qtjM0nW5aBRtXhuXdZ2DZA3v398wvCkGgaN9QsyjuG4Jucd+kpU/4zit3hWAS21V3TZW7w7Jna/3aa1P9f13FqTcpeLS3WLydm62qE6GA2/BGKBTe/Z/Qd1FQDIefAUdOg+GnhkJFmpaXFwJrrwEwZHzD7RXbYXs5bP87bP841I62/z0UsonKUODs2VJX0FRXwz79QqG8/+eTmsYGhKbBA0aFX9aZ/oqt2B5qXsl9Tbs21m+Sq9wTAlS8MqqB5dVv5ssvhInfCT8kDp7csNaZix8WRb1Df9eQo+qn19Q8LT9NrbKxH2cW1YZ6Z73wk72j4JGituaRrWarnRth7V/h70tDwNjwJlTtDtsKiuHgY+CE60JNo7hPdvLQXdX8Wj1gVO7Of2Df0D/VHdT0c0mXpOCRoqKt+zwq94TOxjVRR/emlSG9sCccOCY0DRw0NjQTDDxCnX4i0im0e0llZkOB+4ADgWpgjrv/0sz2Ax4BSoF1wLnu/ml0zPXAJUAC+KG7/zlb+avr89iLZqvqBLzzFJTNDSNVEpWhc/LgY+DL/yd0dB94pIawikinlYufuXHgX939b2bWG1hqZs8BFwEvuPvNZjYTmAlcZ2YjgenAKOAg4HkzOzxbzzHfq9FWsZ2w7EF4/a4whLXfITBxBnzupNA+3aNnG+dWRCQ32j14uPsGYEO0vtPM3gaGAFOBE6Pd7iU82/y6KP1hd48Ba81sNTAReC0b+avrMG9B8Nj+d1jyayibB7HtYfjsaT+Fz39VtQsR6ZJy2sBuZqXAOGAxcEAUWHD3DWZW09M2BHg96bDyKC3d580AZgAcfPDBrcpTXYd5BoX+lvfhlV/Am4+EIZAjpsAxV8DQo1t1bhGRziJnwcPM9gUeA6509x3W+HDHdBvS3mHk7nOAOQATJkxo5C6kptXWPJq6z2PDG/DybFj1ZBghNeESOOb74SY0EZFuICfBw8wKCYHjQXf/Y5S80cwGR7WOwcCmKL0cGJp0eAmwPlt5a7LP46PF8PKt8P6zYTLBL10Fk74P+w7KVnZERDqkXIy2MuB3wNvuPjtp0wLgQuDm6PXJpPTfm9lsQof5cGBJtvLXaLPVi/8Ff70l3CR28n/A0d8JN5CJiHRDuah5HAt8C3jLzJZHaf9OCBrzzewS4CNgGoC7rzSz+cAqwkity7M10goaabbavQVevT3cuPe1OeEOWBGRbiwXo61eIX0/BsApjRwzC5iVtUwlSTvaavGvwtQRp/xEgUNEBD2GtoFYVQIz6JEffTUVO2DxHBhxZngWhoiIKHikisXDg6BqR3+VzQ33bnzp6txmTESkA1HwSBGCR9RZXlUR7hY/9MSGM4aKiHRjCh4pYvFEXX/H8gfDlNKqdYiI1KPgkSJWVR1GWiXi8Oovw3Mhhh2f62yJiHQoCh4paputVj4eJjf80tVd/5GVIiItpOCRIhZPUJwPvDI7PIv5iK/kOksiIh2OgkeKWLyaydVLw/OYj70yPAZURETqUcmYIlaZ4Bt75kPfg+HIb+Q6OyIiHZKCR4rDKt7g8Kq34dgfQn5hrrMjItIhKXik+Nru+ezI7xeeLS4iImkpeCSrTlA54POsPPQ7ULhPrnMjItJh5fRJgh1OXj6TL7s717kQEenwVPMQEZEWU/AQEZEWU/AQEZEWU/AQEZEW6zTBw8zOMLN3zWy1mc3MdX5ERLqzThE8zCwf+H/APwEjgfPMbGRucyUi0n11iuABTARWu/sH7l4JPAxMzXGeRES6rc4SPIYAHye9L4/S6jGzGWZWZmZlmzdvbrfMiYh0N53lJsF0D9TwBgnuc4A5AGa22cw+bOX5BgJbWnlsZ6br7l503d1Lptd9SCYf1lmCRzkwNOl9CbC+qQPcfVBrT2ZmZe4+obXHd1a67u5F1929tPV1d5Zmq/8FhpvZMDPrAUwHFuQ4TyIi3VanqHm4e9zMrgD+DOQDc919ZY6zJSLSbXWK4AHg7v8D/E87nW5OO52no9F1dy+67u6lTa/b3Bv0O4uIiDSps/R5iIhIB6LgISIiLabgkaSrzZ9lZnPNbJOZrUhK28/MnjOz96PX/knbro+u/V0zOz0pfbyZvRVtu93M0t1302GY2VAze9HM3jazlWb2oyi9S1+7mRWb2RIzeyO67huj9C593TXMLN/MlpnZU9H77nLd66I8Lzezsigt+9fu7lpCv08+sAY4FOgBvAGMzHW+9vKajgeOAlYkpf0MmBmtzwRuidZHRtdcBAyLvov8aNsS4BjCzZpPA/+U62tr5roHA0dF672B96Lr69LXHuVx32i9EFgMTOrq1510/VcDvweeit53l+teBwxMScv6tavmUafLzZ/l7i8Bn6QkTwXujdbvBc5OSn/Y3WPuvhZYDUw0s8FAH3d/zcO/sPuSjumQ3H2Du/8tWt8JvE2YzqZLX7sHu6K3hdHidPHrBjCzEuCrwG+Tkrv8dTch69eu4FEno/mzuoAD3H0DhEIW2D9Kb+z6h0TrqemdgpmVAuMIv8K7/LVHTTfLgU3Ac+7eLa4buA34N6A6Ka07XDeEHwjPmtlSM5sRpWX92jvNfR7tIKP5s7qwxq6/034vZrYv8BhwpbvvaKIJt8tcu7sngLFm1g943MxGN7F7l7huMzsT2OTuS83sxEwOSZPW6a47ybHuvt7M9geeM7N3mti3za5dNY86LZ4/q5PaGFVRiV43RemNXX95tJ6a3qGZWSEhcDzo7n+MkrvFtQO4+zZgIXAGXf+6jwWmmNk6QnPzyWb2AF3/ugFw9/XR6ybgcUITfNavXcGjTneZP2sBcGG0fiHwZFL6dDMrMrNhwHBgSVTl3Wlmk6LRF99OOqZDivL5O+Btd5+dtKlLX7uZDYpqHJjZPsCXgXfo4tft7te7e4m7lxL+3/7F3S+gi183gJn1MrPeNevAacAK2uPacz1SoCMtwFcII3PWAD/OdX7a4HoeAjYAVYRfFpcAA4AXgPej1/2S9v9xdO3vkjTSApgQ/YNcA9xJNDNBR12ALxGq3G8Cy6PlK1392oExwLLoulcAN0TpXfq6U76DE6kbbdXlr5swOvSNaFlZU261x7VrehIREWkxNVuJiEiLKXiIiEiLKXiIiEiLKXiIiEiLKXiIiEiLKXiItBEz62dm329i+6IMPmNXc/uIdAQKHiJtpx/QIHiYWT6Au09u7wyJZIvmthJpOzcDn4smJqwCdhFu0hwLjDSzXe6+bzTn1pNAf8LMt//h7h36TmaRVLpJUKSNRDP4PuXuo6MJ+v4EjPYw9TVJwaMA6OlhssaBwOvAcHf3mn1ydAkiGVPNQyR7ltQEjhQG/JeZHU+YQnwIcADwj/bMnMjeUPAQyZ7djaSfDwwCxrt7VTQbbHG75UqkDajDXKTt7CQ89rY5fQnPn6gys5OAQ7KbLZG2p5qHSBtx961m9qqZrQA+AzY2suuDwH+bWRlhxt+mHt4j0iGpw1xERFpMzVYiItJiCh4iItJiCh4iItJiCh4iItJiCh4iItJiCh4iItJiCh4iItJi/x8Urbljm0J3cgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1237,18 +678,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "#### Conclusions\n", + "None so far." + ] }, { "cell_type": "code", diff --git a/XCS_Experiments/XCS_compared_Maze5_without_#.ipynb b/XCS_Experiments/XCS_compared_Maze5_without_#.ipynb deleted file mode 100644 index 2349d39..0000000 --- a/XCS_Experiments/XCS_compared_Maze5_without_#.ipynb +++ /dev/null @@ -1,1358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "exploration_cycles = 4000\n", - "exploitation_cycles = 1000\n", - "input_size = 8\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(input_size)\n", - "scenario.maze.reset()\n", - "scenario.maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "algorithm = XCSAlgorithm()\n", - "algorithm.max_population_size = 200\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 25\n", - "algorithm.crossover_probability = 0.5\n", - "algorithm.mutation_probability = 0.01\n", - "algorithm.initial_prediction = float(np.finfo(np.float32).tiny) # p_I\n", - "algorithm.initial_error = float(np.finfo(np.float32).tiny) # epsilon_I\n", - "algorithm.initial_fitness = float(np.finfo(np.float32).tiny) # F_I\n", - "algorithm.wildcard_probability = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n", - "Executing 1 experiment\n", - "Executing 2 experiment\n", - "Executing 3 experiment\n", - "Executing 4 experiment\n" - ] - } - ], - "source": [ - "other_metrics = other_avg_experiment(\n", - " maze=scenario,\n", - " algorithm=algorithm,\n", - " number_of_tests=5,\n", - " explore_trials=exploration_cycles,\n", - " exploit_trials=exploitation_cycles\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " steps_in_trial population numerosity\n", - "trial \n", - "0 30.8 28.8 29.6\n", - "100 44.0 141.6 200.0\n", - "200 41.2 143.2 200.0\n", - "300 35.6 137.4 200.0\n", - "400 39.2 139.6 200.0\n", - "500 45.0 136.2 200.0\n", - "600 32.8 137.0 200.0\n", - "700 36.6 137.6 200.0\n", - "800 38.4 142.6 200.0\n", - "900 31.4 135.2 200.0\n", - "1000 30.0 139.2 200.0\n", - "1100 24.6 141.0 200.0\n", - "1200 49.4 139.8 200.0\n", - "1300 48.8 136.6 200.0\n", - "1400 40.2 140.0 200.0\n", - "1500 44.8 138.4 200.0\n", - "1600 44.6 142.0 200.0\n", - "1700 16.0 141.4 200.0\n", - "1800 49.8 138.0 200.0\n", - "1900 25.8 138.6 200.0\n", - "2000 36.2 137.2 200.0\n", - "2100 43.0 140.8 200.0\n", - "2200 32.0 138.2 200.0\n", - "2300 25.0 138.8 200.0\n", - "2400 30.4 136.6 200.0\n", - "2500 34.2 140.0 200.0\n", - "2600 31.4 136.4 200.0\n", - "2700 41.4 135.8 200.0\n", - "2800 32.6 136.8 200.0\n", - "2900 33.4 141.4 200.0\n", - "3000 42.8 135.6 200.0\n", - "3100 16.8 140.4 200.0\n", - "3200 50.0 140.8 200.0\n", - "3300 39.0 140.8 200.0\n", - "3400 30.4 136.0 200.0\n", - "3500 34.0 138.8 200.0\n", - "3600 43.6 132.2 200.0\n", - "3700 25.2 139.0 200.0\n", - "3800 41.2 139.0 200.0\n", - "3900 17.8 139.0 200.0\n", - "4000 20.8 142.6 200.0\n", - "4100 22.4 143.4 200.0\n", - "4200 42.4 150.4 200.0\n", - "4300 26.4 148.8 200.0\n", - "4400 48.8 142.4 200.0\n", - "4500 38.4 145.2 200.0\n", - "4600 37.8 142.8 200.0\n", - "4700 37.2 149.4 200.0\n", - "4800 36.6 138.4 200.0\n", - "4900 32.2 144.6 200.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(other_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = other_metrics[\"steps_in_trial\"].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps other XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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yf/J48zbIbQVD/uSvnr94GRZOA8z/cXf/EXw40d/heupYf0Ku7cqqaCffgXfgSJ//8SfBgAvhuL/4bTeu3Fbz2PiNr2HtfIAPQImuuD57Hv5zpb9yHvwHXxvMzfczgL4zFt69x980dfAoOODHfn8bVsTVcFZC+RYfVA86z1/1JbLkDf/38cOXcMBwf9PWxDNhwAVwwk31a9KLRv2EdG/f7ss5/GHfBHreMzDuRHjsHLjgBdh5/8rbRcp9gP5oki/fqWPrNghj/7N9UHn9RtiyGoZPTP43vXKeH/Gz4z7+7yEV7/3Ln/ATz8iTXF5rOOkWGPiLplXDqY+infzfyvv3+eUT/1q/psR6MOfq+AtpQoqLi93s2fW/jf3dJesY+cB7TD27Pf2fH+r/+Gv7hZSVwCNn+Qh/+K/h8xdhzUI/i2T/kf4EWNTN3xwz637//tTbYe9Ta8+Qc/4KKXZiWrfYnxSKdvZXiYmCwPdfwn1H+X/KC1+s/M//7QJ46gLfhzFoDBx5Vd2uAsMhf5X91t/8Cf7I38IRV/g26uUf+Hbg1Z/4wHPybdtGWzkH3873weKLl/xIsV5H+au9Tr1SP35MaDPM+H++Gg4+L5WYD/BlQZt6xx4+CPYeDN0O9CfV+U/4eXrOuHfbTUzx1i3xtbKPJlW+os3J9+Vqv6tPX/GBD4j7nuF/1z0O9UFry1p4+Y8+GHfuA0PvgN0H+VrFGzf7zssO3f3ghz7H1P07KN8KU3/lm+EO/llwos/f9vkPX/lJ6VwELnrJ1zLAX40+OcoPyjjmj36M/vb2qc191Aebnfb1TZ7xo4RWf+bLuXCa/34iZb45MVGNO97SN2HiGb6m1feEOmTGoO9x28rZkmxcCfceBv1/6oNEA/aBmtkc51xK1RIFCWDG56u5YPwHvHRWK/Z+4cf+imyPY2vfcOt6mDAUvvsYdjnIdxzufza0alN5vRWzfbPA6k98O+bJt0G7nXzHbWxoXuy1YXlwtVpSeR/dDoSfPlVzM8jHT8Pki/0JYMiftrX9vzjGN42d/e/aO9lqsuEbeOVP/gTVsafvHPxoUuoBsKzEB5b6/rGvnAufTIE2XSqPhirqBjl5viP3yzeD19u+eRD8Z0ddA0ddXXuQXL/cB7iinf2oq7ZdK1+lrl4Is8f78oc2+n6cPU+CORP88pFX+WNV7RBfPssPYVy32AeX4/5Se8dquMzX6r79GGaP8wHquBvgiN8k/i5Xf+ZrXAXt4aKXfcdpbCTcaXf64FJfi17zwzrb7uD/X3LyfNPa/Ccgv43vfD3kUj953tI3fXNYsmbC9cvh/kH+O77ktcYbONEchENpGcWkIFFHL3/yLb+YOIcZZ0To9dL5cPGrvtM2FaUb/RX/jvvUvF6k3DfbvHmrX45GfJNUTEF735basee2k177Xf2rw67QbufUqtJTf+Xbqkc85vsePnkG+hzrx2XH+gXq68u3fJv+ms99B+GQP9WvKS2dohF/clw+y5+kGnqocNkWH5xnPwirPvId/af9w19lJ1O+1dcw3/0n4KDNDpV/1+138Vfh333q877ms21/KwUdYNjdsO+wmvO1Yg48dJqvTZWX+PsDzpngR+s1lG8+9DeVhUP+GDl5vmn1iKt88ABfI37gWN88O3oGdNyt8j7KS30T2fdL4edvQJc9Gi5/kpSCRB1N+2glV0yay7vDNtHt5V/AL2fCTvs1QA4T+H6pb3IoKPJjtmOvtl0apjoZ2uyvytYt9sPlhvwJjriy4dtqI+WwebU/qYmvtW361tcQU/2uv5njR5hV9Kms9P0fsZpPu518UNv5ANhpf9i5n7+QyElx+pilM3wNoqDI10K7D9iektXs+6Uw5VKfv6N/l/jGzjVfwL+P9U2MF728rabtnB8CPe8Rf4Pa3qdU31bSoi5BIuVeq+BxojvFb+Oc+7ru2Wt6QuW+47pVZKtPSMdQxZjOu8PQsenbf0E7f8X42l/8P22PQ9NznNx8BYh4ZnW/833XAdtGqMUr2+KvsGNX49tr98H+6r11J187SYfOu8PFr9S8Ttc9fVPnYz/xz0748Tj/fc0e5wPE0f+jANGEpRQkzOzX+FlcvwNivYUOSNDz1/zE7pNoFU1hdFNzsPMBcN7kTOdCtlertg13oZKuGnFd7XkiHHudHzq+8/5+AMOLv/ed1IMb+DkN0qBSrUn8BtjLObcunZnJlFiQyG+MmoRItjryKn+vz/Qb/ZDdDt39HcUtZdhqC5Xqb2c5sCGdGcmk2H0SeeES346f14ynaBBpqsz80N9u/Xxn908e8U1h0qSlWpNYCswws+chdlsyOOfS2LjeeGJ3XOeGt/impkxP3ifSUrVq4+/jKfm++kgnaZJSDRJfB69WwatFCYWjtMrLwco2q6lJJN0ass9F0i6lIOGcuwHAzIr8okthJrDmIxSO+GdJlG3RH6+ISJyU+iTMbH8zmwssAD4xszlm1kSGTdSff751roKEiEgVqXZc3w/81jnX0znXE7gaeCB92WpcpeVxNQlNCSAiUiHVINHWOfdGbME5NwOo8ZLbzMaZ2WozWxCX9jcz+8zM5pvZFDPrGKT3MrOtZjYveN1X96Jsv1A4SkF+jp9PXzUJEZEKqQaJpWb25+Bk3svM/gR8Wcs2E4CTqqS9CuzvnOsHfAHE30WzxDnXP3hdmmK+GkSoPGhuCilIiIjESzVIXAR0BZ4BpgTva5z71zn3FvB9lbRXnHOx+ZffA5rEsxDVcS0ikliqo5t+AK5o4GNfBDwRt9w76BzfCPzJOfd2oo3MbDQwGqBHjx4NkhHfcR0LEs18Sg4RkQZUY5Awszudc1ea2X9I8Lgo59x2PSrJzP4IhIFHg6RVQA/n3DozGwBMNbP9nHMbExzzfnxHOsXFxQ0yhW0oHKVjYV7QJ6EgISISU1tNYmLw8/aGOqCZjQKGAse6YJ5y51yI4E5u59wcM1sC7AnUfx7wFITKIxS1dYBTc5OISJwag4Rzbk7wtr9z7h/xn5nZb4A363IwMzsJ+D0wyDlXEpfeFfjeORcxs92BvvipQBpFWThK+9zgoS4KEiIiFVLtuB6VIO2CmjYws0nAu8BeZrbCzC4G7gGKgFerDHU9GphvZh8BTwOXOue+T7jjNAiFoxTlBFNSqblJRKRCbX0SI4Gf4juVp8V9VATUOG24c25kguQHk6w7GcjYAxBC4QjtrNQvqCYhIlKhtj6JmfhO5S7A3+PSNwHz05WpxhYqj9LOgppEgWoSIiIxtfVJfAV8BRzWONnJjFA4StuKmoSChIhITKoT/B1qZh+Y2WYzKzOziJlVG57aHEWijrJIlDaouUlEpKpUO67vAUYCi4DWwCXA3enKVGMqCx5d2sYpSIiIVJXqQ4dwzi02s1znXAQYb2Yz05ivRhN7dGlr1NwkIlJVqkGixMxaAfPM7DZ8Z3aLuOQOBTWJQrfVJyhIiIhUSLW56XwgF7gc2ALsBpydrkw1ptjzrQujW8FyIa8gwzkSEWk6Up3g76vg7VbghvRlp/HFmpsK3VZfizDLcI5ERJqO2m6m+5gEE/vFBM+FaNZizU2tolvVaS0iUkVtNYmhjZKLDIrVJPIjJQoSIiJVpHIzXYsW65PIj5TobmsRkSpS6pMws01sa3ZqBeQDW5xz7dOVscYSa27KD5dAawUJEZF4qXZcF8Uvm9kZwMB0ZKixxZqb8iIl0KpzhnMjItK0pDoEthLn3FRgSMNmJTNiNYmccj3fWkSkqlSbm86KW8wBiqlh1FNzEuuTyA2r41pEpKpU77g+Le59GFgGDGvw3GRAadDcZOVboFVRLWuLiGSXVPskLkx3RjLF1yQcVqbmJhGRqlKdKnx3M/uPma0xs9Vm9mzwLOpmLxSO0JoQhlOQEBGpItWO68eAJ4FuwC7AU8CkdGWqMYXCcU+lU5AQEakk1SBhzrmJzrlw8HqEltJxHY7SMS8WJHSfhIhIvFSDxBtmNsbMeplZTzP7H+B5M+tsZglvLjCzcUHT1IK4tM5m9qqZLQp+dor77FozW2xmn5vZifUrVupC5RE65ZX7Bd1xLSJSSapB4ifAL4A3gBnAL4GLgDnA7CTbTABOqpI2BpjunOsLTA+WMbN9gRHAfsE295pZbqqFqI9QOErHXDU3iYgkkuropt513bFz7i0z61UleRgwOHj/ED7g/D5If9w5FwK+NLPF+Du6363rcesqFI7SPrfMD+xVc5OISCWp3kyXj689HB0kzQD+zzlXXsfj7eScWwXgnFtlZjsG6bsC78WttyJIS5SX0cBogB49etTx8NWFwhF2zlFNQkQkkVSbm/4FDADuDV4DgrSGkuhJPwk7xp1z9zvnip1zxV27dq33gUPlUYoUJEREEkr1jusfOecOjFt+3cw+2o7jfWdm3YJaRDdgdZC+Av9I1JjuwMrt2H+dhcLxQUJ3XIuIxEu1JhExsz6xheBGush2HG8aMCp4Pwp4Ni59hJkVmFlvoC8wazv2X2ehcIR2VuoXVJMQEakk1ZrE7/DDYJcGy72AGqfqMLNJ+E7qLma2ArgeuAV40swuBr4GzgFwzn1iZk8Cn+K7kC9zzm1PEKqz0vIobSkFy4W8gsY4pIhIs5FqkPgv8H/AscHy/1HLyCPn3MgkHx2bKNE5dzNwc4r5aTChcIS2FvIjmyxR14iISPZKtbnpYaA3cGPw6g1MTFemGlMoHKUNpbqRTkQkgVRrEntV6bh+Yzs7rpucUHmU1rlb1R8hIpJAqjWJuWZ2aGzBzA7BN0E1e6FwhNZOQUJEJJFUaxKHAD8zs6+D5R7AQjP7GHDOuX5pyV0jCIWjFLpSDX8VEUkg1SBRdQ6mFiMUjlLgtkKrnTOdFRGRJifVuZu+SndGMiEciRKJOlpFt2reJhGRBFLtk2iRQuEoAAWREvVJiIgkoCAB5EdKVJMQEUkgy4NEBCNKXkSjm0REEsnuIFEepZAyDKcgISKSQFYHidJwhLYEM8DqjmsRkWqyOkiEyqO0ta1+QX0SIiLVZHeQCAczwIKam0REEsjyIBHxk/uBgoSISALZHSTKo36acFBzk4hIAtkdJGLThIOChIhIAlkeJCK0q+i4VnOTiEhVWR4korRBzU0iIslkd5Aoj2h0k4hIDVKdKrzBmNlewBNxSbsD1wEdgZ8Da4L0PzjnXkhnXkLhKG2sFGe5WF5BOg8lItIsNXqQcM59DvQHMLNc4BtgCnAhcIdz7vbGyksoHKUo9nxrs8Y6rIhIs5Hp5qZjgSWZel6F77guxdQfISKSUKaDxAhgUtzy5WY238zGmVmndB88VB6lKCek/ggRkSQyFiTMrBVwOvBUkPQvoA++KWoV8Pck2402s9lmNnvNmjWJVklZaThCO1OQEBFJJpM1iZOBD51z3wE4575zzkWcc1HgAWBgoo2cc/c754qdc8Vdu3atVwYq7rhWc5OISEKZDBIjiWtqMrNucZ+dCSxIdwZC4ShtrVRBQkQkiUYf3QRgZm2A44FfxCXfZmb9AQcsq/JZWoTCEdqip9KJiCSTkSDhnCsBdqiSdn5j5yMUjtLalSpIiIgkkenRTRkVKo/SGjU3iYgkk9VBoqy8nALVJEREksrqIEF4Kzk4Pd9aRCSJrA4SOeUl/o1qEiIiCWV1kMgNb/Fv1CchIpJQlgcJ1SRERGqS1UEiX0FCRKRG2R0kIrHmpqLMZkREpInK2iDhnCM/oudbi4jUJGuDRFkkmLcJFCRERJLI2iARCkdpQ8gvaHSTiEhC2RskyqO0RTUJEZGaZG+QCEdoa1uJWh7kFWQ6OyIiTVIWBwnf3BTOawNmmc6OiEiTlL1BImhuiuS1yXRWRESarOwNEuEIbayUaL6ChIhIMlkcJHxNwuWr01pEJJnsDhJWSjRfw19FRJLJ3iBRHvFDYDX8VUQkqawNEqXhKG0oxQoUJEREksnLxEHNbBmwCYgAYedcsZl1Bp4AegHLgOHOuR/SlYdQeYS2FsJ0t7VINeXl5axYsYLS0tJMZ0XqobCwkO7du5Ofn7/d+8hIkAgc45xbG7c8BpjunLvFzMYEy79P18FDQU0iR48uFalmxYoVFBUV0atXL0z3ETVLzjnWrVvHihUr6N2793bvpyk1Nw0DHgrePwSckc6DhcrDtLNScgo1TbhIVaWlpeywww4KEM2YmbHDDjvUuzaYqSDhgFfMbI6ZjQ7SdnLOrQIIfu6YaEMzG21ms81s9po1a7Y7A5GQf5ZEbqFqEiKJKEA0fw3xO8xUc9MRzrmVZrYj8KqZfZbqhs65+4H7AYqLi932ZsCFNgOQq5qEiEhSGalJOOdWBj9XA1OAgcB3ZtYNIPi5Oq15KPM1CfVJiEh9TJs2jVtuuQWAqVOn8umnn2Y4Rw2r0YOEmbU1s6LYe+AEYAEwDRgVrDYKeDatGSnzNQndJyEi8cLhcJ3WP/300xkzZgzQMoNEJpqbdgKmBG1lecBjzrmXzOwD4Ekzuxj4GjgnnZnIKYs931o1CZGa3PCfT/h05cYG3ee+u7Tn+tP2S/r5smXLOPnkkznyyCOZOXMmu+66K88++ywnn3wyt99+O8XFxaxdu5bi4mKWLVvGhAkTmDp1KpFIhAULFnD11VdTVlbGxIkTKSgo4IUXXqBz584sWbKEyy67jDVr1tCmTRseeOAB9t57by644AI6d+7M3LlzOfjggzn//PO59NJLKSkpoU+fPowbN45OnTpx1113cd9995GXl8e+++7L448/zoQJE5g9ezY//elPmTZtGm+++SY33XQTkydP5pxzzuHDDz8EYNGiRYwYMYI5c+Y06HeZbo0eJJxzS4EDE6SvA45trHyYgoRIk7Zo0SImTZrEAw88wPDhw5k8eXKN6y9YsIC5c+dSWlrKHnvswa233srcuXO56qqrePjhh7nyyisZPXo09913H3379uX999/nV7/6Fa+//joAX3zxBa+99hq5ubn069ePu+++m0GDBnHddddxww03cOedd3LLLbfw5ZdfUlBQwPr16ysd//DDD+f0009n6NCh/PjHPwagQ4cOzJs3j/79+zN+/HguuOCCdHxVaZXJ+yQyKiccCxJqbhKpSU1X/OnUu3dv+vfvD8CAAQNYtmxZjesfc8wxFBUVUVRURIcOHTjttNMAOOCAA5g/fz6bN29m5syZnHPOtkaKUChU8f6cc84hNzeXDRs2sH79egYNGgTAqFGjKrbp168f5557LmeccQZnnHFGrWW45JJLGD9+PGPHjuWJJ55g1qxZdfgGmoamdJ9Eo8oJl/g3ChIiTVJBwbYnRubm5hIOh8nLyyMajQJUG/8fv35OTk7Fck5ODuFwmGg0SseOHZk3b17Fa+HChRXbtG1b+7ng+eef57LLLmPOnDkMGDCg1v6Ls88+mxdffJHnnnuOAQMGsMMOO9Re8CYma4NEXljNTSLNTa9evSra9J9++uk6bdu+fXt69+7NU089Bfg7kj/66KNq63Xo0IFOnTrx9ttvAzBx4kQGDRpENBpl+fLlHHPMMdx2222sX7+ezZs3V9q2qKiITZs2VSwXFhZy4okn8stf/pILL7ywTvltKrI4SAQ1CQ2BFWk2rrnmGv71r39x+OGHs3bt2to3qOLRRx/lwQcf5MADD2S//fbj2WcTD6J86KGH+N3vfke/fv2YN28e1113HZFIhPPOO48DDjiAgw46iKuuuoqOHTtW2m7EiBH87W9/46CDDmLJkiUAnHvuuZgZJ5xwQp3z2xSYc9t9P1rGFRcXu9mzZ2/Xtk/d+nPO3DqZvOvX6RnXIlUsXLiQffbZJ9PZaBFuv/12NmzYwI033piR4yf6XZrZHOdccSrbZ23HdatICaGc1uQpQIhImpx55pksWbKkYgRVc5S9QSK6lbKc1qjbWkTSZcqUKZnOQr1lbZ9EQbSEspzWmc6GiEiTlr1Bwm2lPFf1CBGRmmRtkCiMllKe1ybT2RARadKyNki0ppSwgoSISI2yMkhEo47WbisRBQmRrDN48GC2d+h8Xdx1113ss88+nHvuudu9j8bKa02ycnRTWSRKOytlq4KEiNRBbGqQVNx77728+OKLNT5fui77y5Smnbs0CZVHaUMp0XzdbS1SqxfHwLcfN+w+dz4ATr4l6cfJpgpv3bo1gwcPrtd04QCPPPIIV1xxBRs3bmTcuHEMHDiQLVu28Otf/5qPP/6YcDjMX/7yF4YNG8aECRN4/vnnKS0tZcuWLdXueRg7dizjxo0D/IR+V155JZdeeilLly7l9NNP56KLLuKqq66qWL/q/q677jpuv/12nnvuOQAuv/xyiouLq80Y+8orr3D99dcTCoXo06cP48ePp127dowZM4Zp06aRl5fHCSecwO23394Qv6EKWRokyulgIaKtVJMQaaoSTRV+3nnn1bhNKtOFA2zZsoWZM2fy1ltvcdFFF7FgwQJuvvlmhgwZwrhx41i/fj0DBw7kuOOOA+Ddd99l/vz5FUEmZs6cOYwfP573338f5xyHHHIIgwYN4r777uOll17ijTfeoEuXLtXyGb+/GTNm1PpdrF27lptuuonXXnuNtm3bcuuttzJ27Fguv/xypkyZwmeffYaZVZu+vCFkZ5AoCSb3U01CpHY1XPGnU12nCofapwuPGTlyJABHH300GzduZP369bzyyitMmzat4kq8tLSUr7/+GoDjjz++WoAAeOeddzjzzDMrZpA966yzePvttznooINqzGey/SXz3nvv8emnn3LEEUcAUFZWxmGHHUb79u0pLCzkkksu4dRTT2Xo0KEp7zNVWRkkykuDp2xpmnCRJqvqVOFbt24FqNd04TFWZToeM8M5x+TJk9lrr70qffb+++8nnUZ8e+e+i99ffHmgeplixzn++OOZNGlStc9mzZrF9OnTefzxx7nnnnsafAqQrBzdFN7qp/I1zQAr0uzUZ7rwmCeeeALwNYEOHTrQoUMHTjzxRO6+++6KE//cuXNr3c/RRx/N1KlTKSkpYcuWLUyZMoWjjjqqTnnp2bMnn376KaFQiA0bNjB9+vRq6xx66KH897//ZfHixQCUlJTwxRdfsHnzZjZs2MApp5zCnXfeybx58+p07FRkZU2i0PkrkrZFHTKcExGpq2uuuYbhw4czceJEhgwZsl376NSpE4cffnhFxzXAn//8Z6688kr69euHc45evXpVdCYnc/DBB3PBBRcwcOBAwHdc19bUVNVuu+3G8OHD6devH3379k24fdeuXZkwYQIjR46seJreTTfdRFFREcOGDaO0tBTnHHfccUedjp2K7JwqfO1ieP1GOOq30K3a47ZFsp6mCm856jtVeKM3N5nZbmb2hpktNLNPzOw3QfpfzOwbM5sXvE5JWya67AHDH1KAEBGpRSaam8LA1c65D82sCJhjZq8Gn93hnGvYQb4iIrLdGj1IOOdWAauC95vMbCGwa2PnQ0Rq5pyrNgpImpeG6E7I6OgmM+sFHAS8HyRdbmbzzWycmXVKss1oM5ttZrPXrFnTWFkVySqFhYWsW7euQU4ykhnOOdatW0dhYWG99pOxjmszawe8CdzsnHvGzHYC1gIOuBHo5py7qKZ91OcZ1yKSXHl5OStWrEg4Zl+aj8LCQrp3705+fn6l9Cb/jGszywcmA486554BcM59F/f5A0DNY89EJG3y8/NrnJhOskcmRjcZ8CCw0Dk3Ni69W9xqZwILGjtvIiJSWSZqEkcA5wMfm9m8IO0PwEgz649vbloG/CIDeRMRkTiZGN30DpBoyMQLjZ0XERGpWbO+49rM1gBf1WMXXfCd5dlG5c4uKnd2SaXcPZ1zXVPZWbMOEvVlZrNT7eFvSVTu7KJyZ5eGLndWzgIrIiKpUZAQEZGksj1I3J/pDGSIyp1dVO7s0qDlzuo+CRERqVm21yRERKQGChIiIpJUVgYJMzvJzD43s8VmNibT+amvYNbc1Wa2IC6ts5m9amaLgp+d4j67Nij752Z2Ylz6ADP7OPjsLmvi80TX8ACrFl12Mys0s1lm9lFQ7huC9BZd7hgzyzWzuWb2XLDc4sttZsuC/M4zs9lBWuOU2zmXVS8gF1gC7A60Aj4C9s10vupZpqOBg4EFcWm3AWOC92OAW4P3+wZlLgB6B99FbvDZLOAw/B3xLwInZ7pstZS7G3Bw8L4I+CIoX4sue5DHdsH7fPxU+4e29HLHlf+3wGPAc8Fyiy83fqqiLlXSGqXc2ViTGAgsds4tdc6VAY8DwzKcp3pxzr0FfF8leRjwUPD+IeCMuPTHnXMh59yXwGJgYDDBYnvn3LvO/zU9HLdNk+ScW+Wc+zB4vwmIPcCqRZfdeZuDxfzg5Wjh5QYws+7AqcC/45JbfLmTaJRyZ2OQ2BVYHre8gpb5ZLydnH8KIMHPHYP0ZOXfNXhfNb1ZsMoPsGrxZQ+aXOYBq4FXnXNZUW7gTuB/gGhcWjaU2wGvmNkcMxsdpDVKuTPyPIkMS9QGl03jgJOVv9l+L8EDrCYDVzrnNtbQzNpiyu6ciwD9zawjMMXM9q9h9RZRbjMbCqx2zs0xs8GpbJIgrdmVO3CEc26lme0IvGpmn9WwboOWOxtrEiuA3eKWuwMrM5SXdPouqF7GntWxOkhPVv4Vwfuq6U2aJXiAFVlSdgDn3HpgBnASLb/cRwCnm9kyfDPxEDN7hJZfbpxzK4Ofq4Ep+GbzRil3NgaJD4C+ZtbbzFoBI4BpGc5TOkwDRgXvRwHPxqWPMLMCM+sN9AVmBdXVTWZ2aDDi4Wdx2zRJQT6rPcCKFl52M+sa1CAws9bAccBntPByO+eudc51d871wv/fvu6cO48WXm4za2tmRbH3wAn4h7I1Trkz3WufiRdwCn4kzBLgj5nOTwOUZxKwCijHXy1cDOwATAcWBT87x63/x6DsnxM3ugEoDv74lgD3ENyR31RfwJH46vJ8YF7wOqWllx3oB8wNyr0AuC5Ib9HlrvIdDGbb6KYWXW78SMyPgtcnsXNWY5Vb03KIiEhS2djcJCIiKVKQEBGRpBQkREQkKQUJERFJSkFCRESSUpAQqSMz62hmv6rh85kp7GNzbeuINAUKEiJ11xGoFiTMLBfAOXd4Y2dIJF2yce4mkfq6BegTTLBXDmzG38zYH9jXzDY759oFc0o9C3TCz9T6J+dck72zVyQR3UwnUkfBjLPPOef2Dyaaex7Y3/lpmYkLEnlAG+cnHewCvAf0dc652DoZKoJIylSTEKm/WbEAUYUBfzWzo/FTW+8K7AR825iZE6kPBQmR+tuSJP1coCswwDlXHsxeWthouRJpAOq4Fqm7TfjHpdamA/75B+VmdgzQM73ZEml4qkmI1JFzbp2Z/dfMFgBbge+SrPoo8J/gwfXz8NN5izQr6rgWEZGk1NwkIiJJKUiIiEhSChIiIpKUgoSIiCSlICEiIkkpSIiISFIKEiIiktT/B7PnWaSQSiePAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = other_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import Configuration\n", - "from utils.xcs_utils import *\n", - " \n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=200,\n", - " covering_wildcard_chance = 1,\n", - " metrics_trial_frequency=100,\n", - " user_metrics_collector_fcn=xcs_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [5.1200643157495e-41, 1.0254604841732145e-40, 2.9915190908737574e-41, 1.9710514767964755e-40, 9.228969786532952e-41, 5.725119250207043e-41, 1.938962294654073e-40, 4.910922728877399e-41], 'perf_time': 0.01915710000002946, 'population': 104, 'numerosity': 110}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [25.907780274684487, 19.29998613185881, 19.86598910983664, 18.98040521730779, 20.628074920025604, 14.27556207648116, 20.416264345156392, 16.517131822043538], 'perf_time': 0.037464900000031776, 'population': 178, 'numerosity': 200}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 14, 'reward': [46.515721273231144, 50.576360486849744, 220.58487249407153, 49.216564473423304, 40.51124497570612, 39.0326580826752, 44.042351873115365, 41.518059002934585], 'perf_time': 0.016042799999922863, 'population': 180, 'numerosity': 200}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 50, 'reward': [26.907094597807593, 31.178039235437755, 34.44224107010148, 33.15180403842226, 33.67967597043441, 32.3088691251137, 28.295786484686563, 25.186343454965407], 'perf_time': 0.04064949999997225, 'population': 177, 'numerosity': 201}\n", - "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 50, 'reward': [13.295173890748659, 12.78247404719778, 18.77484727163302, 17.184103284931933, 18.940148178442197, 15.506828150417002, 12.384311833567777, 19.07113948663577], 'perf_time': 0.029006900000013047, 'population': 172, 'numerosity': 201}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 50, 'reward': [17.821096011218255, 19.8440153731198, 24.71089249515874, 18.605576708765813, 19.28529420790987, 18.006514701584017, 21.84841670031169, 15.428419912285598], 'perf_time': 0.0343391999999767, 'population': 169, 'numerosity': 201}\n", - "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 50, 'reward': [25.163053858049803, 37.989394933821984, 18.918544321484173, 24.534715116482523, 18.806513041175315, 37.26328607415514, 24.984123280210635, 24.285077572206042], 'perf_time': 0.032653500000151325, 'population': 166, 'numerosity': 200}\n", - "INFO:lcs.agents.Agent:{'trial': 2800, 'steps_in_trial': 50, 'reward': [25.347607205901145, 76.4404916868534, 27.360165327210808, 29.135985564679086, 29.803844167765757, 23.421694214659357, 24.62719334483451, 26.71503983405549], 'perf_time': 0.0435067999999319, 'population': 177, 'numerosity': 201}\n", - "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 16, 'reward': [78.66373701951368, 21.34238044436787, 202.38259711279164, 18.15224742765841, 27.403452916383227, 20.59208358252585, 23.452122018632338, 22.44399999123691], 'perf_time': 0.008620300000075076, 'population': 185, 'numerosity': 200}\n", - "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 50, 'reward': [76.39250391586903, 37.171825324757016, 118.56544706928905, 43.850162160857685, 55.2803457465704, 51.712627213397084, 47.698361491939124, 36.18771632250961], 'perf_time': 0.032460000000128275, 'population': 174, 'numerosity': 200}\n", - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [6.275587523171267, 0, 8.090094965152167, 0, 6.0687787293609965, 0, 19.622865611277263, 4.942088351403461], 'perf_time': 0.027591600000050676, 'population': 177, 'numerosity': 200}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [29.553311010498557, 11.572683864571097, 16.777027707293133, 28.994111537823198, 10.434853461042925, 1.0924138873695337, 17.270337991736127, 17.14721908657729], 'perf_time': 0.027446900000086316, 'population': 189, 'numerosity': 200}\n", - 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"INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 50, 'reward': [55.35448035603408, 53.52775211720623, 54.11884976603645, 55.04568194287782, 52.85869655705945, 20.546460615223793, 16.414088262659668, 38.33240742827019], 'perf_time': 0.029735099999925296, 'population': 196, 'numerosity': 200}\n" - ] - } - ], - "source": [ - "from lcs.agents.xcs import XCS\n", - "\n", - "\n", - "agent = XCS(cfg)\n", - "my_metrics = avg_experiment(scenario,\n", - " cfg,\n", - " number_of_tests=5,\n", - " explore_trials=exploration_cycles,\n", - " exploit_metrics=exploitation_cycles)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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50031.00.025600177.8200.2
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130050.00.034666174.2200.2
140038.00.023551173.4200.0
150037.80.027908175.0200.4
160045.40.032533175.0200.2
170036.80.027934175.6200.0
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200045.00.029622169.0200.4
210041.20.031296175.4200.2
220012.00.007534174.0200.2
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240050.00.038837173.6200.0
250047.20.031363176.0200.2
260050.00.031221170.2200.2
270041.80.029264175.4200.2
280029.60.024288171.2200.4
290050.00.037588175.4200.4
300039.60.027838175.6200.2
310035.40.025880178.0200.2
320031.80.026403182.0200.2
330039.80.035170173.6200.4
340027.00.017081177.6200.2
350041.60.027984177.2200.6
360035.00.023267178.6200.2
370036.80.023686174.4200.0
380025.20.021637174.6200.0
390038.80.026464174.6200.2
400050.00.031966179.4200.0
410040.40.023590191.6200.0
420050.00.028753193.0200.0
430040.40.023696195.8200.0
440050.00.029643195.4200.0
450050.00.028493196.0200.0
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470050.00.029543196.2200.0
480050.00.028361198.0200.0
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\n", - "
" - ], - "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 50.0 0.017250 84.6 88.6\n", - "100 50.0 0.034758 174.4 200.8\n", - "200 33.0 0.024187 173.6 200.0\n", - "300 36.6 0.029357 177.0 200.2\n", - "400 42.8 0.033050 174.8 200.2\n", - "500 31.0 0.025600 177.8 200.2\n", - "600 40.8 0.035055 174.6 200.2\n", - "700 39.2 0.029996 173.2 200.0\n", - "800 42.8 0.031525 173.4 200.0\n", - "900 49.6 0.038133 174.4 200.8\n", - "1000 50.0 0.044239 173.2 200.4\n", - "1100 42.2 0.034534 175.6 201.0\n", - "1200 39.2 0.028957 177.6 200.6\n", - "1300 50.0 0.034666 174.2 200.2\n", - "1400 38.0 0.023551 173.4 200.0\n", - "1500 37.8 0.027908 175.0 200.4\n", - "1600 45.4 0.032533 175.0 200.2\n", - "1700 36.8 0.027934 175.6 200.0\n", - "1800 38.6 0.028733 172.2 200.0\n", - "1900 36.6 0.029495 176.6 200.0\n", - "2000 45.0 0.029622 169.0 200.4\n", - "2100 41.2 0.031296 175.4 200.2\n", - "2200 12.0 0.007534 174.0 200.2\n", - "2300 43.4 0.030157 174.4 200.4\n", - "2400 50.0 0.038837 173.6 200.0\n", - "2500 47.2 0.031363 176.0 200.2\n", - "2600 50.0 0.031221 170.2 200.2\n", - "2700 41.8 0.029264 175.4 200.2\n", - "2800 29.6 0.024288 171.2 200.4\n", - "2900 50.0 0.037588 175.4 200.4\n", - "3000 39.6 0.027838 175.6 200.2\n", - "3100 35.4 0.025880 178.0 200.2\n", - "3200 31.8 0.026403 182.0 200.2\n", - "3300 39.8 0.035170 173.6 200.4\n", - "3400 27.0 0.017081 177.6 200.2\n", - "3500 41.6 0.027984 177.2 200.6\n", - "3600 35.0 0.023267 178.6 200.2\n", - "3700 36.8 0.023686 174.4 200.0\n", - "3800 25.2 0.021637 174.6 200.0\n", - "3900 38.8 0.026464 174.6 200.2\n", - "4000 50.0 0.031966 179.4 200.0\n", - "4100 40.4 0.023590 191.6 200.0\n", - "4200 50.0 0.028753 193.0 200.0\n", - "4300 40.4 0.023696 195.8 200.0\n", - "4400 50.0 0.029643 195.4 200.0\n", - "4500 50.0 0.028493 196.0 200.0\n", - "4600 50.0 0.028793 194.8 200.0\n", - "4700 50.0 0.029543 196.2 200.0\n", - "4800 50.0 0.028361 198.0 200.0\n", - "4900 50.0 0.029207 196.6 200.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(my_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = my_metrics['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = my_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "None so far." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_compared_Maze5_without_#_high_pop.ipynb b/XCS_Experiments/XCS_compared_Maze5_without_#_high_pop.ipynb deleted file mode 100644 index a697d9b..0000000 --- a/XCS_Experiments/XCS_compared_Maze5_without_#_high_pop.ipynb +++ /dev/null @@ -1,717 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "exploration_cycles = 1000\n", - "exploitation_cycles = 500\n", - "input_size = 8\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(input_size)\n", - "scenario.maze.reset()\n", - "scenario.maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "algorithm = XCSAlgorithm()\n", - "algorithm.max_population_size = 1600\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 25\n", - "algorithm.crossover_probability = 1\n", - "algorithm.mutation_probability = 0.01\n", - "algorithm.initial_prediction = 0.000001 # p_I\n", - "algorithm.initial_error = 0.000001 # epsilon_I\n", - "algorithm.initial_fitness = 0.000001 # F_I\n", - "algorithm.wildcard_probability = 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - } - ], - "source": [ - "other_metrics = other_avg_experiment(\n", - " maze=scenario,\n", - " algorithm=algorithm,\n", - " number_of_tests=1,\n", - " explore_trials=exploration_cycles,\n", - " exploit_trials=exploitation_cycles\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " steps_in_trial population numerosity\n", - "trial \n", - "0 50 42 44\n", - "100 16 377 1600\n", - "200 6 391 1600\n", - "300 2 406 1600\n", - "400 50 426 1600\n", - "500 50 444 1600\n", - "600 50 449 1600\n", - "700 2 468 1600\n", - "800 14 470 1600\n", - "900 50 458 1600\n", - "1000 1 518 1600\n", - "1100 31 532 1600\n", - "1200 50 506 1600\n", - "1300 50 464 1600\n", - "1400 50 477 1600" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(other_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = other_metrics[\"steps_in_trial\"].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps other XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = other_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import Configuration\n", - "from utils.xcs_utils import *\n", - " \n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " covering_wildcard_chance = 1,\n", - " ga_threshold = 25,\n", - " metrics_trial_frequency=100,\n", - " mutation_chance=0.03,\n", - " chi=1, # crossover\n", - " initial_prediction = 0.000001, # p_I\n", - " initial_error = 0.000001, # epsilon_I\n", - " initial_fitness = 0.000001, # F_I\n", - " user_metrics_collector_fcn=xcs_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 4, 'reward': [0, 100.00000000000007, 0, 2.5205456210545373e-09, 0, 0, 0, 1.349e-13], 'perf_time': 0.0010627999999996973, 'population': 16, 'numerosity': 16}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 49, 'reward': [43.484988911585006, 155.8801151880337, 25.899420844188477, 27.394917025619986, 26.44408670571981, 25.766672900415497, 29.961322059865527, 29.01554115718213], 'perf_time': 0.027437300000087816, 'population': 212, 'numerosity': 422}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': [39.187000242492935, 33.6946701445112, 29.95530843292193, 31.015277518436182, 37.75189563350876, 51.031719648243865, 40.92758657756191, 37.92694442758516], 'perf_time': 0.03000869999993938, 'population': 215, 'numerosity': 514}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 50, 'reward': [135.8813424010642, 54.62575901289569, 38.02975863106683, 53.93782200756955, 43.89146734206789, 78.60386221284136, 52.21542181564877, 54.56380856282243], 'perf_time': 0.02858409999998912, 'population': 215, 'numerosity': 534}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [11.028770725424632, 8.036558896840486, 11.152270698537862, 11.938716632531456, 8.369187263391087, 10.192073332779183, 10.660576277140526, 9.991671784408583], 'perf_time': 0.027828600000020742, 'population': 215, 'numerosity': 574}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 50, 'reward': [5.422615823419046, 71.4420710309193, 26.7895438786806, 5.4517037500402985, 3.3897934462998225, 7.406041186166473, 5.663023624677907, 8.61396758133133], 'perf_time': 0.028523299999960727, 'population': 215, 'numerosity': 574}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 50, 'reward': [10.411625294142818, 24.714116375966604, 49.82606327002898, 13.728374550492436, 17.160893509241358, 22.834571426005958, 11.436024671878481, 20.84157225566885], 'perf_time': 0.027706800000032672, 'population': 215, 'numerosity': 574}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 29, 'reward': [128.89278216980534, 26.479497375211743, 68.4533093440447, 24.815704702919735, 11.523050395228893, 19.619368617446252, 28.028641890757022, 40.2243970355051], 'perf_time': 0.015212500000075124, 'population': 215, 'numerosity': 574}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 21, 'reward': [106.5712081832744, 16.846532827112792, 123.43721829716101, 15.639046398934886, 10.340423296307735, 8.661523791584335, 18.26595136624244, 14.428231492492541], 'perf_time': 0.01086350000002767, 'population': 215, 'numerosity': 574}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 35, 'reward': [59.93796394539037, 43.476047709965634, 188.42515509447708, 17.352785714924458, 23.494364283236273, 15.77726047497663, 22.58617556259116, 19.738777589431084], 'perf_time': 0.01988600000004226, 'population': 215, 'numerosity': 574}\n", - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 21, 'reward': [102.25417594330445, 3.2758146905298373, 2.889553900119542, 1.5235041858199434, 0, 0.6698977388631, 0, 1.0798961171588901], 'perf_time': 0.011633499999902597, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 21, 'reward': [2.942876206738658, 251.3443699823993, 3.3137146311630676, 1.0260173605547003, 1.2583490243432027, 1.3467468248804502, 1.250420200624438, 0.9232115899555582], 'perf_time': 0.011633499999902597, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 5, 'reward': [115.10988459964275, 435.4274692106625, 3.9237716843111743, 1.640798443427785, 1.976389751983341, 0.6602806581070816, 2.0211497461614414, 0.9805641743642121], 'perf_time': 0.002584300000080475, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 5, 'reward': [41.01102062813288, 438.0738151641403, 2.5961991378758458, 1.8337744012325405, 2.0861126991614953, 0.535965866788736, 2.3444670791899145, 0.8832075558695726], 'perf_time': 0.002584300000080475, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': [104.01233175359825, 430.40682460177493, 4.255513382585892, 1.8231034849047894, 2.057778520658027, 0.47105525730401704, 1.9777722103770659, 0.8832075558695726], 'perf_time': 0.003091700000027231, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': [15.463331703834978, 440.8374902961353, 4.732419307260924, 1.718277094565807, 1.7095066261611827, 0.44100145008045233, 2.600316874930196, 0.8832075558695726], 'perf_time': 0.003091700000027231, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 1, 'reward': [147.01179636044228, 416.49272635130717, 1.6914039365409756, 1.723473822737457, 1.9716476176371516, 0.4332177852632394, 3.687158954513004, 0.8832075558695726], 'perf_time': 0.0006981999999879918, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 1, 'reward': [26.853688666655376, 414.7040811043279, 1.3029947197277327, 1.870606694105669, 1.8576346126166676, 0.4286216090253233, 3.687158954513004, 0.8832075558695726], 'perf_time': 0.0006981999999879918, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 6, 'reward': [4.37262555195136, 371.86937569875477, 1.0368343690128623, 1.9575532423163327, 1.6985260916723495, 1.4502573832045915, 4.328928334085856, 0.8832075558695726], 'perf_time': 0.003091500000095948, 'population': 215, 'numerosity': 576}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 6, 'reward': [3.5417891368560768, 557.1476983279694, 0.7683630253380476, 1.9749750338987246, 0.9996412758013704, 35.736008617470134, 4.504753335134316, 0.8832075558695726], 'perf_time': 0.003091500000095948, 'population': 215, 'numerosity': 576}\n" - ] - } - ], - "source": [ - "from lcs.agents.xcs import XCS\n", - "\n", - "\n", - "agent = XCS(cfg)\n", - "my_metrics = avg_experiment(scenario,\n", - " cfg,\n", - " number_of_tests=1,\n", - " explore_trials=exploration_cycles,\n", - " exploit_metrics=exploitation_cycles)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 4 0.001063 16 16\n", - "100 49 0.027437 212 422\n", - "200 50 0.030009 215 514\n", - "300 50 0.028584 215 534\n", - "400 50 0.027829 215 574\n", - "500 50 0.028523 215 574\n", - "600 50 0.027707 215 574\n", - "700 29 0.015213 215 574\n", - "800 21 0.010864 215 574\n", - "900 35 0.019886 215 574\n", - "1000 21 0.011633 215 576\n", - "1100 5 0.002584 215 576\n", - "1200 5 0.003092 215 576\n", - "1300 1 0.000698 215 576\n", - "1400 6 0.003092 215 576" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(my_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = my_metrics['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = my_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "None so far." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_compared_crossover.ipynb b/XCS_Experiments/XCS_compared_crossover.ipynb deleted file mode 100644 index 749ed86..0000000 --- a/XCS_Experiments/XCS_compared_crossover.ipynb +++ /dev/null @@ -1,1354 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "exploration_cycles = 4000\n", - "exploitation_cycles = 1000\n", - "input_size = 8\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(input_size)\n", - "scenario.maze.reset()\n", - "scenario.maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "algorithm = XCSAlgorithm()\n", - "algorithm.max_population_size = 1600\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 25\n", - "algorithm.crossover_probability = 1\n", - "algorithm.mutation_probability = 0.01\n", - "algorithm.initial_prediction = 0.000001 # p_I\n", - "algorithm.initial_error = 0.000001 # epsilon_I\n", - "algorithm.initial_fitness = 0.000001 # F_I\n", - "algorithm.wildcard_probability = 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - } - ], - "source": [ - "other_metrics = other_avg_experiment(\n", - " maze=scenario,\n", - " algorithm=algorithm,\n", - " number_of_tests=1,\n", - " explore_trials=exploration_cycles,\n", - " exploit_trials=exploitation_cycles\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " steps_in_trial population numerosity\n", - "trial \n", - "0 50 50 50\n", - "100 50 350 1600\n", - "200 28 401 1600\n", - "300 50 427 1600\n", - "400 50 476 1600\n", - "500 50 463 1600\n", - "600 50 482 1600\n", - "700 50 485 1600\n", - "800 50 533 1600\n", - "900 23 558 1600\n", - "1000 50 506 1600\n", - "1100 23 467 1600\n", - "1200 50 502 1600\n", - "1300 10 520 1600\n", - "1400 16 501 1600\n", - "1500 50 510 1600\n", - "1600 50 523 1600\n", - "1700 33 536 1600\n", - "1800 14 481 1600\n", - "1900 50 439 1600\n", - "2000 11 396 1600\n", - "2100 19 335 1600\n", - "2200 1 306 1600\n", - "2300 50 293 1600\n", - "2400 50 287 1600\n", - "2500 50 288 1600\n", - "2600 9 293 1600\n", - "2700 1 278 1600\n", - "2800 50 272 1600\n", - "2900 23 257 1600\n", - "3000 28 260 1600\n", - "3100 50 275 1600\n", - "3200 20 240 1600\n", - "3300 49 229 1600\n", - "3400 50 245 1600\n", - "3500 41 235 1600\n", - "3600 50 242 1600\n", - "3700 28 246 1600\n", - "3800 8 241 1600\n", - "3900 1 247 1600\n", - "4000 50 244 1600\n", - "4100 23 250 1600\n", - "4200 50 252 1600\n", - "4300 50 248 1600\n", - "4400 1 246 1600\n", - "4500 50 238 1600\n", - "4600 50 237 1600\n", - "4700 50 223 1600\n", - "4800 50 220 1600\n", - "4900 50 224 1600" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(other_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = other_metrics[\"steps_in_trial\"].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps other XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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mTG677TYWLap1Ag4Avve973Hrrbdy8sknM3ToULp3717vNVqS+krFOYS+jSVU7ev4d5pxRb900frlIq1HYWEh778fWtqfeeaZBp3bqVMn+vTpw9NPPw2Eh+g+/PDDGsd17tyZrl278o9//AOAadOmceKJJ1JeXs6aNWsYNWoUv/71r9m8eTPbt1edAjAvL49t27bt+Zybm8vo0aO5+uqr+eEPW9+TD3WWjO7+ubu/4e7HufubcdsH7l7aXJlMl1hpGblav1ykVbjhhht44IEHGDlyJBs2bGjw+Y8//jh/+tOfOOaYY+jfvz/PP594Au+pU6dy4403MnDgQBYsWMAvf/lLysrKuOSSSzj66KMZPHgw119/PV26dKly3vjx4/nNb37D4MGDWb58OQAXX3wxZsbpp5/e4Pymm4XVXes5yGwEcC9wFGFa9kxgh7t3Sm32Gm/YsGFeMfKisW58+kPeXraBOTcnnMtRZJ/zySefcNRRR6U7G23GnXfeyZYtW7jtttvScv1E/z3N7H13H1bfuckOWL4PGA88DQwjrKtxeAPz2erESsvJUc1DRFLgvPPOY/ny5XtGdLU2ST/t4u7LzCwzWmfjETObk8J8tQix0jL1eYhISsyYMSPdWdgryQaPnWbWDlhgZr8G1gH7wKy66jAXEUkk2ZLxB4R+jmuBHYT1xr/X2Iua2fVmtsjMFprZE2aWa2bdzOwVM/sseu0ad/zNZrbMzJaa2ejGXrehYiXlmppERCSBpIJHNOrqa3ff6u63uvtP3X1ZYy5oZr0IkywOc/cBhKA0HpgEzHL3vsCs6DNm1i/a358wPPh+M2uWEj1WWqany0VEEqjvIcGPSTAhYgV3H7gX121vZiVAB2AtcDNhzXOAqcAbwE3AWGC6u8eAlWa2DBgOvNPIayctVlpOt44KHiIi1dVXMp4NnFPH1mDu/k/CRIurCX0nW9z9ZeAAd18XHbMO2D86pRewJu4riqK0GsxsopnNM7N5xcXFjcleFaHPQ81WIvuC+IkVU+mee+7hqKOO4uKLL270dzRXXutS38SInzf1BaO+jLFAH2Az8LSZXVLXKYmyluhAd3+IsN46w4YNq/8BlnpotJWIJKMhU7Xff//9vPjii3Uu/9rQqd/TIamS0cy2mdnWaNtlZmVm1tjpNE8FVrp7sbuXEFYnHAl8aWY9o+v1BNZHxxcROugrFBCauVIuVlKuPg+RFqS26diBvZ6SHeCxxx5j5MiRDBgwgLlz5wKwY8cOLr/8cr75zW8yePDgPU+eT5kyhXHjxnHOOeckfEL8rrvuYsCAAQwYMIC7774bgH/5l39hxYoVjBkzht/+9rdVjq/+fW+88QZnn332nv3XXnstU6ZMqXGdl19+meOOO44hQ4Ywbty4PdOiTJo0ac8U8TfccEMj/tp1S3Ylwbz4z2Z2LqHfoTFWAyPMrAPwNXAKMI8wimsCcHv0WjE3wEzgz2Z2F3AQ0BeY28hrN4iarUTq8OIk+OLjpv3OA4+G79xe5yGJpmO/5JK6Gi+Sm5IdQqCYM2cOs2fP5vLLL2fhwoVMnjyZk08+mYcffpjNmzczfPhwTj31VADeeecdPvroI7p161bleu+//z6PPPII7733Hu7Osccey4knnsjvf/97XnrpJV5//XV69OhRI5/x3/fGG2/U++fasGEDv/rVr3j11Vfp2LEjd9xxB3fddRfXXnstM2bMYMmSJZhZjSnim0Kj6kXu/pyZTWrkue+Z2TPAB0ApMJ/Q1LQf8JSZXUEIMOOi4xeZ2VPA4uj4a6IHFVNOzVYiLU9Dp2OHyinZ8/LyakzJ/tFHH+057qKLLgLghBNOYOvWrWzevJmXX36ZmTNncueddwJhxt7Vq1cDcNppp9UIHABvvfUW55133p7ZeL/73e/yj3/8g8GDB9eZz9q+rzbvvvsuixcv5lvf+hYAu3fv5rjjjqNTp07k5uZy5ZVXctZZZ1WpwTSVpIKHmX037mMGYYqSRvcpuPstwC3VkmOEWkii4ycDkxt7vcZwdz0kKFKXemoIqZJoOnZgr6Zkr2BWtYvVzHB3nn32WY488sgq+957771ap2pPZs7AROK/L/5+oOY9VVzntNNO44knnqixb+7cucyaNYvp06dz3333Nfk0KMmWjPEjrEYD2wid3m1WSZnjjua2Emkl9mZK9gpPPvkkEGoOnTt3pnPnzowePZp77713T0CYP39+vd9zwgkn8Nxzz7Fz50527NjBjBkzOP744xuUl0MOOYTFixcTi8XYsmULs2bNqnHMiBEjePvtt1m2LDx2t3PnTj799FO2b9/Oli1bOPPMM7n77rtrXadkbyTb59H6JpvfS7u0frlIq3LDDTdwwQUXMG3aNE4++eRGfUfXrl0ZOXIkW7du5eGHHwbgF7/4Bddddx0DBw7E3SksLOSFF16o83uGDBnCZZddxvDhoWv4yiuvrLfJqrrevXtzwQUXMHDgQPr27Zvw/Pz8fKZMmcJFF11ELBYD4Fe/+hV5eXmMHTuWXbt24e41OuebQrJTsh8K/A4YQWiuege43t1XNHmOmsjeTslevC3GNye/ym1j+/OD4wqbLmMirZimZG9b9mZK9mR/Vv8ZeAroSRjx9DRQs5GtDYntqXmo2UpEpLpkg4e5+zR3L422x9iLDvPWIFYaOqr0nIeISE3JDtV9PRqaO50QNC4E/mZm3QDcfVNdJ7dGsZIoeKjPQ0SkhmSDx4XR61XV0i8nBJNDmyxHLYSarUQSc/caQ1ql9WnscOIKyY62qn0SljZqT7OVah4ie+Tm5rJx40a6d++uANKKuTsbN24kNze30d+R7EOC2cDVwAlR0hvAg9HcVG2S+jxEaiooKKCoqIimmLVa0is3N5eCgoJGn59ss9UDQDZwf/T5B1HalY2+cgsXK1GzlUh12dnZdc4GK/uOZIPHN939mLjPr5nZh6nIUEtRUfPIVc1DRKSGZEvGMjM7rOJD9NBgs0xOmC6VfR6qeYiIVJdszeNGwnDdiifKC4E2PWVJTNOTiIjUKtmS8W3gQaA82h6kGdYQT6fK5zxU8xARqS7ZmsejwFbgtujzRcA0ojU32iKNthIRqV2ywePIah3mr7f9DvPQbNUuU8FDRKS6ZEvG+WY2ouKDmR1LaMpqs2Kl5bTLzCAjQw9CiYhUl2zwOBaYY2arzGwVob/jRDP72Mw+qvvUmsysi5k9Y2ZLzOwTMzvOzLqZ2Stm9ln02jXu+JvNbJmZLTWz0Q29XmPESrSKoIhIbZJttjqjia/7O+Aldz/fzNoBHYCfAbPc/fZoEsZJwE1m1g8YD/QnTAf/qpkdkep1zGOlZervEBGpRbJzW33eVBc0s06EaU4ui757N7DbzMYCJ0WHTSVMgXITYbnb6e4eA1aa2TJgOCke7RXWL9dIKxGRRNLx0/pQoBh4xMzmm9kfzawjcIC7rwOIXvePju8FrIk7vyhKq8HMJprZPDObt7dz74TgoZqHiEgi6Sgds4AhwAPuPhjYQWiiqk2iHuuEcwm7+0PuPszdh+Xn5+9VJmMlZbRT8BARSSgdpWMRUOTu70WfnyEEky/NrCdA9Lo+7vjececXAGtTnclYaTk52Wq2EhFJpNmDh7t/AawxsyOjpFOAxcBMYEKUNgF4Pno/ExhvZjlm1gfoC8xNdT5jpWVqthIRqUWyo62a2k+Ax6ORVisI82RlAE+Z2RXAaqKn1919kZk9RQgwpcA1qR5pBbCrpJy83HT9eVJkxwaY+wfoczwUfjvduRGRViwtpaO7LwCGJdh1Si3HTwYmpzJP1cVKy+nRVkZb7dwEc+6F9x6Ekh3w9u/gBzPgkOPSnTMRaaXULlOLNvGcx64t8Mbt8Ltj4K3fwpHfgcv/Dp0L4M8XwNoF6c6hiLRSbaxdpum06ifMy8tCTeOt38KuzXDUOXDSz+CAfmH/pc/Bw2fAY9+FH74E+UekM7fJ27UVVr0FK16HLxfDd26HA49Od65E9kkKHrVotQ8JlpfBc1fDR09C39Nh1M/hoEFVj+lcAJc+HwLItHPh8pegy8HpyG3dykqgaF4IFiveCO+9DLLaQ1Y7eOx8uPKVlpl3kTZOwaMWrXK0VVkpzLgKFj4DJ/8nnHBj7cd2Pwx+8BeYchY8em4IIPvtX/vxydi1BRbNgKUvhRrB4Eug6yHJn+8OxUtgeRQsPn8bdm8Hy4CDhsC3r4dDT4Lew2Hj8qj2dH7Ie4due5d3EWkQBY9ahOc8WlHwKCuBv/woFN6n3ALH/7T+cw48Gr7/dKh9TPsuXPZXaN+13tOqXrc01AwW/BmW/jeU7oLOveHTl2D2b0JhP+RS+MZZkJVT9dzdO0Lz0xcfwZr3QsDY/mXY1+0wGHhhOL/P8TXzdUA/GP94aHqbfnEYAJCd27C8i0ijKXgk4O7sbk3NVmUl8Mzl8MlMOO02+Na/Jn/uwceGQvjPF8KjY2H8n0OzVn12boK37oKPngoFfvuuIUgcMz7UErYUwYLHYf5j8MwPoX03OOYi6NAVvlgIX3wMm1awZ7KADj3g0BPh0FHhNZmmqD7Hw7kPwLNXhBrX+Y9ARisK+CKtmIJHAntWEWwNzValu0PhvOQFGP1fcNw1Df+Ow06GCx8PAejBE+GCqXU/B7LizVBY7yiGvqNh0EXhNatd5TFdesNJk0LT2YrX4YNpMPchKC+BroWh1jPwQjhwQHjfuTdYI9ZOOfp82LoWXvkFvHwQnPF/Gv4dItJgCh4JVASP3JY2PUlZKcS2hr6Fiu3dB+DTF+E7v4Zjr2r8dx9xOvzoNZj+/VADGf1fMHxi1QK9dDe8Pjk8J9L9cPj+k9DzmNq/EyAjEw4/NWy7tgAGuZ0an89ERv4Etv4T3r0fOvWCkdc27feLSA0KHglULEGbsprH7h1gmfW30ZfuDv0I86fB6ndD53EiZ94Jw3+09/nKPwJ+NAv+chW8+B/hOZCzfxvyuXF5aB5aOx+GXhaCS7uODfv+3M57n8dEzEJ+tq6Fl38egtOQS1NzLREBFDwSipWksNlq5ezQv+DlcPBxoUP40JPgwIGV7fXFS+GDR+HD6bBzQ/g1Pej7oV8gt3Pc1ins69an6fKX2zn0e7x5B7x5OxR/AgPHw6z/DZnZcME06Dem6a7XVDIy4bt/gCe2wcyfwObVYZhyY5rCRKReCh4J7OnzaOpmq5X/gMcvCMNXDx0VRhe9ekvY174b9DkBtn0Ba96FjCw48szwC/qwk0Ph2FwyMmDUzdBzYKiFvHQTFB4P5z0InRMupdIyZOfCxU/DC9eFkV5frYKx/6/mKC8R2WsKHgmkpNlq1dthSpCuh8CEF2C/aL2Rretg5ZshkKx4E3LywoipY8bv/XMXe+sbZ8FVb0LR/8DR45o3gDVWZjaMuQ+6HRpqS1uKQk1Kz4GINCkFjwSafLTV53Pg8XFhRNGEv1YGDoBOPUOgOGZ801yrqXU/LGytiRkc/+9hVNeMq+GPp4YaSWu7D5EWrBWMRW1+lX0eTfBL+/N3wlPQnXtFgSPNtYl9yYDvwYSZ8PVXIYCsfjfdORJpMxQ8EtjTbLW3T5ivfhcePx86HRQCR94BTZA7aZCDR8CVr4Zmq6ljYPHz9Z8jIvVS8Ehgr5utSr6Gt++Bx74HeQfCZS+EV0mP7ofBFa+ECSKfmgDv/j7dORJp9dIWPMws08zmm9kL0eduZvaKmX0WvXaNO/ZmM1tmZkvNbHSq81YZPBrYbFVWGobY3js0PPF88IjQOa7AkX4duoWZhL9xVhg99vefQ3l5unMl0mqls+bxb8AncZ8nAbPcvS8wK/qMmfUDxgP9gTOA+80spcN+YiUNHG3lDotnwgPHhWcM8nqGoHHJs6FDXFqG7PZwwaMw/Cp45z549nIo2ZXuXIm0SmkJHmZWAJwF/DEueSwwNXo/FTg3Ln26u8fcfSWwDBieyvxVPueRxJ+n+NPQGfvUD8LnCx8Lbex9jk9hDqXRMjLhO3eE4dCLZsC088IkjyLSIOmqedwN/AcQ325wgLuvA4heK4Yl9QLWxB1XFKXVYGYTzWyemc0rLi5udOaSbrbatAKmnhMeRhtzH1z9Tli1T081t2xmYebh7/0J/jkv/DeMbUt3rkRalWYPHmZ2NrDe3d9P9pQEaZ7oQHd/yN2Hufuw/Pz8RIckJamHBLcUwdSxULY7jKQa8gPI1GMzrcrR58NFT8D6xfDcj0Pzo4gkJR01j28BY8xsFTAdONnMHgO+NLOeANHr+uj4IqB33PkFwNpUZnBXfXNbbfsyDPvctTmsxlexNri0PoefCqf977AWylu/TXduRFqNZg8e7n6zuxe4eyGhI/w1d78EmAlMiA6bAFQMyJ8JjDezHDPrA/QF5qYyj7HSMtplZWCJmp92bgor721bBxc/AwcNTmVWpDkcdy0MOD9MZ/LZq+nOjUir0JKe87gdOM3MPgNOiz7j7ouAp4DFwEvANe5elsqMxErKE9c6dm0JHawbl4fmjoOPTWU2pLmYwZh74YABYQTWphXpzpFIi5fW4OHub7j72dH7je5+irv3jV43xR032d0Pc/cj3f3FVOcrlmgJ2t07woy4Xy6EC6eFadSl7WjXAcY/BpYR1kSP1bJ2iogALavm0WLESstq1jxemwxFc+F7f4QjUv6coqRD10I4/2EoXgLPX6MOdJE6KHgkECstr/mMxz/nhcWb+p+XnkxJ8zjsZDj1f8Hi5+Dtu9OcGZGWS8EjgdDnEdds5R5W98s/Mn2ZkuYz8l+h/3fh1f8VpnTfvr7eU0T2NQoeCdRottq+PgzLzf9G2vIkzcgMzr0fvn09fPx0mKvs3QfC3GUiAih4JBQ6zOP+NMVLwmuPI9KTIWl+2e1D89WP34WCb8JLk+DB42HVW+nOmUiLoOCRQOjziGu22vBpeFXNY9/T4/AwweWFj4cRWFPOgmcuD8sGa1JF2YdpPo0EYiVl5OblVCYUL4GcTppafV9lBkedHTrT374b3v4dLHwWsnLDtPuHjgpDtw8cCBn6PSb7BgWPBHZXr3lUdJZrwsN9W7sOMOpnoUP987dD7WPFG/DqLWF/+25wQH/ocnDYOveufJ+TB2UlUF4SvZaG193bw2wF276ArWvD67a1sHtnmJn5yLOg11AFJWlxFDwSqNnnsRSOOD19GZKWJWe/8KxPxfM+276AFW/Cyjdh4zJY/lpISzx/Z+0yssNaMHkHQmY2zLk3zLe13wFwxBlw5Jlw6ImhP0YkzRQ8Eqgy2mrnJtixHnpomK7UIu9AOObCsFUojYWZl7esgc2rwwwFmdkhQGS2i95nQbuO4fy8nqHmEl/D+PqrMNfW0r/Bwr/AB1Mhqz10Pxy69K5Zu8n/BmTnNv4+SnfD52/B+k/gmIvC6ositVDwSKDKcx7qLJfGyMoJa6d3P6zx39G+KwwcF7bSGKz6ByybFebe+moVrJwdmr0qZHeEw08JS+32PT25wn/XFvjsFVj63yFQxbaE9Dn3wnkPhpqOSAIKHglUecK8eGl4zdcwXUmjrJwwffzhp1amuYfayZY1sGllaDZb+mKYXt4y4ZCRoalr/6MgtjUEivhtw2dh6HF5CXToAf3OCX0sHXuE9U0eHQvfvg5G/TzUlETiKHhUU17u7C6L6/MoXhqaCjofnN6MiVRnFmoXHbpBz2Og/7lw5v+FdfNhyX+H2sTfb050IuR2Dk1lI64ONZWCb4Yleitc9Sb8/Wehz2XFm2FOt72pRbVU7uEh4C1rQjNjbBuUfA0lO6LXneE1u30IsB17xL12h/ZdILtDaIrcxwbUKHhUs7us2hK0xUugR1+NdpHWISMjjM7qNRRO+UWokWxdGwq5nE4haLTbr/5/z+06wjm/g8NOgZk/gd8fD2fdGfpC6isk3eGf74f5wbYXh8DWa0gYytyuQ+Jzdu+obI7bvh52boQdG2DnhvD69SZolxf6hzodVNlPlNczrOa5eXVl/9Lm1bB5Taht5XauvO+KLTMbtv4zHLNlDZTW8bxOdocwJLtkZ93HWWY4tl2HEGjadw39pPlHhibv/CPDxJvxAbqVU/CoJlZ9FcENn4YJEUVao259wtZY/caEQDTjKnju6jDfV+/h0PvYsPU8JjSplZeHWacXPw+LZ8LWojA4oEM3+Gh6+C7LgPyjoNdg6FIImz8PwW3T8jBcubqcztCxe/il36lXeEhz3Yfw6UuhMK/OMsNxXXpD4bdDoIhtq2ym21IE6xeF/qNOvcIKoEeMhi6HhHM6F4RA065jCABZ7SuDrHsIcDs3wI6NlUEttjWk76ml7AzDrHcUhz6qinsHyMwJ/y2y28cNnMgK77Nywt+572lhXZlWUItR8Khmz/rl2RnhH+uWNZA/oZ6zRNqwzr3g0ufhw+mhX2XNe/DJX8O+zHYhgGwpCgEgs13olznlF2F4cfsusHUdrJ0Paz8Ir0v+O9QkOuZDt0PDQ5bdD4Vuh4XCdb8DQ5NQVrvE+XEPhXbFszGZ2WG0Wd5BoTBOBbMwRDtnv1CDSFZF31LxkrBtWhlqMBXP+uzeGfqcYttgyQsw69ZwH4efEgY9HHoS5HZK/nruIYC169jQO2wwBY9qYqVxzVYaaSUSZGTC4IvDBrDty1DTWDMXiuaFX839zg2/5KsXdp16hu0bZ4bP7uGXem1NWPUxq2yCaukzXed2hoJhYavPti9g2avw2cuhBjd/WhjO3bmgarNbbmfI7RL+jhU1oPgaUeku+MWGlA9yaPbgYWa9gUeBA4Fy4CF3/52ZdQOeBAqBVcAF7v5VdM7NwBVAGfCv7v73VOVvT80jKyNupJWCh0gVeQfAUeeEraHMGh842rK8A2HwJWErKwmBedmroVa3Z4TcsvAa2xqCR0Wz3n4HwP79w+eO+VBe1vaCB1AK/Lu7f2BmecD7ZvYKcBkwy91vN7NJwCTgJjPrB4wH+gMHAa+a2RGpWsd8V3yfx7qloT2y6160GYuINFRmNhR+K2wtVLMPIXL3de7+QfR+G/AJ0AsYC0yNDpsKnBu9HwtMd/eYu68ElgHDU5W/yj6PzFDz6H546tpRRURaqbSOPzWzQmAw8B5wgLuvgxBggP2jw3oBa+JOK4rSEn3fRDObZ2bziouLG5WnKqOtipfo4UARkQTSFjzMbD/gWeA6d99a16EJ0hLOOOfuD7n7MHcflp+f36h8VXSY57I7jDlXf4eISA1pCR5mlk0IHI+7+1+i5C/NrGe0vydQsXB0EdA77vQCYG2q8lbRbJW3YzV4uVYPFBFJoNmDh5kZ8CfgE3e/K27XTKDigYoJwPNx6ePNLMfM+gB9gbmpyl9FzaPjtmUhQTUPEZEa0tET/C3gB8DHZrYgSvsZcDvwlJldAawGxgG4+yIzewpYTBipdU2qRlpBZZ9Hh83LwhOx3Q9P1aVERFqtZg8e7v4WifsxAE6p5ZzJwOSUZSpORbNVu83LwpOke7M+gohIG6XZ/qqpaLbK3vSpmqxERGqh4FFNrLScTMrI2LRcneUiIrVQ8KgmVlJGYcaXWHmJah4iIrVQ8KgmVlrOUZnRSGA9ICgikpCCRzWx0nKOyIzWFlCzlYhIQpq0qZpYaRl97Z+QVwA5eenOjohIi6SaRzWxknIOpajlrxMgIpJGCh7V7C4p5RBX8BARqYuCRzUdd60LkyIqeIiI1ErBo5r8XauiNxqmKyJSGwWPag6IrQpvNNJKRKRWCh7VHLj7c7ZkdIUO3dKdFRGRFkvBo5qC0tV80e7gdGdDRKRF03Me1WzpMRg6HZTubIiItGgKHtWMuPrBdGdBRKTFU7OViIg0WKsJHmZ2hpktNbNlZjYp3fkREdmXtYrgYWaZwP8DvgP0Ay4ys37pzZWIyL6rVQQPYDiwzN1XuPtuYDowNs15EhHZZ7WW4NELWBP3uShKq8LMJprZPDObV1xc3GyZExHZ17SW4GEJ0rxGgvtD7j7M3Yfl5+c3Q7ZERPZNrSV4FAG94z4XAGvTlBcRkX1eawke/wP0NbM+ZtYOGA/MTHOeRET2WeZeo/WnRTKzM4G7gUzgYXefXM/xxcDnjbxcD2BDI89tzXTf+xbd974l2fs+xN3rbfdvNcGjOZnZPHcflu58NDfd975F971vaer7bi3NViIi0oIoeIiISIMpeCT2ULozkCa6732L7nvf0qT3rT4PERFpMNU8RESkwRQ8RESkwRQ84rS1ad/N7GEzW29mC+PSupnZK2b2WfTaNW7fzdG9LzWz0XHpQ83s42jfPWaWaLqYFsPMepvZ62b2iZktMrN/i9Lb9L2bWa6ZzTWzD6P7vjVKb9P3XcHMMs1svpm9EH3eV+57VZTnBWY2L0pL/b27u7bQ75MJLAcOBdoBHwL90p2vvbynE4AhwMK4tF8Dk6L3k4A7ovf9onvOAfpEf4vMaN9c4DjCHGMvAt9J973Vc989gSHR+zzg0+j+2vS9R3ncL3qfDbwHjGjr9x13/z8F/gy8EH3eV+57FdCjWlrK7101j0ptbtp3d58NbKqWPBaYGr2fCpwblz7d3WPuvhJYBgw3s55AJ3d/x8O/sEfjzmmR3H2du38Qvd8GfEKYhblN37sH26OP2dHmtPH7BjCzAuAs4I9xyW3+vuuQ8ntX8KiU1LTvbcAB7r4OQiEL7B+l13b/vaL31dNbBTMrBAYTfoW3+XuPmm4WAOuBV9x9n7hvwtRF/wGUx6XtC/cN4QfCy2b2vplNjNJSfu9ZTZDxtiKpad/bsNruv9X+XcxsP+BZ4Dp331pHE26buXd3LwMGmVkXYIaZDajj8DZx32Z2NrDe3d83s5OSOSVBWqu77zjfcve1ZrY/8IqZLanj2Ca7d9U8Ku0r075/GVVRiV7XR+m13X9R9L56eotmZtmEwPG4u/8lSt4n7h3A3TcDbwBn0Pbv+1vAGDNbRWhuPtnMHqPt3zcA7r42el0PzCA0waf83hU8Ku0r077PBCZE7ycAz8eljzezHDPrA/QF5kZV3m1mNiIafXFp3DktUpTPPwGfuPtdcbva9L2bWX5U48DM2gOnAkto4/ft7je7e4G7FxL+v33N3S+hjd83gJl1NLO8ivfA6cBCmuPe0z1SoCVtwJmEkTnLgZ+nOz9NcD9PAOuAEsIviyuA7sAs4LPotVvc8T+P7n0pcSMtgGHRP8jlwH1EMxO01A34NqHK/RGwINrObOv3DgwE5kf3vRD4ZZTepu+72t/gJCpHW7X5+yaMDv0w2hZVlFvNce+ankRERBpMzVYiItJgCh4iItJgCh4iItJgCh4iItJgCh4iItJgCh4iTcTMupjZj+vYPyeJ79he3zEiLYGCh0jT6QLUCB5mlgng7iObO0MiqaK5rUSazu3AYdHEhCXAdsJDmoOAfma23d33i+bceh7oSpj59j/dvUU/ySxSnR4SFGki0Qy+L7j7gGiCvr8BAzxMfU1c8MgCOniYrLEH8C7Q19294pg03YJI0lTzEEmduRWBoxoD/svMTiBMId4LOAD4ojkzJ7I3FDxEUmdHLekXA/nAUHcviWaDzW22XIk0AXWYizSdbYRlb+vTmbD+RImZjQIOSW22RJqeah4iTcTdN5rZ22a2EPga+LKWQx8H/mpm8wgz/ta1eI9Ii6QOcxERaTA1W4mISIMpeIiISIMpeIiISIMpeIiISIMpeIiISIMpeIiISIMpeIiISIP9f1XjitVoN+yUAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = other_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import Configuration\n", - "from utils.xcs_utils import *\n", - " \n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " covering_wildcard_chance = 1,\n", - " ga_threshold = 25,\n", - " metrics_trial_frequency=100,\n", - " mutation_chance=0.03,\n", - " chi=1, # crossover\n", - " initial_prediction = 0.000001, # p_I\n", - " initial_error = 0.000001, # epsilon_I\n", - " initial_fitness = 0.000001, # F_I\n", - " delta=0.8, \n", - " user_metrics_collector_fcn=xcs_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': 4.431325056160379e-08, 'perf_time': 0.018619200000102865, 'population': 96, 'numerosity': 96}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 70.46571581800626, 'perf_time': 0.10048000000006141, 'population': 350, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 50, 'reward': 44.95473765882148, 'perf_time': 0.1148457999997845, 'population': 377, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 50, 'reward': 31.414392043764956, 'perf_time': 0.09751400000004651, 'population': 376, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 50, 'reward': 35.12634111253022, 'perf_time': 0.11543780000010884, 'population': 385, 'numerosity': 1600}\n", - 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"INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 50, 'reward': 94.4931021516744, 'perf_time': 0.04752760000019407, 'population': 373, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 1, 'reward': 188.32799705225298, 'perf_time': 0.0009927999999490567, 'population': 373, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 50, 'reward': 27.82332216259, 'perf_time': 0.04810200000019904, 'population': 373, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': 4.2151181125486316e-08, 'perf_time': 0.017306600000210892, 'population': 88, 'numerosity': 102}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 35.7983724569229, 'perf_time': 0.11599139999998442, 'population': 371, 'numerosity': 1600}\n", - 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"INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 50, 'reward': 107.10616326070786, 'perf_time': 0.04760269999951561, 'population': 394, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 50, 'reward': 92.74596267439142, 'perf_time': 0.04953939999995782, 'population': 394, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 50, 'reward': 97.93958623646687, 'perf_time': 0.05075630000010278, 'population': 394, 'numerosity': 1600}\n" - ] - } - ], - "source": [ - "from lcs.agents.xcs import XCS\n", - "\n", - "\n", - "agent = XCS(cfg)\n", - "my_metrics = avg_experiment(scenario,\n", - " cfg,\n", - " number_of_tests=3,\n", - " explore_trials=exploration_cycles,\n", - " exploit_metrics=exploitation_cycles)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = my_metrics['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = my_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "None so far." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_compared_no_GA.ipynb b/XCS_Experiments/XCS_compared_no_GA.ipynb deleted file mode 100644 index 3fbd75d..0000000 --- a/XCS_Experiments/XCS_compared_no_GA.ipynb +++ /dev/null @@ -1,774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - 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] - } - ], - "source": [ - "exploration_cycles = 1000\n", - "exploitation_cycles = 500\n", - "input_size = 8\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(input_size)\n", - "scenario.maze.reset()\n", - "scenario.maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "algorithm = XCSAlgorithm()\n", - "algorithm.max_population_size = 1600\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 99999999999999999999999999999999999# 25\n", - "algorithm.crossover_probability = 0.5\n", - "algorithm.mutation_probability = 0.01\n", - "algorithm.initial_prediction = float(np.finfo(np.float32).tiny) # p_I\n", - "algorithm.initial_error = float(np.finfo(np.float32).tiny) # epsilon_I\n", - "algorithm.initial_fitness = float(np.finfo(np.float32).tiny) # F_I\n", - "algorithm.wildcard_probability = 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n", - "Executing 1 experiment\n", - "Executing 2 experiment\n" - ] - } - ], - "source": [ - "other_metrics = other_avg_experiment(\n", - " maze=scenario,\n", - " algorithm=algorithm,\n", - " number_of_tests=3,\n", - " explore_trials=exploration_cycles,\n", - " exploit_trials=exploitation_cycles\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = other_metrics[\"steps_in_trial\"].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps other XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = other_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import Configuration\n", - "from utils.xcs_utils import *\n", - " \n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " covering_wildcard_chance = 1,\n", - " ga_threshold = 9999999999999999999999999,\n", - " metrics_trial_frequency=100,\n", - " mutation_chance=0.01,\n", - " user_metrics_collector_fcn=xcs_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [5.753183395709529e-41, 2.159292778582813e-40, 1.253515642280712e-40, 2.5209629782634306e-40, 7.569508869886019e-41, 2.1873884048928495e-40, 1.6565574643280677e-40, 1.0842949299188363e-40], 'perf_time': 0.013000199999993356, 'population': 80, 'numerosity': 80}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 29, 'reward': [31.364722236947603, 115.30520592778899, 9.886847369331884, 5.45662672155003, 4.638095391004085, 3.4042051167905645, 5.294142650228229, 5.7713915871528], 'perf_time': 0.015804799999997954, 'population': 208, 'numerosity': 208}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 17, 'reward': [194.2522502058606, 53.94857650381515, 98.73732628256941, 4.368861674886088, 7.243342732776246, 4.462264673335366, 4.9651884079276165, 6.027256527488662], 'perf_time': 0.008661399999994046, 'population': 208, 'numerosity': 208}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 4, 'reward': [55.90099092140596, 9.347028821258352, 112.84972165016924, 8.526653060070778, 13.479242086186956, 8.738785085541329, 7.3312894571345035, 9.303370669253274], 'perf_time': 0.00219750000000829, 'population': 208, 'numerosity': 208}\n", - 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"INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 4, 'reward': [50.23965532520499, 37.828162799537324, 452.79195660082746, 4.7282341191127415, 19.834461572036385, 2.8054760435937025, 19.519488191744802, 16.431702533693223], 'perf_time': 0.002166100000010829, 'population': 208, 'numerosity': 208}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 4, 'reward': [59.97680181340553, 47.7864940835952, 401.19358085764844, 4.7282341191127415, 20.70725915837149, 1.6704078840644785, 21.788399511333587, 7.2877031826921765], 'perf_time': 0.002166100000010829, 'population': 208, 'numerosity': 208}\n" - ] - } - ], - "source": [ - "from lcs.agents.xcs import XCS\n", - "\n", - "\n", - "agent = XCS(cfg)\n", - "my_metrics = avg_experiment(scenario,\n", - " cfg,\n", - " number_of_tests=3,\n", - " explore_trials=exploration_cycles,\n", - " exploit_metrics=exploitation_cycles)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - 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"cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = my_metrics['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = my_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "None so far." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_compared_only_exploit.ipynb b/XCS_Experiments/XCS_compared_only_exploit.ipynb deleted file mode 100644 index 1ef93ef..0000000 --- a/XCS_Experiments/XCS_compared_only_exploit.ipynb +++ /dev/null @@ -1,1225 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "logging.root.setLevel(logging.INFO)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "from xcs import XCSAlgorithm\n", - "from xcs.scenarios import Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "exploration_cycles = 0\n", - "exploitation_cycles = 5000\n", - "input_size = 8\n", - "logging.root.setLevel(logging.INFO)\n", - "scenario = MazeScenario(input_size)\n", - "scenario.maze.reset()\n", - "scenario.maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "algorithm = XCSAlgorithm()\n", - "algorithm.max_population_size = 1600\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 25\n", - "algorithm.crossover_probability = 1\n", - "algorithm.mutation_probability = 0.01\n", - "algorithm.initial_prediction = 0.000001 # p_I\n", - "algorithm.initial_error = 0.000001 # epsilon_I\n", - "algorithm.initial_fitness = 0.000001 # F_I\n", - "algorithm.wildcard_probability = 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - } - ], - "source": [ - "other_metrics = other_avg_experiment(\n", - " maze=scenario,\n", - " algorithm=algorithm,\n", - " number_of_tests=1,\n", - " explore_trials=exploration_cycles,\n", - " exploit_trials=exploitation_cycles\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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steps_in_trialpopulationnumerosity
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" - ], - "text/plain": [ - " steps_in_trial population numerosity\n", - "trial \n", - "0 50 50 50\n", - "100 21 334 1600\n", - "200 13 392 1600\n", - "300 50 428 1600\n", - "400 40 450 1600\n", - "500 50 424 1600\n", - "600 50 424 1600\n", - "700 50 435 1600\n", - "800 50 447 1600\n", - "900 50 453 1600\n", - "1000 50 457 1600\n", - "1100 50 469 1600\n", - "1200 50 466 1600\n", - "1300 50 481 1600\n", - "1400 50 476 1600\n", - "1500 50 474 1600\n", - "1600 50 465 1600\n", - "1700 50 472 1600\n", - "1800 50 469 1600\n", - "1900 50 471 1600\n", - "2000 50 468 1600\n", - "2100 50 461 1600\n", - "2200 50 460 1600\n", - "2300 50 467 1600\n", - "2400 1 461 1600\n", - "2500 50 454 1600\n", - "2600 50 464 1600\n", - "2700 50 468 1600\n", - "2800 50 466 1600\n", - "2900 1 467 1600\n", - "3000 50 466 1600\n", - "3100 50 463 1600\n", - "3200 50 456 1600\n", - "3300 1 453 1600\n", - "3400 2 450 1600\n", - "3500 50 452 1600\n", - "3600 50 443 1600\n", - "3700 50 447 1600\n", - "3800 2 452 1600\n", - "3900 50 449 1600\n", - "4000 50 453 1600\n", - "4100 50 452 1600\n", - "4200 50 454 1600\n", - "4300 50 466 1600\n", - "4400 50 466 1600\n", - "4500 2 465 1600\n", - "4600 50 462 1600\n", - "4700 50 462 1600\n", - "4800 50 452 1600\n", - "4900 2 450 1600" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(other_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = other_metrics[\"steps_in_trial\"].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps_in_trial\")\n", - "ax.legend([\"steps other XCS\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = other_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xcs import Configuration\n", - "from utils.xcs_utils import *\n", - " \n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " covering_wildcard_chance = 1,\n", - " ga_threshold = 25,\n", - " metrics_trial_frequency=100,\n", - " mutation_chance=0.03,\n", - " chi=1, # crossover\n", - " initial_prediction = 0.000001, # p_I\n", - " initial_error = 0.000001, # epsilon_I\n", - " initial_fitness = 0.000001, # F_I\n", - " user_metrics_collector_fcn=xcs_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [0, 9.339685532844034e-09, 9.339685532844034e-09, 1.335901139763139e-08, 2.547931123661034e-08, 2.988954611581355e-08, 2.151936563468136e-08, 5.638172962127993e-13], 'perf_time': 0.014018700000008266, 'population': 72, 'numerosity': 74}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 50, 'reward': [45.50452934853669, 11.733219116960623, 2.4382839340538234, 32.14658608623825, 13.284752022932295, 25.961349881478032, 57.276824948606524, 6.970439174217054e-13], 'perf_time': 0.044748500000025615, 'population': 293, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 50, 'reward': [56.268744384172045, 40.40867330401268, 24.70076065694415, 37.374856633974055, 13.284752022932295, 5.571761807579601, 46.51414117918814, 8.581018846811654], 'perf_time': 0.044003500000030726, 'population': 315, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 1, 'reward': [169.8706013641221, 45.386980184579784, 43.353540543526854, 72.09663794626833, 11.800584374962842, 45.0159384065631, 27.893706695942846, 23.29059758684236], 'perf_time': 0.0026578999999742337, 'population': 345, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 50, 'reward': [25.813594805322197, 47.10171232159088, 125.42293453351655, 17.964280311902346, 17.773894766809917, 31.018524791599294, 10.19575284565603, 31.955098596074347], 'perf_time': 0.049107100000014725, 'population': 383, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 2500, 'steps_in_trial': 50, 'reward': [25.497794554186598, 15.248290786852781, 71.88517575185776, 33.654458462994555, 35.84952122904353, 21.899102837862223, 20.413800236666457, 97.34201322507454], 'perf_time': 0.0550429999999551, 'population': 402, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 3000, 'steps_in_trial': 50, 'reward': [40.74851181449875, 68.69241188892474, 24.91616771359776, 72.99405094957865, 74.37168132180497, 37.484376082212506, 70.69589268405976, 171.96527788686436], 'perf_time': 0.06455819999996493, 'population': 432, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 3500, 'steps_in_trial': 50, 'reward': [59.317410974023566, 67.08962300797158, 45.536973613314494, 102.6277070669639, 112.40284800391417, 57.38332613135417, 76.62371177219008, 62.74140010946658], 'perf_time': 0.07726740000009613, 'population': 437, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 4000, 'steps_in_trial': 50, 'reward': [20.018084357519772, 18.939302625391885, 35.24975248875095, 28.10729027463833, 85.70990731671719, 29.57022375539224, 29.814563196621123, 13.62658157625745], 'perf_time': 0.0727706999999782, 'population': 452, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 4500, 'steps_in_trial': 50, 'reward': [89.67933884765844, 57.529092876389726, 58.20596881611598, 82.85273575013208, 100.07473510460402, 52.47408483437317, 36.84138945649649, 38.500112687782035], 'perf_time': 0.06377049999991868, 'population': 440, 'numerosity': 1600}\n" - ] - } - ], - "source": [ - "from lcs.agents.xcs import XCS\n", - "\n", - "\n", - "agent = XCS(cfg)\n", - "my_metrics = avg_experiment(scenario,\n", - " cfg,\n", - " number_of_tests=1,\n", - " explore_trials=exploration_cycles,\n", - " exploit_metrics=exploitation_cycles)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 50 0.014019 72 74\n", - "100 50 0.030024 222 998\n", - "200 50 0.031027 240 1514\n", - "300 50 0.040135 262 1600\n", - "400 50 0.041994 281 1600\n", - "500 50 0.044749 293 1600\n", - "600 2 0.005147 301 1600\n", - "700 50 0.045320 313 1600\n", - "800 50 0.044427 310 1600\n", - "900 50 0.042931 314 1600\n", - "1000 50 0.044004 315 1600\n", - "1100 50 0.042028 310 1600\n", - "1200 50 0.042310 314 1600\n", - "1300 50 0.042681 327 1600\n", - "1400 50 0.046601 343 1600\n", - "1500 1 0.002658 345 1600\n", - "1600 50 0.052513 352 1600\n", - "1700 50 0.047489 363 1600\n", - "1800 50 0.054318 364 1600\n", - "1900 50 0.059778 376 1600\n", - "2000 50 0.049107 383 1600\n", - "2100 50 0.056652 398 1600\n", - "2200 50 0.059129 392 1600\n", - "2300 50 0.063845 384 1600\n", - "2400 50 0.055786 386 1600\n", - "2500 50 0.055043 402 1600\n", - "2600 50 0.064283 401 1600\n", - "2700 50 0.057471 406 1600\n", - "2800 50 0.063067 410 1600\n", - "2900 50 0.054561 419 1600\n", - "3000 50 0.064558 432 1600\n", - "3100 50 0.063220 438 1600\n", - "3200 26 0.043214 437 1600\n", - "3300 50 0.057555 436 1600\n", - "3400 50 0.060323 433 1600\n", - "3500 50 0.077267 437 1600\n", - "3600 50 0.070114 436 1600\n", - "3700 50 0.067924 441 1600\n", - "3800 50 0.070625 448 1600\n", - "3900 50 0.080098 450 1600\n", - "4000 50 0.072771 452 1600\n", - "4100 50 0.075043 449 1600\n", - "4200 50 0.061202 443 1600\n", - "4300 50 0.059867 440 1600\n", - "4400 50 0.073291 449 1600\n", - "4500 50 0.063770 440 1600\n", - "4600 50 0.057721 450 1600\n", - "4700 50 0.069579 465 1600\n", - "4800 50 0.063470 457 1600\n", - "4900 50 0.058956 448 1600" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(my_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "ax = my_metrics['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = my_metrics[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Conclusions\n", - "None so far." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_maze5.ipynb b/XCS_Experiments/XCS_maze5.ipynb deleted file mode 100644 index 5c37bc6..0000000 --- a/XCS_Experiments/XCS_maze5.ipynb +++ /dev/null @@ -1,827 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### XCS in Maze 5\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Maze5 experiment. \n", - "Due to differences in implementation I decided to run this experiment with my default values in hopes of that it will yield similar results this way. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_maze\n", - "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "maze = gym.make('Maze4-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [2.4786035893814066e-08, 2.103715185466775e-08, 3.933899037348334e-08, 2.8035119847182056e-08, 2.0106829085699827e-08, 3.168486574625643e-08, 3.72801533812359e-08, 2.2195475247048022e-08], 'perf_time': 0.009912399999990384, 'population': 31, 'numerosity': 55}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 11, 'reward': [192.249892874772, 26.931986152892236, 22.190624535826586, 20.981276831711654, 16.31917897337844, 13.662676650425665, 31.952498163145627, 19.987772937524518], 'perf_time': 0.0082487000000242, 'population': 129, 'numerosity': 1600}\n", - 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"text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': [49.00961778467261, 64.50762644085759, 29.47468536645948, 54.73970761685219, 58.4310494016315, 52.10349059120672, 53.455571627787855, 57.184789203382394], 'perf_time': 0.0340820999999778, 'population': 140, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 50, 'reward': [95.66607826369977, 99.25735272070943, 86.08740198905025, 100.03968078143936, 95.17013302170523, 71.25877744650279, 91.65480830220858, 93.95127218176061], 'perf_time': 0.041144299999984923, 'population': 198, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 50, 'reward': [38.463824310839165, 113.6670271081694, 41.124174848646284, 40.054776219961646, 36.40078163999772, 41.96335616417129, 32.884863751189165, 37.357518675413004], 'perf_time': 0.06874570000002223, 'population': 220, 'numerosity': 1600}\n", - 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"INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 50, 'reward': [106.41238739001632, 0, 0, 0, 0, 0, 0, 0], 'perf_time': 0.03672890000007101, 'population': 266, 'numerosity': 1600}\n" - ] - } - ], - "source": [ - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " metrics_trial_frequency=100,\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "explore_trials = 4000\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=5,\n", - " explore_trials=4000,\n", - " exploit_metrics=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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120028.00.035459220.01600.0
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210024.00.040871254.61600.0
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270037.80.049161257.61600.0
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290045.60.060217265.81600.0
300029.00.046759260.01600.0
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320035.20.048965263.41600.0
330018.60.027317268.21600.0
340023.80.028769268.21600.0
350046.40.059816268.21600.0
360027.40.036188275.01600.0
370034.40.049033273.01600.0
380032.80.049048269.81600.0
390043.80.060122276.01600.0
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" - ], - "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 47.8 0.010574 35.2 52.8\n", - "100 11.2 0.004748 79.4 1500.8\n", - "200 35.0 0.021365 103.6 1600.0\n", - "300 20.8 0.012909 126.2 1600.0\n", - "400 34.8 0.025986 142.8 1600.0\n", - "500 46.0 0.039345 155.4 1600.0\n", - "600 34.8 0.030813 170.2 1600.0\n", - "700 43.4 0.036726 180.4 1600.0\n", - "800 36.0 0.033502 192.6 1600.0\n", - "900 44.0 0.047701 200.8 1600.0\n", - "1000 35.0 0.043058 215.8 1600.0\n", - "1100 14.0 0.015578 219.8 1600.0\n", - "1200 28.0 0.035459 220.0 1600.0\n", - "1300 20.2 0.024177 224.2 1600.0\n", - "1400 38.0 0.047346 227.6 1600.0\n", - "1500 29.8 0.041776 237.0 1600.0\n", - "1600 31.8 0.043365 234.0 1600.0\n", - "1700 39.6 0.054749 240.4 1600.0\n", - "1800 29.0 0.036766 239.4 1600.0\n", - "1900 26.4 0.037730 250.6 1600.0\n", - "2000 40.6 0.059776 255.8 1600.0\n", - "2100 24.0 0.040871 254.6 1600.0\n", - "2200 17.0 0.027526 257.6 1600.0\n", - "2300 39.6 0.053207 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is hard to say but oking at amount of times algorithm reaches top steps (50) the steps might actually go down over trials. need to somehow smooth it to see it better" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n", - "The results of my implementation of the XCS aredifferent compared to An Analysis of Generalization in the XCS\n", - "Classifier System. \n", - "Unfortunately from 10 experiments my algorithm managed to fail to solve the maze as well as the one presented in An Analysis of Generalization in the XCS Classifier System. With exploitation not really going down below 50 steps, where it should be easily below 50.\n", - "It can be caused by differences in implementation or envirement I am unaware of.\n", - "
\n", - "Unfortunately I cannot find metrics or issues that could showcase what causes what said diffrences are." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_maze5_without_#.ipynb b/XCS_Experiments/XCS_maze5_without_#.ipynb index 7354b96..4007d03 100644 --- a/XCS_Experiments/XCS_maze5_without_#.ipynb +++ b/XCS_Experiments/XCS_maze5_without_#.ipynb @@ -44,12 +44,12 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '1', '0', '0', '0', '1', '1', '1')\n", + "('1', '0', '0', '1', '0', '0', '1', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", @@ -78,7 +78,7 @@ "name": 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"INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1374.5248209001315, 'perf_time': 0.002424400000023752, 'population': 336, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 9, 'reward': 1063.9786572741712, 'perf_time': 0.007083100000016884, 'population': 336, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 8, 'reward': 1112.6830064540356, 'perf_time': 0.0063073999999971875, 'population': 336, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': 0.0, 'perf_time': 0.014774399999993193, 'population': 68, 'numerosity': 88}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 1 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 3, 'reward': 1612.0280961878896, 'perf_time': 0.0054921999999919535, 'population': 326, 'numerosity': 1600}\n", + 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'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 30, 'reward': 1000.0344945484236, 'perf_time': 0.06493780000005245, 'population': 346, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 13, 'reward': 1000.0000004805667, 'perf_time': 0.031009999999980664, 'population': 360, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 14, 'reward': 1000.0, 'perf_time': 0.01364250000000311, 'population': 355, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 2, 'reward': 1641.1002217362761, 'perf_time': 0.0017081000000302993, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 8, 'reward': 1066.6903503236365, 'perf_time': 0.006849299999998948, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 9, 'reward': 1080.0855157222993, 'perf_time': 0.008099200000003748, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 42, 'reward': 1000.000567730361, 'perf_time': 0.03521480000000565, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 9, 'reward': 1047.5325068696472, 'perf_time': 0.007547100000010687, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 3, 'reward': 1449.2451744257053, 'perf_time': 0.002518699999995988, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 10, 'reward': 1034.124780525829, 'perf_time': 0.00869930000004615, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 8, 'reward': 1064.594969331911, 'perf_time': 0.007064500000069529, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 3, 'reward': 1539.0406806601422, 'perf_time': 0.0026307000000542757, 'population': 357, 'numerosity': 1600}\n" ] } ], @@ -133,7 +166,7 @@ "\n", "df = avg_experiment(maze,\n", " cfg,\n", - " number_of_tests=1,\n", + " number_of_tests=2,\n", " explore_trials=4000,\n", " exploit_metrics=1000)" ] @@ -165,6 +198,7 @@ " \n", " \n", " steps_in_trial\n", + " reward\n", " perf_time\n", " population\n", " numerosity\n", @@ -175,416 +209,467 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", - " 50\n", - " 0.008658\n", - " 32\n", - " 40\n", + " 50.0\n", + " 0.000000e+00\n", + " 0.014694\n", + " 69.0\n", + " 84.0\n", " \n", " \n", " 100\n", - " 50\n", - " 0.048931\n", - " 227\n", - " 1600\n", + " 37.0\n", + " 5.000000e+02\n", + " 0.055201\n", + " 295.5\n", + " 1600.0\n", " \n", " \n", " 200\n", - " 50\n", - " 0.053113\n", - " 222\n", - " 1600\n", + " 21.5\n", + " 1.000000e+03\n", + " 0.039413\n", + " 301.5\n", + " 1600.0\n", " \n", " \n", " 300\n", - " 50\n", - " 0.053579\n", - " 223\n", - " 1600\n", + " 50.0\n", + " 7.041987e-13\n", + " 0.092019\n", + " 327.0\n", + " 1600.0\n", " \n", " \n", " 400\n", - " 50\n", - " 0.063952\n", - " 230\n", - " 1600\n", + " 26.5\n", + " 8.060141e+02\n", + " 0.038117\n", + " 324.5\n", + " 1600.0\n", " \n", " \n", " 500\n", - " 50\n", - " 0.048320\n", - " 227\n", - " 1600\n", + " 50.0\n", + " 1.827627e-05\n", + " 0.094100\n", + " 333.5\n", + " 1600.0\n", " \n", " \n", " 600\n", - " 50\n", - " 0.059309\n", - " 215\n", - " 1600\n", + " 25.0\n", + " 1.355000e+03\n", + " 0.046612\n", + " 329.0\n", + " 1600.0\n", " \n", " \n", " 700\n", - " 50\n", - " 0.060466\n", - " 227\n", - " 1600\n", + " 7.5\n", + " 1.106491e+03\n", + " 0.017386\n", + " 341.5\n", + " 1600.0\n", " \n", " \n", " 800\n", - " 50\n", - " 0.048802\n", - " 235\n", - " 1600\n", + " 18.0\n", + " 1.356483e+03\n", + " 0.035508\n", + " 332.5\n", + " 1600.0\n", " \n", " \n", " 900\n", - " 50\n", - " 0.061664\n", - " 231\n", - " 1600\n", + " 20.5\n", + " 1.004184e+03\n", + " 0.036089\n", + " 337.0\n", + " 1600.0\n", " \n", " \n", " 1000\n", - " 47\n", - " 0.055512\n", - " 234\n", - " 1600\n", + " 36.0\n", + " 5.002671e+02\n", + " 0.069729\n", + " 330.0\n", + " 1600.0\n", " \n", " \n", " 1100\n", - " 50\n", - " 0.057060\n", - " 231\n", - " 1600\n", + " 12.0\n", + " 1.000000e+03\n", + " 0.024495\n", + " 339.5\n", + " 1600.0\n", " \n", " \n", " 1200\n", - " 50\n", - " 0.066798\n", - " 233\n", - " 1600\n", + " 35.5\n", + " 5.003770e+02\n", + " 0.065656\n", + " 331.5\n", + " 1600.0\n", " \n", " \n", " 1300\n", - " 50\n", - " 0.054293\n", - " 214\n", - " 1600\n", + " 34.5\n", + " 5.000000e+02\n", + " 0.064152\n", + " 332.5\n", + " 1600.0\n", " \n", " \n", " 1400\n", - " 50\n", - " 0.055717\n", - " 234\n", - " 1600\n", + " 14.0\n", + " 1.032288e+03\n", + " 0.029252\n", + " 345.0\n", + " 1600.0\n", " \n", " \n", " 1500\n", - " 35\n", - " 0.039043\n", - " 213\n", - " 1600\n", + " 11.5\n", + " 1.000000e+03\n", + " 0.026517\n", + " 334.0\n", + " 1600.0\n", " \n", " \n", " 1600\n", - " 50\n", - " 0.057154\n", - " 222\n", - " 1600\n", + " 28.0\n", + " 1.000034e+03\n", + " 0.054088\n", + " 345.0\n", + " 1600.0\n", " \n", " \n", " 1700\n", - " 50\n", - " 0.063802\n", - " 253\n", - " 1600\n", + " 32.5\n", + " 5.000000e+02\n", + " 0.066505\n", + " 334.0\n", + " 1600.0\n", " \n", " \n", " 1800\n", - " 50\n", - " 0.053912\n", - " 246\n", - " 1600\n", + " 32.5\n", + " 5.029366e+02\n", + " 0.070635\n", + " 338.5\n", + " 1600.0\n", " \n", " \n", " 1900\n", - " 50\n", - " 0.058445\n", - " 243\n", - " 1600\n", + " 40.0\n", + " 5.000172e+02\n", + " 0.077617\n", + " 338.5\n", + " 1600.0\n", " \n", " \n", " 2000\n", - " 50\n", - " 0.057146\n", - " 247\n", - " 1600\n", + " 20.5\n", + " 1.127061e+03\n", + " 0.038744\n", + " 342.0\n", + " 1600.0\n", " \n", " \n", " 2100\n", - " 50\n", - " 0.073436\n", - " 250\n", - " 1600\n", + " 32.5\n", + " 5.029366e+02\n", + " 0.066212\n", + " 341.5\n", + " 1600.0\n", " \n", " \n", " 2200\n", - " 50\n", - " 0.056097\n", - " 231\n", - " 1600\n", + " 12.5\n", + " 1.073004e+03\n", + " 0.024406\n", + " 340.0\n", + " 1600.0\n", " \n", " \n", " 2300\n", - " 50\n", - " 0.055020\n", - " 245\n", - " 1600\n", + " 50.0\n", + " 2.061746e-05\n", + " 0.093918\n", + " 353.5\n", + " 1600.0\n", " \n", " \n", " 2400\n", - " 50\n", - " 0.061959\n", - " 247\n", - " 1600\n", + " 35.0\n", + " 5.000000e+02\n", + " 0.070592\n", + " 346.0\n", + " 1600.0\n", " \n", " \n", " 2500\n", - " 2\n", - " 0.003387\n", - " 223\n", - " 1600\n", + " 26.0\n", + " 5.000000e+02\n", + " 0.049916\n", + " 348.0\n", + " 1600.0\n", " \n", " \n", " 2600\n", - " 50\n", - " 0.051482\n", - " 238\n", - " 1600\n", + " 26.5\n", + " 6.818921e+02\n", + " 0.045574\n", + " 346.0\n", + " 1600.0\n", " \n", " \n", " 2700\n", - " 50\n", - " 0.043136\n", - " 220\n", - " 1600\n", + " 27.5\n", + " 6.371674e+02\n", + " 0.066821\n", + " 343.0\n", + " 1600.0\n", " \n", " \n", " 2800\n", - " 50\n", - " 0.049407\n", - " 221\n", - " 1600\n", + " 38.5\n", + " 5.000482e+02\n", + " 0.076081\n", + " 343.5\n", + " 1600.0\n", " \n", " 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+ " 0.002061\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4200\n", - " 50\n", - " 0.034578\n", - " 269\n", - " 1600\n", + " 5.5\n", + " 1.355865e+03\n", + " 0.004629\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4300\n", - " 50\n", - " 0.032525\n", - " 269\n", - " 1600\n", + " 9.0\n", + " 1.065087e+03\n", + " 0.007607\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4400\n", - " 50\n", - " 0.035601\n", - " 269\n", - " 1600\n", + " 23.5\n", + " 1.091336e+03\n", + " 0.019586\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4500\n", - " 50\n", - " 0.033539\n", - " 269\n", - " 1600\n", + " 9.5\n", + " 1.040077e+03\n", + " 0.007704\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4600\n", - " 3\n", - " 0.002200\n", - " 269\n", - " 1600\n", + " 2.0\n", + " 1.614899e+03\n", + " 0.001695\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4700\n", - " 50\n", - " 0.034513\n", - " 269\n", - " 1600\n", + " 6.5\n", + " 1.204325e+03\n", + " 0.005562\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4800\n", - " 3\n", - " 0.002333\n", - " 269\n", - " 1600\n", + " 8.5\n", + " 1.064287e+03\n", + " 0.007074\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", " 4900\n", - " 50\n", - " 0.034051\n", - " 269\n", - " 1600\n", + " 5.5\n", + " 1.325862e+03\n", + " 0.004469\n", + " 346.5\n", + " 1600.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 50 0.008658 32 40\n", - "100 50 0.048931 227 1600\n", - "200 50 0.053113 222 1600\n", - "300 50 0.053579 223 1600\n", - "400 50 0.063952 230 1600\n", - "500 50 0.048320 227 1600\n", - "600 50 0.059309 215 1600\n", - "700 50 0.060466 227 1600\n", - "800 50 0.048802 235 1600\n", - "900 50 0.061664 231 1600\n", - "1000 47 0.055512 234 1600\n", - "1100 50 0.057060 231 1600\n", - "1200 50 0.066798 233 1600\n", - "1300 50 0.054293 214 1600\n", - "1400 50 0.055717 234 1600\n", - "1500 35 0.039043 213 1600\n", - "1600 50 0.057154 222 1600\n", - "1700 50 0.063802 253 1600\n", - "1800 50 0.053912 246 1600\n", - "1900 50 0.058445 243 1600\n", - "2000 50 0.057146 247 1600\n", - "2100 50 0.073436 250 1600\n", - "2200 50 0.056097 231 1600\n", - "2300 50 0.055020 245 1600\n", - "2400 50 0.061959 247 1600\n", - "2500 2 0.003387 223 1600\n", - "2600 50 0.051482 238 1600\n", - "2700 50 0.043136 220 1600\n", - "2800 50 0.049407 221 1600\n", - "2900 50 0.051058 212 1600\n", - "3000 50 0.049651 212 1600\n", - "3100 50 0.048551 208 1600\n", - "3200 50 0.065960 224 1600\n", - "3300 9 0.010725 231 1600\n", - "3400 50 0.056180 219 1600\n", - "3500 50 0.049327 230 1600\n", - "3600 50 0.063814 248 1600\n", - "3700 3 0.007643 257 1600\n", - "3800 37 0.053969 257 1600\n", - "3900 50 0.060112 268 1600\n", - "4000 50 0.035787 269 1600\n", - "4100 50 0.033367 269 1600\n", - "4200 50 0.034578 269 1600\n", - "4300 50 0.032525 269 1600\n", - "4400 50 0.035601 269 1600\n", - "4500 50 0.033539 269 1600\n", - "4600 3 0.002200 269 1600\n", - "4700 50 0.034513 269 1600\n", - "4800 3 0.002333 269 1600\n", - "4900 50 0.034051 269 1600" + " steps_in_trial reward perf_time population numerosity\n", + "trial \n", + "0 50.0 0.000000e+00 0.014694 69.0 84.0\n", + "100 37.0 5.000000e+02 0.055201 295.5 1600.0\n", + "200 21.5 1.000000e+03 0.039413 301.5 1600.0\n", + "300 50.0 7.041987e-13 0.092019 327.0 1600.0\n", + "400 26.5 8.060141e+02 0.038117 324.5 1600.0\n", + "500 50.0 1.827627e-05 0.094100 333.5 1600.0\n", + "600 25.0 1.355000e+03 0.046612 329.0 1600.0\n", + "700 7.5 1.106491e+03 0.017386 341.5 1600.0\n", + "800 18.0 1.356483e+03 0.035508 332.5 1600.0\n", + "900 20.5 1.004184e+03 0.036089 337.0 1600.0\n", + "1000 36.0 5.002671e+02 0.069729 330.0 1600.0\n", + "1100 12.0 1.000000e+03 0.024495 339.5 1600.0\n", + "1200 35.5 5.003770e+02 0.065656 331.5 1600.0\n", + "1300 34.5 5.000000e+02 0.064152 332.5 1600.0\n", + "1400 14.0 1.032288e+03 0.029252 345.0 1600.0\n", + "1500 11.5 1.000000e+03 0.026517 334.0 1600.0\n", + "1600 28.0 1.000034e+03 0.054088 345.0 1600.0\n", + "1700 32.5 5.000000e+02 0.066505 334.0 1600.0\n", + "1800 32.5 5.029366e+02 0.070635 338.5 1600.0\n", + "1900 40.0 5.000172e+02 0.077617 338.5 1600.0\n", + "2000 20.5 1.127061e+03 0.038744 342.0 1600.0\n", + "2100 32.5 5.029366e+02 0.066212 341.5 1600.0\n", + "2200 12.5 1.073004e+03 0.024406 340.0 1600.0\n", + "2300 50.0 2.061746e-05 0.093918 353.5 1600.0\n", + "2400 35.0 5.000000e+02 0.070592 346.0 1600.0\n", + "2500 26.0 5.000000e+02 0.049916 348.0 1600.0\n", + "2600 26.5 6.818921e+02 0.045574 346.0 1600.0\n", + "2700 27.5 6.371674e+02 0.066821 343.0 1600.0\n", + "2800 38.5 5.000482e+02 0.076081 343.5 1600.0\n", + "2900 49.0 5.000000e+02 0.094831 350.0 1600.0\n", + "3000 41.5 1.000003e+03 0.073643 341.0 1600.0\n", + "3100 50.0 3.125273e-05 0.095620 348.0 1600.0\n", + "3200 20.0 1.000017e+03 0.042478 343.0 1600.0\n", + "3300 36.0 5.004019e+02 0.071616 323.5 1600.0\n", + "3400 50.0 1.887400e-05 0.096246 334.5 1600.0\n", + "3500 40.5 5.000000e+02 0.078166 338.5 1600.0\n", + "3600 19.5 1.000068e+03 0.047064 346.5 1600.0\n", + "3700 50.0 3.985382e-05 0.085862 341.0 1600.0\n", + "3800 33.0 5.020850e+02 0.051347 338.0 1600.0\n", + "3900 50.0 8.925648e-28 0.104628 335.0 1600.0\n", + "4000 8.5 1.000000e+03 0.008014 344.0 1600.0\n", + "4100 2.5 1.511096e+03 0.002061 346.5 1600.0\n", + "4200 5.5 1.355865e+03 0.004629 346.5 1600.0\n", + "4300 9.0 1.065087e+03 0.007607 346.5 1600.0\n", + "4400 23.5 1.091336e+03 0.019586 346.5 1600.0\n", + "4500 9.5 1.040077e+03 0.007704 346.5 1600.0\n", + "4600 2.0 1.614899e+03 0.001695 346.5 1600.0\n", + "4700 6.5 1.204325e+03 0.005562 346.5 1600.0\n", + "4800 8.5 1.064287e+03 0.007074 346.5 1600.0\n", + "4900 5.5 1.325862e+03 0.004469 346.5 1600.0" ] }, "metadata": {}, @@ -602,7 +687,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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2jIfWeASlShUnFtO4Yiq8ZtuQ19VyaGh+Nb8mUQxIyerpmL+lhHGmWFbyFqMhD5YMPAKuM8STxSI0ZB8DYQjcTgcmwq3NJeBfoLBPyhGUq0wZY6hFsVxFrlSRk8UiR9CL5EpVxSPgF8FuaQ6drisdVbNnOorDqoTxqXgGxUpVyQ9wgl5Xy30E88n1HgEgVw61GhpSDIHXUGaCK4+Ohrzwu50oVVhbKsICfQbCEACS+Fw7hiAo5wgA4ws7/100UDMEIkfQW+SKZSUxOx6Rqlu6VTmkGIJRDUMwE0UiU1R0tI4qieLImu2CntYMQalSRTxdWOcRAJLUxMmlNKrV5jR9MoWKkreQQkMGOQLZIxgJeeBzS5eq/AbQfepHBsYQbIq1JvdQHxoCjBVIeT4g4nMjJHIEPUmuVFGSo+Nh6SLYrYTxqXgGRMBmjdm+e6blDmM5PPTifBIuByl6QJxWQ0OLqQIYg65HUChXcb6JxVWlypArVRSPYCTkQSJT1DUm8XQBsYAbbqdDMcyihNQeBsYQTMf8OL+Sa1qVkPcAhOTyUQCG3cLcSET8LjjlFn/hEfQWuaIqR6B4BN0xBKfjGWyK+teUjnJeJieM+ejKo3MpbB8LweNa+7UOeJ2G4Uw95uXiCi09I6VyqIk8AU9Yh1ShoUqVYUVnoZTIFDASlKqf/MIQ2MrAGIKpqA/FcrVhJ2M9ikfgcyHilz7Ahh6BfNHnRiPicwm9oR4jW6woF96hgAduJ3Wtl+BUIqtoHtXjczuxcyKMQ+elJrKjdRVDnGCLHoHSQ6BhCLjX0YzUBB9Kw3sbRmU1VL0S0niqqDSeeXloSBgCWxgYQ8B7CeaarBxaWz7KPQL9C/uqKjQESOqnIlncW+RLtfJRh4MwFuped/GZREYzUczZMx3BodkVJPMlnF/JrasYAoBQizkCRWcosl55dTjowXDQ01TCuJZv4zkC46ayeLqgGItaaEjkCOxgYAwB7y5uJqYJ1BK9QY/LVBUQNxKKR+AXoaFeQ10+CkDuLu58aGglW8RKtmRsCGZiuJgt4dGjiwCwrmII4FVDrYSG8vC5HYonXM9lY82NrcwU1oeGAP3ZxfF0AaMiNNQRBsYQtNpdnJGH0jgdpPQFGIeGeI5AeAS9ijo0BEh5gm7kCLjYnFbFEIcnjL99YBYAcHldxRAgCc+1MrdYKh31aw6/AYDt48GmPAKeI1BCQwZ6Q4VyBcl8WdmG/z+E8Jw9DIwhGAq44XU5mu4uTsvaKIDUpRnyuhomiz2uWpVD2Cc8gl4jr6oaAqRegm7kCHjpaH0zmZorJsNwOQg/ORFH2OfCJo14ftDrAmNoOmEsNZPpTzXbPhbCxWzJsARUDfdK+Pcp5nfD6SClX0AN7ziuhYZ4jkCEhuxgYAwBEbU0oEZtCAAp+WtcPlpWQkjS9uaE6gQbg1KlilKFIaDyCMbDXqxkSx0PS5yOZ+EgaRiMHjxhzJhkFLRW78EWh9Nwj0APZVrZkjmpiUxdjsDhIAwHPUq/gBq1zhAAUT5qMwNjCAApPNRsjoBLUHMk4TnjHEFUFVPlHsEgDdPuZfiFxl+XIwA63118OpHBppgfXtf60lE1e2UBuvpGMg5fyDRTOVStMiwk85oVQxxlfrHJ8BAPDQVVCyupu1jfI+AqpSJHYC8DZQimos13F2fqPYIGU8qS+ZKSH+Dbl+VGGsHGhw+uX5sjkC6Gnc4TnI4bVwxxdst5Aq3SUQBK4ruZ0FAiU0SpwtbIT9czHfPD53aYNwQFLUPg0TQEXINoTJSPdoSBMgSbYn4spQsoNtGmnsqX13xwG80k4LMIODzBLPIEvQE32P66ZDGAjuYJGGM4Fc/o9hCo+bkdo9gU9eH67SOav2/FI+BGz8gjcDgI20bNVw7xKWnqsNuYjt4QNw7cIxDlo/YyUIZgOuYHY82t7DLFMsJrcgRuw4u6FBpamyMAIJrKegRlOpk6NBTuvEewki0hmS+b8gi2jATx04/evE5agtNKjkBrII0WzYjPZeUKPIejlscYkT2C+tBpIl1EwONUKoxEaMheBsoQTMWkD3UzeYK0hkfQqLM4UpcjAIBVMcS+J+DhE5/KEAwF3HA7qaNNZacSa+cUt0OwBY+gNqKysSE4v5JTQmpGqCWoOaMhLwrl6rpj47OKOW6nNChIhFjtwXZDQEROIvoZEd0nPx4mooeI6Jh8O2T3MXCU7uImSkjXJ4uldn0tOVzGGFbrPQK/8Ah6iXxxfWiIiDAe9nVUeO603ENQP5msFUKKR2D+Ijq/moPTQRgJ6ZePAiqpCRPhoXShsibfBtSqguoH1PBZxWp8LocIDdlEJzyCDwB4QfX4IwAeYYztAPCI/LgjbIo2N7u4UK6gVGF15aP60tLZYgWVKqsrHxXDaXoJrdAQIMlRdzJHcDqegUNHdbRZeLlmM6Gh+dUCJsJeOB3azWQc7rGcXc42fE8eGlKjN7s4kS6uM0J+jxhOYxe2GgIimgFwG4C/UT39VgB3y/fvBvA2O49Bjd/jxHDQYzo0pFYe5UQNZhKs1slLAFAG2AuPwDqS+VJT0gbNkNXwCABpUlkncwSnEllMD/nXKYm2QrCFAfbzyZxhopjDL+QXs43FHNMF7dAQsN4Q1IeGAMDrcioem8Ba7PYIvgDg9wGo/bkJxtgcAMi341ovJKK7iOgAER1YWlqy7ICmoj7MmTQE3JUO1pWPAtoyE/XyEkDNgzDqRhY0x+cffAnv/uvHbXlv7hHUyz53WmaikdhcMzgchIDH2XSy2Iwh4IuelWzjhU6mWF4XGhqTO4eXVKGhSpVhOVPEWH1oyO0QHoFN2GYIiOh2AIuMsadbeT1j7EuMsX2MsX1jY2OWHdemJrqLUwXpw63pEWhc2Feza5VHAenD63KQ8Ags5OkzFxFvsgzYLHnd0JAPyXy5I1UrvHTUikQxJ+BxIWNyNc0Yw/xqHpMaqqP1+NxO+NwOrJjwCLKFyrq/67AsKqeWqVjOFFFlNXkJjt/jFDkCm7DTI7gBwFuI6DSAbwK4iYj+AcACEU0BgHy7aOMxrEOSmWjOIwjXJYsB7dBQ/SwCQEo0NupGFpinUK7g6Lykv2/m4tMsvPrFX28I5IvSYgcqh5YzRaTyZUsSxZyQ17xHkCqUkS1WMBk1ThRzhgIeUx5BvVwLIFUDxQLuNaEhrj00Ely7f5/LKcpHbcI2Q8AY+yhjbIYxthXALwH4PmPsPQDuBXCnvNmdAO6x6xi0mIr6kCqUTV2Y07JHUF8+CuiEhlTTydQI4TnreGk+jVJFqjlftsEQKOWjrvrQkNxL0AE5ajNic83SzAD7BWUgTWOPAJC+ExfNhIY0cgSALDOh0hvi99dVDbmdonzUJrrRR/AZALcQ0TEAt8iPO0YzA2rSdWqJgDrmby5ZzF8jDIE1HDq/qty/mLHey8qXKvC6HGuanoDOykycjksVOFblCIDmppSZbSbjDAU8WM0ZG+VqlSFbqg2uVzMa8qxRIFUE5+pCQz63KB+1i44YAsbYDxhjt8v3E4yxmxljO+Tb5U4cA6eZuQRaVUN8NoFRsjjsW2sIwj6XUCC1CD6fFzBXqdIs9UNpOLXZxfaHhk4nMnA6CDND1nkEIa9LEX1rhNlmMk4s4G4YGsqVKmAMmh7BSJ3MhGII6kNDbicKwiOwhYHqLAaATU10F/PQkLqhjIgkvSGd8tGw17Wu9lqEhqzj0PlV7JyQmpiWm5w/bYZssbKudBSQvDyPy9GRSWWnE1lMx6wpHeU0M6WMj6gcN5hFoCYWaBwa0hKc44yFvGuG08TTRXic6yejidCQfQycIRgP++B0kKnuYi2RLIDPJFh/YU/mymtKR2vbi2SxFRTKFbw4n8Jrd0hVZBdtMAS5UmWNvARH6i72diRZfCaRwZYR67wBQEoWNxMaGgl6Gspfc2JyaMhIap1XLPHmNjWjIQ9ShVpFVjxdwEjIs262ghQaEobADgbOEDgdhMmIz1QJaTovVTnUx4v1FEhXc6U1FUacsMgRWMKL8ymUKgzXbBlCyOuyJVmcL2qHhgApT9CJHMGZRNZyQxBoYoB9ozkE9cT8bpQqzLA8VfEIPNqhIUCSvgakUtKRukQxIDX5iRyBPQycIQCkElIzoSGpymH9RUFvJkEyX1qXKJa2lxJ1laoYTtMOB2elRPGe6SiGgo3j0q2gFxoCpBJSuw3BaraE1VwJW4atSxQDUkgmW6ygauIzOLeaN50oBqTQEGBczsu9kfryUWD97OJ4uriuqxiohYbEkCfrGUhDMBXzmQwNra97BvSnlCVzJc3QEE8ep4VX0BaHz68iFnBjZsiP4YDHlhxBrlRZ11XM6cTs4jPLUunoZhtCQwCQNRFaadojCEirdyPDnOWD6zUNwVq9IS15CaDW7V2woZFw0BlIQ7Ap5sf8ar7hCl3XEPjcmp3F9bMIatvrN6EJzHNwdhV7pqMgIgwFPbZUDeV1qoYAKXmaypeVi5odnElIpaNWh4bMziTIlypYzhSb8whMyEzUSrG1cgQ1BVLGGBIGHgEAFER4yHIG1hCUKkxzRJ6aeglqTsQvlYPWu6jJ/NrB9RzuEQhD0Dr5UgUvLaSwRx7LaKdHoBcampAH1NiZMD4jN5NZoTqqxuyUMn5uzXgEQ7JMxIpBL4FR1RC/6C+lC0jmyyhWquuayQApWQxA6A3ZwEAagokwrwk3jvfWzyvmRP1uFCvVNS5quSIN19DLEQBiXGU7HJ1PoVxlyqD2WMBjS9VQtlhZJy/BGY+Y+9y0w5lEFmNhrzKZyyp4kraRR8BDplMmu4qBmkdgVELK96t1Xn6PE0GPE/F0odZDoOURyFVMZobgCJpjIA3BeMTcyq5+XjGHr/rVCWN+ka+vfVZvL5rKWod3FPNB7cNBNzLFCgoWrw7zReMcAWDv7OIzy1lstTgsBJifUjavzCo210MAAFE5WbxqEKpTlHx1jOxoWGoq4wljLUPADbTwCKxnMA0BFxBr8IWun1fMqSmQ1i7sPOyj5RGIAfbtc2h2BcNBD6blznAlHGFx5ZBeZzFQCw3Z6RGcTWSx2eKKIcD8lLL5JnWGAGlOQMDjNPxfZIplSYnXqX3JGQl6kEgXlBJSrfJRJTQkcgSWM5CGgK82jLpEGWPr5hVztGYS8PtaOYJIh3MET5xM4H1ff8ZUqWCvcOh8ErvlRDEg5QgAa7uLi+UqylWmmyOI+F3wuhy2eQT5UgXzybzliWIACJicUja3mkfI69IMiRoRayA8lymUNXsIOKMhrwgNdZGBNAQelwPDQY/hF7ogXxS0ksVaU8p4FZFW+Wiowx7B919cxP5Dc32TnOaJ4r1yWAioeQRW5gn0htJwiAjjNg6oObdsT8UQoPIIGlQ8LaUKpqUl1MQaCM/pKY9y1KEhotqcAjVetwgN2cVAGgJACg8tGRgCowYYpRxUVUKqpzwKSJrrAY+zYzmCJTn3YXYqWrXK8NHvHMTfP3Z6Q3oRL8wlUakyJT8ASIqXgLVS1LWhNPoXrImwz7aqIV46anXFEGC+fHQpVcBYg4H1WjQSnksXKsaGQC4HXkgWMBzwaM5K9ivlo8IQWM3AGoKxsPEwci3lUY5WaKg2plL7w95J4Tl+XmY9govZIr7x5Dl8/J7ncceXH1dKGDcKPFHMK4YAYCjYuFKlWZR5xR79r8VExGfbTIIzikdgfY6A62WlG+QIltIFZXxkM0jCc/pGOVss6yaKAckjYAw4tpjSDAsBtRyBEJ6znoE2BEsGLn7aoO5ZqwrIyCPgr+lUqIbnPsx6IPzY37R7EkcuJPHGL/wIX/nJqQ3jHRyaXcVI0LNGFpl7BJaGhnQG16sZC3uVwS1WczaRQdjrwlBA+zPUDg4HIWhibvFSqoDxsPkeAo4UGmqQIzDyCOSL/4vzKYyG14eFgFrITiSLrWdgDcF42IeldEFXt4QbAq2qIY/LAb/budYjyJXgcpDuRaSTHsFSkx4BH7H5zn0zePB3X4vrtg3jk/9+BL/0pcdxKt597+DQ+VXsmYmuUaN0Ox0I+1yWJov5StNvEBq6ZDiATLHSsBmxFc4sZ7F5JLBOddMqGk0pyxbLSBfKrXkEfik0ZPR9MkpAc0OQKVZ0PQK/YgiER2A1A2wIvChVmG5ogX9htJLFANbNJOCCc3pfYkmB1H6PoFiuKudkNkegrniaivrxd+99Jf7PO6/CC/NJvOmLP8SDz8/bdryNyBUrOLaYVjqK1QxbLDPBLzBGHsGOcWkWwksLKcv2yzlrg+qomlCDKWV8AdGKIRgKeFCuMt33zxqougJry0XrZxVzuEcgQkPWM7iGIGJcQmoUGgKkXMDa8lHtWQS17d3KyttOllQrVSNXXU2yLqxFRPjFV8zg4d/9eQwHPPjWU+esP1CTHNFIFHOGLJaZyJoIDe2cCAMAji2kLdsvAFSqDOcuZm3JD3AaeQTtGIJowFhvKG0yNARANzTkdYk+ArsYXEPQQDfGKDQE8JkEtS9VMldSqom0kEJD9nsEi6q8h9nQkOIR1BmyiYgPl44FbRF3M8thjUQxZ8jEiMRmqIWGjJLFXoR9Lss9ggsrOZQqDFtsqBjiBDxOw4YyxRC0UjVkIDzHGNOVa+FEfC545GYzvdCQw0HwuhyiasgGBtgQGHcX86ohXY/A517XUGboEegollqN+nzMJouNuqJjAY8tuv9mOTi7itGQR1MNcyhorUeQLzbOERARdk6ELfcIzsoVQ1bLT6tpNLeYe5MthYYMhOcK5SqqrNbUpgURKeEhLcE5js/tFDkCGxhcQ9AgNJQplEEE3bhm/UyCZN7YEIR9LhQrVds/xNwQ+NwO06GoZK4Mj9OhuN5qhgJurHRRI+nw+Zr0dD3DAWtzBFxe2ig0BAA7J0J4aTFl6YCUmvx0d0NDDp1mrkYYCc8Z9eSo4Z6AnkcASJ9rkSOwnoE1BAGP1EavFxpKFcoIeVy6yd/6cZV6swg4EY1uZDtYSuZBBGwdCTZVPhrRSXQPBTxYyRa7UkqaLZZxbDGlmSgGpFVotlixzLjm5NhzI0OwYzyMlWwJ8bR1RujMcgYep6OpOQDNEvS6DPsIllIFjIS8ms1cjeDDabSE54zGVKoZVTwCfUMgxlXaw8AaAsC4uzid155FwIn4XEgVyqhWGRhj0uB6DZ0h9faA/TITi6kCRoJeDAU8TZSPlnQb4aJ+N6qsO4J5Ry4kUWXAnpmY5u/5ytUqr6AmMWH8tagljK3LE5xNZDEz7G/pImyWkNe4j0DqIWg+LATUwopaYURFedQgNATUDICW4BxHhIbsQfdKR0TXGL2QMfaM9YfTWaTuYp3QUNG4yiHid4MxyXPwuhwoVqrGHkGHpKgX5S9zxO/C6XjW1GuMvBmlcStbVCpDOgXvKNb1COTjuZgpNaWfr0euWIbf7WxYx79zolZC+urLRtveLyAPrLcxUQxIHkGuVEGlyjQNzmKqta5iQOqtCXqcmqEhnpcw+j4BwL6tQzidyMDr0jcYXnluscBajP4zf2zwOwbgJouPpeOMR3w4OLui+btUvkGVg0qK2iPH1vVW1UDnpKgXU3mMR7xNdTIncyXFta+nJuVQxFbYE79++MiCIq+g5ruH5jAW9mJCRwRNbaTM7OM1O0Z1BeUAeTqZQa07ZyzsRdTvxkuL1iSMGWM4u5zFtZcOW/J+eqiF57S816VUAVdMhlt+/1jAo5ksblSKzXn3Kzfj3a/cbLiNz+UQoyptQPc/wxi7sZMH0g3GQvqhoUblblGV3hBPshqFhvi4StsNQbKAXVMRhH3upnIEm3WSlGYGk7fD/Goev/73B6CXd33Hy6d1V+g8NNSocuj4Yhr/9WsH8P+/+yq8/eUzutvlitWG+QFAqnDZMR6yLDS0nCkiXSjbIjanJqCaUlb/Wa1WpdGtrXoEgL7wXFYZStP+1DW/x2nLiNJBx9R/hoh2A9gFQMlkMca+ZtdBdYrxiBfZYkWz/T1dKBtqrqhDPdwjME4W2z/AvlJlSGSKGA/74HY6kClWUK5UdYeBcJL5MqI63kwzq+5W+N7hOTAG3PvbN2hWzBj1ZgyZzBFwET0jtVlA6iw24xEAwI6JML57aA6MsbYlIc7YKD+tJqjMJFgfWlnJlVCusrYMAS8sqKc2r9jc39YIn8sp5hHYQMNkMRF9AsCfyj83AvgcgLfYfFwdQekl0BCfyxQqxsli1YW9pjxqxiOwzxAsZ4qoVJkUGjI5J1lKdJd0vZmhBh2j7bL/4ByumAxj70wMUb973Y/RRZaXLDZaIc5elObwNlIqzco5AjPsnAhhNVdqaFzMcLYDpaOAekrZ+s9EO13FnKhOqbHZ8lEz+NwOMY/ABsxUDf0igJsBzDPGfgXAVQBa/7RsIJTuYo0vcypfMhUaSubKDZVHAWlWq4PM6/+0Ak98j4W8mnOVtcgWKyhXme6xh31uEEFzpdcuc6s5HDhzEbftmWrp9S6nAxGfq6GRmr0oXWgbKZXmSpUmDIEUS3/Jgsay04kMiIBLhttPeBthNJOgna5iDheeq4f3ZxjNeTCL3yPKR+3AjCHIMcaqAMpEFAGwCGCbvYfVGWpNZWsNAWMMmWLFVLJ4NVeqTScz8CCIyHbhOX4e4xGv5hQ1LRp5M04HIdpgDGGrfO+QJGb35r2tGQJAyhOY9wgaGYJqE6Eh68TnziaymIr4DKtlrIB/nrWE4ZbS0iJivI0+Br2ek3ShAo/ToYRQ28HrEuWjdmDGRB8gohiALwN4GkAawJN2HlSn0AsN5UtVVKrMsMoh5HFJK/x8SdFIMQoNSb932So8xyeTjYd9qMiLpkYeiBlvZsjiDl7O/kNSWGj7WKjl9xgyoUB6jnsEDYxZrljGlMkL4VjIi1jAjWMWVA5x+Wm7CaqqhuqxIjQUC0g9J+m6qiRpFoE1Rk70EdhDQxPNGPstxtgKY+yvANwC4E45RNTzRP1ueFyOdXHeVEG6YBjlCBwOaYW/mithNVdCwOOEu0FSNuy12yOQQ0Nhr+nkdM2b0TcEjcYQtsKFlRyePnMRt7fhDQCSzIRZj6BReMts+Sggaw6Nhy2pHJJ6COzNDwC1ZK1Wd/FisgC/22k4RawRSlNZZu1nJVMsWxIWAqTO4lKFobJBhib1C7pXLiK6Qr69hv8AGAbgatRsJr/OR0RPEtFzRPQ8EX1Sfn6YiB4iomPy7ZBVJ9MsRISx0PqRlbyqItRgFcNlJvgsgkaEfS6bcwQFRHwu+NxO0w1sSUV51KA6xwaP4LuH5gAAb24xP8AZCnoMY/+pfEkxYsuZRh6B+dAQIIWHXlpoT3MoUygjni50xCMwTBbLpaPtVEDxCrP6XoJGpdjNwLu+hVdgLUb/nd8FcBe0G8vMNJQVANzEGEsTkRvAj4noewDeAeARxthniOgjAD4C4MPNH7o1jEfWdxfX5hU3DvWsyuWjRivq2vZunNNonLKKxWRBifGa1TYyExqKBdx4cd5a2eX9h+awayqCbW2EhQCpqsko5HN+RfIGtowEcP5izrDcM9dE1RAgJYyT+TIWUwVMtBhbP9uh0lEActe0frK4nbAQIH1OgPUhuEyhYmloCJC8t0YNagLz6HoEjLG7iMgB4GOMsRvrfhp2FTMJHkB1yz8MwFsB3C0/fzeAt7V1Bm0yHvauE55Lm6x7lrp3y5LOkMGKmmP3uMrFVF7Je5itUlKSxUahIb92fXirnF/J4WdnV3Bbm2EhQPIIcqWKbm357LJkCHZPRw0naDHGmqoaAqxJGCuqox0IDRERgh6XZh/BUqrQVsUQoG4+rPMIGsi1NIMYV2kPhkFtuVro/7T65kTkJKJnIVUaPcQYewLABGNsTn7/OQDjrb6/FYyHfetCQ7WhNMarfB4aWm2gPMqxe4D9oko0jIjWSWVrwT2CsFHjVsCNTLGCYtmasr3vWRQWAqQcAaBfEcQTxVyv6KJOeKhYkTTzmwoNjbdfQnp2WWp260RoCJAWN0ahoXbgHkF9yXKmULakqxgAvEpoSJSQWomZeq4HiegXqIXgIWOswhi7GsAMgGvlDmVTENFdRHSAiA4sLS01u2vTjIW9WM2V1qwwGs0r5vDhNMm8fkPW2u2lmbF2SDozxiRDoApRREzITCRzUvzWqPs4FtRe6bXKfQfncOWmCC4dbX8VPNRAZmL2Yg5+t1OpTNIzGPmiOQlqNaMhD4YC7rYSxmcSWcQCblMLCSsIel1I11UNFcoVrGRLbRsCfg71xjZTqBgOpWkGn/AIbMGMIfhdAN8GUCCiJBGliCjZzE4YYysAfgDgjQAWiGgKAOTbRZ3XfIkxto8xtm9sbKyZ3TUFX0HHVbN+UyZDQ9GAtOJONphOxuGKpfVfRCtI5soolqtrZITNlKua8WaGdGK/rXBuOYtnz1kTFgIaS1HPXsxiZsiPYZV4nhbZkjyUpgmPgIiwYyLcVmjo7LK9c4rrCWkMp0nIcxValaDmuJ0OhL2u9cniopXJYmEI7MBM+WiYMeZgjHkYYxH5caTR64hoTO4/ABH5AbwewFEA9wK4U97sTgD3tHz0FqDVVJYxGRqK+FzIl6pI5o0H13PsVCDlDUHqVV39OE0tkvmSYVgIsFZv6HuHpbBQq93E9dSOTfs8Zy/mcMlwQIlf651DzsTgei12ToRwbCHdcuXQ6UTGdvlpNVKOYO3nb9GCHgJOVKPUONNgcH0z1HIEIjRkJWa0hh4x85wGUwAeJaKDAJ6ClCO4D8BnANxCRMcg9SV8prlDthatIfbpfBkOajygRL2SNpsjAOyZSbCoaiZT789M+WgjIxazUG9o/6F57J6OWLYKrs0k0A8NzQz5awZDJ0dQG1zfrCEII1UoY15Dr6oRpUoVF1byHakY4mhNKbOimYxTLzxXLFdRqrC2+hPUiPJRezAaTOMDEAAwKtf68xxBBMCmRm/MGDsI4OUazycgaRdtCLg7vKQqIeVqpI3SIuoLqJG8BMdOKWqtVV3UZLL4kgYr0iGdapBmObecxXPnVvDhN17R1vuokYTptHMEybyUyJ8Z8ivb6Z1Dqx4BTxgfW0g3PRzn/MUcKlVmu/y0Gq0pZVYaglid8FzG5CwCs6jLRwXWYbTk/Q1IkhJXyLf85x4Af27/oXWGkZAXDlobGtKSpdZCnSBuJjRki0eQ4loxdTmCBuWjqbzxiE1Avz68WXgTmVVhIUASnpO0kNZf4Hnp6MxQoKFmUuseQeslpDX56c7lCAIaOQJuCEaCFoSG6oTnzA6lMYsoH7UHo8E0XwTwRSJ6P2PsTzt4TB3F6SCMhLzrQkONKoaAtRd/U6EheRsuYWEli8kCfG4pWafsz+dGriSVfeoJfplJFvvdTnhcjrY9gv2H5rB3Jmp5qaSezARXHZ0ZklbqQwEPli32CEZCXowEPTjWQgnpWXlOQidDQyGva53W0FI6j6GA2xJRuPrQULZo3VAaQFU+alEps0DCTLK4b40AZ7xudrHZBhj1MBcz5aN2JoulHgLfmnCWYnh0wkPlShXpQuNmOCKSO3hbNwTnV3I4OLtqSe9APUNBj2b+gmsMzQxJF9qhgFs/NNSiRwDIUhOLLXgEiSx8bkfb1TrNEPRIBQ7lSu1CakVXMScWkAoUeIm02eZMsyhVQ2I4jaW0vwToAyRDoCofbTCvmLPGIzAx2N3u0FD9BaUmPKdteLhBMuPNSHpDrR/3qSVp9Xv1JbGW30OPoYBbxyPIIehxKgnloYBHP1ncokcASAnj4y1UDp1ZzmLzcKDtCWfNoEwpU11Il+RFhBXEAh5UWe2zlbFwKA0gQkN2IQwB1ncXmxXJWpMjMBFK8rqc8Loc9nkEdUPeG1UpmZGX4MQCbqy2YQh4n8ZomzIGWuiJ4p27mMXMUO1CGzMQz+MeQaAVj2A8hFShjLlV85VDZxNZHJpdxeYOSEuo0RKeW7TSI+AKpHIvgZVDaQCpV8HpIDGlzGJMGQIimiaiVxPRa/mP3QfWScYjXiTSBUXa1myy2OeWLuwOMr/iMSP70ApLyfWrukbCc4oEtWmPoPXQkJWVKfXw4TT1K3JeOlrbTj+8xQ2BrwWPYIcyraxxeKhaZfjqT07h1i/8EJlCGf/l+i1N768d6qeUMcYsDw0BtcKCtKLka51AnM/lQK4ocgRW0vC/Q0SfBfBuAEcAcDPMAPzQxuPqKGNhL6oMSKQliYZ0wVyyGJAuoqVK1bR7H/ZZP5wmV6wgVSiv+zLXPALt/ZlRHuXEGqh8NiKeLsAjj5a0mqGgB4VyFblSZc3Kc/ZiFtduramcxwIe5EtV5Irr5w7kihUQAd4WEqZ8bOWxhTRed7m+dNbpeAa//y8H8eSpZfz8zjH873fswaaYveMp66mfUpYqlFEoV9sWnOPUC89ZObie4/c4hUdgMWa+lW8DcDljrP0p3RsUZVKZvDJqRj894nOhVDEfGzbT5NUsSumobo6gQWjIhHJqTK4GMZJxNsIKvXs9hlXdxdwQrOZKSOXLSqIYWNsh7fesvQDnihVZprn54xsOejAa8uh6BNUqw1d/ehqfe+Ao3E4H/ugX9+IXXzHT0dwAh4e+uAKp1Z5avfAcr1CyUjJajKu0HjP/nZOQJKT71hCMySGVpVQBuVIFVWbelY363U0ZAjukqJeUWcV1oaEGA+yb8QiGAm5FxjlsIqegdYyjIU/TrzNDTNVdPC2vsHnpqHogvFpvqH4lLnkTra9ad4yH8VLd2MqLmSIePDKPbzx5Ds+eW8FNV4zjf719Dyaj1iRmWyFY5xFYbQhqHdw1j8DpoJY8LT18bgcKQmLCUsxc7bIAnpVlJRRjwBj7HduOqsPUPIK8MpTG7Arml165GaWq+Q9lxOdWhqVYhTK0vu7LHPA44XSQfrI410yymLv8pZYMQTxdxCabLoDDGgqk55bXlo4Ca8+hnlyx0lJ+gLNzIoR/fnoWy5kiHnx+HvsPzeGnJxKoVBkuGfbjj995Fd5xzXRXvAA1fIHDk7hWGwIe+uPdxZlCBUFPa56WHj63U3QWW4yZq9298k/fwr8Ei8lCbRaByVj2u155SVP7ivit9wgWk+sF5wCp/t9IZmI1V4LLQaZWwkOqi+glw80f41KqgKtmos2/0ARDGgqk9c1kgLF4XrNDaerZMRFGpljBvk8/hCoDNg8H8Os/tw237ZnC7ulI1w0Apz5ZrBgCi3IELqcDYZ9LMbZWCs5x/GKAveU0/A8xxu5utE2v43M7EfW7sZiqGQKrOiHrCfusH2C/mCrA5SAlVq4mYjAnOZmXBOfMXKRqUtTNVw5VqgzLmYItpaNALUeg9ghmL+YQ8rrWhL2MBOraDQ297vIxvOrSYbxiyxDevGcKV27aOBd/NbVksZwjSBfgdpISXrMCdXexldPJOD63U/FoBNZgJDr3T4yxdxHRIUhVQmtgjO219cg6DO8uVuYV21DdAtSkq41kH5plMSVdZB2O9Rceo3LVZK5suoqnkYyzERezRVSZPaWjgHSODlqrhcRLR9UX45iBZHW2zdDQzFAA3/qN61t+fafwuaVyZ+4RLCalEZVWGi218Fy6YP1sYZ/bgURG5AisxOg/9AH59vZOHEi3kYbY1zwCK+ue1dQUSEsYsWiFrNVMxjGqUjI7YhNoT4qahx/s8ggUQbnM2tCQOiwEAB6XAyGvS9OY5UsVJdfQzxCRLEUth4YsGFFZj1rcL1soWyZBzfG5nSiI0JClGA2v53OFz2j9dO4QO8N42LcmR2CXIWgk+9AKi8n18hLq/enti4eGzMA7RlvxCGpdxfZdaIeCNUE5xpjsEawXc4sF3NqhoWJ7oaFeQj2lzMpmMs5QwINV+X+RtiFH4BM5AssREhMy42EvltQegU2hIb4Ct2r+L8C/zNoVOY08ArOGoD4J2OzxAfaFhgApT8Av8Ku5EtKF8jqPAJAqjLRCQ7lSe6GhXiLgcSr1/XYYAnXzoZVjKjk+t0NUDVmMMAQyY2EvipWqUtppl0fANd/1hq03S6lSRSJTNPAIGuUIzCcJ6yWGzaJ4BDYagqFgTYq6XnVUTUznHHhD2SAQkqeU8SS+VRVDnFjAg2S+hEqVIVuw3tOSqoZEjsBKmjIERDRERH2VJObwZqyTSxm4LG6AUTMih0f4xbFd+Pvo5wik5HShriWfMYZkEzkCALIUdfMeQTxdhNe1dlaC1QwHalLUWqWjnKGAW3MmQbvlo71E0OtCtlBGIlOwJYkf87vBmJQHM6vb1Qw+tyQx0eqcaMF6zMws/gERRYhoGMBzAL5CRJ+3/9A6C18VnYpnEDQxprJVuEcQT1vjEWjNKlZTm0mwNk9QKFdRrFRNyUtw9FbTjViSq5rsLKfkOQKeHwCASzQ8gqGAByt1UtSMsbbLR3sJniyuheysbfTjhQXxdAGFctWWHAFjQLEivAKrMLPsjTLGkgDeAeArjLFXAHi9vYfVefiK+kwiY1tYCJAEs4IeJxIWGYIlna5ijp7MRDNdxZxWhefiNlSm1DMUcKNYriJbrODcchZhr0vTyA0FPEgVyiipLiKFchWMAb4BMQR8SpnWnGsr4I1751ekRkerDWxtOI0wBFZhxhC4iGgKwLsA3Gfz8XQNfiEtVZithgCQxhsmMtaEhhR5CZ3QEA/91CeMm9EZ4rQqRc09AjsZUslMzF7MYUZn4MtQcH0ZbDtDaXqRoNeJTKHScBHRKnxI0/mL9uTbfMq4SvsTxtUqw09PxPs+DGXGEHwKwAMATjDGniKibQCO2XtYnSfkdSkXArsqhjgjIY9lHgFXHtW70OqVq9aUR5vzCFL58poxh2aQPAJ7a/SHVQ1v9XMI1GjJTLQzlKYXqQ8NWW2k+d+Y52osDw25Ojel7MfH4/jPX34Cz55bsX1f3cTMzOJvM8b2MsZ+U358kjH2C/YfWmchImVVbfUHt56RoNeyZPFiqoDhoAdup/a/Um9KWaseAVATFDODVJlStLwypZ61HsH6ZjJluzp1TKA2YH1QykdDHheK5SrmVnMIe10tzWk2gvecXJAr8KycRQDU/k+dKCE9Jxuzs8tZ2/fVTcwki7cR0b8T0RIRLRLRPUR0aScOrtNwF9nO6hZAaqxKWFQ+upgsGLr2elPKlOlkTXg/rXQX88oUO0tHgZoC6al4BpliRbN0FFg/QQuorSwHJTQUkD/fp+NZW3I3kn4VlFJsq3W7/B45NNSBEtIFuRhjIWl+DGkvYiY09I8A/gnAFIBNAL4N4Jt2HlS34JU3Vq9g6hkNebGcKaJabT/uuJTKG36Z9aaUteURNJEniKekbW33COQL/MHZVQDapaNAzWBoh4bsXQBsFELy5/tUPGOLgXY6CBGfW6ne6uXQ0II8h7qZedS9iBlDQIyxv2eMleWff4CGCF0/wC+oIa91SoxajIQ8qFSZ7sCYZlhMrZ9VrMbndsDtJA2PoPkcwZCBaJseSx1oJgMkg+cg4ODsCgDt0lFAJ0fAk8Weweiv5BfmC6s526q5YgG3soq22hB4OxgampfPYV4YAjxKRB8hoq1EtIWIfh/AfiIalnsL+gaeI7A/WSztp93KoWpVGjyuVzEESLkPLZmJZL6EgMepm1vQItaCFHXcZsE5jsNBGAp4cDKeAQBM63gEfo8TXpdjTXhr0HIE/MLMmPUVQ5xYwAPu8FrtYfMQXieE57gxm+/z0JCZK9675dvfqHv+VyF5BtssPaIuwlfWIbtDQ0HeXVzEZfqzzhtyMVtEucoafpklmYn1oaFmeggAdY6gCUOQtl9niDMUlHIvEZ/LMOQ1FPCskfgYtByBupzTNo9A9fe3rXy0IzmCwfAIzAym6cvEsBbjHQsNyR5BmyWktRGVxp2h0nCa9cniZrqKAekL7XJQc6GhVAE+t8NyKWIteAmpXqKYMxRc2yE9aDkCdfLWrtwNXzQQWW9gO1U1lC9VcDFbgttJWEwVUKkyODVmfvQDZqqGAkT0MSL6kvx4BxH15YwCHk6wW5ee6w21Gxpq1EzGifjd6/IRzcwi4BCRLDNh3hDwruJOTOviFx+9RDGnXjMpO2ANZZ3wCHguJuixXq6F/5/sThbzPotdUxFUqsyyku+NiJkA8VcAFAG8Wn48C+DTth1RF9k+FsI//7fr8fqXtRGvMcFQwAOiWvy8Vfis4oahId96BdJkvvnQECBdRJsJDS2l7e8q5nAD3tAjqOuQ5hcU34AkiwOq0KddhoAvMuyowFMkJmwODfG8wFWXxAD0d+WQmU/+dsbY5wCUAIAxlgPQn/4RgH1bh+FqIoHaCk55vnC8zV4CXpHTMDTkd68rH03mm/cIgOZlJuIp+5vJOLyp7JLhBh5BcO1wmlyxAqeD4LH5/75R6IxHIBsCG8JtXBnYbo+A5weumokB6O88gZlPfpGI/JBLRoloO4D+9ZE6hCQz0a5HUDDVGSpNKasLDWXND6VREw24mw4N2V06yjGdIwh4sJorKX0cWXkWwUYcNm8HXpcDTgfBQTU1XKvh86Ht6NJ3yDLxdhsCfuG/enNMfpyzdX/dxIwh+EMA9wO4hIi+DuARAB9u9CIiuoSIHiWiF4joeSL6gPz8MBE9RETH5Nuhdk6gVxkJettOFh9fTDfMDwBSaKhYripfnGqVIVUot2QIpPi6ueMuV6pYzhY7FhoalfWMNg8bGwJe2siN4yBNJwPkucUeJ4aDXtuSn1x4zq7mzE6Mq1xI5uFxOXDpSBAepwPzyf5d/5rRGnoQkgT1ewF8A8A+xtijJt67DOC/M8ZeBuA6AO8jol0APgLgEcbYDkhG5SMtHntPM9KmzMR/vLSEHx+P421XTzfctl5mIlUog7Hm5CU4UmioZEqNcTlTBLNh8Ikeb7xyCn9yx8uxcyJkuN2wrEDKS0jzAzSLgBPyumzrIQDWJovtwOd22J4jWEgWMBnxweEgTES9g+0RENEjjLEEY2w/Y+w+xliciB5p9DrG2Bxj7Bn5fgrACwCmAbwVwN3yZncDeFvLR9/DjIZaF54rlCv4w3ufx9aRAO76+cZtHPyCz/MErXQVc2IBj+xdNP4SKnr3Ng6tV+P3OPGWqzY1DPHE6jqkB2lMJSfsc5vyJlslpiSL7TIETtvLR+eTeUzKkwsnI76+Thbr/peIyAcgAGBUDt/wb1cEkuaQaYhoK4CXA3gCwARjbA6QjAURaZboENFdAO4CgM2bNzezu55gJOhBKl9GoVyB19XcRejLPzyJU/EM7v7Va029tt4jaEVniDOk6i72e4yTsp1sJmuGes2kbKkyMENpOJ94yy6EbeyXGbIxRwDwucX2h4b2TEcBAJNRPw7J8iX9iJFH8BsAngZwhXzLf+4B8Odmd0BEIQD/AuCD8qQzUzDGvsQY28cY2zc2Nmb2ZT0DT6A2O8T+3HIWf/bocbxp9yR+fqe5v0u9FLUyi6CF8tGYhlaPHnwcZ6dyBGbhSWUlNFSswO8ejIohzqu3j2LPTNS29w/7XHCQfV36XrcT+bJ9oSHGGBZUHsFUVPII+nVAja65Zox9EcAXiej9jLE/beXNicgNyQh8nTH2HfnpBSKakr2BKQCLrbx3rzMilzom0kVMRY1X1mo+dd8REAgfv32X6ddE64bT8BBRKx5BM1LUdg0+aZdY3ZSyXKmy4byWXsfhIHz2F/bi5ZvtqQXxuRzIF+3zCJK5MvKlKiajkiGYiPhQKFexmispi6F+wswyaJ6IwgAgdxh/h4iuafQikgK1fwvgBcaYetj9vQDulO/fCcnDGDi4zEQzeYLvH13AQ0cW8Ds378CmmHnjsc4jUHIErSWLAbMeQQEBj9P2QT/NElakMuTQULE8cDmCTvDOfZfgsnHjxH2r+D1OW0dV8maycZVHAPRvU5kZQ/BxxliKiF4D4FZICd6/NPG6GwD8MoCbiOhZ+efNAD4D4BYiOgbgFvnxwDEaqnkEZsiXKvjDe49g+1gQv/aa5uSfeI5gtS401F6OwJxHsNG8AaAmlcHPIV+qDlT5aD/gc9mbI+DNZDw0NCHf9mtTmZmlGv9r3wbgLxlj9xDRHzZ6EWPsx9DvQL7Z3OH1L81KUf/Vf5zA2eUsvv5fXwWPq7l4ttflgMfpWJMsdlBrpX3cLV416RFs1JDLUKDWXZwbwPLRXsfu8lHuEUzIlVXcI+hXOWozV5TzRPTXAN4F4LtE5DX5OoEBQVkX34xHcDaRxV/84ARu3zuFGy4bbXpfRLRGZiKZKyHsc8PRQjORxyUpiZr3CDZmPFUtlZEtli2f2yuwF7/H3vLRRcUQSAZAEk4c7NDQuwA8AOCNjLEVAMMAPmTnQQ0CRCT3EjQ2BJ/efwRuB+Fjt5lPENejlploRXlUTcyk3tBG9ghislRGtcpEaKgH8docGppP5hELuJXPhdvpwFjIq4yu7DfMzCPIAviO6vEcgDk7D2pQGAl5GiaLq1WG/3hpCXdcu1mpYGgF9ZSyZL75WQRqhoKN9YZKlSouZksbMkcASEqlPzu3goJcgihCQ72Fz+1Ewc7Q0GoBE3VijlNRH+YGODQksImRoKdhjmA+mUehXG27+kI9pSzZrkfgb+wRJDZoDwFHmqtQRLYo/U1E1VBv4XM7UKxUUanaU9e/mMpjom7hNRHx9a3MhDAEXWQk1Fh47rQ8g/fS0WBb+4r4XEjlaqGhVprJODETCqQbtauYMxRwo1RhSmhOGILewu7hNPOreUzWSXBMRX19WzUkDEEXkaSoi4bdiqcSkiHY2q4h8NeG07Q6i4AzFPA0HE6zUZvJOHx2wYUVaYUnksW9hc9GQ1CuVBFPF5REMWci6kMyX1a8yH5CGIIuMhr0olipIlXQ/2CdSWThcTkwFWk9PwDwHEEZjDHJI2jLELjX6PlrURuas0ENgVwGe0F29YVH0FvwAfZ2VA7F00VUGdYZAqWEtA+9AmEIusiIiaayU/EMtgwHWir1VBPxu1CsVJHMS63zrUhQc+r1/LXgoaEN6xHIjXFzK9KXWngEvYWd4yrn65rJOJMRqZtfGAKBpShNZQaVQ6fjmbbDQkBNZmL2YhZAa13FnKFg4+7ipVQBQY9zw15g60NDony0t7AzNLRQ10PAmexjmQlhCLoIb7bS6yWoVhnOLGfbThQDNZmJc8u5NY9bIeZvrDcUTxc3bKIYqIWGzsuGQJSP9hbcEBRs0BtSDEF07eeXewj92F0sDEEXGW0gMzGXzKNYrmLLiPHoRTPwUBD3CNoyBIoCqb4hWErlN2xYCJA8InWnqMgR9Bb8/5Ur2hAaWs3D6SCM1s1z9nuciPrdIjQksBa+KtXLESiloyPtewQ8FDR7UfYI2igfrQ12McoRbGyPwOmgNV/qjRrCEmjDk8X2hIYKGA97NfNyU1Gf8AgE1uJxORD1u3VzBKfi1pSOAjUPwJIcQd2oRy3i6Y2pPKpmKOBBsSKtKIUh6C2UHIFNoaH6/ABHaioThkBgMSMhD+I6U8pOxzPwuhzrqhdagXsAtRxB61VDfPqUXmioWK5iZQPLS3B4iAsQoaFeoxYast4QzCfziupoPXxSWb8hDEGXGQ16dT2C04ksto4E2y4dBaSLN6DKEbQRGnI4yFB4juc8NnJoCKiNrHQ5CG6n+Cr0El4eGrJhXKV6RGU9k1EfEpkCijaOyewG4tPfZSThOR2PIJGxJFEMSK601+VApliB1+Vou1wyFnDrhoZqXcUbU4Kaw2criLBQ76FUDVmcI8gWy0jly+t0hjiTER8Yk7SI+glhCLqMJDOx3iOoVBnOJqwpHeXwPEE7FUOcmN+tGxra6DpDHN5UJsJCvYfPZU9oaCEpfXbrlUc5vJdgoc8SxsIQdJmRoBcXsyWUK2tdzQsrORQrVUsSxRxeQtpOopgj6Q1pewTx1MZWHuXwpjLhEfQebifB6SDLk8U8Eawn+d6vTWXCEHQZHj5Zrltdn0lIsfytFpSOchSPoA15Cc54xIcziSyWNRLdSz3jEciGQHgEPQcRweeyflylXlcxZ6pPZSaEIegyNZmJtRfUmuqoNTkCoJYgtsIj+JUbtiJfquBz9x9d97ulVAFhr2vDyzYooSHhEfQkPrf1U8oW6mYV1xPxu+B3O4UhEFjLSFC7qex0PAOf26Ebq2wFK3MEOyfC+NXXXIpvPnUOz5y9uOZ3S+kCRje4NwCoksUb3GAJtPG5rZ9bPJ/MI+hxIqxTVUdEmOzDSWXCEHSZER2ZidPxjGWloxwrcwQA8Ds378BkxIeP/9vhNZOi4qkCxjZ4fgCQxlUCwhD0Kj63w/JxlQvJ9ZPJ6pmM+PpudrEwBF1GT3juVCJjaX4AqBmAdnoI1IS8Lnzs9pfh+QtJfP2JM8rz8XQBo+GNXToKiNBQr2NPaGj9rOJ6JvuwqUwYgi4T9bvhctCaEtJKleHcctbSiiFAHRpqP1nMuW3PFF5z2Sj+6IEXlbLRpR7xCERoqLfx2xEaWs3rVgxxJqM+LCTzhoOZeg1hCLoMESkjKzkXVnIoVRi2WtRMxrEyWcwhInzyrVciX6rgf3/3KArlCpL58oYvHQUkradYwG1JzkTQeaz2CBhjWEzlMa6TKOZMRnwoVxkSOtIwvYh1S0NBy4wEvWtyBFaKzanhnoBVoSHO9rEQfv3ntuEvfnACr905CgA9kSwGgL977ysxE/N3+zAELeBzO7CcsS5HsJwpolRhDbW9JlUjKzd6ibRZhEewAaiXmTgtl45a2VUM1OrmeSOVlfz2TZdhOubHx//tMAD0RGgIAK7ZPIRxC0T9BJ3Hao9Ab0RlPcrs4j6qHBKGYAMwGlrrEZyOZxHwOC0f/H7dthF8/l1X4ZVbhy19XwAIeFz4+O27kMyXAfSORyDoXaw2BIuyvESjhYEyqWw1Z9m+u40wBBuAkeDaHIEkNhcEkXWlo4A0jOUd18zAaWFJqppbr5zAz+8cA7Dxu4oFvY/P7bBUfVTxCBoki0dCXrgc1FeVQyJHsAEYCXmRLVaQLZYR8LhwOp7B5ZPhbh9W0xAR/ugX9+KBIwvY1ODLJBC0i89lcWhIvrA38sSdDsJ42CtCQwJrGQnVuovLlSrO2lA62inGIz788nVbLPdmBIJ6/B6pfJQxa8o4F1N5jIY8pmZTTEb7a1KZ8Ag2ALWmsgIYA8pVZsmcYoGgn/G5nWAMKFaq8Lra7wWZX9UfUVnPVNSPF+aTbe9zo2CbR0BEf0dEi0R0WPXcMBE9RETH5Nshu/bfS4wEa8JzNbE5YQgEAiO8Lj7A3po8wUKyYHosLJ9dbJU30m3sDA19FcAb6577CIBHGGM7ADwiPx54lNBQpoDTvIfA4mYygaDf4NIgVuUJFpJ506XEU1EfssUKUoWyJfvuNrYZAsbYDwEs1z39VgB3y/fvBvA2u/bfS3CPIJ4u4lQ8g6DHKapuBIIG8CllVhiCQrmCRKZo3iNQNZX1A51OFk8wxuYAQL4d19uQiO4iogNEdGBpaaljB9gN/B4ngh4nEukizthUOioQ9Bt83oUVoSE+Z1tvDkE9U302qWzDVg0xxr7EGNvHGNs3NjbW7cOxndGw1FR22uI5xQJBv+L3SJcvK4TnlIE0Jsue+62prNOGYIGIpgBAvl3s8P43LCNBDxaSeVl1VOQHBIJGWBkaml+VPAKzoaHJqA8elwPHF9Nt73sj0GlDcC+AO+X7dwK4p8P737CMhLw4fD6JcpVhiygdFQga4nVbaAgazCqux+104GWTYRw6v9r2vjcCdpaPfgPAYwAuJ6JZIvo1AJ8BcAsRHQNwi/xYAKmXIC1XIIjQkEDQGL+FOYLvH13AVNSnDCsyw56ZKA6fT/bFXAI7q4buYIxNMcbcjLEZxtjfMsYSjLGbGWM75Nv6qqKBhVcOAbB8MplA0I/43LyPoD2P4KWFFH5yPIH3NNkRv2c6inShrKgFt8rpeAZ/86OTXTUoorN4g8B7CUJel9JpLBAI9BkNe0EEnElk23qfr/zkNLwuB+64dnNTr9szHQMAHDq/im1joZb2nSmU8Wt3P4UTSxlMRn24fe+mlt6nXTZs1dCgwYfYbxkJiNJRgcAEEZ8bL5uM4IlTiZbfYyVbxL/+bBZvu3oaw03O6dgxEYLH5cCh2dbyBIwx/I9/PYRT8QwmIl588eFjqHTJKxCGYIMwKn8IhbSEQGCe67eP4OkzF1EotxYe+tZT55AvVfHeG7Y2/Vq304FdUxEcbDFh/M2nzuHfnr2A//f1O/Gx23bh2GIa+w/NtfRe7SIMwQaBewRCbE4gMM9120ZQKFfx7NmVpl9brlTxtcfO4Lptw3jZVKSl/e+ZjuL586tNx/ePXEjiE/c+j5/bMYr33XgZbtszhR3jIfzJI93xCoQh2CDMDPlxybAf128f6fahCAQ9w7WXDoMIePxk83UnD7+wgPMrObz31Ze2vP89M1FkihWcjJtPGKfyJbzvH5/BUMCNL7z7ajgcBIeD8IHX78DxxTTuO3ih5eNpFWEINghBrws/+v2bcMNlo90+FIGgZ4j63bhyUwSPnYw3/dqv/OQ0pmN+3LJrouX975mOAgAOmwwPMcbw0e8cwtnlLP70jmuUSAAAvHn3FC6fCHfFKxCGQCAQ9DTXXTqCZ86uNFVGeuRCEk+cWsadr97S1ujWHeMheF0O041l//DEWdx3cA7//Q07ce2la2eHc6/gxFKm416BMAQCgaCnuX77CIrlKp49t2L6NV/96Sn43U68e19zJaP1uJwO7NoUMVU5dPj8Kv7nvx/BjZeP4b+9drvmNm+8chJXTIbxxQ57BcIQCASCnmbf1mE4CHjshLky0uVMEf/27AW8/ZppRJvoJNZj73QUz19YbXjh/uz9RxENuPHH75LyAlo4HIQP3LwDJ5cyuPe5820fm1mEIRAIBD2NlCeI4vGT5gzBN548i2K5il959VZL9r97WkoYn4rrC9AtZ4r46YkE3vmKmYb9CrfKXsGfPHIc5Yo109caIQyBQCDoea7fPoKfnWucJyhVqvj7x87gNZeNYsdE2JJ9752JAYBhnuCB5+dRqTLctneq4fs5HIQPvn4nTsUzuPe5zuQKhCEQCAQ9z3XbhlEsV/HM2YuG2z3w/Dzmk3n8SgsNZHpsHwvC53bgoEGeYP/BOWwdCWCXyX6FW6+cwK6pCP7kkWMd8QqEIRAIBD0PzxMY9RMwxvDlH57ElpEAXne57nDEpnHJHcZ6JaSJdAGPnUzgtr1TpuVjiKQKotOJLO5/ft6yY9VDGAKBQNDzRHxu7JmO4nGDhPGjLy7iudlV/NbrtrdVMqrF3pkYDp9PaiaMH3h+QQoL7WlOUO4NuybwpV9+Bd60u3E4qV2EIRAIBH3BddtG8Oy5FeSK6/MEjDF84eFjuGTYj3dcM2P5vndPR5ErVXByaX3CeP+hC9g2GsTLpprLSRAR3nDlpOVGSwthCAQCQV9w3bYRFCtV/EwjT/D9o4s4OLuK99+4A26n9Ze9vTNSh3F9niCeLuCxEwm8eY/5sFA3EIZAIBD0Bfu2DsHpIDxWV0bKvYHNwwG8/ZppW/a9fSwEv9u5rnLo/sPzqDKYqhbqJsIQCASCviDsc2P39Pp+godfWMSh86v47Zsus8UbAACng3Dlpsg6Q/DdQ3PYNhbEFZPWlKrahTAEAoGgb7i+Lk8geQMvYctIAO94uT3eAGf3dBRHLtQSxkupAh4/mcDtGzwsBAhDIBAI+ojrtg2jVGF4+oyUJ3joyAKev5DEb994GVw2eQOcvTNSwviEnDC+/3keFurO+MlmEIZAIBD0Dfu2DsPpIDx+MqHkBraOBPB2m70BoCZJzRPG+w9ewPaxIHZOtDbPuJMIQyAQCPqGkNeFvTNSnuCB5xdwZC6J99+0w3ZvAAC2jYUQ8Dhx+PwqFlN5PHFqGbft3bThw0KAMAQCgaDP4P0Ef/zgi7h0NIi3Xt2Z0AxPGB+cXcH9h+fBGHD7Bq8W4ghDIBAI+orrto2gXGU4tpjG+2+yPzegZs90DEfmkrj32QvYMR7CTouE7exGGAKBQNBX7NsyBJeDsG00iLdc1dlE7Z6ZCPKlKg6cubjhewfUuLp9AAKBQGAlQa8L/9/bd2PHRLij3gAgeQSc2/YIQyAQCARd492vbG8EZatsGw0i6HFieshv2byDTiAMgUAgEFiEw0H42O27sCnm7/ahNIUwBAKBQGAhd1zbHW+kHUSyWCAQCAYcYQgEAoFgwBGGQCAQCAacrhgCInojEb1IRMeJ6CPdOAaBQCAQSHTcEBCRE8CfA3gTgF0A7iCiXZ0+DoFAIBBIdMMjuBbAccbYScZYEcA3Aby1C8chEAgEAnTHEEwDOKd6PCs/twYiuouIDhDRgaWlpY4dnEAgEAwa3TAEWpqsbN0TjH2JMbaPMbZvbGysA4clEAgEg0k3GspmAVyiejwD4ILRC55++uk4EZ1pcX+jAOItvraXEec9eAzquYvz1meLmTcixtYtxm2FiFwAXgJwM4DzAJ4C8J8ZY8/btL8DjLF9drz3Rkac9+AxqOcuzrt9Ou4RMMbKRPTbAB4A4ATwd3YZAYFAIBA0pitaQ4yx7wL4bjf2LRAIBIK1DEJn8Ze6fQBdQpz34DGo5y7Ou006niMQCAQCwcZiEDwCgUAgEBggDIFAIBAMOH1tCPpN3I6I/o6IFonosOq5YSJ6iIiOybdDqt99VD73F4noVtXzryCiQ/Lv/oSItJr8NgREdAkRPUpELxDR80T0Afn5fj9vHxE9SUTPyef9Sfn5vj5vDhE5iehnRHSf/HhQzvu0fMzPEtEB+Tn7z50x1pc/kEpTTwDYBsAD4DkAu7p9XG2e02sBXAPgsOq5zwH4iHz/IwA+K9/fJZ+zF8Cl8t/CKf/uSQDXQ+ry/h6AN3X73AzOeQrANfL9MKQelF0DcN4EICTfdwN4AsB1/X7eqvP/XQD/COC+Qficq877NIDRuudsP/d+9gj6TtyOMfZDAMt1T78VwN3y/bsBvE31/DcZYwXG2CkAxwFcS0RTACKMsceY9In5muo1Gw7G2Bxj7Bn5fgrAC5C0qfr9vBljLC0/dMs/DH1+3gBARDMAbgPwN6qn+/68DbD93PvZEJgSt+sDJhhjc4B00QQwLj+vd/7T8v365zc8RLQVwMshrY77/rzl8MizABYBPMQYG4jzBvAFAL8PoKp6bhDOG5CM/YNE9DQR3SU/Z/u59/PwelPidn2M3vn35N+FiEIA/gXABxljSYOQZ9+cN2OsAuBqIooB+Fci2m2weV+cNxHdDmCRMfY0Eb3OzEs0nuu581ZxA2PsAhGNA3iIiI4abGvZufezR9C0uF2PsiC7gpBvF+Xn9c5/Vr5f//yGhYjckIzA1xlj35Gf7vvz5jDGVgD8AMAb0f/nfQOAtxDRaUjh3JuI6B/Q/+cNAGCMXZBvFwH8K6QQt+3n3s+G4CkAO4joUiLyAPglAPd2+Zjs4F4Ad8r37wRwj+r5XyIiLxFdCmAHgCdl1zJFRNfJlQT/RfWaDYd8jH8L4AXG2OdVv+r38x6TPQEQkR/A6wEcRZ+fN2Pso4yxGcbYVkjf2e8zxt6DPj9vACCiIBGF+X0AbwBwGJ04925nye38AfBmSFUmJwD8j24fjwXn8w0AcwBKkKz+rwEYAfAIgGPy7bBq+/8hn/uLUFUNANgnf8BOAPgzyB3mG/EHwGsgubUHATwr/7x5AM57L4Cfyed9GMAfyM/39XnX/Q1eh1rVUN+fN6QKx+fkn+f5NasT5y4kJgQCgWDA6efQkEAgEAhMIAyBQCAQDDjCEAgEAsGAIwyBQCAQDDjCEAgEAsGAIwyBQKADEcWI6LcMfv9TE++RbrSNQNBthCEQCPSJAVhnCIjICQCMsVd3+oAEAjvoZ60hgaBdPgNguyz8VgKQhtTQdzWAXUSUZoyFZB2kewAMQVIJ/RhjbEN3sQoEakRDmUCgg6x2eh9jbLcsgLYfwG4mSf5CZQhcAAJMEsMbBfA4gB2MMca36dIpCASmEB6BQGCeJ7kRqIMA/C8iei0k6eRpABMA5jt5cAJBqwhDIBCYJ6Pz/P8DYAzAKxhjJVk509exoxII2kQkiwUCfVKQxmM2IgpJQ79ERDcC2GLvYQkE1iI8AoFAB8ZYgoh+QkSHAeQALOhs+nUA/y4PG38Wkly0QNAziGSxQCAQDDgiNCQQCAQDjjAEAoFAMOAIQyAQCAQDjjAEAoFAMOAIQyAQCAQDjjAEAoFAMOAIQyAQCAQDzv8FCi1pqJpUuXYAAAAASUVORK5CYII=\n", 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" ] @@ -657,6 +742,41 @@ "ax.legend([\"steps\"])" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['population'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"number of rules\"])" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/XCS_Experiments/XCS_multiplexer.ipynb b/XCS_Experiments/XCS_multiplexer.ipynb index efc19b8..9a37ca0 100644 --- a/XCS_Experiments/XCS_multiplexer.ipynb +++ b/XCS_Experiments/XCS_multiplexer.ipynb @@ -13,9 +13,9 @@ "metadata": {}, "outputs": [], "source": [ - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", + "# import logging\n", + "# logging.basicConfig(level=logging.INFO)\n", + "# logger = logging.getLogger(__name__)\n", "\n", "# import pandas as pd\n", "# import numpy as np\n", @@ -55,20 +55,9 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "mpx.reset()" + "state = mpx.reset()" ] }, { @@ -87,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -107,7 +96,10 @@ "cfg = Configuration(number_of_actions=2,\n", " max_population=1000,\n", " gamma=0.9,\n", + " # ga_threshold = 9999999999999999,\n", + " # covering_wildcard_chance=1,\n", " metrics_trial_frequency=100,\n", + " # mutation_chance=0,\n", " environment_adapter=MultiplexerAdapter(),\n", " user_metrics_collector_fcn=mpx_metrics)\n", "\n", @@ -123,31 +115,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': [570.5508815523286, 530.0908629315612], 'perf_time': 0.0009067079954547808, 'population': 15, 'numerosity': 372}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 1, 'reward': [517.6587436649028, 369.43516701389626], 'perf_time': 0.00020945801225025207, 'population': 22, 'numerosity': 833}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 1, 'reward': [554.7412290238851, 364.03865588843746], 'perf_time': 0.0002216589928139001, 'population': 28, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 3000, 'steps_in_trial': 1, 'reward': [563.2684490900601, 580.2952947713835], 'perf_time': 0.00021999901218805462, 'population': 31, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 4000, 'steps_in_trial': 1, 'reward': [679.0461013929137, 443.2434086252577], 'perf_time': 0.00022068100224714726, 'population': 32, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': [652.4201243655311, 372.368657015231], 'perf_time': 0.00020892500469926745, 'population': 30, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 6000, 'steps_in_trial': 1, 'reward': [340.3967433715899, 213.5879698145288], 'perf_time': 0.00021586401271633804, 'population': 30, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 7000, 'steps_in_trial': 1, 'reward': [619.0533085256677, 577.8538634747493], 'perf_time': 0.00046395001118071377, 'population': 35, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 8000, 'steps_in_trial': 1, 'reward': [255.10991554049463, 443.8515888568786], 'perf_time': 0.0005984580056974664, 'population': 35, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 9000, 'steps_in_trial': 1, 'reward': [688.5152682593864, 538.1776283400435], 'perf_time': 0.00039033699431456625, 'population': 35, 'numerosity': 1000}\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.06 s, sys: 0 ns, total: 3.06 s\n", - "Wall time: 3.05 s\n" + "Wall time: 3.98 s\n" ] } ], @@ -158,34 +133,43 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 6, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explore population count: 33\n", + "Explore population count: 40\n", "\n", "Top 15 classifiers:\n", - "###00#\t0\n", - "0#01##\t0\n", - "#00#10\t1\n", - "1#####\t0\n", - "01#00#\t1\n", - "000##1\t1\n", - "#1##1#\t0\n", - "#1####\t0\n", - "#1##1#\t1\n", - "10#11#\t0\n", - "#1##1#\t0\n", - "11####\t1\n", - "#11101\t1\n", - "#1##1#\t1\n", - "111###\t1\n", - "###001\t1\n", - "####0#\t0\n", - "####0#\t1\n" + "#01110\t1\n", + "#01111\t0\n", + "#####0\t0\n", + "#011#0\t1\n", + "##0001\t1\n", + "0##011\t0\n", + "#1#10#\t0\n", + "###1##\t0\n", + "1#00#1\t0\n", + "###11#\t0\n", + "0###10\t1\n", + "11##01\t0\n", + "0##011\t1\n", + "1#00#1\t1\n", + "000#0#\t0\n", + "#10#1#\t1\n", + "######\t0\n", + "#11#10\t1\n", + "0###10\t0\n", + "1#00#0\t1\n", + "00#0##\t0\n", + "#0##10\t1\n", + "01###1\t0\n", + "##01##\t0\n", + "#0#1##\t1\n" ] } ], @@ -206,16 +190,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "31" + "40" ] }, - "execution_count": 40, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -231,6 +215,215 @@ "source": [ "There are duplicate classifiers found." ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "for rule in explore_population:\n", + " for second_rule in explore_population:\n", + " if rule.condition == second_rule.condition and rule.action == second_rule.action and id(rule) != id(second_rule):\n", + " print(\"Duplicate Classifier found:\")\n", + " print(rule)\n", + " print(second_rule)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cond:01###1 - Act:0 - Num:2 [fit: 0.000, exp: 198.00, pred: 521.742]\n", + "Cond:#01111 - Act:0 - Num:27 [fit: 0.000, exp: 111.00, pred: 476.774]\n", + "Cond:00#0## - Act:0 - Num:6 [fit: 0.000, exp: 208.00, pred: 521.456]\n", + "Cond:1#00#0 - Act:1 - Num:11 [fit: 0.000, exp: 165.00, pred: 521.855]\n", + "Cond:0###10 - Act:0 - Num:11 [fit: 0.000, exp: 173.00, pred: 485.328]\n", + "Cond:0###10 - Act:1 - Num:35 [fit: 0.000, exp: 148.00, pred: 453.594]\n", + "Cond:1#00#1 - Act:1 - Num:24 [fit: 0.000, exp: 175.00, pred: 530.429]\n", + "Cond:0#1#11 - Act:1 - Num:55 [fit: 0.000, exp: 119.00, pred: 503.860]\n", + "Cond:#011#0 - Act:1 - Num:21 [fit: 0.000, exp: 102.00, pred: 574.861]\n", + "Cond:#11#10 - Act:1 - Num:19 [fit: 0.000, exp: 187.00, pred: 501.388]\n", + "Cond:1#00#1 - Act:0 - Num:32 [fit: 0.000, exp: 152.00, pred: 412.653]\n", + "Cond:#11111 - Act:0 - Num:38 [fit: 0.000, exp: 84.00, pred: 520.065]\n", + "Cond:0##011 - Act:0 - Num:60 [fit: 0.000, exp: 157.00, pred: 528.572]\n", + "Cond:0##011 - Act:1 - Num:32 [fit: 0.000, exp: 158.00, pred: 483.638]\n", + "Cond:11##01 - Act:0 - Num:29 [fit: 0.000, exp: 160.00, pred: 417.714]\n", + "Cond:000#0# - Act:0 - Num:35 [fit: 0.000, exp: 189.00, pred: 549.371]\n", + "Cond:#0##10 - Act:1 - Num:3 [fit: 0.000, exp: 193.00, pred: 499.302]\n", + "Cond:#01110 - Act:1 - Num:31 [fit: 0.000, exp: 101.00, pred: 584.680]\n", + "Cond:#0#1## - Act:1 - Num:1 [fit: 0.000, exp: 225.00, pred: 448.306]\n", + "Cond:##0001 - Act:1 - Num:61 [fit: 0.000, exp: 117.00, pred: 520.513]\n", + "Cond:#11111 - Act:1 - Num:33 [fit: 0.000, exp: 45.00, pred: 584.617]\n", + "Cond:##01## - Act:0 - Num:4 [fit: 0.000, exp: 211.00, pred: 417.614]\n", + "Cond:1#00#0 - Act:0 - Num:13 [fit: 0.000, exp: 44.00, pred: 530.567]\n", + "Cond:###11# - Act:0 - Num:42 [fit: 0.000, exp: 139.00, pred: 398.852]\n", + "Cond:#10#1# - Act:1 - Num:39 [fit: 0.000, exp: 190.00, pred: 459.080]\n", + "Cond:#1#10# - Act:0 - Num:39 [fit: 0.000, exp: 136.00, pred: 451.514]\n", + "Cond:#####0 - Act:0 - Num:30 [fit: 0.000, exp: 143.00, pred: 425.275]\n", + "Cond:##0001 - Act:0 - Num:20 [fit: 0.000, exp: 37.00, pred: 467.753]\n", + "Cond:#11#1# - Act:0 - Num:53 [fit: 0.000, exp: 89.00, pred: 482.093]\n", + "Cond:###1## - Act:0 - Num:31 [fit: 0.000, exp: 131.00, pred: 376.467]\n", + "Cond:#1#0#1 - Act:0 - Num:46 [fit: 0.000, exp: 85.00, pred: 424.991]\n", + "Cond:#10#1# - Act:0 - Num:15 [fit: 0.000, exp: 77.00, pred: 488.714]\n", + "Cond:#01#00 - Act:1 - Num:8 [fit: 0.007, exp: 16.00, pred: 447.688]\n", + "Cond:#1#### - Act:1 - Num:10 [fit: 0.000, exp: 76.00, pred: 442.122]\n", + "Cond:###### - Act:0 - Num:28 [fit: 0.000, exp: 158.00, pred: 354.008]\n", + "Cond:#1#0#1 - Act:1 - Num:19 [fit: 0.000, exp: 35.00, pred: 423.557]\n", + "Cond:10###1 - Act:1 - Num:7 [fit: 0.019, exp: 10.00, pred: 468.895]\n", + "Cond:0#1### - Act:1 - Num:11 [fit: 0.003, exp: 12.00, pred: 461.700]\n", + "Cond:###### - Act:1 - Num:18 [fit: 0.000, exp: 51.00, pred: 423.200]\n", + "Cond:#01#00 - Act:0 - Num:1 [fit: 0.001, exp: 8.00, pred: 522.568]\n" + ] + } + ], + "source": [ + "for rule in explore_population:\n", + " print(rule)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experiment 0: No duplicates\n", + "Experiment 1: Duplicate found: 44, 42\n", + "Experiment 2: No duplicates\n", + "Experiment 3: Duplicate found: 36, 35\n", + "Experiment 4: No duplicates\n", + "Experiment 5: Duplicate found: 39, 37\n", + "Experiment 6: Duplicate found: 46, 45\n", + "Experiment 7: No duplicates\n", + "Experiment 8: Duplicate found: 38, 37\n", + "Experiment 9: No duplicates\n", + "Experiment 10: No duplicates\n", + "Experiment 11: Duplicate found: 36, 35\n", + "Experiment 12: Duplicate found: 34, 33\n", + "Experiment 13: Duplicate found: 47, 45\n", + "Experiment 14: Duplicate found: 31, 30\n", + "Experiment 15: No duplicates\n", + "Experiment 16: Duplicate found: 31, 30\n", + "Experiment 17: No duplicates\n", + "Experiment 18: No duplicates\n", + "Experiment 19: Duplicate found: 44, 43\n", + "Experiment 20: No duplicates\n", + "Experiment 21: Duplicate found: 38, 37\n", + "Experiment 22: No duplicates\n", + "Experiment 23: No duplicates\n", + "Experiment 24: No duplicates\n", + "Experiment 25: Duplicate found: 48, 46\n", + "Experiment 26: No duplicates\n", + "Experiment 27: No duplicates\n", + "Experiment 28: Duplicate found: 42, 40\n", + "Experiment 29: Duplicate found: 37, 36\n", + "Experiment 30: No duplicates\n", + "Experiment 31: Duplicate found: 37, 36\n", + "Experiment 32: Duplicate found: 55, 52\n", + "Experiment 33: Duplicate found: 45, 44\n", + "Experiment 34: Duplicate found: 43, 41\n", + "Experiment 35: No duplicates\n", + "Experiment 36: Duplicate found: 38, 36\n", + "Experiment 37: Duplicate found: 46, 45\n", + "Experiment 38: Duplicate found: 38, 37\n", + "Experiment 39: No duplicates\n", + "Experiment 40: No duplicates\n", + "Experiment 41: Duplicate found: 44, 43\n", + "Experiment 42: No duplicates\n", + "Experiment 43: Duplicate found: 42, 41\n", + "Experiment 44: Duplicate found: 36, 35\n", + "Experiment 45: No duplicates\n", + "Experiment 46: Duplicate found: 31, 30\n", + "Experiment 47: No duplicates\n", + "Experiment 48: Duplicate found: 38, 36\n", + "Experiment 49: Duplicate found: 45, 44\n", + "Experiment 50: Duplicate found: 45, 43\n", + "Experiment 51: Duplicate found: 40, 39\n", + "Experiment 52: No duplicates\n", + "Experiment 53: No duplicates\n", + "Experiment 54: Duplicate found: 43, 41\n", + "Experiment 55: Duplicate found: 30, 29\n", + "Experiment 56: Duplicate found: 34, 33\n", + "Experiment 57: No duplicates\n", + "Experiment 58: Duplicate found: 45, 44\n", + "Experiment 59: No duplicates\n", + "Experiment 60: Duplicate found: 43, 42\n", + "Experiment 61: No duplicates\n", + "Experiment 62: Duplicate found: 39, 37\n", + "Experiment 63: No duplicates\n", + "Experiment 64: No duplicates\n", + "Experiment 65: Duplicate found: 42, 41\n", + "Experiment 66: No duplicates\n", + "Experiment 67: No duplicates\n", + "Experiment 68: Duplicate found: 39, 38\n", + "Experiment 69: No duplicates\n", + "Experiment 70: Duplicate found: 43, 42\n", + "Experiment 71: Duplicate found: 41, 40\n", + "Experiment 72: Duplicate found: 36, 35\n", + "Experiment 73: Duplicate found: 40, 39\n", + "Experiment 74: No duplicates\n", + "Experiment 75: No duplicates\n", + "Experiment 76: Duplicate found: 43, 42\n", + "Experiment 77: No duplicates\n", + "Experiment 78: No duplicates\n", + "Experiment 79: Duplicate found: 38, 37\n", + "Experiment 80: Duplicate found: 43, 42\n", + "Experiment 81: Duplicate found: 37, 35\n", + "Experiment 82: Duplicate found: 40, 39\n", + "Experiment 83: No duplicates\n", + "Experiment 84: No duplicates\n", + "Experiment 85: Duplicate found: 37, 36\n", + "Experiment 86: Duplicate found: 30, 29\n", + "Experiment 87: No duplicates\n", + "Experiment 88: No duplicates\n", + "Experiment 89: Duplicate found: 41, 40\n", + "Experiment 90: No duplicates\n", + "Experiment 91: Duplicate found: 35, 34\n", + "Experiment 92: No duplicates\n", + "Experiment 93: No duplicates\n", + "Experiment 94: No duplicates\n", + "Experiment 95: No duplicates\n", + "Experiment 96: No duplicates\n", + "Experiment 97: No duplicates\n", + "Experiment 98: No duplicates\n", + "Experiment 99: No duplicates\n", + "0.51\n" + ] + } + ], + "source": [ + "detected_duplicates = 0\n", + "for i in range(100):\n", + " agent = XCS(cfg)\n", + " explore_population, explore_metrics = agent.explore(mpx, 10_000, decay=False)\n", + " rules = [(cl.condition, cl.action) for cl in explore_population]\n", + " if len(explore_population) > len(set(rules)):\n", + " print(f\"Experiment {i}: Duplicate found: {len(explore_population)}, {len(set(rules))}\")\n", + " detected_duplicates += 1\n", + " else:\n", + " print(f\"Experiment {i}: No duplicates\")\n", + "print(f\"{detected_duplicates / 100}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XCS_woods14-without_#.ipynb b/XCS_Experiments/XCS_woods14-without_#.ipynb deleted file mode 100644 index 7b5493e..0000000 --- a/XCS_Experiments/XCS_woods14-without_#.ipynb +++ /dev/null @@ -1,988 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### XCS in Woods 14\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Wood14 version without generalization" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_woods\n", - "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "['O', 'O', '.', 'O', 'O', 'O', '.', 'O']\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "maze = gym.make('Woods14-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [3.154577245521147e-40, 8.426386911065009e-40, 2.552379700264007e-40, 6.154537527909824e-40, 1.1409518512263796e-39, 3.343389917687717e-40, 1.1020098568328202e-39, 6.499256715350867e-40], 'perf_time': 0.009184100000084072, 'population': 24, 'numerosity': 34}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 4, 'reward': [1.4512454053062271, 0.5818823619639897, 0.5302856071727265, 0.23178782842795262, 100.4345253432717, 1.1632277044996782, 0.6967189743549652, 0.5719084455366454], 'perf_time': 0.0006842000000233384, 'population': 29, 'numerosity': 60}\n", - "INFO:lcs.agents.Agent:{'trial': 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crossover\n", - " metrics_trial_frequency=100,\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=10,\n", - " explore_trials=4000,\n", - " exploit_metrics=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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trial
043.30.01139321.832.3
10045.60.01257228.458.6
20050.00.01168628.358.6
30050.00.00973328.358.6
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50050.00.01126128.358.6
60050.00.01024728.358.6
70050.00.01108228.358.6
80050.00.00993328.358.6
90042.80.00812128.358.6
100044.70.00894928.358.8
110045.20.00907828.358.8
120049.90.00988828.358.8
130050.00.00942428.358.8
140046.20.00872928.358.8
150040.30.00875828.358.8
160050.00.01240328.358.8
170047.20.00931528.358.8
180049.30.00923128.358.8
190045.90.00906728.358.8
200044.90.00885628.358.8
210050.00.00951528.358.8
220045.20.00857728.358.8
230050.00.00939628.358.8
240045.80.01024928.358.8
250050.00.00970228.358.8
260047.80.00850128.358.8
270045.90.00860628.358.8
280050.00.00956128.358.8
290050.00.01061528.358.8
300050.00.00946028.358.8
310037.50.00714228.358.8
320040.10.00824228.358.8
330042.70.00864528.358.8
340050.00.00965528.358.8
350046.10.00873028.358.8
360049.50.00981428.358.8
370050.00.00949528.358.8
380045.20.00964128.358.8
390049.70.01243628.358.8
400045.10.00816728.358.8
410050.00.00973628.358.8
420050.00.00951828.358.8
430050.00.00985828.358.8
440050.00.01016828.358.8
450050.00.00969328.358.8
460050.00.00991028.358.8
470050.00.01087028.358.8
480050.00.00947328.358.8
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\n", - "
" - ], - "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 43.3 0.011393 21.8 32.3\n", - "100 45.6 0.012572 28.4 58.6\n", - "200 50.0 0.011686 28.3 58.6\n", - "300 50.0 0.009733 28.3 58.6\n", - "400 29.7 0.005734 28.3 58.6\n", - "500 50.0 0.011261 28.3 58.6\n", - "600 50.0 0.010247 28.3 58.6\n", - "700 50.0 0.011082 28.3 58.6\n", - "800 50.0 0.009933 28.3 58.6\n", - "900 42.8 0.008121 28.3 58.6\n", - "1000 44.7 0.008949 28.3 58.8\n", - "1100 45.2 0.009078 28.3 58.8\n", - "1200 49.9 0.009888 28.3 58.8\n", - "1300 50.0 0.009424 28.3 58.8\n", - "1400 46.2 0.008729 28.3 58.8\n", - "1500 40.3 0.008758 28.3 58.8\n", - "1600 50.0 0.012403 28.3 58.8\n", - "1700 47.2 0.009315 28.3 58.8\n", - "1800 49.3 0.009231 28.3 58.8\n", - "1900 45.9 0.009067 28.3 58.8\n", - "2000 44.9 0.008856 28.3 58.8\n", - "2100 50.0 0.009515 28.3 58.8\n", - "2200 45.2 0.008577 28.3 58.8\n", - "2300 50.0 0.009396 28.3 58.8\n", - "2400 45.8 0.010249 28.3 58.8\n", - "2500 50.0 0.009702 28.3 58.8\n", - "2600 47.8 0.008501 28.3 58.8\n", - "2700 45.9 0.008606 28.3 58.8\n", - "2800 50.0 0.009561 28.3 58.8\n", - "2900 50.0 0.010615 28.3 58.8\n", - "3000 50.0 0.009460 28.3 58.8\n", - "3100 37.5 0.007142 28.3 58.8\n", - "3200 40.1 0.008242 28.3 58.8\n", - "3300 42.7 0.008645 28.3 58.8\n", - "3400 50.0 0.009655 28.3 58.8\n", - "3500 46.1 0.008730 28.3 58.8\n", - "3600 49.5 0.009814 28.3 58.8\n", - "3700 50.0 0.009495 28.3 58.8\n", - "3800 45.2 0.009641 28.3 58.8\n", - "3900 49.7 0.012436 28.3 58.8\n", - "4000 45.1 0.008167 28.3 58.8\n", - "4100 50.0 0.009736 28.3 58.8\n", - "4200 50.0 0.009518 28.3 58.8\n", - "4300 50.0 0.009858 28.3 58.8\n", - "4400 50.0 0.010168 28.3 58.8\n", - "4500 50.0 0.009693 28.3 58.8\n", - "4600 50.0 0.009910 28.3 58.8\n", - "4700 50.0 0.010870 28.3 58.8\n", - "4800 50.0 0.009473 28.3 58.8\n", - "4900 50.0 0.009381 28.3 58.8" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is hard to say but oking at amount of times algorithm reaches top steps (50) the steps might actually go down over trials. need to somehow smooth it to see it better" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_woods2.ipynb b/XCS_Experiments/XCS_woods2.ipynb deleted file mode 100644 index d31d50e..0000000 --- a/XCS_Experiments/XCS_woods2.ipynb +++ /dev/null @@ -1,412 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### XCS in Woods 14\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Wood14 version without generalization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_woods\n", - "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "['.', '.', '.', '.', '.', 'O', 'F', '.']\n", - "\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - 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"\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" - ] - } - ], - "source": [ - "maze = gym.make('Woods2-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - 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"INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [29.457312286172304, 75.84764020867995, 55.87418706624759, 53.56670000668446, 60.68865076362752, 117.78919079474184, 72.40449989611754, 47.310951149443895], 'perf_time': 0.022635000000008176, 'population': 147, 'numerosity': 1000}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': [29.457312286172304, 74.44616712188858, 82.03801204198257, 53.56670000668446, 97.77512802862347, 117.78919079474184, 71.67566012830272, 47.310951149443895], 'perf_time': 0.022635000000008176, 'population': 147, 'numerosity': 1000}\n" - ] - } - ], - "source": [ - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1000,\n", - " gamma=0.9,\n", - " chi=1, # crossover\n", - " metrics_trial_frequency=100,\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=2,\n", - " explore_trials=800,\n", - " exploit_metrics=200)" - ] - }, - { - "cell_type": "code", - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is hard to say but oking at amount of times algorithm reaches top steps (50) the steps might actually go down over trials. need to somehow smooth it to see it better" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 62fc4d3837d400cb9bc7ec2283c5d5428c158786 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Mon, 10 May 2021 12:02:30 +0200 Subject: [PATCH 03/19] Experiments with new RP --- XCS_Experiments/XCS_maze5.ipynb | 1080 +++++++++++++++++++++ XCS_Experiments/XCS_maze5_without_#.ipynb | 816 ---------------- 2 files changed, 1080 insertions(+), 816 deletions(-) create mode 100644 XCS_Experiments/XCS_maze5.ipynb delete mode 100644 XCS_Experiments/XCS_maze5_without_#.ipynb diff --git a/XCS_Experiments/XCS_maze5.ipynb b/XCS_Experiments/XCS_maze5.ipynb new file mode 100644 index 0000000..3d541d5 --- /dev/null +++ b/XCS_Experiments/XCS_maze5.ipynb @@ -0,0 +1,1080 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### XCS in Maze 5 without #\n", + "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Maze5 experiment. \n", + "In this experiment I am repeating experiment that involves XCS without wildcards in classifiers similarly to experiment in An Analysis of Generalization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '1', '0', '1', '0', '1', '1', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.035937099999999944, 'population': 103, 'numerosity': 173}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 25, 'reward': 1000.1911904827269, 'perf_time': 0.04771840000000083, 'population': 324, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 100, 'reward': 1.7853718879791583e-27, 'perf_time': 0.16699319999999318, 'population': 326, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 95, 'reward': 1000.0000000000074, 'perf_time': 0.1838887999999912, 'population': 337, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 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"INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 13, 'reward': 1011.719302823544, 'perf_time': 0.027023199999803182, 'population': 345, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 100, 'reward': 2.0096090746111906e-12, 'perf_time': 0.167609099999936, 'population': 333, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 2800, 'steps_in_trial': 3, 'reward': 1657.8763107205452, 'perf_time': 0.002399999999852298, 'population': 345, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 16, 'reward': 1004.1699762311637, 'perf_time': 0.022209299999758514, 'population': 334, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 10, 'reward': 1032.6881783924425, 'perf_time': 0.019478799999887997, 'population': 336, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.08121080000000802, 'population': 353, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 12, 'reward': 1017.361073058201, 'perf_time': 0.009911799999827053, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 11, 'reward': 1031.5831479297788, 'perf_time': 0.008957999999893218, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 13, 'reward': 1020.825106097668, 'perf_time': 0.010641500000019732, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 2, 'reward': 1513.1350731443135, 'perf_time': 0.0016259999997600971, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 4, 'reward': 1297.9197762349804, 'perf_time': 0.0032400000000052387, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 7, 'reward': 1114.20550790856, 'perf_time': 0.005712199999834411, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 10, 'reward': 1054.907212919428, 'perf_time': 0.008112200000141456, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 1, 'reward': 1840.311398508435, 'perf_time': 0.0008250999999290798, 'population': 357, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 2, 'reward': 2201.9687521774654, 'perf_time': 0.0018755000000965083, 'population': 357, 'numerosity': 1600}\n" + ] + } + ], + "source": [ + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "df = avg_experiment(maze,\n", + " cfg,\n", + " number_of_tests=10,\n", + " explore_trials=4000,\n", + " exploit_metrics=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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100052.2700.0829660.098841333.81600.0
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210042.5935.9595240.079401340.11600.0
220039.41001.7915080.078162344.51600.0
230053.9750.9015500.105645339.91600.0
240054.8735.8168470.098968340.21600.0
250040.41078.4171300.083718340.31600.0
260064.3600.4361320.126997338.11600.0
270061.6612.2437480.117623341.81600.0
280027.11096.0710890.052056342.41600.0
290024.0980.5828260.045605340.41600.0
300055.6800.0437730.103892339.31600.0
310053.6861.6607800.096595338.81600.0
320022.41033.4349880.041260340.21600.0
330039.6934.5134190.074734344.91600.0
340037.9918.6573550.074431343.71600.0
350064.5725.4129780.125586345.31600.0
360049.3725.8901370.093838337.61600.0
370054.9802.7434720.109023334.91600.0
380053.3677.2400340.103832340.91600.0
390041.8900.8679830.075486341.81600.0
400031.0800.0000000.025720340.21600.0
41006.91236.7837030.005875346.91600.0
42007.61216.3974770.006493346.91600.0
43008.81206.6480800.007760347.01600.0
44006.31278.7751260.005465347.01600.0
45006.41244.4529440.005389347.01600.0
46007.01198.6158840.005860347.01600.0
47008.21146.7969140.007198347.01600.0
48006.51298.8148170.005528347.01600.0
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['population'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"number of rules\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/XCS_Experiments/XCS_maze5_without_#.ipynb b/XCS_Experiments/XCS_maze5_without_#.ipynb deleted file mode 100644 index 4007d03..0000000 --- a/XCS_Experiments/XCS_maze5_without_#.ipynb +++ /dev/null @@ -1,816 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### XCS in Maze 5 without #\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Maze5 experiment. \n", - "In this experiment I am repeating experiment that involves XCS without wildcards in classifiers similarly to experiment in An Analysis of Generalization." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_maze\n", - "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "('1', '0', '0', '1', '0', '0', '1', '0')\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "maze = gym.make('Maze5-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': 0.0, 'perf_time': 0.014613500000000279, 'population': 70, 'numerosity': 80}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 3.6552529472225224e-05, 'perf_time': 0.07074129999999812, 'population': 323, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 35, 'reward': 1000.0062236076386, 'perf_time': 0.07021239999999551, 'population': 334, 'numerosity': 1600}\n", - 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"INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 3, 'reward': 1539.0406806601422, 'perf_time': 0.0026307000000542757, 'population': 357, 'numerosity': 1600}\n" - ] - } - ], - "source": [ - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " learning_rate=0.2,\n", - " alpha=0.1,\n", - " gamma=0.71,\n", - " mutation_chance=0.01,\n", - " delta=0.1,\n", - " ga_threshold=25,\n", - " covering_wildcard_chance = 0.7,\n", - " chi=1, # crossover\n", - " metrics_trial_frequency=100,\n", - " initial_prediction =10, # p_i\n", - " initial_error = 0, # epsilon_i\n", - " initial_fitness = 10, # f_i\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=2,\n", - " explore_trials=4000,\n", - " exploit_metrics=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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48008.51.064287e+030.007074346.51600.0
49005.51.325862e+030.004469346.51600.0
\n", - "
" - ], - "text/plain": [ - " steps_in_trial reward perf_time population numerosity\n", - "trial \n", - "0 50.0 0.000000e+00 0.014694 69.0 84.0\n", - "100 37.0 5.000000e+02 0.055201 295.5 1600.0\n", - "200 21.5 1.000000e+03 0.039413 301.5 1600.0\n", - "300 50.0 7.041987e-13 0.092019 327.0 1600.0\n", - "400 26.5 8.060141e+02 0.038117 324.5 1600.0\n", - "500 50.0 1.827627e-05 0.094100 333.5 1600.0\n", - "600 25.0 1.355000e+03 0.046612 329.0 1600.0\n", - "700 7.5 1.106491e+03 0.017386 341.5 1600.0\n", - "800 18.0 1.356483e+03 0.035508 332.5 1600.0\n", - "900 20.5 1.004184e+03 0.036089 337.0 1600.0\n", - "1000 36.0 5.002671e+02 0.069729 330.0 1600.0\n", - "1100 12.0 1.000000e+03 0.024495 339.5 1600.0\n", - "1200 35.5 5.003770e+02 0.065656 331.5 1600.0\n", - "1300 34.5 5.000000e+02 0.064152 332.5 1600.0\n", - "1400 14.0 1.032288e+03 0.029252 345.0 1600.0\n", - "1500 11.5 1.000000e+03 0.026517 334.0 1600.0\n", - "1600 28.0 1.000034e+03 0.054088 345.0 1600.0\n", - "1700 32.5 5.000000e+02 0.066505 334.0 1600.0\n", - "1800 32.5 5.029366e+02 0.070635 338.5 1600.0\n", - "1900 40.0 5.000172e+02 0.077617 338.5 1600.0\n", - "2000 20.5 1.127061e+03 0.038744 342.0 1600.0\n", - "2100 32.5 5.029366e+02 0.066212 341.5 1600.0\n", - "2200 12.5 1.073004e+03 0.024406 340.0 1600.0\n", - "2300 50.0 2.061746e-05 0.093918 353.5 1600.0\n", - "2400 35.0 5.000000e+02 0.070592 346.0 1600.0\n", - "2500 26.0 5.000000e+02 0.049916 348.0 1600.0\n", - "2600 26.5 6.818921e+02 0.045574 346.0 1600.0\n", - "2700 27.5 6.371674e+02 0.066821 343.0 1600.0\n", - "2800 38.5 5.000482e+02 0.076081 343.5 1600.0\n", - "2900 49.0 5.000000e+02 0.094831 350.0 1600.0\n", - "3000 41.5 1.000003e+03 0.073643 341.0 1600.0\n", - "3100 50.0 3.125273e-05 0.095620 348.0 1600.0\n", - "3200 20.0 1.000017e+03 0.042478 343.0 1600.0\n", - "3300 36.0 5.004019e+02 0.071616 323.5 1600.0\n", - "3400 50.0 1.887400e-05 0.096246 334.5 1600.0\n", - "3500 40.5 5.000000e+02 0.078166 338.5 1600.0\n", - "3600 19.5 1.000068e+03 0.047064 346.5 1600.0\n", - "3700 50.0 3.985382e-05 0.085862 341.0 1600.0\n", - "3800 33.0 5.020850e+02 0.051347 338.0 1600.0\n", - "3900 50.0 8.925648e-28 0.104628 335.0 1600.0\n", - "4000 8.5 1.000000e+03 0.008014 344.0 1600.0\n", - "4100 2.5 1.511096e+03 0.002061 346.5 1600.0\n", - "4200 5.5 1.355865e+03 0.004629 346.5 1600.0\n", - "4300 9.0 1.065087e+03 0.007607 346.5 1600.0\n", - "4400 23.5 1.091336e+03 0.019586 346.5 1600.0\n", - "4500 9.5 1.040077e+03 0.007704 346.5 1600.0\n", - "4600 2.0 1.614899e+03 0.001695 346.5 1600.0\n", - "4700 6.5 1.204325e+03 0.005562 346.5 1600.0\n", - "4800 8.5 1.064287e+03 0.007074 346.5 1600.0\n", - "4900 5.5 1.325862e+03 0.004469 346.5 1600.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['population'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From d50bff85e753152f8220fc51338a0b7774eae6ed Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Tue, 18 May 2021 16:48:18 +0200 Subject: [PATCH 04/19] XCS Experiments --- XCS_Experiments/XCS_maze5.ipynb | 1005 +++++----- .../XCS_maze5_multiple_variants.ipynb | 1673 +++++++++++++++++ XCS_Experiments/utils/nxcs_utils.py | 42 +- examples/xcs/XCS_maze5.py | 19 + examples/xncs/XNCS_maze5.py | 18 + 5 files changed, 2243 insertions(+), 514 deletions(-) create mode 100644 XCS_Experiments/XCS_maze5_multiple_variants.ipynb create mode 100644 examples/xcs/XCS_maze5.py create mode 100644 examples/xncs/XNCS_maze5.py diff --git a/XCS_Experiments/XCS_maze5.ipynb b/XCS_Experiments/XCS_maze5.ipynb index 3d541d5..087650c 100644 --- a/XCS_Experiments/XCS_maze5.ipynb +++ b/XCS_Experiments/XCS_maze5.ipynb @@ -44,13 +44,13 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '1', '0', '1', '0', '1', '1', '1')\n", + "('0', '1', '1', '1', '1', '0', '0', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", @@ -78,7 +78,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.035937099999999944, 'population': 103, 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-884,56 +884,56 @@ "text/plain": [ " steps_in_trial reward perf_time population numerosity\n", "trial \n", - "0 100.0 0.000000 0.037112 107.6 163.3\n", - "100 65.4 601.752477 0.107259 302.3 1600.0\n", - "200 47.5 806.490461 0.081540 318.5 1600.0\n", - "300 27.7 1049.848999 0.049116 320.2 1599.6\n", - "400 44.2 812.877949 0.083761 328.0 1600.0\n", - "500 40.8 800.724114 0.083254 329.4 1600.0\n", - "600 45.1 1012.173942 0.086531 332.5 1600.0\n", - "700 46.9 973.311289 0.089079 328.9 1600.0\n", - "800 54.3 700.431015 0.096503 331.1 1600.0\n", - "900 34.1 926.656246 0.058906 332.5 1600.0\n", - "1000 52.2 700.082966 0.098841 333.8 1600.0\n", - "1100 53.2 812.453049 0.099922 336.9 1600.0\n", - "1200 48.1 1000.542133 0.087014 341.5 1600.0\n", - "1300 44.5 940.509922 0.083042 340.5 1600.0\n", - "1400 48.2 839.908354 0.092162 342.5 1600.0\n", - "1500 62.0 619.583962 0.115459 342.2 1600.0\n", - "1600 43.4 844.296116 0.084207 341.7 1600.0\n", - "1700 49.5 835.378038 0.097699 342.3 1600.0\n", - 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1600.0\n", + "4800 16.9 1130.998406 0.037435 408.4 1600.4\n", + "4900 15.2 1130.398310 0.035527 409.8 1600.0" ] }, "metadata": {}, @@ -941,6 +941,7 @@ } ], "source": [ + "\n", "display(df)" ] }, @@ -951,7 +952,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] diff --git a/XCS_Experiments/XCS_maze5_multiple_variants.ipynb b/XCS_Experiments/XCS_maze5_multiple_variants.ipynb new file mode 100644 index 0000000..f5fedb5 --- /dev/null +++ b/XCS_Experiments/XCS_maze5_multiple_variants.ipynb @@ -0,0 +1,1673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### XCS in Maze 5 without #\n", + "Comparison of XCS in Maze 5 with and without #." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '0', '1', '0', '0', '1', '0', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test with #" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.03938660000000027, 'population': 87, 'numerosity': 124}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 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'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 7, 'reward': 1116.4103631939581, 'perf_time': 0.013397499999996398, 'population': 452, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.07061119999991661, 'population': 165, 'numerosity': 203}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 1 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 3, 'reward': 1374.3206827406984, 'perf_time': 0.012460499999974672, 'population': 453, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 22, 'reward': 1000.9134395653258, 'perf_time': 0.08041149999996833, 'population': 430, 'numerosity': 1607}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 100, 'reward': 1.379580580777137e-12, 'perf_time': 0.379826200000025, 'population': 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1186.7634429865655, 'perf_time': 0.010863900000003923, 'population': 447, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 41, 'reward': 1000.0008487298285, 'perf_time': 0.08915609999985463, 'population': 447, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 9, 'reward': 1050.0200785772788, 'perf_time': 0.019271699999990233, 'population': 447, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 4, 'reward': 1255.8075209830047, 'perf_time': 0.008945200000198383, 'population': 447, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 9, 'reward': 1046.6980496023373, 'perf_time': 0.019656299999951443, 'population': 447, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 4, 'reward': 1258.6957806233113, 'perf_time': 0.009097699999983888, 'population': 447, 'numerosity': 1600}\n" + ] + } + ], + "source": [ + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "df = avg_experiment(maze,\n", + " cfg,\n", + " number_of_tests=10,\n", + " explore_trials=4000,\n", + " exploit_metrics=1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tests without #" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.062294399999700545, 'population': 88, 'numerosity': 154}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 8, 'reward': 1069.7362843455132, 'perf_time': 0.020595300000422867, 'population': 285, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 17, 'reward': 1003.497997986562, 'perf_time': 0.04291319999992993, 'population': 283, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 33, 'reward': 1000.0123459880984, 'perf_time': 0.10462439999992057, 'population': 285, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 10, 'reward': 1032.6208636593763, 'perf_time': 0.0765161999997872, 'population': 286, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 8, 'reward': 1064.5753531245764, 'perf_time': 0.02856419999989157, 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4, 'reward': 1333.3345105628343, 'perf_time': 0.0058894999983749585, 'population': 288, 'numerosity': 1600}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 3, 'reward': 1465.8326431713056, 'perf_time': 0.004226199998811353, 'population': 288, 'numerosity': 1600}\n" + ] + } + ], + "source": [ + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 1,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "df_without = avg_experiment(maze,\n", + " cfg,\n", + " number_of_tests=10,\n", + " explore_trials=4000,\n", + " exploit_metrics=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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30038.9842.6938490.149336412.21603.936.0283.51600.0
40032.1947.7131350.124457420.71601.213.0281.21600.0
50017.31129.8618920.067204406.61607.921.3281.31600.0
60039.5800.9099680.151993418.81608.030.2282.91600.0
70059.0628.9042360.235333416.01604.930.5281.71600.0
80033.3904.7847900.135967420.11612.722.6282.81600.0
90052.3802.7180140.211393412.11603.428.5283.61600.0
100046.6752.0433140.245118416.31609.026.5282.31600.0
110050.1708.2112340.208092419.01603.727.5283.41600.0
120048.8745.0669190.200061406.31610.421.1283.31600.0
130038.8905.8220230.143877414.21609.227.7282.01600.0
140057.9666.7867430.230458410.81609.736.5282.91600.0
150046.0900.0197530.191005415.61605.813.6282.21600.0
160036.71098.0159930.154292418.31605.640.2284.61600.0
170041.7981.1577630.171120413.71608.551.6282.51600.0
180058.7804.6083690.241749424.81606.929.2282.61600.0
190054.6802.2036940.220643421.81606.443.9283.11600.0
200060.8673.9872350.233318418.41604.033.4283.21600.0
210060.2691.8020730.229124416.01610.426.7280.81600.0
220055.4800.3038790.226953419.51606.022.3282.11600.0
230030.9955.0588830.120142416.81605.833.0284.51600.0
240043.2900.0151930.183235420.01604.534.2282.61600.0
250028.11000.5334300.110761408.01606.424.2281.81600.0
260048.8922.5596880.184506420.41606.225.0282.41600.0
270047.8881.9748380.182179411.31607.233.3282.31600.0
280028.11043.6368330.132279409.91607.321.8283.01600.0
290033.7849.3970030.150458408.61602.035.0282.41600.0
300049.0757.4389720.204505421.71606.334.1282.81600.0
310059.3728.7501010.224976417.61604.850.2283.31600.0
320040.1871.2066830.163756415.31606.836.3283.51600.0
330033.0915.8910840.134094428.51609.114.2282.91600.0
340048.9714.1411160.196507421.71614.023.8282.01600.0
350052.4844.7003870.226562414.81609.733.8282.51600.0
360042.5904.4436390.163510406.01606.910.7283.01600.0
370034.71119.0604480.148523421.31605.525.0282.91600.0
380048.4800.0446780.194762427.81610.039.4282.91600.0
390031.21077.4133430.123849408.11610.815.6282.51600.0
400023.21000.0000000.052659421.71604.126.9284.81600.0
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42008.01179.2069950.016115427.21600.05.3288.01600.0
43007.31199.3056070.018155427.31600.06.3288.01600.0
44006.91201.8761090.013948427.31600.05.0288.01600.0
45009.81244.9716970.020832427.31600.05.3288.01600.0
46008.01194.1633160.016126427.31600.05.7288.01600.0
47004.41317.9750020.008909427.31600.06.8288.01600.0
48009.21217.0627430.019675427.31600.06.0288.01600.0
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 100.0 0.000000 0.055389 113.6 157.3 \n", + "100 31.5 1008.230363 0.125652 400.7 1606.6 \n", + "200 41.3 955.049116 0.159102 413.0 1609.1 \n", + "300 38.9 842.693849 0.149336 412.2 1603.9 \n", + "400 32.1 947.713135 0.124457 420.7 1601.2 \n", + "500 17.3 1129.861892 0.067204 406.6 1607.9 \n", + "600 39.5 800.909968 0.151993 418.8 1608.0 \n", + "700 59.0 628.904236 0.235333 416.0 1604.9 \n", + "800 33.3 904.784790 0.135967 420.1 1612.7 \n", + "900 52.3 802.718014 0.211393 412.1 1603.4 \n", + "1000 46.6 752.043314 0.245118 416.3 1609.0 \n", + "1100 50.1 708.211234 0.208092 419.0 1603.7 \n", + "1200 48.8 745.066919 0.200061 406.3 1610.4 \n", + "1300 38.8 905.822023 0.143877 414.2 1609.2 \n", + "1400 57.9 666.786743 0.230458 410.8 1609.7 \n", + "1500 46.0 900.019753 0.191005 415.6 1605.8 \n", + "1600 36.7 1098.015993 0.154292 418.3 1605.6 \n", + "1700 41.7 981.157763 0.171120 413.7 1608.5 \n", + "1800 58.7 804.608369 0.241749 424.8 1606.9 \n", + "1900 54.6 802.203694 0.220643 421.8 1606.4 \n", + "2000 60.8 673.987235 0.233318 418.4 1604.0 \n", + "2100 60.2 691.802073 0.229124 416.0 1610.4 \n", + "2200 55.4 800.303879 0.226953 419.5 1606.0 \n", + "2300 30.9 955.058883 0.120142 416.8 1605.8 \n", + "2400 43.2 900.015193 0.183235 420.0 1604.5 \n", + "2500 28.1 1000.533430 0.110761 408.0 1606.4 \n", + "2600 48.8 922.559688 0.184506 420.4 1606.2 \n", + "2700 47.8 881.974838 0.182179 411.3 1607.2 \n", + "2800 28.1 1043.636833 0.132279 409.9 1607.3 \n", + "2900 33.7 849.397003 0.150458 408.6 1602.0 \n", + "3000 49.0 757.438972 0.204505 421.7 1606.3 \n", + "3100 59.3 728.750101 0.224976 417.6 1604.8 \n", + "3200 40.1 871.206683 0.163756 415.3 1606.8 \n", + "3300 33.0 915.891084 0.134094 428.5 1609.1 \n", + "3400 48.9 714.141116 0.196507 421.7 1614.0 \n", + "3500 52.4 844.700387 0.226562 414.8 1609.7 \n", + "3600 42.5 904.443639 0.163510 406.0 1606.9 \n", + "3700 34.7 1119.060448 0.148523 421.3 1605.5 \n", + "3800 48.4 800.044678 0.194762 427.8 1610.0 \n", + "3900 31.2 1077.413343 0.123849 408.1 1610.8 \n", + "4000 23.2 1000.000000 0.052659 421.7 1604.1 \n", + "4100 12.1 1125.082428 0.024684 427.0 1600.0 \n", + "4200 8.0 1179.206995 0.016115 427.2 1600.0 \n", + "4300 7.3 1199.305607 0.018155 427.3 1600.0 \n", + "4400 6.9 1201.876109 0.013948 427.3 1600.0 \n", + "4500 9.8 1244.971697 0.020832 427.3 1600.0 \n", + "4600 8.0 1194.163316 0.016126 427.3 1600.0 \n", + "4700 4.4 1317.975002 0.008909 427.3 1600.0 \n", + "4800 9.2 1217.062743 0.019675 427.3 1600.0 \n", + "4900 7.8 1152.985162 0.015758 427.3 1600.0 \n", + "\n", + " steps_in_trial_without population_without numerosity_without \n", + "trial \n", + "0 91.4 109.6 157.8 \n", + "100 25.7 284.5 1600.0 \n", + "200 20.7 285.1 1600.0 \n", + "300 36.0 283.5 1600.0 \n", + "400 13.0 281.2 1600.0 \n", + "500 21.3 281.3 1600.0 \n", + "600 30.2 282.9 1600.0 \n", + "700 30.5 281.7 1600.0 \n", + "800 22.6 282.8 1600.0 \n", + "900 28.5 283.6 1600.0 \n", + "1000 26.5 282.3 1600.0 \n", + "1100 27.5 283.4 1600.0 \n", + "1200 21.1 283.3 1600.0 \n", + "1300 27.7 282.0 1600.0 \n", + "1400 36.5 282.9 1600.0 \n", + "1500 13.6 282.2 1600.0 \n", + "1600 40.2 284.6 1600.0 \n", + "1700 51.6 282.5 1600.0 \n", + "1800 29.2 282.6 1600.0 \n", + "1900 43.9 283.1 1600.0 \n", + "2000 33.4 283.2 1600.0 \n", + "2100 26.7 280.8 1600.0 \n", + "2200 22.3 282.1 1600.0 \n", + "2300 33.0 284.5 1600.0 \n", + "2400 34.2 282.6 1600.0 \n", + "2500 24.2 281.8 1600.0 \n", + "2600 25.0 282.4 1600.0 \n", + "2700 33.3 282.3 1600.0 \n", + "2800 21.8 283.0 1600.0 \n", + "2900 35.0 282.4 1600.0 \n", + "3000 34.1 282.8 1600.0 \n", + "3100 50.2 283.3 1600.0 \n", + "3200 36.3 283.5 1600.0 \n", + "3300 14.2 282.9 1600.0 \n", + "3400 23.8 282.0 1600.0 \n", + "3500 33.8 282.5 1600.0 \n", + "3600 10.7 283.0 1600.0 \n", + "3700 25.0 282.9 1600.0 \n", + "3800 39.4 282.9 1600.0 \n", + "3900 15.6 282.5 1600.0 \n", + "4000 26.9 284.8 1600.0 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial', 'steps_in_trial_without']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['population'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"number of rules\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index 42185aa..6ea0b02 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -13,13 +13,18 @@ def fraction_accuracy(xncs): action_sets_percentages = [] for action in range(xncs.cfg.number_of_actions): action_set = xncs.population.generate_action_set(action) - total_accuracy = 0 - most_numerous = action_set[0] - for cl in action_set: - total_accuracy += cl_accuracy(cl, xncs.cfg) - if cl.numerosity > most_numerous.numerosity: - most_numerous = cl - action_sets_percentages.append(cl_accuracy(most_numerous, xncs.cfg) / total_accuracy) + if action_set is not None: + total_accuracy = 0 + most_numerous = action_set[0] + for cl in action_set: + total_accuracy += cl_accuracy(cl, xncs.cfg) + if cl.numerosity > most_numerous.numerosity: + most_numerous = cl + action_sets_percentages.append( + cl_accuracy(most_numerous, xncs.cfg) / total_accuracy + ) + else: + action_sets_percentages.append("1") return sum(action_sets_percentages) / xncs.cfg.number_of_actions @@ -39,18 +44,24 @@ def xcs_maze_metrics(xncs: XNCS, environment): } -def avg_experiment(maze, cfg, number_of_tests=1, trials=3000): +def avg_experiment(maze, cfg, number_of_tests=1, explore_trials=3000, exploit_trials=1000): test_metrics =[] for i in range(number_of_tests): print(f'Executing {i} experiment') - test_metrics.append(start_single_experiment(maze, cfg, trials)) + test_metrics.append(start_single_experiment(maze, + cfg, + explore_trials, + exploit_trials)) return pd.concat(test_metrics).groupby(['trial']).mean() -def start_single_experiment(maze, cfg, trials=3000): +def start_single_experiment(maze, cfg, explore_trials, exploit_trials): agent = XNCS(cfg) - _, current_metrics = agent.explore(maze, trials, True) - df = parse_results(current_metrics, cfg) + _, explore_metrics = agent.explore(maze, explore_trials) + _, exploit_metrics = agent.exploit(maze, exploit_trials) + df = parse_results(explore_metrics, cfg) + df_exploit = parse_results_exploit(exploit_metrics, cfg, explore_trials) + df = df.append(df_exploit) return df @@ -59,3 +70,10 @@ def parse_results(metrics, cfg): df['trial'] = df.index * cfg.metrics_trial_frequency df.set_index('trial', inplace=True) return df + + +def parse_results_exploit(metrics, cfg, explore_trials): + df = pd.DataFrame(metrics) + df['trial'] = df.index * cfg.metrics_trial_frequency + explore_trials + df.set_index('trial', inplace=True) + return df diff --git a/examples/xcs/XCS_maze5.py b/examples/xcs/XCS_maze5.py new file mode 100644 index 0000000..a5cb5fe --- /dev/null +++ b/examples/xcs/XCS_maze5.py @@ -0,0 +1,19 @@ +from lcs.agents.xcs import Configuration +from XCS_Experiments.utils.xcs_utils import * +from lcs.agents.xcs import Configuration, XCS, GeneticAlgorithm + + +env = MazeScenario(input_size=8) +env.maze.reset() +env.maze.render() + +cfg = Configuration(number_of_actions=8, + max_population=400, + metrics_trial_frequency=100, + covering_wildcard_chance=0.9, + mutation_chance=1, + delta=0.1, + user_metrics_collector_fcn=xcs_metrics) + +agent = XCS(cfg) +population, metrics = agent.explore(env, 1000) diff --git a/examples/xncs/XNCS_maze5.py b/examples/xncs/XNCS_maze5.py new file mode 100644 index 0000000..efb9b0a --- /dev/null +++ b/examples/xncs/XNCS_maze5.py @@ -0,0 +1,18 @@ +from XCS_Experiments.utils.xcs_utils import * +from lcs.agents.xncs import Configuration, XNCS + + +env = MazeScenario(input_size=8) +env.maze.reset() +env.maze.render() + +cfg = Configuration(number_of_actions=8, + max_population=400, + metrics_trial_frequency=100, + covering_wildcard_chance=0.9, + mutation_chance=1, + delta=0.1, + user_metrics_collector_fcn=xcs_metrics) + +agent = XNCS(cfg) +population, metrics = agent.explore(env, 1000) From 7ec2fc6ab0858c5035440ab7878cf0838fe655f8 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Wed, 19 May 2021 11:41:35 +0200 Subject: [PATCH 05/19] Multiple varians pop comparison --- .../XCS_maze5_multiple_variants.ipynb | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/XCS_Experiments/XCS_maze5_multiple_variants.ipynb b/XCS_Experiments/XCS_maze5_multiple_variants.ipynb index f5fedb5..a3e3b24 100644 --- a/XCS_Experiments/XCS_maze5_multiple_variants.ipynb +++ b/XCS_Experiments/XCS_maze5_multiple_variants.ipynb @@ -1566,22 +1566,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1596,27 +1596,27 @@ "ax = df[['steps_in_trial', 'steps_in_trial_without']].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" + "ax.legend([\"steps\", \"steps without #\"])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1628,10 +1628,10 @@ } ], "source": [ - "ax = df['population'].plot()\n", + "ax = df[['population', 'population_without']].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"population\")\n", - "ax.legend([\"number of rules\"])" + "ax.legend([\"number of rules\", \"number of rules without #\"])" ] }, { From 8a35998998f315821619826551fb2e680e6a6309 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Wed, 19 May 2021 11:45:02 +0200 Subject: [PATCH 06/19] Description --- XCS_Experiments/XCS_maze5_multiple_variants.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/XCS_Experiments/XCS_maze5_multiple_variants.ipynb b/XCS_Experiments/XCS_maze5_multiple_variants.ipynb index a3e3b24..123760f 100644 --- a/XCS_Experiments/XCS_maze5_multiple_variants.ipynb +++ b/XCS_Experiments/XCS_maze5_multiple_variants.ipynb @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### XCS in Maze 5 without #\n", - "Comparison of XCS in Maze 5 with and without #." + "### XCS in Maze 5\n", + "Comparison of XCS in Maze5 enviroment with and without #. The test is supposed to be replication of same test in An Analysis of Generalization in XCS. For the test implementation of XCS in PyAlcs library was used." ] }, { From 11b6934f7f728a7251886c154cddf60fe531cae9 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 30 May 2021 11:04:15 +0200 Subject: [PATCH 07/19] XNCS Test --- XCS_Experiments/Untitled.ipynb | 200 +-- XCS_Experiments/XCS_Woods14.ipynb | 988 ----------- XCS_Experiments/XCS_XNCS_Maze5.ipynb | 1562 +++++++++++++++++ .../XCS_XNCS_Maze5_pre_populated.ipynb | 1014 +++++++++++ XCS_Experiments/XCS_XNCS_Woods1.ipynb | 318 ++++ XCS_Experiments/XCS_maze5_XNCS_metrics.ipynb | 547 ++++++ XCS_Experiments/XCS_multiplexer.ipynb | 450 ----- XCS_Experiments/XCS_woods.ipynb | 937 ++++++++++ XCS_Experiments/XCS_woods1.ipynb | 942 ++++++++++ XCS_Experiments/XCS_woods1_XNCS_metrics.ipynb | 538 ++++++ .../XNCS_Woods1_pre_populated.ipynb | 771 ++++++++ ...oods1_pre_populated_default_settings.ipynb | 513 ++++++ XCS_Experiments/XNCS_maze.ipynb | 282 +-- .../XNCS_maze5_avg_pre_populated.ipynb | 998 +++++++++++ XCS_Experiments/XNCS_woods.ipynb | 1491 +++++++++++++--- XCS_Experiments/XNCS_woods_avg.ipynb | 1082 +++++++----- XCS_Experiments/utils/nxcs_utils.py | 74 +- XCS_Experiments/utils/xcs_utils.py | 54 +- examples/xncs/XNCS_maze5.py | 1 + 19 files changed, 10273 insertions(+), 2489 deletions(-) delete mode 100644 XCS_Experiments/XCS_Woods14.ipynb create mode 100644 XCS_Experiments/XCS_XNCS_Maze5.ipynb create mode 100644 XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb create mode 100644 XCS_Experiments/XCS_XNCS_Woods1.ipynb create mode 100644 XCS_Experiments/XCS_maze5_XNCS_metrics.ipynb delete mode 100644 XCS_Experiments/XCS_multiplexer.ipynb create mode 100644 XCS_Experiments/XCS_woods.ipynb create mode 100644 XCS_Experiments/XCS_woods1.ipynb create mode 100644 XCS_Experiments/XCS_woods1_XNCS_metrics.ipynb create mode 100644 XCS_Experiments/XNCS_Woods1_pre_populated.ipynb create mode 100644 XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb create mode 100644 XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb diff --git a/XCS_Experiments/Untitled.ipynb b/XCS_Experiments/Untitled.ipynb index 303f39d..bc8fe7d 100644 --- a/XCS_Experiments/Untitled.ipynb +++ b/XCS_Experiments/Untitled.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 120, + "execution_count": 13, "id": "sitting-christmas", "metadata": {}, "outputs": [ @@ -12,12 +12,12 @@ "text": [ "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] } @@ -25,51 +25,14 @@ "source": [ "from utils.xcs_utils import *\n", "\n", - "scenario = MazeScenario(input_size=8)\n", - "scenario.maze.reset()\n", - "scenario.maze.render()" + "env = MazeScenario(input_size=8)\n", + "env.maze.reset()\n", + "env.maze.render()" ] }, { "cell_type": "code", - "execution_count": 121, - "id": "coastal-upper", - "metadata": {}, - "outputs": [], - "source": [ - "from xcs import XCSAlgorithm\n", - "import numpy as np\n", - "\n", - "algorithm = XCSAlgorithm()\n", - "\n", - "algorithm.max_population_size = 1600\n", - "algorithm.learning_rate = .1\n", - "algorithm.error_threshold = .01 # epsilon_0\n", - "algorithm.ga_threshold = 25\n", - "algorithm.crossover_probability = 0.5\n", - "algorithm.mutation_probability = 0.1\n", - "algorithm.initial_prediction = float(np.finfo(np.float32).tiny) # p_I\n", - "algorithm.initial_error = float(np.finfo(np.float32).tiny) # epsilon_I\n", - "algorithm.initial_fitness = float(np.finfo(np.float32).tiny) # F_I\n", - "algorithm.wildcard_probability = 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "employed-laser", - "metadata": {}, - "outputs": [], - "source": [ - "model = algorithm.new_model(scenario)\n", - "for i in range(100):\n", - " scenario.reset()\n", - " model.run(scenario, learn=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, + "execution_count": 14, "id": "referenced-affiliation", "metadata": {}, "outputs": [], @@ -88,20 +51,19 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 15, "id": "elementary-punch", "metadata": {}, "outputs": [], "source": [ "from lcs.agents.xcs import Configuration, XCS, GeneticAlgorithm\n", "\n", - "agent = XCS(cfg)\n", - "population, metrics = agent.explore(scenario, 100, False)" + "agent = XCS(cfg)\n" ] }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 16, "id": "productive-recipe", "metadata": {}, "outputs": [], @@ -111,138 +73,56 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 17, "id": "august-anatomy", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "0.05739385325559033\n" - ] - } - ], + "outputs": [], "source": [ - "average_fitness = sum(cl.fitness for cl in model) / sum(cl.numerosity for cl in model)\n", - "number = 0\n", - "for cl in model:\n", - " if cl.experience > algorithm.deletion_threshold and \\\n", - " cl.fitness / cl.numerosity < \\\n", - " algorithm.fitness_threshold * average_fitness:\n", - " number += cl.numerosity\n", - " print(str(cl) + f\" {population._deletion_vote(cl, average_fitness)}\")\n", - "print(number)\n", - "print(average_fitness)" + "prev_action_set = None\n", + "prev_reward = agent.reward\n", + "prev_state = None # state is known as situation\n", + "prev_time_stamp = agent.time_stamp # steps\n", + "done = False # eop\n", + "\n", + "raw_state = env.reset()\n", + "state = agent.cfg.environment_adapter.to_genotype(raw_state)" ] }, { - "cell_type": "code", - "execution_count": 139, - "id": "isolated-chile", + "cell_type": "markdown", + "id": "freelance-austin", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "0.05487554256814123\n" - ] - } - ], "source": [ - "average_fitness = sum(cl.fitness for cl in population) / sum(cl.numerosity for cl in population)\n", - "number = 0\n", - "for cl in population:\n", - " if cl.experience > cfg.deletion_threshold and \\\n", - " cl.fitness / cl.numerosity < \\\n", - " cfg.delta * average_fitness:\n", - " number += cl.numerosity\n", - " print(str(cl) + f\" {population._deletion_vote(cl, average_fitness)}\")\n", - "print(number)\n", - "print(average_fitness)" + "Main Loop" ] }, { - "cell_type": "code", - "execution_count": 135, - "id": "welcome-instrument", + "cell_type": "markdown", + "id": "potential-plaza", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cond:09#1##1# - Act:6 - Num:2 [fit: 0.491, exp: 14.00, pred: 155.252]\n", - "Cond:0901#01# - Act:4 - Num:1 [fit: 0.274, exp: 5.00, pred: 142.435]\n", - "Cond:119#01#1 - Act:0 - Num:1 [fit: 0.308, exp: 7.00, pred: 133.162]\n", - "Cond:11900101 - Act:1 - Num:4 [fit: 0.285, exp: 5.00, pred: 129.223]\n", - "Cond:1#900101 - Act:4 - Num:2 [fit: 0.455, exp: 10.00, pred: 144.590]\n", - "Cond:11001111 - Act:0 - Num:1 [fit: 0.256, exp: 4.00, pred: 153.930]\n", - "Cond:1#011##0 - Act:3 - Num:6 [fit: 0.405, exp: 21.00, pred: 191.785]\n", - "Cond:10#1#10# - Act:4 - Num:5 [fit: 0.283, exp: 72.00, pred: 221.712]\n", - "Cond:0#010#1# - Act:3 - Num:4 [fit: 0.531, exp: 14.00, pred: 160.401]\n", - "Cond:11001#11 - Act:1 - Num:2 [fit: 0.494, exp: 43.00, pred: 153.595]\n", - "Cond:0#1#0010 - Act:0 - Num:4 [fit: 0.239, exp: 2.00, pred: 167.607]\n", - "Cond:01110100 - Act:6 - Num:1 [fit: 0.331, exp: 6.00, pred: 150.775]\n", - "Cond:#100000# - Act:2 - Num:1 [fit: 0.441, exp: 9.00, pred: 154.946]\n", - "Cond:09010010 - Act:1 - Num:3 [fit: 0.212, exp: 4.00, pred: 136.754]\n", - "Cond:10101000 - Act:5 - Num:1 [fit: 0.306, exp: 5.00, pred: 153.101]\n", - "Cond:11001111 - Act:4 - Num:5 [fit: 0.384, exp: 14.00, pred: 151.107]\n", - "Cond:000#1000 - Act:1 - Num:4 [fit: 0.228, exp: 4.00, pred: 149.296]\n", - "Cond:10000010 - Act:3 - Num:1 [fit: 0.380, exp: 7.00, pred: 158.779]\n", - "Cond:##000000 - Act:5 - Num:1 [fit: 0.575, exp: 15.00, pred: 174.065]\n", - "Cond:#010#01# - Act:1 - Num:1 [fit: 0.216, exp: 9.00, pred: 166.332]\n", - "Cond:0001#010 - Act:0 - Num:4 [fit: 0.212, exp: 4.00, pred: 132.836]\n", - "Cond:00000101 - Act:1 - Num:6 [fit: 0.227, exp: 5.00, pred: 149.043]\n", - "Cond:1#1#0101 - Act:1 - Num:1 [fit: 0.217, exp: 4.00, pred: 148.928]\n", - "Cond:01111110 - Act:0 - Num:3 [fit: 0.225, exp: 5.00, pred: 138.989]\n", - "Cond:01011101 - Act:0 - Num:3 [fit: 0.290, exp: 5.00, pred: 163.930]\n", - "Cond:00#11101 - Act:2 - Num:5 [fit: 0.240, exp: 4.00, pred: 187.022]\n", - "Cond:0011110# - Act:7 - Num:2 [fit: 0.240, exp: 4.00, pred: 180.617]\n", - "Cond:10101#0# - Act:6 - Num:4 [fit: 0.225, exp: 4.00, pred: 151.885]\n", - "Cond:0010000# - Act:7 - Num:3 [fit: 0.261, exp: 7.00, pred: 143.091]\n", - "Cond:1#00##11 - Act:5 - Num:3 [fit: 0.485, exp: 11.00, pred: 167.041]\n", - "Cond:011#0100 - Act:7 - Num:7 [fit: 0.385, exp: 9.00, pred: 167.215]\n", - "Cond:10100#1# - Act:1 - Num:3 [fit: 0.262, exp: 9.00, pred: 174.929]\n", - "Cond:10001000 - Act:3 - Num:1 [fit: 0.345, exp: 6.00, pred: 166.836]\n", - "Cond:00#010## - Act:4 - Num:8 [fit: 0.745, exp: 42.00, pred: 211.002]\n", - "Cond:0#0#00## - Act:5 - Num:7 [fit: 0.378, exp: 28.00, pred: 218.349]\n", - "Cond:1#00001# - Act:1 - Num:3 [fit: 0.314, exp: 6.00, pred: 196.347]\n", - "Cond:001#00## - Act:6 - Num:3 [fit: 0.312, exp: 7.00, pred: 179.387]\n", - "Cond:100111## - Act:7 - Num:4 [fit: 0.349, exp: 6.00, pred: 175.641]\n", - "Cond:0#01##01 - Act:2 - Num:8 [fit: 0.454, exp: 13.00, pred: 206.904]\n", - "Cond:1#001000 - Act:2 - Num:6 [fit: 0.288, exp: 5.00, pred: 158.223]\n", - "Cond:10011#00 - Act:5 - Num:8 [fit: 0.544, exp: 45.00, pred: 239.238]\n", - "Cond:01111110 - Act:4 - Num:3 [fit: 0.337, exp: 6.00, pred: 198.607]\n" - ] - } - ], "source": [ - "for cl in population:\n", - " if cl.fitness > 0.2:\n", - " print(cl)" + "Before Update Set" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "id": "theoretical-projector", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ - "unique_actions = {1,2,3}\n", - "print(type(unique_actions))" + "assert len(agent.population) == len(set(agent.population)), 'duplicates found'\n", + "agent.population.delete_from_population()\n", + "# We are in t+1 here\n", + "match_set = agent.population.generate_match_set(state, agent.time_stamp)\n", + "prediction_array = match_set.prediction_array\n", + "action = agent.select_action(prediction_array, match_set)\n", + "action_set = match_set.generate_action_set(action)\n", + "# apply action to environment\n", + "raw_state, step_reward, done, _ = env.step(action)\n", + "state = agent.cfg.environment_adapter.to_genotype(raw_state)\n", + "if agent.cfg.multistep_enfiroment:\n", + " agent.reward = step_reward + agent.cfg.gamma * agent.reward" ] }, { diff --git a/XCS_Experiments/XCS_Woods14.ipynb b/XCS_Experiments/XCS_Woods14.ipynb deleted file mode 100644 index 7b5493e..0000000 --- a/XCS_Experiments/XCS_Woods14.ipynb +++ /dev/null @@ -1,988 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### XCS in Woods 14\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Wood14 version without generalization" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_woods\n", - "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "['O', 'O', '.', 'O', 'O', 'O', '.', 'O']\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "maze = gym.make('Woods14-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [3.154577245521147e-40, 8.426386911065009e-40, 2.552379700264007e-40, 6.154537527909824e-40, 1.1409518512263796e-39, 3.343389917687717e-40, 1.1020098568328202e-39, 6.499256715350867e-40], 'perf_time': 0.009184100000084072, 'population': 24, 'numerosity': 34}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 4, 'reward': [1.4512454053062271, 0.5818823619639897, 0.5302856071727265, 0.23178782842795262, 100.4345253432717, 1.1632277044996782, 0.6967189743549652, 0.5719084455366454], 'perf_time': 0.0006842000000233384, 'population': 29, 'numerosity': 60}\n", - 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] - } - ], - "source": [ - "cfg = Configuration(number_of_actions=8,\n", - " max_population=60,\n", - " gamma=0.9,\n", - " chi=1, # crossover\n", - " metrics_trial_frequency=100,\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=10,\n", - " explore_trials=4000,\n", - " exploit_metrics=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " steps_in_trial perf_time population numerosity\n", - "trial \n", - "0 43.3 0.011393 21.8 32.3\n", - "100 45.6 0.012572 28.4 58.6\n", - "200 50.0 0.011686 28.3 58.6\n", - "300 50.0 0.009733 28.3 58.6\n", - "400 29.7 0.005734 28.3 58.6\n", - "500 50.0 0.011261 28.3 58.6\n", - "600 50.0 0.010247 28.3 58.6\n", - "700 50.0 0.011082 28.3 58.6\n", - "800 50.0 0.009933 28.3 58.6\n", - "900 42.8 0.008121 28.3 58.6\n", - "1000 44.7 0.008949 28.3 58.8\n", - "1100 45.2 0.009078 28.3 58.8\n", - "1200 49.9 0.009888 28.3 58.8\n", - "1300 50.0 0.009424 28.3 58.8\n", - "1400 46.2 0.008729 28.3 58.8\n", - "1500 40.3 0.008758 28.3 58.8\n", - "1600 50.0 0.012403 28.3 58.8\n", - "1700 47.2 0.009315 28.3 58.8\n", - "1800 49.3 0.009231 28.3 58.8\n", - "1900 45.9 0.009067 28.3 58.8\n", - "2000 44.9 0.008856 28.3 58.8\n", - "2100 50.0 0.009515 28.3 58.8\n", - "2200 45.2 0.008577 28.3 58.8\n", - "2300 50.0 0.009396 28.3 58.8\n", - "2400 45.8 0.010249 28.3 58.8\n", - "2500 50.0 0.009702 28.3 58.8\n", - "2600 47.8 0.008501 28.3 58.8\n", - "2700 45.9 0.008606 28.3 58.8\n", - "2800 50.0 0.009561 28.3 58.8\n", - "2900 50.0 0.010615 28.3 58.8\n", - "3000 50.0 0.009460 28.3 58.8\n", - "3100 37.5 0.007142 28.3 58.8\n", - "3200 40.1 0.008242 28.3 58.8\n", - "3300 42.7 0.008645 28.3 58.8\n", - "3400 50.0 0.009655 28.3 58.8\n", - "3500 46.1 0.008730 28.3 58.8\n", - "3600 49.5 0.009814 28.3 58.8\n", - "3700 50.0 0.009495 28.3 58.8\n", - "3800 45.2 0.009641 28.3 58.8\n", - "3900 49.7 0.012436 28.3 58.8\n", - "4000 45.1 0.008167 28.3 58.8\n", - "4100 50.0 0.009736 28.3 58.8\n", - "4200 50.0 0.009518 28.3 58.8\n", - "4300 50.0 0.009858 28.3 58.8\n", - "4400 50.0 0.010168 28.3 58.8\n", - "4500 50.0 0.009693 28.3 58.8\n", - "4600 50.0 0.009910 28.3 58.8\n", - "4700 50.0 0.010870 28.3 58.8\n", - "4800 50.0 0.009473 28.3 58.8\n", - "4900 50.0 0.009381 28.3 58.8" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is hard to say but oking at amount of times algorithm reaches top steps (50) the steps might actually go down over trials. need to somehow smooth it to see it better" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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n5McERgn0Dk3mLAmSTSwS8v3lr7ZJKhIOsbY+WnUfhhd1ki/LO5udnU0VaRjNddFMuYs9bvHBXGapBw+e5qL1TRmzwu6NzRw8NVbQ8ZwPLwfDExjdrfWcGpvOqymeGp0mldbMgiMibO9ozJuLkUilOT48mdPvcN1F65hOpPl5z7CvuXoRUuVoGOA424OIkjoz6QiG9iZn01LM8e0J92Iahh+B4QmHLW31bFpTR0jgeAHH92zAggmMUjGBUQJHhydylgTJpiZSitO7uiYpcBzfw1WOkiolBHHXuiZOnJksqcS7x9h0gpa6We1tY0st65trzxMYZydn2H/sDG+5eLavw+4NzYzHkxltoRS8HAxPQ+turUd1brmQbHItOIVCa0+MTJJMayYHI5trtrVSGw3xsM9oqSOD4zTGIgXDmwvREIsEEiXlldWf1TAKZ/4f7B9DBC7e0FTwczuba6kJh4pqGI6WF6UmEmLT2rqCGsYJ9+9qTu/SMYHhk/F4kkHXsVaIWLQEk1SiuiYpcARGtTWM4yOTbGyp8yXYdnU2lh0pNTqVmFNqWkS4asva8wTGwy8NkkrrnEZAnmnj4KnSuxd6ORheyHCxXW12DobH9vYGTo1OM5lj9+4JklwaRm00zN4d7Tz00qCvhMuewQl2uNni5dAYCwdikvISHdtdH8bGlloaasL5NYxTo2xrbygaGhwOCV1r6wqaCI8Ozd3IbWlt4FiB8/tGJqmJFM4pMnJjAsMnmQipYgIjEvJdrbbaJikgkIq1x4YnfNt7vZ1lOSVCRqfmahjg+DFOnp2aUyvqwRdO09EU47JNLZljuzqbCIekLD+Gl4PhUSwX4/jIJJGQzOn34Dm+c2V8e6aqXBoGOGap4yOTBaOsPMqNkPJoqAnGh+H5zbzQbhHhgnWNBQRGcYe3R7FcjGPDk2zJFhht9QWzvY+POBuEIHqCrHRMYPikN8uxVohYKSYpV7B4CX/VICiTVLEIKY+tbfXUhEMcKiO0NpfA2OMm8HlaxkwyzU9fGuTNF62b84WvjYbZ0dFQVqRU35mpOealjqYYsUh+M4i34GRHPHnaQy6zVM+g03gqX9/vN7rhtcWS+MbjSU6NTpftvwAnFyOIKCmvjpQXJQWOnyVXLsboVIITI1NFHd4e3a31eaOepmZSvDI2zdas/88tbfWcnUwwmqfK7Ykzk2aOKhMTGD45Olg8pBZKjZJymieVa17IRUdTjHPxZMV9KTwm4kmGxmd8Owgj4RDbOxrKSqIbc53e2eze2ExtNJQRGPuODjMeT87xX2TOLSNSKjsHw0NECobWnsgRw7+tvQGR/AKjUKJd19p6dnU2Fm2qdLSMLnvzaYgFo2EMj88Qi4QyyYHghNa+MjbN2PTchfuFU/4c3h7drfWMTSdzCgCvTe6W9mwNo2HOe/M5YY2TyiZwgSEiYRF5UkTucV/fJSJPuY9eEXkqz7heEXnWPW9/0PMsxtHhCdY31xa1udaWkoeRrE63vWzavNDaKpmlvEXTr4YBXqRUaRqGqubUMKLhEJd1rckUIvzRwdPURkOZEujZ7N7YTP/oNGdKyEOZn4Ph4QiM/E7v+QtObTTMxpa6nJFSPUPjec1RHtdduI7Hjo6ct7hmU2mEFHgCIwCn93icdjdpz2NnnkipTISUTw1jcwGfkpeDsW2eSQpyV60dnXI2CJaDUR4LoWF8BKdrHgCq+h5VvUJVrwD+D/CPBcZe5567J+A5FsWpUlt80YxFwiVVq61WHSkPz4ZcLbPUbEit/13trnWN9J2ZKmknO51Ik0jpeQIDHLPU8/1jTM2k+NELA7zugo5MV7Zsdm9wfBovlGCWmp+D4eHlYsx3RI9OJTg7mcgpQLd3nB8p5VVtLVbK41cuWU8ipfzo4Om85xwZHCckpQnv+TTGwsyk0mWVVS/EcFbSnsdON/P/8Dxt8+CpMdobY6xrqvX12YWCEDxfRXaOkHd+Lj/GbJVa0zDKIVCBISJdwM3A13K8J8CtwLeDnEO16B2eLOrwBi/T23/HvdoqRkiB00QJqqlhnP+FLIbn+C4lUipTR6rufA3uqi1rSaaV7+4/wcmzU7x197qcn+GFaJbix5jNwThfwxiPJzkzzwxSqMz7jo5Gegbn9oHwnODbiziqX715DZvW1HHfs6fyntMz6AQfVBKGHVRPjOGJeGaz4tG1tp5YJHReaO3B/jHf2gWQ0QZyahjDTk2v7I1GfY0TdpxLw/D+3paDUR5BaxhfBj4B5FpBXw+cVtWX84xV4AEROSAitwU0P1+MTiUYmZgp6vAGz+ldikmquhpGECaplrpozp1/PryaUqWYpbJLm8/nym7H8f3nP3H+Va67KLfAaGuMsb65tiQ/Rt+ZKeprwqyd55DOt6s9USDpa3tHAxMzKQayMu17fJYiFxFuunQ9jxwaylvSvdIIKZgVGNV2fA+dm8n873mEQ8KOjsY5/b1nkmleHjjn238B0FQbpbWhJo9JamKOw9tja1tDToFhSXuVEZjAEJFbgAFVPZDnlPdRWLvYq6pXAjcCvyMib8hzndtEZL+I7B8cLL9HciG8kNpiORhQemmQavswOpqqW+LcCVks7cu1pa2BmkhozkJRjEICY21DDTs6Ghgan+GKzWsKmjK8jG+/zM/B8MjX7a3QguNpoEeyakr1DE4QDokvJ+vNl21kJpXOaZZKpZWeoYmK/BcQTBMlVWV4Ik5b4/l5DTs754bWHh4YJ5HSkjQMyF+u5VieZNrutvqcTu8TI1M010ZK2gAZswSpYewF3i4ivcB3gDeJyDcBRCQCvBO4K99gVe13fw4A3wOuznPeHaq6R1X3dHR0VPcOXAr18Z5PLBJiJpn2lYQVhNO7NhqmMRapqoZRakSJt7OsloYBZPpjvHX3+dFR2eze0MzhgXHfUWJeWfP5eDbu+YvU8RGnRW9zjnpis0UIZxeqnqFxulvrifoInb68q4VNa+q4N4dZ6uSZKWaS6YoipCAYk9TYVJJESjNJe9nsdP1ZXkLjwRIjpDxyRa1NJ1L0j07PycHw2NpWz+mx+HkVB06cmSzJvGrMJTCBoaqfUtUuVd0KvBf4iaq+3337LcCLqtqXa6yINIhIk/ccuB54Lqi5FuPo0AQi/oqVldJ1L56ovkkKvOS9yjWMZCrNyTNTZTlZne571dEwAH5pRxsicH0xgbGxmWRafftP5iftedTVhOloip0X/19IgG5orqU2GporMAYnfC/yIsLNl23gX14ePC+EtBoRUuA4vaG6GsbQxNykvWy8mlJe0cSD/V4PjNIEX3drHSfPTs2pXOwJkFzBKN2uEMmlIZrDu3wWKw/jvcwzR4nIRhG5z33ZCTwqIk8DjwH3quoPF3iOGXqHJtjY4vRKKEamr7cfgZFMVbUsiEd7Y6wqFWtPjU6TzCqyVwq7Ops4eXbK98I0VkRgvOPyTfzkv7wx41DPh7dz9ePHyJWDkU2uXW2uHAyPUEjY1t7IUTe0Np1WjuaoUluImy/dQCKlPHDwlTnHj+RowFQOsxpG9UJrvYi83AJjbk2pg6dGuahID4xcdLfWk0orp7Iy/jOm4jwaBjCn+146reclaRqlsSACQ1UfVtVbsl7/hqr+5bxz+lX1Jvd5j6pe7j5epap/uBDzzMdRnxFSMFvmw4/jOwiTFDj1fLzM20rwnIbFOu3lIl8Mfj48DSNf6XhnMS4+j1J6Y+TLwcj+rGyBkXIXnEICdHtHQ6bER//oFPFkumiEVDaXdbXQtfZ8s9SRwQnW1kczBRLLpaGm+iYpz/w5P6wWnBDgaFh4ecCJHis1QsojVy6G9/+ZS2B4YeDZGuLgeJyZZNoERgVYpncRVJWjg+O+cjAgS2D4yMUIIkoKvAKElZukMlm0ZZmkSqspNTqVoCkWKXnnOZ9SemPky8Hw2Nxaz6nRqUzOwqnRqaIa1472Bk6MTBJPpmaLDpZgfvHMUo++PMTZydm/YTUipACaaqvv9B4uIDBme6SMc/LsFGPTyZL9F5A7aq13eII19dGcJVda6qOsqY/O0TAyAQs+qi4buTGBUYQzkwnGppO+QmqBTCKePx9G9aOkwAkvPTM5k7NTXSkcH56kJhxifbO/BKtsNre6Mfg+BUausiDl4rc3Rr4cDI/ued3eclWpnc/2jkbS6vzuZkNqS1vob7l0I8m08sDzs9FSPVUSGEE4vYfGZxCB1vrc2o/Tfe9cyRne2WxoqSMSkvMERqHv5ZZ5GmKhkGjDHyYwinDUZ5Vaj5JNUgH4MDoaa1CFkcnKtIzjI5N0tZZX1TMccqqV+m2mlKssSLl4vTFOnCncZjVfDobH/F2tnwXH81ccGZygZ2iCptpIzuihQlyyqZnu1nrucc1SZydnfGWL+yEaDlETCTFexSZKQ+Nx1tbXEMkTCXbBukaOj0zyxPGziMBF6wv7oXLhlTmfIzCGJnPmYHhsaWuYo2GcGJly+rqsMQ2jXExgFKGUHAzIFhiLa5ICJ5mqEo4NT/rqspePXZ1NvjWMqgqMjf4c3/lyMDzmC4zZsub5NS5vY9EzNO4WHWwsubikZ5b62eEhzkzMcMQ1bVVDwwAnF6OaGsbw+PlJe9ns7HS0rnuf7ffVAyMf2bkY8WSK/tGpnCG1Hlva6jPhyOD8/Tqban0Frxi5MYFRhN7hCULiv/ZMJkrKlw8jOJMUVJbtrapuWfPyd7U7OxvpH53mXIGCeh7zu+1VQqY3RhHHd74cDI91TTFqssqcHxueZNPaurw7aXCc9h1NMXoGJ+gZHC87b+LmSzeQSiv3P/9K1UJqPRpi4epGSeUoC5LNTjdS6sTIVFn+C4/sIASnzlfh6tFb2hpIK5x0TYonzkxa0cEKMYFRhKNDE3StrafG58I+m4dR+AuZSiuJlAakYTi7vUoipUYmZhiPJysqA70rE1JZ3Czl9PMub+c5n0xvjKIaRu4cDI9QSNi8ti4TaXPCZxLj9vYGnu8fo390umyB8aqNzWxtq+feZ0/RMzhBNCxVc9Y21FS3J8ZQjsKD2Wxtr88EM5Tjv/DobnX7XEwlMlVqC/owMlVrHQ2tr0BItOEPExhF6B2e8G2OglmT1HQRDcNTkwPJw2iq3CR1zIeDtxhepJQfs1Q1TVLg9sYooGEUy8HwyN7VHve54GzvaMxUzC3V4e3hmaX+9cgw+3tH2NrWUFCzKYVqm6SGxgtrGLFIOGParFTDAEdw+2loll3mPJ5McWps2pL2KsQERgFUld6hyZJ2ibOJe4U1DO/9IExSTbEINeFQRSYpz1ZcSSntrrV11IRDRVuPxpMpphPp6gqMjc2cGp1mJE9vjGI5GB5b2pww2bHpBGcmE74E6I4s53QljuqbL91IKq3sP3amav4LqG4TpelEinPTyaKOfS/juxINY3OWwDg2PElzbYQ1eQIWADoaY9TXhDk2PEn/2WlULUKqUkxgFGBo3DHLFIrEmI9fp/dsP+/qm6REhPbGGgYrEBheUlQlX7BQSOhsiXE6Kzs3F8XKgpRDsd4YxXIwPDa31nMunuTZvlHAn8blCQkR/9F1ubh4Q1Nms1KNCCmParZp9QRyrsKD2bz54nW8dkeb7x4YucguCNk7POF2OcwfUOB1Tjw2PFGwLL3hHxMYBThaYoQU+K8l5TnFg9AwwDFLVdJE6djwJOubK48o2dBcN6ecQy7GMr0wqicwvN4Yz/eP5ny/WA6Gh7fAPHp4aM7rQnjd9fyWk8mHZ5aC6kVIQXWd3oXKgmTzntd0863/cG1F12qujbK2PpoRGH4CMra01XNsZDKryrA5vSvBBEYBekvMwYDsKKnCX8hpzyQVgA8DnL4YlZikjo9MVGU3tr6lllfGimkYzm63mhpGW2OMi9Y38Tc/P5aplJpNsRwMD+938DNXYPjRuLrW1hENS1W0gndd1cXuDc1cs7214s/yaKiihlGoLEgQdLfWc2RwnJNnpnxp/lvbGjjuCoyacIjOCjQcwwRGQY4OTxAJSUmJPr5NUongTFLglQepRGBUpwz0+pZaXhmdLljuvVjhwXL5v//NJfSdmeJLDxw6771iORge3o702ZOjvhtJRcIh3nVVF7e42kElbGlr4L6PvL6oJlQKjbEIEzNJXyX4i+H9j7U3FNYwqsXm1nqeOH6WtPrT/Lvb6plJpnm8d4SuteUloRqzmMAoQO+Qs8suJTrFvw8jOKc3zJqkylkUphMpTo/FK0ra81jfXEs8mebsZP5cjNEATFIAr9nayr+9ppuv/+woT584O+e9YjkYHvU1EdobY6iWZv/+f955Ge95TXepU14QGmIRVGFypnKz1LDrw2hvWjgNw4sw9GOS8qKonj5xli7zX1SMCYw8qCpPHj/LxSWGAYqIrzats07v4ExSybTmbfdZiEzNpCpoGF5WdCE/RhBOb4/bb7yI9sYYt//jsySyamsVy8HIptvVMlaKw7Sa9aSGzsWpi4bLzt4uley/gR+TlHd+Wq3oYDUwgZGHlwfGeWVsmtfvbC95bCwSKprpndEwAipTMNuqtXSz1GxZ88oXyE5XYJwu4McIUmA010b5/Dsu4YVTY3ztX44C/nMwPLzfw0oJyaxmE6XhiZkF0y5g9m/RFIv4KvW+cY3jT8oea5SPCYw8PHLI6Q/+hl2lt32NRYv39Q48Sqqx/N7exzM5GJU7bf1qGPU1YV9tTMvhhkvW8yuv6uTLPzpE79CE7xwMD2+hWSkLTmPMEczViJQaGo/TtkD+C5gV2luLhNR6hEOSSdZbKQJ/MQlcYIhIWESeFJF73NefFZGTIvKU+7gpz7gbROQlETksIrcHPc/5/PTQIBesa2RjGZUtl4RJyo1aKVXDSKeVA8dGaIpFikYQ+aGjMUZI4JXRqbznjFU5yzsXn3/HJdSEQ/xf33s2E5PvV8PYvMIERkMVNYyh8ZmSq/FWwoaWWiIhKSmh1DvXsrwrZyEMjx8BXgCynQF/qqr/M98AEQkDXwHeCvQBj4vI3ap6MNCZukwnUjx2dIRfv2ZLWeMdgeHPJBVU5czZirX+BcboVIKP3fUUP35xgA/u3VpyldVcRMIhOppiBUNrq10WJBedzbXcftNFfPp7zzHhOnv9ahhvvHAdt+7p4sotawKc4cLRWEUfxvB4nMu7Wir+HL9EwiE+8uadXLllre8xjqY8uGIE/mISqMAQkS7gZuAPgY+VMPRq4LCq9rif8x3gHcCCCIx9R0eIJ9O8YVfp/gtwQmWL+zCC1TDW1tcQktkolmK8cGqM3/7mAU6emeJzb38V//6XyhOWuVjfUjh5b3QqQXOe1qzV5H2v6eb7T/bzWO+IrxwMj46mGH/8rssDnt3CkXF6V9gTI51WhicKFx4Mgt99886Szn/XVV0010ZyduYzSiNok9SXgU8A81fPD4vIMyLydRHJtVXYBJzIet3nHjsPEblNRPaLyP7BwcFqzJlHDg1SEwlxzba2ssbHoj5MUp4PIyANIxwSWhv85WL805Mn+dX/72dMzaS467eu5QOvrY524bGh2cnFyMdoFbvtFSIUEv77Oy+lJhzylYOxUvE0jEpNUqNTCVJpXVAfRjlcsqmFj11/4WJPY0UQmMAQkVuAAVU9MO+trwI7gCuAU8Cf5Bqe41jOhAJVvUNV96jqno6O0h3UuXjk0CDXbGulrqa8xbwUk1RQGgY4Zc4HC1SsnUmm+ezdz/PRu57isk1ruOc/v46rtlQvo9jDS97Lx0L4MDwuWNfIn9x6Ob/7ptJ2qSuJaoXVeuXzverIxsonSJPUXuDtrlO7FmgWkW+q6vu9E0TkfwH35BjbB2zOet0F9Ac41wz9Z6d4eWCcW/dsLn5yHmKRMGeL5D/Ek2lCApEAM0+LZXv/7ref4P7nT/Obr9vG7TdeFFiU0vqWWs7Fk4zHk5ndbTYL4cPI5m2Xb1yway1F6qOe07uyKClvM9LuI7zVWBkEtr1V1U+papeqbgXeC/xEVd8vItn1En4VeC7H8MeBnSKyTURq3PF3BzXXbP7l5fLDaT2cPIziUVKxSDhQs0h7Y03eJkr9Z6e4//nT/Mc37uC/3rI7MGEBs6G1ubSMZCrNxExqQQXGaicUEhpqwqZhGCWzGHkYfywiz4rIM8B1wO8BiMhGEbkPQFWTwIeB+3EirL6rqs8vxOQeOTTE+uZadnWWXx00Fg1nyhfkI55IBVZ40KOtMZa3idJ9z54C4N1XdQU6B3AilCC3wBib9goPLkymsOFQjZ4YXgReoX7exspiQb6lqvow8LD7/N/lOacfuCnr9X3AfQswvQyptPLo4SGu391Z0c7fnw8jHaj/AhyT1FQixUQ8mbFbe9z37Cl2b2guuyNcKWQ0jByhtUHVkTIKU42eGMMTM4QE1tSbwFgtWKZ3Fk/3nWV0KlGROQr8J+4FVanWI9Pbe16298mzUzxx/Gym10LQzGoY5yfvBVkWxMhPNUqcD43P0NoQy/TrNlY+JjCyeOTQICLwugvKy7/wiEXCRXt6x5OpBdEwgPM67/3ANUfdfOnCCIzaqJPzkCsXwwTG4uA0UapUYMQXNMvbWHxMYGTxyKFBLutaw9oKbbJ+8zCC9mHM1pOaKzDueeYUl2xqLqmTYKWsb6nLWYDQBMbi4JikKouSGh6PL3jSnrG4mMBwGZ1M8NSJs/xyGdVp5xOLhEiklFQ6fy+KBTFJNZ1vkjoxMslTJ85y86ULG1q6oaU2p4YRVPMkozDVcHoPT8wUbc1qrCxMYLj87MgQaa0snNbDEwSFIqUWwiTllX/O1jB+8NzCmqM8OvNke5vTe3GoVpTUUs/yNqqLCQyXRw4N0lQb4YrNayr+rNmue/lV/oWIkopFwjTXRuYIjHufOcVlXS1VaY5UChtaahmemDnvdzI2laAmEgqsCKORm6YKnd5TMykmZlIL2gvDWHxMYOB013vk0CB7d7SX1I41H55volBobTwRvEkKnKQqT2CcGJnk6b7RBdcuwMn2BhgYm+tPWegsb8OhIRYhnkyTTBUOzsjHQvfyNpYGJjCAI4Pj9I9O8/oyq9POxxMEhSrWxpPBJ+6B84X2mijd60ZH3bQYAqM5dyMlExiLw2w9qfIc314VZHN6ry5MYAA/PTQEwBt2Vqd4YW10aZikwHF8e7vB+549xeWb1yxK57HZzntzczFMYCwOmTatZZY4H/Y0DHN6rypMYOD4L7a3N1RtIc1oGAVMUtOJ1MKYpBpjDJ2Lc3x4kmf6RrllEbQLmDVJzQ+tHZs2gbEYVFqx1tuEmIaxulj1AmM6kWLf0eGqREd5LBWnNzgCY2w6yT89dRKAGy9dH/g1c9FUG6WhJmwmqSVCQ4U9MTwzp2kYq4tVX/EtFgnxj/9xL/Vl9r7I95lQzIcRfOIezO4A/27fMV7dvcZ3W9IgyNUXY3QyQXPtqv83XHAqbdM6PD5DYyxi0W2rjFX/TRURdm9sLn5iCXhd9PKZpJKpNKm0LphJCuD0WJz/8PrtgV+vEBta6uYUIEynlXPxpGkYi0BDTeUmKTNHrT5WvUkqCIqZpILu551NtslgMaKjspmfvHduOomqJe0tBrNtWsuNkoqbOWoVYgIjAGYFRm4NY2EFhrMLvLJ7DRvX1AV+vUJsaKll4Fw8UzLF6kgtHg1elNR04c6Q+Rgen7E+GKuQvCYpEbmy0EBVfcLPBUQkDOwHTqrqLSLyP4C3ATPAEeCDqno2x7he4ByQApKqusfP9ZYCGZNUHh+Gp3kshP23s7mWNfVR3vOa8lvOVov1LbWk0srQeJzO5lrGpk1gLBaZKKmZ8jSMofE4r+5eW80pGcuAQj6MPynwngJv8nmNj+B0zfMcBQ8Cn1LVpIj8EfAp4JN5xl6nqkM+r7NkKGqScgXJQji9a6NhHv/0WwLtHe6X7OS9zuZa0zAWkVgkRCQkZUVJjU0nGJ6YyeTWGKuHvAJDVa+r9MNFpAu4GfhD4GPu5z6QdcovgHdVep2lhn+T1MJEmATZr7sU1rdkNVLavMYKDy4iIlJ2AcIDvWdQhT1bTMNYbfiKkhKRS4DdQGZLoap/42Pol4FPAE153v8QcFee9xR4QEQU+CtVvSPP3G4DbgPo7u72MaXgKZa452keC+HDWEpkWrW6jm/TMBaXctu0/uLoMNGwmElqFVJ0xRKRzwB/7j6uA/4YeLuPcbcAA6p6IM/7nwaSwN/l+Yi9qnolcCPwOyLyhlwnqeodqrpHVfd0dFQv+a4SomFBBOKJYlFSqyuGvbWhhppwiFNjJjCWAuV23dvXM8LlXWuoq2LukrE88LPFfRfwZuAVVf0gcDngJ55uL/B213n9HeBNIvJNABH5AHAL8OuqmrPLkKr2uz8HgO8BV/u45pJARNy+3nk0jAX0YSwlRITOltgcDSMSkqomTRr+cUxSpTm9J+JJnj05yjXbWwOalbGU8bNiTalqGkiKSDMwABTNAFPVT6lql6puBd4L/ERV3y8iN+A4ud+uqpO5xopIg4g0ec+B64HnfN3REiEWCZtJKgcbmusy5UHG3LIgIovvkF+NlGOSeuL4GVJp5ZptbQHNyljK+Fmx9ovIGuB/AQeAJ4DHKrjmX+D4NB4UkadE5C8BRGSjiNznntMJPCoiT7vXuldVf1jBNRecWCTEtJmkzqOzpTZTgNDqSC0ujWU4vff1jBAOCVeaw3tVUtTprar/yX36lyLyQ6BZVZ8p5SKq+jDwsPv8gjzn9AM3uc97cExfy5ZYtIBJajVrGC213P/8NKrK6FTCIqQWkXKipPYdHeaSTS2ZTHFjdZF3xRKRi9yfV3oPoBWIFEvqMzyT1OLnYSw11jfXMpNMc2YywZgJjEWlVJPUdCLF0ydGuXab+S9WK4W2CR/DCVfNlcBXSuLeqiQWCRXI9F69Jqn1WaG1o1MJutsaFnlGq5eGWJiJmRSq6suP9MTxM8yk0ubwXsUUSty7TURCwB+o6s8WcE4rgoJRUqvYJJURGGNTrg/DTBuLRUMsQiqtxJNpX2Vq9vWMIAJ7tprAWK0UXLHc6Kj/uUBzWVH4MkmtQoEx26p1mrFpK22+mDSW2ETpsaMj7N7QTHOt/c1WK35WrAdE5NfEYh9LorDTO004JESWSMmOhaSjMUZI4MjABKm0msBYRErpiRFPpnji+BkLp13l+LEHfAxowMnDmAYEUFWtbtehFUZtJFywWu1q1C4AIuEQHU0xXjo9BliW92LiVaw9N11cYDzTN0o8af6L1Y6fsNp8daCMAjgaRv48jNUqMADWt9Tx0ivnAMy8sYiU0qZ1X88wAFeb/2JV46eW1I/9HDPmUqw0yGqMkPJY3xxjaHwGMA1jMfGaKE3M+BAYR0e4aH0Ta61p0qqmUAOlWqAeaBeRtTimKHD6WmxcgLktawqVBplOplZlDobHhpbZzn+Wh7F4+G3TmkilOXDsDO+6qmshpmUsYQqZpH4L+CiOcDjArMAYA74S7LSWP04eRv4oqdVtkpptvGMaxuLR4NMk9dzJUSZnUubwNgrmYfwZ8Gci8ruq+ucLOKcVQbHSIKvbJJUlMOpNYCwWfgXGvqMjAFxtGd6rnqLbXBMW5RGLhEmmlWTqfKFhTm9HYIQEGmsscW+xaHDLyhfLw9jXM8yOjgY6mvx0NTBWMqt31QoYTyDM5BMYq9qH4QiMptoooSXQa3y1EgmHqIsWbqKUSiv7e89wzXYzRxkmMAIj09c7Ry7GajdJdbomKfNfLD4NsUhBp/fB/jHOxZNcY+YoA/89vTcBW7LPV9VHgprUSiAWzd/Xe7U7vWujYdbWR01gLAEai7Rp3XfUyb8wh7cBPgSGiPwR8B7gIOBtRRTwJTBEJAzsB06q6i0i0grcBWwFeoFbVfVMjnE3AH8GhIGvqeoX/VxvqZDRMHIk7/kt9raS2bS2jlaL6V90ivXE2Hd0hC1t9XMi24zVix8N498AF6pqvMxrfAR4ASd/A+B24Meq+kURud19/cnsAa6Q+QrwVqAPeFxE7lbVg2XOYcHxTE45NYxVXBrE449/7XJqIua/WGwaCvTESKeVx3tHuH535wLPyliq+Fm1eoCybAci0gXcDHwt6/A7gDvd53fiCKT5XA0cVtUeVZ0BvuOOWzYU9mGsbpMUwO6NzVywzqrOLDaNsUjeTO+XTp/j7GTCzFFGBj8axiTwlFsOJKNlqOp/9jH2y8AncHp4e3Sq6in3M06JyLoc4zYBJ7Je9wHX+LjeksGLgprOZZJKpDM+DsNYTBpiESaGcju9vfpRVnDQ8PAjMO52HyUhIrcAA6p6QETeWOrwHMc0z3Vuw+kMSHd3d4mXCY6MSWqehqGqZpIylgyNsXBek9RjvSNsWlNH19r6BZ6VsVTxU632zmLn5GEv8HYRuQmoBZpF5JvAaRHZ4GoXG4CBHGP7gM1Zr7uA/jzzuwO4A2DPnj05hcpikM/pnUwraV2dzZOMpUdDTYTxHOXNVZXHjo7whp0dizArY6mSd9USke+6P58VkWfmP4p9sKp+SlW7VHUr8F7gJ6r6fhxt5QPuaR8Avp9j+OPAThHZJiI17viStZzFxDNJzXd6r+Z+3sbSoyEWYSqRIpWeu9c6MjjO0PiMmaOMORTSMD7i/rylytf8IvBdEflN4DjwbgAR2YgTPnuTqiZF5MPA/ThhtV9X1eerPI9AmY2SmqtheAUJV3Omt7F0yPTEmEnO6U3yix6nfpQ5vI1sChUf9BzTxyq9iKo+DDzsPh8G3pzjnH7gpqzX9wH3VXrtxSJflNSshmECw1h8sgsQZguMfUdH6GyOsaXN/BfGLLZqBcSsD8NMUsbSJdNEKcvx7fgvhrl6WxsilitjzGICIyBmS4PMM0m5r03DMJYCuZooHRue5PRY3OpHGedR0qolImtF5LKgJrOSqM1nknJfmw/DWArk6onh1Y+61hzexjz89PR+WESa3RpQTwPfEJEvBT+15U0kHCIcEjNJGUuaWQ0jS2D0jNDeWMOOjsbFmpaxRPGzzW1R1THgncA3VPUq4C3BTmtlEIuEzCRlLGkac2oYI1y9rdX8F8Z5+Fm1Im6C3a3APQHPZ0XhCIw8JinTMIwlwHyTVN+ZSU6eneLqrWaOMs7Hj8D4PE4+xBFVfVxEtgMvBzutlUEsEs4fVms+DGMJMN/pvc/Lv7AOe0YO/JQG+Xvg77Ne9wC/FuSkVgqx6PkmqemEmaSMpUNtNERIZjWMfUeHWVMf5cJOqyRsnI8fp/d2EflnERkUkQER+b6IbFuIyS13cpqkzOltLCFEZE5PjH1HR3jN1lbrtW7kxM8291vAd4ENwEYcbeM7QU5qpRCLhHMIDNMwjKVFo9t175XRaY4NT1r+hZEXP6uWqOrfqmrSfXyTPKXGjbnkjpIyH4axtPA0DOvfbRTDTz+Mh9xWqt/BERTvAe518zJQ1ZEA57esiUVDeRP3asImMIylwazAGKEpFmH3xubig4xViR+B8R7352/NO/4hHAGyvaozWkHEImHGpub2GognU0RCQsQEhrFEaIyFmYgn2dczzJ6tawmb/8LIg58oKXNwl0k+k5T5L4ylRENNhJdeGWdoPM6792wuPsBYtfiJkqoXkT8QkTvc1zvd9qtGEXJHSaWotX7exhKiMRZhaDwOYA5voyB+trrfAGaA17qv+4AvBDajFUQsEs7kXXjEE6ZhGEsLL9u7vibMJZtaFnk2xlLGjw9jh6q+R0TeB6CqU+KjyIyI1AKPADH3Ov+gqp8RkbuAC93T1gBnVfWKHON7gXNACkiq6h4fc11SOIl75+dhxEzDMJYQnsC4astaouZbMwrgR2DMiEgdbiitiOwA4j7GxYE3qeq4iESBR0XkB6rqOdERkT8BRgt8xnWqOuTjWkuSWCRHlFQyZRqGsaRodJsomTnKKIYfgfFZ4IfAZhH5O2Av8MFig1RVgXH3ZdR9ZPI3XC3lVuBNpU15+eAk7qVQ1UzlT3N6G0sNr56U1Y8yiuEnSuoBETkAXAsI8BG/u34RCQMHgAuAr6jqvqy3Xw+cVtV8hQwVeEBEFPgrVb0jzzVuA24D6O7u9jOtBSMWCZFWSKaVaNgVGIm0lQUxlhTXXbSOYyOTXLF5zWJPxVji+ImS+rGqDqvqvap6j6oOiciP/Xy4qqZc/0QXcLWIXJL19vuAbxcYvldVrwRuBH5HRN6Q5xp3qOoeVd3T0dHhZ1oLhpfNne3HiCdTluVtLCm2tDXwmbe9yvwXRlHy/oeISK2bzd3utmZtdR9bcWpK+UZVzwIPAze4nx3Bach0V4Ex/e7PAeB7wNWlXHMp4GkS8axIKTNJGYaxXCm0cv0WjjnpIven9/g+8JViHywiHSKyxn1eh9Ol70X37bcAL6pqX56xDSLS5D0Hrgee83E/SwpPMMzVMMwkZRjG8iSvD0NV/wz4MxH5XVX98zI+ewNwp+vHCAHfVVWvY997mWeOEpGNwNdU9SagE/ie6yiOAN9S1R+WMYdFxUvQO88kZRqGYRjLED9RUq+ISJOqnhORPwCuBL6gqk8UGqSqzwCvzvPeb+Q41g/c5D7vAS73MbclzayGkWWSSqTNh2EYxrLEz8r1X11h8TrgV4A7ga8GO62VQcbpnTCTlGEYyx8/AsPbHt8MfFVVvw/UBDellUPG6W0mKcMwVgB+Vq6TIvJXOEl294lIzOe4Vc98k5SqWpSUYRjLFj8r163A/cANbnhsK/DxICe1UpgNq3U0jERKUcVqSRmGsSzxk+k9Cfxj1utTwKkgJ7VSmJ+4Z/28DcNYztjKFSDzTVKZft4mMAzDWIbYyhUg853eXm8Mi5IyDGM5YgIjQDIaRmKehmF5GIZhLENs5QqQ83wYCTNJGYaxfLGVK0Bqwvmc3maSMgxj+WECI0Ai4RCRkGR8F+b0NgxjOWMrV8DEIqEsDcN8GIZhLF9s5QqYWDQ8G1ZrUVKGYSxjTGAETCwSyji7PQ2j1jQMwzCWIbZyBUxOk5RpGIZhLEMCExhui9fHRORpEXleRD7nHv+siJwUkafcx015xt8gIi+JyGERuT2oeQZNLJJlkrLSIIZhLGP8NFAqlzjwJlUdF5Eo8KiI/MB9709V9X/mG+h26fsK8FagD3hcRO5W1YMBzjcQYtFQjjwM0zAMw1h+BLbVVYdx92XUfajP4VcDh1W1R1VngO8A7whgmoGTy4dhUVKGYSxHAl25RCQsIk8BA8CDqrrPfevDIvKMiHxdRNbmGLoJOJH1us89lusat4nIfhHZPzg4WM3pV4VcJikvoc8wDGM5EejKpaopVb0C6AKuFpFLcNq77gCuwCmT/ic5hkquj8tzjTtUdY+q7uno6KjKvKtJbXSu07smHCIUynV7hmEYS5sF2eq6jZcexmnCdNoVJGngf+GYn+bTB2zOet0F9Ac9zyBwNIxZH4Y5vA3DWK4EGSXVISJr3Od1wFuAF0VkQ9Zpvwo8l2P448BOEdkmIjXAe4G7g5prkDhhtbMmKfNfGIaxXAkySmoDcKcb8RQCvquq94jI34rIFTgmpl7gtwBEZCPwNVW9SVWTIvJhnNawYeDrqvp8gHMNjFh0rtPbIqQMw1iuBCYwVPUZ4NU5jv+7POf3Azdlvb4PuC+o+S0Uc0xSSTNJGYaxfLHVK2DmmKQSKWpMYBiGsUyx1StgvNIgqupoGFEzSRmGsTwxgREwsWgYVUik1HF6m4ZhGMYyxVavgMn09U6mzIdhGMayxlavgJkVGGk3D8NMUoZhLE9MYASMJyDiyTTTlodhGMYyxlavgPEERDyRskxvwzCWNbZ6BYwnIKYTaUvcMwxjWWMCI2BmTVIpi5IyDGNZY6tXwMxxeifT5sMwDGPZYqtXwHgCYjqRYsZMUoZhLGNMYASMJyDOTSfd1/YrNwxjeWKrV8B4AmJsOgFArZUGMQxjmWICI2A8DWNsyjQMwzCWN7Z6BYznw/A0DBMYhmEsVwLrhyEitcAjQMy9zj+o6mdE5H8AbwNmgCPAB90WrvPH9wLngBSQVNU9Qc01SDImqSlXYJhJyjCMZUqQ29048CZVvRy4ArhBRK4FHgQuUdXLgEPApwp8xnWqesVyFRYw67MYM6e3YRjLnMBWL3UYd19G3Yeq6gOqmnSP/wLoCmoOS4Ga8DwNwwSGYRjLlEBXLxEJi8hTwADwoKrum3fKh4Af5BmuwAMickBEbgtwmoESCgk14VCWD8NMUoZhLE8CFRiqmlLVK3C0iKtF5BLvPRH5NJAE/i7P8L2qeiVwI/A7IvKGXCeJyG0isl9E9g8ODlb3BqpELBLK8mGYhmEYxvJkQVYv16n9MHADgIh8ALgF+HVV1Txj+t2fA8D3gKvznHeHqu5R1T0dHR3Vn3wViEVD5sMwDGPZE9jqJSIdIrLGfV4HvAV4UURuAD4JvF1VJ/OMbRCRJu85cD3wXFBzDZpYJJzlwzCTlGEYy5PAwmqBDcCdIhLGEUzfVdV7ROQwTqjtgyIC8AtV/W0R2Qh8TVVvAjqB77nvR4BvqeoPA5xroMQiIeLJdOa5YRjGciQwgaGqzwCvznH8gjzn9wM3uc97gMuDmttCU5MlJMyHYRjGcsVWrwUgO1nPTFKGYSxXTGAsANlmKDNJGYaxXLHVawEwgWEYxkrAVq8FwDND1URCuI58wzCMZYcJjAXAc3SbdmEYxnLGVrAFwBMU5vA2DGM5YwJjAfAEhWkYhmEsZ2wFWwAyGoblYBiGsYyxFWwBmPVhmEnKMIzlS5ClQQwXM0kZxtIikUjQ19fH9PT0Yk9lwaitraWrq4toNFr2Z5jAWABmnd4mMAxjKdDX10dTUxNbt25dFaHuqsrw8DB9fX1s27at7M+xFWwB8ARFrfXzNowlwfT0NG1tbatCWACICG1tbRVrVCYwFgCvlpRpGIaxdFgtwsKjGvdrK9gCUJuJkjINwzCM5YsJjAXANAzDMIrx5S9/mcnJnD3llgy2gi0A5vQ2DKMYy0FgBBYlJSK1wCM43fUiwD+o6mdEpBW4C9gK9AK3quqZHONvAP4MCON04vtiUHMNGisNYhhLl8/98/Mc7B+r6mfu3tjMZ972qrzvT0xMcOutt9LX10cqleLd7343/f39XHfddbS3t/PQQw/xwAMP8JnPfIZ4PM6OHTv4xje+QWNjI1u3buU973kPDz30EADf+ta3uOCCC/j7v/97Pve5zxEOh2lpaeGRRx6p6j1BsBpGHHiTql4OXAHcICLXArcDP1bVncCP3ddzcNu6fgW4EdgNvE9Edgc410DJ5GFYprdhGMAPf/hDNm7cyNNPP81zzz3HRz/6UTZu3MhDDz3EQw89xNDQEF/4whf40Y9+xBNPPMGePXv40pe+lBnf3NzMY489xoc//GE++tGPAvD5z3+e+++/n6effpq77747kHkH2aJVgXH3ZdR9KPAO4I3u8TuBh4FPzht+NXDYbdWKiHzHHXcwqPkGiVWrNYylSyFNICguvfRSfv/3f59PfvKT3HLLLbz+9a+f8/4vfvELDh48yN69ewGYmZnhl37plzLvv+9978v8/L3f+z0A9u7dy2/8xm9w66238s53vjOQeQeauOdqCgeAC4CvqOo+EelU1VMAqnpKRNblGLoJOJH1ug+4Js81bgNuA+ju7q7m9KuGmaQMw8hm165dHDhwgPvuu49PfepTXH/99XPeV1Xe+ta38u1vfzvn+OwQWe/5X/7lX7Jv3z7uvfderrjiCp566ina2tqqOu9At7yqmlLVK4Au4GoRucTn0FwBw5rnGneo6h5V3dPR0VHmTIPFSoMYhpFNf38/9fX1vP/97+f3f//3eeKJJ2hqauLcuXMAXHvttfzsZz/j8OHDAExOTnLo0KHM+Lvuuivz09M8jhw5wjXXXMPnP/952tvbOXHiBNVmQUqDqOpZEXkYuAE4LSIbXO1iAzCQY0gfsDnrdRfQH/xMg8ETFDUmMAzDAJ599lk+/vGPEwqFiEajfPWrX+XnP/85N954Ixs2bOChhx7ir//6r3nf+95HPB4H4Atf+AK7du0CIB6Pc80115BOpzNayMc//nFefvllVJU3v/nNXH755VWftziuhuojIh1AwhUWdcADwB8BvwwMq+oXReR2oFVVPzFvbAQ4BLwZOAk8DvxbVX2+0DX37Nmj+/fvD+BuKiOdVv70R4f49Wu2sL6ldrGnYxirnhdeeIGLL754sadRFlu3bmX//v20t7eXPDbXfYvIAVXd42d8kBrGBuBO148RAr6rqveIyM+B74rIbwLHgXcDiMhGnPDZm1Q1KSIfBu7HCav9ejFhsZQJhYT/cv2Fiz0NwzCMiggySuoZ4NU5jg/jaA7zj/cDN2W9vg+4L6j5GYZhLEd6e3sX7dpmVDcMY1USlDl+qVKN+zWBYRjGqqO2tpbh4eFVIzS8fhi1tZX5UK2BkmEYq46uri76+voYHBxc7KksGF7HvUowgWEYxqojGo1W1HlutWImKcMwDMMXJjAMwzAMX5jAMAzDMHwRWKb3YiAig8CxMoe3A0NVnM5ywe57dWH3vbrwc99bVNVXIb4VJTAqQUT2+02PX0nYfa8u7L5XF9W+bzNJGYZhGL4wgWEYhmH4wgTGLHcs9gQWCbvv1YXd9+qiqvdtPgzDMAzDF6ZhGIZhGL4wgWEYhmH4YtULDBG5QUReEpHDbgfAZY2IfF1EBkTkuaxjrSLyoIi87P5cm/Xep9x7f0lEfiXr+FUi8qz73v8r2V3nlyAisllEHhKRF0TkeRH5iHt8Rd+7iNSKyGMi8rR7359zj6/o+/YQkbCIPCki97ivV/x9i0ivO9+nRGS/e2xh7ltVV+0Dp5vfEWA7UAM8Dexe7HlVeE9vAK4Enss69sfA7e7z24E/cp/vdu85Bmxzfxdh973HgF8CBPgBcONi31uR+94AXOk+b8Jp8bt7pd+7O8dG93kU2Adcu9LvO+v+PwZ8C7jHfb3i7xvoBdrnHVuQ+17tGsbVwGFV7VHVGeA7wDsWeU4VoaqPACPzDr8DuNN9fifwb7KOf0dV46p6FDgMXC0iG4BmVf25Ov9Zf5M1ZkmiqqdU9Qn3+TngBWATK/ze1WHcfRl1H8oKv28AEekCbga+lnV4xd93Hhbkvle7wNgEnMh63eceW2l0quopcBZWYJ17PN/9b3Kfzz++LBCRrTjtgfexCu7dNcs8BQwAD6rqqrhv4MvAJ4B01rHVcN8KPCAiB0TkNvfYgtz3au+Hkctmt5rijPPd/7L9vYhII/B/gI+q6lgBs+yKuXdVTQFXiMga4HsickmB01fEfYvILcCAqh4QkTf6GZLj2LK7b5e9qtovIuuAB0XkxQLnVvW+V7uG0QdsznrdBfQv0lyC5LSrguL+HHCP57v/Pvf5/ONLGhGJ4giLv1PVf3QPr4p7B1DVs8DDwA2s/PveC7xdRHpxTMlvEpFvsvLvG1Xtd38OAN/DMa0vyH2vdoHxOLBTRLaJSA3wXuDuRZ5TENwNfMB9/gHg+1nH3ysiMRHZBuwEHnNV2nMicq0bOfHvs8YsSdx5/m/gBVX9UtZbK/reRaTD1SwQkTrgLcCLrPD7VtVPqWqXqm7F+d7+RFXfzwq/bxFpEJEm7zlwPfAcC3Xfi+3xX+wHcBNORM0R4NOLPZ8q3M+3gVNAAmcX8ZtAG/Bj4GX3Z2vW+Z927/0lsqIkgD3uP+IR4C9wqwIs1QfwOhyV+hngKfdx00q/d+Ay4En3vp8D/pt7fEXf97zfwRuZjZJa0feNE9H5tPt43luzFuq+rTSIYRiG4YvVbpIyDMMwfGICwzAMw/CFCQzDMAzDFyYwDMMwDF+YwDAMwzB8YQLDMCpARNaIyH8q8P6/+viM8WLnGMZSwASGYVTGGuA8gSEiYQBVfe1CT8gwgmK115IyjEr5IrDDLf6XAMZxEievAHaLyLiqNro1rr4PrMWpKPsHqrpkM4oNIxeWuGcYFeBWxr1HVS9xi+DdC1yiTilpsgRGBKhXpyBiO/ALYKeqqnfOIt2CYfjGNAzDqC6PecJiHgL8dxF5A0457k1AJ/DKQk7OMCrBBIZhVJeJPMd/HegArlLVhFtltXbBZmUYVcCc3oZRGedwWsIWowWnf0NCRK4DtgQ7LcOoPqZhGEYFqOqwiPxMRJ4DpoDTeU79O+CfRWQ/TiXdQk1vDGNJYk5vwzAMwxdmkjIMwzB8YQLDMAzD8IUJDMMwDMMXJjAMwzAMX5jAMAzDMHxhAsMwDMPwhQkMwzAMwxf/P719wF+ahwzhAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_XNCS_Maze5.ipynb b/XCS_Experiments/XCS_XNCS_Maze5.ipynb new file mode 100644 index 0000000..a2907db --- /dev/null +++ b/XCS_Experiments/XCS_XNCS_Maze5.ipynb @@ -0,0 +1,1562 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('1', '1', '0', '1', '1', '0', '0', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import xcs_metrics\n", + "from utils.nxcs_utils import xcs_maze_metrics\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "XNCScfg = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_maze_metrics,\n", + " lmc=10,\n", + " lem=200)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.06253820000000587, 'population': 126, 'numerosity': 165, 'average_specificity': 7.096969696969697, 'fraction_accuracy': 1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 78, 'reward': 1000.000000002505, 'perf_time': 0.27959270000002334, 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70054.6807.1648620.210975408.01607.91.5993902.500000e-0239.8663.41600.04.3071251.250000e-02
80060.9519.3341000.234345413.61605.11.6181441.250000e-0234.6669.61600.04.2939381.196585e-15
90040.21062.4171790.155673417.91605.71.6182051.250000e-0245.0671.01600.04.3953131.250000e-02
100030.21071.1800090.113604410.81608.41.7193361.284551e-1446.7687.11600.04.6163133.750000e-02
110038.01006.8759210.144240413.71603.21.5711062.037540e-1436.9665.81600.04.2240001.250000e-02
120030.2957.5992730.117028416.41607.11.6272037.909460e-1454.7666.21600.04.0665623.750000e-02
130038.4941.1861580.147198422.91609.21.6355732.500000e-0240.5674.21600.04.2189381.250000e-02
140052.8604.6976680.186518410.51611.31.5405162.500000e-0240.4681.11600.04.2005632.397498e-15
150031.41051.2883330.117136419.81605.51.6766945.779108e-1551.1685.21600.04.3442501.250000e-02
160064.8607.7058270.242201424.81605.51.6600072.500000e-0227.3690.61600.04.4818125.683935e-15
170037.3972.0797490.149581413.61604.61.6552491.250000e-0251.9698.01600.04.6389371.250000e-02
180051.9717.9625020.186570412.51604.51.7076042.366642e-1447.9703.21600.04.4044371.192077e-14
190024.41102.6415790.091645408.91608.91.6459343.750000e-0260.8690.31600.04.2260631.250000e-02
200022.0900.0000000.049691421.11605.61.7044711.250000e-0257.8699.21600.04.3900622.500000e-02
210012.81119.1353160.025083425.31600.01.7136251.250000e-0210.1751.51600.05.2767501.625000e-01
22009.21176.7697880.018771425.31600.01.7136251.250000e-0261.3793.51600.05.9658754.375000e-01
23009.21222.9989030.020475425.31600.01.7136251.250000e-0228.2786.51600.05.6898134.500000e-01
24005.41283.9622740.010418425.31600.01.7121251.250000e-028.5786.21600.05.6036885.000000e-01
\n", + "
" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 91.8 100.000000 0.052199 108.1 152.7 \n", + "100 52.0 800.411296 0.204609 399.4 1605.4 \n", + "200 39.8 914.138554 0.145111 395.9 1605.5 \n", + "300 44.6 916.551896 0.166921 410.7 1613.2 \n", + "400 33.8 1077.355982 0.139215 404.6 1606.6 \n", + "500 69.0 614.028490 0.266113 420.3 1604.0 \n", + "600 54.4 736.694854 0.195789 422.9 1602.9 \n", + "700 54.6 807.164862 0.210975 408.0 1607.9 \n", + "800 60.9 519.334100 0.234345 413.6 1605.1 \n", + "900 40.2 1062.417179 0.155673 417.9 1605.7 \n", + "1000 30.2 1071.180009 0.113604 410.8 1608.4 \n", + "1100 38.0 1006.875921 0.144240 413.7 1603.2 \n", + "1200 30.2 957.599273 0.117028 416.4 1607.1 \n", + "1300 38.4 941.186158 0.147198 422.9 1609.2 \n", + "1400 52.8 604.697668 0.186518 410.5 1611.3 \n", + "1500 31.4 1051.288333 0.117136 419.8 1605.5 \n", + "1600 64.8 607.705827 0.242201 424.8 1605.5 \n", + "1700 37.3 972.079749 0.149581 413.6 1604.6 \n", + "1800 51.9 717.962502 0.186570 412.5 1604.5 \n", + "1900 24.4 1102.641579 0.091645 408.9 1608.9 \n", + "2000 22.0 900.000000 0.049691 421.1 1605.6 \n", + "2100 12.8 1119.135316 0.025083 425.3 1600.0 \n", + "2200 9.2 1176.769788 0.018771 425.3 1600.0 \n", + "2300 9.2 1222.998903 0.020475 425.3 1600.0 \n", + "2400 5.4 1283.962274 0.010418 425.3 1600.0 \n", + "\n", + " average_specificity fraction_accuracy steps_in_trial_other \\\n", + "trial \n", + "0 6.084159 1.000000e+00 89.0 \n", + "100 1.582136 3.003213e-13 42.5 \n", + "200 1.520576 3.750000e-02 27.9 \n", + "300 1.682143 3.031430e-14 48.0 \n", + "400 1.617811 3.700835e-14 28.1 \n", + "500 1.619119 2.041384e-14 55.5 \n", + "600 1.728276 1.250000e-02 35.6 \n", + "700 1.599390 2.500000e-02 39.8 \n", + "800 1.618144 1.250000e-02 34.6 \n", + "900 1.618205 1.250000e-02 45.0 \n", + "1000 1.719336 1.284551e-14 46.7 \n", + "1100 1.571106 2.037540e-14 36.9 \n", + "1200 1.627203 7.909460e-14 54.7 \n", + "1300 1.635573 2.500000e-02 40.5 \n", + "1400 1.540516 2.500000e-02 40.4 \n", + "1500 1.676694 5.779108e-15 51.1 \n", + "1600 1.660007 2.500000e-02 27.3 \n", + "1700 1.655249 1.250000e-02 51.9 \n", + "1800 1.707604 2.366642e-14 47.9 \n", + "1900 1.645934 3.750000e-02 60.8 \n", + "2000 1.704471 1.250000e-02 57.8 \n", + "2100 1.713625 1.250000e-02 10.1 \n", + "2200 1.713625 1.250000e-02 61.3 \n", + "2300 1.713625 1.250000e-02 28.2 \n", + "2400 1.712125 1.250000e-02 8.5 \n", + "\n", + " population_other numerosity_other average_specificity_other \\\n", + "trial \n", + "0 97.5 142.1 5.778131 \n", + "100 479.1 1600.0 2.437562 \n", + "200 547.6 1600.0 2.819063 \n", + "300 595.0 1600.0 3.256438 \n", + "400 621.1 1600.0 3.648625 \n", + "500 641.8 1600.0 3.975375 \n", + "600 642.4 1600.0 4.006437 \n", + "700 663.4 1600.0 4.307125 \n", + "800 669.6 1600.0 4.293938 \n", + "900 671.0 1600.0 4.395313 \n", + "1000 687.1 1600.0 4.616313 \n", + "1100 665.8 1600.0 4.224000 \n", + "1200 666.2 1600.0 4.066562 \n", + "1300 674.2 1600.0 4.218938 \n", + "1400 681.1 1600.0 4.200563 \n", + "1500 685.2 1600.0 4.344250 \n", + "1600 690.6 1600.0 4.481812 \n", + "1700 698.0 1600.0 4.638937 \n", + "1800 703.2 1600.0 4.404437 \n", + "1900 690.3 1600.0 4.226063 \n", + "2000 699.2 1600.0 4.390062 \n", + "2100 751.5 1600.0 5.276750 \n", + "2200 793.5 1600.0 5.965875 \n", + "2300 786.5 1600.0 5.689813 \n", + "2400 786.2 1600.0 5.603688 \n", + "\n", + " fraction_accuracy_other \n", + "trial \n", + "0 8.875000e-01 \n", + "100 1.142850e-15 \n", + "200 3.380321e-15 \n", + "300 2.500000e-02 \n", + "400 2.511887e-15 \n", + "500 1.250000e-02 \n", + "600 1.250000e-02 \n", + "700 1.250000e-02 \n", + "800 1.196585e-15 \n", + "900 1.250000e-02 \n", + "1000 3.750000e-02 \n", + "1100 1.250000e-02 \n", + "1200 3.750000e-02 \n", + "1300 1.250000e-02 \n", + "1400 2.397498e-15 \n", + "1500 1.250000e-02 \n", + "1600 5.683935e-15 \n", + "1700 1.250000e-02 \n", + "1800 1.192077e-14 \n", + "1900 1.250000e-02 \n", + "2000 2.500000e-02 \n", + "2100 1.625000e-01 \n", + "2200 4.375000e-01 \n", + "2300 4.500000e-01 \n", + "2400 5.000000e-01 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['steps_in_trial_other']=df_other['steps_in_trial']\n", + "df['population_other']=df_other['population']\n", + "df['numerosity_other']=df_other['numerosity']\n", + "df['average_specificity_other']=df_other['average_specificity']\n", + "df['fraction_accuracy_other']=df_other['fraction_accuracy']\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df[['average_specificity', \"average_specificity_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\"])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['fraction_accuracy', \"fraction_accuracy_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'numerosity_other']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"numerosity\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population', \"population_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial', 'steps_in_trial_other']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb new file mode 100644 index 0000000..da7d030 --- /dev/null +++ b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb @@ -0,0 +1,1014 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '0', '0', '1', '0', '0', '1', '0')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m 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\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import xcs_metrics\n", + "from utils.nxcs_utils import xncs_metrics\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "XNCScfg = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 91, 'reward': 1000.0, 'perf_time': 1.182630300000028, 'population': 1511, 'numerosity': 1600, 'average_specificity': 8.2225}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 8, 'reward': 1071.0358873685009, 'perf_time': 0.06847820000007232, 'population': 1187, 'numerosity': 1600, 'average_specificity': 6.52625}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 8, 'reward': 1066.4893540049404, 'perf_time': 0.03988800000001902, 'population': 989, 'numerosity': 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"INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 8, 'reward': 1069.9780074220732, 'perf_time': 0.09137409999993906, 'numerosity': 1600, 'population': 1061, 'average_specificity': 9.759375, 'fraction_accuracy': 0.0}\n" + ] + } + ], + "source": [ + "from utils.xcs_utils import avg_experiment as XCSExp\n", + "from utils.nxcs_utils import avg_experiment as XNCSExp\n", + "\n", + "number_of_experiments = 3\n", + "explore = 2000\n", + "exploit = 500\n", + "\n", + "df = XCSExp(maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0, # explore,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "\n", + "df_other = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2006.3333331240.1124740.0698191130.0000001600.06.8937504.0000001158.3333331600.06.8100000.004630
30023.6666671021.2638120.1756281071.3333331600.06.9968759.0000001141.0000001600.07.0056250.000000
40031.3333331044.4538480.2569531010.6666671600.06.62687535.3333331111.3333331600.06.9677080.000000
50013.6666671028.1210580.113802988.3333331600.06.66416740.3333331083.6666671600.07.2462500.000000
6008.0000001078.7703260.059470941.3333331600.06.4683336.3333331071.6666671600.07.1552080.000000
7009.6666671083.3324030.101436922.6666671600.06.58437539.3333331079.0000001600.07.5556250.000000
80042.666667826.8591460.283500898.3333331600.06.45958337.3333331071.6666671600.07.7810420.041228
9004.3333331264.9099470.023765865.0000001600.06.7329176.0000001064.6666671600.08.3872920.000000
100037.6666671050.9016640.223763855.6666671600.06.46812539.3333331052.6666671600.08.9035420.000000
11006.3333331251.0497110.038740804.0000001600.06.38354237.0000001075.3333331600.09.2789580.000000
120037.666667744.0118920.188545798.0000001600.06.31270813.3333331063.3333331600.09.0233330.000000
130036.666667849.7812440.249282770.6666671600.06.2635427.6666671069.6666671600.08.2685420.000000
14007.0000001142.9851760.048491731.0000001600.05.9429178.6666671065.6666671600.08.4287500.000000
150038.000000736.1433750.176672722.6666671600.06.00375012.3333331063.6666671600.08.5758330.000000
160036.333333990.6202170.186637722.6666671600.06.4512505.6666671061.0000001600.08.3752080.000000
170031.6666671342.6739490.149967697.3333331600.05.82250069.0000001058.3333331600.07.7339580.040114
180013.6666671018.7521730.131911696.6666671600.05.9552086.6666671050.0000001600.07.8039580.000000
190037.000000786.8292860.151505673.0000001600.05.7350006.6666671055.3333331600.08.6358330.000000
20007.3333331230.5753540.059764657.3333331600.05.65375010.6666671058.0000001600.08.3429170.040850
210040.000000695.2343120.238204644.3333331600.05.9812503.6666671049.6666671600.09.4345830.000000
22006.0000001289.3124420.027333632.3333331600.05.7547927.0000001062.3333331600.08.6229170.000000
230038.000000742.1897860.159315640.6666671600.05.8275003.6666671054.0000001600.08.1535420.000000
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 97.000000 333.333333 1.319633 1495.333333 1600.0 \n", + "100 10.666667 1224.131331 0.089096 1195.000000 1600.0 \n", + "200 6.333333 1240.112474 0.069819 1130.000000 1600.0 \n", + "300 23.666667 1021.263812 0.175628 1071.333333 1600.0 \n", + "400 31.333333 1044.453848 0.256953 1010.666667 1600.0 \n", + "500 13.666667 1028.121058 0.113802 988.333333 1600.0 \n", + "600 8.000000 1078.770326 0.059470 941.333333 1600.0 \n", + "700 9.666667 1083.332403 0.101436 922.666667 1600.0 \n", + "800 42.666667 826.859146 0.283500 898.333333 1600.0 \n", + "900 4.333333 1264.909947 0.023765 865.000000 1600.0 \n", + "1000 37.666667 1050.901664 0.223763 855.666667 1600.0 \n", + "1100 6.333333 1251.049711 0.038740 804.000000 1600.0 \n", + "1200 37.666667 744.011892 0.188545 798.000000 1600.0 \n", + "1300 36.666667 849.781244 0.249282 770.666667 1600.0 \n", + "1400 7.000000 1142.985176 0.048491 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6.333333 1071.666667 \n", + "700 6.584375 39.333333 1079.000000 \n", + "800 6.459583 37.333333 1071.666667 \n", + "900 6.732917 6.000000 1064.666667 \n", + "1000 6.468125 39.333333 1052.666667 \n", + "1100 6.383542 37.000000 1075.333333 \n", + "1200 6.312708 13.333333 1063.333333 \n", + "1300 6.263542 7.666667 1069.666667 \n", + "1400 5.942917 8.666667 1065.666667 \n", + "1500 6.003750 12.333333 1063.666667 \n", + "1600 6.451250 5.666667 1061.000000 \n", + "1700 5.822500 69.000000 1058.333333 \n", + "1800 5.955208 6.666667 1050.000000 \n", + "1900 5.735000 6.666667 1055.333333 \n", + "2000 5.653750 10.666667 1058.000000 \n", + "2100 5.981250 3.666667 1049.666667 \n", + "2200 5.754792 7.000000 1062.333333 \n", + "2300 5.827500 3.666667 1054.000000 \n", + "2400 6.160417 38.000000 1053.666667 \n", + "\n", + " numerosity_other average_specificity_other fraction_accuracy_other \n", + "trial \n", + "0 1600.0 8.233333 0.000000 \n", + "100 1600.0 7.122917 0.000000 \n", + "200 1600.0 6.810000 0.004630 \n", + "300 1600.0 7.005625 0.000000 \n", + "400 1600.0 6.967708 0.000000 \n", + "500 1600.0 7.246250 0.000000 \n", + "600 1600.0 7.155208 0.000000 \n", + "700 1600.0 7.555625 0.000000 \n", + "800 1600.0 7.781042 0.041228 \n", + "900 1600.0 8.387292 0.000000 \n", + "1000 1600.0 8.903542 0.000000 \n", + "1100 1600.0 9.278958 0.000000 \n", + "1200 1600.0 9.023333 0.000000 \n", + "1300 1600.0 8.268542 0.000000 \n", + "1400 1600.0 8.428750 0.000000 \n", + "1500 1600.0 8.575833 0.000000 \n", + "1600 1600.0 8.375208 0.000000 \n", + "1700 1600.0 7.733958 0.040114 \n", + "1800 1600.0 7.803958 0.000000 \n", + "1900 1600.0 8.635833 0.000000 \n", + "2000 1600.0 8.342917 0.040850 \n", + "2100 1600.0 9.434583 0.000000 \n", + "2200 1600.0 8.622917 0.000000 \n", + "2300 1600.0 8.153542 0.000000 \n", + "2400 1600.0 7.553958 0.000000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['steps_in_trial_other']=df_other['steps_in_trial']\n", + "df['population_other']=df_other['population']\n", + "df['numerosity_other']=df_other['numerosity']\n", + "df['average_specificity_other']=df_other['average_specificity']\n", + "df['fraction_accuracy_other']=df_other['fraction_accuracy']\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df[['average_specificity', \"average_specificity_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\"])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[\"fraction_accuracy_other\"].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'numerosity_other']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"numerosity\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population', \"population_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial', 'steps_in_trial_other']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "208.33333333333337\n", + "184.7777777777778\n" + ] + } + ], + "source": [ + "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_other\"])/number_of_experiments)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XCS_XNCS_Woods1.ipynb b/XCS_Experiments/XCS_XNCS_Woods1.ipynb new file mode 100644 index 0000000..57ed02a --- /dev/null +++ b/XCS_Experiments/XCS_XNCS_Woods1.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like:\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like:\")\n", + "situation = maze.reset()\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import xcs_metrics\n", + "from utils.nxcs_utils import xncs_metrics\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover chi\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "XNCScfg = XNCSConfig(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=200)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 23, 'reward': 1000.0, 'perf_time': 0.007642699999905744, 'population': 49, 'numerosity': 49, 'average_specificity': 10.122448979591837}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1000.0000047592259, 'perf_time': 0.06348840000009659, 'population': 560, 'numerosity': 1800, 'average_specificity': 11.29}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 11, 'reward': 1023.3815253191882, 'perf_time': 0.09047039999995832, 'population': 570, 'numerosity': 1800, 'average_specificity': 12.271666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 6, 'reward': 1219.4517144196761, 'perf_time': 0.051353199999994104, 'population': 615, 'numerosity': 1800, 'average_specificity': 8.837777777777777}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 7, 'reward': 1119.2946802624729, 'perf_time': 0.07016810000004625, 'population': 683, 'numerosity': 1800, 'average_specificity': 10.881666666666666}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 6, 'reward': 1198.548849904951, 'perf_time': 0.03116200000022218, 'population': 658, 'numerosity': 1800, 'average_specificity': 9.328333333333333}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 1, 'reward': 1856.0710776395063, 'perf_time': 0.002941199999895616, 'population': 643, 'numerosity': 1800, 'average_specificity': 7.533333333333333}\n", + "INFO:lcs.agents.Agent:{'trial': 1400, 'steps_in_trial': 2, 'reward': 1858.9006350447112, 'perf_time': 0.02093990000003032, 'population': 689, 'numerosity': 1800, 'average_specificity': 10.705555555555556}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 29, 'reward': 1000.0488758087808, 'perf_time': 0.26839010000003327, 'population': 621, 'numerosity': 1800, 'average_specificity': 12.005}\n", + "INFO:lcs.agents.Agent:{'trial': 1800, 'steps_in_trial': 1, 'reward': 1981.709623413387, 'perf_time': 0.008604000000104861, 'population': 662, 'numerosity': 1800, 'average_specificity': 11.557777777777778}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 3, 'reward': 1515.5396463219226, 'perf_time': 0.011408899999878486, 'population': 693, 'numerosity': 1800, 'average_specificity': 10.162222222222223}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 3, 'reward': 1515.5396463219226, 'perf_time': 0.011408899999878486, 'population': 693, 'numerosity': 1800, 'average_specificity': 10.162222222222223}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': 3.603503893364359e-42, 'perf_time': 0.41862639999999374, 'population': 650, 'numerosity': 1800, 'average_specificity': 11.04}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': 3.603503893364359e-42, 'perf_time': 0.41862639999999374, 'population': 650, 'numerosity': 1800, 'average_specificity': 11.04}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': 3.280893545262122e-42, 'perf_time': 0.19023050000032526, 'population': 623, 'numerosity': 1800, 'average_specificity': 10.921666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': 3.280893545262122e-42, 'perf_time': 0.19023050000032526, 'population': 623, 'numerosity': 1800, 'average_specificity': 10.921666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 3, 'reward': 1374.6976988250246, 'perf_time': 0.05012029999988954, 'population': 630, 'numerosity': 1800, 'average_specificity': 13.93}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 3, 'reward': 1374.6976988250246, 'perf_time': 0.05012029999988954, 'population': 630, 'numerosity': 1800, 'average_specificity': 13.93}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 3, 'reward': 1657.0671567344918, 'perf_time': 0.043464899999889894, 'population': 638, 'numerosity': 1800, 'average_specificity': 13.890555555555556}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 3, 'reward': 1657.0671567344918, 'perf_time': 0.043464899999889894, 'population': 638, 'numerosity': 1800, 'average_specificity': 13.890555555555556}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 47, 'reward': 1000.0, 'perf_time': 0.028675699999894277, 'population': 106, 'numerosity': 117, 'average_specificity': 7.418803418803419}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 1 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': 1181.0153545395274, 'perf_time': 0.0542500999999902, 'population': 595, 'numerosity': 1800, 'average_specificity': 11.3}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 2, 'reward': 1553.815150522942, 'perf_time': 0.023037799999656272, 'population': 646, 'numerosity': 1800, 'average_specificity': 10.501666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 13, 'reward': 1015.2142659998252, 'perf_time': 0.0900723999998263, 'population': 613, 'numerosity': 1800, 'average_specificity': 11.326666666666666}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 26, 'reward': 1000.2353637398902, 'perf_time': 0.19270460000007006, 'population': 659, 'numerosity': 1800, 'average_specificity': 9.516666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 19, 'reward': 1001.494064763673, 'perf_time': 0.11153289999992921, 'population': 652, 'numerosity': 1800, 'average_specificity': 8.839444444444444}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 3, 'reward': 1496.9296369235176, 'perf_time': 0.009201999999731925, 'population': 644, 'numerosity': 1800, 'average_specificity': 8.87111111111111}\n", + "INFO:lcs.agents.Agent:{'trial': 1400, 'steps_in_trial': 2, 'reward': 1775.740969811019, 'perf_time': 0.007334499999615218, 'population': 697, 'numerosity': 1800, 'average_specificity': 10.988333333333333}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 2, 'reward': 1504.580060635059, 'perf_time': 0.006967299999814713, 'population': 678, 'numerosity': 1800, 'average_specificity': 11.297777777777778}\n", + "INFO:lcs.agents.Agent:{'trial': 1800, 'steps_in_trial': 1, 'reward': 2257.3176445859986, 'perf_time': 0.010476899999957823, 'population': 679, 'numerosity': 1800, 'average_specificity': 9.463333333333333}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1758.3525528823438, 'perf_time': 0.005924100000356702, 'population': 649, 'numerosity': 1800, 'average_specificity': 10.102777777777778}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1758.3525528823438, 'perf_time': 0.005924100000356702, 'population': 649, 'numerosity': 1800, 'average_specificity': 10.102777777777778}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 6, 'reward': 1206.3474190320126, 'perf_time': 0.039665599999807455, 'population': 648, 'numerosity': 1800, 'average_specificity': 8.85388888888889}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 6, 'reward': 1206.3474190320126, 'perf_time': 0.039665599999807455, 'population': 648, 'numerosity': 1800, 'average_specificity': 8.85388888888889}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 2, 'reward': 1638.9658287322043, 'perf_time': 0.005959100000382023, 'population': 624, 'numerosity': 1800, 'average_specificity': 8.575}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 2, 'reward': 1638.9658287322043, 'perf_time': 0.005959100000382023, 'population': 624, 'numerosity': 1800, 'average_specificity': 8.575}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 3, 'reward': 1358.655570602341, 'perf_time': 0.009969600000204082, 'population': 696, 'numerosity': 1800, 'average_specificity': 14.39}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 3, 'reward': 1358.655570602341, 'perf_time': 0.009969600000204082, 'population': 696, 'numerosity': 1800, 'average_specificity': 14.39}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 9.396090502804839e-139, 'perf_time': 0.34492560000035155, 'population': 688, 'numerosity': 1800, 'average_specificity': 14.430555555555555}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 9.396090502804839e-139, 'perf_time': 0.34492560000035155, 'population': 688, 'numerosity': 1800, 'average_specificity': 14.430555555555555}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000.0, 'perf_time': 0.00036150000005363836, 'population': 8, 'numerosity': 8, 'average_specificity': 5.875}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 2 experiment\n" + ] + } + ], + "source": [ + "from utils.xcs_utils import avg_experiment as XCSExp\n", + "from utils.nxcs_utils import avg_experiment as XNCSExp\n", + "\n", + "number_of_experiments = 3\n", + "explore = 2000\n", + "exploit = 500\n", + "\n", + "df = XCSExp(maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit)\n", + "\n", + "df_other = XNCSExp(maze=maze,\n", + " cfg=XNCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit+explore)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df['steps_in_trial_other']=df_other['steps_in_trial']\n", + "df['population_other']=df_other['population']\n", + "df['numerosity_other']=df_other['numerosity']\n", + "df['average_specificity_other']=df_other['average_specificity']\n", + "df['fraction_accuracy_other']=df_other['fraction_accuracy']\n", + "display(df_other)\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df[['average_specificity', \"average_specificity_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[ \"fraction_accuracy_other\"].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[['population', \"population_other\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[['numerosity', 'numerosity_other']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"numerosity\")\n", + "ax.legend([\"XCS\", \"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[['steps_in_trial', 'steps_in_trial_other']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(sum(df['steps_in_trial']))\n", + "print(sum(df['steps_in_trial_other']))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XCS_maze5_XNCS_metrics.ipynb b/XCS_Experiments/XCS_maze5_XNCS_metrics.ipynb new file mode 100644 index 0000000..790d13a --- /dev/null +++ b/XCS_Experiments/XCS_maze5_XNCS_metrics.ipynb @@ -0,0 +1,547 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '1', '0', '1', '0', '1', '1', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *\n", + "\n", + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.06490789999998015, 'population': 118, 'numerosity': 167, 'average_specificity': 7.251497005988024, 'fraction_accuracy': 1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 30, 'reward': 1000.0345501267201, 'perf_time': 0.10261690000015733, 'population': 389, 'numerosity': 1600, 'average_specificity': 6.7275, 'fraction_accuracy': 2.6545478371694405e-16}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 9, 'reward': 1000.0000000000001, 'perf_time': 0.04221030000007886, 'population': 427, 'numerosity': 1600, 'average_specificity': 6.305, 'fraction_accuracy': 7.133214498104759e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 19, 'reward': 1001.4924803628722, 'perf_time': 0.07440409999981057, 'population': 417, 'numerosity': 1602, 'average_specificity': 6.504369538077404, 'fraction_accuracy': 2.1510520454084546e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 58, 'reward': 1000.0000024149464, 'perf_time': 0.22334860000000845, 'population': 418, 'numerosity': 1600, 'average_specificity': 6.29125, 'fraction_accuracy': 2.073694525858471e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 100, 'reward': 1.675609681536095e-12, 'perf_time': 0.40097160000004806, 'population': 414, 'numerosity': 1600, 'average_specificity': 6.56875, 'fraction_accuracy': 6.395394106590811e-13}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 2, 'reward': 1504.1000000098427, 'perf_time': 0.007696799999848736, 'population': 408, 'numerosity': 1610, 'average_specificity': 7.822360248447205, 'fraction_accuracy': 3.0104455562010197e-13}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 23, 'reward': 1000.0, 'perf_time': 0.08154340000010052, 'population': 438, 'numerosity': 1600, 'average_specificity': 6.03125, 'fraction_accuracy': 3.797291047644312e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 17, 'reward': 1002.9606831241265, 'perf_time': 0.06626740000001519, 'population': 385, 'numerosity': 1618, 'average_specificity': 6.177379480840544, 'fraction_accuracy': 2.2127582131626325e-13}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 99, 'reward': 1000.0, 'perf_time': 0.3405913999999939, 'population': 411, 'numerosity': 1610, 'average_specificity': 6.719875776397515, 'fraction_accuracy': 5.4687322008958656e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 53, 'reward': 1000.0, 'perf_time': 0.13541850000001432, 'population': 434, 'numerosity': 1600, 'average_specificity': 5.989375, 'fraction_accuracy': 2.0454617467649185e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 53, 'reward': 1000.0, 'perf_time': 0.13541850000001432, 'population': 434, 'numerosity': 1600, 'average_specificity': 5.989375, 'fraction_accuracy': 2.0454617467649185e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 2, 'reward': 1762.8384355046353, 'perf_time': 0.004093699999884848, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.6879632850454125e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 2, 'reward': 1762.8384355046353, 'perf_time': 0.004093699999884848, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.6879632850454125e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': 1196.2871582235587, 'perf_time': 0.010165700000015931, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.6852274143598479e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': 1196.2871582235587, 'perf_time': 0.010165700000015931, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.6852274143598479e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 11, 'reward': 1026.1106184795624, 'perf_time': 0.021779000000151427, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.83519028097717e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 11, 'reward': 1026.1106184795624, 'perf_time': 0.021779000000151427, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.83519028097717e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 7, 'reward': 1206.7188092770646, 'perf_time': 0.014286399999946298, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.694373250286204e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 7, 'reward': 1206.7188092770646, 'perf_time': 0.014286399999946298, 'population': 436, 'numerosity': 1600, 'average_specificity': 5.98875, 'fraction_accuracy': 1.694373250286204e-15}\n" + ] + } + ], + "source": [ + "\n", + "df = avg_experiment(maze=maze,\n", + " cfg=cfg,\n", + " number_of_tests=1,\n", + " explore_trials=1000,\n", + " exploit_trials=500)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 100 0.000000e+00 0.064908 118 167 \n", + "100 30 1.000035e+03 0.102617 389 1600 \n", + "200 9 1.000000e+03 0.042210 427 1600 \n", + "300 19 1.001492e+03 0.074404 417 1602 \n", + "400 58 1.000000e+03 0.223349 418 1600 \n", + "500 100 1.675610e-12 0.400972 414 1600 \n", + "600 2 1.504100e+03 0.007697 408 1610 \n", + "700 23 1.000000e+03 0.081543 438 1600 \n", + "800 17 1.002961e+03 0.066267 385 1618 \n", + "900 99 1.000000e+03 0.340591 411 1610 \n", + "1000 53 1.000000e+03 0.135419 434 1600 \n", + "1100 2 1.762838e+03 0.004094 436 1600 \n", + "1200 5 1.196287e+03 0.010166 436 1600 \n", + "1300 11 1.026111e+03 0.021779 436 1600 \n", + "1400 7 1.206719e+03 0.014286 436 1600 \n", + "\n", + " average_specificity fraction_accuracy \n", + "trial \n", + "0 7.251497 1.000000e+00 \n", + "100 6.727500 2.654548e-16 \n", + "200 6.305000 7.133214e-15 \n", + "300 6.504370 2.151052e-15 \n", + "400 6.291250 2.073695e-15 \n", + "500 6.568750 6.395394e-13 \n", + "600 7.822360 3.010446e-13 \n", + "700 6.031250 3.797291e-15 \n", + "800 6.177379 2.212758e-13 \n", + "900 6.719876 5.468732e-15 \n", + "1000 5.989375 2.045462e-15 \n", + "1100 5.988750 1.687963e-15 \n", + "1200 5.988750 1.685227e-15 \n", + "1300 5.988750 1.835190e-15 \n", + "1400 5.988750 1.694373e-15 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XCS_multiplexer.ipynb b/XCS_Experiments/XCS_multiplexer.ipynb deleted file mode 100644 index 9a37ca0..0000000 --- a/XCS_Experiments/XCS_multiplexer.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# XCS in Multiplexer" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# import logging\n", - "# logging.basicConfig(level=logging.INFO)\n", - "# logger = logging.getLogger(__name__)\n", - "\n", - "# import pandas as pd\n", - "# import numpy as np\n", - "# import matplotlib.pyplot as plt\n", - "\n", - "import gym\n", - "import gym_multiplexer \n", - "\n", - "from lcs.agents import EnvironmentAdapter\n", - "from lcs.agents.xcs import XCS, Configuration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Environment" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mpx = gym.make('boolean-multiplexer-6bit-v0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Example of return value" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "state = mpx.reset()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The last bit is changed after executing an action. It if was valid it's set to `1`, otherwise `0`. This behavior is not needed for the XCS, therefore signal can becompacted" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Agent" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "class MultiplexerAdapter(EnvironmentAdapter):\n", - " @classmethod\n", - " def to_genotype(cls, env_state):\n", - " return list(map(str, map(int, env_state[:-1]))) # skip last bit\n", - "\n", - "def mpx_metrics(xcs: XCS, environment):\n", - " return {\n", - " 'population': len(xcs.population),\n", - " 'numerosity': sum(cl.numerosity for cl in xcs.population)\n", - " }\n", - " \n", - "cfg = Configuration(number_of_actions=2,\n", - " max_population=1000,\n", - " gamma=0.9,\n", - " # ga_threshold = 9999999999999999,\n", - " # covering_wildcard_chance=1,\n", - " metrics_trial_frequency=100,\n", - " # mutation_chance=0,\n", - " environment_adapter=MultiplexerAdapter(),\n", - " user_metrics_collector_fcn=mpx_metrics)\n", - "\n", - "agent = XCS(cfg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 3.98 s\n" - ] - } - ], - "source": [ - "%%time\n", - "explore_population, explore_metrics = agent.explore(mpx, 10_000, decay=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Explore population count: 40\n", - "\n", - "Top 15 classifiers:\n", - "#01110\t1\n", - "#01111\t0\n", - "#####0\t0\n", - "#011#0\t1\n", - "##0001\t1\n", - "0##011\t0\n", - "#1#10#\t0\n", - "###1##\t0\n", - "1#00#1\t0\n", - "###11#\t0\n", - "0###10\t1\n", - "11##01\t0\n", - "0##011\t1\n", - "1#00#1\t1\n", - "000#0#\t0\n", - "#10#1#\t1\n", - "######\t0\n", - "#11#10\t1\n", - "0###10\t0\n", - "1#00#0\t1\n", - "00#0##\t0\n", - "#0##10\t1\n", - "01###1\t0\n", - "##01##\t0\n", - "#0#1##\t1\n" - ] - } - ], - "source": [ - "print(f'Explore population count: {len(explore_population)}\\n')\n", - "\n", - "print('Top 15 classifiers:')\n", - "for cl in sorted(explore_population, key=lambda cl: -cl.fitness)[15:]:\n", - " print(f'{cl.condition}\\t{cl.action}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See if there are some duplicate classifiers." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rules = [(cl.condition, cl.action) for cl in explore_population]\n", - "len(set(rules))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are duplicate classifiers found." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "for rule in explore_population:\n", - " for second_rule in explore_population:\n", - " if rule.condition == second_rule.condition and rule.action == second_rule.action and id(rule) != id(second_rule):\n", - " print(\"Duplicate Classifier found:\")\n", - " print(rule)\n", - " print(second_rule)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cond:01###1 - Act:0 - Num:2 [fit: 0.000, exp: 198.00, pred: 521.742]\n", - "Cond:#01111 - Act:0 - Num:27 [fit: 0.000, exp: 111.00, pred: 476.774]\n", - "Cond:00#0## - Act:0 - Num:6 [fit: 0.000, exp: 208.00, pred: 521.456]\n", - "Cond:1#00#0 - Act:1 - Num:11 [fit: 0.000, exp: 165.00, pred: 521.855]\n", - "Cond:0###10 - Act:0 - Num:11 [fit: 0.000, exp: 173.00, pred: 485.328]\n", - "Cond:0###10 - Act:1 - Num:35 [fit: 0.000, exp: 148.00, pred: 453.594]\n", - "Cond:1#00#1 - Act:1 - Num:24 [fit: 0.000, exp: 175.00, pred: 530.429]\n", - "Cond:0#1#11 - Act:1 - Num:55 [fit: 0.000, exp: 119.00, pred: 503.860]\n", - "Cond:#011#0 - Act:1 - Num:21 [fit: 0.000, exp: 102.00, pred: 574.861]\n", - "Cond:#11#10 - Act:1 - Num:19 [fit: 0.000, exp: 187.00, pred: 501.388]\n", - "Cond:1#00#1 - Act:0 - Num:32 [fit: 0.000, exp: 152.00, pred: 412.653]\n", - "Cond:#11111 - Act:0 - Num:38 [fit: 0.000, exp: 84.00, pred: 520.065]\n", - "Cond:0##011 - Act:0 - Num:60 [fit: 0.000, exp: 157.00, pred: 528.572]\n", - "Cond:0##011 - Act:1 - Num:32 [fit: 0.000, exp: 158.00, pred: 483.638]\n", - "Cond:11##01 - Act:0 - Num:29 [fit: 0.000, exp: 160.00, pred: 417.714]\n", - "Cond:000#0# - Act:0 - Num:35 [fit: 0.000, exp: 189.00, pred: 549.371]\n", - "Cond:#0##10 - Act:1 - Num:3 [fit: 0.000, exp: 193.00, pred: 499.302]\n", - "Cond:#01110 - Act:1 - Num:31 [fit: 0.000, exp: 101.00, pred: 584.680]\n", - "Cond:#0#1## - Act:1 - Num:1 [fit: 0.000, exp: 225.00, pred: 448.306]\n", - "Cond:##0001 - Act:1 - Num:61 [fit: 0.000, exp: 117.00, pred: 520.513]\n", - "Cond:#11111 - Act:1 - Num:33 [fit: 0.000, exp: 45.00, pred: 584.617]\n", - "Cond:##01## - Act:0 - Num:4 [fit: 0.000, exp: 211.00, pred: 417.614]\n", - "Cond:1#00#0 - Act:0 - Num:13 [fit: 0.000, exp: 44.00, pred: 530.567]\n", - "Cond:###11# - Act:0 - Num:42 [fit: 0.000, exp: 139.00, pred: 398.852]\n", - "Cond:#10#1# - Act:1 - Num:39 [fit: 0.000, exp: 190.00, pred: 459.080]\n", - "Cond:#1#10# - Act:0 - Num:39 [fit: 0.000, exp: 136.00, pred: 451.514]\n", - "Cond:#####0 - Act:0 - Num:30 [fit: 0.000, exp: 143.00, pred: 425.275]\n", - "Cond:##0001 - Act:0 - Num:20 [fit: 0.000, exp: 37.00, pred: 467.753]\n", - "Cond:#11#1# - Act:0 - Num:53 [fit: 0.000, exp: 89.00, pred: 482.093]\n", - "Cond:###1## - Act:0 - Num:31 [fit: 0.000, exp: 131.00, pred: 376.467]\n", - "Cond:#1#0#1 - Act:0 - Num:46 [fit: 0.000, exp: 85.00, pred: 424.991]\n", - "Cond:#10#1# - Act:0 - Num:15 [fit: 0.000, exp: 77.00, pred: 488.714]\n", - "Cond:#01#00 - Act:1 - Num:8 [fit: 0.007, exp: 16.00, pred: 447.688]\n", - "Cond:#1#### - Act:1 - Num:10 [fit: 0.000, exp: 76.00, pred: 442.122]\n", - "Cond:###### - Act:0 - Num:28 [fit: 0.000, exp: 158.00, pred: 354.008]\n", - "Cond:#1#0#1 - Act:1 - Num:19 [fit: 0.000, exp: 35.00, pred: 423.557]\n", - "Cond:10###1 - Act:1 - Num:7 [fit: 0.019, exp: 10.00, pred: 468.895]\n", - "Cond:0#1### - Act:1 - Num:11 [fit: 0.003, exp: 12.00, pred: 461.700]\n", - "Cond:###### - Act:1 - Num:18 [fit: 0.000, exp: 51.00, pred: 423.200]\n", - "Cond:#01#00 - Act:0 - Num:1 [fit: 0.001, exp: 8.00, pred: 522.568]\n" - ] - } - ], - "source": [ - "for rule in explore_population:\n", - " print(rule)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Experiment 0: No duplicates\n", - "Experiment 1: Duplicate found: 44, 42\n", - "Experiment 2: No duplicates\n", - "Experiment 3: Duplicate found: 36, 35\n", - "Experiment 4: No duplicates\n", - "Experiment 5: Duplicate found: 39, 37\n", - "Experiment 6: Duplicate found: 46, 45\n", - "Experiment 7: No duplicates\n", - "Experiment 8: Duplicate found: 38, 37\n", - "Experiment 9: No duplicates\n", - "Experiment 10: No duplicates\n", - "Experiment 11: Duplicate found: 36, 35\n", - "Experiment 12: Duplicate found: 34, 33\n", - "Experiment 13: Duplicate found: 47, 45\n", - "Experiment 14: Duplicate found: 31, 30\n", - "Experiment 15: No duplicates\n", - "Experiment 16: Duplicate found: 31, 30\n", - "Experiment 17: No duplicates\n", - "Experiment 18: No duplicates\n", - "Experiment 19: Duplicate found: 44, 43\n", - "Experiment 20: No duplicates\n", - "Experiment 21: Duplicate found: 38, 37\n", - "Experiment 22: No duplicates\n", - "Experiment 23: No duplicates\n", - "Experiment 24: No duplicates\n", - "Experiment 25: Duplicate found: 48, 46\n", - "Experiment 26: No duplicates\n", - "Experiment 27: No duplicates\n", - "Experiment 28: Duplicate found: 42, 40\n", - "Experiment 29: Duplicate found: 37, 36\n", - "Experiment 30: No duplicates\n", - "Experiment 31: Duplicate found: 37, 36\n", - "Experiment 32: Duplicate found: 55, 52\n", - "Experiment 33: Duplicate found: 45, 44\n", - "Experiment 34: Duplicate found: 43, 41\n", - "Experiment 35: No duplicates\n", - "Experiment 36: Duplicate found: 38, 36\n", - "Experiment 37: Duplicate found: 46, 45\n", - "Experiment 38: Duplicate found: 38, 37\n", - "Experiment 39: No duplicates\n", - "Experiment 40: No duplicates\n", - "Experiment 41: Duplicate found: 44, 43\n", - "Experiment 42: No duplicates\n", - "Experiment 43: Duplicate found: 42, 41\n", - "Experiment 44: Duplicate found: 36, 35\n", - "Experiment 45: No duplicates\n", - "Experiment 46: Duplicate found: 31, 30\n", - "Experiment 47: No duplicates\n", - "Experiment 48: Duplicate found: 38, 36\n", - "Experiment 49: Duplicate found: 45, 44\n", - "Experiment 50: Duplicate found: 45, 43\n", - "Experiment 51: Duplicate found: 40, 39\n", - "Experiment 52: No duplicates\n", - "Experiment 53: No duplicates\n", - "Experiment 54: Duplicate found: 43, 41\n", - "Experiment 55: Duplicate found: 30, 29\n", - "Experiment 56: Duplicate found: 34, 33\n", - "Experiment 57: No duplicates\n", - "Experiment 58: Duplicate found: 45, 44\n", - "Experiment 59: No duplicates\n", - "Experiment 60: Duplicate found: 43, 42\n", - "Experiment 61: No duplicates\n", - "Experiment 62: Duplicate found: 39, 37\n", - "Experiment 63: No duplicates\n", - "Experiment 64: No duplicates\n", - "Experiment 65: Duplicate found: 42, 41\n", - "Experiment 66: No duplicates\n", - "Experiment 67: No duplicates\n", - "Experiment 68: Duplicate found: 39, 38\n", - "Experiment 69: No duplicates\n", - "Experiment 70: Duplicate found: 43, 42\n", - "Experiment 71: Duplicate found: 41, 40\n", - "Experiment 72: Duplicate found: 36, 35\n", - "Experiment 73: Duplicate found: 40, 39\n", - "Experiment 74: No duplicates\n", - "Experiment 75: No duplicates\n", - "Experiment 76: Duplicate found: 43, 42\n", - "Experiment 77: No duplicates\n", - "Experiment 78: No duplicates\n", - "Experiment 79: Duplicate found: 38, 37\n", - "Experiment 80: Duplicate found: 43, 42\n", - "Experiment 81: Duplicate found: 37, 35\n", - "Experiment 82: Duplicate found: 40, 39\n", - "Experiment 83: No duplicates\n", - "Experiment 84: No duplicates\n", - "Experiment 85: Duplicate found: 37, 36\n", - "Experiment 86: Duplicate found: 30, 29\n", - "Experiment 87: No duplicates\n", - "Experiment 88: No duplicates\n", - "Experiment 89: Duplicate found: 41, 40\n", - "Experiment 90: No duplicates\n", - "Experiment 91: Duplicate found: 35, 34\n", - "Experiment 92: No duplicates\n", - "Experiment 93: No duplicates\n", - "Experiment 94: No duplicates\n", - "Experiment 95: No duplicates\n", - "Experiment 96: No duplicates\n", - "Experiment 97: No duplicates\n", - "Experiment 98: No duplicates\n", - "Experiment 99: No duplicates\n", - "0.51\n" - ] - } - ], - "source": [ - "detected_duplicates = 0\n", - "for i in range(100):\n", - " agent = XCS(cfg)\n", - " explore_population, explore_metrics = agent.explore(mpx, 10_000, decay=False)\n", - " rules = [(cl.condition, cl.action) for cl in explore_population]\n", - " if len(explore_population) > len(set(rules)):\n", - " print(f\"Experiment {i}: Duplicate found: {len(explore_population)}, {len(set(rules))}\")\n", - " detected_duplicates += 1\n", - " else:\n", - " print(f\"Experiment {i}: No duplicates\")\n", - "print(f\"{detected_duplicates / 100}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_woods.ipynb b/XCS_Experiments/XCS_woods.ipynb new file mode 100644 index 0000000..b558091 --- /dev/null +++ b/XCS_Experiments/XCS_woods.ipynb @@ -0,0 +1,937 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like:\n", + "\n", + "['O', '.', '.', '.', '.', '.', '.', 'O']\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like:\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents import EnvironmentAdapter\n", + "class adapter(EnvironmentAdapter):\n", + " def to_genotype(cls, phenotype): \n", + " return [\"1\" if x=='.' else x for x in phenotype]\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.nxcs_utils import *\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['O', '1', '1', '1', '1', '1', '1', 'O']\n" + ] + } + ], + "source": [ + "ada = adapter()\n", + "print(ada.to_genotype(situation))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " mutation_chance=0.08,\n", + " chi=0.8,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " delta=0.1,\n", + " initial_error=0.01,\n", + " metrics_trial_frequency=50,\n", + " covering_wildcard_chance = 0.9,\n", + " user_metrics_collector_fcn=xcs_maze_metrics,\n", + "\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 30, 'reward': 1000.0, 'perf_time': 0.016425999999999163, 'numerosity': 97, 'population': 97, 'average_specificity': 1.9381443298969072, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 41, 'reward': 1000.0014729710549, 'perf_time': 0.2196277000000002, 'numerosity': 1800, 'population': 520, 'average_specificity': 4.366666666666666, 'fraction_accuracy': 2.4000124223680607e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 13, 'reward': 1011.685958540322, 'perf_time': 0.09741280000000074, 'numerosity': 1800, 'population': 612, 'average_specificity': 4.749444444444444, 'fraction_accuracy': 3.629848402362404e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 46, 'reward': 1000.000143949667, 'perf_time': 0.42157530000000065, 'numerosity': 1800, 'population': 674, 'average_specificity': 4.987777777777778, 'fraction_accuracy': 2.801088873368434e-16}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 2, 'reward': 1636.7205566203402, 'perf_time': 0.013088100000000935, 'numerosity': 1800, 'population': 692, 'average_specificity': 5.42, 'fraction_accuracy': 2.722084872433967e-16}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 4, 'reward': 1479.3033470374432, 'perf_time': 0.01846919999999841, 'numerosity': 1800, 'population': 698, 'average_specificity': 4.923333333333333, 'fraction_accuracy': 2.4174521215758003e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 2, 'reward': 1672.8653645356364, 'perf_time': 0.032798999999997136, 'numerosity': 1800, 'population': 691, 'average_specificity': 4.716666666666667, 'fraction_accuracy': 5.207155858665223e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1552.4485844378219, 'perf_time': 0.009335000000000093, 'numerosity': 1800, 'population': 648, 'average_specificity': 4.810555555555555, 'fraction_accuracy': 5.097075244004837e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 1, 'reward': 1964.1192425229885, 'perf_time': 0.00767420000000385, 'numerosity': 1800, 'population': 673, 'average_specificity': 4.595555555555555, 'fraction_accuracy': 3.1243348872291638e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 1, 'reward': 1901.148030262215, 'perf_time': 0.003277500000002931, 'numerosity': 1800, 'population': 650, 'average_specificity': 4.591111111111111, 'fraction_accuracy': 3.1777843478879033e-18}\n" + ] + } + ], + "source": [ + "agent = XCS(cfg)\n", + "explore_population, explore_metrics = agent.explore(maze, 1000, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cond:.OOO.... - Act:1 - Num:6 [fit: 0.001, exp: 24.00, pred: 818.172, Error:270.63331843209056]\n", + "Cond:.#...OF. - Act:1 - Num:2 [fit: 0.033, exp: 6.00, pred: 840.921, Error:90.16031611667614]\n", + "Cond:.....O#. - Act:2 - Num:1 [fit: 0.000, exp: 35.00, pred: 870.631, Error:249.3473898290552]\n", + "Cond:.....OF# - Act:3 - Num:4 [fit: 0.000, exp: 46.00, pred: 1624.522, Error:339.5095767425465]\n", + "Cond:.....OF. - Act:7 - Num:4 [fit: 0.013, exp: 10.00, pred: 875.535, Error:262.8004001575878]\n", + "Cond:....FO.. - Act:0 - Num:1 [fit: 0.011, exp: 1.00, pred: 654.482, Error:0.0]\n", + "Cond:#...#O.. - Act:1 - Num:2 [fit: 0.000, exp: 25.00, pred: 794.709, Error:264.277408649657]\n", + "Cond:#...F#.. - Act:2 - Num:10 [fit: 0.001, exp: 22.00, pred: 837.888, Error:115.8448684994715]\n", + "Cond:....#O.. - Act:5 - Num:10 [fit: 0.005, exp: 14.00, pred: 720.038, Error:159.5325820151578]\n", + "Cond:#.#.FO.# - Act:7 - Num:7 [fit: 0.002, exp: 19.00, pred: 797.565, Error:85.98819479367896]\n", + "Cond:#....F.. - Act:0 - Num:3 [fit: 0.080, exp: 2.00, pred: 652.288, Error:326.14396895621655]\n", + "Cond:.....F.. - Act:3 - Num:4 [fit: 0.000, exp: 27.00, pred: 917.924, Error:172.75929004369047]\n", + "Cond:O......O - Act:0 - Num:5 [fit: 0.012, exp: 14.00, pred: 831.361, Error:270.7613845474693]\n", + "Cond:O..#.#.O - Act:6 - Num:3 [fit: 0.051, exp: 4.00, pred: 721.919, Error:258.54325952438876]\n", + "Cond:O..##..O - Act:7 - Num:4 [fit: 0.001, exp: 25.00, pred: 843.243, Error:213.27675023014655]\n", + "Cond:.......O - Act:1 - Num:3 [fit: 0.000, exp: 25.00, pred: 812.521, Error:228.15193862697137]\n", + "Cond:...#...O - Act:6 - Num:2 [fit: 0.000, exp: 46.00, pred: 1100.813, Error:278.6922038993029]\n", + "Cond:OO..#..O - Act:0 - Num:5 [fit: 0.012, exp: 13.00, pred: 799.592, Error:134.93671693405923]\n", + "Cond:OO.....O - Act:1 - Num:4 [fit: 0.001, exp: 16.00, pred: 878.434, Error:231.69477418437663]\n", + "Cond:OO....#O - Act:6 - Num:7 [fit: 0.041, exp: 5.00, pred: 780.378, Error:293.8642390015473]\n", + "Cond:OO.....O - Act:7 - Num:4 [fit: 0.115, exp: 4.00, pred: 453.540, Error:257.51356873206436]\n", + "Cond:...#OO.. - Act:0 - Num:1 [fit: 0.073, exp: 2.00, pred: 817.945, Error:2.669712307774148]\n", + "Cond:#..FOO.# - Act:1 - Num:1 [fit: 0.000, exp: 45.00, pred: 808.079, Error:251.90802327290345]\n", + "Cond:...FOO#. - Act:5 - Num:6 [fit: 0.005, exp: 14.00, pred: 798.575, Error:162.99655231911527]\n", + "Cond:...#OO#. - Act:7 - Num:4 [fit: 0.009, exp: 12.00, pred: 843.064, Error:240.6169174450742]\n", + "Cond:OO...... - Act:6 - Num:2 [fit: 0.001, exp: 23.00, pred: 799.655, Error:242.48729607963907]\n", + "Cond:OO...... - Act:7 - Num:3 [fit: 0.007, exp: 15.00, pred: 1089.220, Error:269.2946403336793]\n", + "Cond:.OO..... - Act:1 - Num:2 [fit: 0.017, exp: 9.00, pred: 792.853, Error:126.26799630594708]\n", + "Cond:......OO - Act:5 - Num:3 [fit: 0.041, exp: 5.00, pred: 646.358, Error:183.4186264049896]\n", + "Cond:.....OOF - Act:0 - Num:5 [fit: 0.003, exp: 17.00, pred: 1326.980, Error:359.1679042132547]\n", + "Cond:....#OOF - Act:1 - Num:3 [fit: 0.001, exp: 20.00, pred: 852.487, Error:361.1730110803099]\n", + "Cond:.....OOF - Act:2 - Num:2 [fit: 0.009, exp: 12.00, pred: 908.855, Error:208.86877258724766]\n", + "Cond:...##OOF - Act:3 - Num:1 [fit: 0.000, exp: 22.00, pred: 873.829, Error:224.31434500029263]\n", + "Cond:.....OOF - Act:6 - Num:6 [fit: 0.067, exp: 7.00, pred: 826.461, Error:99.81965255540715]\n", + "Cond:.OOO..O. - Act:6 - Num:2 [fit: 0.010, exp: 1.00, pred: 1572.011, Error:0.0]\n", + "Cond:#..#.... - Act:1 - Num:1 [fit: 0.000, exp: 43.00, pred: 1241.952, Error:397.295352305611]\n", + "Cond:.OO.#.#. - Act:1 - Num:2 [fit: 0.017, exp: 9.00, pred: 792.853, Error:126.26799630594708]\n", + "Cond:..OO...# - Act:1 - Num:2 [fit: 0.064, exp: 3.00, pred: 850.399, Error:85.73598811162655]\n", + "Cond:.OOO.#.. - Act:1 - Num:2 [fit: 0.000, exp: 24.00, pred: 818.172, Error:270.63331843209056]\n", + "Cond:.#..#... - Act:3 - Num:1 [fit: 0.000, exp: 31.00, pred: 733.627, Error:136.10141926415218]\n", + "Cond:.#...... - Act:3 - Num:2 [fit: 0.000, exp: 31.00, pred: 733.627, Error:136.10141926415218]\n", + "Cond:........ - Act:7 - Num:1 [fit: 0.000, exp: 6.00, pred: 1226.548, Error:59.40708246808876]\n", + "Cond:O#...... - Act:7 - Num:2 [fit: 0.007, exp: 15.00, pred: 1089.220, Error:269.2946403336793]\n", + "Cond:.OO.##.# - Act:1 - Num:1 [fit: 0.002, exp: 9.00, pred: 792.853, Error:103.08830571512246]\n", + "Cond:.#O..... - Act:1 - Num:2 [fit: 0.002, exp: 9.00, pred: 792.853, Error:97.98409254440836]\n", + "Cond:.OO..... - Act:0 - Num:5 [fit: 0.000, exp: 22.00, pred: 1322.536, Error:426.79526777378317]\n", + "Cond:.......O - Act:7 - Num:1 [fit: 0.000, exp: 28.00, pred: 776.745, Error:219.35450459756413]\n", + "Cond:........ - Act:5 - Num:2 [fit: 0.003, exp: 2.00, pred: 1207.222, Error:14.59880844754178]\n", + "Cond:.....O#F - Act:2 - Num:3 [fit: 0.009, exp: 12.00, pred: 908.855, Error:208.86877258724766]\n", + "Cond:.O.##F.. - Act:5 - Num:3 [fit: 0.002, exp: 8.00, pred: 1083.843, Error:27.36564199167589]\n", + "Cond:..#.##OF - Act:1 - Num:1 [fit: 0.000, exp: 20.00, pred: 852.487, Error:361.1730110803099]\n", + "Cond:...#.OOF - Act:2 - Num:4 [fit: 0.009, exp: 12.00, pred: 908.855, Error:208.86877258724766]\n", + "Cond:.##..OF# - Act:7 - Num:2 [fit: 0.001, exp: 14.00, pred: 903.830, Error:224.9547462386263]\n", + "Cond:..#.#..O - Act:7 - Num:2 [fit: 0.001, exp: 27.00, pred: 776.456, Error:220.406391601963]\n", + "Cond:...#...O - Act:7 - Num:3 [fit: 0.001, exp: 27.00, pred: 776.456, Error:220.406391601963]\n", + "Cond:.OO....F - Act:7 - Num:2 [fit: 0.001, exp: 12.00, pred: 1162.888, Error:63.36159428085364]\n", + "Cond:#...O.#. - Act:0 - Num:1 [fit: 0.002, exp: 2.00, pred: 1065.211, Error:34.08996207594636]\n", + "Cond:.#..#OOF - Act:1 - Num:3 [fit: 0.000, exp: 20.00, pred: 852.487, Error:359.04535325981897]\n", + "Cond:OO.#.##. - Act:6 - Num:1 [fit: 0.001, exp: 23.00, pred: 799.655, Error:242.48729607963907]\n", + "Cond:OO.#.... - Act:6 - Num:3 [fit: 0.001, exp: 23.00, pred: 799.655, Error:242.48729607963907]\n", + "Cond:O....#.O - Act:0 - Num:1 [fit: 0.000, exp: 13.00, pred: 896.719, Error:273.00336053656684]\n", + "Cond:OO.#...# - Act:7 - Num:2 [fit: 0.000, exp: 17.00, pred: 1088.273, Error:217.8172902899307]\n", + "Cond:....FO.. - Act:2 - Num:1 [fit: 0.000, exp: 28.00, pred: 844.374, Error:122.97714219765678]\n", + "Cond:.#..O... - Act:3 - Num:2 [fit: 0.002, exp: 7.00, pred: 1156.827, Error:65.73319318680305]\n", + "Cond:O..FOO.# - Act:1 - Num:4 [fit: 0.005, exp: 9.00, pred: 1187.521, Error:77.94648628485494]\n", + "Cond:..OO.O.. - Act:6 - Num:7 [fit: 0.004, exp: 5.00, pred: 1053.598, Error:25.533067239542977]\n", + "Cond:...O.F.# - Act:6 - Num:2 [fit: 0.000, exp: 7.00, pred: 1196.499, Error:49.82740440999355]\n", + "Cond:.OO.#..F - Act:7 - Num:3 [fit: 0.000, exp: 21.00, pred: 967.055, Error:34.88065167079016]\n", + "Cond:..OO.#.. - Act:5 - Num:3 [fit: 0.000, exp: 26.00, pred: 902.812, Error:157.20732341097397]\n", + "Cond:.OO..... - Act:2 - Num:1 [fit: 0.000, exp: 24.00, pred: 820.481, Error:138.70276697501095]\n", + "Cond:.#..#O.. - Act:5 - Num:2 [fit: 0.001, exp: 14.00, pred: 720.038, Error:150.39142825090155]\n", + "Cond:#..##OOF - Act:3 - Num:1 [fit: 0.000, exp: 22.00, pred: 874.098, Error:224.2815339369546]\n", + "Cond:#....... - Act:1 - Num:4 [fit: 0.001, exp: 7.00, pred: 1042.922, Error:28.877996274096876]\n", + "Cond:..OO##.. - Act:5 - Num:1 [fit: 0.000, exp: 26.00, pred: 902.812, Error:154.18002250023397]\n", + "Cond:OO#.#... - Act:7 - Num:1 [fit: 0.001, exp: 15.00, pred: 1089.220, Error:238.58781323934178]\n", + "Cond:...##O#F - Act:3 - Num:1 [fit: 0.000, exp: 22.00, pred: 873.829, Error:222.93323654935176]\n", + "Cond:O.#....O - Act:0 - Num:4 [fit: 0.012, exp: 14.00, pred: 831.361, Error:270.7613845474693]\n", + "Cond:O.....#O - Act:0 - Num:2 [fit: 0.012, exp: 14.00, pred: 831.361, Error:270.7613845474693]\n", + "Cond:..#....O - Act:0 - Num:1 [fit: 0.000, exp: 45.00, pred: 831.168, Error:135.39653286148533]\n", + "Cond:....#..O - Act:7 - Num:3 [fit: 0.001, exp: 27.00, pred: 776.456, Error:220.406391601963]\n", + "Cond:O.#..... - Act:3 - Num:3 [fit: 0.001, exp: 10.00, pred: 1234.280, Error:61.475881052493015]\n", + "Cond:.OO..#.. - Act:1 - Num:1 [fit: 0.002, exp: 9.00, pred: 792.853, Error:89.79305038764355]\n", + "Cond:#OO.#.#. - Act:1 - Num:1 [fit: 0.002, exp: 9.00, pred: 792.853, Error:89.79305038764355]\n", + "Cond:..O#O..# - Act:5 - Num:2 [fit: 0.027, exp: 5.00, pred: 1126.094, Error:141.90971049344233]\n", + "Cond:#.#FOOO. - Act:3 - Num:1 [fit: 0.000, exp: 18.00, pred: 1398.511, Error:64.51031334160476]\n", + "Cond:#.#.##OF - Act:1 - Num:5 [fit: 0.001, exp: 20.00, pred: 852.487, Error:361.1730110803099]\n", + "Cond:#....OOF - Act:0 - Num:2 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:359.1679042132547]\n", + "Cond:...##OO# - Act:3 - Num:2 [fit: 0.000, exp: 22.00, pred: 873.829, Error:209.8536748167675]\n", + "Cond:#....##F - Act:0 - Num:1 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:354.5882409410898]\n", + "Cond:##.#.O.. - Act:1 - Num:1 [fit: 0.032, exp: 3.00, pred: 1283.141, Error:249.1051121888766]\n", + "Cond:.#...OOF - Act:0 - Num:2 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:350.8258970185674]\n", + "Cond:#.#.#.OF - Act:1 - Num:4 [fit: 0.003, exp: 7.00, pred: 1148.008, Error:42.611445999298574]\n", + "Cond:#.#.###F - Act:1 - Num:2 [fit: 0.000, exp: 20.00, pred: 852.487, Error:354.14524402742325]\n", + "Cond:....#O.O - Act:5 - Num:2 [fit: 0.003, exp: 3.00, pred: 1764.939, Error:60.76424395907791]\n", + "Cond:.#O.#... - Act:2 - Num:2 [fit: 0.000, exp: 21.00, pred: 812.036, Error:89.53712232271691]\n", + "Cond:.....OO# - Act:2 - Num:1 [fit: 0.001, exp: 12.00, pred: 908.855, Error:194.05498957211563]\n", + "Cond:#..O#OOF - Act:2 - Num:1 [fit: 0.004, exp: 5.00, pred: 881.662, Error:23.545873983910944]\n", + "Cond:.....OOF - Act:3 - Num:2 [fit: 0.000, exp: 22.00, pred: 867.812, Error:194.5410776235181]\n", + "Cond:..##.#.O - Act:1 - Num:1 [fit: 0.000, exp: 25.00, pred: 813.013, Error:229.1571019626362]\n", + "Cond:..#..OOF - Act:6 - Num:4 [fit: 0.007, exp: 7.00, pred: 826.461, Error:83.02404046628541]\n", + "Cond:.....#OF - Act:6 - Num:5 [fit: 0.007, exp: 7.00, pred: 826.461, Error:83.02404046628541]\n", + "Cond:#OOO...# - Act:1 - Num:4 [fit: 0.000, exp: 24.00, pred: 818.172, Error:269.8971236711451]\n", + "Cond:.OOO##.. - Act:2 - Num:2 [fit: 0.000, exp: 26.00, pred: 834.702, Error:153.65873685671812]\n", + "Cond:.OOO.... - Act:2 - Num:1 [fit: 0.000, exp: 26.00, pred: 834.702, Error:153.65873685671812]\n", + "Cond:..OO.... - Act:3 - Num:7 [fit: 0.002, exp: 17.00, pred: 908.525, Error:197.39389502113053]\n", + "Cond:.#...F#. - Act:1 - Num:3 [fit: 0.002, exp: 17.00, pred: 889.413, Error:260.8099653777782]\n", + "Cond:#....F#. - Act:1 - Num:3 [fit: 0.001, exp: 17.00, pred: 889.413, Error:260.8099653777782]\n", + "Cond:#O.....O - Act:6 - Num:1 [fit: 0.000, exp: 24.00, pred: 1096.618, Error:283.7640046722766]\n", + "Cond:.....O.O - Act:5 - Num:3 [fit: 0.004, exp: 4.00, pred: 1293.984, Error:47.309729858205635]\n", + "Cond:.#...OOF - Act:2 - Num:5 [fit: 0.000, exp: 17.00, pred: 925.806, Error:209.59944082016347]\n", + "Cond:...O.... - Act:7 - Num:5 [fit: 0.026, exp: 7.00, pred: 886.714, Error:338.7473087991509]\n", + "Cond:.....OOF - Act:1 - Num:1 [fit: 0.001, exp: 20.00, pred: 852.487, Error:361.1730110803099]\n", + "Cond:#..O.F.. - Act:6 - Num:1 [fit: 0.000, exp: 13.00, pred: 1007.008, Error:44.76904384512106]\n", + "Cond:O#.....O - Act:0 - Num:1 [fit: 0.000, exp: 27.00, pred: 791.171, Error:145.1624924611465]\n", + "Cond:#....O.. - Act:1 - Num:2 [fit: 0.003, exp: 4.00, pred: 1233.048, Error:56.09972634917226]\n", + "Cond:OO.#.#.. - Act:6 - Num:4 [fit: 0.000, exp: 23.00, pred: 799.655, Error:236.52920493798385]\n", + "Cond:OO....## - Act:6 - Num:1 [fit: 0.000, exp: 27.00, pred: 751.844, Error:220.96272709339522]\n", + "Cond:.#.#.F#. - Act:1 - Num:5 [fit: 0.002, exp: 17.00, pred: 889.413, Error:260.8099653777782]\n", + "Cond:..O#..## - Act:1 - Num:1 [fit: 0.006, exp: 3.00, pred: 850.399, Error:85.73598811162655]\n", + "Cond:..O#...# - Act:1 - Num:2 [fit: 0.006, exp: 3.00, pred: 850.399, Error:85.73598811162655]\n", + "Cond:..OO##.F - Act:5 - Num:2 [fit: 0.000, exp: 19.00, pred: 906.009, Error:52.47957854029005]\n", + "Cond:O..#O... - Act:0 - Num:2 [fit: 0.000, exp: 25.00, pred: 963.093, Error:47.205350623438704]\n", + "Cond:..OOO..# - Act:5 - Num:3 [fit: 0.000, exp: 15.00, pred: 991.488, Error:55.69841324365485]\n", + "Cond:..#.#O.. - Act:1 - Num:1 [fit: 0.000, exp: 25.00, pred: 794.709, Error:261.55469904489485]\n", + "Cond:....F### - Act:2 - Num:3 [fit: 0.000, exp: 24.00, pred: 838.783, Error:114.06043725257807]\n", + "Cond:.#.#OO.. - Act:0 - Num:1 [fit: 0.045, exp: 2.00, pred: 817.945, Error:2.669712307774148]\n", + "Cond:.O.#.F#. - Act:1 - Num:2 [fit: 0.001, exp: 10.00, pred: 902.408, Error:35.894045752761436]\n", + "Cond:.#.#.F.. - Act:1 - Num:1 [fit: 0.000, exp: 17.00, pred: 889.413, Error:238.22738442622966]\n", + "Cond:..OO.### - Act:5 - Num:2 [fit: 0.000, exp: 26.00, pred: 902.812, Error:99.7075872963982]\n", + "Cond:O#.#..#. - Act:6 - Num:1 [fit: 0.001, exp: 23.00, pred: 799.655, Error:242.48729607963907]\n", + "Cond:.#.....O - Act:1 - Num:2 [fit: 0.000, exp: 25.00, pred: 812.686, Error:228.31958917734016]\n", + "Cond:#...F#.# - Act:2 - Num:2 [fit: 0.000, exp: 22.00, pred: 838.363, Error:112.95128561728556]\n", + "Cond:....FO#. - Act:1 - Num:2 [fit: 0.000, exp: 21.00, pred: 792.212, Error:257.81731382402313]\n", + "Cond:...O...# - Act:7 - Num:2 [fit: 0.003, exp: 7.00, pred: 886.714, Error:338.7473087991509]\n", + "Cond:O..#...O - Act:0 - Num:1 [fit: 0.001, exp: 14.00, pred: 831.361, Error:231.93578649002575]\n", + "Cond:.#...O#. - Act:1 - Num:3 [fit: 0.033, exp: 6.00, pred: 840.921, Error:90.16031611667614]\n", + "Cond:##...OF# - Act:1 - Num:2 [fit: 0.033, exp: 6.00, pred: 840.921, Error:90.16031611667614]\n", + "Cond:...OO... - Act:2 - Num:6 [fit: 0.003, exp: 11.00, pred: 913.286, Error:227.7648993144003]\n", + "Cond:..OO.... - Act:2 - Num:3 [fit: 0.090, exp: 2.00, pred: 853.644, Error:5.492122843734194]\n", + "Cond:#O#.#OOF - Act:3 - Num:1 [fit: 0.001, exp: 11.00, pred: 1109.165, Error:225.32977702379327]\n", + "Cond:OO##..#. - Act:6 - Num:3 [fit: 0.000, exp: 23.00, pred: 799.655, Error:234.8195603403938]\n", + "Cond:O......O - Act:4 - Num:8 [fit: 0.001, exp: 19.00, pred: 889.640, Error:285.8995089056546]\n", + "Cond:.O.#O... - Act:7 - Num:1 [fit: 0.000, exp: 17.00, pred: 1063.196, Error:52.323626805959535]\n", + "Cond:.O...#.. - Act:3 - Num:1 [fit: 0.000, exp: 27.00, pred: 760.197, Error:141.36911292162728]\n", + "Cond:.OO#.... - Act:1 - Num:1 [fit: 0.000, exp: 28.00, pred: 768.323, Error:203.4121665792198]\n", + "Cond:O..#OO.# - Act:1 - Num:2 [fit: 0.001, exp: 17.00, pred: 992.785, Error:48.8951231426374]\n", + "Cond:.##...#F - Act:0 - Num:3 [fit: 0.001, exp: 16.00, pred: 985.345, Error:54.57084722413062]\n", + "Cond:.##.##O. - Act:3 - Num:1 [fit: 0.000, exp: 12.00, pred: 1113.647, Error:56.868962714552254]\n", + "Cond:O..#.##O - Act:6 - Num:1 [fit: 0.005, exp: 4.00, pred: 721.919, Error:169.3510178483445]\n", + "Cond:..#....O - Act:1 - Num:1 [fit: 0.000, exp: 29.00, pred: 813.073, Error:228.90390880444343]\n", + "Cond:#....O#F - Act:3 - Num:1 [fit: 0.000, exp: 22.00, pred: 873.591, Error:149.76309860890794]\n", + "Cond:.####OOF - Act:3 - Num:2 [fit: 0.000, exp: 17.00, pred: 873.591, Error:149.76309860890794]\n", + "Cond:#.###O.O - Act:1 - Num:4 [fit: 0.000, exp: 20.00, pred: 969.406, Error:45.9846029176032]\n", + "Cond:#...FO.. - Act:3 - Num:6 [fit: 0.001, exp: 19.00, pred: 816.242, Error:175.09753205971566]\n", + "Cond:...#.#OF - Act:1 - Num:3 [fit: 0.005, exp: 15.00, pred: 853.941, Error:355.26164847477537]\n", + "Cond:...F#O.. - Act:7 - Num:4 [fit: 0.000, exp: 19.00, pred: 895.082, Error:248.2693928541624]\n", + "Cond:OO#..... - Act:6 - Num:2 [fit: 0.000, exp: 23.00, pred: 799.655, Error:231.1137727120809]\n", + "Cond:OO....#O - Act:0 - Num:1 [fit: 0.000, exp: 18.00, pred: 800.543, Error:135.03555405941287]\n", + "Cond:OO..#..O - Act:1 - Num:4 [fit: 0.001, exp: 15.00, pred: 890.468, Error:243.94652948341727]\n", + "Cond:...#.O#F - Act:0 - Num:2 [fit: 0.000, exp: 15.00, pred: 1320.795, Error:324.32691677338505]\n", + "Cond:..OO##.# - Act:5 - Num:2 [fit: 0.000, exp: 26.00, pred: 903.402, Error:92.45439286836243]\n", + "Cond:.....O#F - Act:0 - Num:6 [fit: 0.003, exp: 17.00, pred: 1326.980, Error:359.1679042132547]\n", + "Cond:..#.#..# - Act:7 - Num:2 [fit: 0.000, exp: 27.00, pred: 776.456, Error:215.84206849853751]\n", + "Cond:.OOO.#.. - Act:2 - Num:4 [fit: 0.000, exp: 25.00, pred: 847.320, Error:129.91404389393568]\n", + "Cond:#.OOO... - Act:5 - Num:4 [fit: 0.001, exp: 19.00, pred: 949.812, Error:207.7958128260665]\n", + "Cond:#...#O#. - Act:1 - Num:2 [fit: 0.000, exp: 26.00, pred: 788.466, Error:216.41680212935853]\n", + "Cond:.#O...#O - Act:1 - Num:1 [fit: 0.000, exp: 13.00, pred: 935.565, Error:29.549511481378563]\n", + "Cond:.##...OF - Act:0 - Num:1 [fit: 0.000, exp: 20.00, pred: 1023.047, Error:37.376027533136536]\n", + "Cond:...OO... - Act:3 - Num:1 [fit: 0.090, exp: 2.00, pred: 693.712, Error:4.725221478619574]\n", + "Cond:OO.####. - Act:6 - Num:2 [fit: 0.001, exp: 23.00, pred: 799.655, Error:242.48729607963907]\n", + "Cond:..#..O#F - Act:2 - Num:2 [fit: 0.001, exp: 12.00, pred: 908.855, Error:140.4726225223268]\n", + "Cond:...#.O#F - Act:2 - Num:2 [fit: 0.001, exp: 12.00, pred: 908.855, Error:140.4726225223268]\n", + "Cond:.....#OO - Act:5 - Num:2 [fit: 0.004, exp: 5.00, pred: 646.358, Error:57.85196307715155]\n", + "Cond:....#.OO - Act:5 - Num:2 [fit: 0.004, exp: 5.00, pred: 646.358, Error:57.85196307715155]\n", + "Cond:.....##F - Act:0 - Num:4 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:321.6690972645988]\n", + "Cond:..#..O#F - Act:0 - Num:3 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:321.6690972645988]\n", + "Cond:##..F#.# - Act:2 - Num:1 [fit: 0.000, exp: 22.00, pred: 837.888, Error:112.50416832529147]\n", + "Cond:##.FOO.O - Act:1 - Num:1 [fit: 0.000, exp: 22.00, pred: 975.929, Error:36.579859768933794]\n", + "Cond:.O.#.##O - Act:7 - Num:2 [fit: 0.000, exp: 21.00, pred: 1254.883, Error:79.08032371381663]\n", + "Cond:OO.O.... - Act:4 - Num:4 [fit: 0.002, exp: 8.00, pred: 910.237, Error:91.1127297272264]\n", + "Cond:OO..#..# - Act:0 - Num:2 [fit: 0.001, exp: 13.00, pred: 799.592, Error:134.54555921954358]\n", + "Cond:OO.....# - Act:7 - Num:3 [fit: 0.074, exp: 6.00, pred: 802.342, Error:411.2636133475885]\n", + "Cond:O..##.#O - Act:7 - Num:1 [fit: 0.000, exp: 25.00, pred: 843.865, Error:209.8055616546284]\n", + "Cond:O#..#.OO - Act:1 - Num:1 [fit: 0.000, exp: 10.00, pred: 924.586, Error:43.32209116442323]\n", + "Cond:.....O#F - Act:5 - Num:6 [fit: 0.000, exp: 28.00, pred: 838.368, Error:145.55158688703574]\n", + "Cond:#....#F. - Act:1 - Num:1 [fit: 0.000, exp: 24.00, pred: 1026.299, Error:76.21777173403449]\n", + "Cond:....#F#. - Act:2 - Num:5 [fit: 0.000, exp: 18.00, pred: 976.824, Error:188.83446195468622]\n", + "Cond:##..#O.. - Act:1 - Num:1 [fit: 0.000, exp: 16.00, pred: 789.285, Error:238.07036130905115]\n", + "Cond:O..#...O - Act:5 - Num:1 [fit: 0.000, exp: 39.00, pred: 1082.322, Error:233.1817470875938]\n", + "Cond:#..#FO#. - Act:1 - Num:1 [fit: 0.000, exp: 16.00, pred: 789.285, Error:236.63608154244864]\n", + "Cond:...#.F.. - Act:3 - Num:1 [fit: 0.000, exp: 27.00, pred: 917.924, Error:171.45335409551762]\n", + "Cond:.....F.# - Act:3 - Num:1 [fit: 0.000, exp: 27.00, pred: 917.924, Error:171.45335409551762]\n", + "Cond:...#.... - Act:4 - Num:2 [fit: 0.000, exp: 22.00, pred: 830.286, Error:132.53465312877915]\n", + "Cond:...#...# - Act:7 - Num:1 [fit: 0.000, exp: 33.00, pred: 748.953, Error:240.88730441115263]\n", + "Cond:.#...O#F - Act:5 - Num:1 [fit: 0.000, exp: 28.00, pred: 838.368, Error:139.44250282559173]\n", + "Cond:..#..O#F - Act:5 - Num:1 [fit: 0.000, exp: 28.00, pred: 838.368, Error:139.44250282559173]\n", + "Cond:###.F#.. - Act:2 - Num:3 [fit: 0.000, exp: 22.00, pred: 837.888, Error:103.41241153149636]\n", + "Cond:#O...#.O - Act:2 - Num:1 [fit: 0.000, exp: 44.00, pred: 1088.857, Error:166.96024436764287]\n", + "Cond:#...##OF - Act:1 - Num:1 [fit: 0.000, exp: 15.00, pred: 853.214, Error:329.4341718448116]\n", + "Cond:...#.OOF - Act:1 - Num:4 [fit: 0.000, exp: 20.00, pred: 853.214, Error:329.4341718448116]\n", + "Cond:OO....#O - Act:2 - Num:1 [fit: 0.000, exp: 44.00, pred: 1088.857, Error:166.96024436764287]\n", + "Cond:#....F## - Act:1 - Num:1 [fit: 0.000, exp: 17.00, pred: 889.413, Error:193.18758557468868]\n", + "Cond:OO..#... - Act:2 - Num:8 [fit: 0.000, exp: 21.00, pred: 880.302, Error:260.4853364671974]\n", + "Cond:...##F## - Act:2 - Num:2 [fit: 0.000, exp: 18.00, pred: 976.824, Error:175.0442012775198]\n", + "Cond:.O...... - Act:5 - Num:1 [fit: 0.005, exp: 5.00, pred: 1357.181, Error:334.6735520504819]\n", + "Cond:.....F.. - Act:4 - Num:1 [fit: 0.065, exp: 2.00, pred: 906.979, Error:0.9201912520277915]\n", + "Cond:#...FO## - Act:7 - Num:1 [fit: 0.000, exp: 19.00, pred: 797.565, Error:79.28729872683144]\n", + "Cond:OO.#..## - Act:0 - Num:4 [fit: 0.000, exp: 20.00, pred: 875.756, Error:270.18775689595043]\n", + "Cond:#O....## - Act:0 - Num:2 [fit: 0.000, exp: 25.00, pred: 841.445, Error:206.50289513727327]\n", + "Cond:.#.#.F#. - Act:4 - Num:2 [fit: 0.002, exp: 9.00, pred: 1021.972, Error:70.45063910726864]\n", + "Cond:..#..OF. - Act:1 - Num:1 [fit: 0.003, exp: 2.00, pred: 936.703, Error:86.6056821343505]\n", + "Cond:#O...OF# - Act:3 - Num:2 [fit: 0.000, exp: 24.00, pred: 1286.919, Error:52.20310522170469]\n", + "Cond:.OO#..#O - Act:0 - Num:1 [fit: 0.001, exp: 16.00, pred: 1097.562, Error:166.06781542951748]\n", + "Cond:....#..# - Act:7 - Num:1 [fit: 0.000, exp: 28.00, pred: 776.581, Error:210.99246364018597]\n", + "Cond:.O#....# - Act:1 - Num:1 [fit: 0.000, exp: 24.00, pred: 719.754, Error:185.45141878790614]\n", + "Cond:....#..O - Act:1 - Num:3 [fit: 0.000, exp: 25.00, pred: 812.521, Error:223.43161261262924]\n", + "Cond:.O#...## - Act:1 - Num:4 [fit: 0.000, exp: 24.00, pred: 719.754, Error:183.41449004013674]\n", + "Cond:..#.#OOF - Act:1 - Num:3 [fit: 0.000, exp: 20.00, pred: 852.487, Error:322.3260783141617]\n", + "Cond:##..F#.. - Act:2 - Num:5 [fit: 0.000, exp: 22.00, pred: 837.888, Error:101.05366233899903]\n", + "Cond:..#..O.# - Act:5 - Num:2 [fit: 0.004, exp: 12.00, pred: 1130.205, Error:254.84104279477714]\n", + "Cond:....#O#F - Act:1 - Num:4 [fit: 0.000, exp: 20.00, pred: 852.851, Error:313.402799270678]\n", + "Cond:#..#.F#. - Act:1 - Num:1 [fit: 0.000, exp: 17.00, pred: 889.413, Error:166.58319064742065]\n", + "Cond:##...F#. - Act:1 - Num:6 [fit: 0.000, exp: 17.00, pred: 889.413, Error:166.58319064742065]\n", + "Cond:.OO...#. - Act:0 - Num:9 [fit: 0.000, exp: 22.00, pred: 1322.536, Error:426.79526777378317]\n", + "Cond:##..FF.# - Act:2 - Num:1 [fit: 0.001, exp: 12.00, pred: 1110.014, Error:235.71265565286672]\n", + "Cond:.....F.# - Act:4 - Num:1 [fit: 0.065, exp: 2.00, pred: 906.979, Error:0.9201912520277915]\n", + "Cond:....#F.. - Act:4 - Num:1 [fit: 0.065, exp: 2.00, pred: 906.979, Error:0.9201912520277915]\n", + "Cond:...OO... - Act:7 - Num:4 [fit: 0.003, exp: 11.00, pred: 871.909, Error:380.53515501849284]\n", + "Cond:O.###..O - Act:7 - Num:2 [fit: 0.000, exp: 25.00, pred: 843.243, Error:208.13749385390014]\n", + "Cond:#...F#.. - Act:0 - Num:5 [fit: 0.001, exp: 14.00, pred: 989.822, Error:115.57721363880248]\n", + "Cond:O..#...O - Act:6 - Num:1 [fit: 0.005, exp: 4.00, pred: 721.919, Error:101.7765109485545]\n", + "Cond:O......O - Act:3 - Num:10 [fit: 0.000, exp: 26.00, pred: 1045.183, Error:163.1348510792824]\n", + "Cond:..#.FO.. - Act:3 - Num:2 [fit: 0.000, exp: 29.00, pred: 816.703, Error:174.82433827057156]\n", + "Cond:OO#.#..O - Act:0 - Num:4 [fit: 0.001, exp: 13.00, pred: 799.592, Error:133.40173117775734]\n", + "Cond:#..OO... - Act:2 - Num:4 [fit: 0.003, exp: 11.00, pred: 913.286, Error:227.7648993144003]\n", + "Cond:.O#.#... - Act:1 - Num:2 [fit: 0.000, exp: 25.00, pred: 728.567, Error:213.7553566978856]\n", + "Cond:O.#.#.#O - Act:7 - Num:2 [fit: 0.000, exp: 25.00, pred: 843.243, Error:206.64622409408258]\n", + "Cond:OO...#.O - Act:1 - Num:3 [fit: 0.000, exp: 14.00, pred: 865.293, Error:143.7215994985463]\n", + "Cond:#...##.. - Act:2 - Num:2 [fit: 0.000, exp: 28.00, pred: 870.132, Error:113.04207767023377]\n", + "Cond:..#.#O.. - Act:2 - Num:1 [fit: 0.000, exp: 23.00, pred: 836.646, Error:84.08632468564495]\n", + "Cond:##.OO... - Act:2 - Num:2 [fit: 0.000, exp: 11.00, pred: 913.286, Error:189.46587642598726]\n", + "Cond:O.#....O - Act:4 - Num:2 [fit: 0.000, exp: 35.00, pred: 891.752, Error:282.3278838561756]\n", + "Cond:.O...... - Act:2 - Num:3 [fit: 0.016, exp: 3.00, pred: 708.267, Error:44.35494320199363]\n", + "Cond:.O....## - Act:1 - Num:3 [fit: 0.000, exp: 20.00, pred: 765.083, Error:203.3117896853994]\n", + "Cond:...OO.#. - Act:2 - Num:4 [fit: 0.000, exp: 11.00, pred: 913.286, Error:137.0219763840618]\n", + "Cond:.####O#F - Act:3 - Num:1 [fit: 0.000, exp: 17.00, pred: 873.457, Error:99.4057450047306]\n", + "Cond:#...###F - Act:3 - Num:1 [fit: 0.000, exp: 22.00, pred: 873.457, Error:99.4057450047306]\n", + "Cond:...O..#. - Act:7 - Num:1 [fit: 0.000, exp: 19.00, pred: 898.179, Error:301.98489359141394]\n", + "Cond:...O.##. - Act:7 - Num:4 [fit: 0.000, exp: 29.00, pred: 877.443, Error:325.8098681585035]\n", + "Cond:.O.#.F#. - Act:3 - Num:5 [fit: 0.000, exp: 15.00, pred: 1025.720, Error:63.286023308903296]\n", + "Cond:OO..#.#O - Act:1 - Num:3 [fit: 0.000, exp: 15.00, pred: 884.451, Error:140.37900606885802]\n", + "Cond:OO.#...O - Act:1 - Num:5 [fit: 0.000, exp: 16.00, pred: 884.451, Error:140.37900606885802]\n", + "Cond:#...F##. - Act:3 - Num:3 [fit: 0.001, exp: 19.00, pred: 816.242, Error:175.09753205971566]\n", + "Cond:#O..#... - Act:2 - Num:6 [fit: 0.000, exp: 24.00, pred: 862.080, Error:229.71594261842944]\n", + "Cond:.##.FO#. - Act:1 - Num:1 [fit: 0.000, exp: 25.00, pred: 793.461, Error:219.7812663493749]\n", + "Cond:....#O.. - Act:1 - Num:2 [fit: 0.000, exp: 21.00, pred: 793.461, Error:219.7812663493749]\n", + "Cond:#...FF.. - Act:2 - Num:6 [fit: 0.000, exp: 15.00, pred: 970.845, Error:57.18558848880748]\n", + "Cond:..#..F.. - Act:4 - Num:1 [fit: 0.065, exp: 2.00, pred: 906.979, Error:0.9201912520277915]\n", + "Cond:.#...F.# - Act:4 - Num:1 [fit: 0.065, exp: 2.00, pred: 906.979, Error:0.9201912520277915]\n", + "Cond:.....F## - Act:3 - Num:1 [fit: 0.000, exp: 27.00, pred: 917.924, Error:170.9859489910823]\n", + "Cond:OO.##.#. - Act:2 - Num:7 [fit: 0.000, exp: 21.00, pred: 880.302, Error:260.4853364671974]\n", + "Cond:.#.FO..# - Act:6 - Num:4 [fit: 0.001, exp: 4.00, pred: 1142.442, Error:111.51968607383438]\n", + "Cond:..OO.##. - Act:5 - Num:1 [fit: 0.000, exp: 26.00, pred: 902.812, Error:85.72057422114494]\n", + "Cond:.#...OF# - Act:7 - Num:1 [fit: 0.001, exp: 10.00, pred: 875.535, Error:236.72258712457483]\n", + "Cond:O..###.O - Act:7 - Num:4 [fit: 0.000, exp: 25.00, pred: 844.808, Error:206.63589156546635]\n", + "Cond:....##.O - Act:2 - Num:4 [fit: 0.003, exp: 21.00, pred: 788.532, Error:173.8446690230955]\n", + "Cond:.#..#..O - Act:1 - Num:1 [fit: 0.000, exp: 25.00, pred: 808.526, Error:206.04171589271488]\n", + "Cond:O..#...O - Act:7 - Num:4 [fit: 0.000, exp: 24.00, pred: 847.841, Error:200.82803601001663]\n", + "Cond:O..##..O - Act:5 - Num:1 [fit: 0.000, exp: 39.00, pred: 1082.284, Error:233.08361623828517]\n", + "Cond:O...O..O - Act:5 - Num:2 [fit: 0.003, exp: 4.00, pred: 832.009, Error:25.420119502758677]\n", + "Cond:..##OO#. - Act:7 - Num:1 [fit: 0.001, exp: 12.00, pred: 843.064, Error:212.9665749199443]\n", + "Cond:....OO#. - Act:7 - Num:1 [fit: 0.006, exp: 3.00, pred: 626.902, Error:73.24839307236647]\n", + "Cond:O#.##.#. - Act:6 - Num:1 [fit: 0.000, exp: 23.00, pred: 799.655, Error:161.0196644264775]\n", + "Cond:O#.#.#.. - Act:6 - Num:4 [fit: 0.000, exp: 23.00, pred: 799.655, Error:161.0196644264775]\n", + "Cond:.O..#.## - Act:1 - Num:3 [fit: 0.000, exp: 20.00, pred: 777.888, Error:215.63357497557973]\n", + "Cond:.O#..... - Act:1 - Num:2 [fit: 0.000, exp: 25.00, pred: 720.371, Error:182.96800504444312]\n", + "Cond:....#OF. - Act:1 - Num:6 [fit: 0.000, exp: 15.00, pred: 1243.802, Error:149.26410838090987]\n", + "Cond:..##FO.# - Act:5 - Num:1 [fit: 0.002, exp: 7.00, pred: 850.287, Error:310.5583088675694]\n", + "Cond:OO.F#... - Act:3 - Num:2 [fit: 0.040, exp: 2.00, pred: 2033.855, Error:291.67095061515977]\n", + "Cond:...FOO## - Act:7 - Num:2 [fit: 0.001, exp: 11.00, pred: 902.295, Error:268.7391520323101]\n", + "Cond:..##.F.O - Act:7 - Num:4 [fit: 0.000, exp: 19.00, pred: 1201.368, Error:99.58750039017549]\n", + "Cond:#.#.FO## - Act:7 - Num:2 [fit: 0.000, exp: 19.00, pred: 797.565, Error:73.36261638208705]\n", + "Cond:......OO - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.OO..... - Act:3 - Num:7 [fit: 0.000, exp: 21.00, pred: 1346.933, Error:137.02979443325717]\n", + "Cond:.#...OO. - Act:7 - Num:3 [fit: 0.000, exp: 15.00, pred: 1738.951, Error:87.9977608046385]\n", + "Cond:#OOO..#. - Act:1 - Num:3 [fit: 0.000, exp: 24.00, pred: 818.172, Error:260.2370971624401]\n", + "Cond:.OOO...# - Act:1 - Num:6 [fit: 0.000, exp: 24.00, pred: 818.172, Error:260.2370971624401]\n", + "Cond:O.....#O - Act:4 - Num:5 [fit: 0.000, exp: 18.00, pred: 1026.726, Error:421.06393846539834]\n", + "Cond:..OO..#. - Act:3 - Num:2 [fit: 0.000, exp: 17.00, pred: 908.525, Error:185.51209177173675]\n", + "Cond:..OO#... - Act:3 - Num:2 [fit: 0.000, exp: 16.00, pred: 912.051, Error:188.26310776988763]\n", + "Cond:..O#.... - Act:3 - Num:4 [fit: 0.000, exp: 16.00, pred: 915.225, Error:183.5826355964861]\n", + "Cond:.OOO.... - Act:7 - Num:4 [fit: 0.002, exp: 11.00, pred: 929.649, Error:289.4028255580876]\n", + "Cond:..OO##.. - Act:3 - Num:2 [fit: 0.000, exp: 15.00, pred: 921.084, Error:175.36557489675255]\n", + "Cond:..#..O.. - Act:2 - Num:4 [fit: 0.000, exp: 11.00, pred: 1065.907, Error:55.9257044209477]\n", + "Cond:.OOO#... - Act:1 - Num:5 [fit: 0.001, exp: 23.00, pred: 821.951, Error:279.36633215672225]\n", + "Cond:...#.#OO - Act:1 - Num:5 [fit: 0.000, exp: 20.00, pred: 1334.541, Error:539.4593933317574]\n", + "Cond:.#.#.OF. - Act:4 - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:..OO.... - Act:4 - Num:11 [fit: 0.001, exp: 17.00, pred: 880.377, Error:200.80630381649732]\n", + "Cond:...#...O - Act:1 - Num:3 [fit: 0.000, exp: 25.00, pred: 812.521, Error:228.15193862697137]\n", + "Cond:.......# - Act:7 - Num:4 [fit: 0.000, exp: 12.00, pred: 797.777, Error:213.1952730485737]\n", + "Cond:OO.#O... - Act:3 - Num:2 [fit: 0.000, exp: 26.00, pred: 1633.852, Error:399.54902810177833]\n", + "Cond:..O#.... - Act:4 - Num:3 [fit: 0.000, exp: 25.00, pred: 882.504, Error:206.22843462343087]\n", + "Cond:.OO..#.. - Act:0 - Num:7 [fit: 0.000, exp: 22.00, pred: 1322.536, Error:426.79526777378317]\n", + "Cond:#OO..... - Act:0 - Num:4 [fit: 0.000, exp: 22.00, pred: 1322.536, Error:426.79526777378317]\n", + "Cond:##.FOO.. - Act:4 - Num:1 [fit: 0.000, exp: 35.00, pred: 803.605, Error:119.87129561566039]\n", + "Cond:#OO..#.. - Act:0 - Num:3 [fit: 0.000, exp: 22.00, pred: 1322.536, Error:373.95676542616235]\n", + "Cond:#.O..... - Act:6 - Num:3 [fit: 0.000, exp: 9.00, pred: 1388.167, Error:310.2075856340766]\n", + "Cond:.....OO. - Act:7 - Num:5 [fit: 0.001, exp: 5.00, pred: 1806.540, Error:88.74386518001083]\n", + "Cond:..O#.... - Act:2 - Num:4 [fit: 0.000, exp: 12.00, pred: 1426.763, Error:61.48144042886055]\n", + "Cond:...#OOF. - Act:6 - Num:5 [fit: 0.003, exp: 11.00, pred: 1551.459, Error:436.87285756594116]\n", + "Cond:#O..#... - Act:0 - Num:2 [fit: 0.000, exp: 15.00, pred: 1348.970, Error:68.02362002915262]\n", + "Cond:.O##...# - Act:0 - Num:1 [fit: 0.003, exp: 1.00, pred: 752.656, Error:0.0]\n", + "Cond:.OO....# - Act:0 - Num:7 [fit: 0.000, exp: 22.00, pred: 1322.536, Error:192.18083016486824]\n", + "Cond:.#.#.OO# - Act:1 - Num:4 [fit: 0.000, exp: 10.00, pred: 1130.893, Error:442.48574387106726]\n", + "Cond:...O.#.O - Act:3 - Num:8 [fit: 0.000, exp: 23.00, pred: 1851.396, Error:220.4669798280467]\n", + "Cond:...#.... - Act:3 - Num:3 [fit: 0.040, exp: 2.00, pred: 832.023, Error:37.6473959590254]\n", + "Cond:..#....# - Act:7 - Num:2 [fit: 0.000, exp: 26.00, pred: 794.805, Error:184.92155182141897]\n", + "Cond:..O..... - Act:6 - Num:8 [fit: 0.000, exp: 16.00, pred: 1622.352, Error:77.92632524580719]\n", + "Cond:..O##... - Act:2 - Num:7 [fit: 0.000, exp: 16.00, pred: 1622.352, Error:77.92632524580719]\n", + "Cond:.O..#.#. - Act:1 - Num:3 [fit: 0.000, exp: 22.00, pred: 791.964, Error:193.1445633166514]\n", + "Cond:...O..#. - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.O...... - Act:7 - Num:3 [fit: 0.050, exp: 1.00, pred: 741.903, Error:0.0]\n", + "Cond:...O.#.. - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:..##..#O - Act:7 - Num:2 [fit: 0.000, exp: 28.00, pred: 738.754, Error:147.55418204355655]\n", + "Cond:...##..O - Act:7 - Num:3 [fit: 0.000, exp: 26.00, pred: 799.282, Error:159.21532306097043]\n", + "Cond:OO#....O - Act:4 - Num:3 [fit: 0.020, exp: 1.00, pred: 709.694, Error:0.0]\n", + "Cond:..O#.O.. - Act:6 - Num:8 [fit: 0.000, exp: 19.00, pred: 1829.466, Error:75.32712233781564]\n", + "Cond:.OO....# - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:OO.#.... - Act:7 - Num:4 [fit: 0.000, exp: 13.00, pred: 1654.994, Error:107.2244645524614]\n", + "Cond:....F#.. - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#.##FO.# - Act:7 - Num:4 [fit: 0.000, exp: 19.00, pred: 797.565, Error:72.65763471871648]\n", + "Cond:..##FO.. - Act:5 - Num:2 [fit: 0.000, exp: 13.00, pred: 834.296, Error:190.05423739084594]\n", + "Cond:.#..#O.# - Act:5 - Num:2 [fit: 0.000, exp: 6.00, pred: 792.596, Error:178.2798680575407]\n", + "Cond:#..#F##. - Act:1 - Num:2 [fit: 0.000, exp: 25.00, pred: 793.461, Error:205.27554982464366]\n", + "Cond:....#O#. - Act:1 - Num:2 [fit: 0.000, exp: 22.00, pred: 787.467, Error:169.01551325684352]\n", + "Cond:O.#....O - Act:3 - Num:5 [fit: 0.000, exp: 26.00, pred: 1045.183, Error:148.20909152677666]\n", + "Cond:.#...F.. - Act:0 - Num:2 [fit: 0.002, exp: 10.00, pred: 1134.128, Error:58.1231662105796]\n", + "Cond:....#F.. - Act:3 - Num:1 [fit: 0.000, exp: 27.00, pred: 917.924, Error:168.8328379983771]\n", + "Cond:.....#F# - Act:7 - Num:3 [fit: 0.001, exp: 10.00, pred: 875.535, Error:169.8801389469771]\n", + "Cond:..OO#... - Act:4 - Num:2 [fit: 0.000, exp: 17.00, pred: 880.377, Error:200.80630381649732]\n", + "Cond:..###O.# - Act:5 - Num:1 [fit: 0.000, exp: 18.00, pred: 788.752, Error:165.9516807890421]\n", + "Cond:OO#O...O - Act:5 - Num:3 [fit: 0.001, exp: 12.00, pred: 989.583, Error:41.99403525458993]\n", + "Cond:O#..#... - Act:2 - Num:8 [fit: 0.000, exp: 21.00, pred: 880.302, Error:236.9787464458394]\n", + "Cond:OO...#.. - Act:2 - Num:9 [fit: 0.000, exp: 21.00, pred: 880.302, Error:236.9787464458394]\n", + "Cond:#...F#F# - Act:3 - Num:2 [fit: 0.002, exp: 2.00, pred: 827.903, Error:9.989530588733132]\n", + "Cond:.#...F.# - Act:3 - Num:4 [fit: 0.000, exp: 27.00, pred: 917.924, Error:172.75929004369047]\n", + "Cond:.....#O. - Act:7 - Num:3 [fit: 0.000, exp: 13.00, pred: 1607.333, Error:108.39634289961674]\n", + "Cond:.###.#.O - Act:1 - Num:1 [fit: 0.000, exp: 25.00, pred: 810.769, Error:161.79206849963916]\n", + "Cond:..OO#.#. - Act:4 - Num:1 [fit: 0.000, exp: 17.00, pred: 880.377, Error:193.94944768547967]\n", + "Cond:.O..#... - Act:0 - Num:2 [fit: 0.000, exp: 18.00, pred: 1039.630, Error:69.49008728113515]\n", + "Cond:.#..#..O - Act:7 - Num:1 [fit: 0.000, exp: 26.00, pred: 806.509, Error:118.3045132354259]\n", + "Cond:O....#.O - Act:3 - Num:7 [fit: 0.000, exp: 26.00, pred: 1045.183, Error:144.89701240201538]\n", + "Cond:#.##F#.. - Act:2 - Num:8 [fit: 0.000, exp: 22.00, pred: 838.336, Error:59.986708189604165]\n", + "Cond:..#.#..O - Act:2 - Num:2 [fit: 0.000, exp: 21.00, pred: 788.532, Error:104.10060703145277]\n", + "Cond:.#.#.F#. - Act:0 - Num:5 [fit: 0.000, exp: 16.00, pred: 961.673, Error:48.71520426825123]\n", + "Cond:#...#F.. - Act:1 - Num:1 [fit: 0.000, exp: 17.00, pred: 889.413, Error:96.78008776879648]\n", + "Cond:..#..OOO - Act:0 - Num:9 [fit: 0.000, exp: 22.00, pred: 1018.462, Error:9.38986646511859]\n", + "Cond:...O#.#. - Act:2 - Num:1 [fit: 0.014, exp: 1.00, pred: 711.996, Error:0.0]\n", + "Cond:....#F.. - Act:2 - Num:6 [fit: 0.000, exp: 25.00, pred: 946.586, Error:114.29216226726246]\n", + "Cond:#O.##... - Act:2 - Num:8 [fit: 0.000, exp: 23.00, pred: 865.265, Error:242.4022453733367]\n", + "Cond:.OOO.... - Act:6 - Num:2 [fit: 0.025, exp: 1.00, pred: 690.201, Error:0.0]\n", + "Cond:.OO....# - Act:2 - Num:1 [fit: 0.000, exp: 23.00, pred: 864.231, Error:129.62822311969154]\n", + "Cond:.OO#.... - Act:2 - Num:1 [fit: 0.000, exp: 30.00, pred: 825.739, Error:130.4695542989627]\n", + "Cond:...O#... - Act:3 - Num:1 [fit: 0.050, exp: 1.00, pred: 703.162, Error:0.0]\n", + "Cond:#OO....# - Act:4 - Num:1 [fit: 0.000, exp: 33.00, pred: 793.133, Error:209.0675248649937]\n", + "Cond:...FOO.. - Act:7 - Num:1 [fit: 0.001, exp: 12.00, pred: 879.282, Error:207.07374132397052]\n", + "Cond:.....#.O - Act:3 - Num:4 [fit: 0.009, exp: 6.00, pred: 796.593, Error:218.44161650233156]\n", + "Cond:OO.##..O - Act:0 - Num:5 [fit: 0.002, exp: 12.00, pred: 792.719, Error:112.23107918854755]\n", + "Cond:O#..#.#O - Act:0 - Num:5 [fit: 0.002, exp: 12.00, pred: 792.719, Error:112.23107918854755]\n", + "Cond:#OO....# - Act:6 - Num:3 [fit: 0.000, exp: 30.00, pred: 786.256, Error:204.38185518206458]\n", + "Cond:#O#.O#.. - Act:1 - Num:3 [fit: 0.000, exp: 18.00, pred: 1111.647, Error:43.4941267917952]\n", + "Cond:O#.O.... - Act:4 - Num:9 [fit: 0.000, exp: 21.00, pred: 1419.105, Error:145.38723616986397]\n", + "Cond:.O..#### - Act:0 - Num:5 [fit: 0.000, exp: 24.00, pred: 1125.664, Error:88.2189362872778]\n", + "Cond:...##FO. - Act:5 - Num:2 [fit: 0.000, exp: 22.00, pred: 1413.167, Error:28.957314780006225]\n", + "Cond:#...#FO# - Act:5 - Num:2 [fit: 0.000, exp: 28.00, pred: 1786.349, Error:237.13238787560007]\n", + "Cond:.#.#.... - Act:7 - Num:1 [fit: 0.000, exp: 15.00, pred: 874.563, Error:299.5224117055316]\n", + "Cond:.OOO..O. - Act:5 - Num:6 [fit: 0.000, exp: 20.00, pred: 1982.582, Error:135.10784405529986]\n", + "Cond:.OO...#O - Act:5 - Num:1 [fit: 0.000, exp: 32.00, pred: 2535.305, Error:727.5871013842868]\n", + "Cond:#.#...#. - Act:6 - Num:2 [fit: 0.000, exp: 15.00, pred: 2537.713, Error:83.08404239306027]\n", + "Cond:.....OOF - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:....#OF. - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.....O.. - Act:5 - Num:5 [fit: 0.000, exp: 20.00, pred: 1518.325, Error:108.93424133853078]\n", + "Cond:.#OO.... - Act:6 - Num:6 [fit: 0.002, exp: 17.00, pred: 907.543, Error:198.17971752768022]\n", + "Cond:...#..OO - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#..#.... - Act:7 - Num:4 [fit: 0.000, exp: 19.00, pred: 1214.090, Error:636.8045206115229]\n", + "Cond:##.O.... - Act:4 - Num:5 [fit: 0.000, exp: 25.00, pred: 1196.796, Error:531.010136881062]\n", + "Cond:.....F.. - Act:5 - Num:2 [fit: 0.000, exp: 61.00, pred: 1306.751, Error:209.63810455825947]\n", + "Cond:...FOO.. - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:......O# - Act:0 - Num:4 [fit: 0.009, exp: 3.00, pred: 743.747, Error:32.6470124642146]\n", + "Cond:.OO#...# - Act:1 - Num:4 [fit: 0.000, exp: 25.00, pred: 771.129, Error:212.3593407195533]\n", + "Cond:O..F...O - Act:5 - Num:3 [fit: 0.000, exp: 17.00, pred: 1340.009, Error:68.20722241739978]\n", + "Cond:.......O - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.......# - Act:0 - Num:3 [fit: 0.001, exp: 15.00, pred: 824.577, Error:140.70298617317846]\n", + "Cond:O.##.... - Act:1 - Num:7 [fit: 0.000, exp: 23.00, pred: 1274.408, Error:54.92984070576826]\n", + "Cond:...#..#. - Act:4 - Num:1 [fit: 0.000, exp: 22.00, pred: 830.286, Error:74.94729001124477]\n", + "Cond:.....O.. - Act:6 - Num:4 [fit: 0.000, exp: 17.00, pred: 1187.672, Error:48.22514931822723]\n", + "Cond:..#OO... - Act:7 - Num:4 [fit: 0.000, exp: 11.00, pred: 871.909, Error:380.53515501849284]\n", + "Cond:...OO..# - Act:7 - Num:2 [fit: 0.000, exp: 11.00, pred: 871.909, Error:380.53515501849284]\n", + "Cond:.#.#...O - Act:1 - Num:4 [fit: 0.000, exp: 25.00, pred: 812.521, Error:228.15193862697137]\n", + "Cond:....##.O - Act:1 - Num:3 [fit: 0.000, exp: 25.00, pred: 812.521, Error:228.15193862697137]\n", + "Cond:.###OO#. - Act:7 - Num:1 [fit: 0.000, exp: 12.00, pred: 872.679, Error:192.26798109614106]\n", + "Cond:#..FOO#. - Act:4 - Num:3 [fit: 0.000, exp: 12.00, pred: 848.828, Error:178.70806081803005]\n", + "Cond:#..OO... - Act:0 - Num:7 [fit: 0.000, exp: 19.00, pred: 949.621, Error:218.4811914816932]\n", + "Cond:...O#... - Act:7 - Num:4 [fit: 0.000, exp: 13.00, pred: 773.704, Error:297.68144698225336]\n", + "Cond:.....OOF - Act:7 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:...#.#O# - Act:1 - Num:2 [fit: 0.000, exp: 20.00, pred: 1310.289, Error:544.3991435428758]\n", + "Cond:#..#O... - Act:0 - Num:2 [fit: 0.000, exp: 19.00, pred: 949.621, Error:215.3728878841493]\n", + "Cond:#.#OO... - Act:3 - Num:2 [fit: 0.000, exp: 4.00, pred: 1854.385, Error:29.447767223624183]\n", + "Cond:...FOO.. - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#..FOO#. - Act:1 - Num:3 [fit: 0.000, exp: 14.00, pred: 837.627, Error:244.28779886320925]\n", + "Cond:...#O##. - Act:7 - Num:4 [fit: 0.001, exp: 20.00, pred: 711.193, Error:120.81889366773973]\n", + "Cond:...##O## - Act:7 - Num:4 [fit: 0.000, exp: 25.00, pred: 779.209, Error:135.97694088396412]\n", + "Cond:O.#.FO.# - Act:7 - Num:7 [fit: 0.007, exp: 13.00, pred: 859.222, Error:187.87248267439995]\n", + "Cond:...####O - Act:0 - Num:7 [fit: 0.001, exp: 22.00, pred: 790.852, Error:118.99145901349016]\n", + "Cond:...###.. - Act:7 - Num:3 [fit: 0.000, exp: 27.00, pred: 670.654, Error:63.35042035985107]\n", + "Cond:..#####O - Act:0 - Num:3 [fit: 0.000, exp: 22.00, pred: 790.852, Error:106.92692761825035]\n", + "Cond:...OO... - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:....O.#. - Act:7 - Num:4 [fit: 0.001, exp: 11.00, pred: 1045.719, Error:54.06810418028191]\n", + "Cond:...#O#.. - Act:7 - Num:4 [fit: 0.000, exp: 20.00, pred: 711.193, Error:99.04819936670552]\n", + "Cond:.#.##.O# - Act:1 - Num:1 [fit: 0.000, exp: 17.00, pred: 1366.424, Error:406.1397009365083]\n", + "Cond:.#.###.O - Act:2 - Num:2 [fit: 0.000, exp: 21.00, pred: 788.532, Error:91.44422009504254]\n", + "Cond:....FO.. - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#.##FO## - Act:7 - Num:2 [fit: 0.000, exp: 19.00, pred: 819.570, Error:54.288090527487064]\n", + "Cond:#..OO..# - Act:0 - Num:4 [fit: 0.000, exp: 19.00, pred: 949.621, Error:191.82690007180707]\n", + "Cond:...#.F## - Act:3 - Num:1 [fit: 0.000, exp: 27.00, pred: 917.924, Error:151.36038621700226]\n", + "Cond:...###OF - Act:1 - Num:2 [fit: 0.000, exp: 15.00, pred: 853.941, Error:243.91850843733752]\n", + "Cond:....##.# - Act:1 - Num:2 [fit: 0.000, exp: 26.00, pred: 770.092, Error:216.46514403945133]\n", + "Cond:.#...F.. - Act:3 - Num:2 [fit: 0.000, exp: 27.00, pred: 917.924, Error:68.80299411072076]\n", + "Cond:.OO##..# - Act:3 - Num:3 [fit: 0.000, exp: 24.00, pred: 1347.652, Error:137.3153641137399]\n", + "Cond:...#.... - Act:7 - Num:5 [fit: 0.003, exp: 7.00, pred: 886.714, Error:129.0662107415224]\n", + "Cond:...####O - Act:4 - Num:9 [fit: 0.000, exp: 21.00, pred: 759.280, Error:86.89548932986267]\n", + "Cond:#..O##OF - Act:1 - Num:5 [fit: 0.000, exp: 19.00, pred: 1066.518, Error:58.61702782798356]\n", + "Cond:..#.#.OF - Act:1 - Num:3 [fit: 0.000, exp: 14.00, pred: 1066.518, Error:58.61702782798356]\n", + "Cond:#..FO#.. - Act:4 - Num:3 [fit: 0.000, exp: 11.00, pred: 852.983, Error:94.11982600178878]\n", + "Cond:...OO##. - Act:7 - Num:4 [fit: 0.000, exp: 16.00, pred: 817.220, Error:178.1562418621723]\n", + "Cond:...#O... - Act:7 - Num:2 [fit: 0.000, exp: 11.00, pred: 817.220, Error:178.1562418621723]\n", + "Cond:..####.O - Act:4 - Num:5 [fit: 0.000, exp: 20.00, pred: 801.007, Error:16.72324378861213]\n", + "Cond:.#...#.O - Act:2 - Num:3 [fit: 0.000, exp: 21.00, pred: 788.532, Error:70.78093678983794]\n", + "Cond:.###.#.O - Act:7 - Num:2 [fit: 0.000, exp: 26.00, pred: 820.755, Error:59.090394471793076]\n", + "Cond:O.....#O - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.....#.# - Act:1 - Num:1 [fit: 0.000, exp: 26.00, pred: 770.092, Error:79.57398086326852]\n", + "Cond:.#.....# - Act:1 - Num:1 [fit: 0.000, exp: 27.00, pred: 732.572, Error:93.45302380947501]\n", + "Cond:#OO...#. - Act:1 - Num:1 [fit: 0.002, exp: 8.00, pred: 833.046, Error:72.04772821819732]\n", + "Cond:.O#.#.## - Act:2 - Num:2 [fit: 0.013, exp: 10.00, pred: 769.697, Error:121.15258714884892]\n", + "Cond:#..##O## - Act:7 - Num:3 [fit: 0.000, exp: 25.00, pred: 779.209, Error:123.3268461321649]\n", + "Cond:##.OO..# - Act:0 - Num:4 [fit: 0.000, exp: 19.00, pred: 949.621, Error:177.05649891235916]\n", + "Cond:O#.OO..# - Act:0 - Num:7 [fit: 0.000, exp: 10.00, pred: 1010.898, Error:69.78101249632977]\n", + "Cond:O#.####. - Act:6 - Num:1 [fit: 0.000, exp: 23.00, pred: 799.655, Error:122.08183920791123]\n", + "Cond:OO....#. - Act:6 - Num:2 [fit: 0.000, exp: 23.00, pred: 799.655, Error:122.08183920791123]\n", + "Cond:.#..#.#. - Act:3 - Num:3 [fit: 0.000, exp: 31.00, pred: 733.627, Error:136.10141926415218]\n", + "Cond:###FOO.. - Act:1 - Num:2 [fit: 0.000, exp: 14.00, pred: 822.802, Error:181.90030020398808]\n", + "Cond:O...#..# - Act:3 - Num:5 [fit: 0.000, exp: 26.00, pred: 1045.183, Error:133.32538410751346]\n", + "Cond:...##F#. - Act:2 - Num:4 [fit: 0.000, exp: 18.00, pred: 976.824, Error:61.112192654004325]\n", + "Cond:...#...# - Act:4 - Num:2 [fit: 0.000, exp: 22.00, pred: 1013.541, Error:131.92929656709595]\n", + "Cond:#.O#.... - Act:4 - Num:8 [fit: 0.000, exp: 17.00, pred: 880.377, Error:131.76779114352112]\n", + "Cond:#O#O..#. - Act:1 - Num:4 [fit: 0.000, exp: 22.00, pred: 844.352, Error:182.55581514929514]\n", + "Cond:......OO - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.#.#.#OO - Act:1 - Num:4 [fit: 0.000, exp: 20.00, pred: 1334.541, Error:423.16398356965]\n", + "Cond:.#....O# - Act:0 - Num:3 [fit: 0.001, exp: 3.00, pred: 743.747, Error:27.518159429412055]\n", + "Cond:...#..O# - Act:0 - Num:3 [fit: 0.001, exp: 3.00, pred: 743.747, Error:27.518159429412055]\n", + "Cond:.#.#..O# - Act:1 - Num:4 [fit: 0.000, exp: 13.00, pred: 1457.414, Error:362.1088069008897]\n", + "Cond:#.#.#OOF - Act:1 - Num:5 [fit: 0.000, exp: 20.00, pred: 991.690, Error:100.45734436892215]\n", + "Cond:#O..##.. - Act:2 - Num:8 [fit: 0.000, exp: 23.00, pred: 865.265, Error:242.4022453733367]\n", + "Cond:#O.#...# - Act:2 - Num:1 [fit: 0.000, exp: 46.00, pred: 1084.445, Error:161.73362320959413]\n", + "Cond:.O...... - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:..##O##. - Act:7 - Num:4 [fit: 0.000, exp: 20.00, pred: 733.593, Error:93.32654023996919]\n", + "Cond:#...F### - Act:3 - Num:4 [fit: 0.000, exp: 19.00, pred: 816.242, Error:172.3968269759866]\n", + "Cond:.O...#.O - Act:2 - Num:3 [fit: 0.000, exp: 21.00, pred: 788.532, Error:17.695234197459484]\n", + "Cond:.#...##O - Act:2 - Num:3 [fit: 0.000, exp: 21.00, pred: 788.532, Error:17.695234197459484]\n", + "Cond:#O##...# - Act:5 - Num:5 [fit: 0.001, exp: 11.00, pred: 1038.102, Error:253.75683645727585]\n", + "Cond:.#...... - Act:5 - Num:1 [fit: 0.005, exp: 5.00, pred: 1357.181, Error:334.6735520504819]\n", + "Cond:##OO...# - Act:4 - Num:2 [fit: 0.000, exp: 35.00, pred: 1604.532, Error:157.22286515672369]\n", + "Cond:..O#...# - Act:4 - Num:10 [fit: 0.000, exp: 17.00, pred: 880.377, Error:68.1066906673225]\n", + "Cond:.OOO#... - Act:6 - Num:1 [fit: 0.003, exp: 1.00, pred: 690.201, Error:0.0]\n", + "Cond:#O#.#.## - Act:2 - Num:1 [fit: 0.000, exp: 46.00, pred: 1084.422, Error:161.51924032042282]\n", + "Cond:.#.#...# - Act:2 - Num:2 [fit: 0.001, exp: 10.00, pred: 793.056, Error:69.02222338821224]\n", + "Cond:#.###O## - Act:7 - Num:3 [fit: 0.000, exp: 25.00, pred: 779.209, Error:120.48458399864334]\n", + "Cond:O....#.O - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#O#.#.## - Act:7 - Num:7 [fit: 0.000, exp: 15.00, pred: 796.025, Error:16.66177355373079]\n", + "Cond:#..OO.#. - Act:0 - Num:8 [fit: 0.000, exp: 19.00, pred: 949.621, Error:160.09487908615318]\n", + "Cond:O..OO... - Act:0 - Num:7 [fit: 0.000, exp: 13.00, pred: 848.900, Error:74.24203541506455]\n", + "Cond:.O...... - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:O#.#...# - Act:6 - Num:1 [fit: 0.000, exp: 24.00, pred: 756.409, Error:109.11725398907859]\n", + "Cond:OO.#..#. - Act:6 - Num:2 [fit: 0.000, exp: 26.00, pred: 793.949, Error:96.18049214965333]\n", + "Cond:...O.... - Act:5 - Num:5 [fit: 0.026, exp: 7.00, pred: 886.714, Error:338.7473087991509]\n", + "Cond:.##O...# - Act:7 - Num:2 [fit: 0.000, exp: 48.00, pred: 1148.435, Error:236.27617531067852]\n", + "Cond:..#..#.O - Act:3 - Num:4 [fit: 0.001, exp: 6.00, pred: 796.593, Error:54.61040412558289]\n", + "Cond:.....#.O - Act:1 - Num:4 [fit: 0.001, exp: 6.00, pred: 796.593, Error:54.61040412558289]\n", + "Cond:#.OO...# - Act:1 - Num:3 [fit: 0.006, exp: 3.00, pred: 850.399, Error:21.43399702790664]\n", + "Cond:..#.#O#F - Act:3 - Num:1 [fit: 0.000, exp: 22.00, pred: 873.829, Error:56.07858625007316]\n", + "Cond:#.....O# - Act:0 - Num:2 [fit: 0.001, exp: 3.00, pred: 743.747, Error:8.16175311605365]\n", + "Cond:.##..O#F - Act:0 - Num:3 [fit: 0.003, exp: 17.00, pred: 1326.980, Error:359.1679042132547]\n", + "Cond:O....#.O - Act:4 - Num:7 [fit: 0.000, exp: 19.00, pred: 889.640, Error:264.80256757963747]\n", + "Cond:#...FO#. - Act:3 - Num:3 [fit: 0.000, exp: 19.00, pred: 816.242, Error:169.77228606581394]\n", + "Cond:..##.#OO - Act:1 - Num:5 [fit: 0.000, exp: 20.00, pred: 1334.541, Error:363.0759246439793]\n", + "Cond:...#.##O - Act:1 - Num:5 [fit: 0.000, exp: 20.00, pred: 1334.541, Error:363.0759246439793]\n", + "Cond:#OOO##.. - Act:2 - Num:1 [fit: 0.000, exp: 26.00, pred: 834.702, Error:153.65873685671812]\n", + "Cond:.OO##... - Act:3 - Num:1 [fit: 0.000, exp: 24.00, pred: 1347.652, Error:136.94434252999866]\n", + "Cond:.OO##..# - Act:1 - Num:5 [fit: 0.000, exp: 22.00, pred: 834.695, Error:67.29335359694214]\n", + "Cond:...#OO#. - Act:5 - Num:3 [fit: 0.001, exp: 13.00, pred: 794.063, Error:143.99946197977494]\n", + "Cond:...#OO#. - Act:0 - Num:3 [fit: 0.001, exp: 10.00, pred: 926.233, Error:70.46178447861966]\n", + "Cond:...#OO## - Act:7 - Num:3 [fit: 0.001, exp: 12.00, pred: 843.064, Error:105.33029124612442]\n", + "Cond:OO##..#. - Act:4 - Num:2 [fit: 0.000, exp: 25.00, pred: 836.940, Error:67.03309126033191]\n", + "Cond:O#.#.##. - Act:7 - Num:1 [fit: 0.000, exp: 23.00, pred: 799.655, Error:60.621824019909766]\n", + "Cond:.#.##... - Act:3 - Num:1 [fit: 0.000, exp: 31.00, pred: 733.627, Error:136.10141926415218]\n", + "Cond:#..OO#.. - Act:0 - Num:7 [fit: 0.000, exp: 19.00, pred: 949.621, Error:150.73021750207988]\n", + "Cond:#..#OO#. - Act:4 - Num:2 [fit: 0.000, exp: 11.00, pred: 894.822, Error:44.34789887027637]\n", + "Cond:...FOO#. - Act:7 - Num:7 [fit: 0.000, exp: 22.00, pred: 818.284, Error:91.9629713494957]\n", + "Cond:..OO.... - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#.OO.... - Act:4 - Num:10 [fit: 0.000, exp: 17.00, pred: 880.377, Error:50.20157595412433]\n", + "Cond:...O.... - Act:6 - Num:2 [fit: 0.026, exp: 3.00, pred: 1288.888, Error:174.85129323463403]\n", + "Cond:OO..#.#. - Act:2 - Num:8 [fit: 0.000, exp: 21.00, pred: 880.302, Error:214.6328929247786]\n", + "Cond:OO..#... - Act:1 - Num:2 [fit: 0.000, exp: 31.00, pred: 851.065, Error:91.95260542074793]\n", + "Cond:OO....#O - Act:1 - Num:4 [fit: 0.000, exp: 16.00, pred: 878.434, Error:57.92369354609416]\n", + "Cond:OO#....O - Act:1 - Num:4 [fit: 0.000, exp: 16.00, pred: 878.434, Error:57.92369354609416]\n", + "Cond:O......O - Act:6 - Num:5 [fit: 0.001, exp: 11.00, pred: 973.893, Error:48.999844428420104]\n", + "Cond:.....F.# - Act:6 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:...#.F.. - Act:5 - Num:1 [fit: 0.000, exp: 61.00, pred: 1306.751, Error:209.63810455825947]\n", + "Cond:.#.#.#O. - Act:1 - Num:4 [fit: 0.000, exp: 19.00, pred: 1123.060, Error:101.34290294696999]\n", + "Cond:.OOO.... - Act:4 - Num:8 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:134.69557378901595]\n", + "Cond:.O#O.... - Act:7 - Num:4 [fit: 0.000, exp: 11.00, pred: 929.649, Error:237.76318672308915]\n", + "Cond:OO..#... - Act:4 - Num:8 [fit: 0.000, exp: 22.00, pred: 1082.899, Error:161.7574504676473]\n", + "Cond:#O#.##.. - Act:2 - Num:7 [fit: 0.000, exp: 23.00, pred: 865.265, Error:194.37293523661384]\n", + "Cond:#.#.#O.. - Act:3 - Num:2 [fit: 0.000, exp: 19.00, pred: 816.242, Error:162.93181342294295]\n", + "Cond:..#OO##. - Act:1 - Num:2 [fit: 0.004, exp: 12.00, pred: 1001.253, Error:133.7011536675054]\n", + "Cond:#...#..O - Act:5 - Num:5 [fit: 0.002, exp: 10.00, pred: 1144.724, Error:225.535867996287]\n", + "Cond:.....OF. - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.O#O##.. - Act:4 - Num:3 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:131.33636041030633]\n", + "Cond:OO###.#. - Act:4 - Num:1 [fit: 0.000, exp: 25.00, pred: 836.940, Error:67.03309126033191]\n", + "Cond:OO.....O - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#O#.#.#O - Act:0 - Num:9 [fit: 0.000, exp: 24.00, pred: 1125.871, Error:83.25448230208667]\n", + "Cond:#....O#F - Act:0 - Num:1 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:354.5882409410898]\n", + "Cond:.#.####. - Act:7 - Num:4 [fit: 0.000, exp: 12.00, pred: 810.617, Error:142.83992054159063]\n", + "Cond:#....OF# - Act:7 - Num:1 [fit: 0.001, exp: 10.00, pred: 875.535, Error:149.34956072184676]\n", + "Cond:###.F#.. - Act:3 - Num:5 [fit: 0.000, exp: 19.00, pred: 816.242, Error:110.28436519826698]\n", + "Cond:.OOO..#. - Act:7 - Num:4 [fit: 0.002, exp: 11.00, pred: 929.649, Error:289.4028255580876]\n", + "Cond:##..#OF# - Act:1 - Num:2 [fit: 0.003, exp: 6.00, pred: 840.921, Error:22.540079029169036]\n", + "Cond:#.##.... - Act:1 - Num:2 [fit: 0.000, exp: 43.00, pred: 1241.952, Error:397.295352305611]\n", + "Cond:##...... - Act:1 - Num:1 [fit: 0.000, exp: 40.00, pred: 885.831, Error:154.0637641277305]\n", + "Cond:OO..#... - Act:3 - Num:3 [fit: 0.000, exp: 44.00, pred: 1021.196, Error:192.86595287808]\n", + "Cond:OO...... - Act:2 - Num:8 [fit: 0.000, exp: 21.00, pred: 880.302, Error:260.4853364671974]\n", + "Cond:#..#..#O - Act:5 - Num:7 [fit: 0.000, exp: 28.00, pred: 1091.541, Error:223.18891363871927]\n", + "Cond:O#.....O - Act:4 - Num:8 [fit: 0.000, exp: 20.00, pred: 849.001, Error:128.95288020487595]\n", + "Cond:.....OF. - Act:5 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:#....O#. - Act:2 - Num:1 [fit: 0.000, exp: 35.00, pred: 870.631, Error:249.3473898290552]\n", + "Cond:..#..O#. - Act:2 - Num:1 [fit: 0.000, exp: 35.00, pred: 870.631, Error:249.3473898290552]\n", + "Cond:#.#OO... - Act:4 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:0.01]\n", + "Cond:.#.#OO#. - Act:7 - Num:3 [fit: 0.001, exp: 12.00, pred: 843.064, Error:86.42901730565414]\n", + "Cond:....FO.# - Act:2 - Num:3 [fit: 0.000, exp: 24.00, pred: 841.579, Error:29.629697431279354]\n", + "Cond:....F##. - Act:2 - Num:1 [fit: 0.000, exp: 28.00, pred: 841.579, Error:29.629697431279354]\n", + "Cond:.....#F# - Act:3 - Num:5 [fit: 0.000, exp: 46.00, pred: 1624.522, Error:339.5095767425465]\n", + "Cond:#O#O.... - Act:4 - Num:4 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:125.46246356434268]\n", + "Cond:.O#O.... - Act:4 - Num:6 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:125.46246356434268]\n", + "Cond:.#O##... - Act:3 - Num:2 [fit: 0.000, exp: 24.00, pred: 1347.293, Error:135.61077118067297]\n", + "Cond:OO###... - Act:4 - Num:1 [fit: 0.000, exp: 25.00, pred: 836.940, Error:16.75827281508298]\n", + "Cond:OO#O..#. - Act:4 - Num:2 [fit: 0.000, exp: 25.00, pred: 836.940, Error:16.75827281508298]\n", + "Cond:#O.##... - Act:3 - Num:1 [fit: 0.000, exp: 44.00, pred: 1021.196, Error:191.58616024301475]\n", + "Cond:#OO#..#. - Act:4 - Num:4 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:108.00508633826055]\n", + "Cond:.OO.#.#. - Act:3 - Num:5 [fit: 0.000, exp: 21.00, pred: 1347.293, Error:125.55796350617484]\n", + "Cond:.OO#...# - Act:3 - Num:2 [fit: 0.000, exp: 24.00, pred: 1347.293, Error:125.55796350617484]\n", + "Cond:#..#.O.. - Act:1 - Num:1 [fit: 0.000, exp: 33.00, pred: 1197.543, Error:33.77857913502802]\n", + "Cond:OO..O#.. - Act:3 - Num:5 [fit: 0.000, exp: 29.00, pred: 1262.148, Error:43.57664353205007]\n", + "Cond:.OO.#... - Act:3 - Num:5 [fit: 0.000, exp: 21.00, pred: 1347.293, Error:119.93251437704524]\n", + "Cond:.OO#.... - Act:3 - Num:2 [fit: 0.000, exp: 24.00, pred: 1347.293, Error:119.93251437704524]\n", + "Cond:#...##.O - Act:5 - Num:7 [fit: 0.002, exp: 10.00, pred: 1144.724, Error:225.535867996287]\n", + "Cond:#.#.#..O - Act:3 - Num:3 [fit: 0.002, exp: 11.00, pred: 1127.524, Error:212.7687492498865]\n", + "Cond:#.###... - Act:1 - Num:1 [fit: 0.000, exp: 55.00, pred: 982.703, Error:150.1322870533698]\n", + "Cond:#....#.. - Act:1 - Num:1 [fit: 0.000, exp: 39.00, pred: 1376.290, Error:351.55809040883474]\n", + "Cond:#.#OO##. - Act:1 - Num:3 [fit: 0.004, exp: 12.00, pred: 1001.253, Error:133.7011536675054]\n", + "Cond:..#O...# - Act:7 - Num:2 [fit: 0.000, exp: 48.00, pred: 1148.435, Error:236.27617531067852]\n", + "Cond:#.###..O - Act:3 - Num:4 [fit: 0.002, exp: 11.00, pred: 1127.524, Error:212.7687492498865]\n", + "Cond:#...#..O - Act:3 - Num:3 [fit: 0.002, exp: 11.00, pred: 1127.524, Error:212.7687492498865]\n", + "Cond:.####..# - Act:5 - Num:3 [fit: 0.000, exp: 7.00, pred: 1281.580, Error:264.9831978001772]\n", + "Cond:.##O.#O# - Act:1 - Num:4 [fit: 0.000, exp: 13.00, pred: 1383.852, Error:113.31349380547069]\n", + "Cond:.#.#.#O# - Act:1 - Num:2 [fit: 0.000, exp: 20.00, pred: 1383.852, Error:113.31349380547069]\n", + "Cond:.OO####. - Act:3 - Num:5 [fit: 0.000, exp: 21.00, pred: 1346.933, Error:137.02979443325717]\n", + "Cond:..#O###. - Act:1 - Num:4 [fit: 0.000, exp: 15.00, pred: 970.926, Error:112.37071270316561]\n", + "Cond:#..##..O - Act:3 - Num:3 [fit: 0.000, exp: 11.00, pred: 1127.524, Error:152.6292652443462]\n", + "Cond:#.#.#..# - Act:3 - Num:3 [fit: 0.000, exp: 11.00, pred: 1127.524, Error:152.6292652443462]\n", + "Cond:...#.OF# - Act:3 - Num:1 [fit: 0.000, exp: 46.00, pred: 1624.522, Error:173.28208650050593]\n", + "Cond:.##O..#. - Act:4 - Num:5 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:76.46333444330676]\n", + "Cond:.OO..... - Act:6 - Num:6 [fit: 0.000, exp: 17.00, pred: 1447.704, Error:43.73932994224854]\n", + "Cond:..##O##. - Act:0 - Num:4 [fit: 0.000, exp: 14.00, pred: 1053.054, Error:162.32195174162806]\n", + "Cond:#O##O... - Act:3 - Num:1 [fit: 0.000, exp: 38.00, pred: 1279.162, Error:43.04364491854261]\n", + "Cond:#.#....O - Act:3 - Num:3 [fit: 0.000, exp: 11.00, pred: 1127.524, Error:66.54504058358242]\n", + "Cond:#...##.# - Act:3 - Num:3 [fit: 0.000, exp: 11.00, pred: 1127.524, Error:66.54504058358242]\n", + "Cond:..#O###. - Act:3 - Num:2 [fit: 0.000, exp: 12.00, pred: 1001.253, Error:125.74818205593458]\n", + "Cond:#.##O#.# - Act:1 - Num:3 [fit: 0.003, exp: 13.00, pred: 1000.834, Error:107.29592068522129]\n", + "Cond:.#O##..# - Act:5 - Num:3 [fit: 0.000, exp: 5.00, pred: 1007.776, Error:98.57002205005955]\n", + "Cond:.####..# - Act:6 - Num:3 [fit: 0.000, exp: 8.00, pred: 1172.214, Error:183.65991927503404]\n", + "Cond:..#O.... - Act:7 - Num:1 [fit: 0.000, exp: 48.00, pred: 1148.435, Error:236.27617531067852]\n", + "Cond:.#..#O#F - Act:0 - Num:6 [fit: 0.000, exp: 17.00, pred: 1326.980, Error:89.79197605331368]\n", + "Cond:##OO.... - Act:4 - Num:7 [fit: 0.000, exp: 22.00, pred: 1616.732, Error:59.413365890145215]\n", + "Cond:.OO#..#. - Act:3 - Num:2 [fit: 0.000, exp: 24.00, pred: 1347.293, Error:76.32858006570162]\n", + "Cond:...O..#. - Act:6 - Num:1 [fit: 0.003, exp: 3.00, pred: 1288.888, Error:174.85129323463403]\n", + "Cond:OO..#.## - Act:3 - Num:1 [fit: 0.000, exp: 44.00, pred: 1021.196, Error:47.80862709964447]\n", + "Cond:####O### - Act:0 - Num:3 [fit: 0.003, exp: 13.00, pred: 1000.834, Error:107.29592068522129]\n", + "Cond:#..#O#.# - Act:1 - Num:3 [fit: 0.003, exp: 13.00, pred: 1000.834, Error:107.29592068522129]\n", + "Cond:##..#..O - Act:3 - Num:3 [fit: 0.000, exp: 11.00, pred: 1127.524, Error:53.192187312471624]\n", + "Cond:#O##.#.# - Act:5 - Num:4 [fit: 0.000, exp: 11.00, pred: 1038.102, Error:63.43920911431896]\n", + "Cond:.#.#.F.. - Act:5 - Num:1 [fit: 0.000, exp: 61.00, pred: 1306.751, Error:96.92085241641422]\n" + ] + } + ], + "source": [ + "for cl in explore_population:\n", + " print(str(cl))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "df = pd.DataFrame(metric[\"average_specificity\"] for metric in explore_metrics)\n", + "ax = df.plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(metric[\"fraction_accuracy\"] for metric in explore_metrics)\n", + "ax = df.plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "df = pd.DataFrame(zip([metric[\"population\"] for metric in explore_metrics], [metric[\"numerosity\"] for metric in explore_metrics]))\n", + "ax = df.plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"pop\", \"num\"])\n", + "\n", + "steps_averaged = []\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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nIttEZNvhw4fdPu1V9EZCjMbmGJr0d77rvIvmLYdIqPxSHJkoczpEmst7ME2u9EWnWd1azzHtDQDsH40V2CKlnFjS8Rtj3mxfrzfGbEi7rDfGbMjkIMaYUeCXwCXAoIh0A9jXh5Z4zleNMVuNMVvD4XAmh0vR29UEwE6fOx8PT86SSJpla/gdwmU4dD2bHL/TxKWTuBZnXzTGMe0NrLGrv7SWX/ESN3X897vZtsg+YRFptW/XA28EXgJuB662d7saS/3TFzZ1WV2hOwcnV9gzN1IDWFZI9YDt+MvM2UWn4gSrAzTUVrl+TrkPpsmV/pEYa9vraW2ooaG2Sh2/4ilL1h6KSB3QAHTaJZdOuUozsNrFa3cDN4lIFdYXzH8ZY+4QkUeA/xKR9wL7gHfk8gaWIxIKEqqr9j3iX24Ay9E21XF4sryUKYcmZ+lorM3o/aRm75bZeocXTM3OMzQZp6etARFhTWu9lnQqnrJcA9f7gWuwnPz2tO3jwH+s9MLGmGeB0xbZPgxcmJGVWSIibOoK+R7xHxhduWvXIRwKMpcwjMbmaMsgNVLMRKfiGS3sgnbvLocT3Tv5/TVt9bq4q3jKcjn+Lxhj1gMfsfP6zuUUY8y/59HGnOiNNLHzkL+O/+DYDHU1AVrqa1bc19GpKac8fyY6PQ6hYDX1NVUMaqrnKJwa/rWO49eIX/EYNw1cYyLyJwsvvlvmERsjTUSn4gz76GgHxmdY3bL45K2FlGNue3gynlFFD1i/xnQE4+I4Nfxr26w1o562Bsam55jUCijFI9xo9fxe2u06rDTNduDbvljkMc4C747BSc7OMB3hloNjM67y+1CeQ9ezifjBKW0tn/PgFX0jMRprq1LndI39BbB/ZFplrBVPcNO5+8G0y19g5e1LJjntlHT6OcxiYHTaveNvLq+h67H4PNNziYxz/GAvdJfJefCSvug0a9sbUr8gnZJOreVXvMJNqmchMaDXa0P8YlVzHaFgNTt8WuBNJA2DE7OuSjkBGmurqK+pKpsUx/Bk5s1bDuFQkEGN+I+iLxqjp60hdb8nLeJXFC9YMdUjIj/F0tgB64viROC//DTKS0SEjV1NvpV0DmXQvOXYE2kunxGM2TRvOXQ11zEVTzA1O09j0E3WsfwxxtA3EuOcjZ2pbeGmILVVAa3lVzzDzX/b/0m7PQ/sNcb0+2SPL2yKhLj/pUFfXjuTUk6HcFP5SBKnHL/LsYvppE/iWq+OH7DOZyyeYG37kV+QgYDQ3VpHv1b2KB7hJsf/q7TLw6Xm9MHK8w9NxlNOyksyad5yKKeIPxtlTodULb+me1IcqehpeNX2Na1ay694x3LqnBMiMr7IZUJExvNpZK70pqQbvE/3ZCLX4GBF/OXh+LNR5nTQ7t2j6XOatzoWcfwa8SsesVwDV8gY07zIJWSMac6nkbnSG7Eqe3b40Mh1cHyGYHWA1oaVm7ccIs11TMzMMzOXWHnnIic6Fae2OkBjBjo9Dl12xK8LvEdwmrecBV2HnrYGDk/MlsVnRik8rqp6ROQUEfkr+3Ky30Z5TXdLHU3Banb5EPEfGJ2me4XJWwtxmrjKId0zPBXPWKfHoaW+htrqQFmcB6/oi8bobKqlofbVax5OLb/zC1NRcsGNOueHgZuxBqZEgJtF5IN+G+YlIsLGSJMvJZ1uBrAspJxGMGbbvAXW36Wc0l5e0DcSS0k1pJOq5dc8v+IBbiL+9wJnGmM+Zoz5GHAW8Bf+muU9fmn2uBm5uJBIOUX8k7NZO36wZ++WwRegV/RFp49a2IW0Wn5t4lI8wI3jFyA9sZjgiERzybCpK8TQ5CwjHlb2JJKGwXH3cg0O4bQyxlJneCqemiWcDV2hOhVqs5lPJNk/Ov2qUk6HVS11BEQHsije4Mbx3wg8JiIfF5FPAI8C3/DXLO/ZmJrG5V3UPzw5y3zSZBzxdzQGCUh5RPy5pHrAjvh1cRewfj0mkiYlx5xOTVWAruY6TfUonuCmjv8G4D1A1L68xxjzeZ/t8pzUNC4PO3idhbZMc/xVAaGjqfRn707HE8TiidwcfyjIeJlUOOVK38jiNfwOa1rrtYlL8QQ3i7vHAS8YY74IPAOc64xULCVWt9TRWFvl6VCWgSyatxwiZTB7d3gq++YtB6eWvxx+/eTKQh3+hehAFsUr3KR6fgIkRGQj8HVgPfA9X63yAaeyx9uIP3O5BodIqPQXNXNp3nIIay1/ir7oNFUBWfLz1NNWz8HxGeYTyTxbppQbbhx/0hgzD7wd+IIx5q+x5umWHL1dIU9LOg+OzVBbHcgq1REOlX6qZzgHgTaHLu3eTdE3EmN1ax3VVYv/W65pbUipwSpKLrhx/HMichXwJ8Ad9jb3bapFRG+kicMTs4zGvKnscUo5s2leioTqGJ6Kk0ialXcuUqI5SDI7qF7PEfZFY4su7DqsUXlmxSPcOP73AGcD/2KMeUVE1gPf9dcsfziywOtN1D8wNs2q5szTPGBF/Imk8UU4Ll/koszp0N5QS3VANOJn6Rp+Bx3IoniFm6qe3xpjPmSM+b59/xVjzGf8N817NtqaPV4t8GbTvOVQDk1cQ1Oz1FQJoRwklQMBoVO7d5mOJxianF1yYRe0e1fxjmwmcJUsa1rraait8mSBN2k3b3W3ZlbK6VAOsg3RyTgdjcGsUl3pdDXrJK5UKecyjr++toqOxlpt4lJypqIcfyBgV/Z4EPEPTc0yl8i8ecuhHMoYc23ecgjr7N0jpZxtywcSa9pUnlnJnWUdv4hUicjn8mVMPuiNhDyJ+FMDWHLI8UNpV7MMT8XpyCG/72Dp9ZTuefCClWr4HXQgi+IFyzp+Y0wCeK3k+lu+iOjtamJwfJax6bmcXic1gCXLVE99bRWhYHVJR7peRfyRUJDoVJz4fOXWp++LTtNgp3KWwxnIYkzpVoMphcdNqucp4DYRebeIvN25+G2YXzhDWXblGPVnM3JxIeESH8E4PDlLR2P2zVsOXfavplLvZM6FvpEYa9saVlwv6WmrZ3Y+ydBk6VaDKYXHjeNvB4aBNwBvsS9v9tMoP3FKOnNt5DowNk1tVYD2huwj3lIeuj4zl2AqnvAm1RPSWv6+aGxRVc6FrLHLPTXPr+TCinV4xpj35MOQfLGmtZ76mtw1ew6OzdDVEiQQyD4LFmmu47n+0ZzsKBRRD7p2HSp99q4xhr5ojLOP61hx3/SSzlPXtvpsmVKuuBFp6xGRW0XkkIgMishPRKQnH8b5QaqyJ8dUz0AWk7cWUsrTpzx1/M2lv9CdCyOxOabiiWWbtxzW6EAWxQPc6vHfDqwG1gA/tbeVLL0elHQOjE1nXcrpEGkOEosnmJqdz+l1CsHQZO7KnA7WzN7KTfXsc1nRA9ac4lCwWmv5lZxw4/jDxpgbjTHz9uVbQNhnu3yltyvEwfEZxmeyq+xJJg2DY7M5LexCWm67BCNdL5Q5HaqrAlb3bomL1mWLU8q5nE5POirPrOSKG8c/JCLvsmv6q0TkXViLvSVLb47SDdFYnHgiyepcUz0lvKjpZaoHykOmOlucrt2eFZq3HJySTkXJFjeO/8+APwQOAgPAH9jbShansifbkk4vSjkhrXu3BMsYh6fi1FQJzXXZ6/SkYzn+0jsPXtAXjdHRWEujS80jjfiVXFnS8YvI9fbNM40xbzXGhI0xEWPM24wxe/Nkny+saaunriaQdUnngdHsB7CkcyTiLz2HF52M09ZQm7NOj0MkVFfBjn/aVX7foaetnonZ+ZybEJXKZbmI/zIRqQH+PpsXFpG1IvKAiLwoIi+IyIft7e0i8nMR2Wlft2Xz+rlQFRCOCzdlLc98cNybiL+1voaaKinRiH/WszQPWEJtQ5OzFTldqm8klpHjX9Nq1/Jr1K9kyXKO/2fAEHCyiIyLyET6tYvXngf+1hhzAnAW8D9F5ETgOuB+Y0wvcL99P+9s6gqxczC7VM/A2Aw1VUJnjl2rKUniEoz4h6fidHqwsOsQbq7DmCNTvSqFRNKwf2R6RXG2dI6UdKrjV7JjScdvjLnWGNMC3GmMaTbGhNKvV3phY8yAMWa7fXsCeBGrHPQK4CZ7t5uAt+X6JrJhY6SJgbEZJrKo7BkYnaaruS6n5i2HUh267pVOj0OkhNNeuTAwNs180riu6IH0Ji6t5Veyw80glityPYiIrANOAx4DuowxA/ZrDwCRJZ7zPhHZJiLbDh8+nKsJR3FkgTfzdE8uA1gWEg7VlWZVz6RPjr/CKnv6olbUnkmqp7OplmB1QCN+JWt81+MXkSbgJ8A1xhg3KSIAjDFfNcZsNcZsDYe9bxvIpaTz4HjuXbsO4VDpCbXNzieYmJ33pHnLwRFqG6ywiP+IDr97xy8irGmt1yYuJWt8dfz24vBPgJuNMbfYmwdFpNt+vBs45KcNS7G2vYFgdSBj6QZjjKcRfyQUJBqLM1dCi5peNm85OOsFFRfxj8SoCgjdrZl9nnQgi5ILGTl+EWkTkZNd7ivAN4AXjTE3pD10O3C1fftq4LZMbPAKp7In05JORzc+14oeh3AoaC1qlpDMrmOrl6me2uoA7Y21FVfS2ReN0d1SR01VZjGYDmRRcsGNSNsvRaRZRNqBZ4AbReSGlZ4HnAO8G3iDiDxtXy4DPgNcJCI7gYvs+wVhU1dTxjl+ZwCLlxE/lNYIxiMRv3eOH+wmrgpL9eyLxjJK8zj0tNUzPBVnOp7wwSql3HHTKthijBkXkT8HbjTG/C8ReXalJxljfg0sVfZyYSZG+kVvV4j/fvoAk7PzNLnsmjyYcvze5fjBSXG0ePKafuO1XINDuAJlG/pGpnnD5kXrG5YlvaRzo71epShucfP7strOxf8hcIfP9uSVjalpXO6j/oExb7p2HSLO9KkSivi9VOZMp6u5rqIi/ul4gsMTs64GsCwk1cSleX4lC9w4/k8C9wC7jTFPiMgGYKe/ZuUHp6Qzk0augbEZqgPi2cJmp50uKaXcdnQqTnVAaK6r8fR1IyGrezeZrIx5sv0j7uWYF5KK+DXPr2SBmwlcPwJ+lHb/ZeD3/TQqXxzT3kBtdSAj6YaDYzN0NddR5UHzFkCwuorWhpqSSnFEp+K0NdZ60sCWTiQUZD5piMa87QouVvpycPxdoSBVAdGBLEpWuFnc3SAiPxWRw/YUrttEZH0+jPOblGZPhhG/V2keh0iJ1fIPT8U9T/PAkbRXpaR7Us1bWSzuVlcFWNVcp7X8Sla4SfV8D/gvoBtrCtePgB/4aVQ+6Y1kVtI5MDbtWSmnQ7jEJIm9lmtw6LJHMA6W0K+fXNgXjVFfU5VK92WKyjMr2eLG8Ysx5jtpE7i+C5RNEnZTVxP7R6ddjT/0unnLIRKqK62If3LW0+Yth9R8goqJ+GOsba/PWtq6RweyKFnixvE/ICLXicg6ETlWRP4OuNOWV27320C/2Rhxr9kzGptjdj7pWSmngxPxG1Ma36d+pXrCFabX0zcynVWax2FNWz2D4zMl1fWtFAduitf/yL5+/4Ltf4YV+W/w1KI8s6nL1uw5NMkpa1uX3feAx6WcDpFQkPh8kvGZeVrqva2U8Zr4fJKJmXlfUj11NVU011WXVNorW4wx9EVjnLk++9ipp62epLEKDrJZIFYqFzdVPWWxkLsUx7Q3UFsVcLXA69XIxYWEU927M0Xv+Edi/jRvOXQ11zFYgmqlmTIam2Nydj4nh+3U8vePZDbBS1HcVPU0iMg/ichX7fu9IvJm/03LD9VVATaEG12VdDpyDatbvU/1QGlUs/jVvOUQaS6the5s2ZdS5cz+s6QDWZRscZPjvxGIA6+z7/cDn/LNogLQ2xVypdJ5cGyGKntqlpeU0tB1P5Q504mEKqN716nhP6Yj+0jdSTlqZY+SKW4c/3HGmM8CcwDGmGmW1uApSXojTfRFp4nFl6/sOTA2nWqc8ZJSivj90ulxcHoaSmWhO1tyqeF3qKupIhwKpjqAFcUtbhx/XETqsUs4ReQ4oPg9VAY4C7y7D00tu9/BsRnP8/sAzXXVBKsDJRHxO5LMfqV6wqEg8USS0VjmIzFLiX3RGO2NtTS6FAdcijVa0qlkgRvH/3GswetrReRmrAHpH/XTqHzjlHTuWGGB9+DYDN0e5/fBmqgUDgVLYgRjdCpOVUB8W4R2JnGVe56/fyTmyYKsDmRRssHNzN17gbcDfwp8H9hqjHnAZ7vyyrqOBmqqZNkFXmMMB8am6W72PuKH0hm6Pjw1S1tDjec6PQ6VMnu3LxrLaWHXoae1noHRmYoRtlO8wU1Vz/3GmGFjzJ3GmDuMMUMicn8+jMsX1VUBNnQur9kzNj3HzJx3k7cWUiqLmsOTcToa/RNQqwS9nkTSsH/UmxLMnrZ64olkSQQNSvGwpOMXkTq7M7fTHrnYbl/WYWn2lBW9XU3LRvx+lXI6lIpej186PQ5HIv7iPxfZcnB8hrmE4RiPUj2AirUpGbFcxP9+4EngePvaudwG/If/puWX3kiIvpHYkqPsnAEs/kX8Qcam55idL+5RetGpOO0ej1xMpzFYTVOwuqybuPpSNfweOH4dyKJkwZKO3xjzBbtr9yPGmA3GmPX25RRjzL/n0ca80NvVhDGw+/DiUb/Xs3YXEi6R2bt+6fSkU2oy1ZmSat7KYvLWQnQgi5INbqp6DopICMDu4L1FRE732a68c0SzZ/E8/8GxGQICYb8al5qL3/HPJZKMTc/5muOH8p+92x+NERBv0oZNwWpa6mt0IIuSEW4c/z8bYyZE5PXAm4CbgC/7a1b+ObajkZoqWVKbf8CevFVd5eaUZU64qfjLGEec5i0fUz1gLfAW83nIlb6Rabpb6qnx6LO0prVec/xKRrj55DlJ58uBLxtjbgP8/c8vADVVAdZ3NrJzScfv/QCWdEoh4h+e8rd5y6ErFGRwfKZsu3f32Tr8XqEDWZRMceP494vI/wX+ELhLRIIun1dy9EaW1uzxYwBLOh2NtYgUd8Tvt1yDQ6Q5yMxckgkXw3FKkb5ozJOKHgene7dcvygV73HjwP8QuAe4xBgzCrQD1/ppVKHo7WpiXzTGzNyrK2uMMVbXrscDWNKprgrQ0Vhb1BG/38qcDo5oXTnW8s/MJTg0MetJRY9DT1s9sXii7GUuFO9w07kbM8bcYozZad8fsLt5y47eSAhjjp7GNT49Tyye8DXiBwiH6jjs4aLm3uEpfrStz7PX81uZ06Gcu3cdQTUv9fN7VJ5ZyZCyTNlkS69d2bPQ8Q+M+1vD7+B1E9f1P3uJa3/8LPuGvan4iE7FCQi0+jwsphTWO7IlpcrpaarnyEAWRXGDOv401nU0Uh2Qo/L8ftfwO3hZvz42Pcd9Lx4C4M7nBjx5zeGpOG0Ntb7p9Dg4sg3l2MTVN+JdDb+DDmRRMkUdfxq11QHWdTYeVdJ5MOX4/cvxgxXxH56Y9URw667nBojPJ+lsquUujxx/dNJfuQaHULCauppAWeb49w3HqKsJeNoP0tZQQ31NlVb2KK5Rx7+ATV1NR6d6Rqet5q2Q/7nt+aRhdDr3RbpbtvdzXLiRvzh3A8/tH/Mk3TM8NUuHzzX8YMlUR0LlWcvfNxJjbVsDIt79ahIR1rTV60AWxTXq+BewMRJi7/DUqyp7BsZmCIeCnjXcLEWqmiXHRc2+aIwn9ozw9tN7uGxLNwB3PZ971G/JNfj75ecQKdPu3b6oP4PRdSCLkgnq+BewqauJpIGXDx+ZxnVw3N9STgev9HpufWo/AG87bQ1r2xs4uaeFuz1I9/itzJlOV3NpyFRngjHGMx3+hehAFiUT1PEvoNeexpW+wHtgdNr3hV1IK2PMweEZY7hlez9nbWhnja0Fc9mWbp7pH0upQmbDvD0OMV+Ov1RkqjNhbHqOidl53yL+0dgcU2Xa9KZ4izr+BazvbKQqICnpBmMMAz7N2l1I2AMt+qf6RtkzHOPtp/Wktl1up3vuziHdE405Nfz5cfyR5iCTs/PE4uXjyPwo5XTQWn4lE9TxL6C2OsC6joZUxD8xm5/mLbC06Btrq3JK9dy6fT/B6gCXblmV2ra2vYEta1q487mDWb9uqnkrbzn+8uve3eehDv9CelSeWckAdfyL0BsJpSL+fJVyOuQiSRyfT/LTZw9w8WtWEap7dZPVpVtW8UzfaNaVH9HJ/Oj0OHTZTVzlVMvvRw2/Q6qJSyN+xQW+OX4R+aaIHBKR59O2tYvIz0Vkp33d5tfxc2FTVxN7hqeYnU9wwP5HykfED1akm23E/8DvDjEam+Ptp6856rFUuifLqD+lzJmvVE+o+GWqM2VfNEZbQ81RX8peEAkFqakSjfgVV/gZ8X8LuGTBtuuA+40xvcD99v2iY2NXKFXZ40T8+cjxA4Sbs+/evXX7fjqbgpy7sfOox47taOQ1q5uz7uLNlzKnQznO3u2LxnzJ7wMEAkJ3i1b2KO7wzfEbYx4Eogs2X4E1yAX7+m1+HT8XjkzjmmRgbAYRq7wwH4SbsnP8o7E49780yFtPWb3ksJjLtnTzdN9oVs5heHIWEWhryI/jb22oobYqUFa1/P0j/tTwO1gDWbSJS1mZfOf4u4wxA2CpfAKRpXYUkfeJyDYR2Xb48OG8GQjplT0TDIxNE27yv3nLIdIcZGJ2fsmh70txx7MDzCXMomkehyPpnsyjfkenp8pnnR4HEbEkLMpkcTeRNPTbXbt+oQNZFLcU7eKuMearxpitxpit4XA4r8cOVldxbEcDOwcnfR/AshBHwyXTSPfWp/azqauJ16xuXnKfdZ2NnNjdnJV2Tz6btxwizUEGyyTiHxyfYS5hfFnYdVjTWs+hiVlm5zMLGpTKI9+Of1BEugHs60N5Pr5reiNN7Dw0wcE81fA7OMqUmaR79gxN8eTeEa48rWdFDZjLT+5m+77R1KK1W4YL4fhDwbIp53Sa57ycvLUQp6RzYLQ8viwV/8i3478duNq+fTVwW56P75pNXSH2DMfYPzqdt1JOSI/43Tu8W5/ajwi87bTVK+576UlWff/dz2dW3ROdivs+eWsh5STU5mcNv4PKMytu8bOc8/vAI8BmEekXkfcCnwEuEpGdwEX2/aJkY6SJRNLkrXnLIdMhJMYYbn1qP687rsPVF9SGcBPHrwplnO4ZnsyPMmc6Xc1BxqbnGLErikqZvpFpRGB1q39BRI9dy695fmUl/KzqucoY022MqTHG9BhjvmGMGTbGXGiM6bWvF1b9FA2OZg/kr5QToN1eQHWb439y7wj7ojGuTJNoWInLt3Tz5N4RBsbcOYiELRXdnqeuXYc3ntiFCHztoZfzelw/6I/GWN1ST221fz+yV7XUIaJNXMrKFO3ibqHZEG7EKWDxM0pbSCAgdDa5H7p+y1P7qa+p4pKTVq28s81lJ2fWzDUSi2OM/0PWF3L8qmbecvJqbnx4T8mXde6LxlI5eL+orQ7QFarTiF9ZEXX8S1BXU8WxHY0ArMpTDb+D29z2zFyCO545wJte00VTsNr16x9np3vcirblu3krnb++aBPxRJL/fGB33o/tJX0j/jVvpaMDWRQ3qONfht6I1ciVr+Yth7DLapYHXjrE+Mw8V57uPs3jcNmWbrbtHXGlhTM0admS74gfrJ6Kd7y2h+89tq9kFy1n5hIMjs/6WtHjoANZFDeo41+GS7es4rItq3zNyy5GJBTk8OTKjv+Wp/YTCQU557iOjI9x2ZZujHHXzJVS5vRwTmwmfPDCXgC+eN/Oghw/V/pHHDlm/1OGa9rqOTg2Q8KDuc1K+aKOfxmuPK2H/3zna/N+3HAoyPDk7LL/vNGpOA+8dIgrTl1aomE5Nkaa2NwV4i4Xef5CpnrAimLfedYx/Hh7Py8fnlz5CUVGSpXTx1JOh562euaTpqxUTRXvUcdfhERCQZLGGm6+FHc8e4D5pOHtWaR5HC7dsoon9kY5tIKTGLYlmdsavFeVdMsHzt9IbVWAfyvBqL8/D81bDs7UNU33KMuhjr8ICbsYQnLL9v0cvyrECd1LSzSsxOVOumeFZq7oVJzWhpqsfll4RTgU5D3nrOOnzxzgxYHxgtmRDfuiMYLVgdSENT/RgSyKG9TxFyGpoetL5Pl3H57k6b7RZQXZ3NDbFaI30rSiVPPw1GxBFnYX8v7zjiNUV82/3ruj0KZkRF/UUuVcSU7DC1ZrxK+4QB1/EeJo0S+lTPnfT+0nIHDFqbk5frAWeZ/Ys3y6Z3gynreRi8vR0lDD+8/bwH0vDrJ930ihzXFN30iMtT7X8Ds01FbT3libWlBWlMVQx1+EHBm6frQzTiYtiYZzNnZ6UmZ6+clWuueeF5ZO9xRCmXMp3nPOejoaa/nXe39XaFNcs8/HASyLoSWdykqo4y9C6mqqaK6rXrR794k9UfpHpnNO8zhs6gqxcYV0T3QqTnuedXqWojFYzQcu2MjDu4b5za6hQpuzImOxOSZm5vOysOugA1mUlVDHX6RYQ9ePdvy3PrWfhtoq3vQa9xINK3HZlm4efyW66BdNImkYieVfmXM53nnmMXS31PG5e3+HMcVdr+6UcvbkoZTTYU1bPQdGp4v+3CiFQx1/kbLY0PWZuQR3PjfAJSetoqHWvUTDSly2ZRVJAz9bJN0zGouTLIBOz3LU1VTxwTf08tS+UX7xUtGOdADS5Jjz0LzlsKa1npm5JMNloGqq+IM6/iIl0nx0xH/fi4NMzMzz9gyUON2wuSvEhnAjdz17dLon1bxVoK7dpXjH1h6O7Wjgc/f8jmQRd6n2pRx//iJ+LelUVkIdf5HiDF1P/7l+6/b9rGqu4+wsJBqWQ0S4fEs3j70ynNLlcXCixmKK+AFqqgL89Rs38dLBiRXLUQtJ30iM1oYamuvy1/ymA1mUlVDHX6REmoNMzyWYnJ0HLKG0X+04zBWnrfZl4PllW7qtdM+CZq5CyzUsx1tOWc3mrhD/9vMdzCeShTZnUfZFp/Mi1ZCODmRRVkIdf5FypKTTisB/+owt0eBxmsfh+FUhNnQ2HjWZa7iAypwrURUQ/ubiTbw8NMUt2/cX2pxF6Y/G8lrRA9BcX01TsFojfmVJ1PEXKZHQq4eu3/rUfl6zupnNq0LLPS1rRITLtnTz6MvDKWcPR1I9bUXo+AEuPrGLU3pa+ML9O5mdTxTanFeRTBr6R6bpyePCLlh/S6ukUx2/sjjq+IuUSFrEv+vQBM/2j3Hlad7U7i+Fk+6554XB1LboVJyW+hpqCqjTsxwiwkfetJn9o9P84PG+QpvzKgYnZognknlP9YAOZFGWpzj/m5Ujej0Ts9yyfT9VAeGtp6729ZgndIdYvyDdMzxVXDX8i/H6jZ2cub6dL/1iF7H4fKHNSdEXtSLufKd6QLt3leVRx1+ktNTXUFsVYHB8htuePsC5vZ2p9I9fiAiXnrSKR14eTi3qRieLR65hKUSEa9+0maHJWW76zd6C2jI7n+A3u4b4zN0v8Q+3PgcUyPG31TMxM8/4zFzej60UP951ASmeIiKEQ0Hufn6A/aPT/N0lm/Ny3Mu2dPOfv9zNPS8c5KozjmF4apb1nY15OXYubF3XzgWbw3zlV7t551nH5K180hjD7sOTPLhjiId2HubRl6NMzyWoDgivPbaNP778BI7tyL/jT6/lb+4u3BwFpThRx1/EhENBnu4bpSlYzcUneifRsByvWd3MsR0N3PXcAFedcQzRqTivPbY9L8fOlb+9eDNv/tKv+fqDL/M3F/v3RTkyFefXuyxH/9DOIQbGLDG9DZ2N/NHvreXc3k7O3NBBU7Bw/16pgSwj0znNbFDKE3X8RYyT57/0pFXU11bl5ZhOdc9XH3yZ4clZRmJzRZ/jdzhpTQuXbVnFN379Cle/bp1nM4Lj80me2jfCQzuHeHDnYZ7bP4Yx0FxXzet7O/lQb5jXb+zMa3fuSmgTl7Ic6viLGKey50qPlDjdcvmWbr78y93817Z+EklT9Dn+dP7mok387PmDfPmXu/mnN5+Y1WsYY3hlaIqHdlpR/SO7h5mKJ6gKCKetbeWaCzdx7qZOTl7TUtCpZMvR2RiktjqQtePvi8Z4aOcQz/aP5jS4vaG2ineffSwbI/6UIfvJ8/vHuPv5AS7fspoTV5fXryZ1/EXM+ZsjHJqY5az13ko0rMRrVjdzTHsDNz9mLZR2FIkksxs2RkJceVoP3350L39+7gZWtbhbEB+LzfGb3UM8aDt7pwZ+bXs9bzttDef2hjn7uA5a6ksjXx4IWLX8brt3J2fneXT3MA/a6atXhqYAa85yfU32vzajsTjffWwff/R7a7nmjb2+Fyh4Qf9IjH+9dwe3PmU1Bf7nL3dz5Wlr+MjFm1MTzkoddfxFzEUndnHRiV15P66T7vnKr3YDFMX0rUy45o293P7Mfr70i538y5VbFt1nPpHkmf7R1KLs032jJA00Bas5+7gO3n/eBs7bFObYjuJf2F6K5XT5E0nDCwfGeHDHYR7cOcT2vSPMJw31NVWctaGdd591LOdt6uS4cFNOIyOHJ2f54v07ufmxffz3U/t533kb+ItzN9BYwPWPpRibnuM/H9jFjb/ZgwB/ef5xvPPMY/jOo3u58eE93PnsAO85Zz0fuOC4vGov+YGUgmb31q1bzbZt2wptRkXxbP8ob/33hwG460PnltxP3X/+7+f5/uP7+MXfns8xdlVNXzRmRbQ7hnh49xATM/MEBE7uaeW83k7O3RTm1LWtRduslikf/fGz3P/SINv+6SIABsameWiHtU7x8K4hRmJWqedrVjdzbm+Y83o7ee26NoLV3q8nvTI0xefueYm7njtIZ1OQv76olz/aurYoUmWz8wm+88he/v2BXYxNz/H203r424s3vSq67x+JccO9O7jlqf20NdTwwTf08q6zjqW2uvD2L4eIPGmM2XrUdnX8ymIYYzj3sw/QPzLNY/9woSdjHvPJ4PgM5332Ac7c0MG6jgYe3HGYPcNW9Lu6pY7zNoU5tzfMORs7aG0onVRWJnzx/p3c8PMd/Onr1vHwriF2HpoErLWjc3vDnLepk3M2dtKZR8ntJ/eO8Om7XmTb3hGOCzdy3aUn8MYTInkZRL+QZNJwx3MDfO6el+iLTnPepjDXXXL8skHO8/vH+PTdL/LwrmGOaW/g7y7ZzOVbun2xPz6fZPu+Ebasacn6F5I6fiVjPnfPS9z48B6e/tjFRR/ZLMan736R//url2moreLsDR2ca0f1GzobC+Jo8s1dzw3wgZu3E6wOcMb6ds7rDXPupk42d4UK+v6NMdz720Guv/slXh6a4oz17fzDZSdw6trWvNnw6MvDfPquF3mmf4wTupv5h8uO59zesKvnGmN4cOcQn77rRV46OMEpa1v5x8tO4Iz1uZU9G2N4eWiKh3ZY6yyPvDxMLJ7g63+ylTdmmfJVx69kTHw+ycDYdMnmuePzSV4cGOf47pAv6YtiJ5E0vDgwzsZIE3U5LND6xVwiyQ+e6OML9+1gaDLOm0/u5to3bfb187ZzcILP3P0S9790iNUtdfztxZt522lrspI6TyQNP9nezw337uDg+AxvPKGL6y49no2RJtevMRab4+Hd1jrTgzuGUlVY6zoa7F9lVlFBtj0h6vgVRSlKJmfn+eqvdvO1h15hPpnkXWcdy4fe0OupIuyh8Rn+7b4d/PCJPhprq/nABRt5zznrPPlCnI4n+ObDr/DlX+5mei7BH//eWj68RAXTXCLJM32jqUX1Z/utooJQsJrXbeywUpAbw6l1qVxRx68oSlEzOD7D5x3nHKzmA+fn7pwnZ+f56oMv87UHX059qXzwDb2+9KYMT87ypV/s4ruP7qW2OpCqYBqanLXKhHdYPSETs1ZRwSlrWznPXms5pafVl4VudfyKopQEOwYnuN5Ox7Q31ubUOX5oYpax6TkuP7mbv/M5jeSwZ2iKz9oVTLXVAeLz1nS4Na31nLfJqp563XGdtDT4XxKqjl9RlJLikd3D/PCJfcRzGKtZV1PFu886ltOOafPQMnds3zfCj5/sZ3NXiHN7O1lfgKKCpRx/QbooROQS4AtAFfB1Y8xnCmGHoijFy9nHdXD2cfntWveS049p4/QCfOG4Ie81eiJSBfwHcClwInCViGQnqqIoiqJkTCGKs88AdhljXjbGxIEfAFcUwA5FUZSKpBCOfw2QPhy13972KkTkfSKyTUS2HT58OG/GKYqilDuFcPyLrW4ctcJsjPmqMWarMWZrOOyuo05RFEVZmUI4/n5gbdr9HuBAAexQFEWpSArh+J8AekVkvYjUAn8M3F4AOxRFUSqSvJdzGmPmReSvgHuwyjm/aYx5Id92KIqiVCoFqeM3xtwF3FWIYyuKolQ6JdG5KyKHgb1ZPr0TGPLQHK9R+3JD7csNtS93itnGY40xR1XHlITjzwUR2bZYy3KxoPblhtqXG2pf7pSCjQspvekaiqIoSk6o41cURakwKsHxf7XQBqyA2pcbal9uqH25Uwo2voqyz/EriqIor6YSIn5FURQlDXX8iqIoFUbZOH4RuUREficiu0TkukUeFxH5ov34syJyeh5tWysiD4jIiyLygoh8eJF9zheRMRF52r58LF/22cffIyLP2cc+atxZgc/f5rTz8rSIjIvINQv2yev5E5FvisghEXk+bVu7iPxcRHba14tO4Vjps+qjfZ8TkZfsv9+tItK6xHOX/Sz4aN/HRWR/2t/wsiWeW6jz98M02/aIyNNLPNf385czxpiSv2BJP+wGNgC1wDPAiQv2uQy4G0sd9CzgsTza1w2cbt8OATsWse984I4CnsM9QOcyjxfs/C3ytz6I1ZhSsPMHnAecDjyftu2zwHX27euA65ewf9nPqo/2XQxU27evX8w+N58FH+37OPARF3//gpy/BY//K/CxQp2/XC/lEvG7Ge5yBfBtY/Eo0Coi3fkwzhgzYIzZbt+eAF5kkRkERU7Bzt8CLgR2G2Oy7eT2BGPMg0B0weYrgJvs2zcBb1vkqXkZRLSYfcaYe40x8/bdR7GUcQvCEufPDQU7fw5iDc79Q+D7Xh83X5SL43cz3MXVABi/EZF1wGnAY4s8fLaIPCMid4vIa/JrGQa4V0SeFJH3LfJ4UZw/LDXXpf7hCnn+ALqMMQNgfdkDkUX2KZbz+GdYv+AWY6XPgp/8lZ2K+uYSqbJiOH/nAoPGmJ1LPF7I8+eKcnH8boa7uBoA4yci0gT8BLjGGDO+4OHtWOmLU4AvAf+dT9uAc4wxp2PNQv6fInLegseL4fzVAm8FfrTIw4U+f24phvP4j8A8cPMSu6z0WfCLLwPHAacCA1jplIUU/PwBV7F8tF+o8+eacnH8boa7FHQAjIjUYDn9m40xtyx83BgzboyZtG/fBdSISGe+7DPGHLCvDwG3Yv2kTqcYBuhcCmw3xgwufKDQ589m0El/2deHFtmn0J/Dq4E3A+80dkJ6IS4+C75gjBk0xiSMMUnga0sct9Dnrxp4O/DDpfYp1PnLhHJx/G6Gu9wO/IldnXIWMOb8LPcbOyf4DeBFY8wNS+yzyt4PETkD628znCf7GkUk5NzGWgR8fsFuBTt/aSwZaRXy/KVxO3C1fftq4LZF9inYICIRuQT4KPBWY0xsiX3cfBb8si99zejKJY5b6EFObwReMsb0L/ZgIc9fRhR6ddmrC1bVyQ6sFf9/tLf9D+B/2LcF+A/78eeArXm07fVYP0efBZ62L5ctsO+vgBewqhQeBV6XR/s22Md9xrahqM6fffwGLEfekratYOcP6wtoAJjDikLfC3QA9wM77et2e9/VwF3LfVbzZN8urPy48xn8ykL7lvos5Mm+79ifrWexnHl3MZ0/e/u3nM9c2r55P3+5XlSyQVEUpcIol1SPoiiK4hJ1/IqiKBWGOn5FUZQKQx2/oihKhaGOX1EUpcJQx6/kDRFpFZEPrLDPb1y8zqR3VvmPrTr5EY9eK2GrPq5O61v4uH3t3P+lrV7pKElG7O1BW2Fyl4g8ZsuHICLH2fuV1HlVskcdv5JPWoFFHb+IVAEYY17npwF252UpM22MOdVY3aEXi8i/AI0i8ufANWn7vdPe71RjdZCCVSs/YozZCPwblkInxpjdxphT8/cWlEKjjl/JJ58BnOjyc2Jp6D8gIt/DatxJRfMi0iQi94vIdlvbfEUFRhH5Z7H05n8uIt93omw7Av7fIvIr4MMicqGIPGW/7jdFJGjvt8eReRCRrSLyS/v2x0XkOyLyC7G09v8i7ZjXisgTtrDYJ9K2/6Mddd8HbF7C3m+JyB+k3Xfe+/ki8qBYmvm/FZGviMhR/6vGmHuAe4APAR3GmH9b4RSlq4f+GLjQ+ZWgVBalHv0opcV1wElOdCki52PpmJxkjHllwb4zwJXGmHHbGT8qIrebJToORWQr8PtYyqfVWKJtT6bt0mqM+f9EpA6rs/ZCY8wOEfk28JfA51ew/WSsOQSNwFMicidwEtBrvwcBbhdLkGsKS0pgKVvccAZwIrAX+BmWPsyPF7zni7DmEHwRGBaRDxtjvmA/fKOIJLD0oT5ln7eUsqUxZl5ExrC6jYcytE0pcTTiVwrN44s4fbAc6f8WkWeB+7CcVtcyr/N64DZjzLSxZh78dMHjjqjWZuAVY8wO+/5NWEM3VsJ57SHgASzHfLF9eQrLuR+P9UVwLnCrMSZmLBXWbLRkHjeW5nwCSz7g9Yvsc58x5h+BKWPM17G+AMBK82yx7TgXeLe9vRiULZUiQB2/Umimltj+TiAMvNb+hTAI1C3zOiulLJzjLLffPEf+JxYea6GDNPZrfTotl77RGPONJfZf9nh2yqV2heO9eoP968cY8/EF9/fb1xPA9ziiDplStrTXOlrIbhiKUuKo41fyyQTW6Ek3tACHjDFzInIBcOwK+/8aeIuI1Ik19+DyJfZ7CVgnIhvt++8GfmXf3gO81r79+wued4X92h1Y6ZUnsPLrf2YfDxFZY1fQPAhcKSL1tlLjW5awJf14VwA1aY+dYStQBoA/st/fiohIddo6RQ2WBLOjDpmuHvoHwC+WSp0p5Y3m+JW8YYwZFpGHxRpgfTdw5zK73wz8VKxh1U9jOezlXvsJEbkdSxVxL7ANGFtkvxkReQ/wIzvqfQL4iv3wJ4BviMg/cPSEtMdte48B/n+7quaAiJwAPGKvkU4C7zLGbBeRH9p27wUeWsLsrwG3icjjWGqe6b9+HsFaDN+C9UVy63LvP40gcI/t9Kuw0mRfsx/7BvAdEdmFFen/scvXVMoMVedUygYRaTLGTIpIA5azfJ+xZx3n+LofByaNMf8n19dyebzzsYaOv3mRxyaNMU0+Hde311aKC031KOXEV0XkaayF1p944fSLkHG7HHa1Vy/oNHBhraMoFYBG/IqiKBWGRvyKoigVhjp+RVGUCkMdv6IoSoWhjl9RFKXCUMevKIpSYfw/FjfhOvIytNcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "xf = [metric[\"steps_in_trial\"] for metric in explore_metrics]\n", + "temp_df = np.array_split(xf, 20)\n", + "for i in range(len(temp_df)):\n", + " temp_df[i] = np.mean(temp_df[i]) \n", + "df = pd.DataFrame(temp_df)\n", + "# df = pd.DataFrame(metric[\"steps_in_trial\"] for metric in explore_metrics)\n", + "ax = df.plot()\n", + "ax.set_xlabel(\"trial grouped up[*50]\")\n", + "ax.set_ylabel(\"steps for solution\")\n", + "ax.legend([\"steps\"])\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XCS_woods1.ipynb b/XCS_Experiments/XCS_woods1.ipynb new file mode 100644 index 0000000..1f03c0f --- /dev/null +++ b/XCS_Experiments/XCS_woods1.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### XCS in Woods" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['.', '.', '.', 'O', '.', '.', '.', '.']\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 36, 'reward': 1000.0, 'perf_time': 0.0187924000001658, 'population': 84, 'numerosity': 89, 'average_specificity': 9.561797752808989, 'fraction_accuracy': 1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 1, 'reward': 2006.1456306207742, 'perf_time': 0.002873100000215345, 'population': 603, 'numerosity': 1800, 'average_specificity': 9.613888888888889, 'fraction_accuracy': 2.7820701675500964e-15}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 2, 'reward': 1504.3374538297148, 'perf_time': 0.014788999998927466, 'population': 647, 'numerosity': 1800, 'average_specificity': 10.213333333333333, 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p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "df = avg_experiment(maze,\n", + " cfg,\n", + " number_of_tests=10,\n", + " explore_trials=2000,\n", + " exploit_trials=500)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 20.2 900.000000 0.010359 55.4 56.9 \n", + "100 9.0 1403.378939 0.060909 572.8 1776.8 \n", + "200 8.1 1427.678391 0.056508 632.2 1800.0 \n", + "300 15.2 1200.347487 0.129537 643.7 1800.0 \n", + "400 9.8 1276.199333 0.082102 617.3 1800.0 \n", + "500 16.4 970.890341 0.122038 608.3 1800.0 \n", + "600 15.7 1041.671933 0.140665 624.3 1800.0 \n", + "700 10.2 1312.478853 0.101546 641.6 1800.0 \n", + "800 9.4 1234.059260 0.073682 646.6 1800.0 \n", + "900 7.9 1256.795770 0.060683 640.2 1800.0 \n", + "1000 6.0 1443.320311 0.055585 668.9 1800.0 \n", + "1100 4.5 1470.918304 0.038603 670.0 1800.0 \n", + "1200 10.8 1162.646816 0.121073 662.4 1800.0 \n", + "1300 7.1 1197.080436 0.056639 670.1 1800.0 \n", + "1400 16.2 1164.455481 0.144555 674.2 1800.0 \n", + "1500 17.2 1107.341741 0.148655 669.1 1800.0 \n", + "1600 13.8 1161.718883 0.113599 651.8 1800.0 \n", + "1700 12.6 1205.006781 0.097266 666.3 1800.0 \n", + "1800 16.5 1195.225247 0.155213 660.9 1800.0 \n", + "1900 15.0 1139.903337 0.132232 676.6 1800.0 \n", + "2000 9.3 1000.000000 0.037497 672.8 1800.0 \n", + "2100 3.6 1503.631405 0.012683 672.9 1800.0 \n", + "2200 2.8 1790.839386 0.009350 672.9 1800.0 \n", + "2300 4.4 1601.859564 0.014384 672.9 1800.0 \n", + "2400 5.5 1431.873868 0.017843 672.9 1800.0 \n", + "\n", + " average_specificity fraction_accuracy \n", + "trial \n", + "0 8.558113 1.000000e+00 \n", + "100 12.177655 6.953332e-16 \n", + "200 10.461222 7.718738e-16 \n", + "300 10.450944 1.611548e-15 \n", + "400 10.329389 2.836941e-15 \n", + "500 10.315500 6.008248e-15 \n", + "600 10.254000 1.802908e-15 \n", + "700 10.389222 2.112054e-15 \n", + "800 9.898389 1.312832e-15 \n", + "900 10.176167 9.353784e-16 \n", + "1000 10.563722 8.861977e-16 \n", + "1100 10.731833 7.022988e-16 \n", + "1200 10.458000 4.883559e-15 \n", + "1300 10.155833 7.444679e-15 \n", + "1400 10.610611 8.436648e-14 \n", + "1500 10.372056 3.682139e-15 \n", + "1600 10.444056 4.204611e-16 \n", + "1700 10.905056 1.027987e-14 \n", + "1800 10.489722 2.001767e-15 \n", + "1900 10.052944 1.210971e-14 \n", + "2000 9.737000 1.277823e-14 \n", + "2100 9.737000 1.275005e-14 \n", + "2200 9.737000 1.274822e-14 \n", + "2300 9.737000 1.275061e-14 \n", + "2400 9.737000 1.274875e-14 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is hard to say but oking at amount of times algorithm reaches top steps (50) the steps might actually go down over trials. need to somehow smooth it to see it better" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/XCS_Experiments/XCS_woods1_XNCS_metrics.ipynb b/XCS_Experiments/XCS_woods1_XNCS_metrics.ipynb new file mode 100644 index 0000000..84957d7 --- /dev/null +++ b/XCS_Experiments/XCS_woods1_XNCS_metrics.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like:\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like:\")\n", + "situation = maze.reset()\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *\n", + "\n", + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000.0, 'perf_time': 0.00047239999999959537, 'population': 8, 'numerosity': 8, 'average_specificity': 12.75, 'fraction_accuracy': 1.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 2, 'reward': 1504.1548361035784, 'perf_time': 0.006607600000000602, 'population': 264, 'numerosity': 1603, 'average_specificity': 10.123518402994385, 'fraction_accuracy': 2.0223529468227565e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 4, 'reward': 1255.6096947680135, 'perf_time': 0.00807600000000086, 'population': 221, 'numerosity': 1636, 'average_specificity': 7.235941320293398, 'fraction_accuracy': 9.116482292505803e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 6, 'reward': 1130.2101830360214, 'perf_time': 0.018849900000001085, 'population': 269, 'numerosity': 1857, 'average_specificity': 9.990845449649973, 'fraction_accuracy': 5.7811789702408024e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 7, 'reward': 1097.75560647724, 'perf_time': 0.012708599999999848, 'population': 237, 'numerosity': 1821, 'average_specificity': 7.786381109280615, 'fraction_accuracy': 4.1736124655626805e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 16, 'reward': 1004.1789444831745, 'perf_time': 0.04987759999999852, 'population': 264, 'numerosity': 2117, 'average_specificity': 8.61076995748701, 'fraction_accuracy': 7.256041304979413e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 17, 'reward': 1003.4381828772223, 'perf_time': 0.05070580000000291, 'population': 260, 'numerosity': 2129, 'average_specificity': 7.046970408642555, 'fraction_accuracy': 3.408548664653742e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 26, 'reward': 1000.2321215204573, 'perf_time': 0.0633636000000024, 'population': 270, 'numerosity': 2462, 'average_specificity': 8.03127538586515, 'fraction_accuracy': 5.9762455819293225e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 5, 'reward': 1248.7320916970837, 'perf_time': 0.01531310000000019, 'population': 293, 'numerosity': 2670, 'average_specificity': 7.344194756554307, 'fraction_accuracy': 6.225021203088247e-18}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 11, 'reward': 1023.6607895777664, 'perf_time': 0.04438640000000049, 'population': 366, 'numerosity': 3615, 'average_specificity': 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9.655811742334205, 'fraction_accuracy': 2.8473404621539156e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1588.393744412426, 'perf_time': 0.0079521999999983, 'population': 371, 'numerosity': 3788, 'average_specificity': 9.538806758183737, 'fraction_accuracy': 3.082901510373173e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1588.393744412426, 'perf_time': 0.0079521999999983, 'population': 371, 'numerosity': 3788, 'average_specificity': 9.538806758183737, 'fraction_accuracy': 3.082901510373173e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 13, 'reward': 1000.000000425869, 'perf_time': 0.026899200000002566, 'population': 299, 'numerosity': 2466, 'average_specificity': 8.036090835360909, 'fraction_accuracy': 3.497510101315578e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 13, 'reward': 1000.000000425869, 'perf_time': 0.026899200000002566, 'population': 299, 'numerosity': 2466, 'average_specificity': 8.036090835360909, 'fraction_accuracy': 3.497510101315578e-17}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 3.6552529472225224e-05, 'perf_time': 0.05360910000000274, 'population': 218, 'numerosity': 1600, 'average_specificity': 9.2375, 'fraction_accuracy': 3.8878450171860393e-14}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 3.6552529472225224e-05, 'perf_time': 0.05360910000000274, 'population': 218, 'numerosity': 1600, 'average_specificity': 9.2375, 'fraction_accuracy': 3.8878450171860393e-14}\n" + ] + } + ], + "source": [ + "df = avg_experiment(maze=maze,\n", + " cfg=cfg,\n", + " number_of_tests=1,\n", + " explore_trials=1000,\n", + " exploit_trials=500)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"population\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb new file mode 100644 index 0000000..7b38e4c --- /dev/null +++ b/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb @@ -0,0 +1,771 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['.', '.', '.', 'F', 'O', 'O', '.', '.']\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xncs import Configuration\n", + "from utils.nxcs_utils import *\n", + "\n", + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2, # beta\n", + " epsilon_0=0.01,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction = 10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 30, 'reward': 1000.0, 'perf_time': 0.4908828000006906, 'numerosity': 1800, 'population': 1630, 'average_specificity': 8.407222222222222, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1563.7665263868944, 'perf_time': 0.03216950000023644, 'numerosity': 1800, 'population': 1540, 'average_specificity': 18.010555555555555, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 13, 'reward': 1011.8631483990195, 'perf_time': 0.160649999999805, 'numerosity': 1800, 'population': 1543, 'average_specificity': 20.341666666666665, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 8, 'reward': 1070.0230026698277, 'perf_time': 0.09758580000016082, 'numerosity': 1800, 'population': 1551, 'average_specificity': 21.015555555555554, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 8, 'reward': 1069.0362559493183, 'perf_time': 0.09980910000012955, 'numerosity': 1800, 'population': 1575, 'average_specificity': 19.20888888888889, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 7, 'reward': 1097.1024221614744, 'perf_time': 0.07686180000018794, 'numerosity': 1800, 'population': 1578, 'average_specificity': 19.611666666666668, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 1, 'reward': 2217.4535717217086, 'perf_time': 0.025234700000510202, 'numerosity': 1800, 'population': 1584, 'average_specificity': 19.805555555555557, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 5, 'reward': 1198.3710964017487, 'perf_time': 0.06566600000041944, 'numerosity': 1800, 'population': 1567, 'average_specificity': 20.302222222222223, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 3, 'reward': 1409.5230724117976, 'perf_time': 0.03123330000016722, 'numerosity': 1800, 'population': 1575, 'average_specificity': 20.464444444444446, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 8, 'reward': 1075.7290135762564, 'perf_time': 0.09921990000111691, 'numerosity': 1800, 'population': 1568, 'average_specificity': 20.086666666666666, 'fraction_accuracy': 0.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 1 experiment\n" + ] + }, + { + "name": "stderr", + 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1392.394373 0.064630 1800.0 1448.8 \n", + "1800 7.4 1157.446494 0.081245 1800.0 1449.6 \n", + "1900 11.8 1239.067146 0.129333 1800.0 1446.2 \n", + "2000 2.8 1539.534878 0.028273 1800.0 1452.8 \n", + "2100 4.8 1397.112791 0.058439 1800.0 1453.6 \n", + "2200 6.0 1185.790393 0.068443 1800.0 1451.6 \n", + "2300 7.2 1129.381893 0.090482 1800.0 1461.4 \n", + "2400 5.4 1423.324405 0.069817 1800.0 1461.2 \n", + "\n", + " average_specificity fraction_accuracy \n", + "trial \n", + "0 8.369556 0.000000 \n", + "100 11.567222 0.020652 \n", + "200 14.321778 0.025034 \n", + "300 17.568000 0.075165 \n", + "400 19.637444 0.050001 \n", + "500 20.665000 0.038012 \n", + "600 21.490444 0.038501 \n", + "700 23.247222 0.046341 \n", + "800 23.817000 0.052659 \n", + "900 23.252222 0.042387 \n", + "1000 25.584444 0.019927 \n", + "1100 27.055111 0.013034 \n", + "1200 29.789444 0.020771 \n", + "1300 30.880111 0.013929 \n", + "1400 30.821444 0.014106 \n", + "1500 31.602556 0.013922 \n", + "1600 32.647778 0.013637 \n", + "1700 32.844889 0.012886 \n", + "1800 33.736778 0.012378 \n", + "1900 33.532889 0.008997 \n", + "2000 32.882444 0.008764 \n", + "2100 32.853000 0.012493 \n", + "2200 32.758333 0.008879 \n", + "2300 32.383444 0.033714 \n", + "2400 32.737333 0.029473 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42.56000000000002\n" + ] + } + ], + "source": [ + "print(sum(df['steps_in_trial'])/number_of_experiments)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb b/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb new file mode 100644 index 0000000..976101f --- /dev/null +++ b/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb @@ -0,0 +1,513 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['.', '.', '.', '.', '.', '.', '.', 'O']\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xncs import Configuration\n", + "from utils.nxcs_utils import *\n", + "\n", + "cfg = Configuration(number_of_actions=8,\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " metrics_trial_frequency=10,\n", + " max_population=1000,\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 15, 'reward': 1000.0, 'perf_time': 0.1265051999998832, 'numerosity': 1000, 'population': 806, 'average_specificity': 60.11, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 4, 'reward': 1000.0000092886122, 'perf_time': 0.025052799999912168, 'numerosity': 1000, 'population': 692, 'average_specificity': 70.576, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 50, 'reward': 1.8142877965529527e-12, 'perf_time': 0.28711920000000646, 'numerosity': 1000, 'population': 682, 'average_specificity': 56.262, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 50, 'reward': 3.655253076036953e-05, 'perf_time': 0.29238710000004176, 'numerosity': 1000, 'population': 633, 'average_specificity': 55.454, 'fraction_accuracy': 0.009540117416829745}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 50, 'reward': 1.5562945950803363e-79, 'perf_time': 0.23974199999997836, 'numerosity': 1000, 'population': 567, 'average_specificity': 50.586, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 10, 'reward': 1035.6984443065674, 'perf_time': 0.04180100000007769, 'numerosity': 1000, 'population': 535, 'average_specificity': 42.112, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 20, 'reward': 1001.0597655484517, 'perf_time': 0.09176580000007561, 'numerosity': 1000, 'population': 562, 'average_specificity': 38.994, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 3, 'reward': 1358.102259434339, 'perf_time': 0.011067800000091665, 'numerosity': 1000, 'population': 560, 'average_specificity': 38.526, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 10, 'reward': 1041.2217947216698, 'perf_time': 0.05651409999995849, 'numerosity': 1000, 'population': 575, 'average_specificity': 35.321, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 5, 'reward': 1196.1816232805677, 'perf_time': 0.025229599999875063, 'numerosity': 1000, 'population': 581, 'average_specificity': 35.385, 'fraction_accuracy': 0.06944444444444445}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 1 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 12, 'reward': 1000.0, 'perf_time': 0.1048000000000684, 'numerosity': 1000, 'population': 808, 'average_specificity': 63.404, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 4, 'reward': 1271.4399322294107, 'perf_time': 0.018099200000051496, 'numerosity': 1000, 'population': 701, 'average_specificity': 52.824, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 4, 'reward': 1278.2960306875025, 'perf_time': 0.017446099999915532, 'numerosity': 1000, 'population': 675, 'average_specificity': 47.536, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 7, 'reward': 1091.6214180314962, 'perf_time': 0.03658799999993789, 'numerosity': 1000, 'population': 636, 'average_specificity': 45.459, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 5, 'reward': 1219.35614631337, 'perf_time': 0.021666899999900124, 'numerosity': 1000, 'population': 652, 'average_specificity': 43.837, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 6, 'reward': 1175.643591521514, 'perf_time': 0.02585999999996602, 'numerosity': 1000, 'population': 647, 'average_specificity': 40.477, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 9, 'reward': 1074.844471182247, 'perf_time': 0.058271200000035606, 'numerosity': 1000, 'population': 674, 'average_specificity': 39.439, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 9, 'reward': 1049.0586011431242, 'perf_time': 0.04900720000000547, 'numerosity': 1000, 'population': 639, 'average_specificity': 36.87, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 50, 'reward': 5.497866015975701e-05, 'perf_time': 0.23830449999991288, 'numerosity': 1000, 'population': 654, 'average_specificity': 37.573, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 4, 'reward': 1292.0071188549996, 'perf_time': 0.021888200000148572, 'numerosity': 1000, 'population': 648, 'average_specificity': 34.501, 'fraction_accuracy': 0.0}\n" + ] + } + ], + "source": [ + "\n", + "\n", + "number_of_experiments = 2\n", + "explore = 0\n", + "exploit = 2500\n", + "\n", + "df = avg_experiment(\n", + " maze=maze,\n", + " cfg=cfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=True\n", + " )\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 13.5 1.000000e+03 0.115653 1000.0 807.0 \n", + "10 26.0 7.520500e+02 0.175509 1000.0 791.0 \n", + "20 50.0 6.680437e-13 0.306126 1000.0 772.5 \n", + "30 50.0 1.827626e-05 0.285788 1000.0 771.5 \n", + "40 50.0 1.827626e-05 0.311911 1000.0 768.5 \n", + "... ... ... ... ... ... \n", + "2450 3.5 1.360962e+03 0.015389 1000.0 608.0 \n", + "2460 8.5 1.297603e+03 0.039121 1000.0 611.5 \n", + "2470 7.5 1.094273e+03 0.037157 1000.0 609.5 \n", + "2480 11.0 1.003666e+03 0.074811 1000.0 612.5 \n", + "2490 26.5 6.797017e+02 0.148874 1000.0 605.0 \n", + "\n", + " average_specificity fraction_accuracy \n", + "trial \n", + "0 61.757 0.0 \n", + "10 60.261 0.0 \n", + "20 58.846 0.0 \n", + "30 58.108 0.0 \n", + "40 58.681 0.0 \n", + "... ... ... \n", + "2450 34.806 0.0 \n", + "2460 34.482 0.0 \n", + "2470 34.444 0.0 \n", + "2480 34.002 0.0 \n", + "2490 33.261 0.0 \n", + "\n", + "[250 rows x 7 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/54AX4pqjYSSVIHOQv7jXcp8fqJVQoEGYfDAKiFODOA7YoKqvqGo/cAuwouiYFcCP1eURoFNEZgKIyGzgL4EfxThHw0gkQUlt/vNaHNXlivX528xJbRRSUUCIyJkiUosgmQVsKnje5W0Le8y3gS8AZb+xIrJSRNaKyNrt27fXME3DSB5BhfV8U1BtJiZ/THNSG+EIs/CfC6wXkatE5PAqxi6V0V/8tS55jIicCWxT1ccrvYiqXqeqS1V16fTp06uYnmEkl6BEueL9tY05fF/anNRGCSoKCFX9a+BoYCPw7yLyJ++ufWKFU7uAOQXPZwNbQh5zInCWiLyGa5r6gIj8R6W5GsZowQnSIKQODcIpPSYUCAhTIWLDcZQ3d/Xy5q5e3trTN9LTCUUo05Gq7gZ+ibtYzwTOAZ4QkUvLnLYGmC8i80SkHVcTWV10zGrgAi+aaRmwS1XfUNUvqupsVZ3rnfd7T1AZRkvgl8UoVe67cH8UY4I5qRvBV1c/y7Jv3seyb97HMf9wL/c+v3Wkp1SRMD6ID4vIr4HfA23Acap6BrAYuCzoPFXNApcAd+NGIv1cVZ8TkVUisso77E7gFWAD8EPgonouxjBGC+XKfUNtyXJaxgeREtMg4mTb7l5+vqaLUw/fn2/+1SI6x7Vx5zNvjPS0KpIJcczHgKtV9cHCjaraIyJ/U+5EVb0TVwgUbru24LECF1cY4wHggRDzNIxRQ9BinoopUS5tGkRs3PH0Flav20LWcfjKXy5g7rTxPPrKWzzw8nZyjubf+yQSxsT0NeAx/4mIjBWRuQCqel9M8zKMlsYJCHMd3F/LmO5/q8XUOJ7p2sUlP32S3z2/lbOPmsXcaeMBOOWw/dm5t5+nut4Z2QlWIIyA+AVDQ01z3jbDMGKiXMMgqE+DKFuLyaKYIuWGh19jfHuax770F/zz/7U4v/39h04nJXDudY/wdz9/agRnWJ4wAiLjJboB4D1uj29KhmHk1+kAH0Qt63i5RDlzUkfPjj19/OapLXz0mNnsP6ljiGDuHNfOVR9dzFFzOrlt3WZ29QyM4EyDCSMgtovIWf4TEVkB7IhvSoZhVPJB1JZJ7Y8xfJ/VYoqeP27YQX/O4WNL55Tc/9FjZvPFMw4j5yi/fymZEU1hBMQq4Esi8rqIbAIuB/5HvNMyjNYmqPvbYJhr9YRqOWryITKe3byL9kyK9xwQnDK2eHYn+08cwz0JDXmtGMWkqhuBZSIyARBV7Y5/WobR2gQ6lOvQIMo5vi3MNXqe3bybww+YSFs6+D48lRL+4vAZ3L5uM46jeU0uKYQJc0VE/hI4AuiQQSfZN2Kcl2G0NEH+gvz6UUe573JO6lyt3YiMIagqz23ZxZmLD6x47KzODnr6c2QdpT1hAiJMoty1wMeBS3FvPj4GvCvmeRlGSxO0TvstSOtLlBu+b7AWU/XjGsPpensfu3uzLDxwcsVjpQ6tMG7C+CBOUNULgLdV9evAexlaP8kwjIippEHU5aQuISGsFlO0PLt5FwALZ02qeGy6jgq9cRNGQPR6/3tE5EBgAJgX35QMw/DXisA8iBrGDNVy1ORDJDz66k7aMykOnVGppung55HESrphfBC/EZFO4H8DT+B+N38Y56QMo9UJLM3taxA12JjK+SDMSR0dOUe54+k3+MB79qejLV3x+HpCl+OmrIDwGgXdp6rvAL/0ekN3qOquRkzOMFqVwIZBAf0hqhmz1AhWiyk6/rTxLXbs6WPFUZUd1FAgIBIYIFBWQKiqIyL/jOt3QFX7gOYoZG4YTYwTkEpdT8vRoPId7jb3fxLNHM3E5255kj+8vJ2JYzKcctj+oc4Z9CvFOLEaCeOD+J2IfESCWlsZhhE5wS1H3f9Rl/sWEdIpMRNTHfT0Z7l93RZmTxnL1846IpR5CZIdYhzGB/G3wHggKyK9uDcxqqqV3fOGYdREnOW+g271XAFR9bCGxyvb9wJw8cnv5oxFM0OfJ3V8pnETJpO6shveMIxIqbSY11PuO8iPkRbTIOph4/Y9AByy/4SqzhuspBv5lOqmooAQkZNKbS9uIGQYRnRUCnOtJdBVKwidjGkQdbFx+15SAu/ab1xV5yXZ/xPGxPT/FDzuAI4DHgc+EMuMDMMYdEJH2HK0kgaRMh9EXWzcvoeDpo5jTCac78GnaaOYAFT1w4XPRWQOcFVsMzIMI4QPovoxyyXKgeeDSOBdbLOwcdseDplenXkJkp0HESaKqZguYGHUEzEMY5C8Oahoez1hrvnA2SAfhJmYamZff45Xd+yt2v8A9UWmxU0YH8R3KDCJAkcBye2RZxijgCBzUD2F3bSSBmFO6pr4/Ytb+fSNa1GFd9ehQTRrmOvagsdZ4GZV/WNM8zEMg+DFPN8wqC4Tk2kQUXLH028wqaONz586n788Mnx4q089octxE0ZA3Ar0qmoOQETSIjJOVXvinZphtC75m8lhTuo6fBCOP0bp/ZYoVz2qyn+t38H75k/jwhNrq2Ga5DDXMD6I+4CxBc/HAvfGMx3DMKCw5Wjpct9aQ5hrKA0igYtUknnhjW62d/fx/kOn1zxGPsw1gRIijAbRoap7/CequkdEqgv0NQyjKoIK69UT5jropC69P52SRIZaJg1V5Vu/e4nXd+7j9Z2uIeWkugREcqOYwgiIvSKyRFWfABCRY4B98U7LMFqb/N1+Kg4ndXAmddZMTBXp6c/xvfs3MnV8O51j2/irJbOYMamj5vGaXUB8HviFiGzxns/EbUFaERE5HfhXIA38SFX/qWi/ePs/BPQAn1LVJ0SkA3gQGOPN8VZV/VqY1zSM0UClct+1OamHjlGMOanDkfXeyItOPoTPvO/gusdr6jBXVV0jIocB78HVeF9U1YFK54lIGvgecBpu7sQaEVmtqs8XHHYGMN/7Ox74gfe/D/iAZ85qAx4SkbtU9ZHqLs8wmpNK5b7jK9ZnEqISvhkuHRQvXCVJ1iAqOqlF5GJgvKo+q6rPABNE5KIQYx8HbFDVV1S1H7gFWFF0zArgx+ryCNApIjO9577fo837S967ZxgxMViLaej2+lqOuv/LCgj7lVXEzzaPXEAkUIUIE8X0Wa+jHACq+jbw2RDnzQI2FTzv8raFOsYLp10HbAPuUdVHS72IiKwUkbUisnb79u0hpmUYyWewsF6xD8L9X8tiUtEHYRpEKMq1bq2FZg9zTRU2C/JMR+0hziv17hW/BYHHqGpOVY8CZgPHiUjJ8h6qep2qLlXVpdOn1x5JYBhJIsgH4f8Sa1lLyjUMAj+TOoGrVMLwZWg6IgHhD5PE9z6MgLgb+LmI/IWIfAC4GfhtiPO6gDkFz2cDW6o9xtNeHgBOD/GahjEqyJuDhvkgardXhynWZwpEZQZNTNGMl05wJnWYS7wc+D3wP4GLcRPnvhDivDXAfBGZJyLtwLnA6qJjVgMXiMsyYJeqviEi00WkE0BExgKnAi+GuSDDGA0EOZTrK7Xh/i8WOj7plIW5hsE37wVpYtXihzInsZJumCgmBze66AfVDKyqWRG5BFcDSQPXq+pzIrLK238tcCduiOsG3DDXC73TZwI3euasFPBzVb2jmtc3jGYmsNx3qvYw17xfI+C20JzU4chFHsXk/k+ghSlUNdf5wDeBBbgNgwBQ1YoBwKp6J64QKNx2bcFjxdVKis97Gji60viGMVoJ6v5WT7nvcMX6TIOoRGxRTAnUIMKYmP4dV3vIAqcAPwZuinNShtHqBLUclTrCXIMc3z4psUS5MERuYmryMNexqnofIKr6Z1W9Ems3ahixEuSDGKzFVIsG4f4PWtgyVospFJXex2pp9jDXXhFJAetF5BIROQfYP+Z5GUZL4wTc7dfTOyBMJvWA4/Dqjr1Vj91KDPogohmv2cNcPw+MA/5v4Bjgr4FPxjgnw2h5ghLl6oliGmxjGuyDeGX7Xk751gPs2NNX/Qu0CJV8OdWS5IZBFQWEqq5R1T2q2qWqF6rqR6wmUvR8/P/8iZ+v3VT5QKMlCCz3Te3miCCtxKdw+57ebPUv0CJEHcWUTnCYa0RKklEv6za9w0tvdo/0NIyEENQwKJ9JHUMU05Ob3sk/7jdvdSD+Ql5cir1WkhzmagIiIeQcTaQN0hgZgtqD1tUwqEKxvlmdg40j+7MmIILIV3M1E5PRKHJqAsIYJCjMdfB5bcX6RIKLzH3/vCV8/awjANMgyhF1FJM/ThJ//2ES5abjVm+dW3i8qv5NfNNqLRxHUU2mDdIYGYLCWOvRIBwtXR3Tp3NcO/P3nwDAgGkQgfgLeSqqWkwJDnMN01HuduC/gHuBXLzTaU18wZCzOgeGhwbYuevJunVUK971tmXcVc80iGD89z7qaq5JzEEJIyDGqerlsc+khfHvSEyDMHyCW44O3V8NjlY2i7R7wf0DJiACib4WU3OX2rhDRD4U+0xaGP+LkcQ7CGNkCK68Wvtiomigg9qnzRMQ5qQOJuoopiSbmMIIiM/hColeEen2/nbHPbFWwjQIo5jBMNeh2+tZkzSMBpE3Mdl3MYioo5jymdQJ/P2HKfc9sRETaWV8AZFN4i2EMSLkvwrDwlzr8EE4WlHAtJsGUZHIazElOMw1jA8CETkLOMl7+oD1ZogWX0CYicnwCeofnU+qqmH9drRyH2VfgzAfRDBRRzElOcy14iWKyD/hmpme9/4+520zIiIfxZTAL4gxMgQ2DKqj3LejYXwQ7gGmQQSTj2KKykmdYB9EGA3iQ8BRXmc5RORG4Engijgn1krkNYgEqpjGyJCvvFphf7WE9UGYBhFMLvJMavd/Ei0IYZWkzoLHk2OYR0tjPgijmEotR2tRIdw8iPLH+FFMfaZBBOJEHMWU5DDXMBrEN4EnReR+3Buak4AvxjqrFsO3J5uJyfDJLxYRtxy1PIj6iVqDSHKYa5gopptF5AHgWNzv5+Wq+mbcE2slfB9EEu8gjJElsGFQDWO5TuoKr5cSMikxH0QZok6Uq6dLYNwEmphE5DDv/xJgJtAFbAIO9LYZEeE3is9a7LnhMdj9rXS575oS5VQrRjGB64cwDSKYSlVxq8XXRJLogyinQfwtsBL45xL7FOtLHRn+bzGJdxDGyBDU3KeuYn1OuES7tnTKNIgy5KKOYvLDXBP4+w8UEKq60nt4hqr2Fu4TkY5YZ9Vi5DOpE3gHYYwMlcJcaynGpFT2QYCrQVgmdTCRRzEl2AcRJorp4ZDbjBoZLLUxwhMxEkNgue/8/lrGDJf9224aRFmijmICV7NrKhOTiBwAzALGisjRDH43JwHjGjC3lmEwUc5+lMZQAhPlaoxiCnPTaz6I8kStQYD7uSbRxFzOB/FB4FPAbFw/hP9u7Aa+FO+0WotBE9MIT8RIDP7dZNQtR8OsaW1pi2Iqx2CpjQgFREqay8Skqjeq6inAp1T1A6p6ive3QlV/FWZwETldRF4SkQ0iMizzWlyu8fY/7UdHicgcEblfRF4QkedE5HM1X2ETYLWYjGKCCsLVVawvRB4EmAZRiaBeHfWQkmQGqYTxQRwjIp3+ExGZIiL/UOkkEUkD3wPOABYAnxCRBUWHnQHM9/5WAj/wtmeBv1PVw4FlwMUlzh01DGZS24/ScBmpct/gRTGZgAgk6igmcM1VSbxBDCMgzlDVd/wnqvo2bn2mShwHbFDVV1S1H7gFWFF0zArgx+ryCNApIjNV9Q1VfcJ7vW7gBVx/yKgk3zAoed8PY4TINwyKWIMI5YMwJ3VZ8iamiH0QSQxzDSMg0iIyxn8iImOBMWWO95mFm1jn08XwRb7iMSIyFzgaeDTEazYlFuZqDCNgMa+n5WhYDcINczUBEYQTcSY1uD6IBMqHULWY/gO4T0T+HTdB7m+AG0OcV+rdK34Lyh4jIhOAXwKfV9WSXexEZCWueYqDDjooxLSShwkIoxhHS/84JN9ytJYxNbA6bCFtafNBlCNvYopUg0imDyJMLaarROQZ4C9wv7P/r6reHWLsLmBOwfPZwJawx4hIG65w+Ek5p7iqXgdcB7B06dLkvcMhMAFhFBOU1FZPqY3QTmozMZXFiSOKSSSRv/9QHeVU9S7grirHXgPMF5F5wGbgXOC/Fx2zGrhERG4Bjgd2qeob4hpa/w14QVX/pcrXbTryeRAJvIMwRoagwnr13LSGKdYH0JZJMWBZm4G4CYfRjtl0Ya4+IrJMRNaIyB4R6ReRnIiUNPcUoqpZ4BLgblwn889V9TkRWSUiq7zD7gReATYAPwQu8rafCJwPfEBE1nl/YRzjTYmFuRrFOAGF9fK9A2r4roT2QZgGUZacaqT+B2jCTOoCvot79/8LYClwAfDuMIOr6p24QqBw27UFjxW4uMR5DxHcTGvUYQ2DjGEE3KXWU+5bVUP1UW7PiDmpy+A44Ux11ZBuwkzqPKq6QUTSqpoD/l1ErBZThOTDXE1AGB6uQ7mED6Jgf1RjFmMaRHlyTvQahCQ0zDWMgOgRkXZgnYhcBbwBjI93Wq3FYLG+5H1BjJFBAzSIOoq5hradWxRTeXKqkUYwgRsym8Sff5g8iPO94y4B9uJGHX0kzkm1GlmLYjKKcB3KpaKY6i3WFzIPwjSIQBxHI41ggiYNc/XKZfyjqv410At8vSGzajEcExBGEeWynt3FpLZxw2oQWUdjWQhHA/E4qZMZ5lpWg/B8DtM9E5MRExbmapQiyBGaEsnXaqqGaor1AeaoDiCuMNck/vzD+CBeA/4oIqtxTUwAtEJ+QqPw7xxU41FfjeajnAYhNWoQbsvRcE5qgIGcQ0dbuvoXGuXEEcWUkmRaEMIIiC3eXwqYGO90WpPCL0ZOlVTEEb7/++4X6R1w+PszR21B3FFHuZwFkdruNh0NqN9RRF6DMD9ESeKIYmq6hkEicpOqng+8o6r/2sA5tRxDBISjRH3Ttva1t+m1H3tTUa5uklCbk1qVUHkQbXkNInkLVhLIhTTVVUNSBUS5r8sxIvIu4G+8HhBTC/8aNcFWoFBAxPElyTpK1uzJTUVQFBPUvphU64OwUNfSOHFoEKlklvsvZ2K6FvgtcDDwOEOVU/W2GxFQ6JyOww45kHPsx950lPdB1FTum7ANg9xj+kzrLElOoy31DcnNpC7XcvQar6Pb9ap6sKrOK/gz4RAhjhO3gFCyZi5oKlyHcul9rgZRw5ghGwaNMQ2iLGHfx2qQZgxzBVDV/9mIibQyhb/DOL4k2ZzDgLUzbSqCyn2Dp0HUFOYabLYqxPdBmJO6NI5jmdRGA8kVLN5x5EIM5BwGsgn89hmBBDUMAt9JXf2Yqhoqft98EOWJJ4opmWGuJiASQPw+CCVrGkRToeWc1CmpudRGOB+EaRDlCPs+VkMzRjEZDSJ2E5PjWMhik6HlEuWoLeIlqABgMZZJXZ648iASKB9MQCSBQhNTHDf6AzltGnPBM127eGtP30hPY8Qpd5dae5hrOB9Eu2kQZclptO1GwQ1zTWKpHRMQCWCIBhGTD6JZopguuP5RfvTQqyM9jRHHDUktvU9Eam8YVJUPojm+M40m7PtYDWZiMgJxhvggor9rG8g59OecmuzWjWZPX5Y9vdmRnsaIU+5u382DiK9hUN4HkctV/RqtQC6GKKaUSCIbhpmASABDS21EP76vPSQxSqIQx1EGcmqmDcr7IFI1Jso5IUtt5DUIi3wrSS6GgprpVG25LXFjAiIBZGNMlFPV/PhJ73nt52o0i78kTsrV1RNqM0doyIZB+Uxq+xxK4sTQUc7CXI1A4sykLrQjJz0qxdccbGGq5KSuJ4qp8sI2Ju1WixwwTa4krVTN1QREAhiSBxHxl6Qw/yHpjmpfmNnC5OdBlN5XT7nvUB3lMu5BSb+hGCliiWKyMFcjiOJy31FSaEdOuunG1yCSPs9GUE6DqN1JHaodxGDDIBPUJXFLbUQ7poW5GoHEWe67sAZT0hdef3525+qGuZYr913LtyRsBnA6JYjY5xCEZVIbDaXwziFqM1DheEk3MfkLkkXPeA7lgH1uy9HaGgaFcVKLCO3plAmIAOKIYrIwVyOQXC5GDSLXPBqEOakHKdf9rVZ7dTUJXu3plIUbBxBHFJOFuRqBxFmsb6iASOA3sICBvAZhC1O5pDahthsJJ2QUE7i5EEm/oRgp4ohiklYMcxWR00XkJRHZICJXlNgvInKNt/9pEVlSsO96EdkmIs/GOcckMCTMNfIopgITU8Iruvp3rGba8Bfz0vtq7ShXTaObNtMgAnFiiGJKS20VeuMmNgEhImnge8AZwALgEyKyoOiwM4D53t9K4AcF+24ATo9rfkkiV/DDzUV8l1/4I0/6HWHeB5HweTaCyk7q+Ir1ga9BJG/BSgK5OKKYauwSGDdxahDHARtU9RVV7QduAVYUHbMC+LG6PAJ0ishMAFV9ENgZ4/wSQ87RfGhhnBpE0n/w+TBXu3MtX+5baqv6W40Poi0tTaNBvLWnj1O+9QBPbXqnIa8XSxRTC4a5zgI2FTzv8rZVe0xZRGSliKwVkbXbt2+vaaIjTc7RfP2bqCMZmslJ7QswMzGVz3quVYNQqvFBpJvmc1j757d5dcdeHny5Mb9/J6YoppYyMVE6J6f4HQhzTFlU9TpVXaqqS6dPn17NqYmhUIOIul5SoVBIfJir74NokjvXOHHKhLm6+2sbM3wUU/NoEM9t3gXAi292N+T1crHUYpKWc1J3AXMKns8GttRwzKgn52i+xHLUYa6FQiH5GoQ5qX0qNQyqKZPaCVesD5oriukZT0C88ObuhrxezonBSd2CYa5rgPkiMk9E2oFzgdVFx6wGLvCimZYBu1T1jRjnlEhyOmhiauUw18FSG8meZyMoV4splaotiqncmMU0UxTTs1tcwfDajr3s64+/h4WjSjrilbPW5Me4iU1AqGoWuAS4G3gB+LmqPiciq0RklXfYncArwAbgh8BF/vkicjPwJ+A9ItIlIp+Oa64jjePEKSCaKMzVE2Y5RxOpbjeSssX6aiz3XY1ztVk0iG27e9ne3cfx86biKKzfFr+ZKY6GQemEZlJn4hxcVe/EFQKF264teKzAxQHnfiLOuSWJnA6amKJeGLNO82kQ4Go+6VR6BGczsihKSkrfv6WkSkedR7ncimLa0in6mkCDeHaLa1766DGzefTVnbz4RjdHzu6M9TWdkH01qiHVgiYmIyTZXIEG0cKlNgrn1wyLU5w45cxBNcbMu0JndGkQm9/pBeCkQ6fT0ZZqiAbhxJVJ3UomJiM8jipj0nGFuRYW60v2D76ZhFncaIWGQTWX+w4rIJqkWF937wAAk8e2Mamjjd374u9nntPoBUTLZVIb4ck5mm/SEmeYa3+TmZhamXJfA6FWJ3X4Uhvt6VRTVNXdvS9LezrFmEyKce1pegYa4KR2wueThKUVw1yNkAzJpI7aB9FEGkShAGuWCJq4KK9B1OqkrsIHkZGm0SAmdmQQEca2Z9jX3ygNItoxzQdhBFIY5hpnue+otZOoMQ1iEDfrufS+Wst9VxXFlE43RcmT7t4sEzvcWJtx7Wl6GhDmGkcUk/9ZJ83MZAIiATgOBVFM0Y490ER35eakHqRspEzMDYPA1SAa2Zfj1se72La7t+rzdvcOMGlsG9AYAeEv4HGU2oDklfw2AVElj726k23d1X+RyzHUxBTtjzI7RINI9qI7VINI1g+l0WgZc1AtYa75hS3kujYm7UYxNeKOdnfvAJf94il++cTmqs8t1CDGtqVjT5TzF/CofRC+0zth8sEERDU4jnLB9Y/ybw+9Gum42SGJcpEOnb8rH9ME5ZuHONRbXoOA0qXK3ES5ahduf+EJu7C1pVOoNsYs2d2b9f4P1HDuABPHFGgQA/H6IPxQ1DjCXCF52dSxJsqNNt7u6ad3wOHtvf2RjuuokvEKzEeeB+EobWm3x3Cj7Prrt3bzn8+8wYGTx/LRY2aHVsf7LMw1T7nS3KkUOFXeKPsLT9hlzb9hGcg5efNnXOzxBMSevvCL+/cf2MCMiR1DNYj2TOwahK+ER65BiK9BmIBoWrbu7gOq+yKHwXd6pVMSi4mpLZ2iLZNqWDXX792/gdvWuTUXFeVjx8wZvPMSCRQYhU7RVtcgKpb7rlqDqM527guF/qzDuPaqXqpq9vS5moMvKMLwszWbmNU5lt37Bn0Q4xvggxjUIKIdN6k+CBMQVbDV8z10V/FFDoPb4zblCYhIh2Ygp2RSQiYlDbsrf3N3L0sO6iSTSvEPd7zAP931Im/3uIvArM6x3LJyGXOmjht2Xn/O8d4DbYoQyzip1B602nXElyeh8yA8DaIRn0N3DRrE7n0DCLC3PzckimnfQM7L94i45ZtHXD6IlPkgmh8/yiIeAeEV7IohzLUtnaIt3TgfxLbuPg6Y3ME/nLOQse1pjp+3H5ctP5S/Pe1Q9vRlufCGNSVNAQM5h/Ht6fzjVqZccx+3YVCV41Xpg2gv0CDiZm+f+10IKyBUle7eLK/v7AFgYoerQYxtz6AKvQPxzdmvdBC1DyKpYa6mQVSBb2KqxZlWjpy6Har8u+coyebUExDSsCim7bv7OGn+dA6dMZHHvnzqkH3zpo3n0puf5Kmud1h28H5D9vVnHSaMybC7N9vyJiZHNdBhIDWU2nCqjGIa9EHEv2DlTUwhBcS+gdwQ5/mkAg0CoKc/y9j2eAo9Dr6PFuZqFLHV0yDi8kGkJJ5+EJm0kGmQk3pff47uvizTJ44puf/g6eMBeKdnuKO/P6eMH+P+2Ftdg6CiD6K64Qad1NX7IOImb2IKqZkX11sa1CB8ARGfHyJXpS8nLGZiGgUMahDRC4hMyl3EIxcQXrc61wcR/7fPzxHZP0BAdHoeT98nUchA1skLCNMggluOCtVHuzg1+iAaIaj9G66wN17FGnyxBrEvxnpMvhIeVyZ10qKYTEBUwXZv8evpz0W2kPs2zVRK3IJdUfsgso4b5tqg8s3bul0huv+kjpL7p4xz7/beKSEg+nOuicl9nKwfSqMpVzdJaij3rVWaRtq8sOtGZLRXG+a6u0hA+BrEuAZqEFFHMSU1zNUERBX4GgRUF5JXjsLwz3QKchEvjFnHIZNyNYhGhLlu896jIA1ibFua9kyqpIlpIOcwfoz7I291DaJc74Zayn0POqnDHd9IDWKvV2Av7I1XsYlp0lg/kzrjjRNfspwTVxSTmImpqck5yvY9fcyY5C583X3ROKr9H0Q6LWRSqRgaBiltGT+KqREaRHkTk4jQObattAZRYGJqdR+E41DBSV3leFXazhsZxVRosg2jRVTSIOJMlsvFFcXk+yASJiFMQITkrb195BzlkOkTgOj8EPkvnIibIRuDk7otJQ0UEH1kUsKUMtlVU8a183aABtHRliYlJiCgUphrjT6IkMePhA+i+HEQu73f3rQJ7nds4rAophh9ELFFMQ0dPymYgAjJ7U+6mcF+v9uoIpkKa7ukY/BBZHPqRTFJQ+rqbNvdx/SJY8reqXaOK61B9GUd2r2cjVY3MZV1UkstiXJeFFMVtZigQQKi4GZrbxgBsc/97hw6YyJj29L5uY5tgAZRrSYWFgtzbWJeerObq+5+kQ8eMYMPHjEDiC4Xwvc5pLwSFFEv4v0FiXJxLro5R8nmHLZ19waal3w6x7Xxzr7SGkR7JkV7pjnaXcZJud4NUkNCZbXF+nwNoiFO6r4sY7zXC6OZd/e6XeQWzprM7Clj89vHtcfvg8jFFcWU0DBXS5QLwb0vbGUgp/yvcxblwzMjMzF5P/RM2i2HEbWJKes4BYly8Xz7ntuyi3O+/3BeAC1fMKPs8VPGtfNEzzvDtvf7EVemQbi1mAJu31JSfb1v3yQV2kmdblyiXHdvlpmTO3jtrZ7QPohJYzP87WmHctHJh+S3501MMYa5Dvogoh03qSYmExAh2LhtDwdO7mC/CWPyi2xUAqIwKiKOvrRuJrXrg4ir5eiaV3fSn3W45JR3MyaT4rQjyguIyePa2NUzMKRmTs5RHHU7mZULyV372k6+/pvnuXnlMn79RBe7e7N8aNFM5k0bH/l1jSTly33XngdRTblvaFCpjf4shx0w0RUQIX5Xu/cNMKmjjY62NB1tgxnTYzIpRBpkYrJqrobPxu17OGR/1zntO8Ri8UHEUGqjP+eQSafIpOKrxfTS1j10jmvj75YfGsrGPWVcO/05h57+3LDEuLaMlDWH3f3cmzyzeRd/3LCDr65+DlW469k3uOPS90V3QYmgTLnvGhoG+TciSUuUU1X29GY5wMubCeOD6O7NMtGr4FqIiDCuLd6KrnFFMfm/m6T19DIfRAVUlY3b9+ajl8a2pUmnJDIfhJ+bkPYqrsbhpHajmOKr5rp+azeH7j8xtAPUT5YrjGTyfQ7t6ZSnQZR+H57ZvAuAnz76OqqweE4nz2/ZHXn5k5HG0eDFvBYfxGA11+oS5eLWIPqyDllHOWCy60voDmti6ih9bzu2PdOYKKaIBcRgR7lkaRAmICqwrbuPPX1ZDvFqCIkIE8ZkIkuUcwoS5VKxFOsbdFLH4YNQVV7a2s2hB0wIfc7ksW54YmEkky+82r2cjVLOUcdRntu8G4AH128H4MIT5uIoPL3pnVovIZFoWSd1HXkQCSv37ZtqD/Dyi6oxMZXC7UsdY6KchblGh4icLiIvicgGEbmixH4RkWu8/U+LyJKw5zaKjdv2AHDw9MEFcGJHJvo8CC/MNeovSH9OXRNTWoY05ImKrbv76O7NcuiMiaHPKVVuw79THdQghs/1zzt76O7L0pZ2i9W9a79xnPKe/QF44vW367mMxOEEF3P1Wo5WO16VpTZSjfFB+Jrf5HFtjG1L5yu7lmN3bzafPV3MuJibBsUWxdRqYa4ikga+B5wBLAA+ISILig47A5jv/a0EflDFuQ1h43ZXQBxSICAmjMmEUoXDkG9AkvLCXGMoteE7qQdiMHC+tLUboDoBMd7TIApCXX2B0JZO0R5gDnu66x0ATl84E4DFszuZPK6NQ6aP54nX36ll+onFKdP0JiXV32n6R4dd11IxmyV9fJ/D+PYMEzoy7OmrvLh39w7ks6eLGduebkgmdVCEWa20YpjrccAGVX0FQERuAVYAzxccswL4sbpZPI+ISKeIzATmhjg3Mj78nYfoDQiNe2tvP+Pb0/kSGwCTOtr444YdnPYvf6j7tX1TSlpcH8QTr78dybg+u/YNkEm5Ya69A06kY/vjQ3UCotNzMH7jN8/zr/euBwZNGX5ZkFLvw9s9/bRnUvzVkln85qktHDWnE4AlB03h9nVbIr+2kWRPXzZwMU+JsK27r6rr9d/fajqttaVT/PSx17nn+a2hz6kWv/LqhDEZJo7JcMdTW1j72s6y5/QOOEwcE6xBrP3zzti+C752Elc118/d8iRj26rvZTFlXDs/X/XeSOcE8QqIWcCmguddwPEhjpkV8lwARGQlrvbBQQcdVNNED5k+PtDWOh9Y+q6pQ35YnzxhLv/5zJaaXqsUS981hePmTSUlbhJZlBx6wEQ+vHgmbekUf36rJxYb58HTJjB1fPjGxdMnjuGz75vH5nf2Ddl+zEFTWDZvKm0pCXwflhw0hRMO2Y/Pvm8eH158IAAXvHcuPV6rydGC+7kdWHLfOUtmsacvW3W5Df/9DculH5jPM5vfqeo1auG9B+/H4jmdrDzp4LxvqRyHzZzEGYsOKLnv/GVzmVwiwilKTnz3fhwxa3KkYx45u5OPHjO7Zv9JkE+mXiSuH5WIfAz4oKp+xnt+PnCcql5acMx/At9U1Ye85/cBXwAOrnRuKZYuXapr166N5XoMwzBGIyLyuKouLbUvTg2iC5hT8Hw2UHzbHXRMe4hzDcMwjBiJM4ppDTBfROaJSDtwLrC66JjVwAVeNNMyYJeqvhHyXMMwDCNGYtMgVDUrIpcAdwNp4HpVfU5EVnn7rwXuBD4EbAB6gAvLnRvXXA3DMIzhxOaDGAnMB2EYhlEd5XwQlkltGIZhlMQEhGEYhlESExCGYRhGSUxAGIZhGCUZVU5qEdkO/LnG06cBOyKcTjNg19wa2DW3BrVe87tUdXqpHaNKQNSDiKwN8uSPVuyaWwO75tYgjms2E5NhGIZREhMQhmEYRklMQAxy3UhPYASwa24N7Jpbg8iv2XwQhmEYRklMgzAMwzBKYgLCMAzDKEnLCwgROV1EXhKRDSJyxUjPJ0pE5DUReUZE1onIWm/bVBG5R0TWe/+nFBz/Re99eElEPjhyMw+PiFwvIttE5NmCbVVfo4gc471XG0TkGqmmN+cIEHDdV4rIZu/zXiciHyrY19TXLSJzROR+EXlBRJ4Tkc9520ftZ13mmhv3Oatqy/7hlhLfiNvBrh14Clgw0vOK8PpeA6YVbbsKuMJ7fAXw/3mPF3jXPwaY570v6ZG+hhDXeBKwBHi2nmsEHgPeCwhwF3DGSF9bDdd9JXBZiWOb/rqBmcAS7/FE4GXvukbtZ13mmhv2Obe6BnEcsEFVX1HVfuAWYMUIzyluVgA3eo9vBM4u2H6Lqvap6qu4PTqOa/z0qkNVHwSKu9xXdY0iMhOYpKp/UvfX9OOCcxJJwHUH0fTXrapvqOoT3uNu4AXc3vWj9rMuc81BRH7NrS4gZgGbCp53Uf4DaDYU+J2IPC4iK71tM9Tt2of3f39v+2h6L6q9xlne4+LtzcglIvK0Z4LyzS2j6rpFZC5wNPAoLfJZF10zNOhzbnUBUcoON5rifk9U1SXAGcDFInJSmWNH+3sBwdc4Wq79B8AhwFHAG8A/e9tHzXWLyATgl8DnVXV3uUNLbBst19ywz7nVBUQXMKfg+WxgywjNJXJUdYv3fxvwa1yT0VZP5cT7v807fDS9F9VeY5f3uHh7U6GqW1U1p6oO8EMGTYSj4rpFpA13ofyJqv7K2zyqP+tS19zIz7nVBcQaYL6IzBORduBcYPUIzykSRGS8iEz0HwPLgWdxr++T3mGfBG73Hq8GzhWRMSIyD5iP69hqRqq6Rs800S0iy7zojgsKzmka/IXS4xzczxtGwXV78/s34AVV/ZeCXaP2sw665oZ+ziPtqR/pP+BDuNEBG4Evj/R8Iryug3EjGp4CnvOvDdgPuA9Y7/2fWnDOl7334SUSGtlR4jpvxlWzB3DvlD5dyzUCS70f2kbgu3hVBpL6F3DdNwHPAE97i8XM0XLdwH/DNYs8Dazz/j40mj/rMtfcsM/ZSm0YhmEYJWl1E5NhGIYRgAkIwzAMoyQmIAzDMIySmIAwDMMwSmICwjAMwyiJCQjDqAMR6RSRi8rsfzjEGHuinZVhRIMJCMOoj05gmIAQkTSAqp7Q6AkZRlRkRnoChtHk/BNwiIisw01a24ObwHYUsEBE9qjqBK+ezu3AFKAN+IqqJjKD1zB8LFHOMOrAq7J5h6ouFJGTgf8EFqpbbpkCAZEBxqnqbhGZBjwCzFdV9Y8ZoUswjEBMgzCMaHnMFw5FCPC/vIq6Dm655RnAm42cnGFUgwkIw4iWvQHbzwOmA8eo6oCIvAZ0NGxWhlED5qQ2jProxm0HWYnJwDZPOJwCvCveaRlG/ZgGYRh1oKpvicgfReRZYB+wNeDQnwC/EZG1uFU5X2zQFA2jZsxJbRiGYZTETEyGYRhGSUxAGIZhGCUxAWEYhmGUxASEYRiGURITEIZhGEZJTEAYhmEYJTEBYRiGYZTk/wfE+bqk5IFUfgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_maze.ipynb b/XCS_Experiments/XNCS_maze.ipynb index cbf9c74..72e177d 100644 --- a/XCS_Experiments/XNCS_maze.ipynb +++ b/XCS_Experiments/XNCS_maze.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -23,13 +23,13 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '1', '9', '0', '0', '1', '0', '1')\n", + "('1', '0', '0', '0', '1', '0', '0', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -77,264 +77,38 @@ " initial_error=0.01,\n", " metrics_trial_frequency=50,\n", " covering_wildcard_chance = 0.9,\n", - " user_metrics_collector_fcn=xcs_maze_metrics,\n", + " user_metrics_collector_fcn=xncs_metrics,\n", " lmc=10,\n", - " lem=200\n", + " lem=20\n", " )\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': [2.8566112218933495e-40, 8.400832354756411e-40, 3.0089755020947694e-40, 5.373945905528416e-40, 1.331157464532907e-39, 7.2462590953615124e-40, 1.2413732752587846e-39, 8.502887850913455e-40], 'numerosity': 74, 'population': 62, 'average_specificity': 2.324324324324324, 'fraction_accuracy': 0.09002976190476192}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 4, 'reward': [19.04115636378121, 282.52017309674295, 226.5067539676668, 8.421850892652117, 5.431990731337813, 15.731977597089834, 11.963625607846652, 14.502821779440746], 'numerosity': 366, 'population': 206, 'average_specificity': 1.7978142076502732, 'fraction_accuracy': 0.00012562392327923093}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 3, 'reward': [9.141033961819875, 503.015057902867, 8.863140156841938, 9.473248851504252, 10.954050664477583, 5.113548666757833, 10.610099188209634, 6.427631106549245], 'numerosity': 376, 'population': 207, 'average_specificity': 1.8085106382978724, 'fraction_accuracy': 0.002685303593785624}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 8, 'reward': [236.62182341932353, 17.71831643714007, 169.77052904218897, 9.885835547509476, 3.7531533272066295, 3.492515487209963, 4.602117471447815, 8.500254071547586], 'numerosity': 380, 'population': 207, 'average_specificity': 1.7973684210526315, 'fraction_accuracy': 0.029532007613889643}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 4, 'reward': [135.7803478314048, 450.7886225104653, 14.72247029751609, 4.572676939519304, 11.63699361165655, 15.083527445343089, 25.43823816003597, 5.356680235746249], 'numerosity': 382, 'population': 207, 'average_specificity': 1.7958115183246073, 'fraction_accuracy': 0.0064821793669427264}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 1, 'reward': [218.9779987445338, 560.3080029864477, 4.628089864393865, 2.8052160415666796, 11.859517588053768, 4.325274560832783, 6.284963398815211, 33.54393462509168], 'numerosity': 382, 'population': 207, 'average_specificity': 1.7958115183246073, 'fraction_accuracy': 0.005001010385987953}\n", - "INFO:lcs.agents.Agent:{'trial': 1800, 'steps_in_trial': 7, 'reward': [13.135194486657284, 493.6535088133593, 12.417384480151332, 22.74253293618887, 10.932168768078794, 3.2971373023255612, 4.570089035733947, 19.641580876438702], 'numerosity': 382, 'population': 207, 'average_specificity': 1.7958115183246073, 'fraction_accuracy': 0.007293011429806728}\n", - "INFO:lcs.agents.Agent:{'trial': 2100, 'steps_in_trial': 5, 'reward': [33.69239728997236, 564.1246357266482, 3.3293423929079964, 2.3605428093942398, 8.754942465846353, 2.873620086579977, 6.588360230435732, 14.707460577876544], 'numerosity': 382, 'population': 207, 'average_specificity': 1.7958115183246073, 'fraction_accuracy': 0.0019079329181071096}\n", - "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 7, 'reward': [95.27548590841683, 400.8054428471048, 51.28659289721928, 9.397675259440012, 10.264134401651521, 2.873620086579977, 14.182604637030222, 16.305140503616084], 'numerosity': 382, 'population': 207, 'average_specificity': 1.7958115183246073, 'fraction_accuracy': 0.0013329544909790438}\n", - "INFO:lcs.agents.Agent:{'trial': 2700, 'steps_in_trial': 4, 'reward': [3.8719475782675903, 598.8377444943224, 43.41676649934165, 1.7303620527514036, 11.178729589211162, 2.873620086579977, 0.6959811681736219, 52.13301090746528], 'numerosity': 382, 'population': 207, 'average_specificity': 1.7958115183246073, 'fraction_accuracy': 0.00041098423726583603}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 50, 'reward': 0.0, 'perf_time': 0.024100099999998292, 'numerosity': 74, 'population': 66, 'average_specificity': 1.864864864864865, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 26, 'reward': 1000.138880235737, 'perf_time': 0.329812000000004, 'numerosity': 1800, 'population': 1192, 'average_specificity': 8.991666666666667, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': 1.3360874108178934e-12, 'perf_time': 0.8133116999999856, 'numerosity': 1800, 'population': 1330, 'average_specificity': 11.657222222222222, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 14, 'reward': 1011.437914927404, 'perf_time': 0.24458859999998595, 'numerosity': 1800, 'population': 1355, 'average_specificity': 13.273888888888889, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 35, 'reward': 1000.0110957432273, 'perf_time': 0.7310306000000253, 'numerosity': 1800, 'population': 1390, 'average_specificity': 14.805, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 3, 'reward': 1663.377965961677, 'perf_time': 0.08162349999997787, 'numerosity': 1800, 'population': 1399, 'average_specificity': 16.635, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 1, 'reward': 2144.250513788887, 'perf_time': 0.01140459999999166, 'numerosity': 1800, 'population': 1412, 'average_specificity': 16.419444444444444, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 4, 'reward': 1306.9938981928083, 'perf_time': 0.08042490000002545, 'numerosity': 1800, 'population': 1449, 'average_specificity': 17.11722222222222, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 9, 'reward': 1046.6069274511074, 'perf_time': 0.17500010000003385, 'numerosity': 1800, 'population': 1455, 'average_specificity': 17.447222222222223, 'fraction_accuracy': 0.09821428571428571}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 2, 'reward': 1624.0372437156136, 'perf_time': 0.052321100000028764, 'numerosity': 1800, 'population': 1464, 'average_specificity': 17.857222222222223, 'fraction_accuracy': 0.0}\n" ] } ], "source": [ "agent = XNCS(cfg)\n", "explore_population, explore_metrics =\\\n", - " agent.explore(maze, 3000, True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cond:00#000## - Act:0 - Num:3 [fit: 0.000, exp: 769.00, pred: 362.000]\n", - "Cond:001##001 - Act:1 - Num:2 [fit: 0.000, exp: 299.00, pred: 735.269]\n", - "Cond:0010#001 - Act:2 - Num:1 [fit: 0.000, exp: 153.00, pred: 273.097]\n", - "Cond:0#100001 - Act:3 - Num:3 [fit: 0.006, exp: 21.00, pred: 209.456]\n", - "Cond:001000#1 - Act:4 - Num:3 [fit: 0.000, exp: 65.00, pred: 253.011]\n", - "Cond:00100#01 - Act:5 - Num:1 [fit: 0.004, exp: 156.00, pred: 279.322]\n", - "Cond:10100001 - Act:6 - Num:2 [fit: 0.244, exp: 3.00, pred: 6.916]\n", - "Cond:00100001 - Act:7 - Num:1 [fit: 0.001, exp: 82.00, pred: 273.815]\n", - "Cond:0#01#01# - Act:0 - Num:3 [fit: 0.000, exp: 814.00, pred: 428.572]\n", - "Cond:#9010010 - Act:1 - Num:3 [fit: 0.000, exp: 2354.00, pred: 541.384]\n", - "Cond:09010010 - Act:2 - Num:1 [fit: 0.000, exp: 235.00, pred: 247.591]\n", - "Cond:09010010 - Act:3 - Num:3 [fit: 0.000, exp: 56.00, pred: 306.198]\n", - "Cond:#9010010 - Act:4 - Num:3 [fit: 0.000, exp: 94.00, pred: 300.504]\n", - "Cond:09##001# - Act:5 - Num:3 [fit: 0.000, exp: 59.00, pred: 371.361]\n", - "Cond:#9#10010 - Act:6 - Num:3 [fit: 0.000, exp: 61.00, pred: 291.819]\n", - "Cond:0#01#0## - Act:7 - Num:3 [fit: 0.000, exp: 85.00, pred: 284.094]\n", - "Cond:11001011 - Act:0 - Num:3 [fit: 0.000, exp: 62.00, pred: 203.801]\n", - "Cond:11001011 - Act:1 - Num:3 [fit: 0.000, exp: 200.00, pred: 255.488]\n", - "Cond:110010#1 - Act:2 - Num:3 [fit: 0.000, exp: 80.00, pred: 273.577]\n", - "Cond:11001#11 - Act:3 - Num:3 [fit: 0.000, exp: 617.00, pred: 422.712]\n", - "Cond:###0#01# - Act:4 - Num:3 [fit: 0.000, exp: 163.00, pred: 320.334]\n", - "Cond:1##0#011 - Act:5 - Num:3 [fit: 0.000, exp: 34.00, pred: 285.247]\n", - "Cond:#1001011 - Act:6 - Num:3 [fit: 0.000, exp: 34.00, pred: 209.057]\n", - "Cond:#1001011 - Act:7 - Num:3 [fit: 0.000, exp: 47.00, pred: 250.444]\n", - "Cond:119#0101 - Act:0 - Num:1 [fit: 0.000, exp: 244.00, pred: 226.670]\n", - "Cond:119###01 - Act:1 - Num:4 [fit: 0.000, exp: 242.00, pred: 313.680]\n", - "Cond:1##001#1 - Act:2 - Num:3 [fit: 0.000, exp: 371.00, pred: 248.102]\n", - "Cond:11900#01 - Act:3 - Num:3 [fit: 0.000, exp: 74.00, pred: 222.356]\n", - "Cond:#190##01 - Act:4 - Num:3 [fit: 0.000, exp: 265.00, pred: 407.212]\n", - "Cond:119#0101 - Act:5 - Num:2 [fit: 0.000, exp: 44.00, pred: 225.464]\n", - "Cond:11900101 - Act:6 - Num:1 [fit: 0.064, exp: 30.00, pred: 226.547]\n", - "Cond:1190#101 - Act:7 - Num:3 [fit: 0.000, exp: 45.00, pred: 225.734]\n", - "Cond:10101#00 - Act:0 - Num:3 [fit: 0.000, exp: 100.00, pred: 229.454]\n", - "Cond:1010#0#0 - Act:1 - Num:3 [fit: 0.000, exp: 377.00, pred: 762.358]\n", - "Cond:1010#000 - Act:2 - Num:1 [fit: 0.000, exp: 60.00, pred: 266.907]\n", - "Cond:#01####0 - Act:3 - Num:3 [fit: 0.293, exp: 541.00, pred: 538.738]\n", - "Cond:101010#0 - Act:4 - Num:1 [fit: 0.003, exp: 43.00, pred: 322.756]\n", - "Cond:1010#00# - Act:5 - Num:3 [fit: 0.000, exp: 63.00, pred: 260.278]\n", - "Cond:1#10##00 - Act:6 - Num:3 [fit: 0.000, exp: 48.00, pred: 288.850]\n", - "Cond:1#101000 - Act:7 - Num:3 [fit: 0.077, exp: 35.00, pred: 175.312]\n", - "Cond:1##00101 - Act:0 - Num:3 [fit: 0.000, exp: 551.00, pred: 212.303]\n", - "Cond:1110##01 - Act:1 - Num:3 [fit: 0.000, exp: 103.00, pred: 180.127]\n", - "Cond:1#1#0101 - Act:3 - Num:2 [fit: 0.315, exp: 164.00, pred: 552.258]\n", - "Cond:1110#10# - Act:4 - Num:3 [fit: 0.000, exp: 61.00, pred: 218.462]\n", - "Cond:1#10#101 - Act:5 - Num:1 [fit: 0.000, exp: 48.00, pred: 175.658]\n", - "Cond:1110010# - Act:6 - Num:2 [fit: 0.002, exp: 23.00, pred: 167.197]\n", - "Cond:1110010# - Act:7 - Num:3 [fit: 0.000, exp: 34.00, pred: 200.439]\n", - "Cond:11#01111 - Act:0 - Num:3 [fit: 0.060, exp: 8.00, pred: 236.590]\n", - "Cond:11#0#111 - Act:1 - Num:3 [fit: 0.056, exp: 11.00, pred: 210.205]\n", - "Cond:1100111# - Act:2 - Num:3 [fit: 0.001, exp: 23.00, pred: 178.690]\n", - "Cond:1#0011#1 - Act:4 - Num:1 [fit: 0.001, exp: 25.00, pred: 189.596]\n", - "Cond:11001111 - Act:5 - Num:1 [fit: 0.038, exp: 9.00, pred: 147.714]\n", - "Cond:11001111 - Act:6 - Num:3 [fit: 0.024, exp: 11.00, pred: 159.321]\n", - "Cond:11#0111# - Act:7 - Num:1 [fit: 0.047, exp: 8.00, pred: 126.576]\n", - "Cond:0#0101#0 - Act:0 - Num:1 [fit: 0.000, exp: 80.00, pred: 241.403]\n", - "Cond:0#0#0110 - Act:1 - Num:1 [fit: 0.000, exp: 271.00, pred: 324.762]\n", - "Cond:0101#11# - Act:2 - Num:1 [fit: 0.080, exp: 466.00, pred: 523.418]\n", - "Cond:010101## - Act:3 - Num:3 [fit: 0.000, exp: 83.00, pred: 230.883]\n", - "Cond:01010110 - Act:4 - Num:1 [fit: 0.000, exp: 30.00, pred: 194.867]\n", - "Cond:010##110 - Act:5 - Num:1 [fit: 0.000, exp: 29.00, pred: 228.229]\n", - "Cond:01010110 - Act:6 - Num:1 [fit: 0.000, exp: 84.00, pred: 256.103]\n", - "Cond:01010#10 - Act:7 - Num:1 [fit: 0.041, exp: 44.00, pred: 223.438]\n", - "Cond:01000000 - Act:0 - Num:3 [fit: 0.000, exp: 82.00, pred: 268.571]\n", - "Cond:0100000# - Act:1 - Num:3 [fit: 0.000, exp: 78.00, pred: 360.871]\n", - "Cond:#1#00000 - Act:2 - Num:3 [fit: 0.000, exp: 38.00, pred: 265.867]\n", - "Cond:01000000 - Act:3 - Num:3 [fit: 0.010, exp: 16.00, pred: 180.552]\n", - "Cond:#10#0#00 - Act:4 - Num:3 [fit: 0.003, exp: 17.00, pred: 251.009]\n", - "Cond:010#0#00 - Act:5 - Num:3 [fit: 0.006, exp: 23.00, pred: 274.079]\n", - "Cond:01000#00 - Act:6 - Num:3 [fit: 0.000, exp: 92.00, pred: 273.765]\n", - "Cond:#100#000 - Act:7 - Num:3 [fit: 0.000, exp: 294.00, pred: 591.685]\n", - "Cond:10#0#010 - Act:0 - Num:1 [fit: 0.000, exp: 283.00, pred: 500.807]\n", - "Cond:#0000010 - Act:1 - Num:2 [fit: 0.000, exp: 1644.00, pred: 826.590]\n", - "Cond:100000#0 - Act:2 - Num:3 [fit: 0.000, exp: 324.00, pred: 278.687]\n", - "Cond:10#00010 - Act:3 - Num:2 [fit: 0.000, exp: 39.00, pred: 277.021]\n", - "Cond:10#0#0#0 - Act:5 - Num:3 [fit: 0.000, exp: 58.00, pred: 283.264]\n", - "Cond:1#000010 - Act:6 - Num:2 [fit: 0.000, exp: 47.00, pred: 286.466]\n", - "Cond:10000010 - Act:7 - Num:3 [fit: 0.005, exp: 55.00, pred: 294.252]\n", - "Cond:1#00100# - Act:0 - Num:1 [fit: 0.000, exp: 102.00, pred: 290.634]\n", - "Cond:#000100# - Act:1 - Num:3 [fit: 0.000, exp: 345.00, pred: 250.207]\n", - "Cond:10001#0# - Act:2 - Num:3 [fit: 0.000, exp: 334.00, pred: 427.273]\n", - "Cond:##001000 - Act:3 - Num:3 [fit: 0.000, exp: 33.00, pred: 219.538]\n", - "Cond:10001#0# - Act:4 - Num:3 [fit: 0.006, exp: 17.00, pred: 262.323]\n", - "Cond:001010#0 - Act:5 - Num:3 [fit: 0.244, exp: 3.00, pred: 0.000]\n", - "Cond:10001000 - Act:6 - Num:3 [fit: 0.003, exp: 21.00, pred: 265.582]\n", - "Cond:#000#00# - Act:7 - Num:3 [fit: 0.000, exp: 427.00, pred: 404.759]\n", - "Cond:0010000# - Act:1 - Num:2 [fit: 0.000, exp: 302.00, pred: 735.269]\n", - "Cond:001#0##1 - Act:2 - Num:3 [fit: 0.000, exp: 208.00, pred: 260.829]\n", - "Cond:0010001# - Act:3 - Num:3 [fit: 0.000, exp: 498.00, pred: 370.348]\n", - "Cond:#01#00## - Act:5 - Num:3 [fit: 0.000, exp: 351.00, pred: 314.932]\n", - "Cond:00##0011 - Act:6 - Num:3 [fit: 0.001, exp: 26.00, pred: 198.943]\n", - "Cond:001#0##1 - Act:7 - Num:2 [fit: 0.000, exp: 108.00, pred: 292.972]\n", - "Cond:0101#1#1 - Act:0 - Num:3 [fit: 0.000, exp: 230.00, pred: 349.281]\n", - "Cond:01010101 - Act:1 - Num:1 [fit: 0.000, exp: 85.00, pred: 232.502]\n", - "Cond:0101###1 - Act:2 - Num:2 [fit: 0.000, exp: 215.00, pred: 296.369]\n", - "Cond:01010101 - Act:4 - Num:1 [fit: 0.002, exp: 24.00, pred: 219.968]\n", - "Cond:#1010101 - Act:5 - Num:1 [fit: 0.013, exp: 16.00, pred: 241.500]\n", - "Cond:01010101 - Act:6 - Num:1 [fit: 0.019, exp: 12.00, pred: 235.113]\n", - "Cond:010#010# - Act:7 - Num:1 [fit: 0.003, exp: 24.00, pred: 223.643]\n", - "Cond:1000111# - Act:0 - Num:1 [fit: 0.000, exp: 97.00, pred: 274.680]\n", - "Cond:100#1#11 - Act:1 - Num:1 [fit: 0.000, exp: 394.00, pred: 757.293]\n", - "Cond:10001#1# - Act:2 - Num:1 [fit: 0.001, exp: 29.00, pred: 267.503]\n", - "Cond:1000#111 - Act:3 - Num:1 [fit: 0.004, exp: 25.00, pred: 193.396]\n", - "Cond:10#01#11 - Act:5 - Num:3 [fit: 0.003, exp: 20.00, pred: 255.466]\n", - "Cond:10#01111 - Act:6 - Num:3 [fit: 0.003, exp: 20.00, pred: 213.324]\n", - "Cond:#000111# - Act:7 - Num:3 [fit: 0.003, exp: 23.00, pred: 218.067]\n", - "Cond:0#10001# - Act:0 - Num:1 [fit: 0.000, exp: 362.00, pred: 361.997]\n", - "Cond:00100010 - Act:2 - Num:1 [fit: 0.195, exp: 4.00, pred: 63.311]\n", - "Cond:00100010 - Act:6 - Num:1 [fit: 0.033, exp: 12.00, pred: 196.543]\n", - "Cond:00100010 - Act:7 - Num:1 [fit: 0.056, exp: 13.00, pred: 272.173]\n", - "Cond:10011100 - Act:0 - Num:1 [fit: 0.004, exp: 19.00, pred: 312.415]\n", - "Cond:10011#00 - Act:1 - Num:1 [fit: 0.000, exp: 452.00, pred: 723.399]\n", - "Cond:10#11#00 - Act:2 - Num:1 [fit: 0.032, exp: 14.00, pred: 308.447]\n", - "Cond:10011100 - Act:3 - Num:1 [fit: 0.080, exp: 8.00, pred: 132.007]\n", - "Cond:#0011100 - Act:4 - Num:1 [fit: 0.083, exp: 14.00, pred: 196.091]\n", - "Cond:10#111#0 - Act:5 - Num:1 [fit: 0.015, exp: 13.00, pred: 259.164]\n", - "Cond:1001110# - Act:6 - Num:1 [fit: 0.011, exp: 11.00, pred: 225.322]\n", - "Cond:10011##0 - Act:7 - Num:1 [fit: 0.017, exp: 9.00, pred: 262.670]\n", - "Cond:001#0##1 - Act:5 - Num:1 [fit: 0.000, exp: 256.00, pred: 265.613]\n", - "Cond:00010010 - Act:0 - Num:2 [fit: 0.000, exp: 507.00, pred: 428.572]\n", - "Cond:000100#0 - Act:1 - Num:3 [fit: 0.041, exp: 5.00, pred: 218.078]\n", - "Cond:0#0##01# - Act:2 - Num:3 [fit: 0.000, exp: 303.00, pred: 267.933]\n", - "Cond:00010010 - Act:3 - Num:1 [fit: 0.051, exp: 10.00, pred: 181.937]\n", - "Cond:00010010 - Act:4 - Num:3 [fit: 0.000, exp: 53.00, pred: 246.024]\n", - "Cond:0001##10 - Act:5 - Num:3 [fit: 0.000, exp: 51.00, pred: 265.694]\n", - "Cond:00#1001# - Act:6 - Num:3 [fit: 0.120, exp: 11.00, pred: 233.528]\n", - "Cond:00#10010 - Act:7 - Num:3 [fit: 0.003, exp: 24.00, pred: 251.434]\n", - "Cond:01011101 - Act:1 - Num:1 [fit: 0.097, exp: 9.00, pred: 213.165]\n", - "Cond:01#10101 - Act:2 - Num:2 [fit: 0.000, exp: 152.00, pred: 243.607]\n", - "Cond:#101#101 - Act:3 - Num:1 [fit: 0.000, exp: 51.00, pred: 229.215]\n", - "Cond:010111#1 - Act:4 - Num:1 [fit: 0.074, exp: 6.00, pred: 159.277]\n", - "Cond:01011#01 - Act:5 - Num:1 [fit: 0.110, exp: 10.00, pred: 196.715]\n", - "Cond:0#011#01 - Act:6 - Num:1 [fit: 0.011, exp: 11.00, pred: 257.289]\n", - "Cond:01011101 - Act:7 - Num:1 [fit: 0.144, exp: 3.00, pred: 91.832]\n", - "Cond:100111#1 - Act:0 - Num:1 [fit: 0.074, exp: 6.00, pred: 233.990]\n", - "Cond:10011111 - Act:2 - Num:1 [fit: 0.038, exp: 9.00, pred: 255.342]\n", - "Cond:1#011111 - Act:3 - Num:1 [fit: 0.010, exp: 15.00, pred: 239.023]\n", - "Cond:10#111#1 - Act:4 - Num:1 [fit: 0.047, exp: 8.00, pred: 217.477]\n", - "Cond:#0011111 - Act:5 - Num:3 [fit: 0.051, exp: 4.00, pred: 164.841]\n", - "Cond:10011111 - Act:6 - Num:1 [fit: 0.038, exp: 9.00, pred: 163.989]\n", - "Cond:1#011111 - Act:7 - Num:1 [fit: 0.074, exp: 6.00, pred: 135.800]\n", - "Cond:0#111101 - Act:0 - Num:1 [fit: 0.074, exp: 6.00, pred: 186.316]\n", - "Cond:#0111#01 - Act:1 - Num:1 [fit: 0.000, exp: 267.00, pred: 746.083]\n", - "Cond:00111101 - Act:2 - Num:1 [fit: 0.032, exp: 17.00, pred: 216.929]\n", - "Cond:00111101 - Act:3 - Num:1 [fit: 0.011, exp: 11.00, pred: 229.266]\n", - "Cond:00111101 - Act:4 - Num:1 [fit: 0.003, exp: 21.00, pred: 195.710]\n", - "Cond:00111##1 - Act:5 - Num:1 [fit: 0.026, exp: 13.00, pred: 214.319]\n", - "Cond:001111#1 - Act:6 - Num:1 [fit: 0.125, exp: 6.00, pred: 155.548]\n", - "Cond:0#11#101 - Act:7 - Num:1 [fit: 0.010, exp: 15.00, pred: 189.243]\n", - "Cond:00#01000 - Act:0 - Num:1 [fit: 0.001, exp: 27.00, pred: 240.113]\n", - "Cond:0000##00 - Act:2 - Num:1 [fit: 0.001, exp: 30.00, pred: 225.981]\n", - "Cond:0000100# - Act:3 - Num:1 [fit: 0.000, exp: 46.00, pred: 217.728]\n", - "Cond:00001000 - Act:4 - Num:1 [fit: 0.003, exp: 20.00, pred: 241.520]\n", - "Cond:000010#0 - Act:6 - Num:1 [fit: 0.000, exp: 39.00, pred: 256.732]\n", - "Cond:00001000 - Act:7 - Num:1 [fit: 0.012, exp: 316.00, pred: 440.521]\n", - "Cond:#0000##1 - Act:0 - Num:1 [fit: 0.000, exp: 1278.00, pred: 615.179]\n", - "Cond:0#000101 - Act:1 - Num:1 [fit: 0.000, exp: 149.00, pred: 370.228]\n", - "Cond:00000101 - Act:2 - Num:1 [fit: 0.000, exp: 40.00, pred: 326.216]\n", - "Cond:00000101 - Act:3 - Num:1 [fit: 0.000, exp: 36.00, pred: 235.609]\n", - "Cond:00000101 - Act:4 - Num:1 [fit: 0.000, exp: 44.00, pred: 291.699]\n", - "Cond:#000010# - Act:5 - Num:1 [fit: 0.000, exp: 48.00, pred: 292.828]\n", - "Cond:000#0101 - Act:6 - Num:1 [fit: 0.000, exp: 31.00, pred: 259.395]\n", - "Cond:00000101 - Act:7 - Num:1 [fit: 0.000, exp: 35.00, pred: 298.455]\n", - "Cond:91111##0 - Act:0 - Num:1 [fit: 0.000, exp: 396.00, pred: 238.241]\n", - "Cond:911110#0 - Act:1 - Num:1 [fit: 0.000, exp: 300.00, pred: 230.258]\n", - "Cond:91111#00 - Act:2 - Num:1 [fit: 0.000, exp: 134.00, pred: 233.228]\n", - "Cond:91111000 - Act:3 - Num:1 [fit: 0.000, exp: 43.00, pred: 232.649]\n", - "Cond:91#11000 - Act:4 - Num:1 [fit: 0.000, exp: 47.00, pred: 218.217]\n", - "Cond:91111000 - Act:5 - Num:1 [fit: 0.000, exp: 41.00, pred: 220.108]\n", - "Cond:91#11#00 - Act:6 - Num:1 [fit: 0.000, exp: 237.00, pred: 402.970]\n", - "Cond:91111000 - Act:7 - Num:1 [fit: 0.000, exp: 54.00, pred: 225.116]\n", - "Cond:001##001 - Act:7 - Num:1 [fit: 0.001, exp: 80.00, pred: 273.815]\n", - "Cond:01#10100 - Act:0 - Num:3 [fit: 0.000, exp: 43.00, pred: 245.339]\n", - "Cond:0111#1#0 - Act:1 - Num:3 [fit: 0.000, exp: 403.00, pred: 297.699]\n", - "Cond:0#110100 - Act:2 - Num:3 [fit: 0.041, exp: 35.00, pred: 215.618]\n", - "Cond:0#110#00 - Act:3 - Num:3 [fit: 0.035, exp: 18.00, pred: 156.300]\n", - "Cond:011##100 - Act:4 - Num:3 [fit: 0.002, exp: 31.00, pred: 208.535]\n", - "Cond:#111#100 - Act:5 - Num:3 [fit: 0.001, exp: 24.00, pred: 201.008]\n", - "Cond:01#1#1#0 - Act:6 - Num:3 [fit: 0.001, exp: 245.00, pred: 214.019]\n", - "Cond:01#100## - Act:7 - Num:3 [fit: 0.180, exp: 2.00, pred: 0.000]\n", - "Cond:000#1000 - Act:5 - Num:3 [fit: 0.466, exp: 102.00, pred: 253.971]\n", - "Cond:01111#10 - Act:0 - Num:1 [fit: 0.400, exp: 160.00, pred: 366.265]\n", - "Cond:#1111110 - Act:1 - Num:1 [fit: 0.000, exp: 204.00, pred: 298.253]\n", - "Cond:0111#110 - Act:2 - Num:1 [fit: 0.004, exp: 23.00, pred: 216.716]\n", - "Cond:01111110 - Act:3 - Num:1 [fit: 0.051, exp: 10.00, pred: 137.266]\n", - "Cond:01111#10 - Act:4 - Num:1 [fit: 0.030, exp: 10.00, pred: 183.919]\n", - "Cond:0#111110 - Act:5 - Num:1 [fit: 0.062, exp: 11.00, pred: 197.428]\n", - "Cond:01111110 - Act:6 - Num:1 [fit: 0.000, exp: 55.00, pred: 193.188]\n", - "Cond:011#111# - Act:7 - Num:2 [fit: 0.000, exp: 37.00, pred: 221.720]\n", - "Cond:10001000 - Act:5 - Num:1 [fit: 0.001, exp: 21.00, pred: 295.456]\n", - "Cond:11110000 - Act:0 - Num:1 [fit: 0.000, exp: 117.00, pred: 219.779]\n", - "Cond:1111000# - Act:1 - Num:1 [fit: 0.000, exp: 287.00, pred: 222.121]\n", - "Cond:111100#0 - Act:2 - Num:1 [fit: 0.000, exp: 72.00, pred: 220.338]\n", - "Cond:11110000 - Act:3 - Num:1 [fit: 0.000, exp: 35.00, pred: 226.311]\n", - "Cond:11110000 - Act:4 - Num:1 [fit: 0.000, exp: 34.00, pred: 204.920]\n", - "Cond:1111#000 - Act:5 - Num:1 [fit: 0.001, exp: 149.00, pred: 225.083]\n", - "Cond:11110000 - Act:6 - Num:1 [fit: 0.337, exp: 151.00, pred: 436.397]\n", - "Cond:111100#0 - Act:7 - Num:1 [fit: 0.000, exp: 116.00, pred: 227.816]\n", - "Cond:01110100 - Act:7 - Num:1 [fit: 0.068, exp: 153.00, pred: 458.150]\n", - "Cond:#0000010 - Act:0 - Num:1 [fit: 0.000, exp: 283.00, pred: 500.807]\n", - "Cond:1110010# - Act:2 - Num:1 [fit: 0.000, exp: 151.00, pred: 234.003]\n", - "Cond:00010010 - Act:2 - Num:1 [fit: 0.000, exp: 66.00, pred: 267.071]\n", - "Cond:1#1#0101 - Act:5 - Num:1 [fit: 0.000, exp: 41.00, pred: 175.676]\n", - "Cond:10100001 - Act:5 - Num:1 [fit: 0.244, exp: 0.00, pred: 6.916]\n", - "Cond:0010000# - Act:5 - Num:1 [fit: 0.004, exp: 151.00, pred: 279.322]\n", - "Cond:1#000010 - Act:2 - Num:1 [fit: 0.000, exp: 316.00, pred: 278.687]\n", - "Cond:10#00010 - Act:1 - Num:1 [fit: 0.000, exp: 1579.00, pred: 826.590]\n", - "Cond:011#111# - Act:2 - Num:1 [fit: 0.026, exp: 9.00, pred: 220.959]\n" - ] - } - ], - "source": [ - "for cl in explore_population:\n", - " print(str(cl))" + " agent.explore(maze, 1000, True)" ] }, { @@ -345,7 +119,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -354,7 +128,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -419,7 +193,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb new file mode 100644 index 0000000..f9403d6 --- /dev/null +++ b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb @@ -0,0 +1,998 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('1', '1', '0', '0', '0', '1', '1', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m 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"print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xncs import XNCS, Configuration\n", + "from utils.nxcs_utils import *\n", + "\n", + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " mutation_chance=0.08,\n", + " chi=0.8,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " delta=0.1,\n", + " initial_error=0.01,\n", + " metrics_trial_frequency=50,\n", + " covering_wildcard_chance=0.9,\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=200\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 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13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_woods.ipynb b/XCS_Experiments/XNCS_woods.ipynb index 0dc4a5e..d7ff931 100644 --- a/XCS_Experiments/XNCS_woods.ipynb +++ b/XCS_Experiments/XNCS_woods.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -24,10 +24,10 @@ "This is how maze looks like:\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } ], @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -68,9 +68,10 @@ " deletion_threshold=25,\n", " delta=0.1,\n", " initial_error=0.01,\n", + " epsilon_0=0.01,\n", " metrics_trial_frequency=50,\n", " covering_wildcard_chance = 0.9,\n", - " user_metrics_collector_fcn=xcs_maze_metrics,\n", + " user_metrics_collector_fcn=xncs_metrics,\n", " lmc=10,\n", " lem=200\n", " )\n" @@ -78,202 +79,1317 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 12, 'reward': [0, 0, 0, 0, 0, 2.1332401280982545e-40, 1.9720619850863863e-40, 200.0], 'numerosity': 51, 'population': 51, 'average_specificity': 1.8627450980392157, 'fraction_accuracy': 0.16488095238095238}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 9, 'reward': [3.242513785040816, 4.007511220460857, 3.755400723031541, 5.718705424140142, 203.99357594410674, 4.16502083955891, 131.8650716024955, 251.6666137052352], 'numerosity': 299, 'population': 133, 'average_specificity': 1.7759197324414715, 'fraction_accuracy': 0.025498295364304785}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 6, 'reward': [12.027011313229846, 5.781253374659476, 2.089086454040064, 1.0849649364456135, 1.03967442216596, 1.432177796361129, 12.922073032888735, 746.5488957150206], 'numerosity': 315, 'population': 134, 'average_specificity': 1.7333333333333334, 'fraction_accuracy': 0.035467335107153226}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 9, 'reward': [1.8021887695852694, 0.1641835547578232, 1.1445634273693566, 99.85939727754356, 406.66430063060625, 103.90841179260266, 1.0742443230729424, 47.527137949315595], 'numerosity': 317, 'population': 134, 'average_specificity': 1.7602523659305993, 'fraction_accuracy': 0.02326778365233266}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 6, 'reward': [0.02118622641878736, 0.05523429811698611, 0.38798854673609356, 14.017161035528767, 1.2197640077544785, 408.1563418422502, 27.089362059349675, 65.5861521511334], 'numerosity': 321, 'population': 134, 'average_specificity': 1.7414330218068537, 'fraction_accuracy': 0.05446225764374553}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 2, 'reward': [0.022086023009902214, 0.1190131480781886, 0.0020142729099989504, 1.6525426882568843, 424.7237460501094, 0.014007758931381491, 0.18765529185525254, 55.564970287087704], 'numerosity': 321, 'population': 134, 'average_specificity': 1.7414330218068537, 'fraction_accuracy': 0.06032217360179199}\n", - "INFO:lcs.agents.Agent:{'trial': 1800, 'steps_in_trial': 4, 'reward': [0.06069980692144186, 0.0008488812551224147, 4.315305232314489, 0.2784780355196849, 456.57185462747236, 0.031183833875812528, 6.363499965879443, 4.947467737164551], 'numerosity': 321, 'population': 134, 'average_specificity': 1.7414330218068537, 'fraction_accuracy': 0.05926564718646832}\n", - "INFO:lcs.agents.Agent:{'trial': 2100, 'steps_in_trial': 4, 'reward': [0.04884168449638756, 0.5257956823098625, 1.0133160350464565, 0.28397557101834386, 563.6133328804822, 167.07011388526234, 4.079628146818933, 6.862163275593859], 'numerosity': 321, 'population': 134, 'average_specificity': 1.7414330218068537, 'fraction_accuracy': 0.054242116467880165}\n", - "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 5, 'reward': [0.08584597690007044, 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'population': 957, 'average_specificity': 18.73090909090909, 'fraction_accuracy': 0.017783101045296165}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1628.1335076814366, 'perf_time': 0.033511500000003025, 'numerosity': 1800, 'population': 1146, 'average_specificity': 21.773888888888887, 'fraction_accuracy': 0.027564102564102563}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 2, 'reward': 1775.4082438248972, 'perf_time': 0.024715899999989688, 'numerosity': 1800, 'population': 1173, 'average_specificity': 22.769444444444446, 'fraction_accuracy': 0.05624485696961823}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 1, 'reward': 2485.5120197202286, 'perf_time': 0.00967719999999872, 'numerosity': 1800, 'population': 1202, 'average_specificity': 24.182777777777776, 'fraction_accuracy': 0.05673272357723578}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 1, 'reward': 1712.9694491357925, 'perf_time': 0.009268800000000965, 'numerosity': 1800, 'population': 1189, 'average_specificity': 24.768333333333334, 'fraction_accuracy': 0.16367382617382614}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 30, 'reward': 1000.0642385171988, 'perf_time': 0.3304250000000195, 'numerosity': 1800, 'population': 1163, 'average_specificity': 29.625, 'fraction_accuracy': 0.05153715868169495}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 2, 'reward': 1913.9094897297305, 'perf_time': 0.022648800000013125, 'numerosity': 1800, 'population': 1188, 'average_specificity': 31.697222222222223, 'fraction_accuracy': 0.0561127618315661}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 9, 'reward': 1046.700518721966, 'perf_time': 0.15424020000000382, 'numerosity': 1800, 'population': 1145, 'average_specificity': 37.638888888888886, 'fraction_accuracy': 0.02087473652514303}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 4, 'reward': 1279.408090436091, 'perf_time': 0.03775390000001266, 'numerosity': 1800, 'population': 1137, 'average_specificity': 39.653333333333336, 'fraction_accuracy': 0.020817103134176305}\n" ] } ], "source": [ "agent = XNCS(cfg)\n", - "explore_population, explore_metrics = agent.explore(maze, 3000, True)" + "explore_population, explore_metrics = agent.explore(maze, 1000, True)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cond:.O...... - Act:0 - Num:3 [fit: 0.000, exp: 33.00, pred: 212.399]\n", - "Cond:.O...#.. - Act:1 - Num:3 [fit: 0.000, exp: 34.00, pred: 197.219]\n", - "Cond:#O.#.#.. - Act:2 - Num:3 [fit: 0.000, exp: 113.00, pred: 233.260]\n", - "Cond:#O...O.. - Act:3 - Num:3 [fit: 0.151, exp: 10.00, pred: 304.418]\n", - "Cond:.O.#..#. - Act:4 - Num:3 [fit: 0.000, exp: 121.00, pred: 207.194]\n", - "Cond:.O.##.#. - Act:5 - Num:3 [fit: 0.000, exp: 187.00, pred: 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- Num:3 [fit: 0.000, exp: 373.00, pred: 401.396]\n", - "Cond:...O#..# - Act:0 - Num:3 [fit: 0.000, exp: 365.00, pred: 263.844]\n", - "Cond:#.##O... - Act:1 - Num:3 [fit: 0.000, exp: 560.00, pred: 192.944]\n", - "Cond:#..OO... - Act:2 - Num:3 [fit: 0.000, exp: 56.00, pred: 218.966]\n", - "Cond:..#O##.. - Act:3 - Num:3 [fit: 0.000, exp: 292.00, pred: 290.921]\n", - "Cond:...OO..# - Act:4 - Num:3 [fit: 0.000, exp: 124.00, pred: 206.277]\n", - "Cond:..#O#..# - Act:5 - Num:3 [fit: 0.000, exp: 654.00, pred: 224.385]\n", - "Cond:...OO... - Act:6 - Num:3 [fit: 0.000, exp: 131.00, pred: 408.461]\n", - "Cond:...OO... - Act:7 - Num:3 [fit: 0.000, exp: 224.00, pred: 212.393]\n", - "Cond:.#...#F. - Act:0 - Num:1 [fit: 0.000, exp: 50.00, pred: 217.402]\n", - "Cond:.....OF. - Act:1 - Num:2 [fit: 0.000, exp: 78.00, pred: 215.805]\n", - "Cond:#....#F. - Act:2 - Num:3 [fit: 0.000, exp: 33.00, pred: 220.131]\n", - "Cond:###.#OF# - Act:3 - Num:3 [fit: 0.000, exp: 142.00, pred: 215.050]\n", - "Cond:..###OF. - Act:4 - Num:3 [fit: 0.000, exp: 220.00, pred: 230.335]\n", - "Cond:##...OF. - Act:5 - Num:3 [fit: 0.000, exp: 82.00, pred: 225.259]\n", - "Cond:..#..OF# - Act:6 - Num:3 [fit: 0.000, exp: 42.00, pred: 219.861]\n", - "Cond:##...OF. - Act:7 - Num:3 [fit: 0.000, exp: 979.00, pred: 247.994]\n", - "Cond:....FO.. - Act:0 - Num:3 [fit: 0.000, exp: 100.00, pred: 284.493]\n", - "Cond:.....O## - Act:1 - Num:2 [fit: 0.000, exp: 139.00, pred: 207.795]\n", - "Cond:.#..FO.. - Act:2 - Num:1 [fit: 0.000, exp: 61.00, pred: 225.174]\n", - "Cond:....FO.. - Act:3 - Num:3 [fit: 0.000, exp: 123.00, pred: 235.353]\n", - "Cond:....FO.. - Act:4 - Num:2 [fit: 0.000, exp: 2092.00, pred: 313.056]\n", - "Cond:....F#.. - Act:5 - Num:3 [fit: 0.000, exp: 142.00, pred: 224.601]\n", - "Cond:....FO.. - Act:6 - Num:3 [fit: 0.000, exp: 144.00, pred: 231.881]\n", - "Cond:....F##. - Act:7 - Num:3 [fit: 0.000, exp: 232.00, pred: 259.438]\n", - "Cond:#..O..#. - Act:0 - Num:1 [fit: 0.000, exp: 62.00, pred: 230.028]\n", - "Cond:...O#... - Act:1 - Num:1 [fit: 0.000, exp: 669.00, pred: 192.304]\n", - "Cond:#..O.... - Act:2 - Num:1 [fit: 0.000, exp: 44.00, pred: 194.650]\n", - "Cond:##.#..#. - Act:3 - Num:3 [fit: 0.000, exp: 520.00, pred: 290.287]\n", - "Cond:..#O.... - Act:4 - Num:2 [fit: 0.000, exp: 441.00, pred: 562.066]\n", - "Cond:...O.... - Act:5 - Num:1 [fit: 0.000, exp: 154.00, pred: 247.516]\n", - "Cond:..#O.... - Act:6 - Num:2 [fit: 0.000, exp: 90.00, pred: 217.167]\n", - "Cond:#..O#... - Act:7 - Num:3 [fit: 0.000, exp: 383.00, pred: 202.241]\n", - "Cond:..#O...# - Act:0 - Num:3 [fit: 0.000, exp: 103.00, pred: 237.159]\n", - "Cond:.#OO.... - Act:1 - Num:1 [fit: 0.000, exp: 56.00, pred: 193.538]\n", - "Cond:..#O.... - Act:2 - Num:1 [fit: 0.000, exp: 72.00, pred: 199.222]\n", - "Cond:#.OO.... - Act:3 - Num:1 [fit: 0.000, exp: 87.00, pred: 225.191]\n", - "Cond:..OO.#.. - Act:5 - Num:1 [fit: 0.000, exp: 203.00, pred: 232.427]\n", - "Cond:..O##... - Act:7 - Num:1 [fit: 0.000, exp: 356.00, pred: 259.427]\n", - "Cond:.#OO..#. - Act:0 - Num:1 [fit: 0.000, exp: 80.00, pred: 208.204]\n", - "Cond:#OOO...# - Act:2 - Num:1 [fit: 0.000, exp: 58.00, pred: 190.889]\n", - "Cond:.OOO...# - Act:3 - Num:3 [fit: 0.000, exp: 81.00, pred: 205.885]\n", - "Cond:#.#O##.# - Act:5 - Num:3 [fit: 0.000, exp: 531.00, pred: 224.385]\n", - "Cond:.OOO.... - Act:6 - Num:3 [fit: 0.000, exp: 126.00, pred: 210.514]\n", - "Cond:.OOO.... - Act:7 - Num:3 [fit: 0.000, exp: 309.00, pred: 211.958]\n", - "Cond:O#.##... - Act:3 - Num:3 [fit: 0.000, exp: 168.00, pred: 222.932]\n", - "Cond:.O#.#... - Act:3 - Num:3 [fit: 0.000, exp: 520.00, pred: 194.396]\n", - "Cond:O#.....O - Act:5 - Num:1 [fit: 0.000, exp: 357.00, pred: 212.179]\n", - "Cond:O#.....O - Act:7 - Num:1 [fit: 0.000, exp: 296.00, pred: 202.552]\n", - "Cond:#..FO#.. - Act:0 - Num:1 [fit: 0.000, exp: 136.00, pred: 223.777]\n", - "Cond:.......O - Act:2 - Num:1 [fit: 0.000, exp: 28.00, pred: 209.111]\n", - "Cond:###.#.OO - Act:5 - Num:3 [fit: 0.000, exp: 46.00, pred: 203.237]\n", - "Cond:....FO.. - Act:1 - Num:3 [fit: 0.000, exp: 66.00, pred: 234.714]\n", - "Cond:#O.....# - Act:4 - Num:1 [fit: 0.000, exp: 1692.00, pred: 556.146]\n", - "Cond:#..#...O - Act:0 - Num:1 [fit: 0.000, exp: 62.00, pred: 222.786]\n", - "Cond:#OOO..#. - Act:4 - Num:2 [fit: 0.000, exp: 275.00, pred: 460.740]\n", - "Cond:..#O.... - Act:5 - Num:1 [fit: 0.000, exp: 262.00, pred: 231.964]\n", - "Cond:.....#OO - Act:1 - Num:1 [fit: 0.000, exp: 45.00, pred: 205.184]\n", - "Cond:.....#.. - Act:3 - Num:3 [fit: 0.000, exp: 88.00, pred: 231.717]\n", - "Cond:OO###... - Act:6 - Num:1 [fit: 0.000, exp: 44.00, pred: 208.692]\n", - "Cond:....FO.. - Act:0 - Num:1 [fit: 0.000, exp: 70.00, pred: 284.493]\n", - "Cond:.....#.O - Act:4 - Num:1 [fit: 0.000, exp: 561.00, pred: 363.509]\n", - "Cond:#OOO..#. - Act:7 - Num:1 [fit: 0.000, exp: 227.00, pred: 211.958]\n", - "Cond:..#O.... - Act:4 - Num:1 [fit: 0.000, exp: 359.00, pred: 562.066]\n" + "Cond:....FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.002, exp: 50.00, pred: 353.055, error:6337.943477712033]acc: 0.54\n", + "Cond:.#..FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 182.00, pred: 496.703, error:4613.81658032312]acc: 0.6593406593406593\n", + "Cond:#....OF. - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 90.00, pred: 297.611, error:2670.2316972579392]acc: 0.0\n", + "Cond:..#O.... - Act:1 - effect:........ - Num:2 [fit: 0.001, exp: 28.00, pred: 449.250, error:10524.783774084593]acc: 0.0\n", + "Cond:...O.... - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 83.00, pred: 544.878, error:4259.89056855382]acc: 0.5301204819277109\n", + "Cond:#...#F.. - Act:1 - effect:.F...F.. - Num:6 [fit: 0.004, exp: 17.00, pred: 399.260, error:9865.895799661299]acc: 0.0\n", + "Cond:O......O - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 45.00, pred: 571.826, error:7137.170298870321]acc: 0.0\n", + "Cond:O......O - Act:2 - effect:.O....OO - Num:1 [fit: 0.009, exp: 13.00, pred: 520.655, error:10075.382209840245]acc: 0.0\n", + "Cond:O......O - Act:3 - effect:..O..O.O - Num:1 [fit: 0.001, exp: 26.00, pred: 405.061, error:11348.755852271704]acc: 0.0\n", + "Cond:#O.....O - Act:0 - effect:O.OOOOOO - Num:4 [fit: 0.022, exp: 9.00, pred: 499.453, error:6266.4934723170445]acc: 0.0\n", + "Cond:#O#....O - Act:2 - effect:.O.O.... - Num:1 [fit: 0.008, exp: 13.00, pred: 490.633, error:9097.96850556846]acc: 0.0\n", + "Cond:OO...... - Act:1 - effect:...O.... - Num:3 [fit: 0.002, exp: 23.00, pred: 465.361, error:7687.653083812652]acc: 0.0\n", + "Cond:OO...... - Act:2 - effect:...O.... - Num:1 [fit: 0.001, exp: 20.00, pred: 530.600, error:10584.571505347523]acc: 0.0\n", + "Cond:.O...... - Act:1 - effect:O.O....O - Num:5 [fit: 0.011, exp: 41.00, pred: 373.449, error:2837.6100851686674]acc: 0.0\n", + "Cond:.O...... - Act:2 - effect:.O...... - Num:2 [fit: 0.002, exp: 23.00, pred: 471.095, error:10553.639474215453]acc: 0.043478260869565216\n", + "Cond:.#OO.... - Act:1 - effect:OO.O...O - Num:4 [fit: 0.005, exp: 19.00, pred: 430.562, error:6595.173042392595]acc: 0.0\n", + "Cond:.#.#.OOF - Act:5 - effect:......O. - Num:4 [fit: 0.020, exp: 124.00, pred: 591.861, error:6424.406081388435]acc: 0.0\n", + "Cond:.....OOF - Act:6 - effect:.OF..O.. - Num:3 [fit: 0.026, exp: 127.00, pred: 278.127, error:895.2348863473378]acc: 0.0\n", + "Cond:...FOO.. - Act:0 - effect:O....OOO - Num:1 [fit: 0.000, exp: 36.00, pred: 523.670, error:9727.334891880051]acc: 0.0\n", + "Cond:...FO#.. - Act:5 - effect:O....F.. - Num:3 [fit: 0.027, exp: 114.00, pred: 596.498, error:2275.633615268892]acc: 0.0\n", + "Cond:..#.FO.. - Act:1 - effect:....FO.. - Num:5 [fit: 0.009, exp: 49.00, pred: 353.057, error:4918.1454716668095]acc: 0.5510204081632653\n", + "Cond:O..#...O - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 43.00, pred: 571.831, error:5911.4023850470285]acc: 0.0\n", + "Cond:O.#...#O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 43.00, pred: 571.831, error:6362.6815813585445]acc: 0.0\n", + "Cond:....F#.# - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 182.00, pred: 496.703, error:3981.4632590278857]acc: 0.34065934065934067\n", + "Cond:#.#.#F.. - Act:1 - effect:.F...F.. - Num:2 [fit: 0.003, exp: 16.00, pred: 403.872, error:9610.638275638881]acc: 0.0\n", + "Cond:.OO..#.. - Act:1 - effect:OO.O.... - Num:2 [fit: 0.023, exp: 8.00, pred: 333.509, error:5004.856920830573]acc: 0.0\n", + "Cond:....F#.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.003, exp: 48.00, pred: 353.065, error:5568.61262428654]acc: 0.5625\n", + "Cond:.#..#O.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 48.00, pred: 353.065, error:4918.410473251848]acc: 0.5625\n", + "Cond:#..##F.. - Act:1 - effect:.F...F.. - Num:3 [fit: 0.004, exp: 15.00, pred: 415.398, error:9480.03334319077]acc: 0.0\n", + "Cond:O#.....O - Act:2 - effect:.......O - Num:5 [fit: 0.001, exp: 25.00, pred: 432.290, error:9826.476675593618]acc: 0.08\n", + "Cond:O..##..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 41.00, pred: 571.844, error:5298.021750311862]acc: 0.0\n", + "Cond:O......# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 41.00, pred: 571.844, error:6714.8405197902575]acc: 0.0\n", + "Cond:..#.#..O - Act:1 - effect:........ - Num:1 [fit: 0.000, exp: 34.00, pred: 650.216, error:8131.760368723735]acc: 0.0\n", + "Cond:..#.O### - Act:1 - effect:OO.O.... - Num:1 [fit: 0.021, exp: 0.00, pred: 525.647, error:347.1414781693665]acc: 0\n", + "Cond:...FOO#. - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 33.00, pred: 523.551, error:9156.453988333025]acc: 0.0\n", + "Cond:OO.....O - Act:7 - effect:.......O - Num:3 [fit: 0.010, exp: 16.00, pred: 446.449, error:7981.043445140795]acc: 0.0\n", + "Cond:O#..#..O - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 24.00, pred: 432.353, error:9681.644302688486]acc: 0.08333333333333333\n", + "Cond:O.#....# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 39.00, pred: 571.896, error:5714.3103867511145]acc: 0.0\n", + "Cond:..OO..#. - Act:2 - effect:.......O - Num:1 [fit: 0.005, exp: 19.00, pred: 583.076, error:8670.392565902705]acc: 0.0\n", + "Cond:..OO#... - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 51.00, pred: 333.881, error:4712.814784746106]acc: 0.0\n", + "Cond:.....OOF - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 123.00, pred: 591.861, error:5209.092778309505]acc: 0.0\n", + "Cond:#OO..#.. - Act:1 - effect:OO.O.... - Num:1 [fit: 0.004, exp: 7.00, pred: 323.868, error:3677.847246191934]acc: 0.0\n", + "Cond:OO...#.O - Act:7 - effect:.......O - Num:4 [fit: 0.004, exp: 14.00, pred: 453.018, error:7096.441229296728]acc: 0.0\n", + "Cond:..#FOO#. - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 27.00, pred: 523.361, error:9057.722346704897]acc: 0.0\n", + "Cond:O..#...O - Act:3 - effect:...OO... - Num:1 [fit: 0.001, exp: 19.00, pred: 407.115, error:9364.707706747295]acc: 0.0\n", + "Cond:.#..FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.003, exp: 47.00, pred: 353.072, error:5885.377964047417]acc: 0.574468085106383\n", + "Cond:..#.#... - Act:0 - effect:.O..O... - Num:1 [fit: 0.014, exp: 0.00, pred: 818.390, error:462.3555041188563]acc: 0\n", + "Cond:..O#..#. - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 18.00, pred: 584.573, error:7300.06082352122]acc: 0.0\n", + "Cond:....O##. - Act:6 - effect:OO.O.... - Num:1 [fit: 0.005, exp: 0.00, pred: 462.466, error:592.0031570377164]acc: 0\n", + "Cond:.O#O#..# - Act:3 - effect:...O.... - Num:1 [fit: 0.011, exp: 45.00, pred: 495.556, error:2148.4343584777043]acc: 0.0\n", + "Cond:#O.....O - Act:7 - effect:.......O - Num:1 [fit: 0.003, exp: 11.00, pred: 435.347, error:7456.636553044949]acc: 0.0\n", + "Cond:O#.#O..# - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:O#.####. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 19.00, pred: 530.441, error:10074.077212800548]acc: 0.0\n", + "Cond:OO..##.# - Act:1 - effect:.....O.. - Num:1 [fit: 0.000, exp: 35.00, pred: 362.415, error:5999.4905704979365]acc: 0.0\n", + "Cond:O#..##.. - Act:1 - effect:.....O.. - Num:2 [fit: 0.006, exp: 21.00, pred: 464.825, error:6146.43610478317]acc: 0.0\n", + "Cond:O#.##... - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 18.00, pred: 530.100, error:9520.127168511372]acc: 0.0\n", + "Cond:OO#...#. - Act:1 - effect:.....O.. - Num:2 [fit: 0.005, exp: 13.00, pred: 448.523, error:7538.032092309675]acc: 0.0\n", + "Cond:O###.#.. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 17.00, pred: 533.208, error:9339.22729942304]acc: 0.0\n", + "Cond:O##.##.O - Act:1 - effect:.....O.. - Num:1 [fit: 0.000, exp: 32.00, pred: 380.280, error:8335.640163793965]acc: 0.0\n", + "Cond:O.##.#.# - Act:3 - effect:...OO... - Num:2 [fit: 0.000, exp: 18.00, pred: 407.178, error:9633.554050330706]acc: 0.0\n", + "Cond:.O.#..#. - Act:1 - effect:O.O....O - Num:1 [fit: 0.003, exp: 40.00, pred: 373.476, error:2551.842039236233]acc: 0.0\n", + "Cond:..#O#O#. - Act:2 - effect:O...O... - Num:1 [fit: 0.009, exp: 0.00, pred: 689.021, error:1047.596900369857]acc: 0\n", + "Cond:.#O#.O.. - Act:7 - effect:.O.O..O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1053.139, error:234.83598116026235]acc: 0\n", + "Cond:O.#....# - Act:1 - effect:O..O.... - Num:2 [fit: 0.000, exp: 20.00, pred: 467.997, error:8917.596295279574]acc: 0.0\n", + "Cond:O......O - Act:7 - effect:.......O - Num:1 [fit: 0.002, exp: 43.00, pred: 398.207, error:4022.1719304523012]acc: 0.023255813953488372\n", + "Cond:.......# - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 19.00, pred: 622.574, error:9516.631539035749]acc: 0.0\n", + "Cond:.#..##.O - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 19.00, pred: 622.574, error:8913.883526082147]acc: 0.10526315789473684\n", + "Cond:...O.... - Act:1 - effect:........ - Num:1 [fit: 0.001, exp: 19.00, pred: 464.128, error:8859.430762072749]acc: 0.0\n", + "Cond:O#..#... - Act:1 - effect:.....O.. - Num:3 [fit: 0.037, exp: 10.00, pred: 448.691, error:5686.635651199907]acc: 0.0\n", + "Cond:OO..##.. - Act:1 - effect:.....O.. - Num:4 [fit: 0.020, exp: 10.00, pred: 448.691, error:6238.277251199907]acc: 0.0\n", + "Cond:.#.OO... - Act:0 - effect:........ - Num:1 [fit: 0.000, exp: 25.00, pred: 574.879, error:8578.260380881457]acc: 0.0\n", + "Cond:#O#....F - Act:7 - effect:...O.OO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#...OO# - Act:6 - effect:.O...... - Num:1 [fit: 0.009, exp: 122.00, pred: 278.127, error:1092.5495480423253]acc: 0.0\n", + "Cond:##OO..## - Act:1 - effect:...O..OO - Num:1 [fit: 0.000, exp: 17.00, pred: 441.085, error:6025.867898627779]acc: 0.0\n", + "Cond:.#..OO#F - Act:7 - effect:..O..FF. - Num:1 [fit: 0.002, exp: 0.00, pred: 921.678, error:1263.4849207381756]acc: 0\n", + "Cond:.#.#O... - Act:0 - effect:........ - Num:1 [fit: 0.000, exp: 24.00, pred: 575.240, error:9459.694031312512]acc: 0.0\n", + "Cond:##O##.## - Act:1 - effect:...O.... - Num:3 [fit: 0.000, exp: 24.00, pred: 305.462, error:6021.623058693252]acc: 0.0\n", + "Cond:##..O#.. - Act:7 - effect:O....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:..##OO## - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 23.00, pred: 522.943, error:9186.726118856637]acc: 0.0\n", + "Cond:###F##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 522.943, error:8918.868471755985]acc: 0.0\n", + "Cond:OO.###.. - Act:1 - effect:.....O.. - Num:1 [fit: 0.006, exp: 8.00, pred: 463.842, error:5093.599988402337]acc: 0.0\n", + "Cond:#..FO#.. - Act:5 - effect:O....F.. - Num:1 [fit: 0.013, exp: 112.00, pred: 596.498, error:3185.7110160511634]acc: 0.0\n", + "Cond:O......# - Act:3 - effect:..OOO.O. - Num:2 [fit: 0.000, exp: 17.00, pred: 410.683, error:9045.182060448225]acc: 0.0\n", + "Cond:#.OO..## - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 45.00, pred: 333.890, error:3957.0801520373807]acc: 0.0\n", + "Cond:..OO.### - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 45.00, pred: 333.890, error:4192.436673411008]acc: 0.0\n", + "Cond:..OO#... - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 11.00, pred: 619.222, error:7159.218141919737]acc: 0.0\n", + "Cond:OO#..#.O - Act:7 - effect:.......O - Num:1 [fit: 0.007, exp: 9.00, pred: 437.253, error:6686.121235567013]acc: 0.0\n", + "Cond:#O.FOO.. - Act:7 - effect:..O....O - Num:1 [fit: 0.005, exp: 0.00, pred: 387.950, error:634.3292197483906]acc: 0\n", + "Cond:O#...#.O - Act:2 - effect:.......O - Num:3 [fit: 0.001, exp: 20.00, pred: 435.794, error:9153.83308925144]acc: 0.1\n", + "Cond:OO#....O - Act:2 - effect:.O.O.... - Num:2 [fit: 0.001, exp: 9.00, pred: 581.029, error:8982.962343868741]acc: 0.0\n", + "Cond:O...F#.O - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 493.170, error:1341.6924102555245]acc: 0\n", + "Cond:...FOO## - Act:0 - effect:O....OOO - Num:1 [fit: 0.000, exp: 21.00, pred: 527.539, error:9334.236872492673]acc: 0.0\n", + "Cond:..##OO#. - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 21.00, pred: 527.539, error:8941.900926083099]acc: 0.0\n", + "Cond:...#FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.002, exp: 46.00, pred: 353.085, error:4951.564378704332]acc: 0.5869565217391305\n", + "Cond:.#.F.##. - Act:7 - effect:FOO....O - Num:1 [fit: 0.004, exp: 0.00, pred: 373.133, error:673.6872299099866]acc: 0\n", + "Cond:..##...O - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 26.00, pred: 650.666, error:7045.348894668639]acc: 0.34615384615384615\n", + "Cond:##.#.OOF - Act:5 - effect:......O. - Num:2 [fit: 0.010, exp: 120.00, pred: 591.861, error:6321.603940344311]acc: 0.0\n", + "Cond:...#OO## - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 20.00, pred: 532.481, error:9375.223570547509]acc: 0.0\n", + "Cond:....F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.007, exp: 180.00, pred: 496.703, error:4646.1842866706265]acc: 0.5833333333333334\n", + "Cond:..O....O - Act:1 - effect:.......O - Num:1 [fit: 0.004, exp: 0.00, pred: 428.073, error:746.9090835819317]acc: 0\n", + "Cond:O##.##.. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 15.00, pred: 551.383, error:9014.65802655628]acc: 0.0\n", + "Cond:.#.FOO.. - Act:2 - effect:....OO.F - Num:3 [fit: 0.000, exp: 25.00, pred: 934.462, error:6669.367840166487]acc: 0.0\n", + "Cond:..#.##.O - Act:1 - effect:........ - Num:1 [fit: 0.000, exp: 23.00, pred: 654.011, error:7878.826723978138]acc: 0.0\n", + "Cond:O#..OO.. - Act:7 - effect:O....F.. - Num:1 [fit: 0.004, exp: 0.00, pred: 398.233, error:678.6737672231693]acc: 0\n", + "Cond:..O#.### - Act:1 - effect:.......O - Num:1 [fit: 0.002, exp: 8.00, pred: 722.686, error:6322.500651323787]acc: 0.0\n", + "Cond:OO..#... - Act:1 - effect:.....O.. - Num:1 [fit: 0.010, exp: 4.00, pred: 478.147, error:3871.9829623297064]acc: 0.0\n", + "Cond:O.#....O - Act:1 - effect:O..O.... - Num:1 [fit: 0.000, exp: 18.00, pred: 474.682, error:8858.523434652972]acc: 0.0\n", + "Cond:..##..#. - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 25.00, pred: 452.339, error:9024.960386303828]acc: 0.0\n", + "Cond:.....F## - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 659.913, error:8696.762631059104]acc: 0.13636363636363635\n", + "Cond:..O.##F. - Act:3 - effect:.....F.. - Num:1 [fit: 0.004, exp: 0.00, pred: 726.080, error:556.1666191940266]acc: 0\n", + "Cond:.##.O### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 885.769, error:805.999306844893]acc: 0\n", + "Cond:..#.O#.. - Act:1 - effect:OO.O.... - Num:2 [fit: 0.003, exp: 0.00, pred: 644.803, error:1059.8649949242924]acc: 0\n", + "Cond:O...#..O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 37.00, pred: 571.887, error:6522.126372083032]acc: 0.0\n", + "Cond:O..#.#.O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 37.00, pred: 571.887, error:5973.310157826459]acc: 0.0\n", + "Cond:##.#.OO# - Act:5 - effect:.O...... - Num:1 [fit: 0.004, exp: 118.00, pred: 591.861, error:5596.286968621487]acc: 0.0\n", + "Cond:#.##..O# - Act:2 - effect:.....O.O - Num:1 [fit: 0.000, exp: 23.00, pred: 607.849, error:9571.301267637094]acc: 0.0\n", + "Cond:.O####F. - Act:7 - effect:.O.O..O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1012.022, error:740.125350469487]acc: 0\n", + "Cond:..#..FO. - Act:5 - effect:O.....O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1045.385, error:992.3975870070019]acc: 0\n", + "Cond:..##.O## - Act:5 - effect:........ - Num:5 [fit: 0.018, exp: 201.00, pred: 575.159, error:5666.270307430446]acc: 0.0\n", + "Cond:.....#.O - Act:0 - effect:.O...... - Num:2 [fit: 0.001, exp: 16.00, pred: 631.860, error:8348.084344770876]acc: 0.0625\n", + "Cond:O.##..#. - Act:1 - effect:..OOO.O. - Num:1 [fit: 0.012, exp: 0.00, pred: 714.596, error:592.963499385789]acc: 0\n", + "Cond:...#FO## - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 180.00, pred: 496.703, error:3981.4635981063934]acc: 0.6611111111111111\n", + "Cond:..###.O# - Act:2 - effect:.....O.O - Num:1 [fit: 0.000, exp: 23.00, pred: 607.849, error:9210.736426807618]acc: 0.0\n", + "Cond:.....O.# - Act:5 - effect:O.....O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1141.640, error:1578.1259378398556]acc: 0\n", + "Cond:#..O#..# - Act:3 - effect:...O.... - Num:3 [fit: 0.028, exp: 157.00, pred: 512.640, error:5059.64966221754]acc: 0.4012738853503185\n", + "Cond:###...#F - Act:0 - effect:......O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1609.351, error:0.0]acc: 0\n", + "Cond:.....#FF - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 910.461, error:973.7211020861592]acc: 0\n", + "Cond:..##.#O. - Act:2 - effect:.....O.O - Num:1 [fit: 0.002, exp: 0.00, pred: 699.818, error:1154.0500303958465]acc: 0\n", + "Cond:...#.O## - Act:5 - effect:......O. - Num:2 [fit: 0.009, exp: 199.00, pred: 575.159, error:5152.772420245019]acc: 0.0\n", + "Cond:O##.#.#O - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 42.00, pred: 556.418, error:7149.3829943641185]acc: 0.0\n", + "Cond:O##.##.# - Act:1 - effect:O..O.... - Num:1 [fit: 0.000, exp: 29.00, pred: 320.095, error:6717.539796007581]acc: 0.0\n", + "Cond:O.#..##O - Act:1 - effect:.....O.. - Num:1 [fit: 0.000, exp: 17.00, pred: 474.894, error:9665.872107625324]acc: 0.0\n", + "Cond:##O.#.## - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 24.00, pred: 502.989, error:10324.285224091193]acc: 0.0\n", + "Cond:#OO#.O.. - Act:6 - effect:O.O..... - Num:1 [fit: 0.001, exp: 0.00, pred: 877.163, error:833.6618838674718]acc: 0\n", + "Cond:...#..O# - Act:2 - effect:.....O.O - Num:1 [fit: 0.000, exp: 22.00, pred: 607.545, error:9767.595135596894]acc: 0.0\n", + "Cond:#.#..O.. - Act:6 - effect:.....F.. - Num:1 [fit: 0.001, exp: 0.00, pred: 937.800, error:978.2963845425596]acc: 0\n", + "Cond:.....O.F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.001, exp: 0.00, pred: 798.308, error:926.7844360640581]acc: 0\n", + "Cond:...#.##F - Act:6 - effect:...O.... - Num:4 [fit: 0.053, exp: 119.00, pred: 278.127, error:2768.0843709811365]acc: 0.0\n", + "Cond:.#####.O - Act:1 - effect:O.....O. - Num:2 [fit: 0.000, exp: 22.00, pred: 651.818, error:8589.429600835692]acc: 0.0\n", + "Cond:.O.#OO.. - Act:0 - effect:........ - Num:1 [fit: 0.002, exp: 0.00, pred: 766.801, error:1148.0721027462323]acc: 0\n", + "Cond:.O..##.. - Act:2 - effect:.O...... - Num:1 [fit: 0.000, exp: 15.00, pred: 489.584, error:9405.707394314819]acc: 0.06666666666666667\n", + "Cond:O#####.. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 13.00, pred: 549.192, error:9530.97673561599]acc: 0.0\n", + "Cond:.###OO## - Act:0 - effect:O....OOO - Num:1 [fit: 0.000, exp: 18.00, pred: 531.270, error:9221.91449517896]acc: 0.0\n", + "Cond:...#OO.. - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 18.00, pred: 531.270, error:8984.458972387536]acc: 0.0\n", + "Cond:.#..O.#. - Act:7 - effect:..O....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1172.832, error:1139.8565547531255]acc: 0\n", + "Cond:O..####. - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1165.389, error:250.69490745098673]acc: 0\n", + "Cond:#.##F##O - Act:4 - effect:.O...... - Num:1 [fit: 0.004, exp: 0.00, pred: 1108.561, error:281.8267606571851]acc: 0\n", + "Cond:.O....#. - Act:2 - effect:........ - Num:1 [fit: 0.000, exp: 15.00, pred: 489.584, error:8930.255772938817]acc: 0.0\n", + "Cond:...O...# - Act:1 - effect:..O.OO.. - Num:1 [fit: 0.001, exp: 17.00, pred: 477.095, error:8567.67732449247]acc: 0.0\n", + "Cond:.#..OO## - Act:6 - effect:...O.F.F - Num:1 [fit: 0.002, exp: 0.00, pred: 1586.301, error:613.9652097552853]acc: 0\n", + "Cond:#.##.OO# - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 117.00, pred: 591.861, error:6030.870856717871]acc: 0.0\n", + "Cond:.#O..### - Act:0 - effect:OO...... - Num:1 [fit: 0.000, exp: 20.00, pred: 505.139, error:8374.36097465027]acc: 0.0\n", + "Cond:#######. - Act:5 - effect:....FO.. - Num:4 [fit: 0.002, exp: 1829.00, pred: 1223.458, error:2031.1534559951656]acc: 0.0683433570256971\n", + "Cond:..#O#O.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#..O#.. - Act:1 - effect:OO.O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 977.954, error:1322.7975716551146]acc: 0\n", + "Cond:.#OO#O.# - Act:6 - effect:O.O..... - Num:1 [fit: 0.000, exp: 0.00, pred: 1209.541, error:874.5607862650131]acc: 0\n", + "Cond:...#.#F# - Act:5 - effect:.O.OOOO. - Num:1 [fit: 0.009, exp: 81.00, pred: 297.611, error:2480.781863054097]acc: 0.0\n", + "Cond:OO#...#. - Act:2 - effect:.O.O.... - Num:1 [fit: 0.001, exp: 12.00, pred: 557.625, error:8297.733676617663]acc: 0.0\n", + "Cond:.OOO.#F. - Act:7 - effect:.O.O..O. - Num:1 [fit: 0.005, exp: 0.00, pred: 1086.044, error:1222.6600975588067]acc: 0\n", + "Cond:.OO..#.# - Act:1 - effect:O.O..... - Num:1 [fit: 0.004, exp: 7.00, pred: 323.868, error:3677.847246191934]acc: 0.0\n", + "Cond:.....#.F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 1028.187, error:1645.4258553781983]acc: 0\n", + "Cond:..###..# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 220.00, pred: 481.467, error:5487.271805117372]acc: 0.29545454545454547\n", + "Cond:...F.O.. - Act:7 - effect:FOO....O - Num:1 [fit: 0.001, exp: 0.00, pred: 619.622, error:1684.3817246586636]acc: 0\n", + "Cond:OO##..## - Act:0 - effect:......O. - Num:2 [fit: 0.001, exp: 34.00, pred: 313.077, error:5835.651217844026]acc: 0.0\n", + "Cond:O##.#... - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 11.00, pred: 585.373, error:9509.71963109912]acc: 0.0\n", + "Cond:..#.#.O. - Act:1 - effect:..O..FF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1171.668, error:857.85087288033]acc: 0\n", + "Cond:###O.#O# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1061.941, error:816.8595806231785]acc: 0\n", + "Cond:....#OOF - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.012, exp: 117.00, pred: 278.127, error:735.4195057757368]acc: 0.0\n", + "Cond:...#.O.F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#...O.. - Act:7 - effect:....O..O - Num:1 [fit: 0.004, exp: 0.00, pred: 553.104, error:1095.300615351723]acc: 0\n", + "Cond:....OO#. - Act:5 - effect:O.....O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:###.#OO# - Act:6 - effect:FOO....O - Num:1 [fit: 0.009, exp: 117.00, pred: 278.127, error:1097.6824027245507]acc: 0.0\n", + "Cond:##..O... - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:O#..#..# - Act:2 - effect:.....O.. - Num:6 [fit: 0.004, exp: 23.00, pred: 372.033, error:7311.767484177768]acc: 0.0\n", + "Cond:#O.O..#. - Act:5 - effect:.O.O.... - Num:1 [fit: 0.002, exp: 0.00, pred: 1023.109, error:640.6608834805902]acc: 0\n", + "Cond:.#.O..## - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 18.00, pred: 710.421, error:7809.517807394174]acc: 0.0\n", + "Cond:O...#.#O - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 35.00, pred: 571.922, error:5782.698143639375]acc: 0.0\n", + "Cond:O##.#..# - Act:1 - effect:O..O.... - Num:1 [fit: 0.000, exp: 26.00, pred: 320.033, error:6904.070537158301]acc: 0.0\n", + "Cond:###..OO. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:..#...#. - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1034.909, error:1016.9640257142541]acc: 0\n", + "Cond:..#..#.# - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 28.00, pred: 523.371, error:8101.894724442725]acc: 0.14285714285714285\n", + "Cond:#...FO.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 45.00, pred: 353.091, error:5434.3116534327755]acc: 0.6\n", + "Cond:##.OO#.. - Act:0 - effect:........ - Num:1 [fit: 0.000, exp: 17.00, pred: 583.353, error:8499.885856641697]acc: 0.0\n", + "Cond:....##O. - Act:1 - effect:O.....O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1447.035, error:1021.2983724191758]acc: 0\n", + "Cond:.##..O.. - Act:5 - effect:..O.OO.. - Num:1 [fit: 0.007, exp: 0.00, pred: 1317.925, error:1200.4001466835905]acc: 0\n", + "Cond:O#####.. - Act:0 - effect:O..O.... - Num:2 [fit: 0.001, exp: 26.00, pred: 400.116, error:6475.23889139357]acc: 0.0\n", + "Cond:O##.##.. - Act:1 - effect:O..O.... - Num:1 [fit: 0.106, exp: 2.00, pred: 211.654, error:2501.99040667384]acc: 0.0\n", + "Cond:.###OFO. - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:..#F.O.. - Act:7 - effect:FOO....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:...#..F# - Act:5 - effect:...O..OO - Num:1 [fit: 0.001, exp: 0.00, pred: 979.638, error:569.264858588701]acc: 0\n", + "Cond:......F. - Act:3 - effect:...OF... - Num:1 [fit: 0.003, exp: 0.00, pred: 1149.718, error:954.3909187689369]acc: 0\n", + "Cond:...#.... - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 80.00, pred: 544.878, error:3657.415947561034]acc: 0.475\n", + "Cond:...O##OO - Act:1 - effect:....O..O - Num:1 [fit: 0.000, exp: 0.00, pred: 1138.897, error:1429.4210671856476]acc: 0\n", + "Cond:#####.#F - Act:6 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#O...## - Act:3 - effect:.OO..OO. - Num:1 [fit: 0.000, exp: 22.00, pred: 474.567, error:7522.249874251819]acc: 0.0\n", + "Cond:.#OO##.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 43.00, pred: 763.512, error:6650.574831572045]acc: 0.023255813953488372\n", + "Cond:##....#. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 20.00, pred: 363.230, error:6972.925625261002]acc: 0.25\n", + "Cond:#..FO##. - Act:1 - effect:..O...F. - Num:1 [fit: 0.000, exp: 23.00, pred: 395.165, error:7352.981700699038]acc: 0.0\n", + "Cond:.#.#.##F - Act:6 - effect:.O...... - Num:1 [fit: 0.010, exp: 113.00, pred: 278.127, error:1479.971534652448]acc: 0.0\n", + "Cond:.#.F###. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 20.00, pred: 934.940, error:7724.784789156964]acc: 0.0\n", + "Cond:...#.OOF - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 115.00, pred: 591.861, error:5465.156334326693]acc: 0.0\n", + "Cond:#..F#OO. - Act:3 - effect:..O...F. - Num:1 [fit: 0.001, exp: 0.00, pred: 1361.214, error:1105.2169014662325]acc: 0\n", + "Cond:..#.FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 179.00, pred: 496.703, error:5013.809343364639]acc: 0.5810055865921788\n", + "Cond:..##F### - Act:0 - effect:.......F - Num:2 [fit: 0.000, exp: 27.00, pred: 501.558, error:8196.98208577534]acc: 0.0\n", + "Cond:O#.#FO## - Act:6 - effect:.....O.O - Num:1 [fit: 0.000, exp: 0.00, pred: 1455.523, error:1090.9086930498254]acc: 0\n", + "Cond:#....FO. - Act:5 - effect:O.....O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.##.#... - Act:2 - effect:.O...... - Num:1 [fit: 0.004, exp: 20.00, pred: 365.780, error:7793.7487469393745]acc: 0.05\n", + "Cond:#O.....# - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 25.00, pred: 367.263, error:6056.625259449768]acc: 0.2\n", + "Cond:#..#F.## - Act:7 - effect:.......F - Num:1 [fit: 0.004, exp: 0.00, pred: 1326.152, error:703.0549951380294]acc: 0\n", + "Cond:OO#.#..# - Act:1 - effect:.....O.. - Num:1 [fit: 0.044, exp: 9.00, pred: 371.756, error:4762.2090882854145]acc: 0.0\n", + "Cond:...OO#.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 663.429, error:4738.355922102305]acc: 0.0\n", + "Cond:.##..#O. - Act:5 - effect:......O. - Num:1 [fit: 0.003, exp: 0.00, pred: 1351.551, error:799.5216151326937]acc: 0\n", + "Cond:.###OF#. - Act:5 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 1351.551, error:799.5216151326937]acc: 0\n", + "Cond:..##.OF. - Act:5 - effect:..O..O.. - Num:1 [fit: 0.014, exp: 80.00, pred: 297.611, error:2182.3884262221527]acc: 0.0\n", + "Cond:#OO.##.. - Act:1 - effect:.O...... - Num:1 [fit: 0.008, exp: 7.00, pred: 323.868, error:4810.647246191935]acc: 0.0\n", + "Cond:O....#.# - Act:3 - effect:..O..O.O - Num:2 [fit: 0.001, exp: 14.00, pred: 429.453, error:9296.244248507992]acc: 0.0\n", + "Cond:#..FOO.# - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 18.00, pred: 938.496, error:6736.3508350884185]acc: 0.0\n", + "Cond:O...#..O - Act:7 - effect:O..O.... - Num:1 [fit: 0.002, exp: 35.00, pred: 398.199, error:4710.856138125791]acc: 0.0\n", + "Cond:####.OOF - Act:5 - effect:......O. - Num:3 [fit: 0.008, exp: 113.00, pred: 591.861, error:6469.6475834343455]acc: 0.0\n", + "Cond:#O.O###F - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1127.096, error:1360.6018909333363]acc: 0\n", + "Cond:#O..#O## - Act:5 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#O#.##O# - Act:7 - effect:.......O - Num:2 [fit: 0.020, exp: 0.00, pred: 1010.378, error:42.428467478511834]acc: 0\n", + "Cond:.#.FO##. - Act:1 - effect:..O...F. - Num:1 [fit: 0.000, exp: 17.00, pred: 392.958, error:6974.27593295062]acc: 0.0\n", + "Cond:#.#.OO## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1116.335, error:564.012229283444]acc: 0\n", + "Cond:###.#F.. - Act:1 - effect:.......O - Num:1 [fit: 0.002, exp: 11.00, pred: 427.548, error:8477.167280708258]acc: 0.0\n", + "Cond:.##O##.. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 216.00, pred: 481.614, error:5548.695761950081]acc: 0.2037037037037037\n", + "Cond:..#.#O.# - Act:0 - effect:..O..O.. - Num:2 [fit: 0.000, exp: 18.00, pred: 503.141, error:7574.780093615441]acc: 0.0\n", + "Cond:..###O## - Act:5 - effect:....FO.. - Num:2 [fit: 0.013, exp: 477.00, pred: 715.928, error:4441.088962206786]acc: 0.2557651991614256\n", + "Cond:.#....O# - Act:1 - effect:........ - Num:2 [fit: 0.000, exp: 22.00, pred: 667.257, error:9111.363028440863]acc: 0.0\n", + "Cond:###OO##. - Act:4 - effect:.......O - Num:2 [fit: 0.008, exp: 44.00, pred: 609.394, error:5604.508906454034]acc: 0.06818181818181818\n", + "Cond:#..#..#. - Act:7 - effect:.......O - Num:3 [fit: 0.001, exp: 25.00, pred: 406.737, error:6133.304751365514]acc: 0.04\n", + "Cond:##OO#... - Act:1 - effect:OO.O...O - Num:4 [fit: 0.010, exp: 16.00, pred: 431.295, error:5947.311541610065]acc: 0.0\n", + "Cond:..#O..## - Act:1 - effect:.......O - Num:2 [fit: 0.001, exp: 23.00, pred: 451.462, error:8792.72463556831]acc: 0.0\n", + "Cond:...###O. - Act:1 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 1642.364, error:700.0339329461576]acc: 0\n", + "Cond:#.##.OFF - Act:6 - effect:.O...... - Num:1 [fit: 0.002, exp: 0.00, pred: 1445.585, error:118.70060570467754]acc: 0\n", + "Cond:.#.###O. - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1445.585, error:118.70060570467754]acc: 0\n", + "Cond:...FOO.# - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 15.00, pred: 515.125, error:8935.28964472972]acc: 0.0\n", + "Cond:..#F#O#. - Act:2 - effect:....OO.F - Num:2 [fit: 0.000, exp: 17.00, pred: 938.399, error:6795.512619369188]acc: 0.0\n", + "Cond:.###..#F - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1187.890, error:548.2118457740978]acc: 0\n", + "Cond:....##OF - Act:6 - effect:.OF..O.. - Num:2 [fit: 0.027, exp: 112.00, pred: 278.127, error:2368.703319091791]acc: 0.0\n", + "Cond:#..#.O#F - Act:6 - effect:.F.O...O - Num:1 [fit: 0.009, exp: 112.00, pred: 278.127, error:1094.9129189627897]acc: 0.0\n", + "Cond:#..##O## - Act:5 - effect:....FO.. - Num:2 [fit: 0.014, exp: 474.00, pred: 715.928, error:5907.094814130806]acc: 0.21940928270042195\n", + "Cond:.....#F. - Act:1 - effect:..F..... - Num:3 [fit: 0.001, exp: 25.00, pred: 382.834, error:7569.8258370587055]acc: 0.0\n", + "Cond:.###.O## - Act:5 - effect:......O. - Num:3 [fit: 0.008, exp: 185.00, pred: 575.159, error:5746.736892729852]acc: 0.0\n", + "Cond:#.##.OOF - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 108.00, pred: 591.861, error:5868.669275707169]acc: 0.0\n", + "Cond:........ - Act:7 - effect:.......O - Num:2 [fit: 0.014, exp: 0.00, pred: 1160.936, error:707.3455795776557]acc: 0\n", + "Cond:##.F.#.# - Act:7 - effect:FOO....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1105.720, error:120.78234019162151]acc: 0\n", + "Cond:O##.#.#O - Act:1 - effect:.....O.. - Num:2 [fit: 0.000, exp: 19.00, pred: 380.365, error:7809.520204502078]acc: 0.0\n", + "Cond:..#..#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 29.00, pred: 377.816, error:10230.152938838537]acc: 0.0\n", + "Cond:##OO#..# - Act:3 - effect:.OOOOO.. - Num:2 [fit: 0.009, exp: 63.00, pred: 321.804, error:3701.378056040754]acc: 0.0\n", + "Cond:O...##.O - Act:3 - effect:..O..O.O - Num:1 [fit: 0.000, exp: 13.00, pred: 426.451, error:8982.803301749775]acc: 0.0\n", + "Cond:####..#O - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 77.00, pred: 384.090, error:5458.683205953437]acc: 0.12987012987012986\n", + "Cond:.#...... - Act:1 - effect:O.O....O - Num:1 [fit: 0.002, exp: 38.00, pred: 373.412, error:2937.3939931076056]acc: 0.0\n", + "Cond:..#.#### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 440.00, pred: 1655.017, error:7284.710455599785]acc: 0.15\n", + "Cond:OO##.#.O - Act:1 - effect:.....O.. - Num:1 [fit: 0.020, exp: 5.00, pred: 407.976, error:3282.844748523742]acc: 0.0\n", + "Cond:....##F. - Act:1 - effect:..F..... - Num:2 [fit: 0.002, exp: 24.00, pred: 381.469, error:7023.003084408075]acc: 0.0\n", + "Cond:##...#.# - Act:1 - effect:.O...... - Num:1 [fit: 0.000, exp: 79.00, pred: 245.224, error:4576.776555035348]acc: 0.26582278481012656\n", + "Cond:O#..#.#O - Act:2 - effect:.....O.. - Num:1 [fit: 0.003, exp: 11.00, pred: 417.503, error:8368.852584064824]acc: 0.0\n", + "Cond:....O### - Act:5 - effect:....OO.F - Num:1 [fit: 0.011, exp: 0.00, pred: 1085.685, error:505.6652056526437]acc: 0\n", + "Cond:.###.O#F - Act:6 - effect:...O.... - Num:1 [fit: 0.011, exp: 110.00, pred: 278.127, error:741.3711930910256]acc: 0.0\n", + "Cond:#...#OO# - Act:6 - effect:FOO....O - Num:1 [fit: 0.009, exp: 110.00, pred: 278.127, error:892.6861869596568]acc: 0.0\n", + "Cond:#.##.OOF - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 23.00, pred: 809.785, error:9453.63069956985]acc: 0.0\n", + "Cond:.#..#.#O - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 38.00, pred: 301.236, error:3972.580036003398]acc: 0.15789473684210525\n", + "Cond:#O#O###. - Act:2 - effect:.......O - Num:1 [fit: 0.014, exp: 14.00, pred: 710.377, error:5857.038785561664]acc: 0.0\n", + "Cond:.#.#OO.. - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 13.00, pred: 520.926, error:8489.520756534312]acc: 0.0\n", + "Cond:#O....#O - Act:0 - effect:O.OOOOOO - Num:1 [fit: 0.069, exp: 4.00, pred: 368.901, error:2444.9819578950837]acc: 0.0\n", + "Cond:...F#O#. - Act:2 - effect:..O...O. - Num:2 [fit: 0.003, exp: 16.00, pred: 949.903, error:6519.936622501989]acc: 0.0\n", + "Cond:..#FOO#. - Act:2 - effect:....OO.F - Num:1 [fit: 0.001, exp: 16.00, pred: 949.903, error:7602.505184780391]acc: 0.0\n", + "Cond:.....O#F - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.013, exp: 104.00, pred: 278.127, error:2568.0843593325085]acc: 0.0\n", + "Cond:.#####OF - Act:6 - effect:.F.O...O - Num:1 [fit: 0.012, exp: 104.00, pred: 278.127, error:735.4567760476085]acc: 0.0\n", + "Cond:.....#F# - Act:1 - effect:..F..... - Num:4 [fit: 0.001, exp: 21.00, pred: 377.976, error:8174.237787296627]acc: 0.0\n", + "Cond:O....#.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.005, exp: 33.00, pred: 572.026, error:6425.600889925429]acc: 0.0\n", + "Cond:O#..#.## - Act:2 - effect:.....O.. - Num:4 [fit: 0.004, exp: 17.00, pred: 368.144, error:8106.861134472726]acc: 0.0\n", + "Cond:O#.##..# - Act:2 - effect:.....O.. - Num:1 [fit: 0.004, exp: 17.00, pred: 368.144, error:7334.5506005744855]acc: 0.0\n", + "Cond:#..##..# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 193.00, pred: 512.133, error:4505.418757555612]acc: 0.33678756476683935\n", + "Cond:#.#####F - Act:0 - effect:..O..O.. - Num:2 [fit: 0.000, exp: 23.00, pred: 809.785, error:9965.056958171037]acc: 0.0\n", + "Cond:...O###F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 892.435, error:1252.516038205788]acc: 0\n", + "Cond:..###.#F - Act:2 - effect:..O..FF. - Num:2 [fit: 0.000, exp: 0.00, pred: 892.435, error:1252.516038205788]acc: 0\n", + "Cond:.##.#O#F - Act:6 - effect:.F.O...O - Num:1 [fit: 0.009, exp: 102.00, pred: 278.127, error:897.6828808207779]acc: 0.0\n", + "Cond:##.#.OO# - Act:6 - effect:FOO....O - Num:2 [fit: 0.019, exp: 102.00, pred: 278.127, error:1687.869211410583]acc: 0.0\n", + "Cond:##..#.OF - Act:3 - effect:O.....O. - Num:1 [fit: 0.002, exp: 0.00, pred: 840.076, error:1335.176981049846]acc: 0\n", + "Cond:#OO#.##. - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 331.713, error:6318.894697554077]acc: 0.0\n", + "Cond:##O..### - Act:2 - effect:.......O - Num:2 [fit: 0.068, exp: 7.00, pred: 442.930, error:3040.2692103956697]acc: 0.0\n", + "Cond:##O##F## - Act:7 - effect:.......O - Num:1 [fit: 0.007, exp: 0.00, pred: 807.327, error:646.0636550177974]acc: 0\n", + "Cond:.O#..... - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 43.00, pred: 290.181, error:4262.099099921874]acc: 0.0\n", + "Cond:.#..#.#. - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 12.00, pred: 394.899, error:8444.590532575156]acc: 0.0\n", + "Cond:#O#.##.# - Act:1 - effect:.O...... - Num:1 [fit: 0.002, exp: 48.00, pred: 259.171, error:5058.6255576803505]acc: 0.5416666666666666\n", + "Cond:####F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 177.00, pred: 496.703, error:3981.4705217001365]acc: 0.6101694915254238\n", + "Cond:....F#.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.006, exp: 41.00, pred: 353.067, error:5349.645151495246]acc: 0.6585365853658537\n", + "Cond:....FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.004, exp: 41.00, pred: 353.067, error:5252.618541716733]acc: 0.6585365853658537\n", + "Cond:######## - Act:5 - effect:........ - Num:68 [fit: 0.043, exp: 2334.00, pred: 1210.169, error:1714.0389427135808]acc: 0.0\n", + "Cond:#..FO#.# - Act:5 - effect:........ - Num:1 [fit: 0.010, exp: 108.00, pred: 596.498, error:2068.6618017569153]acc: 0.0\n", + "Cond:#..FOO.. - Act:5 - effect:O....F.. - Num:2 [fit: 0.027, exp: 108.00, pred: 596.498, error:2210.3366856089747]acc: 0.0\n", + "Cond:.#.##O#O - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#OO###.. - Act:2 - effect:.......O - Num:3 [fit: 0.008, exp: 17.00, pred: 640.363, error:5931.649484248948]acc: 0.0\n", + "Cond:###FO#.# - Act:4 - effect:.O...... - Num:4 [fit: 0.027, exp: 116.00, pred: 488.760, error:3869.797046761752]acc: 0.0\n", + "Cond:..##.O.# - Act:5 - effect:......O. - Num:1 [fit: 0.003, exp: 0.00, pred: 1385.514, error:457.9784365998413]acc: 0\n", + "Cond:.#....#. - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 25.00, pred: 637.482, error:5551.218701334168]acc: 0.04\n", + "Cond:#####O#F - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 104.00, pred: 591.861, error:5334.329935574037]acc: 0.0\n", + "Cond:###.O#.. - Act:5 - effect:......O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:####.#OO - Act:6 - effect:.O...... - Num:1 [fit: 0.002, exp: 68.00, pred: 373.857, error:3881.6822917453333]acc: 0.0\n", + "Cond:.######O - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 117.00, pred: 336.932, error:5364.967631546808]acc: 0.017094017094017096\n", + "Cond:.#####.F - Act:5 - effect:......O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1423.306, error:938.5080153743152]acc: 0\n", + "Cond:#O...#.# - Act:2 - effect:...O.... - Num:2 [fit: 0.002, exp: 22.00, pred: 364.071, error:5955.988703519521]acc: 0.13636363636363635\n", + "Cond:#...#.#. - Act:2 - effect:.......O - Num:1 [fit: 0.007, exp: 0.00, pred: 1254.420, error:883.9785429329164]acc: 0\n", + "Cond:##OO...# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 25.00, pred: 511.057, error:7159.913096518207]acc: 0.0\n", + "Cond:####.O## - Act:5 - effect:.O...... - Num:1 [fit: 0.005, exp: 180.00, pred: 575.159, error:5615.660009462372]acc: 0.0\n", + "Cond:###.#### - Act:5 - effect:....FO.. - Num:4 [fit: 0.001, exp: 1285.00, pred: 1582.586, error:4047.1964077835983]acc: 0.11673151750972763\n", + "Cond:..#####. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 663.00, pred: 1501.499, error:2541.895310687191]acc: 0.09502262443438914\n", + "Cond:###F#### - Act:4 - effect:.O...... - Num:3 [fit: 0.008, exp: 112.00, pred: 488.760, error:3644.2827655667184]acc: 0.0\n", + "Cond:##.##O#. - Act:5 - effect:.O...... - Num:1 [fit: 0.010, exp: 340.00, pred: 597.351, error:1870.2967593637395]acc: 0.0\n", + "Cond:#O#O#### - Act:2 - effect:.......O - Num:1 [fit: 0.003, exp: 9.00, pred: 674.217, error:4891.290796572414]acc: 0.0\n", + "Cond:#.#..##O - Act:1 - effect:.....F.. - Num:3 [fit: 0.003, exp: 24.00, pred: 352.798, error:8569.491524636658]acc: 0.0\n", + "Cond:#....... - Act:7 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1049.813, error:538.7368909740012]acc: 0\n", + "Cond:..###OO# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 22.00, pred: 811.207, error:9792.035938324832]acc: 0.0\n", + "Cond:#OOO..## - Act:0 - effect:O..O.... - Num:3 [fit: 0.003, exp: 9.00, pred: 420.011, error:7445.55052754914]acc: 0.0\n", + "Cond:.#OO.### - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 22.00, pred: 513.862, error:7048.307883108745]acc: 0.0\n", + "Cond:#.####O. - Act:6 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 0.00, pred: 942.618, error:718.766108231561]acc: 0\n", + "Cond:O..O##.# - Act:1 - effect:.....F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1170.649, error:123.62884893330869]acc: 0\n", + "Cond:..O..O## - Act:1 - effect:.....F.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1008.226, error:779.4421561141331]acc: 0\n", + "Cond:##O##... - Act:1 - effect:OO.O...O - Num:2 [fit: 0.000, exp: 19.00, pred: 301.738, error:5599.2198561562145]acc: 0.0\n", + "Cond:#.#OO##. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 24.00, pred: 315.988, error:6647.986375771467]acc: 0.041666666666666664\n", + "Cond:##.#..#O - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 230.00, pred: 311.393, error:3969.132861207423]acc: 0.0\n", + "Cond:..O..### - Act:5 - effect:......O. - Num:1 [fit: 0.014, exp: 0.00, pred: 876.723, error:421.45930910212934]acc: 0\n", + "Cond:....#.#O - Act:2 - effect:.......O - Num:2 [fit: 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"Cond:..###OO. - Act:6 - effect:.....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 1001.471, error:1389.208260695104]acc: 0\n", + "Cond:.##.#..# - Act:1 - effect:.O...... - Num:6 [fit: 0.046, exp: 41.00, pred: 277.005, error:5362.820858610951]acc: 0.5365853658536586\n", + "Cond:#.#.###F - Act:0 - effect:..O..O.. - Num:5 [fit: 0.002, exp: 21.00, pred: 810.828, error:10688.920098385375]acc: 0.0\n", + "Cond:.###.O.. - Act:5 - effect:......O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1021.983, error:410.669088037938]acc: 0\n", + "Cond:..#.##.. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 20.00, pred: 429.482, error:7655.435370677475]acc: 0.0\n", + "Cond:#.OO.... - Act:2 - effect:...O.... - Num:1 [fit: 0.003, exp: 5.00, pred: 483.574, error:5209.847874209787]acc: 0.0\n", + "Cond:##.OO##. - Act:4 - effect:.O...... - Num:1 [fit: 0.006, exp: 30.00, pred: 608.991, error:6046.540885747135]acc: 0.0\n", + "Cond:#.#OO... - Act:4 - effect:.O...... - Num:2 [fit: 0.019, exp: 30.00, pred: 608.991, error:4968.915880237747]acc: 0.0\n", + "Cond:##..##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 84.00, pred: 501.213, error:7321.051773682124]acc: 0.0\n", + "Cond:###F#..# - Act:3 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 957.493, error:662.954673892797]acc: 0\n", + "Cond:.O#..### - Act:1 - effect:.O...... - Num:1 [fit: 0.004, exp: 35.00, pred: 290.221, error:4467.559677450754]acc: 0.5714285714285714\n", + "Cond:O#..##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 39.00, pred: 497.818, error:7250.177165025325]acc: 0.0\n", + "Cond:#.#.##.. - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 17.00, pred: 418.873, error:8132.343727729604]acc: 0.0\n", + "Cond:#.###..# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 218.00, pred: 481.374, error:5022.566696835398]acc: 0.3211009174311927\n", + "Cond:#...##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 47.00, pred: 565.915, error:7454.920115659406]acc: 0.0\n", + "Cond:##O#.### - Act:2 - effect:.......O - Num:1 [fit: 0.003, exp: 18.00, pred: 736.031, error:6617.280601435991]acc: 0.0\n", + "Cond:#..O.### - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 79.00, pred: 544.878, error:3856.090012953712]acc: 0.7215189873417721\n", + "Cond:..###O#F - Act:6 - effect:.F.O...O - Num:1 [fit: 0.013, exp: 99.00, pred: 278.127, error:2368.3938326927264]acc: 0.0\n", + "Cond:.#####OF - Act:5 - effect:.F.O...O - Num:1 [fit: 0.005, exp: 103.00, pred: 591.861, error:6327.55686961147]acc: 0.0\n", + "Cond:..#.##.# - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 19.00, pred: 398.185, error:7918.390553515605]acc: 0.0\n", + "Cond:##.#FO## - Act:5 - effect:....FO.. - Num:1 [fit: 0.020, exp: 154.00, pred: 496.703, error:4442.491574836091]acc: 0.6233766233766234\n", + "Cond:..#O..#. - Act:1 - effect:OO.O.... - Num:1 [fit: 0.000, exp: 17.00, pred: 443.100, error:8756.975315099824]acc: 0.0\n", + "Cond:..##.#F. - Act:2 - effect:...O..O. - Num:1 [fit: 0.000, exp: 22.00, pred: 755.918, error:7761.244310375663]acc: 0.0\n", + "Cond:##.#.OOF - Act:3 - effect:......O. - Num:1 [fit: 0.000, exp: 22.00, pred: 440.994, error:9643.159346139673]acc: 0.0\n", + "Cond:.....#.. - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 22.00, pred: 746.197, error:10556.498193661186]acc: 0.045454545454545456\n", + "Cond:#.###### - Act:7 - effect:.O...... - Num:2 [fit: 0.001, exp: 1015.00, pred: 1343.672, error:2075.5516091641402]acc: 0.008866995073891626\n", + "Cond:#...#O## - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 315.00, pred: 676.251, error:6653.3806006171835]acc: 0.3238095238095238\n", + "Cond:###F###. - Act:4 - effect:.O...... - Num:2 [fit: 0.012, exp: 104.00, pred: 488.760, error:3572.8135521330214]acc: 0.0\n", + "Cond:#.#.#O.. - Act:2 - effect:OO...... - Num:1 [fit: 0.000, exp: 22.00, pred: 888.344, error:9888.334146479247]acc: 0.0\n", + "Cond:.#.#FO.. - Act:0 - effect:.......O - Num:2 [fit: 0.003, exp: 8.00, pred: 428.207, error:6101.589986358933]acc: 0.0\n", + "Cond:O#.O###. - Act:5 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1182.514, error:407.1922349318695]acc: 0\n", + "Cond:#####O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.007, exp: 316.00, pred: 597.351, error:2495.189789320173]acc: 0.22468354430379747\n", + "Cond:###..F.O - Act:7 - effect:.......O - Num:1 [fit: 0.002, exp: 0.00, pred: 943.947, error:605.5508845908984]acc: 0\n", + "Cond:#..##O.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.018, exp: 243.00, pred: 597.351, error:768.5579449698157]acc: 0.42386831275720166\n", + "Cond:###F##.# - Act:4 - effect:.O...... - Num:3 [fit: 0.019, exp: 102.00, pred: 488.760, error:3643.7199522565293]acc: 0.0\n", + "Cond:O##...## - Act:6 - effect:O....O.O - Num:2 [fit: 0.001, exp: 172.00, pred: 284.895, error:4344.240101852718]acc: 0.0\n", + "Cond:.##...## - Act:1 - effect:.O...... - Num:1 [fit: 0.005, exp: 26.00, pred: 269.621, error:5324.595181816107]acc: 0.4230769230769231\n", + "Cond:.O#..##. - Act:1 - effect:.O...... - Num:1 [fit: 0.015, exp: 21.00, pred: 287.200, error:4207.58234537588]acc: 0.5238095238095238\n", + "Cond:#.O#.### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 22.00, pred: 396.058, error:6367.178997922822]acc: 0.0\n", + "Cond:##..##FF - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 793.315, error:623.7827062017626]acc: 0\n", + "Cond:###O###. - Act:4 - effect:.O...... - Num:2 [fit: 0.001, exp: 88.00, pred: 743.895, error:6562.428185488546]acc: 0.2159090909090909\n", + "Cond:.####### - Act:5 - effect:.O...... - Num:22 [fit: 0.006, exp: 1687.00, pred: 1206.937, error:2163.408786861434]acc: 0.004149377593360996\n", + "Cond:....#F.# - Act:7 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 23.00, pred: 793.041, error:9187.295147820674]acc: 0.0\n", + "Cond:O#..#..# - Act:0 - effect:O..O.... - Num:2 [fit: 0.001, exp: 36.00, pred: 497.740, error:7184.502136854692]acc: 0.0\n", + "Cond:O.##.### - Act:3 - effect:...OO... - Num:3 [fit: 0.004, exp: 9.00, pred: 390.022, error:7953.8481713361525]acc: 0.0\n", + "Cond:##.##F.. - Act:1 - effect:.F...F.. - Num:2 [fit: 0.004, exp: 6.00, pred: 271.122, error:6137.160101761207]acc: 0.0\n", + "Cond:#OO..### - Act:6 - effect:.......O - Num:3 [fit: 0.009, exp: 16.00, pred: 547.587, error:8464.960499984114]acc: 0.125\n", + "Cond:O...#### - Act:7 - effect:O..O.... - Num:1 [fit: 0.008, exp: 22.00, pred: 401.037, error:4490.697494662628]acc: 0.0\n", + "Cond:.O.##O.. - Act:7 - effect:....O..O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:##.O#### - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.002, exp: 41.00, pred: 622.380, error:7228.292953677383]acc: 0.0\n", + "Cond:#..O##.# - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 147.00, pred: 512.640, error:4423.341646539215]acc: 0.4217687074829932\n", + "Cond:###O#### - Act:4 - effect:.O...... - Num:3 [fit: 0.000, exp: 85.00, pred: 743.895, error:6787.203658618655]acc: 0.2235294117647059\n", + "Cond:#.O.###. - Act:7 - effect:...O.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1023.067, error:1162.5038570294116]acc: 0\n", + "Cond:.#O#.### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 56.00, pred: 406.105, error:9301.344391667684]acc: 0.0\n", + "Cond:O###.##. - Act:3 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 50.00, pred: 673.006, error:9213.482043637343]acc: 0.0\n", + "Cond:..#..#F. - Act:1 - effect:..F..... - Num:1 [fit: 0.004, exp: 17.00, pred: 377.319, error:7221.28743867438]acc: 0.0\n", + "Cond:#.OO.##. - Act:0 - effect:OO..O... - Num:1 [fit: 0.002, exp: 10.00, pred: 628.857, error:8639.076983377097]acc: 0.0\n", + "Cond:..#..O#. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 19.00, pred: 372.623, error:9617.240457646203]acc: 0.0\n", + "Cond:O#.####. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.020, exp: 7.00, pred: 295.142, error:5264.487144757223]acc: 0.0\n", + "Cond:###.O### - Act:4 - effect:.O...... - Num:1 [fit: 0.006, exp: 0.00, pred: 899.875, error:734.1128744461657]acc: 0\n", + "Cond:.####..# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 268.00, pred: 477.463, error:6728.467761293084]acc: 0.291044776119403\n", + "Cond:OO.##..# - Act:7 - effect:......O. - Num:1 [fit: 0.003, exp: 20.00, pred: 471.381, error:8601.810212912911]acc: 0.0\n", + "Cond:##O.#.#. - Act:7 - effect:.......O - Num:1 [fit: 0.012, exp: 2.00, pred: 394.415, error:2980.7332048225508]acc: 0.0\n", + "Cond:#.OO.#.. - Act:2 - effect:.......O - Num:1 [fit: 0.005, exp: 5.00, pred: 483.574, error:5249.847874209787]acc: 0.0\n", + "Cond:#..#.O## - Act:5 - effect:..O....O - Num:1 [fit: 0.005, exp: 163.00, pred: 575.159, error:5907.932418780404]acc: 0.0\n", + "Cond:.#####.# - Act:5 - effect:....FO.. - Num:2 [fit: 0.001, exp: 1410.00, pred: 1206.758, error:1832.3391111770547]acc: 0.15319148936170213\n", + "Cond:..###O## - Act:4 - effect:.......O - Num:2 [fit: 0.000, exp: 268.00, pred: 591.450, error:6135.422404710654]acc: 0.15298507462686567\n", + "Cond:..O..O## - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#O###.# - Act:3 - effect:...O.... - Num:1 [fit: 0.006, exp: 65.00, pred: 299.353, error:3399.8632238621312]acc: 0.03076923076923077\n", + "Cond:....##OF - Act:1 - effect:...OOO.. - Num:1 [fit: 0.000, exp: 35.00, pred: 315.648, error:8346.192379523967]acc: 0.0\n", + "Cond:#.#.O#.# - Act:5 - effect:..O....O - Num:1 [fit: 0.001, exp: 0.00, pred: 860.468, error:1193.2309308825807]acc: 0\n", + "Cond:.##..#.. - Act:0 - effect:O..O.... - Num:2 [fit: 0.014, exp: 14.00, pred: 362.104, error:6195.293094330063]acc: 0.0\n", + "Cond:#.###O## - Act:5 - effect:....FO.. - Num:3 [fit: 0.021, exp: 404.00, pred: 715.928, error:4564.867446431187]acc: 0.04950495049504951\n", + "Cond:#OO#.##. - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 47.00, pred: 630.663, error:6253.572662395977]acc: 0.02127659574468085\n", + "Cond:#O#O###. - Act:4 - effect:.O...... - Num:2 [fit: 0.040, exp: 23.00, pred: 682.470, error:4004.743693169525]acc: 0.8260869565217391\n", + "Cond:.#.####O - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 27.00, pred: 300.968, error:4130.728302811267]acc: 0.2222222222222222\n", + "Cond:..O##O#F - Act:1 - effect:......O. - Num:1 [fit: 0.000, exp: 0.00, pred: 787.871, error:906.996416750163]acc: 0\n", + "Cond:O####... - Act:3 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 47.00, pred: 673.006, error:9606.760246992913]acc: 0.0\n", + "Cond:#...O### - Act:5 - effect:..O....O - Num:1 [fit: 0.000, exp: 0.00, pred: 999.128, error:1314.4362254009297]acc: 0\n", + "Cond:##OO###. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 15.00, pred: 510.292, error:6228.342815664939]acc: 0.0\n", + "Cond:.##.#O## - Act:5 - effect:....FO.. - Num:2 [fit: 0.005, exp: 300.00, pred: 676.251, error:7102.905171768876]acc: 0.15\n", + "Cond:.#.#...# - Act:3 - effect:...O.... - Num:2 [fit: 0.000, exp: 143.00, pred: 552.013, error:7345.168721930051]acc: 0.5034965034965035\n", + "Cond:.O.###O# - Act:3 - effect:...O.... - Num:1 [fit: 0.006, exp: 0.00, pred: 996.030, error:287.5143249372769]acc: 0\n", + "Cond:####.##F - Act:6 - effect:.......O - Num:1 [fit: 0.009, exp: 99.00, pred: 278.127, error:893.2510474953638]acc: 0.0\n", + "Cond:##.#.#O# - Act:6 - effect:FOO....O - Num:1 [fit: 0.002, exp: 160.00, pred: 373.857, error:2945.569533035551]acc: 0.0\n", + "Cond:##.###.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.000, exp: 75.00, pred: 490.912, error:8215.341771104793]acc: 0.0\n", + "Cond:..##.#F. - Act:1 - effect:..F..... - Num:3 [fit: 0.013, exp: 13.00, pred: 375.287, error:7346.92204780589]acc: 0.0\n", + "Cond:...#.#F# - Act:1 - effect:..F..... - Num:1 [fit: 0.014, exp: 13.00, pred: 375.287, error:7056.721983805889]acc: 0.0\n", + "Cond:.##.#.O# - Act:3 - effect:...O.... - Num:3 [fit: 0.015, exp: 14.00, pred: 580.603, error:8183.555262193819]acc: 0.2857142857142857\n", + "Cond:.#..##F# - Act:0 - effect:..O..O.. - Num:2 [fit: 0.005, exp: 14.00, pred: 386.022, error:9720.764899560123]acc: 0.0\n", + "Cond:.##..#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.005, exp: 14.00, pred: 386.022, error:9005.008335080121]acc: 0.0\n", + "Cond:.##.F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 149.00, pred: 496.703, error:4383.3117407324735]acc: 0.8859060402684564\n", + "Cond:.#.....O - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 5.00, pred: 461.863, error:5492.662438411722]acc: 0.0\n", + "Cond:O##O#.#. - Act:5 - effect:........ - Num:1 [fit: 0.002, exp: 0.00, pred: 934.723, error:1297.2513999816692]acc: 0\n", + "Cond:..###..# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 21.00, pred: 519.476, error:8847.729912412618]acc: 0.0\n", + "Cond:.#.#..#. - Act:2 - effect:.OO..... - Num:1 [fit: 0.000, exp: 23.00, pred: 460.774, error:4172.713641162449]acc: 0.043478260869565216\n", + "Cond:.O.FO#.. - Act:7 - effect:FOO....O - Num:1 [fit: 0.000, exp: 0.00, pred: 981.285, error:1552.166260721175]acc: 0\n", + "Cond:..##.### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 275.00, pred: 1653.047, error:8179.074374008822]acc: 0.12363636363636364\n", + "Cond:.#.##### - Act:4 - effect:..O.OO.. - Num:2 [fit: 0.000, exp: 431.00, pred: 1479.746, error:7384.564715543683]acc: 0.0069605568445475635\n", + "Cond:#.....#O - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 20.00, pred: 385.409, error:8218.715837919914]acc: 0.0\n", + "Cond:##..#O.. - Act:2 - effect:OO...... - Num:1 [fit: 0.003, exp: 17.00, pred: 905.563, error:9914.426924782434]acc: 0.0\n", + "Cond:##.##### - Act:5 - effect:........ - Num:9 [fit: 0.002, exp: 1624.00, pred: 1320.994, error:2613.878512108236]acc: 0.014778325123152709\n", + "Cond:####..#F - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#..#O## - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 297.00, pred: 676.251, error:6583.786001041673]acc: 0.3468013468013468\n", + "Cond:...FO.## - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.004, exp: 0.00, pred: 1503.755, error:530.8077017923646]acc: 0\n", + "Cond:##O.#.#. - Act:0 - effect:.O...... - Num:1 [fit: 0.002, exp: 6.00, pred: 378.027, error:6958.899050422108]acc: 0.0\n", + "Cond:#...#F#. - Act:2 - effect:...O.... - Num:1 [fit: 0.002, exp: 12.00, pred: 735.276, error:8662.148593033493]acc: 0.0\n", + "Cond:.##O.#OF - Act:6 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#O..#O#. - Act:4 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#O##... - Act:1 - effect:OO.O...O - Num:1 [fit: 0.008, exp: 17.00, pred: 302.315, error:5848.835933227193]acc: 0.0\n", + "Cond:.#.##... - Act:2 - effect:OO...... - Num:1 [fit: 0.000, exp: 33.00, pred: 538.321, error:3848.7563493506855]acc: 0.24242424242424243\n", + "Cond:##..#..# - Act:0 - effect:O..O.... - Num:2 [fit: 0.002, exp: 47.00, pred: 504.190, error:7603.740006362076]acc: 0.0\n", + "Cond:#O..#O.. - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1021.076, error:160.06554608447567]acc: 0\n", + "Cond:.##..O#. - Act:0 - effect:..O..O.. - Num:2 [fit: 0.002, exp: 11.00, pred: 357.769, error:8633.561988580816]acc: 0.0\n", + "Cond:.#.##.#. - Act:2 - effect:OO...... - Num:1 [fit: 0.000, exp: 32.00, pred: 538.248, error:3563.93608336706]acc: 0.125\n", + "Cond:###.#O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.034, exp: 218.00, pred: 483.016, error:4700.323736154113]acc: 0.44495412844036697\n", + "Cond:.##..O#F - Act:5 - effect:.O...... - Num:3 [fit: 0.016, exp: 77.00, pred: 591.861, error:5854.14559437308]acc: 0.0\n", + "Cond:..O#..## - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 18.00, pred: 396.939, error:7002.051698185215]acc: 0.0\n", + "Cond:O#.###.. - Act:0 - effect:O..O.... - Num:1 [fit: 0.021, exp: 7.00, pred: 295.142, error:4874.087144757223]acc: 0.0\n", + "Cond:.....F## - Act:7 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 21.00, pred: 797.849, error:9287.086527396166]acc: 0.0\n", + "Cond:.O..F### - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1221.548, error:554.6220856208612]acc: 0\n", + "Cond:.#...O.# - Act:5 - effect:..O....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1631.061, error:297.0688674537016]acc: 0\n", + "Cond:....##.O - Act:7 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 9.00, pred: 344.626, error:4667.656188194025]acc: 0.0\n", + "Cond:....O##. - Act:3 - effect:....OO.F - Num:1 [fit: 0.000, exp: 0.00, pred: 1444.239, error:637.1258636088164]acc: 0\n", + "Cond:.##...O# - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.003, exp: 18.00, pred: 338.679, error:3574.3694669696256]acc: 0.0\n", + "Cond:.O#..##. - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 13.00, pred: 388.276, error:8530.86294394073]acc: 0.0\n", + "Cond:O..##### - Act:2 - effect:.O.OF..O - Num:1 [fit: 0.008, exp: 3.00, pred: 314.479, error:3603.3674673742876]acc: 0.0\n", + "Cond:.#.###.# - Act:2 - effect:.O...... - Num:1 [fit: 0.000, exp: 57.00, pred: 760.164, error:5786.234266702058]acc: 0.10526315789473684\n", + "Cond:..OO##.F - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1659.907, error:1009.4067550339951]acc: 0\n", + "Cond:....F..O - Act:5 - effect:.OO..... - Num:1 [fit: 0.009, exp: 0.00, pred: 1080.827, error:426.79126173403404]acc: 0\n", + "Cond:.#..F#.# - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 149.00, pred: 496.703, error:4111.311692272174]acc: 0.6845637583892618\n", + "Cond:#.O#.### - Act:2 - effect:........ - Num:1 [fit: 0.005, exp: 5.00, pred: 483.574, error:4769.847874209787]acc: 0.0\n", + "Cond:.#..#... - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 11.00, pred: 387.500, error:8414.544477878724]acc: 0.0\n", + "Cond:...#FO.# - Act:1 - effect:....FO.. - Num:6 [fit: 0.022, exp: 36.00, pred: 353.044, error:5539.574500283724]acc: 0.7222222222222222\n", + "Cond:.#..#O.# - Act:2 - effect:..O..FF. - Num:1 [fit: 0.002, exp: 8.00, pred: 775.557, error:8347.69055930361]acc: 0.0\n", + "Cond:#..###.. - Act:2 - effect:OO...... - Num:1 [fit: 0.001, exp: 40.00, pred: 765.035, error:5626.406735164864]acc: 0.2\n", + "Cond:.#...O#. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 9.00, pred: 338.183, error:8116.758128362655]acc: 0.0\n", + "Cond:#O.###.# - Act:2 - effect:.....F.. - Num:2 [fit: 0.000, exp: 17.00, pred: 360.421, error:7929.978367977126]acc: 0.0\n", + "Cond:..#O.### - Act:2 - effect:........ - Num:1 [fit: 0.000, exp: 18.00, pred: 490.781, error:6147.092872631598]acc: 0.0\n", + "Cond:....O### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#..##O.. - Act:2 - effect:OO...... - Num:1 [fit: 0.004, exp: 8.00, pred: 758.366, error:7965.867722013965]acc: 0.0\n", + "Cond:.#.###.. - Act:2 - effect:.O...... - Num:3 [fit: 0.002, exp: 45.00, pred: 761.974, error:5328.024100964356]acc: 0.15555555555555556\n", + "Cond:.###.#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.005, exp: 8.00, pred: 322.173, error:7982.162480079689]acc: 0.0\n", + "Cond:...#..#O - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 11.00, pred: 403.655, error:7054.291908154859]acc: 0.09090909090909091\n", + "Cond:#...FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.016, exp: 26.00, pred: 353.516, error:5174.758692774016]acc: 0.6923076923076923\n", + "Cond:.##..#.# - Act:1 - effect:...O.... - Num:4 [fit: 0.004, exp: 28.00, pred: 249.354, error:6660.6000231897115]acc: 0.03571428571428571\n", + "Cond:##.#.... - Act:0 - effect:O..O.... - Num:1 [fit: 0.004, exp: 14.00, pred: 266.731, error:7144.291419714787]acc: 0.0\n", + "Cond:.##..#.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 17.00, pred: 385.693, error:6466.448170716505]acc: 0.0\n", + "Cond:##O###.. - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 16.00, pred: 734.013, error:5789.181318241539]acc: 0.0\n", + "Cond:#OO##... - Act:2 - effect:.......O - Num:1 [fit: 0.018, exp: 11.00, pred: 618.754, error:5103.286795335611]acc: 0.0\n", + "Cond:#O#..O## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 841.818, error:1112.50008423622]acc: 0\n", + "Cond:#.#.#OO# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 15.00, pred: 445.767, error:8807.30927092852]acc: 0.0\n", + "Cond:.##.##.. - Act:2 - effect:.O...... - Num:1 [fit: 0.000, exp: 24.00, pred: 802.261, error:7499.688391408275]acc: 0.0\n", + "Cond:..##O#O# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1197.896, error:788.1111123008943]acc: 0\n", + "Cond:#..#F#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.027, exp: 25.00, pred: 352.897, error:5425.191227136094]acc: 0.68\n", + "Cond:##O#..#. - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 19.00, pred: 478.997, error:6434.270032392207]acc: 0.0\n", + "Cond:#OOO...# - Act:0 - effect:O..O.... - Num:1 [fit: 0.006, exp: 5.00, pred: 319.336, error:4782.484491451696]acc: 0.0\n", + "Cond:.....#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.004, exp: 7.00, pred: 309.722, error:7005.050361913452]acc: 0.0\n", + "Cond:.#.OO... - Act:7 - effect:....O..O - Num:2 [fit: 0.000, exp: 21.00, pred: 316.518, error:6164.649007456267]acc: 0.0\n", + "Cond:.#....#. - Act:2 - effect:...O.... - Num:1 [fit: 0.002, exp: 5.00, pred: 326.296, error:4219.765375790463]acc: 0.2\n", + "Cond:O####O#. - Act:3 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:##..FO.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.041, exp: 19.00, pred: 353.753, error:6195.988236233408]acc: 0.5789473684210527\n", + "Cond:##.#...O - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 33.00, pred: 388.045, error:6479.957717130735]acc: 0.15151515151515152\n", + "Cond:#....#.# - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 32.00, pred: 340.758, error:6122.56362511412]acc: 0.15625\n", + "Cond:O#...#.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.002, exp: 32.00, pred: 497.659, error:6978.106079872781]acc: 0.0\n", + "Cond:O...#.## - Act:2 - effect:.O....OO - Num:1 [fit: 0.010, exp: 2.00, pred: 233.216, error:2706.9458102429157]acc: 0.0\n", + "Cond:O#...##O - Act:2 - effect:.....O.. - Num:1 [fit: 0.002, exp: 7.00, pred: 348.461, error:7575.496963640455]acc: 0.0\n", + "Cond:.OO#.### - Act:6 - effect:.......O - Num:1 [fit: 0.003, exp: 22.00, pred: 660.479, error:8532.059353393699]acc: 0.045454545454545456\n", + "Cond:##.#.F.# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 30.00, pred: 596.760, error:10532.493611895115]acc: 0.0\n", + "Cond:.#O##... - Act:3 - effect:.OO..OO. - Num:6 [fit: 0.019, exp: 59.00, pred: 299.353, error:2988.393877525843]acc: 0.0\n", + "Cond:...#.#.# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.002, exp: 8.00, pred: 422.564, error:5614.700362304513]acc: 0.0\n", + "Cond:.##O#.## - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 49.00, pred: 301.014, error:6658.557837769128]acc: 0.40816326530612246\n", + "Cond:O#...#.# - Act:2 - effect:.....O.. - Num:1 [fit: 0.003, exp: 11.00, pred: 350.940, error:7092.818708623942]acc: 0.0\n", + "Cond:#O.##.## - Act:2 - effect:...O.... - Num:2 [fit: 0.015, exp: 14.00, pred: 355.839, error:5452.945129336869]acc: 0.07142857142857142\n", + "Cond:OO###..# - Act:1 - effect:.....O.. - Num:2 [fit: 0.259, exp: 4.00, pred: 212.203, error:2954.800967022076]acc: 0.0\n", + "Cond:.O#.##.. - Act:1 - effect:..O.OO.. - Num:1 [fit: 0.008, exp: 18.00, pred: 286.523, error:4699.02858282333]acc: 0.0\n", + "Cond:....#.#. - Act:0 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 0.00, pred: 563.315, error:1093.333466498043]acc: 0\n", + "Cond:.##.#.#. - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 12.00, pred: 380.633, error:8124.322451598014]acc: 0.0\n", + "Cond:......#O - Act:2 - effect:OO...... - Num:2 [fit: 0.002, exp: 8.00, pred: 406.642, error:4598.827279185254]acc: 0.25\n", + "Cond:#..##### - Act:2 - effect:OO...... - Num:4 [fit: 0.003, exp: 52.00, pred: 760.583, error:6324.026647679455]acc: 0.19230769230769232\n", + "Cond:##..#.## - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 78.00, pred: 492.038, error:6629.52454864763]acc: 0.0\n", + "Cond:.######O - Act:1 - effect:.......O - Num:1 [fit: 0.010, exp: 4.00, pred: 328.348, error:4754.073320722432]acc: 0.0\n", + "Cond:#.#.#.#O - Act:1 - effect:.....F.. - Num:3 [fit: 0.006, exp: 8.00, pred: 322.639, error:7284.331142732702]acc: 0.0\n", + "Cond:#..#..#O - Act:1 - effect:..O...F. - Num:1 [fit: 0.002, exp: 8.00, pred: 322.639, error:6770.731142732701]acc: 0.0\n", + "Cond:#OO#.#.# - Act:6 - effect:..O..O.. - Num:1 [fit: 0.004, exp: 19.00, pred: 658.141, error:9575.554664369201]acc: 0.0\n", + "Cond:.#.##.O# - Act:3 - effect:...O.... - Num:1 [fit: 0.004, exp: 9.00, pred: 606.196, error:8146.629432549256]acc: 0.2222222222222222\n", + "Cond:.#####.. - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 282.00, pred: 771.025, error:4754.648434958876]acc: 0.0673758865248227\n", + "Cond:#O#.##.# - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.000, exp: 126.00, pred: 1346.367, error:3408.902814211071]acc: 0.0\n", + "Cond:##.###.. - Act:2 - effect:OO...... - Num:2 [fit: 0.002, exp: 42.00, pred: 761.870, error:6066.864712613375]acc: 0.21428571428571427\n", + "Cond:#..###.# - Act:2 - effect:OO...... - Num:2 [fit: 0.001, exp: 42.00, pred: 762.474, error:5305.9131113819]acc: 0.23809523809523808\n", + "Cond:.##F###. - Act:4 - effect:.......O - Num:1 [fit: 0.005, exp: 100.00, pred: 488.760, error:3777.5534281358823]acc: 0.0\n", + "Cond:.######. - Act:5 - effect:....FO.. - Num:1 [fit: 0.001, exp: 1206.00, pred: 1206.728, error:1646.225456448609]acc: 0.17827529021558872\n", + "Cond:#...#.## - Act:2 - effect:O..O.... - Num:2 [fit: 0.003, exp: 9.00, pred: 365.875, error:6738.529417947816]acc: 0.0\n", + "Cond:.###.##O - Act:0 - effect:.O...... - Num:2 [fit: 0.002, exp: 41.00, pred: 328.063, error:4181.158531457421]acc: 0.0\n", + "Cond:.#..#OOF - Act:1 - effect:...OOO.. - Num:1 [fit: 0.000, exp: 28.00, pred: 315.724, error:8640.014290323408]acc: 0.0\n", + "Cond:..#.#... - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 0.00, pred: 569.620, error:936.4555205331078]acc: 0\n", + "Cond:....#O#. - Act:1 - effect:....FO.. - Num:1 [fit: 0.000, exp: 24.00, pred: 292.652, error:8603.61728212125]acc: 0.041666666666666664\n", + "Cond:..##FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 10.00, pred: 341.562, error:4560.556699805004]acc: 0.3\n", + "Cond:.####O.. - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 130.00, pred: 1051.486, error:8304.801885327211]acc: 0.0\n", + "Cond:...FOO.. - Act:1 - effect:....OO.F - Num:3 [fit: 0.005, exp: 11.00, pred: 353.575, error:6222.790795805741]acc: 0.0\n", + "Cond:..####.# - Act:5 - effect:....FO.. - Num:3 [fit: 0.001, exp: 1084.00, pred: 1205.989, error:2010.6054677850102]acc: 0.16512915129151293\n", + "Cond:...#O#.# - Act:4 - effect:...O.... - Num:2 [fit: 0.004, exp: 102.00, pred: 653.345, error:4831.958339724557]acc: 0.0\n", + "Cond:#..O###. - Act:3 - effect:...O.... - Num:1 [fit: 0.005, exp: 98.00, pred: 512.640, error:4265.49446193219]acc: 0.5510204081632653\n", + "Cond:.#.#.... - Act:1 - effect:..F..... - Num:1 [fit: 0.007, exp: 18.00, pred: 388.235, error:7033.770579177785]acc: 0.0\n", + 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14.00, pred: 398.417, error:7735.99150520128]acc: 0.0\n", + "Cond:.###O.#. - Act:7 - effect:....O..O - Num:2 [fit: 0.014, exp: 12.00, pred: 297.731, error:7890.008744187779]acc: 0.0\n", + "Cond:.#.O###. - Act:3 - effect:...O.... - Num:2 [fit: 0.018, exp: 82.00, pred: 512.640, error:5046.879778936711]acc: 0.524390243902439\n", + "Cond:O#.####. - Act:3 - effect:...O.... - Num:3 [fit: 0.001, exp: 38.00, pred: 672.969, error:9102.347198350044]acc: 0.0\n", + "Cond:.#.#F#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.003, exp: 9.00, pred: 348.271, error:4975.818935275545]acc: 0.3333333333333333\n", + "Cond:O#.##.## - Act:2 - effect:.....O.. - Num:4 [fit: 0.053, exp: 8.00, pred: 333.724, error:5915.294428793219]acc: 0.0\n", + "Cond:#O.##..# - Act:2 - effect:...O.... - Num:2 [fit: 0.036, exp: 10.00, pred: 345.389, error:6084.648701783818]acc: 0.1\n", + "Cond:.#O..#.# - Act:1 - effect:...O.... - Num:2 [fit: 0.096, exp: 6.00, pred: 290.307, error:3797.0896929461996]acc: 0.0\n", + "Cond:#O#.O.#. - 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0.0\n", + "Cond:O..##.## - Act:0 - effect:O..O.... - Num:3 [fit: 0.045, exp: 16.00, pred: 570.990, error:6368.265216783599]acc: 0.0\n", + "Cond:.###...# - Act:1 - effect:...O.... - Num:2 [fit: 0.001, exp: 14.00, pred: 346.045, error:8240.256395765085]acc: 0.0\n", + "Cond:.#..##.F - Act:1 - effect:...OOO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 601.081, error:719.1799693328664]acc: 0\n", + "Cond:##....#O - Act:2 - effect:OO...... - Num:1 [fit: 0.003, exp: 9.00, pred: 360.132, error:5797.152688986372]acc: 0.2222222222222222\n", + "Cond:.###.#.. - Act:0 - effect:..O..O.. - Num:2 [fit: 0.002, exp: 16.00, pred: 494.539, error:7232.725623493198]acc: 0.0\n", + "Cond:##.#FO## - Act:3 - effect:...O.... - Num:3 [fit: 0.001, exp: 22.00, pred: 668.717, error:9815.728236010857]acc: 0.045454545454545456\n", + "Cond:.####O.F - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 852.141, error:333.6233280084333]acc: 0\n", + "Cond:.##F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 135.00, pred: 1431.491, error:5142.002156827013]acc: 0.5259259259259259\n", + "Cond:#..#FO## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 19.00, pred: 669.410, error:9155.030228919488]acc: 0.05263157894736842\n", + "Cond:###..### - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 782.00, pred: 1582.586, error:4594.8011443380365]acc: 0.0\n", + "Cond:..#O##F# - Act:6 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1154.507, error:314.3767441221915]acc: 0\n", + "Cond:.#.#..#. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 81.00, pred: 552.056, error:8858.927377431834]acc: 0.5802469135802469\n", + "Cond:######## - Act:6 - effect:...O.... - Num:4 [fit: 0.021, exp: 812.00, pred: 275.537, error:3396.2269400353703]acc: 0.06527093596059114\n", + "Cond:##.#F#F. - Act:3 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1114.205, error:1026.6930939616996]acc: 0\n", + "Cond:..#.#.## - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 22.00, pred: 300.309, error:4309.447124394454]acc: 0.0\n", + "Cond:#.#..OF. - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 15.00, pred: 572.702, error:8892.366517832546]acc: 0.0\n", + "Cond:##.##### - Act:6 - effect:.O...... - Num:3 [fit: 0.048, exp: 731.00, pred: 275.651, error:3680.330253216206]acc: 0.06155950752393981\n", + "Cond:.#####O. - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 768.152, error:393.26430400777997]acc: 0\n", + "Cond:.#.##OOF - Act:1 - effect:...OOO.. - Num:1 [fit: 0.001, exp: 23.00, pred: 315.944, error:8130.2429661707765]acc: 0.0\n", + "Cond:...##.## - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 63.00, pred: 657.384, error:5324.469114154322]acc: 0.5238095238095238\n", + "Cond:.####.O. - Act:3 - effect:...O.... - Num:1 [fit: 0.020, exp: 0.00, pred: 462.534, error:42.61522724851508]acc: 0\n", + "Cond:##.###.# - Act:6 - effect:.O...... - Num:1 [fit: 0.009, exp: 517.00, pred: 271.650, error:3985.427803416029]acc: 0.08897485493230174\n", + "Cond:.#..#O#. - 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error:1833.8170765267917]acc: 0.011002444987775062\n", + "Cond:.#.#.... - Act:0 - effect:...O.... - Num:1 [fit: 0.008, exp: 1.00, pred: 309.547, error:2000.0]acc: 0.0\n", + "Cond:...#..#. - Act:1 - effect:..F..... - Num:1 [fit: 0.004, exp: 4.00, pred: 418.213, error:5131.572679117815]acc: 0.0\n", + "Cond:##..#### - Act:6 - effect:...O.... - Num:3 [fit: 0.023, exp: 531.00, pred: 275.083, error:2845.202835512542]acc: 0.005649717514124294\n", + "Cond:.##F.##. - Act:7 - effect:...O.... - Num:2 [fit: 0.072, exp: 0.00, pred: 750.521, error:69.16038788515706]acc: 0\n", + "Cond:.O#F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.072, exp: 0.00, pred: 750.521, error:69.16038788515706]acc: 0\n", + "Cond:##....## - Act:6 - effect:.O...... - Num:1 [fit: 0.007, exp: 311.00, pred: 266.063, error:3510.7268650086294]acc: 0.1189710610932476\n", + "Cond:..#####O - Act:7 - effect:.O...... - Num:1 [fit: 0.001, exp: 22.00, pred: 300.309, error:3669.780263301504]acc: 0.045454545454545456\n", + "Cond:O..##O#. 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error:8047.045071643977]acc: 0.0\n", + "Cond:#O....## - Act:6 - effect:O....O.O - Num:2 [fit: 0.001, exp: 144.00, pred: 274.734, error:3071.561730561595]acc: 0.0\n", + "Cond:.######O - Act:7 - effect:.......O - Num:2 [fit: 0.001, exp: 22.00, pred: 300.309, error:4376.624530327978]acc: 0.22727272727272727\n", + "Cond:#######F - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 8.00, pred: 765.368, error:8384.089925643155]acc: 0.0\n", + "Cond:....O##. - Act:5 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 1290.835, error:321.8459485751058]acc: 0\n", + "Cond:..#O#O## - Act:6 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 775.342, error:374.7828915131181]acc: 0\n", + "Cond:######F# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 21.00, pred: 1177.061, error:8537.578097611564]acc: 0.0\n", + "Cond:##..#O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.025, exp: 126.00, pred: 483.016, error:4901.201995289743]acc: 0.5\n", + "Cond:#O...### - Act:6 - effect:O....O.O - Num:1 [fit: 0.000, exp: 136.00, pred: 274.734, error:4749.925607632031]acc: 0.0\n", + "Cond:.#.###F# - Act:5 - effect:.O...... - Num:1 [fit: 0.014, exp: 50.00, pred: 297.622, error:2581.70030422921]acc: 0.0\n", + "Cond:...#.OOF - Act:0 - effect:......O. - Num:2 [fit: 0.003, exp: 8.00, pred: 765.368, error:7583.769925643154]acc: 0.0\n", + "Cond:.#..#O.F - Act:1 - effect:...OOO.. - Num:2 [fit: 0.014, exp: 0.00, pred: 653.208, error:978.5352812008947]acc: 0\n", + "Cond:##..#O#F - Act:1 - effect:...OOO.. - Num:2 [fit: 0.003, exp: 11.00, pred: 299.938, error:7514.9296982767155]acc: 0.0\n", + "Cond:#.#####F - Act:6 - effect:...O.... - Num:1 [fit: 0.009, exp: 47.00, pred: 278.137, error:1298.5975146746619]acc: 0.0\n", + "Cond:..####OF - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.009, exp: 47.00, pred: 278.137, error:1493.718691537472]acc: 0.0\n", + "Cond:..####F# - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 11.00, pred: 1160.305, error:7760.0416130393805]acc: 0.0\n", + "Cond:O#.#O### - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 0.00, pred: 595.109, error:693.9974767453382]acc: 0\n", + "Cond:##...O#. - Act:0 - effect:....FO.. - Num:1 [fit: 0.015, exp: 5.00, pred: 284.376, error:5837.513036683071]acc: 0.0\n", + "Cond:.##.FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.005, exp: 68.00, pred: 496.703, error:4219.469126114722]acc: 0.8823529411764706\n", + "Cond:....##F. - Act:0 - effect:....FO.. - Num:1 [fit: 0.008, exp: 4.00, pred: 258.577, error:4517.218119971063]acc: 0.0\n", + "Cond:.#...O#F - Act:1 - effect:...OOO.. - Num:3 [fit: 0.011, exp: 9.00, pred: 279.804, error:6540.977102045524]acc: 0.0\n", + "Cond:##.##.## - Act:6 - effect:...O.... - Num:2 [fit: 0.013, exp: 345.00, pred: 266.294, error:3634.8916219078683]acc: 0.02608695652173913\n", + "Cond:.##F###. - Act:2 - effect:...O.... - Num:1 [fit: 0.024, exp: 0.00, pred: 608.111, error:849.7216848178907]acc: 0\n", + "Cond:##.#...# - Act:6 - effect:O....O.O - Num:2 [fit: 0.013, exp: 254.00, pred: 266.082, error:3679.4884268190917]acc: 0.0\n", + "Cond:#####.#. - Act:5 - effect:........ - Num:3 [fit: 0.001, exp: 819.00, pred: 1215.005, error:2294.985237649877]acc: 0.0\n", + "Cond:##O##.#. - Act:2 - effect:.......O - Num:6 [fit: 0.240, exp: 8.00, pred: 723.332, error:5444.782682022333]acc: 0.0\n", + "Cond:...O.#F# - Act:1 - effect:..F..... - Num:1 [fit: 0.003, exp: 0.00, pred: 482.812, error:701.0958461641058]acc: 0\n", + "Cond:#..#.OO# - Act:6 - effect:.O...... - Num:1 [fit: 0.014, exp: 27.00, pred: 277.677, error:2563.8288459991772]acc: 0.0\n", + "Cond:###O...# - Act:0 - effect:O..O.... - Num:4 [fit: 0.004, exp: 11.00, pred: 481.257, error:5812.148501554744]acc: 0.0\n", + "Cond:.#####.. - Act:0 - effect:..O..O.. - Num:2 [fit: 0.000, exp: 24.00, pred: 488.393, error:8129.269716786038]acc: 0.0\n", + "Cond:.###O..# - Act:7 - effect:....O..O - Num:1 [fit: 0.018, exp: 4.00, pred: 272.156, error:3976.0308854861705]acc: 0.0\n", + "Cond:.#.##... - Act:7 - effect:....O..O - Num:3 [fit: 0.000, exp: 21.00, pred: 284.451, error:7068.259949686054]acc: 0.0\n", + "Cond:#.##.#O# - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 8.00, pred: 471.303, error:7590.281594796087]acc: 0.0\n", + "Cond:#...#O## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 550.935, error:8173.272515728394]acc: 0.0\n", + "Cond:##O..#.# - Act:6 - effect:.O...... - Num:1 [fit: 0.003, exp: 8.00, pred: 522.883, error:8020.059834009292]acc: 0.0\n", + "Cond:.#####O# - Act:3 - effect:...O.... - Num:3 [fit: 0.005, exp: 7.00, pred: 468.021, error:7359.179029065638]acc: 0.0\n", + "Cond:.##.#.#. - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 18.00, pred: 340.837, error:5452.803823930583]acc: 0.2222222222222222\n", + "Cond:##.#FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.085, exp: 4.00, pred: 274.133, error:3787.8249570336934]acc: 0.5\n", + "Cond:.#..F#.# - Act:1 - effect:....FO.. - Num:3 [fit: 0.086, exp: 4.00, pred: 274.133, error:3087.8249570336934]acc: 0.5\n", + "Cond:...####. - Act:0 - effect:OO...... - Num:1 [fit: 0.000, exp: 16.00, pred: 304.327, error:6963.50601868045]acc: 0.0\n", + "Cond:...##.#. - Act:2 - effect:OO...... - Num:1 [fit: 0.012, exp: 15.00, pred: 554.276, error:3396.1824230415186]acc: 0.5333333333333333\n", + "Cond:...###.. - Act:2 - effect:OO...... - Num:3 [fit: 0.005, exp: 25.00, pred: 764.657, error:5942.8744599945485]acc: 0.32\n", + "Cond:##O#.#.# - Act:4 - effect:..O.OO.. - Num:3 [fit: 0.002, exp: 63.00, pred: 597.239, error:5357.799740204838]acc: 0.0\n", + "Cond:#OO#.#.. - Act:2 - effect:.......O - Num:1 [fit: 0.004, exp: 4.00, pred: 663.227, error:3888.002921669111]acc: 0.0\n", + "Cond:.OO##..# - Act:2 - effect:...O.... - Num:1 [fit: 0.013, exp: 4.00, pred: 663.227, error:4588.002921669111]acc: 0.0\n", + "Cond:.##O..#. - Act:1 - effect:.O...... - Num:1 [fit: 0.005, exp: 4.00, pred: 418.213, error:3331.572679117815]acc: 0.25\n", + "Cond:###....# - Act:6 - effect:.O...... - Num:2 [fit: 0.017, exp: 232.00, pred: 266.088, error:3905.5176590669953]acc: 0.16810344827586207\n", + "Cond:..###..# - Act:7 - effect:.O...... - Num:3 [fit: 0.006, exp: 18.00, pred: 259.401, error:5719.016882837783]acc: 0.1111111111111111\n", + "Cond:..##O.## - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 9.00, pred: 309.986, error:3710.0466784191676]acc: 0.1111111111111111\n", + "Cond:#.####.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.000, exp: 22.00, pred: 687.614, error:8330.900721585684]acc: 0.0\n", + "Cond:.#.F###. - Act:5 - effect:........ - Num:1 [fit: 0.012, exp: 70.00, pred: 596.498, error:1851.8274063047402]acc: 0.0\n", + "Cond:.####OOF - Act:5 - effect:........ - Num:1 [fit: 0.004, exp: 51.00, pred: 591.863, error:6721.233364844495]acc: 0.0\n", + "Cond:.....OO# - Act:5 - effect:........ - Num:1 [fit: 0.004, exp: 51.00, pred: 591.863, error:6373.29279005755]acc: 0.0\n", + "Cond:##..###F - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 7.00, pred: 746.118, error:7641.310595509452]acc: 0.0\n", + "Cond:.#..#O.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.064, exp: 3.00, pred: 265.654, error:3237.5346596303316]acc: 0.3333333333333333\n", + "Cond:#..#FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.064, exp: 3.00, pred: 265.654, error:2904.2013262969986]acc: 0.3333333333333333\n", + "Cond:##.##### - Act:4 - effect:.......O - Num:2 [fit: 0.000, exp: 487.00, pred: 1356.819, error:3682.393791142411]acc: 0.07597535934291581\n", + "Cond:###.#### - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 64.00, pred: 859.870, error:8312.177845336526]acc: 0.0\n", + "Cond:#.#..### - Act:1 - effect:.......O - Num:1 [fit: 0.001, exp: 14.00, pred: 251.094, error:5844.6802219438905]acc: 0.0\n", + "Cond:##..##.O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 8.00, pred: 549.792, error:5837.095124292826]acc: 0.0\n", + "Cond:##..#.## - Act:6 - effect:.O...... - Num:2 [fit: 0.007, exp: 281.00, pred: 266.063, error:3716.832887325856]acc: 0.1387900355871886\n", + "Cond:O###.### - Act:6 - effect:.O...... - Num:1 [fit: 0.001, exp: 142.00, pred: 284.895, error:4155.5472814130135]acc: 0.2746478873239437\n", + "Cond:....#O.# - Act:3 - effect:...O.... - Num:1 [fit: 0.003, exp: 6.00, pred: 660.289, error:6314.822394680403]acc: 0.0\n", + "Cond:##O#.#.O - Act:6 - effect:..O..O.. - Num:1 [fit: 0.006, exp: 0.00, pred: 356.791, error:940.6782499223368]acc: 0\n", + "Cond:..#..##O - Act:0 - effect:.......F - Num:1 [fit: 0.001, exp: 35.00, pred: 328.000, error:4439.122810545058]acc: 0.0\n", + "Cond:.#O#.#.. - Act:7 - effect:.OO..OO. - Num:2 [fit: 0.006, exp: 6.00, pred: 395.592, error:6380.48726795551]acc: 0.0\n", + "Cond:#.###.## - Act:2 - effect:O..O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 537.338, error:3977.8448535756547]acc: 0.0\n", + "Cond:#..###OF - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.018, exp: 23.00, pred: 278.213, error:1838.791156681884]acc: 0.0\n", + "Cond:####.OO# - Act:6 - effect:.O...... - Num:1 [fit: 0.019, exp: 23.00, pred: 278.213, error:708.4314532154426]acc: 0.0\n", + "Cond:#.###### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 113.00, pred: 627.264, error:6213.256063750292]acc: 0.1592920353982301\n", + "Cond:###.F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 40.00, pred: 496.683, error:4183.186324897397]acc: 0.875\n", + "Cond:.#..F### - Act:1 - effect:....FO.. - Num:1 [fit: 0.006, exp: 2.00, pred: 221.611, error:3702.4975696719002]acc: 0.0\n", + "Cond:#.#.##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 11.00, pred: 554.758, error:6800.672033110737]acc: 0.0\n", + "Cond:#....#.# - Act:3 - effect:.......O - Num:1 [fit: 0.002, exp: 11.00, pred: 405.809, error:6063.530977668383]acc: 0.2727272727272727\n", + "Cond:O#.##O## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:O#.###.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.001, exp: 8.00, pred: 484.619, error:5799.854745877408]acc: 0.0\n", + "Cond:##.##..# - Act:0 - effect:O..O.... - Num:2 [fit: 0.010, exp: 12.00, pred: 483.814, error:7824.5859311902805]acc: 0.0\n", + "Cond:...FOO#. - Act:1 - effect:....OO.F - Num:1 [fit: 0.017, exp: 9.00, pred: 364.277, error:6713.7791979318035]acc: 0.0\n", + "Cond:..###### - Act:0 - effect:.......F - Num:2 [fit: 0.000, exp: 54.00, pred: 862.033, error:8814.861816729974]acc: 0.0\n", + "Cond:...#.#OF - Act:0 - effect:......O. - Num:1 [fit: 0.005, exp: 5.00, pred: 838.329, error:6125.3965625392975]acc: 0.0\n", + "Cond:.###.O## - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 8.00, pred: 736.462, error:8046.440484883746]acc: 0.0\n", + "Cond:#.#..##. - Act:1 - effect:.......O - Num:1 [fit: 0.156, exp: 3.00, pred: 233.792, error:2804.557527580674]acc: 0.0\n", + "Cond:....#### - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 46.00, pred: 765.924, error:7246.222157875497]acc: 0.0\n", + "Cond:###F##.. - Act:4 - effect:..O.OO.. - Num:3 [fit: 0.012, exp: 73.00, pred: 488.760, error:4614.8196076623835]acc: 0.0\n", + "Cond:.#.#F#.. - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 5.00, pred: 593.691, error:6610.805831522767]acc: 0.0\n", + "Cond:.#O#.### - Act:2 - effect:..O..O.. - Num:1 [fit: 0.126, exp: 5.00, pred: 730.384, error:4453.7561139629215]acc: 0.0\n", + "Cond:#..##.## - Act:3 - effect:...O.... - Num:2 [fit: 0.002, exp: 41.00, pred: 657.207, error:7030.415409510439]acc: 0.5365853658536586\n", + "Cond:.#.O#..# - Act:3 - effect:...O.... - Num:3 [fit: 0.004, exp: 35.00, pred: 512.635, error:4447.426050795117]acc: 0.6285714285714286\n", + "Cond:#.###.## - Act:7 - effect:.O...... - Num:2 [fit: 0.000, exp: 35.00, pred: 252.625, error:5812.840353839525]acc: 0.0\n", + "Cond:.####..O - Act:7 - effect:.......O - Num:1 [fit: 0.028, exp: 4.00, pred: 296.246, error:2739.7830277372695]acc: 0.5\n", + "Cond:#.#.#.#O - Act:2 - effect:OO...... - Num:1 [fit: 0.006, exp: 4.00, pred: 327.015, error:3410.99512078877]acc: 0.25\n", + "Cond:##....## - Act:2 - effect:O..O.... - Num:1 [fit: 0.001, exp: 10.00, pred: 359.740, error:4645.16987342024]acc: 0.0\n", + "Cond:.###.... - Act:7 - effect:....O..O - Num:2 [fit: 0.000, exp: 21.00, pred: 300.122, error:7660.0312805988615]acc: 0.0\n", + "Cond:#O#.#..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.009, exp: 4.00, pred: 210.242, error:3411.668626919173]acc: 0.0\n", + "Cond:.####OF# - Act:5 - effect:.O...... - Num:1 [fit: 0.021, exp: 22.00, pred: 301.074, error:3076.4313024023495]acc: 0.0\n", + "Cond:##....## - Act:1 - effect:.O...... - Num:1 [fit: 0.132, exp: 4.00, pred: 223.200, error:2508.3315372434317]acc: 0.25\n", + "Cond:#.#.#O## - Act:3 - effect:.......O - Num:4 [fit: 0.009, exp: 17.00, pred: 545.981, error:7386.353416992961]acc: 0.0\n", + "Cond:.#O##### - Act:1 - effect:...O.... - Num:1 [fit: 0.212, exp: 2.00, pred: 226.173, error:1500.1820045954219]acc: 0.0\n", + "Cond:..##.O#O - Act:0 - effect:.......F - Num:1 [fit: 0.004, exp: 0.00, pred: 311.352, error:570.982647472199]acc: 0\n", + "Cond:.#..#O## - Act:1 - effect:...OOO.. - Num:1 [fit: 0.001, exp: 7.00, pred: 252.165, error:4576.260654926502]acc: 0.0\n", + "Cond:###.#.## - Act:6 - effect:.O...... - Num:4 [fit: 0.022, exp: 284.00, pred: 266.060, error:3538.4329406932234]acc: 0.13732394366197184\n", + "Cond:#.##.##. - Act:1 - effect:...O.... - Num:6 [fit: 0.084, exp: 6.00, pred: 349.855, error:6077.5566651442105]acc: 0.0\n", + "Cond:##..#F#. - Act:0 - effect:.F...F.. - Num:1 [fit: 0.005, exp: 1.00, pred: 474.386, error:1800.0]acc: 0.0\n", + "Cond:##.###.# - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.000, exp: 335.00, pred: 1356.819, error:2897.412594323568]acc: 0.0\n", + "Cond:#####..# - Act:4 - effect:..O.OO.. - Num:4 [fit: 0.001, exp: 263.00, pred: 1356.914, error:2893.475468629773]acc: 0.0\n", + "Cond:###O.... - Act:0 - effect:O..O.... - Num:2 [fit: 0.006, exp: 2.00, pred: 728.562, error:2959.186424245373]acc: 0.0\n", + "Cond:.##..#F. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.005, exp: 2.00, pred: 255.516, error:3614.792401476874]acc: 0.0\n", + "Cond:#.#..O#. - Act:3 - effect:.......O - Num:1 [fit: 0.003, exp: 8.00, pred: 654.178, error:8034.581192911398]acc: 0.0\n", + "Cond:#.#.#OF. - Act:3 - effect:...O.... - Num:2 [fit: 0.003, exp: 8.00, pred: 654.178, error:7091.221192911397]acc: 0.0\n", + "Cond:O#..#### - Act:7 - effect:.....F.. - Num:3 [fit: 0.012, exp: 12.00, pred: 428.006, error:6855.834284632549]acc: 0.08333333333333333\n", + "Cond:..#.#.#O - Act:1 - effect:.....F.. - Num:1 [fit: 0.001, exp: 1.00, pred: 229.689, error:2000.0]acc: 0.0\n", + "Cond:.####### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 122.00, pred: 624.761, error:5865.815530086743]acc: 0.18032786885245902\n", + "Cond:#....##. - Act:3 - effect:.......O - Num:1 [fit: 0.000, exp: 13.00, pred: 525.559, error:6330.537331980472]acc: 0.0\n", + "Cond:#.#O###. - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.000, exp: 53.00, pred: 603.374, error:6551.140380742362]acc: 0.0\n", + "Cond:###O.### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 53.00, pred: 600.987, error:2807.2345124648123]acc: 0.05660377358490566\n", + "Cond:.##.#.#. - Act:1 - effect:.O...... - Num:1 [fit: 0.005, exp: 0.00, pred: 280.905, error:951.4836990579992]acc: 0\n", + "Cond:####.#.# - Act:6 - effect:.OO.OO.. - Num:3 [fit: 0.021, exp: 269.00, pred: 266.100, error:3000.4837999486085]acc: 0.0\n", + "Cond:#.#.##.O - Act:7 - effect:.......O - Num:1 [fit: 0.016, exp: 6.00, pred: 293.197, error:3853.93893820953]acc: 0.3333333333333333\n", + "Cond:O...#### - Act:2 - effect:.O.OF..O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:###.#O## - Act:5 - effect:........ - Num:2 [fit: 0.002, exp: 42.00, pred: 676.284, error:6852.498931210781]acc: 0.0\n", + "Cond:..##.#.. - Act:2 - effect:OO...... - Num:1 [fit: 0.004, exp: 11.00, pred: 553.518, error:4629.6251533727755]acc: 0.0\n", + "Cond:.#.####. - Act:2 - effect:OO...... - Num:1 [fit: 0.002, exp: 23.00, pred: 765.835, error:5562.023404521855]acc: 0.391304347826087\n", + "Cond:.#..#O.. - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 19.00, pred: 498.672, error:4076.3738973027607]acc: 0.0\n", + "Cond:.###F#.. - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 19.00, pred: 498.672, error:4584.142606422761]acc: 0.0\n", + "Cond:.....O#. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 1.00, pred: 225.931, error:1600.0]acc: 0.0\n", + "Cond:###.##F# - Act:0 - effect:....FO.. - Num:1 [fit: 0.001, exp: 1.00, pred: 225.931, error:1800.0]acc: 0.0\n", + "Cond:..##.O#F - Act:5 - effect:.O...... - Num:1 [fit: 0.039, exp: 17.00, pred: 593.392, error:5698.164873173553]acc: 0.0\n", + "Cond:.#...O.F - Act:1 - effect:...OOO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 330.572, error:1404.3298237047493]acc: 0\n", + "Cond:.####O#F - Act:5 - effect:.O...... - Num:1 [fit: 0.007, exp: 3.00, pred: 883.000, error:4178.739927391851]acc: 0.0\n", + "Cond:.#.#.O## - Act:0 - effect:....FO.. - Num:2 [fit: 0.120, exp: 5.00, pred: 837.852, error:925.5317760514256]acc: 0.6\n", + "Cond:..#.#O## - Act:7 - effect:...O.... - Num:4 [fit: 0.001, exp: 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1178.220, error:5092.578858917577]acc: 0.0\n", + "Cond:##.####. - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 68.00, pred: 504.266, error:7778.532816199033]acc: 0.3088235294117647\n", + "Cond:.#OO..#. - Act:3 - effect:...O.... - Num:1 [fit: 0.018, exp: 23.00, pred: 322.786, error:3816.8384015040215]acc: 0.0\n", + "Cond:#O..#..# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 71.00, pred: 274.734, error:4744.903659527759]acc: 0.30985915492957744\n", + "Cond:.###.#OF - Act:0 - effect:.O...... - Num:1 [fit: 0.007, exp: 3.00, pred: 1246.996, error:3828.21272383193]acc: 0.0\n", + "Cond:####O### - Act:3 - effect:...O.... - Num:1 [fit: 0.005, exp: 6.00, pred: 453.662, error:3326.119856612714]acc: 0.16666666666666666\n", + "Cond:#..####. - Act:3 - effect:...O.... - Num:4 [fit: 0.002, exp: 36.00, pred: 501.018, error:5412.330591853073]acc: 0.5\n", + "Cond:#O###.## - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 71.00, pred: 274.346, error:4523.6116007688215]acc: 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20.00, pred: 899.768, error:7864.742188610059]acc: 0.0\n", + "Cond:..#.#### - Act:0 - effect:.......F - Num:3 [fit: 0.011, exp: 18.00, pred: 765.440, error:7379.466460116797]acc: 0.0\n", + "Cond:.#.##O#F - Act:1 - effect:...OOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 409.804, error:1291.3154108143]acc: 0\n", + "Cond:###O#### - Act:7 - effect:.O...... - Num:1 [fit: 0.019, exp: 6.00, pred: 263.268, error:4513.933547229262]acc: 0.0\n", + "Cond:.###...# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 16.00, pred: 494.177, error:6202.024495247502]acc: 0.0625\n", + "Cond:.####O## - Act:5 - effect:.O...... - Num:5 [fit: 0.020, exp: 76.00, pred: 715.928, error:4672.652545238236]acc: 0.0\n", + "Cond:.O###O## - Act:7 - effect:.......O - Num:1 [fit: 0.055, exp: 0.00, pred: 986.412, error:61.168073879706284]acc: 0\n", + "Cond:#O...#.O - Act:2 - effect:.O.O.... - Num:1 [fit: 0.008, exp: 2.00, pred: 416.670, error:2594.349759262478]acc: 0.0\n", + "Cond:#O#..#.# - Act:0 - effect:...O.... - Num:1 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"Cond:...F#O.# - Act:1 - effect:....OO.F - Num:1 [fit: 0.007, exp: 8.00, pred: 356.757, error:6363.689104522328]acc: 0.0\n", + "Cond:##.#O.#. - Act:6 - effect:.O...... - Num:1 [fit: 0.005, exp: 1.00, pred: 331.537, error:1600.0]acc: 0.0\n", + "Cond:#..#O#.# - Act:0 - effect:..O.OO.. - Num:1 [fit: 0.008, exp: 1.00, pred: 216.934, error:1600.0]acc: 0.0\n", + "Cond:.####O#. - Act:0 - effect:O....OOO - Num:1 [fit: 0.031, exp: 2.00, pred: 312.816, error:1109.0063813740094]acc: 0.0\n", + "Cond:.#.##O## - Act:7 - effect:.......O - Num:2 [fit: 0.002, exp: 673.00, pred: 1343.672, error:1921.3542874483023]acc: 0.0\n", + "Cond:..#FO#.. - Act:4 - effect:.O...... - Num:2 [fit: 0.020, exp: 20.00, pred: 490.052, error:3855.483763280707]acc: 0.0\n", + "Cond:###F###. - Act:1 - effect:...O.... - Num:1 [fit: 0.006, exp: 6.00, pred: 344.528, error:4979.359356814311]acc: 0.0\n", + "Cond:..##OO#. - Act:1 - effect:....OO.F - Num:1 [fit: 0.008, exp: 6.00, pred: 344.528, error:4731.359356814311]acc: 0.0\n", + 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pred: 287.866, error:3696.631667507477]acc: 0.0\n", + "Cond:...#.##F - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 40.00, pred: 477.163, error:2662.8122782994146]acc: 0.5\n", + "Cond:..#..### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 131.00, pred: 1654.385, error:8134.0225903193095]acc: 0.15267175572519084\n", + "Cond:..#FOO.. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 30.00, pred: 360.797, error:6775.334927439636]acc: 0.03333333333333333\n", + "Cond:#..F##.. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 30.00, pred: 360.797, error:7301.454230938074]acc: 0.03333333333333333\n", + "Cond:...#..#F - Act:4 - effect:.......O - Num:1 [fit: 0.048, exp: 0.00, pred: 607.549, error:67.05954631761213]acc: 0\n", + "Cond:..O##### - Act:4 - effect:.......O - Num:1 [fit: 0.002, exp: 9.00, pred: 434.463, error:7021.68139944434]acc: 0.0\n", + "Cond:.O###### - Act:4 - effect:.......O - Num:2 [fit: 0.002, exp: 23.00, pred: 624.600, error:2729.9182768484197]acc: 0.13043478260869565\n", + "Cond:..#.##.# - Act:2 - effect:.O...... - Num:1 [fit: 0.027, exp: 7.00, pred: 994.734, error:6367.960361361349]acc: 0.0\n", + "Cond:..#..##F - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 32.00, pred: 477.085, error:3513.8673583767268]acc: 0.53125\n", + "Cond:.#.##### - Act:2 - effect:..O.OO.. - Num:2 [fit: 0.001, exp: 28.00, pred: 760.123, error:5720.933330411987]acc: 0.0\n", + "Cond:..#FOO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 24.00, pred: 363.374, error:7102.068650733292]acc: 0.041666666666666664\n", + "Cond:#.#F##.. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 24.00, pred: 363.374, error:6796.247937062478]acc: 0.041666666666666664\n", + "Cond:.###.### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 13.00, pred: 639.293, error:6327.61005049589]acc: 0.07692307692307693\n", + "Cond:.#O..#.# - Act:4 - effect:.......O - Num:1 [fit: 0.002, exp: 7.00, pred: 491.656, error:4712.876179924646]acc: 0.0\n", + "Cond:.##OO.## - Act:3 - effect:...O.... - Num:1 [fit: 0.202, exp: 2.00, pred: 235.514, error:1502.3710324937852]acc: 0.0\n", + "Cond:.#.##.## - Act:1 - effect:....FO.. - Num:4 [fit: 0.049, exp: 13.00, pred: 308.547, error:6065.697095110263]acc: 0.46153846153846156\n", + "Cond:.O#O#### - Act:1 - effect:.......O - Num:1 [fit: 0.001, exp: 0.00, pred: 513.702, error:363.93827822945883]acc: 0\n", + "Cond:O##F#... - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 817.701, error:1678.4490241463577]acc: 0\n", + "Cond:##...##F - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 23.00, pred: 479.018, error:3899.3019428460148]acc: 0.391304347826087\n", + "Cond:.####.## - Act:1 - effect:....FO.. - Num:5 [fit: 0.200, exp: 11.00, pred: 284.923, error:6837.98580305198]acc: 0.36363636363636365\n", + "Cond:.##.###O - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 32.00, pred: 1657.392, error:7715.8424462059975]acc: 0.0\n", + "Cond:..#FOO.# - Act:6 - effect:...O.... - Num:2 [fit: 0.001, exp: 17.00, pred: 358.145, error:6880.028077474584]acc: 0.058823529411764705\n", + "Cond:#.#FOO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 17.00, pred: 358.145, error:7424.662018671383]acc: 0.058823529411764705\n", + "Cond:#.#.##O# - Act:4 - effect:.......O - Num:1 [fit: 0.002, exp: 23.00, pred: 425.737, error:3372.574644771581]acc: 0.30434782608695654\n", + "Cond:.#..###O - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 29.00, pred: 1657.045, error:8032.669562925308]acc: 0.0\n", + "Cond:###..### - Act:4 - effect:.......O - Num:3 [fit: 0.000, exp: 161.00, pred: 1357.120, error:2926.4373127893077]acc: 0.031055900621118012\n", + "Cond:..#FO### - Act:6 - effect:...O.... - Num:2 [fit: 0.002, exp: 15.00, pred: 353.343, error:5954.274211305913]acc: 0.06666666666666667\n", + "Cond:O.#...## - Act:0 - effect:.......F - Num:1 [fit: 0.058, exp: 3.00, pred: 678.442, error:3616.2740146824112]acc: 0.0\n", + "Cond:.##...## - Act:3 - effect:.......O - Num:1 [fit: 0.002, exp: 8.00, pred: 466.819, error:7065.305137777038]acc: 0.0\n", + "Cond:.O###.## - Act:1 - effect:....FO.. - Num:1 [fit: 0.041, exp: 0.00, pred: 456.814, error:1.4680196347900676]acc: 0\n", + "Cond:####.### - Act:5 - effect:........ - Num:3 [fit: 0.001, exp: 858.00, pred: 1316.268, error:2817.029089179964]acc: 0.002331002331002331\n", + "Cond:..##O### - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 13.00, pred: 360.018, error:6393.030765743157]acc: 0.07692307692307693\n", + "Cond:.##O.### - Act:4 - effect:.......O - Num:1 [fit: 0.004, exp: 20.00, pred: 602.011, error:3013.989618930968]acc: 0.15\n", + "Cond:#OO##### - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 16.00, pred: 628.359, error:2830.06563254327]acc: 0.1875\n", + "Cond:.###.### - Act:4 - effect:.O...... - Num:2 [fit: 0.000, exp: 57.00, pred: 1669.781, error:7372.4479959981045]acc: 0.12280701754385964\n", + "Cond:.##.#### - Act:1 - effect:....FO.. - Num:1 [fit: 0.043, exp: 1.00, pred: 221.705, error:1600.0]acc: 0.0\n", + "Cond:..##..## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 28.00, pred: 1653.556, error:7918.62817880637]acc: 0.03571428571428571\n", + "Cond:..#F#.## - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 635.121, error:982.9002276237992]acc: 0\n", + "Cond:..#F#O.# - Act:6 - effect:...O.... - Num:3 [fit: 0.008, exp: 9.00, pred: 333.340, error:6205.095335966489]acc: 0.1111111111111111\n", + "Cond:.#.#O### - Act:4 - effect:.......O - Num:1 [fit: 0.104, exp: 5.00, pred: 715.357, error:3533.6645634313827]acc: 0.0\n", + "Cond:.O#O#### - Act:4 - effect:.O...... - Num:1 [fit: 0.380, exp: 6.00, pred: 634.016, error:4076.7763207087055]acc: 0.6666666666666666\n", + "Cond:.#..O##. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 577.747, error:1654.9793529686635]acc: 0\n", + "Cond:##.##.## - Act:1 - effect:....FO.. - Num:2 [fit: 0.048, exp: 9.00, pred: 244.612, error:3775.921322567716]acc: 0.1111111111111111\n", + "Cond:.##..#O# - Act:4 - effect:.......O - Num:1 [fit: 0.008, exp: 5.00, pred: 407.505, error:1832.2231195902891]acc: 0.2\n", + "Cond:..###### - Act:5 - effect:.......O - Num:5 [fit: 0.002, exp: 777.00, pred: 1206.332, error:2137.4352088268065]acc: 0.0\n", + "Cond:###.##O# - Act:4 - effect:.......O - Num:1 [fit: 0.203, exp: 2.00, pred: 316.840, error:2103.122229708746]acc: 0.5\n", + "Cond:.#..#O## - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 564.00, pred: 1375.127, error:1954.7898328808528]acc: 0.0\n", + "Cond:#..F#O.# - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 3.00, pred: 238.167, error:4812.625377514138]acc: 0.0\n", + "Cond:OO..#### - Act:3 - effect:........ - Num:2 [fit: 0.014, exp: 3.00, pred: 269.078, error:3743.273987036965]acc: 0.0\n", + "Cond:OO..F### - Act:3 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:..#.#.## - Act:3 - effect:.......O - Num:1 [fit: 0.006, exp: 1.00, pred: 1212.005, error:1800.0]acc: 0.0\n", + "Cond:O#####.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.045, exp: 4.00, pred: 508.831, error:4497.137491770538]acc: 0.0\n", + "Cond:..#OO#.. - Act:4 - effect:.O...... - Num:1 [fit: 0.011, exp: 2.00, pred: 1059.750, error:2807.184160187796]acc: 0.0\n", + "Cond:####O.#. - Act:4 - effect:.......O - Num:1 [fit: 0.010, exp: 2.00, pred: 1059.750, error:2807.184160187796]acc: 0.0\n", + "Cond:.######. - Act:4 - effect:.......O - Num:2 [fit: 0.090, exp: 7.00, pred: 793.162, error:3309.57393467238]acc: 0.2857142857142857\n", + "Cond:####.### - Act:0 - effect:.......F - Num:2 [fit: 0.019, exp: 8.00, pred: 954.755, error:7664.061316965305]acc: 0.0\n", + "Cond:#O###### - Act:3 - effect:........ - Num:1 [fit: 0.009, exp: 3.00, pred: 229.233, error:3874.926358033689]acc: 0.0\n", + "Cond:##..##.. - Act:3 - effect:...O.... - Num:1 [fit: 0.004, exp: 3.00, pred: 237.294, error:3802.8857178226895]acc: 0.0\n", + "Cond:.##FOO#. - Act:1 - effect:....OO.F - Num:2 [fit: 0.007, exp: 4.00, pred: 364.386, error:3125.3462967891246]acc: 0.0\n", + "Cond:O.###.#O - Act:0 - effect:O..O.... - Num:1 [fit: 0.009, exp: 1.00, pred: 1524.972, error:1600.0]acc: 0.0\n", + "Cond:#..F.### - Act:7 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 366.323, error:599.7059354379264]acc: 0\n", + "Cond:..O#.#.. - Act:7 - effect:.O...... - Num:1 [fit: 0.006, exp: 1.00, pred: 229.858, error:1600.0]acc: 0.0\n", + "Cond:.#O#.##. - Act:7 - effect:.OO..OO. - Num:1 [fit: 0.006, exp: 1.00, pred: 229.858, error:2000.0]acc: 0.0\n", + "Cond:#..F#### - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 70.00, pred: 596.498, error:2273.691645068624]acc: 0.0\n", + "Cond:#.##O### - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 1.00, pred: 219.560, error:1600.0]acc: 0.0\n", + "Cond:..#F#O.. - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 1.00, pred: 219.560, error:1800.0]acc: 0.0\n", + "Cond:O#.....# - Act:7 - effect:.....F.. - Num:1 [fit: 0.013, exp: 1.00, pred: 837.458, error:1600.0]acc: 0.0\n", + "Cond:#O.#...# - Act:2 - effect:...O.... - Num:2 [fit: 0.015, exp: 3.00, pred: 402.250, error:2137.123730734622]acc: 0.3333333333333333\n", + "Cond:#O..#..# - Act:2 - effect:...O.... - Num:1 [fit: 0.007, exp: 3.00, pred: 402.250, error:1937.1237307346219]acc: 0.3333333333333333\n", + "Cond:#.##.##. - Act:4 - effect:.O...... - Num:1 [fit: 0.102, exp: 2.00, pred: 283.680, error:1716.4458683884743]acc: 0.0\n", + "Cond:###.F#.# - Act:4 - effect:O....O.O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:O..#F##. - Act:7 - effect:.O...... - Num:1 [fit: 0.017, exp: 0.00, pred: 311.704, error:275.9491727128152]acc: 0\n", + "Cond:#O.##..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.006, exp: 1.00, pred: 0.000, error:1600.0]acc: 0.0\n", + "Cond:.##O#### - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 4.00, pred: 994.568, error:5211.976502663918]acc: 0.0\n", + "Cond:.#O#..## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 2.00, pred: 929.387, error:2934.449080344359]acc: 0.0\n", + "Cond:.OO#.### - Act:4 - effect:.O...... - Num:1 [fit: 0.031, exp: 1.00, pred: 1598.285, error:2200.0]acc: 0.0\n", + "Cond:.##.##.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 312.788, error:1001.3297844548603]acc: 0\n", + "Cond:#.#.#F## - Act:4 - effect:.F...F.. - Num:1 [fit: 0.002, exp: 0.00, pred: 312.788, error:1001.3297844548603]acc: 0\n", + "Cond:#..##O.. - Act:6 - effect:...O.... - Num:1 [fit: 0.007, exp: 1.00, pred: 935.716, error:1600.0]acc: 0.0\n", + "Cond:###.#.## - Act:1 - effect:.....O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 362.290, error:637.2549862598926]acc: 0\n", + "Cond:O#...#.. - Act:1 - effect:.....O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 362.290, error:637.2549862598926]acc: 0\n", + "Cond:###.##.. - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 209.556, error:711.285334851536]acc: 0\n", + "Cond:#O.#.### - Act:0 - effect:.......F - Num:1 [fit: 0.003, exp: 0.00, pred: 209.556, error:711.285334851536]acc: 0\n", + "Cond:#O##.### - Act:3 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#####.# - Act:3 - effect:...O.... - Num:3 [fit: 0.443, exp: 4.00, pred: 501.664, error:3012.906544172839]acc: 0.0\n", + "Cond:.###..## - Act:1 - effect:....FO.. - Num:1 [fit: 0.014, exp: 0.00, pred: 317.389, error:1164.4428085245238]acc: 0\n", + "Cond:####..F# - Act:6 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 298.181, error:515.9294345364439]acc: 0\n", + "Cond:.#.#OO## - Act:1 - effect:....OO.F - Num:1 [fit: 0.194, exp: 2.00, pred: 257.372, error:1707.446025990852]acc: 0.0\n", + "Cond:##.##..# - Act:6 - effect:.O...... - Num:2 [fit: 0.141, exp: 13.00, pred: 269.681, error:3566.8609527074714]acc: 0.15384615384615385\n", + "Cond:...##..# - Act:6 - effect:.O...... - Num:1 [fit: 0.019, exp: 0.00, pred: 357.402, error:197.03067495873]acc: 0\n", + "Cond:#.#....# - Act:7 - effect:...O.... - Num:1 [fit: 0.002, exp: 0.00, pred: 281.027, error:162.05913284239492]acc: 0\n", + "Cond:#####O## - Act:5 - effect:....FO.. - Num:5 [fit: 0.021, exp: 73.00, pred: 715.928, error:4753.878483483094]acc: 0.0136986301369863\n", + "Cond:.##F#O#. - Act:1 - effect:....OO.F - Num:1 [fit: 0.001, exp: 1.00, pred: 242.480, error:2400.0]acc: 0.0\n", + "Cond:##.F###. - Act:7 - effect:...O.... - Num:3 [fit: 0.000, exp: 104.00, pred: 1431.491, error:4551.723198512714]acc: 0.6346153846153846\n", + "Cond:#..##### - Act:7 - effect:.O...... - Num:3 [fit: 0.003, exp: 665.00, pred: 1343.672, error:1848.4127209674325]acc: 0.0\n", + "Cond:..#..#O# - Act:2 - effect:...O.... - Num:3 [fit: 0.029, exp: 4.00, pred: 714.424, error:5000.4295844354]acc: 0.0\n", + "Cond:######.# - Act:5 - effect:........ - Num:8 [fit: 0.004, exp: 1046.00, pred: 1210.129, error:2368.1646793496975]acc: 0.0\n", + "Cond:#####O## - Act:7 - effect:.......O - Num:6 [fit: 0.006, exp: 663.00, pred: 1343.672, error:1582.3094434824782]acc: 0.0\n", + "Cond:.##.#O## - Act:7 - effect:...O.... - Num:8 [fit: 0.005, exp: 558.00, pred: 1375.127, error:2227.9561613954756]acc: 0.0\n", + "Cond:#O.O...O - Act:5 - effect:.OO..... - Num:1 [fit: 0.010, exp: 0.00, pred: 379.224, error:812.4755573704872]acc: 0\n", + "Cond:#.###### - Act:5 - effect:........ - Num:5 [fit: 0.001, exp: 753.00, pred: 1206.081, error:2340.740952971882]acc: 0.0\n", + "Cond:.####O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 27.00, pred: 598.630, error:1867.3931966943296]acc: 0.037037037037037035\n", + "Cond:###.#O## - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 549.00, pred: 1375.127, error:2443.7219353497776]acc: 0.0\n", + "Cond:###F.##. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#.####.# - Act:5 - effect:........ - Num:2 [fit: 0.000, exp: 706.00, pred: 1205.812, error:2275.6211367705996]acc: 0.0\n", + "Cond:.##.#### - Act:3 - effect:.......O - Num:1 [fit: 0.030, exp: 0.00, pred: 490.433, error:765.246696155309]acc: 0\n", + "Cond:#...#### - Act:3 - effect:...O.... - Num:1 [fit: 0.030, exp: 0.00, pred: 490.433, error:765.246696155309]acc: 0\n", + "Cond:#O..#.## - Act:2 - effect:........ - Num:1 [fit: 0.007, exp: 1.00, pred: 385.746, error:1600.0]acc: 0.0\n", + "Cond:###F###. - Act:7 - effect:...O.... - Num:2 [fit: 0.000, exp: 98.00, pred: 1431.491, error:4756.768247612424]acc: 0.6122448979591837\n", + "Cond:.####..# - Act:1 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1313.966, error:402.3435338779427]acc: 0\n", + "Cond:OO..#.#. - Act:7 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1302.291, error:584.0409062776037]acc: 0\n", + "Cond:#..#F##O - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1350.767, error:846.37293211164]acc: 0\n", + "Cond:#####OO# - Act:5 - effect:........ - Num:1 [fit: 0.008, exp: 2.00, pred: 949.184, error:2901.0426637134656]acc: 0.0\n", + "Cond:###F##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 96.00, pred: 1431.491, error:4576.196685113501]acc: 0.6041666666666666\n", + "Cond:O##.#### - Act:7 - effect:.O...... - Num:1 [fit: 0.006, exp: 1.00, pred: 837.458, error:1800.0]acc: 0.0\n", + "Cond:.######. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 358.00, pred: 1501.499, error:2922.320013113878]acc: 0.002793296089385475\n", + "Cond:#O.F##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1626.518, error:8.265376374502807]acc: 0\n", + "Cond:######## - Act:7 - effect:.......O - Num:5 [fit: 0.004, exp: 579.00, pred: 1343.672, error:1895.70170036745]acc: 0.0\n", + "Cond:...##O## - Act:7 - effect:.O...... - Num:4 [fit: 0.006, exp: 578.00, pred: 1343.672, error:1816.3871096888995]acc: 0.0\n", + "Cond:#..##O## - Act:7 - effect:.O...... - Num:8 [fit: 0.008, exp: 551.00, pred: 1343.672, error:1499.6091153772782]acc: 0.0\n", + "Cond:###F#### - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 90.00, pred: 1431.491, error:4873.00029774006]acc: 0.5777777777777777\n", + "Cond:.###.#.# - Act:3 - effect:...O.... - Num:1 [fit: 0.044, exp: 0.00, pred: 501.664, error:353.2266360432097]acc: 0\n", + "Cond:.##FO.## - Act:6 - effect:...O.... - Num:1 [fit: 0.037, exp: 0.00, pred: 1666.388, error:71.85256688430697]acc: 0\n", + "Cond:###F.##. - Act:7 - effect:...O.... - Num:1 [fit: 0.037, exp: 0.00, pred: 1666.388, error:71.85256688430697]acc: 0\n", + "Cond:.####### - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1186.755, error:549.8001989838074]acc: 0\n", + "Cond:#O#F#.#. - Act:7 - effect:...O.... - Num:1 [fit: 0.065, exp: 0.00, pred: 1623.942, error:39.46462947190855]acc: 0\n", + "Cond:##.###.# - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 531.00, pred: 1320.530, error:2689.6704798631527]acc: 0.0\n", + "Cond:.##.#### - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 404.00, pred: 1375.127, error:1828.5268019817704]acc: 0.0\n", + "Cond:##.##### - Act:7 - effect:.O...... - Num:1 [fit: 0.001, exp: 487.00, pred: 1343.672, error:1886.1621058547494]acc: 0.0\n", + "Cond:####F#.# - Act:5 - effect:........ - Num:1 [fit: 0.205, exp: 2.00, pred: 447.729, error:1707.4789466037957]acc: 0.0\n", + "Cond:.##.#### - Act:2 - effect:.O...... - Num:2 [fit: 0.011, exp: 12.00, pred: 850.799, error:6942.094720945614]acc: 0.0\n", + "Cond:#O.F###. - Act:7 - effect:...O.... - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:O##F#.## - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:##OO###. - Act:1 - effect:.......O - Num:1 [fit: 0.022, exp: 0.00, pred: 449.472, error:551.8461007980261]acc: 0\n", + "Cond:..O#.#.. - Act:2 - effect:.......O - Num:1 [fit: 0.022, exp: 0.00, pred: 449.472, error:551.8461007980261]acc: 0\n", + "Cond:.#..#OO. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1265.165, error:590.068061242368]acc: 0\n", + "Cond:..####.. - Act:2 - effect:OO...... - Num:1 [fit: 0.001, exp: 20.00, pred: 768.407, error:4825.495497962967]acc: 0.4\n", + "Cond:.#..###. - Act:2 - effect:OO...... - Num:1 [fit: 0.014, exp: 5.00, pred: 709.228, error:5044.084267627385]acc: 0.0\n", + "Cond:.#.##F#. - Act:2 - effect:OO...... - Num:1 [fit: 0.012, exp: 1.00, pred: 396.077, error:2000.0]acc: 0.0\n", + "Cond:.######. - Act:2 - effect:.O...... - Num:2 [fit: 0.003, exp: 21.00, pred: 998.582, error:6707.384470257394]acc: 0.0\n", + "Cond:#O#F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.056, exp: 0.00, pred: 1556.361, error:56.899609112875766]acc: 0\n", + "Cond:..##.##. - Act:2 - effect:OO...... - Num:1 [fit: 0.004, exp: 7.00, pred: 468.390, error:3514.639910080141]acc: 0.0\n", + "Cond:...###.# - Act:2 - effect:OO...... - Num:1 [fit: 0.006, exp: 18.00, pred: 760.881, error:5518.464293654875]acc: 0.4444444444444444\n", + "Cond:#.###.#. - Act:2 - effect:.......O - Num:1 [fit: 0.011, exp: 13.00, pred: 549.915, error:3718.67793823691]acc: 0.0\n", + "Cond:..O#.### - Act:2 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 714.424, error:800.10739610885]acc: 0\n", + "Cond:.O..#..# - Act:2 - effect:.O...... - Num:1 [fit: 0.004, exp: 0.00, pred: 517.254, error:498.8690656223327]acc: 0\n", + "Cond:######## - Act:2 - effect:...O.... - Num:2 [fit: 0.023, exp: 15.00, pred: 994.244, error:6268.989638630434]acc: 0.06666666666666667\n", + "Cond:..####.# - Act:2 - effect:OO...... - Num:3 [fit: 0.096, exp: 9.00, pred: 752.970, error:5449.241461763329]acc: 0.2222222222222222\n", + "Cond:#######O - Act:7 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 756.885, error:786.2527443021846]acc: 0\n", + "Cond:#.#.F#.. - Act:6 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 504.132, error:771.7111561781936]acc: 0\n", + "Cond:.O#####. - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:###FO### - Act:5 - effect:........ - Num:1 [fit: 0.017, exp: 22.00, pred: 598.152, error:2592.9029962077434]acc: 0.0\n", + "Cond:###F#O## - Act:5 - effect:.O...... - Num:1 [fit: 0.016, exp: 22.00, pred: 598.152, error:1864.1630892299515]acc: 0.0\n", + "Cond:.##.OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1110.142, error:529.2566857852762]acc: 0\n", + "Cond:##.F##.. - Act:7 - effect:...O.... - Num:5 [fit: 0.000, exp: 63.00, pred: 1431.492, error:4475.563803113068]acc: 0.3968253968253968\n", + "Cond:#...##.# - Act:1 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 936.455, error:435.92523588126096]acc: 0\n", + "Cond:.####### - Act:2 - effect:OO...... - Num:1 [fit: 0.005, exp: 2.00, pred: 1890.365, error:3427.7857053476973]acc: 0.0\n", + "Cond:O#.F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.036, exp: 0.00, pred: 1079.539, error:9.054931690271541]acc: 0\n", + "Cond:###.#### - Act:7 - effect:.O...... - Num:2 [fit: 0.002, exp: 308.00, pred: 1375.127, error:2147.860452219234]acc: 0.0\n", + "Cond:###F##.. - Act:7 - effect:...O.... - Num:2 [fit: 0.000, exp: 56.00, pred: 1431.494, error:4920.883903827482]acc: 0.32142857142857145\n", + "Cond:#O.F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.054, exp: 0.00, pred: 1243.262, error:52.33005485402675]acc: 0\n", + "Cond:#.###.## - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 346.00, pred: 1195.448, error:2436.5756414308225]acc: 0.0\n", + "Cond:##.#.O## - Act:0 - effect:.......F - Num:1 [fit: 0.014, exp: 2.00, pred: 1222.939, error:2590.869919697658]acc: 0.0\n", + "Cond:####.##. - Act:0 - effect:....FO.. - Num:1 [fit: 0.013, exp: 1.00, pred: 1246.935, error:2600.0]acc: 0.0\n", + "Cond:#######F - Act:5 - effect:........ - Num:1 [fit: 0.011, exp: 2.00, pred: 949.184, error:2701.0426637134656]acc: 0.0\n", + "Cond:#####OO. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1295.178, error:786.5683099539451]acc: 0\n", + "Cond:.O#.###. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1515.358, error:623.442897245877]acc: 0\n", + "Cond:#.#####. - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 291.00, pred: 1205.883, error:2358.391699630094]acc: 0.0\n", + "Cond:######## - Act:4 - effect:........ - Num:4 [fit: 0.001, exp: 109.00, pred: 1356.819, error:3201.5923103753744]acc: 0.0\n", + "Cond:###F##.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#O.F###. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:##..#### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 233.00, pred: 1375.127, error:2666.3484267425592]acc: 0.0\n", + "Cond:#.###.## - Act:4 - effect:........ - Num:2 [fit: 0.001, exp: 28.00, pred: 1625.164, error:7503.286215393206]acc: 0.0\n", + "Cond:##.#.### - Act:4 - effect:........ - Num:5 [fit: 0.000, exp: 103.00, pred: 1357.120, error:3208.79300242492]acc: 0.0\n", + "Cond:.##FO##. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 48.00, pred: 1431.495, error:4368.240966153006]acc: 0.25\n", + "Cond:#######. - Act:0 - effect:.......F - Num:2 [fit: 0.005, exp: 1.00, pred: 1246.935, error:1600.0]acc: 0.0\n", + "Cond:#####O## - Act:4 - effect:........ - Num:1 [fit: 0.024, exp: 1.00, pred: 940.330, error:2600.0]acc: 0.0\n", + "Cond:.##F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 44.00, pred: 1431.480, error:5028.724546209979]acc: 0.25\n", + "Cond:##.FO##. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 44.00, pred: 1431.480, error:4720.622471236122]acc: 0.25\n", + "Cond:#####..# - Act:5 - effect:........ - Num:2 [fit: 0.001, exp: 327.00, pred: 1204.636, error:2319.8492440901687]acc: 0.0\n", + "Cond:#.#.#.## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 22.00, pred: 1704.624, error:5771.894052773212]acc: 0.0\n", + "Cond:..###.## - Act:4 - effect:........ - Num:3 [fit: 0.001, exp: 13.00, pred: 1607.265, error:8718.216065062403]acc: 0.0\n", + "Cond:.#.##O## - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#.F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 39.00, pred: 1431.489, error:4723.362415386215]acc: 0.28205128205128205\n", + "Cond:#######. - Act:6 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1305.647, error:634.5961685684134]acc: 0\n", + "Cond:..###O## - Act:7 - effect:...O.... - Num:5 [fit: 0.004, exp: 236.00, pred: 1343.672, error:1974.9066731342975]acc: 0.046610169491525424\n", + "Cond:O#.F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1524.702, error:1112.6609078471674]acc: 0\n", + "Cond:.####..# - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.001, exp: 13.00, pred: 1607.265, error:7668.729421862402]acc: 0.0\n", + "Cond:##..#### - Act:3 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 1675.136, error:644.4636835808452]acc: 0\n", + "Cond:.#.#OO## - Act:5 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1445.184, error:655.3313232282262]acc: 0\n", + "Cond:.####O## - Act:0 - effect:....FO.. - Num:1 [fit: 0.004, exp: 1.00, pred: 1404.679, error:0.0]acc: 1.0\n", + "Cond:#.#.F.## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:###..### - Act:3 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1462.687, error:749.920479389614]acc: 0\n", + "Cond:#.##..## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 17.00, pred: 1710.652, error:6363.960086726091]acc: 0.0\n", + "Cond:####O### - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.004, exp: 3.00, pred: 1019.944, error:1847.1519759087978]acc: 0.0\n", + "Cond:##.#...# - Act:4 - effect:........ - Num:2 [fit: 0.000, exp: 60.00, pred: 1357.121, error:3310.3994094567634]acc: 0.0\n", + "Cond:#.#.#O## - Act:5 - effect:........ - Num:1 [fit: 0.002, exp: 1.00, pred: 747.099, error:1600.0]acc: 0.0\n", + "Cond:..###O## - Act:0 - effect:...O.... - Num:1 [fit: 0.004, exp: 1.00, pred: 1404.679, error:2600.0]acc: 0.0\n", + "Cond:##.##... - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 23.00, pred: 1245.336, error:5983.208906437757]acc: 0.0\n", + "Cond:.####.## - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.002, exp: 11.00, pred: 1624.658, error:7179.014095472662]acc: 0.0\n", + "Cond:..###..# - Act:4 - effect:........ - Num:1 [fit: 0.003, exp: 11.00, pred: 1624.658, error:8029.612495472662]acc: 0.0\n", + "Cond:#.###.#. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:####..## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 49.00, pred: 1357.129, error:2472.5334686754136]acc: 0.0\n", + "Cond:.####O.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 1835.107, error:399.4585087850263]acc: 0\n", + "Cond:#######. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 80.00, pred: 1501.499, error:2062.42186162631]acc: 0.05\n", + "Cond:.#.F#### - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 24.00, pred: 1432.094, error:5995.129722059982]acc: 0.0\n", + "Cond:#####.## - Act:4 - effect:........ - Num:5 [fit: 0.000, exp: 45.00, pred: 1356.906, error:3435.520478155384]acc: 0.0\n", + "Cond:##.#..## - Act:4 - effect:........ - Num:2 [fit: 0.000, exp: 43.00, pred: 1357.108, error:3066.1611466337417]acc: 0.0\n", + "Cond:.######F - Act:5 - effect:.O...... - Num:1 [fit: 0.002, exp: 1.00, pred: 747.099, error:2000.0]acc: 0.0\n", + "Cond:##.###.. - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 19.00, pred: 1233.229, error:6106.644950719108]acc: 0.0\n", + "Cond:#O#F##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1955.717, error:718.7898186463824]acc: 0\n", + "Cond:#####.#. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1955.717, error:718.7898186463824]acc: 0\n", + "Cond:##.##.## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 36.00, pred: 1356.796, error:2612.0963425235773]acc: 0.0\n", + "Cond:#.#.#### - Act:5 - effect:........ - Num:2 [fit: 0.000, exp: 31.00, pred: 1838.357, error:5412.987160626485]acc: 0.0\n", + "Cond:O.###O## - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1670.538, error:568.8650421987163]acc: 0\n", + "Cond:..###O## - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:.#.##### - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 103.00, pred: 1343.672, error:1469.7055008019365]acc: 0.0\n", + "Cond:.#.F#O## - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 18.00, pred: 1422.015, error:6343.660715644108]acc: 0.0\n", + "Cond:######.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 105.00, pred: 1223.458, error:1801.7313824115113]acc: 0.13333333333333333\n", + "Cond:#..##.## - Act:4 - effect:........ - Num:1 [fit: 0.003, exp: 7.00, pred: 1569.606, error:4497.6588507993465]acc: 0.0\n", + "Cond:##.#.#.# - Act:4 - effect:........ - Num:2 [fit: 0.000, exp: 31.00, pred: 1356.482, error:3011.6344445831337]acc: 0.0\n", + "Cond:..#O##.. - Act:4 - effect:........ - Num:1 [fit: 0.003, exp: 1.00, pred: 845.382, error:1800.0]acc: 0.0\n", + "Cond:O##.#### - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 25.00, pred: 1532.172, error:3704.857436263127]acc: 0.0\n", + "Cond:.#.###.# - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 36.00, pred: 1475.793, error:3356.736751092681]acc: 0.0\n", + "Cond:##.F.#.# - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", + "Cond:#..#.#.# - Act:4 - effect:........ - Num:1 [fit: 0.006, exp: 5.00, pred: 1704.068, error:3856.9752558330542]acc: 0.0\n", + "Cond:.######O - Act:2 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1486.690, error:495.8040119370071]acc: 0\n", + "Cond:######## - Act:1 - effect:........ - Num:1 [fit: 0.006, exp: 0.00, pred: 1637.381, error:365.2649736286069]acc: 0\n", + "Cond:...##O## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1291.702, error:563.3519985533375]acc: 0\n", + "Cond:#.#####. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 31.00, pred: 1502.410, error:2957.059618469304]acc: 0.0\n", + "Cond:.#.#..#O - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1577.031, error:1073.409693174345]acc: 0\n", + "Cond:#.###O## - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 31.00, pred: 1343.607, error:1625.1953870233979]acc: 0.0\n", + "Cond:###O#..# - Act:7 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 1535.768, error:1129.0716800764071]acc: 0\n", + "Cond:.####O#. - Act:7 - effect:.......O - Num:1 [fit: 0.013, exp: 16.00, pred: 1511.414, error:2474.320808728021]acc: 0.0\n", + "Cond:#..##O#. - Act:7 - effect:.O...... - Num:1 [fit: 0.037, exp: 12.00, pred: 1476.304, error:1983.0638386156036]acc: 0.0\n", + "Cond:#.###O## - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 0.00, pred: 1152.223, error:733.0927735762574]acc: 0\n", + "Cond:#..#F#.. - Act:7 - effect:.O...... - Num:1 [fit: 0.017, exp: 2.00, pred: 1629.194, error:947.154082762917]acc: 0.0\n" ] } ], "source": [ "for cl in explore_population:\n", - " print(str(cl))" + " print(str(cl))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Numerous rules:\n", + "Cond:#.#.###F - Act:0 - effect:..O..O.. - Num:5 [fit: 0.002, exp: 21.00, pred: 810.828, error:10688.920098385375]acc: 0.0\n", + "Cond:.#..#O#F - Act:1 - effect:...OOO.. - Num:8 [fit: 0.012, exp: 22.00, pred: 317.322, error:7880.945643824181]acc: 0.0\n", + "Cond:O#..#..# - Act:2 - effect:.....O.. - Num:6 [fit: 0.004, exp: 23.00, pred: 372.033, error:7311.767484177768]acc: 0.0\n", + "Cond:.#O##... - Act:3 - effect:.OO..OO. - Num:6 [fit: 0.019, exp: 59.00, pred: 299.353, error:2988.393877525843]acc: 0.0\n", + "Cond:.####### - Act:4 - effect:.......O - Num:8 [fit: 0.001, exp: 420.00, pred: 1488.579, error:7875.997225382572]acc: 0.09047619047619047\n", + "Cond:######## - Act:5 - effect:........ - Num:68 [fit: 0.043, exp: 2334.00, pred: 1210.169, error:1714.0389427135808]acc: 0.0\n", + "Cond:######.# - Act:6 - effect:.O...... - Num:6 [fit: 0.066, exp: 656.00, pred: 271.508, error:2804.2657259436173]acc: 0.07317073170731707\n", + "Cond:.####### - Act:7 - effect:.O...... - Num:9 [fit: 0.012, exp: 818.00, pred: 1343.672, error:1833.8170765267917]acc: 0.011002444987775062\n" + ] + } + ], + "source": [ + "print(\"Most Numerous rules:\")\n", + "for action in range(agent.cfg.number_of_actions):\n", + " action_set = agent.population.generate_action_set(action)\n", + " most_numerous = action_set[0]\n", + " for cl in action_set:\n", + " if cl.numerosity > most_numerous.numerosity:\n", + " most_numerous = cl\n", + " print(most_numerous)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best rules:\n", + "Cond:O......O - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 45.00, pred: 571.826, error:7137.170298870321]acc: 0.0\n", + "Cond:....FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.002, exp: 50.00, pred: 353.055, error:6337.943477712033]acc: 0.54\n", + "Cond:O......O - Act:2 - effect:.O....OO - Num:1 [fit: 0.009, exp: 13.00, pred: 520.655, error:10075.382209840245]acc: 0.0\n", + "Cond:...O.... - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 83.00, pred: 544.878, error:4259.89056855382]acc: 0.5301204819277109\n", + "Cond:O...F#.O - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 493.170, error:1341.6924102555245]acc: 0\n", + "Cond:.#..FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 182.00, pred: 496.703, error:4613.81658032312]acc: 0.6593406593406593\n", + "Cond:.....OOF - Act:6 - effect:.OF..O.. - Num:3 [fit: 0.026, exp: 127.00, pred: 278.127, error:895.2348863473378]acc: 0.0\n", + "Cond:OO.....O - Act:7 - effect:.......O - Num:3 [fit: 0.010, exp: 16.00, pred: 446.449, error:7981.043445140795]acc: 0.0\n" + ] + } + ], + "source": [ + "print(\"The best rules:\")\n", + "for action in range(agent.cfg.number_of_actions):\n", + " action_set = agent.population.generate_action_set(action)\n", + " most_numerous = action_set[0]\n", + " for cl in action_set:\n", + " if (cl.fitness) > (most_numerous.prediction):\n", + " most_numerous = cl\n", + " print(most_numerous)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Avg Error and pred\n", + "[fit: 0.006, pred: 574.682, error:6100.992211655139]\n", + "[fit: 0.018, pred: 416.524, error:4858.0924376831]\n", + "[fit: 0.009, pred: 586.372, error:5528.83380068283]\n", + "[fit: 0.017, pred: 572.786, error:4750.917418532567]\n", + "[fit: 0.014, pred: 929.342, error:4078.4389407891395]\n", + "[fit: 0.009, pred: 802.240, error:3061.474473908673]\n", + "[fit: 0.010, pred: 479.630, error:2908.2080628997805]\n", + "[fit: 0.010, pred: 809.651, error:2999.963739797117]\n" + ] + } + ], + "source": [ + "print(\"Avg Error and pred\")\n", + "for action in range(agent.cfg.number_of_actions):\n", + " action_set = agent.population.generate_action_set(action)\n", + " error = sum(cl.error for cl in action_set) / len(action_set)\n", + " prediction = sum(cl.prediction for cl in action_set) / len(action_set)\n", + " fitness = sum(cl.fitness for cl in action_set) / len(action_set)\n", + " print( f\"[fit: {fitness:.3f}, pred: {prediction:2.3f}, error:{error}]\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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VWDHpWbtJlYSIlJkwSeILwEnARqABmA9cmo+gik1czU0iUqbCLBW+Gbgoj7EUrYqoYabmJhEpP2FGNx1lZk+Y2arg+SwzuzZ/oRUPMyMei6qSEJGyE6a56fskFvnrBHD3lymjyiJeEVElISJlJ0ySqHH35w44tjeXwRSzKlUSIlKGwiSJZjM7AnAAM/s4sCkvURWheEVEC/yJSNkJM8/hMuB24H1mthF4C7g4L1EVoapYlA4tFS4iZSbMKrBr3f0vgHrgfe5+iru/3dd7zOwOM9uc7Ozu5fwoM3vQzP5kZq+Y2SUp5840s9fNbI2ZXZ1pnPmiSkJEylGY0U1jzey7wFLgd2Z2s5mN7edtS4Az+zh/GYltUWcDC4Bvm1mlmUWBW4GzgOnAQjObnmms+VBVoT4JESk/Yfokfgo0ARcAHw++v6evN7j708DWvl4CjDAzA4YHr90LzAPWBNXLnuDaBd3Poiqm0U0iUn7CJIkx7n69u78VfP0HUDvA698CHA00AiuBK4M1oSYAG1Je1xAcO4iZXWpmy81seVNT0wDDSS+uSkJEylCYJPGkmV1kZpHg6xPAwwO8/keAFcB4YA5wi5mNJLF/9oG8l2O4++3uPtfd59bX1w8wnPTiFVH2qJIQkTITJkn8LXAX0AEkm4C+MsAd6i4Bfu4Ja0iMmHoficphUsrrJpKoNgqmKhZRJSEiZSfM2k352JluPXA6sNTMDgXeS2Jl2W3AkWY2jcSCghcBn8rD9TMWr4ho+1IRKTsZJwkzOxlY4e67zGwRcCzwHXdf38d77iYxaqnOzBqA64AKAHdfDFwPLDGzlSSamK5y9+bgvZcDvwKiwB3u/koW95czVbGolgoXkbITZjLdbcBsM5sN/BPwQ+DHwKnp3uDuC/v6QHdvBM5Ic+4R4JEQ8eWVKgkRKUdh+iT2BjvTnQfc7O43A/logipK8ViUrm6nsyv7RLFnbzc/+ePbmrktIkNGmCSx08yuARYBDwcT3iryE1bxycUWps+saeL/3r+Ke5/f0P+LRUSKQJgk8UkSI5s+5+7vkJi38M28RFWEkluYDmSE09tbdgNw57K3SdkuXESkaIVZu+kdd/8vd18aPF/v7ncmz5vZsnwEWCySW5gOpJLYsLUNgDc2t/KHtX1NRBcRKQ5hKon+xHP4WUUn2dw0kEpi/dbdTKsbRm1NBT/+w7ocRSYikj+5TBIl3X5SFRt4c1NDy26OqB/OJ+ZO4levvMum7W25Ck9EJC9ymSRK2kA7rt2d9Vt3M2lMNYvmT6Hbnbv/mHaKiYhIUchlkuhtvaWSER9gJbF11x527+li8pgaJo+t4bT3HsJdz23QelAiUtRCJQkzm2JmfxF8X21mqfMkPp3TyIpMfICVxIaWRNPSpNE1AHz6xCk0t3bw2Cvv5CZAEZE8CLPp0N8A/wv8v+DQROAXyfPu3uvuc6Ui2SeR7dIc67cmhr9OHptIEqceWc+UsTX8eNm6nMQnIpIPYSqJy4CTgR0A7v4GcEg+gipG8Z7RTVlWEkGSmDi6GoBIxFg0fwrPr2th9aZsF9EVEcmvMEmiI9glDgAzi1HiI5pSVVUk50lkV0ls2LqbuuGV1FTuWy7rwrkTiVdEuHNZn1uFi4gUTJgk8ZSZ/TNQbWYfBn4GPJifsIpPPDbASqJlN5PG1Ox3rLamkvNmT+AXL21ke1vngGMUEcm1MEniahL7Wq8ksQHRI8C1+QiqGA10WY71W3f3dFqn+vSJU2jr7OJLP32Jne1KFCJSXMIsy9Ht7t939wvd/ePB9+XT3BTLfnTT3q5uGre1M3nMwUlixoRR/MfHZrD0jWbO/97vWde8a8CxiojkSpjRTSvN7OUDvpaa2U1mNjafQRaDWDRCLGJZVRKbtrfT1e1MGlPd6/lFJ0zhzs/NY0trB+fd+izPvNE80HBFRHIiTHPTo8DDwMXB14PA08A7wJKcR1aE4hXRrCqJ5Mim3pqbkk46oo4HLjuFw0bG+ex/P8d/P/uWVooVkYILszPdye5+csrzlWb2rLufHGxnWvKqYpGsKokNLUGS6KW5KdXksTXc93cn8ZV7VvC1B1+ls6ubSz94RFaxiojkQphKYriZzU8+MbN5wPDg6d6cRlWk4hXRrEY3rd+6m2jEGDeq/4Vyh1fFWLzoOBa8t57v/e5NdnWUxY9WRIpUmCTxeeAHZvaWma0DfgD8jZkNA/4zH8EVm6pYJKt5Ehu2tjGhtppYNLMfdyRiXHH6kWzb3clPtYudiBRQxs1N7v48MNPMRgHm7ttSTt+b68CKUVWWlURijkTvndbpHDt5NPOnjeEHS9fy6ROmUBnTgr0iMvjCLvB3Dok5EleY2b+a2b/mJ6ziFK/ItpLY3evw1/58ccERbNrezgMrNoZ+r4hILoQZAruYxD7Xf09iWfALgSl5iqsoVcUidISsJHbv2Utz6x4m9jGyKZ1Tj6pn+riRLH7qTbq7NdJJRAZfmEriJHf/DNDi7l8DTgQm5Ses4hSviNIespJI7mvd38im3pgZX1xwBG827eLXr74b+v0iIgMVJkm0B4+7zWw80AlMy31IxSsei4auJJJzJLJpbgI4a8ZhTBlbw21Pval5EyIy6MLMk3jQzGqBbwIvklgB9vv5CKpYVVVEwlcSyTkSo8N1XCfFohEu/eDh/N/7V7Fs7RZOOqIuq88ZqDebWnlt086CXBtg3rQx1I+oKtj1RcpVRknCzCLAE8GIpvvM7CEg7u7b8xlcsYnHoqEn063fupthlVHGDKvM+roXHDuR7zz+Brf97s2CJYnPLXmedVt2F+TaAB+efijf/8zcgl1fpFxllCTcvdvMvk2iHwJ37wA68hlYMaqqiIRelmPD1jYmjanBLPstwOMVUT53yjRufPQ1VjZsZ+bEUVl/Vjbe2d7Oui27uey0IzhvzoRBvTbAD5e+xf0vbWRHeycj4xWDfn2RchamuenXZnYB8PNyWv01VWLGddiO64P3kcjGxfMnc+uTa7jtqTV87+LjBvx5YSx/eysAH3n/YRx16Ih+Xp17nzh+Ivcs38ATq9/l/GMmDvr1RcpZmI7rr5DYaGiPme0ws51mVlb7bsZjiUoi0xzp7mxoyW6OxIFGxCv47IlTeXTVO6zZPLh9A8vXtVBTGWX6uJGDet2kYyaNZtyoOA+/vKkg1xcpZ2H2kxjh7hF3r3D3kcHzwvzWKJCqiijusKcrsyanrbv2sHtPV+jZ1un8n1OmEY9FufXJN3PyeZl6ft1Wjplcm/GyIrkWiRhnzRjH039uZoc2ZhIZVGEm05mZLTKzfwmeTwoW+SsbVSG3MF0/wOGvBxozrJJFJ0zmgRUbeXvL4GxO1Nqxl9WbdnDclDGDcr10zpk1jj1d3Tyu+SIigyrMn4bfI9Fx/angeStwa19vMLM7zGyzma1Kc/4fzWxF8LXKzLrMbExwbl2w0dEKM1seIs68SW5hmunSHBtasp9Il87ffOBwYtEIt/1ucKqJl9a30O1w/NTRg3K9dI6ZVMu4UXEeWakmJ5HBFCZJzHf3ywgm1bl7C9DfuM4lwJnpTrr7N919jrvPAa4BnnL3rSkvOS04XxRjH3u2MM2wkkhOpJuY5RyJ3hwyMs5Fx0/ivhcb2LitLWefm87z61qIGBwzubBJIhIxzp6pJieRwRYmSXSaWZTEJDrMrB7o87eluz8NbO3rNSkWAneHiGfQha4ktu6mbngVNZVhBpH1729PTWxEdPtT+a8mlq/byvTxIxleldt7yMbZM9XkJDLYwiSJ7wL3A4eY2Q3AM8DXcxGEmdWQqDjuSznsJIbdvmBml/bx3kvNbLmZLW9qaspFOGmF7ZPIZonwTEyoreaCYydy9/Mb2Lyzvf83ZKmzq5uX1m9jboH7I5KOmVTLeI1yEhlUYUY3/QT4JxIbDG0CPubuP8tRHH8JPHtAU9PJ7n4scBZwmZl9ME1ct7v7XHefW19fn6NwepesJDKdK7E+yyXCM/HFBUewt6ubHyx9Ky+fD/Bq4w7aOruYW+D+iKRIxDhr5jiWvtHM9jY1OYkMhjCjm24Gxrj7re5+i7uvzmEcF3FAU5O7NwaPm0lUMAUfSbWvuan/SmJvVzeN29qZlMUS4ZmYMnYY582ZwP/84W227tqTl2ssf7sFoGgqCdAoJ5HBFqah+UXgWjM7isQv7XvcfcCjjoKd7k4FFqUcGwZE3H1n8P0ZwL8P9FoDta+5qf9KYtP2drq6PS/NTUl/t+AIfrFiIzc8vJoPve+Qfl8/dngl86eNyXiJkOXrtjJpTDWHZbA392A5ZlItE2qreXjlJi44TrOvRfItzPalPwJ+FAxRvQD4hplNdvcj073HzO4GFgB1ZtYAXAdUBJ+3OHjZ+cCv3T114P+hwP3BL7MYcJe7P5bxXeXJvuam/iuJ5MimXA5/PdCRh47gnJnjuO/FBu57sSGj93znk3P42DH9r7/k7jy/roUPHlmYBQXTMTPOmnEYP1q2ju1tnYyq1lpOIvmUzZCV9wDvA6YCr/b1Qndf2N+HufsSEkNlU4+tBWZnEVtexSuCIbAZjG7at0R4/pIEwLc/MZsrTk+bp/fzlXtX8I3HXuMj7z+M6spon699e8tumls7mDu1eJqaks6ZNY4fPPMWv3n1XT6uakIkrzJOEmb2DeCvgDeBe4Drg6XDy0ZVLPNK4t0diUVyDx2Z36aaqlg040X3/uWc6Xzy9j/w/aVr+00sz69LjCEolk7rVHOCJqd/fWAV3/rV64UOR6QoHHnocH78ufk5/9wwlcRbwEnA4UAVMMvMknMhykKYSqK5tYPamgoqY4VZ76g38w8fy1kzDuO2373JJ+ZO6rOv4YW3WxhVXcF76ocPYoSZMTP+/bz38+tX1HktkjSuNj9/kIZJEl3Ab4GJwArgBGAZ8KHch1WcwlQSTTs7qBtefDupXXPW0TyxejPf/NXrfPsT6Vv0nl+3lblTRhOJZL8PRj6dfvShnH70oYUOQ6Tkhfkz9wrgeOBtdz8NOAbI7+y1IhNmdFNzawf1RZgkJo+t4ZJTpnLfiw2sbOh9Y8EtrR282bSrKPsjRGRwhUkS7e7eDmBmVe7+GvDe/IRVnCIRozKW2e50TTs7qCvSPZkvP+09jB1WyfUPvdrr3hgvBPMjCr2on4gUXpgk0WBmtcAvgN+Y2QNAYz6CKmZVsUiGlcSeoqwkILGB0T+c8V6eW7eVR1e9c9D55W+3UBmNMGPC4G6TKiLFJ8w8ifODb//NzJ4ERgEFn7sw2OIV0X4ribY9XbR27KVuRH+L5BbOJ4+fxJ3L1vGfj65mdE0lqfPrnnmjmVkTR/XMCxGR8pXV0p7u/lSuAxkq4hUROvqpJJpbE8Nfi7WSAIhGjH/56HQW/fCPLPz+Hw46f/lp7ylAVCJSbAq//vMQUxWL0t7PENjNOxNJolj7JJJOfk8dv/nyqQetJBsxY86k2sIEJSJFRUkipEQl0Xdz01CoJJLec8hw3nNI8c2FEJHiUDwzvYaITCqJpqCSOKTIKwkRkf4oSYRUUxmltaP/JGEGY4YVb8e1iEgmlCRCOnRknHe3970bXHNrB2NqKolF9eMVkaFNv8VCGl9bzbs72+nsSt8vUaxLcoiIhKUkEdKE2jju8O6O9NVEc2sH9eqPEJESoCQR0vjaxE5zjdvSJ4mm1g7qhqs/QkSGPiWJkMaNSiaJtl7PuzvNO/eokhCRkqAkEdL4YM32jWmSxK49XbR1dqlPQkRKgpJESDWVMUbXVLBpe+9JojmYI6FKQkRKgZJEFsaNqk7bJ9EUzLZWJSEipUBJIgvja6vT9kmokhCRUqIkkYUJtfG0SUKVhIiUEiWJLIyrrWZH+152tncedK55ZwcRLckhIiVCSSILybkSm3pZnqOptYMxw6qIRuygcyIiQ42SRBYm9DEMtklzJESkhChJZKGnkuhlhJNmW4tIKVGSyMIhI+JEI9Zr53XzTq3bJCKlQ0kiC9GIcdjIg0c4uTtNrR1DYkc6EZFMKElkaXxtnMYDZl3v7NjLnr3dqiREpGQoSWSpt1nXyW1LNUdCREqFkkSWxtdWs2l7G93d3nNMs61FpNQoSWRpQm2czi6nOZhhDZptLSKlJ69JwszuMLPNZrYqzfl/NLMVwdcqM+syszHBuTPN7HUzW2NmV+czzmz07CuRMqGuSZWEiJSYfFcSS4Az051092+6+xx3nwNcAzzl7lvNLArcCpwFTAcWmtn0PMcayr4d6vZ1Xje3dhCNGLXVFYUKS0Qkp/KaJNz9aWBrhi9fCNwdfD8PWOPua919D/BT4Lw8hJi1Cb0kiaadiYl0ES3JISIloij6JMyshkTFcV9waAKwIeUlDcGx3t57qZktN7PlTU1N+Q00xcjqGDWV0f1GODW37lF/hIiUlKJIEsBfAs+6e7Lq6O1Pce/lGO5+u7vPdfe59fX1eQvwQGZ20L4STZptLSIlpliSxEXsa2qCROUwKeX5RKBxUCPKwPja6v0m1DVrtrWIlJiCJwkzGwWcCjyQcvh54Egzm2ZmlSSSyC8LEV9fUjcfck8Mh61TJSEiJSSWzw83s7uBBUCdmTUA1wEVAO6+OHjZ+cCv3X1X8n3uvtfMLgd+BUSBO9z9lXzGmo1xo6ppbt1De2cX7Z1ddHa5KgkRKSl5TRLuvjCD1ywhMVT2wOOPAI/kPqrcSQ6DfWd7O51d3QCqJESkpOQ1SZS68cHmQ43b2nq62lVJiEgpUZIYgPEps64rooksUT9CGw6JSOlQkhiAw0btqySGVSV+lPXD44UMSUQkp5QkBiBeEaVueBWN29qoramkMhphZLV+pCJSOvQbbYDG18bZuK2Nvd1O3fBKzLQkh4iUDiWJARo/qpo1Ta1EzDSySURKTsEn0w11yaU5mnZqtrWIlB4liQEaXxtn954u3mrepcX9RKTkKEkMUHJCXVtnlxb3E5GSoyQxQMkkAVA3XHMkRKS0KEkMUHLWNUD9CM2REJHSoiQxQHXDqnpmW6uSEJFSoyQxQJGIMS5YnkN9EiJSapQkciDZ5KR5EiJSapQkcmD8qGoqYxFGVGluooiUFv1Wy4FFJ05hzuRaLckhIiVHSSIHjp08mmMnjy50GCIiOafmJhERSUtJQkRE0lKSEBGRtJQkREQkLSUJERFJS0lCRETSUpIQEZG0lCRERCQtc/dCx5AzZtYEvD2Aj6gDmnMUTqGV0r1Aad1PKd0L6H6KWab3MsXd63s7UVJJYqDMbLm7zy10HLlQSvcCpXU/pXQvoPspZrm4FzU3iYhIWkoSIiKSlpLE/m4vdAA5VEr3AqV1P6V0L6D7KWYDvhf1SYiISFqqJEREJC0lCRERSUtJAjCzM83sdTNbY2ZXFzqesMzsDjPbbGarUo6NMbPfmNkbweOQ2BXJzCaZ2ZNmttrMXjGzK4PjQ/V+4mb2nJn9KbifrwXHh+T9AJhZ1MxeMrOHgudD+V7WmdlKM1thZsuDY0P5fmrN7H/N7LXg/6ETB3o/ZZ8kzCwK3AqcBUwHFprZ9MJGFdoS4MwDjl0NPOHuRwJPBM+Hgr3AP7j70cAJwGXBf4+hej8dwIfcfTYwBzjTzE5g6N4PwJXA6pTnQ/leAE5z9zkp8wmG8v3cDDzm7u8DZpP47zSw+3H3sv4CTgR+lfL8GuCaQseVxX1MBValPH8dGBd8Pw54vdAxZnlfDwAfLoX7AWqAF4H5Q/V+gInBL5oPAQ8Fx4bkvQTxrgPqDjg2JO8HGAm8RTAgKVf3U/aVBDAB2JDyvCE4NtQd6u6bAILHQwocT2hmNhU4BvgjQ/h+guaZFcBm4DfuPpTv5zvAPwHdKceG6r0AOPBrM3vBzC4Njg3V+zkcaAL+O2gO/IGZDWOA96MkAdbLMY0LLjAzGw7cB3zJ3XcUOp6BcPcud59D4q/weWY2o8AhZcXMPgpsdvcXCh1LDp3s7seSaG6+zMw+WOiABiAGHAvc5u7HALvIQVOZkkSicpiU8nwi0FigWHLpXTMbBxA8bi5wPBkzswoSCeIn7v7z4PCQvZ8kd98G/I5E/9FQvJ+TgXPNbB3wU+BDZvY/DM17AcDdG4PHzcD9wDyG7v00AA1BpQrwvySSxoDuR0kCngeONLNpZlYJXAT8ssAx5cIvgc8G33+WRNt+0TMzA34IrHb3/0o5NVTvp97MaoPvq4G/AF5jCN6Pu1/j7hPdfSqJ/09+6+6LGIL3AmBmw8xsRPJ74AxgFUP0ftz9HWCDmb03OHQ68CoDvB/NuAbM7GwSba1R4A53v6GwEYVjZncDC0gsC/wucB3wC+BeYDKwHrjQ3bcWKMSMmdkpwFJgJfvavf+ZRL/EULyfWcCPSPzbigD3uvu/m9lYhuD9JJnZAuCr7v7RoXovZnY4ieoBEk01d7n7DUP1fgDMbA7wA6ASWAtcQvDvjizvR0lCRETSUnOTiIikpSQhIiJpKUmIiEhaShIiIpKWkoSIiKSlJCEyQMHKm3/Xx/nfZ/AZrbmNSiQ3lCREBq4WOChJBCsM4+4nDXZAIrkSK3QAIiXgRuCIYBG/TqAV2ERiafDpZtbq7sOD9ageAEYDFcC17j4kZvNK+dJkOpEBClarfcjdZwQzkR8GZrj7W8H5ZJKIATXuvsPM6oA/AEe6uydfU6BbEElLlYRI7j2XTBAHMODrwUqj3SSWpD8UeGcwgxMJQ0lCJPd2pTl+MVAPHOfuncFqqvFBi0okC+q4Fhm4ncCIDF43isR+DJ1mdhowJb9hiQycKgmRAXL3LWb2rJmtAtpIrMTbm58AD5rZcmAFiSXDRYqaOq5FRCQtNTeJiEhaShIiIpKWkoSIiKSlJCEiImkpSYiISFpKEiIikpaShIiIpPX/Aals69KBfXPWAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -297,22 +1413,29 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -333,12 +1456,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -362,22 +1485,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -402,30 +1525,6 @@ "ax.set_ylabel(\"steps for solution\")\n", "ax.legend([\"steps\"])\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XNCS_woods_avg.ipynb b/XCS_Experiments/XNCS_woods_avg.ipynb index 065a214..6f9f824 100644 --- a/XCS_Experiments/XNCS_woods_avg.ipynb +++ b/XCS_Experiments/XNCS_woods_avg.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -23,11 +23,11 @@ "text": [ "This is how maze looks like:\n", "\n", - "\u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } ], @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -55,15 +55,20 @@ "cfg = Configuration(number_of_actions=8,\n", " max_population=1800,\n", " learning_rate=0.2,\n", - " mutation_chance=0.08,\n", - " chi=0.8,\n", + " epsilon_0=0.01,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", " ga_threshold=25,\n", " deletion_threshold=25,\n", - " delta=0.1,\n", - " initial_error=0.01,\n", - " metrics_trial_frequency=50,\n", - " covering_wildcard_chance=0.9,\n", - " user_metrics_collector_fcn=xcs_maze_metrics,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", " lmc=10,\n", " lem=200\n", " )\n" @@ -71,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -80,7 +85,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 40, 'reward': [9.481067235992241e-41, 2.133240128098254e-40, 1.9621374957174576e-77, 2.922017516796629e-40, 2.94174852031233e-40, 3.824833107711084e-40, 4.3524600635062645e-40, 200.0], 'perf_time': 0.01856710000000028, 'numerosity': 112, 'population': 106, 'average_specificity': 2.2857142857142856, 'fraction_accuracy': 0.07738095238095237}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 27, 'reward': 1000.0, 'perf_time': 0.016212000000001225, 'numerosity': 64, 'population': 64, 'average_specificity': 8.59375, 'fraction_accuracy': 0.0}\n" ] }, { @@ -94,17 +99,26 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'fraction_accuracy': 0.009408602150537635}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 3, 'reward': 1390.467695717232, 'perf_time': 0.03633810000019366, 'numerosity': 1800, 'population': 1555, 'average_specificity': 20.862222222222222, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 9, 'reward': 1051.5109573174466, 'perf_time': 0.14293810000026497, 'numerosity': 1800, 'population': 1550, 'average_specificity': 20.515555555555554, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 37, 'reward': 1000.0000000001177, 'perf_time': 0.4431442000000061, 'numerosity': 1800, 'population': 1534, 'average_specificity': 19.169444444444444, 'fraction_accuracy': 0.0}\n" ] } ], "source": [ - "\n", "df = avg_experiment(maze=maze,\n", " cfg=cfg,\n", - " number_of_tests=10,\n", - " trials=1000)\n" + " number_of_tests=3,\n", + " explore_trials=2500,\n", + " exploit_trials=2500)\n" ] }, { "cell_type": "code", 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0.001512 179.5 123.3 2.036652 \n", - "650 3.4 0.001368 179.5 123.3 2.036652 \n", - "700 3.3 0.001318 181.9 123.6 2.044400 \n", - "750 4.7 0.001836 182.7 123.6 2.037064 \n", - "800 6.0 0.002455 183.7 123.9 2.032797 \n", - "850 4.1 0.001654 183.7 123.9 2.032797 \n", - "900 4.0 0.001644 184.8 124.0 2.026479 \n", - "950 2.9 0.001219 185.8 124.0 2.021963 \n", + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 20.333333 1000.000000 0.011569 60.666667 60.666667 \n", + "100 6.666667 1209.451280 0.051856 1360.333333 776.333333 \n", + "200 20.333333 785.041917 0.304347 1800.000000 1055.000000 \n", + "300 15.666667 1045.143124 0.252319 1800.000000 1106.333333 \n", + "400 29.666667 709.462192 0.433871 1800.000000 1161.666667 \n", + "500 13.666667 1090.244415 0.198405 1800.000000 1205.666667 \n", + "600 19.333333 1008.224390 0.288814 1800.000000 1222.000000 \n", + "700 9.333333 1084.492052 0.153036 1800.000000 1250.666667 \n", + "800 12.666667 1258.534417 0.216534 1800.000000 1234.666667 \n", + "900 3.666667 1413.465917 0.054013 1800.000000 1260.666667 \n", + "1000 3.333333 1430.265952 0.057651 1800.000000 1290.666667 \n", + "1100 15.666667 1191.411857 0.270642 1800.000000 1291.666667 \n", + "1200 15.333333 1174.545917 0.288776 1800.000000 1331.000000 \n", + "1300 12.333333 1059.004720 0.206261 1800.000000 1331.000000 \n", + "1400 14.333333 1128.607536 0.245075 1800.000000 1368.333333 \n", + "1500 17.666667 1071.446189 0.375244 1800.000000 1367.000000 \n", + "1600 20.666667 1043.285602 0.387420 1800.000000 1390.333333 \n", + "1700 7.000000 1383.407734 0.127791 1800.000000 1381.333333 \n", + "1800 6.000000 1506.843661 0.096391 1800.000000 1393.000000 \n", + "1900 20.333333 1027.420650 0.345792 1800.000000 1401.666667 \n", + "2000 13.333333 1135.410679 0.289465 1800.000000 1412.000000 \n", + "2100 12.000000 1231.843427 0.194471 1800.000000 1426.666667 \n", + "2200 19.000000 1168.096088 0.376105 1800.000000 1424.666667 \n", + "2300 19.000000 1043.423353 0.342171 1800.000000 1452.000000 \n", + "2400 8.000000 1285.111332 0.140494 1800.000000 1458.000000 \n", + "2500 20.666667 806.374751 0.352253 1800.000000 1440.666667 \n", + "2600 18.000000 1201.885676 0.219223 1800.000000 1465.333333 \n", + "2700 23.333333 765.633664 0.300722 1800.000000 1483.666667 \n", + "2800 12.666667 1029.292824 0.216334 1800.000000 1476.666667 \n", + "2900 19.666667 929.801484 0.207916 1800.000000 1479.333333 \n", + "3000 2.333333 1584.691784 0.031993 1800.000000 1492.333333 \n", + "3100 18.666667 1082.638953 0.207062 1800.000000 1505.333333 \n", + "3200 4.000000 1288.586903 0.041993 1800.000000 1506.666667 \n", + "3300 7.000000 1171.438441 0.089677 1800.000000 1500.333333 \n", + "3400 18.666667 1051.286544 0.182575 1800.000000 1491.333333 \n", + "3500 19.000000 894.845332 0.223014 1800.000000 1503.666667 \n", + "3600 20.000000 857.303397 0.231591 1800.000000 1508.333333 \n", + "3700 27.333333 741.651443 0.302267 1800.000000 1516.666667 \n", + "3800 20.666667 666.666686 0.277704 1800.000000 1516.666667 \n", + "3900 5.333333 1479.183766 0.067978 1800.000000 1524.333333 \n", + "4000 19.000000 966.393574 0.236436 1800.000000 1519.333333 \n", + "4100 25.666667 1049.348977 0.359262 1800.000000 1524.000000 \n", + "4200 19.000000 945.753410 0.210907 1800.000000 1532.000000 \n", + "4300 17.000000 1002.806775 0.250447 1800.000000 1529.000000 \n", + "4400 20.333333 904.476119 0.274334 1800.000000 1542.666667 \n", + "4500 7.666667 1200.118281 0.121425 1800.000000 1543.000000 \n", + "4600 17.000000 1285.238940 0.199901 1800.000000 1535.666667 \n", + "4700 30.000000 809.243419 0.397577 1800.000000 1539.000000 \n", + "4800 33.000000 834.718281 0.427849 1800.000000 1537.333333 \n", + "4900 4.333333 1255.325022 0.063624 1800.000000 1531.000000 \n", "\n", - " fraction_accuracy \n", - "trial \n", - "0 0.259619 \n", - "50 0.053213 \n", - "100 0.045706 \n", - "150 0.035817 \n", - "200 0.034720 \n", - "250 0.039040 \n", - "300 0.035501 \n", - "350 0.030666 \n", - "400 0.030513 \n", - "450 0.032513 \n", - "500 0.026641 \n", - "550 0.025052 \n", - "600 0.025535 \n", - "650 0.028255 \n", - "700 0.023228 \n", - "750 0.028790 \n", - "800 0.024835 \n", - "850 0.024593 \n", - "900 0.024945 \n", - "950 0.025245 " + " average_specificity fraction_accuracy \n", + "trial \n", + "0 7.872087 0.000000 \n", + "100 17.462643 0.011789 \n", + "200 14.818704 0.006625 \n", + "300 15.233519 0.000000 \n", + "400 17.285370 0.004167 \n", + "500 18.876111 0.011619 \n", + "600 17.435000 0.000000 \n", + "700 19.326852 0.022637 \n", + "800 19.808148 0.012255 \n", + "900 19.396111 0.015851 \n", + "1000 18.363704 0.018472 \n", + "1100 19.157037 0.011787 \n", + "1200 18.224074 0.019393 \n", + "1300 18.803704 0.005208 \n", + "1400 18.746852 0.007576 \n", + "1500 18.212963 0.012489 \n", + "1600 19.877963 0.009455 \n", + "1700 19.122593 0.014683 \n", + "1800 18.428704 0.012649 \n", + "1900 17.960926 0.007659 \n", + "2000 18.370556 0.040618 \n", + "2100 17.818704 0.030286 \n", + "2200 17.418148 0.019097 \n", + "2300 17.861667 0.015317 \n", + "2400 18.347407 0.027657 \n", + "2500 17.878889 0.014137 \n", + "2600 17.680926 0.005952 \n", + "2700 18.171481 0.052545 \n", + "2800 20.790556 0.016004 \n", + "2900 21.283519 0.042365 \n", + "3000 20.419815 0.020032 \n", + "3100 21.386852 0.015086 \n", + "3200 20.582963 0.021245 \n", + "3300 22.397963 0.014689 \n", + "3400 22.824815 0.034271 \n", + "3500 21.672593 0.022042 \n", + "3600 22.380926 0.052997 \n", + "3700 21.171296 0.072268 \n", + "3800 21.714444 0.028810 \n", + "3900 21.698889 0.039755 \n", + "4000 21.503704 0.043138 \n", + "4100 21.858333 0.040765 \n", + "4200 21.039074 0.064732 \n", + "4300 20.644630 0.055804 \n", + "4400 19.520185 0.046726 \n", + "4500 19.176852 0.012401 \n", + "4600 19.137593 0.042193 \n", + "4700 19.197407 0.000000 \n", + "4800 19.976296 0.017544 \n", + "4900 19.556852 0.094298 " ] }, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -607,22 +863,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -645,22 +901,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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4Xyml7gy+/5ZSakXYey6l1CNKqfeUUnuUUuusXpQgCKOPgw2G6KA9bORoUW5qk96BgOZom5uprlyKch2cMaecZ3cfzzjF3FAOI9M8DKXUXcDHgZsAhdGXMd3CcXbgZ8AFGN7JFUqphQN2uwCYE3xcC/wi7L3/A57TWs/HaBLcE++cgiCMXkzRwXBcuU46e334/IEoR40sDZ29eP2ayuJcAC5cXMHRNje7MqxaKlQllWkGA1ivtb4aaNVafwtYB1RZOG4NcEBrXROsrHoI2Dhgn43Ab7TBZsCllKpQSk0AzgTuAaMyS2vdZu2SBEEYbZiig+H5C4Ci3CwAOjypkTg/2maU1E4NGowPmWGpDKuW8mRwH4Yn+G+PUmoK4AWqLRw3FagNe10X3GZln5lAI4bY4U6l1K+UUvlEICiEuE0pta2xsdHCsgRByDRM0cHwCikAV54pD5Ka5r26YEltVdBgFOU6OH12Gc+8nVlhKU8GexhPKaVcwA+BHcBh4EELx6kI2wb+xKPtkwWsAH6htV4OdAODciAAWuu7tdartNarysvLLSxLEIRMI1KFFJByeRDTYEx1nUy8m2GpN+vaU7IGK2RkDiM4OOlvWus2rfWjGLmL+VprK0nvOvqHriqBeov71AF1Wus3gtsfwTAggiCMQcJFB8OZEDQYbSk0GKX5zn6hnnMXTs64sFRGGgytdQD4n7DXvVprq2Z2KzBHKVWtlHJiDGJ6csA+TwJXB6ul1gLtWutjWuvjQK1Sal5wvw8C71o8ryAIo4xw0cFwXEGJ81RVStW19oQS3iZFeQ5Om13Gn986ljFhKTPpnZ2B0iB/VUpdppSKFD6KitbaB9wI/AWjwukPWut3lFLXKaWuC+72DFADHAB+iVG6a3IT8IBS6i1gGfBfiZxfEITRQ02Y6GA4qQ5JmT0YAzHDUm9lSFjK4/WT47BhsyX0tTxssizs8+9APuBTSnkw8g5aaz0h3oFa62cwjEL4trvCnmvghijH7gJWWVifIAijnIONXZy/qGLQdtNgpKJ5T2ujB+OchZMGvXfuwkl8zWaEpZZWuZK+lnikYxYGWOv0LtRa27TWTq31hODruMZCEATBCq0RRAdNHHYb+U57SjyMxq5een2BQSEpMKq1Tptdxp/fzoywlDsN41nBWuPemZEeqVicIAhjn5omM+EdsXIeV54zJR6GqVIbyWAAfHhxBXWtbt4+mv6wlNvrJyfFPRhgLST1lbDnORgNeduBDyRlRYIgjCsONhgltQN7MEwmpEiAMFJJbTjnnjKJrz2u+PPbx1hS6Ur6emLh8QbIycpAD0Nr/ZGwx4eARcCJ5C9NEITxQCTRwXAMifPkN+6FDEYUD8OV52T97DKeywBtKY/Xn/Iub7AoPjiAOgyjIQiCMGxqGruZXtpfdDCcVEmcH23rwZXnoCA7euDlgkWTeb+5h3ePdSR9PbHI2KS3UuonQUXZO5VSPwVeAd5M/tIEQRgPHAwbyxoJV4okzuta3VHzFybnLpyETcFzu48nfT2xcPf5Uy4LAtY8jG0YOYvtwOvALVrrTyR1VYIgjAuiiQ6GkyoPo67VTWWU/IVJaUE2p1aXpr3rO5NDUo8Av9Na36+1fgDYrJSK/VMVBEGwQDTRwXAm5Dro9QVCQ4OSgdaao63uqPmLcC5cPJmDjd3sP9GZtPXEwwhJpbbLG6wZjL8B4T/FXOCF5CxHEITxxKEoooPhmPIgyfQyWrr7cHv9cUNSAOedMhml4Jm30xeWytgcBpCjte4yXwSfi4chCCmgrrWHM3/wIrUtPeleSlI41h67MglSIw9SF+rBiP/VNnFCDqumF/Ps7vSFpdx96enDsGIwugeMTl0JuJO3JEEQTPYe7+RIS/qrcpJFY2cvNgWl+dlR93HlmjMxkmcwjraZPRjxPQyACxZV8N7xTmoau+LvPMIEAppeXyBjPYwvAn9USr2ilHoFeBhDVFAQhCRj3lW3dKdmgFCqaejspbQgO2pJLaTKw+g/aS8e5y+aDMCzaaiW6g1O20tHlVTcTm+t9Val1HxgHobw4Hta69RNZReEcUzHGDcYjZ29lBdE9y7gZA4jmVP36lrdTMjJGiSvHo0prlyWVbl4bvdxbjh7dtLWFYl0zcIAa30YNwD5WuvdWuu3gQKl1PXxjhMEYfiYs6ybu8amwWjo7GXihNgGY0IKPAyjQiqx1OwFiybz9tH2lOeXMtpgAJ/TWreZL7TWrcDnkrYiQRBCmB5Ga4pmWqcaKx5GYXYWNpX8pLeVCqlwLgjKsae6ic8cnpSpSW9b+PAkpZQdcCZvSYIgmJhfks1jMCQVCGiauuJ7GDabSqoAodaautYeywlvk2mleZwyZQLPpLhaypPhHsZfgD8opT6olPoA8CDwXHKXJQgCQIfHzGH0pnklI09rTx++gI7rYYCR+E5WlVS720t3n7UejIFcuLiCnUfaQuXBqSDTQ1K3AH8HPo8xHe9vwM3JXJQgCAYdbiOH0TIGcxgNnYYRnDghJ+6+riR6GIn0YAzErJZKZVjKDEnlOjOw01trHdBa/0Jr/VGt9WVa6/+ntU5ej74gCCFCHsYYzGE0Bg1GeWF8D2NCroO2pBkMI2k9FA9jVnkB8yYV8mwKu75NDyMjxQeVUnOUUo8opd5VStWYj1QsThDGO+ZdtccboKfPl+bVjCwhD8OCwXDlOUMFACNNXZxJe/G4YPFktr7fQkOnZySXFRVPJhsM4NfALwAfcDbwG+C3yVyUIAgGHW4vecFqmLFWWpuIh1GUm5XUkFRBtvUejIFcsKgCreEv76RmrlymJ71ztdZ/A5TW+n2t9e3IeFZBSDqBgKaz18eMUkOYb6w17zV0eijIziLPGX9StCvXSbvbm5RJd0fb3Ex15RJWDJoQcycVMLM8n7+kKI8RymFkqMHwKKVswH6l1I1KqUuBiUlelyCMezp7fWgN1WVj02A0dvZa8i7AqJLyBzRdvSMflhtKD0Y4SilWTS9mX4rkzt1eQxokU+dhfBFDnfbfgJXAJ4BPJnFNgiBwsmlvRplRvTPWDEZDIgYjJA8y8mGputaeYRkMgLKCbFq6+wgEkj/r20x6Z2elvkrKkpZU8GkXcE1ylyMIgolZITVWQ1JNnb0smDLB0r7hAoRVI7iGdreXTo/PsuhgNEoLsvEFNB0eL6685PY1e4KzMIYaQhsOqTdRgiBYwuzBmFqci8Ouxly3d0Nnr6UKKTD6MGDk5UGODqMHI5yyAsNINKWgMMHdl57xrCAGQxAyFvPLsSjXQUm+c0x1e/f0+ejq9SUckhppgxGSNU9QFmQg5jyP5q7k/47SNW0PxGAIQsZihqQm5DgoznPS0j12pgo0hnow4nd5w8mQ1EjnMMzBScPNYZQGPYxUeIEer5/sNMzzBgs5DKVUOYY67Yzw/bXWn07esgRBMJPeE3IdlBaMLQ8jkR4MODl1bygeRq/Pz7E2DzPKBs8Nr2t1k+uwU5I/vLxDyGCkwMPwpNHDiF8ADX8CXgFeAEQSRBBSRIfHh1KGvHdJfjZvt7ale0kjRiJd3gA5DhtOuy1hg9HnC/Dp+7by2sFmvnzuPK7fMKtfsriutYepxUPvwTApyXOiVIpyGBluMPK01rckfSWCIPSjw+01ZkHYFKX5TsvhjiffrKc038lps8uSvMKhk6iHoZSiKM9Bu9v6F7LWmlsffYtXDzSzanoxP/zLXnYfbedHH1tKfrbx1Xe0bXg9GCZZdhvFeU6aE/ACDzR0MntiYcLncvf5Q+tPNVYCYU8rpS5M+koEQehHh9sbmjZXku+k0+PD6w/EPe77z77Hz148kOzlDYuGTg92m6IkgRLUogQVa+94fh+P7TzKv39oLn+8bh1fv3ABf3nnOJf+/FUON3UDw2/aC6c030lTpzWDtvtoO+f878tsf78l4fO4vYG06EiBNYPxBQyj4VFKdQYfHclemCCMdzo8XibkGAajOBhjb43jZfgDmhMdHmpbUzs2NFEaO3spK3Bis1kPBbkSmInx8NYj3Pn3A3x8VRU3fWA2Sik+d+ZMfvPpU2no7OXin27iz28do63Hy1TX8EpqTUoLrHsYh5sNg3W0LXHBwnTmMKzImxdqrW1a65zg80KttaVuG6XU+UqpvUqpA0qpWyO8r5RSdwbff0sptWLA+3al1E6l1NPWL0kQxgbtbm+oOqg031oVTlNXL76Apr7NY8kbSRdGD4a1CikTqx7GS3sb+Nrjuzlzbjn/eemifvmJ0+eU8dSNpzO1OI8bfr8DGH6FlElpQbZlgciGDsOwtA9Btt7dl8EGA0ApdbFS6kfBx0UWj7EDPwMuABYCVyilFg7Y7QJgTvBxLYYqbjhfAPZYOZ8gWOV7z+zh1kffSvcy4tLh9jEh14hVm1U88bq964Nlov6A5tgQ7l5TRSI6UiZFefE9jN1H27nhgR3Mm1TIz69cgcM++CuuqiSPxz6/nouXTsGmYP7kxPMIkSjLd9JksUrqRFAKfShlwm5vBjfuKaX+G+OL+93g4wvBbfFYAxzQWtdorfuAh4CNA/bZCPxGG2wGXEqpiuB5K4EPA7+yfDWCYIHXDjbz2I6jdCdByG4kCQ9JlVo0GMfaTxqJIy2ZG5ZKpMvbpCjXEXMmxtE2N5++bytFuQ5+fc1qCmIkhnOddv7v8mVs+fo5zJk0MgajtCCbDo+PPl98z64x6GEMZSiUO419GFbOeiHwIa31vVrre4Hzg9viMRWoDXtdF9xmdZ8fY4yCjfnTV0pdq5TappTa1tjYaGFZwninqauXPn+A1w82p3spMQlPehePIYPhD2iauxL3MFy5Tjp7ffiihNr+40+7cff5+fU1a5hkYeyrUooyC/PErWL2YljR/BqqhxEIaPp8gcwOSQGusOdFFo+JlM0aKOUYcZ9g2KtBa7093km01ndrrVdprVeVl5dbXJowXtFah+LML+1rSPNqouP1B+ju84dyGMXBOv94OYxjbW6ys2w47CpjDUZzdy8Bbb0Hw6QoGJ7r8Az2DD1eP5sONHHZykrmjVCIKVFMeRArYalQDiOBMmEAjy99szDAWh/G94CdSqkXMb7gzwS+auG4OugnLFkJ1Fvc56PAxcFy3hxgglLqd1rrT1g4ryBEpcPtoy94h/rie41ordOi+hmPzuCX4oQc47+o3aZw5Tridnsfa/cwJaiLVJuhBiPRHgyTkxLnfYM6s3e834rHG+CMOenrPSlLQB7kRMfQPIzQ8KRMzWForR8E1gKPBR/rtNYPWfjsrcAcpVS1UsoJXA48OWCfJ4Grg9VSa4F2rfUxrfVXtdaVWusZweP+LsZCGAkag3d/a6pLONrm5mBjV5pXFJlwWRCTknwnrXH0pOrb3VQU5VBVkpexHkZDyGAkViUVSx7k5f1NZNkUp84sHf4Ch4gZ3oonD+Lx+kNeUqI5DHca53lDDIOhlJof/HcFUIHhDdQCUwaWv0ZCa+0DbgT+glHp9Aet9TtKqeuUUtcFd3sGqAEOAL8Erh/GtQhCXMxwwUdXVgKGl5GJmMKDRQMMRrw6/+PtHiqKcplWkpuxBqMxQVkQkwkxJM43HWhkxbTimInuZHNSTyq2h2GGo7KzbAl7GOmc5w2xQ1L/jlHq+j8R3tNYmOuttX4GwyiEb7sr7LkGbojzGS8BL8U7lyBYwTQYSytdzJ1UwEv7GvjcmTPTvKrBtEfxMA4FO5Qj4fMHONHhYYorh8KcLNrdXtp7vKFQTqYw1JCUK4rEeXNXL+/Ud/Dv58wdmQUOkYLsLJxZtrg5jIZgwnv2xAL2nehMKCzq7guOZ800g6G1vjb49AKtdb+CbqVUYr6kIGQITcEvq7ICJxvmTeTXrx6iu9eXNm2eaJjDk8yyWoCS/Gy2v98a9ZiGTiOZPLkoJ1SGW9vaQ1Ge1TqV1NDY2UthTlbCYZWiKB7Gqweb0dpoyksnSqlgL0ZsD+NE0MOYO6mQd+o76ElAG8oMSWVsDgN4zeI2Qch4mrr6sNsUxXlONswtx+vXvHqgKd3LGkRoFkbuyS+S0nwnrT3eqHOjj7UbTXtTinKpKjHkLjIxLNXQ6Uk4HAXRZ2Js2t/IhJwsllS6RmJ5w6K0IDtu2ND0MOYG+z8SyWNkcg5jslJqJZCrlFqulFoRfGwARkZ8RRBSTFNXLyX5hobRqhkl5DvtvLQv8/IY4dP2TIrznfiDc6MjYfZgVLhyMtpgNA5BFgTAYbeR77T38zC01mza38Rps8uwJ6BLlSxKC5xxcxgnOnpx2BXVZcbvqC0BeRBPyGBk3gCl84BPYZS6/g8neyY6gK8ld1mCkByaunpD1SzOLBunzS7jH3szr7y2w+0ly6b6xarD9aRcEVReTSmQiqLc4JQ+R0YajIbOXpYO0Rtw5Tn7eRg1Td3Ut3u44QOZIeVemp/NvuOdMfcxPKyc0O+wPYHEd8YmvbXW9wP3K6Uu01o/msI1CULSMFVSTc6eP5G/vnuC/Q1doRBBJtDhMbq8w41YuJ7UrAg9qvXtbvKd9lDvxrSSvIzsxWgcgiyIyYQBAoSvBL3DM2ZnRtNuWYGTpu6+mDcgpo6WmcRPKCSV6X0YwEqllMt8oZQqVkr9Z/KWJAjJo6mrj/IwOYgN84wvmpf2ZlbXd4fb1y8cBScNRrSQx7E2D5OLckJfVOnoxXjyzXqeeftY1Pe7en309PkTrpAyceX2H6K06UAT00rymFaaGVHy0gInfb4AXTF0yk50GDkcs68kkdJad5o9DCsG4wKtdZv5QmvdijUtKUHIKLTWNHb1Uhb2ZVVRlMu8SYUZ14/R7vaGPAUTs86/NUrM+1i7O9TlDYaHcbTVHVV7KRn8+IV9fP+596K+H+rBmDA0gxEuce4N6oGls7t7IKY8SKw8RkNnL5Mm5IR5GNZzGBmb9A7DrpQK/XaVUrnAyCl2CUKK6Ow1lETDQ1IAG+aXs+39lph3hanGDEmFU5wXW9zuWLuHiqKTyeRpJXn4ArqfIGEy8fkDHGnu4f3mHo5HOWdDUBKjvGBolflFYUOUdh5po7vPn1EGw7wZiVYp5fH6aevxMrEwmxyHnewsW2I5jD4/ShlNf+nAyll/B/xNKfUZpdSngeeB+5O7LEEYeU72YPS/39kwd2LGldd2uL39ejDAuKvMd9oj3r32+QI0dvVSUdTfw4DUaUrVtrrxBUt+txyOPHrUlGYZqofhyjvpYWza34hNwbpZmWMwzMKEaL0Ypodlqum6LMz4CMcdnLaXrgINK1pSPwC+CywATgG+E9wmCKMK8z/xQIOxaoYhKZFJeYx2t2+QhwFQUuCMKEB4osOD1vTzMMzS2lSNa60J0+XaciiydLwpi1E+RFnxCbkOen0BPF4/rxxoYmmVa1CuJ52c1JOKbDDMHozyoMF05ToTDkmlK38B1tRq0Vo/Czyb5LUIQlIxJRsGGgyH3cbps8t4KYPKa42Q1OD/niX52bREuCM92YNx0sOoKMohy5Y6mfOaRkO2ZGllEVsORfYwGjqNHgTXEOVKzONqW3p4s7aNG8+ePbTFJomSkIcROSRlGsxJwT4UK1MEw3H3BdKWvwBrE/fWKqW2KqW6lFJ9Sim/UqojFYsThJEkZDAKB/cwbJhXzrF2D/tOpF+91uP10+cLDApJAZTkRZY4P9nlfdLDyLLbmFqcy5EWd/IWG0ZNUzfFeQ7OPWUy+050Rcy1NHb2Ul6QPWSjbHoTz+4+TkDD6XMyo5zWxJllY0JOVlTFWlPWfGLIw7A2p9zE4/OnrWkPrOUwfgpcAewHcoHPAj9J5qIEIRk0dfaiFJREaHrbMG8iAC9mQFgqklKtSUl+Ni0Rwh2RPAyAquLUldbWNHZRXZbPqdUlAGyNkMdo6PRQbmEaXjTMUtQ/v3WMfKed5dNcQ/6sZFFWkE1TlMKEhs5esmwq9DeYaA7D05e+ed5gceKe1voAYNda+7XWvwbOTu6yBGHkaezqoyTPSZZ98J/95KIc5k8uzIg8RqRZGCalBU6ag41h4Rxrc1OYnTVI3rsqhc17NU3dzCwvYHFlEdlZtohhKdPDGCqmEd17opN1s0pxRPhdphtDHiSah2E07dmCMiauvNGVw7Dy0+4JDkDapZT6gVLqS0B+ktclCCNOuCxIJDbMm8i2w61pL69td/efthdOSb6TXl8gVI9vUt/uocI1+M59WkkeLd19dEbRnxopOj1eGjt7mVmeT3aWcecfzWAMtUIK6Jf7OH125lRHhVOanx0z6T0xzMMqynXg8QZCkh/xcHv9mZ3DAK4K7ncj0I0xUvWyZC5KEJJBU1dvxPyFyRlzyvAFdNQKn1RxUqk2Ug4jcre3OThpICdLa5ObxzDndMwsKwBgTXUp79S39zNUXn+Alp6+YXkY4T+TTMtfmJheYCQaOvrLokSb8RENd18GexhKKTvwXa21R2vdobX+ltb634MhKkEYVTR1xQ6HrJxeTHaWjU3702wwIijVmoTrSYVjdHlH9jAg+aq1ZoXUzHIj+HBqdQkBTb/5Hc1dfWg99B4MgMLsLJQykvuzyjMz0FFakE1rT1/EDvuB0u6JyoN4vBmcw9Ba+4HyYEhKEEY1TZ19MUNSOQ47q2eU8NrB9DbwhXIYkaqkCgYbjF6fn6auvjgeRrINRhc2BdODmk7Lp7nIsql+YanQpL1heBg2m2JKUS5nz5+YEeXPkSgrcKI1tA4wAr0+P6093lDTHpz0MKxKnKc7h2GlD+Mw8KpS6kmMkBQAWuv/TdaiBGGk6e714fb6++lIReK02WV8/7n3QhLU6aDDE8xhROjDCJc4NzFlOCYXDV5vUZ6DCTlZyfcwmrqpLM4jO8v4MstzZrF4QD+G2bQ2cRhVUgB/vG5dRjXrDSTUvNfd209kMdIs89BQqARCUpmew6gHng7uWxj2EIRRQ7SmvYGYidTXDw4vLPW9Z/bw5Jv1Qzq2w+0lx2ELffmGUxw0GK1hBqM+OAdjSgQPA2BaafJLa2sau0PhKJM11SW8WdcWSugOdZb3QKa4cjNupG44pVFUhc3RrJE8DKt6Uh5fhjbuKaV+G3zaFsxd9HukaH2CMCKcNBixo6sLp0zAledg0/6hh6U6PV5++UoNv3v9/SEd3x5BR8qkMDsLh1319zA6jIR2pCopSP5cjEBAc6ipm+qy/gbj1OoSvH7NziNtgNGDAPF/B6Od0uBNycBu70ZTFqRf0juYw7BQWusPaPp8gYxNeq9USk0HPh2cgVES/kjVAgVhJGjsjKwjNRC7TbFuZimvHmga1OtglR1H2ghoeOtoG94hSItHUqo1UUpRkt9fT6o+NGkvssGoKsmjrtWNP8os8OFyvMOD2+tnZnlBv+0rp5egFKGwVGNnL648R0TPaSxhGsSBAoQNnYM9jHynnSybspT0Dk3bc2Zmp/ddwHPAfGD7gMe25C9NEEYO827PSjjktNll1Ld7ONw8tLtysyzX4w2wN864zkh0uH0RezBMSvKz+yW9j7W7Kcp1kOeMfMy0kjz6/IGQLMVIY5bUzhrgYRTlOlgweQJbDhs/j4EVQmOVCTkOsmxqUPPeiQ4PdpsKhazAuAFw5Tks5TDSPTwJYhgMrfWdWusFwL1a65la6+qwx8wUrlEQho1pMEry44dDzDzGpiHKnW891Bq62995pDXO3oPp8HhjJnVL8539DUabJ6p3AckvrTVVaqsjlLmuqS5h+/uthvx6Z++w8xejAZvN8AIH5jAaOoyybrPL26Qo12Eph2GOZ83IHIaJ1vrzqViIICSTpq5eivMclqQkppfmMdWVy6tDyGN4vH521bZx0ZIKyguzQ/H7RGh3Rw9JgZH47u9hePpN2htIsg3GwcZu8px2Jkeofjq1ugSPN8Du+nYaOnvTVnmWakoLsgcNUToRpcvdqjzIyZBUBhsMQRgLxOvBCEcpxemzy3jtYFPCcf+36trp8wdYU13K8ioXO4biYcRIeoPhYTQPCEnF8jCmuHKxqeT1YtQEE96R+iJWB4UI36hpGTceBhh5jEE5jI7IpdquXGsChBkdkhKEsUQ8HamBrJ9dSofHx+6j7Qmdx8xfrJpezPJpxRxu7ok6UjUSWms6PL6YIamSfCedHmPcrLvPaAaL5WE47DamuHKT5mEcauoalPA2KSvIZlZ5Pn/bc4JeX2Bc5DDANOr9PYyGKB5GUZ41iXMzJCUGQxCSjKEjlYDBmDW0PMaWw63Mm1RIcb4zJL29q9a6l9HT58cf0BGb9kzMPExbT19oDkakcFA400qS04vh8fqpa3UPKqkNZ011KduCEiHjxcMoLegvQNjnC9DS3RcanBSO5RxG0MPIFoMhCMmlqasvofr/8sJs5k8uTEgmxOcPsP1wC6uriwFYUlmE3aYSymO0x5AFMSkJ6/Y+HpqDEd9gJCMk9X5zD1oTU9fJnI8B48dglBVk09Pnp6fP6NqPNcvcleuks9cXtwTb4zXeFw9DsIzH6w+VMQrW8Hj9dPX6EgpJgVEttfVwq2Xp6T3HOunu87OmuhQw5DHmTy5MyGDEUqo1CRcgrG+P3eVtUlWSR1NXH90jLN1uVkiZKrWRWBNmMMZNSKqgf7d3gzlpL8L1m93eHXHCUpL0FhLmN68f5rw7Xk4oLj7eGaro3WlzyujzBdh22FpI6Y1g/mLNjJNfkMunudhV22Y5ed4RnIURr6wWDA/jWFswJBUj6Q1hIoStI+tl1ARvXiKV1JpMceVSWWwYtPJxUiVlerNmcUIkWRCTkABhHIMhSW8hYfad6KLPH+CV/Y3pXsqoIdYs71ismVGCw64s5zG2Hm5hWklevy/v5VXFdPX6ONhobVZ4IiGp1qCHUZLvjFubHyqtHWIzYjRqGruZWJg9aNLfQE6tLiXXYY/ZkDiWKM0PyoMEb1ZMWZBIHkZIgDBOHkOS3kLCmHHol/aKwbCKWd6YaEgqPzuL5VXFvGrBYGit2Xq4ldUz+qvmmIlvqw18J8ezRv9ideU5USroYcQpqTUZSi+G1ppN+5siznUwqWnqGiQ6GIkvnzeXez65KmMlyUeaUEgqWCl1oqMXmzqpMxWOqSfVHqcXw/QwcjJUGmTYKKXOV0rtVUodUErdGuF9pZS6M/j+W0qpFcHtVUqpF5VSe5RS7yilvpDMdY4mTIPx8r5GAknSBhprWFWqjcRps8vYXd8ed17BwcYuWrr7+iV4AarL8inKdbDj/TZL5wvlMGJ4GHabwpXroKW7N+qkvYG48hwUZmcllPh+dvdxPnHPG9wfRURRax1UqY2evzCpKMplfYaOVE0GIQ/DzGF0eigryMZuG2wwXRY9DI/Xj02BM41zzJN25uC0vp8BFwALgSuUUgsH7HYBMCf4uBb4RXC7D/j/gtIka4EbIhw77ujzBTjW4aG6LJ/m7j7eTrBHYLxihgVKh6CSevqcUrSOL3f+RlBgb/UAg6GUYvk0FzstltaaOYzCOKGbkmC3d31b5El7A1FKUZVAaa3Wmp/83Risee+mQxG9jNYeL+1uLzNjlNSOV3KddvKd9lDS+0RHb8T8BYQPUYofksp12NPqpSXTVK0BDmita7TWfcBDwMYB+2wEfqMNNgMupVSF1vqY1noHgNa6E9gDTE3iWkcFR9vcaA3/smYaSklYyipNXb1MyMkakkrqkkoXBdlZcfMYWw+1UF6YzYzgxLlwllcVs7+hK+Q9xKLd7aUgO4usOHeRpfnZ1LW66fD4LHkYYISlDjZ2W1LhfXFvA3uOdXDBoskcbXPz7O7jg/YJVUhl6KjUdBMuD2LIokT2cAtzHChlLemdTh0pSK7BmArUhr2uY/CXftx9lFIzgOXAG5FOopS6Vim1TSm1rbFxbH+BmuGEpVUullS6eGlfQ0rP39bTx592HU3pOUeCpq6+hJr2wnHYbaydWRI3j7HlUAtrZpREvPtbPs2F1vBWbXyPsMPjtZQYLs538F5QCddKDgPgAwsmcqSlhyfi/A611vz07weY6srlx5cvo7osn7tfrhlkaEJzvGOU1I5nSguc/cpqo00atNsUE3IctMcJe3q86R2eBMk1GJH8poG3NjH3UUoVAI8CX9Rad0Q6idb6bq31Kq31qvLy8iEvdjRghhOqSnLZMLecXbVt/SavJZsfv7CfLzy0a9T1gTQmKAsykPWzyjjc3MN7xyP+CVLX2kN9u6dfv0E4y6a5UMpa4rsjjvCgSUl+Nn0+I0xk1WB8dEUlS6tc/Ncz79EZw9t5vaaZHUfauG7DLLKz7Hz2jGrePtoeCruZHGzqwmFXoZJZoT+l+dk0dfXi9Qdo7u6L2YNiReLc4/WntQcDkmsw6oCqsNeVGONeLe2jlHJgGIsHtNaPJXGdo4ba1h6cdhuTCnPYMK8creHlIZbXaq156s16dtW2Wdrf4/Xz+E7jzvRNi8dkCk1dvQn3YIRz0dIKSvKd3Pj7nXRFaHwzBwQNrJAymZDjYHZ5ATst/NxiDU8KJ3ymQiwdqXBsNsV3Np5CU1cvP35hf9T9fvbiASYWZvOxlZUAXLaikpJ8J796pabffocau5lemh83fDZeKSswRCLNootoOQywJkDo9vrTWlILyTUYW4E5SqlqpZQTuBx4csA+TwJXB6ul1gLtWutjyvDr7wH2aK3/N4lrHFXUtvRQWZyLzaZYUumiOM/BP4aQx/AHNN966l1uenAnX3xop6Vqq7+8czzUI2DVyGQKTZ29wxoLOrEwh59esZyaxi5ueeStQaGZrYdbmJCTxbzJ0UfdL5/mYueR1rj5g3a3L2aFlEn4XI9YX0QDWVLp4oo107jvtcMRPaYdR1p59UAznztjZij8keOwc9Xa6bywp4EDDSf7SWoijGUVTlJWYAy6MuVbYnkYRXnO+DmMvjFsMLTWPuBG4C8YSes/aK3fUUpdp5S6LrjbM0ANcAD4JXB9cPtpwFXAB5RSu4KPC5O11tFCbYubqmA9vd2mOHNuOf9IsLzW4/Vz4+93cN9rhzm1uoTDzT387b34uZCHt9ZSWZzLyunFvFXXNtRLSDm9Pj8dnsRlQQayfnYZN58/nz+/fYxfvXKo33tvHGph1YySiCWTJsunFdPa4+X9OI1zRkgqfg7DrPgqK8jGmZXYf+OvnDuPwpwsbvvTO4MM2M/+fgBXnoN/OXVav+1XrZtOdpaNezYZXobPH+D95m5JeMegtMCJP6DZf8IwsvE8jHg5DLfXT84YDkmhtX5Gaz1Xaz1La/3d4La7tNZ3BZ9rrfUNwfcXa623Bbdv0lorrfUSrfWy4OOZZK51NHCkpSfUgAWwYV45zd197K63Vl7b2t3HJ371Bs+9c5xvfHgBD3z2VKa6cgeFGgadt7mH1w428/FVVSyrcvFOfceQZlWnAzPpONSkdzj/euZMLlg0mf9+7r1QmW1TVy81jd1R8xcmZgNfvPkY8abtmRQHm72slNQOOjbfyc3nzWfLoRaefPNklPjd+g7+9l4Dnz6tmvwBndtlBdlctrKSR3ccpamrl6Ntbrx+zSxJeEfFbNJ795jhyUUSHjSxksNo7OylJC/+30YykeDjKKHdbdS8V5WcjFefOaccpeDF9+KHpWpberjsrtd462g7P71iBZ89YyZZdhufWj+DNw61xJz78IdttdgUfHSVkTTt9Q1tVnU6GE7T3kCUUvzwY0uZUZrHTQ/u4Fi7m61x8hcmcyYWku+0xxQi9Ac0nZ7EQlJWE94D+fjqKpZWFvGff94TSoD/7KUDFGRn8cl1MyIe85nTq+nzBfjN6++frJASDyMqZcHf0bv1HSjVP+80EFeuMRMjWrSgq9fH0TY3cyZFD3umAjEYowSzpDbcwygtyGbJ1KK45bW7j7Zz6c9fo6mzl9995lQ+vKQi9N7H11SR77Rzz6ZDEY/1+QP8cXstZ80tp6Iol6WVRYAxWW40cNJgDD2HEU5Bdhb/76qVuPv8XP/ADl492ESOw8biqUUxj7PbFEurYjfwdXmMhLqlpHeBaTCGVqFktym+vXERTV29/N8L+znQ0MUzbx/jqnXTKYpyFzurvIBzFkzit68fDt01Sw4jOqaHsedYB2UF2TGLA4rynGgNnZ7IasL7Txg3aHPFYAhWMA1GZXH/xrCz5k2MWV773vEOLr97M9lZNh79/PpBoZMJOQ7+eXUVT71ZH0rOhfOPfY2c6Ojl46uNmPa0kjxceY5RUynV1Dk0HalYzJ5YyA8/tpSdR9p44I0jLK8qtpRHWD7NxZ5jnSERuYGYjX1WQlKl+dmU5jvjGqpYLK1ycfnqKn792mG+8cTbZGfZ+Mzp1TGPufbMmbT2ePnlKzUU5Tr6Jd+F/phGvbPXF1fWPSQPEkVPal/IYKQ3BCgGY5RgylJPG9BJHKu8trmrl8/ev408p51HPr8uqjt7zfpq/Frzm9cPD3rvoa21lBU4+eCCiYARlllS6eLNUZL4NgfXjPTgngsXV/CvZ85E68FyINFYXlWMP6CjSrqcVKqNn/R2ZtnY/LUP8k8rhieA8JXz5lOYk8XmmhauWDMtrmFdPaOYpVUu2nq8zCyPPMdbMCgOikRC/Eq2ePIg+050keOwUVU8WEkglYjBGCUcaemhKNcxKL69NEp5bZ8vwOcf2EFDZy93X70qZuhiWmke5y2czO+3HAlNCANDMO3v7zVw2YpKHGHu9LLKIvad6Oy3b7I53u7hf/66l4bOwV5QLJq6einIzkpKh+xXzpvHNy9ayCcGVBRFI55y7UmlWmuJTYfdNuwv7JJ8J//xkYVMdeVy7Zkz4+6vlOJzZxheiHR4x8ZuU5QEixPiehhxZmLsO9HJ7IkF2GJU4qUCMRijhNoWd7/8hYndpjhjTv/yWq01tz/1DlsOtfDDjy5hWZUr7ud/5oxq2nq8PLrjpGzEo9uP4g9o/nl1Vb99l1S6CGh4pz5y53MyuO1Pu/nJ3w9w3h0v8+zbxywfl+ho1kTIshshnGiSDwMpLchmemle1MS3FaXaZHDp8ko23XK25XzI+adM5pwFkzj3lElJXtnoxwxLxfsbKco9Oac9EvtPdKU9fwFiMEYNtS09/Sqkwjl7fv/y2t9ufp/fv3GEz2+YxcZl1kIWq6YXs7SyiF9vOkQgoNFa8/DWI6yZUcKsAfLVS6qMuHmq8hivHWzir++e4MpTp1FZnMfnH9jBlx7eFQrhxMJo2sucsaCrppfw2sEmen2D8xihaXtpKJ1MxFPJstv41SdXcd4pk5O4orGBKXNu1cOI9Dfd7vZyvMMjBkOwRiCgqWs92bQ3ELO89qW9jbx6oIlvPfUu5yyYyFfOnWf5HEopPn16NTVN3by4t4E3DrVwuLlnkHcBRufzlKIc3kxBpZQ/oPnPp/cw1ZXLNy9ayGPXr+eL58zhyTfrOe+Ol+NOHmwapo7USPORpRV0eHy8GKFZ8qSHMT6m0o0HzP6feDmMWFP39mdIwhvEYIwKTnR66PMHoia8zPLaJ3Yd5foHdjCrPJ8fX7484XjnhYsrqCjK4Z5Nh/jD1loKs7O4cHHku8ilVa5heRhWJLYBHt1ex7vHOrjlgvnkOOw47Da+eM5cHr9+PfnZdq66ZwvffGI3Hm/kyqOmrt6ER7Mmk9Nnl1FWkM1jOwYrxra7vdgU5DvFYIwVzN6LeB6Gw26jIDsrosHYF+wUnzNRPAzBArUtboCIOQyTs+ZNpKaxG5uCX129Ou6M5Ug47DY+uX4Grx1s5qm36rl42RTyonx5Lal0caSlZ0hquYGA5tP3beVTv94StcQUjGalH/51LyumufhIWO+Ief4//9sZfOb0an67+f2IYnpef4DWHm9GeRhZdhsbl03hxb0Ng+LVplJtuhObwshRFsphxP8bLMp1RCyr3Xeik3ynnakWRSaTiRiMGLx+sDluyGO4tPd4I8azwzkpax7dYFy0pIIpRTn8/MqVg0pvE+GK1dPIddjx+jWXr45e/bPUzGMMobz20R11vLi3kZf2NvK532yL6h3c9dJBGjt7+eZFCyPG2HMcdr550UI+urKSezbVcLCxq9/7Ld0j34MxEly6fCpev+bpt/on7zssdnkLo4eLlkzhCx+cw2QLhRGuPAftkUJSDZ3MnlSYETcSYjBi8PXH3+a6326noSOxUk6r9PkCnP9/L/Nff94Tc7/alh6UIuYdxtxJhbz21Q+yblbpsNZUlOfgurNm8cH5E1k0dULU/RZPLUKpxDu+23u8/Pez77FyejE/+OgSXj3YxHW/2z7IaNa19nD3KzVcsmwKy6cVx/zMW86fT06Wnduf7C+m19g5crIgI8kpUyYwZ2JBSC7epN2i8KAwephRls+XPjTXUlFBND2pvce7mDsx/fkLEIMRlRMdHmqauunu8/Ojv+5Nyjmef/cEx9o9vBhHory2pYeKCTkJq5IOlS+cM4d7PrU65h95YY6DWeUFCecx/uf5vbT29PHtjafwz6uq+K9LF/PS3kZueGBHaCAQwPef24tNwc3nz4/7meWF2XzpQ3N5Zb9RTWXSFGray5wcBhgFBpeumMr291s5EqZe2+H2iocxjnHlOgeFKVuD8zQyoUIKxGBEZXONoUZ6+uwy/ri9LqY431B5cMsRwAg51bVGl72ube2JGY5KF0sqi3izrt1yAnv30XZ+t/l9rl43g1OmGCGtK9ZM4zsbT+GFPQ3824M78foDbH+/laferOfaM2ZaHg509brpzJtUyLefejcU4mrqysyQFBAqdw73Mqwq1Qpjk6I8x6Cy2pAkSIxZK6lEDEYUNtc0U5iTxU//ZTkleU6+9dTg2QHD4f3mbjYdaOLDi41krimXHYkjLZlpMJZVuWjq6uVYBA2qgQQCmm88sZuSfMMbCOeqdTP45kULee6d43zp4V185+l3mViYzb+eNcvyWrLsNm6/+BSOtrn5xUsHgZFVqh1pprpyWTuzhMd31oX+rjosDk8Sxibm1L3w75l9wYFVmVBSC2IworK5poVTq0tw5Tn5/86dx9bDrfw5gQ7jeDy01ZAM/8ZFCyjJd/J6TWSD4fH6OdHRG7NCKl0sqXQB1hr4/rCtll21bXztwvkR76I/c3o1X71gPk+/dYxdtW185bx5g2YyxGPdrFI+snQKv/jHQY4099DU2Uuuw57w56SKf1peyeHmntAEQ8lhjG9ceQ58AU13WOXgvuOdFGZnWUqapwIxGBE43u7hUFM3a2caCeSPr65iQcUEvvfMe1ErehLB6w/wx211fGD+JCqKjDvNzQebI3owda1GSW20Lu90sqCiEIddxW3ga+3u4/vPvceaGSVcujx65/m/njWLb160kH9aPpXLVlQOaU1fu3A+WTbFd/78bsb1YAzk/MWTyc6y8fjOo/T5Ari9fglJjWNcEeRB9p3oZM6kgowReRSDEQEzf2EaDLtNcdtFCzna5uaXL8eeTmeFv+05QVNXL1esMbqo180qo77dE3F8Z0ilNgM9jOwsOwsqJsT1MH741710eHx8+5JT4v7hf+b0av7348uGXEJYUZTLTR+Yw/PvnuAf+xozMhxlMiHHwTkLJ/HUm/WhEmCrwoPC2KMogmLt/obM0JAyEYMRgc01zUzIyWJBxcmy0nWzSjn/lMn8/KWDnBhmme3vt9RSUZTDWXPLjc8OGqZIYSlzDka6ZY2jsaSyiLePtkedFPZmbRsPbjnCNetnMH9y9DLdkeQzp1czsyw/45r2IvFPy6fS2uPl6beMUamSwxi/mDMxzMR3U1cvLd19YjAync01zaypLsU+4C73axcuwB/QfP+594b82bUtPbyyv5F/XlUVmsA1qzyf8sLsiInv2pYesrNsIz7PYaRYWumiq9dHTVPXoPfae7x89bG3KS/I5gvnzEnZmpxZRgIcMjPhHc6Zc8spyXfym9ffB5AcxjjGlWeGpAyDse94ZkzZC0cMxgCOtbs53NwTsQFuWmkenzmjmsd2HA0lKhPlD9tqUdBP1E8pxfpZpbwWIY9hVkhlSgxzIEuD0ulv1vbPY+w93snFP9vE/oZOvnvpYgpTfOd85txyvrPxFD6x1tqsinThsNv4yJKKUDe/5DDGL0UDpu5lypS9cMRgDOBk/iLyFLUbzp5NeWE23x5Cma3PH+DhrbVsmDdxUNf2upmlNHX1DpK3iDYHI1OYVV5AvtPeTyLkz28d49Kfv4q7z89D167jQwvTMzfhqrB+j0zm0rAEv4Skxi8Dp+7ta+iiKNeRUdEFMRgD2HywhaJcBwuixNsLsrP4wgfnsONIG5trWhL67L+/10BDZy+XR5AMNz2a8LCU1tqYg1GceRVSJnabYtFUo4HPDNfd8PsdzJ9cyFM3nc7K6bFlPQRYWllEdVk+IEnv8UyOw052li2Uw9h/opN5kwozKrogBmMAr9c0c2p1ScwqnY+urKQk38k9mxKrmHpwyxEmFmbzgfkTB703rSSPKUU5/RLf7W4vnb2+jGzaC2dZlYs99R186tdb+MVLB/mXU6fx4LVr484AEAyUUly+uoqC7KzQXaYwPnHlOWjr6UNrzd7jRkltJiEGI4yjbW6OtPSEymmjkeOw84m103lhT8OgEFKsz/7HvkY+vvpksjscpRTrZpXx+sHmUMWRFZXaTGBJpYs+f4DNNc18758W81+XLiY7a+RnaI9lPnfGTF6++Wz5uY1zDD0pLw2dvXR4fBmV8AYxGP14Y0D/RSyuWjsdZ5aNezcdsvTZf9haiwb+edXgcJTJulmltPZ42RtMdpkGI5NzGABnzi3jYysreejadVyxJrOTzJmKzaYoyc/cJkMhNRQFFWvNhLd4GBnM6webceU5mG9B6Ku8MJtLl03lke11oaaraHj9Af6wrZYz55TH9BYG5jHMwUmZ7mEU5jj44ceWSr5CEIaJK9eYiWFO2ZsnHkbmsvlQ/PxFOJ85o5peX4AHNr8fc7+f/G0/x9o9fGr9jJj7TXXlMr00L5THONLSQ0m+c0jT8wRBGH0YMzH62He8k9J8J6UZ1kckBiNIXWsPtS3uUNe1FeZOKuSsueXc//r7UTWmth5u4acvHuCjKys5O0KyeyDrZpayuaYZf0BT15rZFVKCIIwsrjwjh7GvIfMS3iAGI4RZIrs2wYl1nztjJk1dvTz5Zv2g9zo8Xr740C4qi/NCncfxWDerlE6Pj3frOzJW1lwQhORQlOug1xdgz7GOjEt4gxiMEJtrminOczB3YmK/pNNmlzJ/ciH3vHJoUCPfbU/s5niHhx9fvsxyWMn0cDYdaOJoq1sMhiCMI8yyao83wBwxGJnL5ppm1s4sTVglVSnFZ8+Yyd4Tnbyyvym0/YmdR3liVz1f+OAcVsSZSR3OxAk5zCrP5/GddfgCOuMrpARBGDlMiXPIvIQ3iMEADIG/ula3pXLaSHxkaQXlhdn88pWa0Od984ndrJpezPUbrE+NM1k3qzRUJZGpKrWCIIw84Y2bmaQhZZJUg6GUOl8ptVcpdUApdWuE95VS6s7g+28ppVZYPXYkGTj/IlGys+x8av0MXtnfxLv1HXzp4V0A3PHxZRGb9OKxbmZZ6Ll4GIIwfjAFCMsLs0PqtZlE0gyGUsoO/Ay4AFgIXKGUWjhgtwuAOcHHtcAvEjh2xNhc00JJvnNYFv1f1kwjx2Hjk7/ewrb3W/nOJYuGnH8whQ9tCipcIq8hCOMF08PIRO8CkuthrAEOaK1rtNZ9wEPAxgH7bAR+ow02Ay6lVIXFY0cErXUwf1EyLJGv4nwnH1tZRWNnLxuXTeGSGKNI41FakM38yYVMceXiGIKHIgjC6MT0KjKxQgogmR1hU4HasNd1wKkW9plq8VgAlFLXYngnTJuWuCxFry/A+lmlnD6nLP7Ocbjpg7MpzMniuiHkLQZy8/nzQqqVgiCMDwqys/jKefPSNhIgHsk0GJFu1wcOkIi2j5VjjY1a3w3cDbBq1arEBlRgCAn+8GNLEz0sIhMLc7j5/Pkj8lkfmJ+ZfzCCICSXG86ene4lRCWZBqMOCFfaqwQGdrdF28dp4VhBEAQhhSQzQL4VmKOUqlZKOYHLgScH7PMkcHWwWmot0K61PmbxWEEQBCGFJM3D0Fr7lFI3An8B7MC9Wut3lFLXBd+/C3gGuBA4APQA18Q6NllrFQRBEOKjEp1LncmsWrVKb9u2Ld3LEARBGDUopbZrrVdZ2VdqNgVBEARLiMEQBEEQLCEGQxAEQbCEGAxBEATBEmMq6a2UagRiz0uNThnQFHevsYdc9/hCrnt8YeW6p2uty6182JgyGMNBKbXNaqXAWEKue3wh1z2+GOnrlpCUIAiCYAkxGIIgCIIlxGCc5O50LyBNyHWPL+S6xxcjet2SwxAEQRAsIR6GIAiCYAkxGIIgCIIlxr3BUEqdr5Taq5Q6oJS6Nd3rGS5KqXuVUg1Kqd1h20qUUs8rpfYH/y0Oe++rwWvfq5Q6L2z7SqXU28H37lTDmV+bApRSVUqpF5VSe5RS7yilvhDcPqavXSmVo5TaopR6M3jd3wpuH9PXbaKUsiuldiqlng6+HvPXrZQ6HFzvLqXUtuC21Fy31nrcPjCk0w8CMzGGNr0JLEz3uoZ5TWcCK4DdYdt+ANwafH4r8P3g84XBa84GqoM/C3vwvS3AOozph88CF6T72uJcdwWwIvi8ENgXvL4xfe3BNRYEnzuAN4C1Y/26w67/34HfA08HX4/56wYOA2UDtqXkuse7h7EGOKC1rtFa9wEPARvTvKZhobV+GWgZsHkjcH/w+f3AJWHbH9Ja92qtD2HMJVmjlKoAJmitX9fGX9Zvwo7JSLTWx7TWO4LPO4E9GLPhx/S1a4Ou4EtH8KEZ49cNoJSqBD4M/Cps85i/7iik5LrHu8GYCtSGva4LbhtrTNLGJEOC/04Mbo92/VODzwduHxUopWYAyzHutsf8tQfDMruABuB5rfW4uG7gx8DNQCBs23i4bg38VSm1XSl1bXBbSq47mTO9RwORYnbjqc442vWP2p+LUqoAeBT4ota6I0ZYdsxcu9baDyxTSrmAx5VSi2LsPiauWyl1EdCgtd6ulNpg5ZAI20bddQc5TWtdr5SaCDyvlHovxr4jet3j3cOoA6rCXlcC9WlaSzI5EXRBCf7bENwe7frrgs8Hbs9olFIODGPxgNb6seDmcXHtAFrrNuAl4HzG/nWfBlyslDqMEUr+gFLqd4z960ZrXR/8twF4HCO0npLrHu8GYyswRylVrZRyApcDT6Z5TcngSeCTweefBP4Utv1ypVS2UqoamANsCbq0nUqptcHKiavDjslIguu8B9ijtf7fsLfG9LUrpcqDngVKqVzgHOA9xvh1a62/qrWu1FrPwPh/+3et9ScY49etlMpXShWaz4Fzgd2k6rrTnfFP9wO4EKOi5iDw9XSvZwSu50HgGODFuIv4DFAK/A3YH/y3JGz/rwevfS9hVRLAquAf4kHgpwRVATL1AZyO4VK/BewKPi4c69cOLAF2Bq97N3BbcPuYvu4BP4MNnKySGtPXjVHR+Wbw8Y75nZWq6xZpEEEQBMES4z0kJQiCIFhEDIYgCIJgCTEYgiAIgiXEYAiCIAiWEIMhCIIgWEIMhiAMA6WUSyl1fYz3X7PwGV3x9hGETEAMhiAMDxcwyGAopewAWuv1qV6QICSL8a4lJQjD5b+BWUHxPy/QhdE4uQxYqJTq0loXBDWu/gQUYyjKfkNrnbEdxYIQCWncE4RhEFTGfVprvSgogvdnYJE2pKQJMxhZQJ42BBHLgM3AHK21NvdJ0yUIgmXEwxCEkWWLaSwGoID/UkqdiSHHPRWYBBxP5eIEYTiIwRCEkaU7yvYrgXJgpdbaG1RZzUnZqgRhBJCktyAMj06MkbDxKMKY3+BVSp0NTE/usgRh5BEPQxCGgda6WSn1qlJqN+AGTkTZ9QHgKaXUNgwl3VhDbwQhI5GktyAIgmAJCUkJgiAIlhCDIQiCIFhCDIYgCIJgCTEYgiAIgiXEYAiCIAiWEIMhCIIgWEIMhiAIgmCJ/x+c0LXMSQ8GOAAAAABJRU5ErkJggg==\n", 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" ] @@ -680,22 +936,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -715,22 +971,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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VoNeFobDX0qayeCpvSnAOUA2wFx5BZxBP5eB3O5fFO4UUtaBW3v75p/HJfUetXkbLOTefBmPQDA3x3XszKu7mkjkksgXlOfnzWyk+F0/X7hFYsTkQhqAO4qnl8hKASoFUDKcRmGAxk8exqQRes7jhqR1w1VGt0FDI60I05G3KxVpdMcTZNGDtSEyzswgAlUdgQS+BMAR1UCkvAQARMa5SUANHJxIAgJlE1uKVtB7eQ6DlEQDApmigKRdrHgLaNLDcI5hdylmSu2OMmZ5FAAiPoOOIp/PL8gOAVDUEiByBwBxHJ6WhKbNL3W8IzsRS8LgcGAp7Ne8fGwjiZBMqe8bnknA5aFmvwiYLK4eSuSLyRVZHslh4BB3BQmq5zhAARPzCIxCY54jsEcwlcyiVurv35EwshXV9fjgc2iWU0q492/CufXxWqkxyqYbE8zCRFVIT80nz8hKAunxUeAQdgVZoyO92wukgUT4qMAX3CIolppQjdyt6PQQcXjnU6OyAk7NJjA0sf52N/a2djWyEVuOpEeXQkPAIOoJ4OrciWUxEiPjEcBpBdUolhmOTCQzKoZJuDw+dqWIImqFCyhjD+FxyWaIYAPweJ0Z7fJYYAkVwzoS8BKBOFguPwPZk8kVk8iXNBFDY5xYegaAqp2MppHJF/OqFUQDAbBcnjBdSeSxmCpo9BJxyB3D9F+uZRBapXFHxLpY9v0UqpGXlUft7BLpKTER0hdEf8jGUq41Fja5iTtjnEn0EgqrwsNC1W6L43vPnMNPFHsFpDfnpSgIeF4Yj3oYu1tybGBvQMATRIPYdmqj7ueulHBoyWzVknUdgJMmnOTlMhgG4oclr6Qi05CU4EeERCExwZCIBBwF7uEewlLN4Ra3jzDw3BH7Dx40NNNb4Na6Sn65kUzSA+VQeC6k8ekzuzpsB9wgqC0v08Lps6BEwxva2cyGdQnkWgbZH0Oyxe4Lu48jEIjZFgxgKe+F2Ulf3EpjxCADpAv7Iy1N1v87J2RQ8TgfW9K40OEoOYi6JXYHeul+jVuKpPMI+17IqJiOsrBoyJdJNRDsAXAzAx29jjH21VYuyM9zd0/QI/G7RWSyoytHJBC5d1wMiwkDQ29XJ4tOxFPoCbqXzXo+xaBAxebqY1merGuOzSazv98OpUaKq1jPatb635ueuF0lwzlxYCAAcDoLHac2Usqqmioj+G4B/lL/2AvgUgLe0eF22pSxBrZUsFsNpBMYsZQs4HUvhopEwACAa9nS1ITijozpaiaIUWmd4aHwuqRkWAiRvhKj5s5GrIclL1GbUvG6HbfsIfhPAjQAmGWPvAbATgHaL4CqAj57TijVGfG4ksgUUu7xBSFA/xyalRrLtIxEAQDTU3R6BWUOwqQE56lJJLh3VSBQDUjXOmh5/2zWHapGX4PjcTnt6BADSjLESgAIRRQBMA9jc2mXZl4V0HkRAWGP0HVcgXcoKr0CgzZEJqWLoojUqQ5DozmRxscRwLp427CHgbJQbwerZtU8lMsjkSyt6CNRsskCFtJZZBByvy4GsTT2CA0TUC+CLAJ4F8ByAZ1q5KDvDY5ha7fJCgVRQjaOTiwj7XFjTI6XbBsNezCWzXTnidHIxg3yRGfYQcKRde32NX4rYnIEhGItKs5Hb+X/m08lqwed2ImOz8lEAAGPsT+Qf/4mI9gGIMMZebO2y7EtcQ2eII/SGBNU4OpHARSMRZXRhNORFvshk2ZLaLhp25/ScsepoJWPRIE7WITPBBeWMPIKxgSAWMwXMp/LoN9np2wj5YgmJbKGmZDEg9RLYajANEW2Xv1/BvwD0A3BVazbrZuIGVQ1cgVT0Eqzk9FwKV//t40ozVbdxbDKBq//2ccPQBmMMRycT2D4aVm6LhqQLRTeWkJ6Jmesh4IxFgzhVRxx/fC4Jr8uB0YhP9zHcW2hXwphXF/YFaw0NWeMRGIWGPiB//3uNr79r8bpsy0I6jx4dK6+EhoRHsIIHXjiHycWMosPfbbw8sYDJxQy+feCM7mPOzqexlC3gotGIcttgSKq76Mbu4jPzKTgImrX9WkRDXsRT+ZqLLU7OJrFxIKCrbgqoZiO3zRDIzWSd7hEwxu4kIgeAv2GM7a34qtpVTETriWg/ER0hosNE9Ofy7f1E9CgRHZe/9zXxeJbx6Udfwe2f/anpxyezBbzzX36BE9P6F6uF1ErBOY4YYK/PvsOTAMofkIae69AkPvwf9opOziel9/z+g+d1ZaV5onj7iMojUITnui9hfDqWwppeP9wmG6r4cKelGjdS47P6FUOc9X0BOKj2qqQzsRTu+MLPMVejoZ7n8hI19kTY0SOAXC1U7+6/AOAvGGMXAbgawJ8S0cUA7gbwOGNsC4DH5d9bQiZfxLEp8zvQV2eW8NMTs9h3aFL3MQtp/RyBMrdYJIuXcSaWwqFz0kWwGZLL+49O45sHzthq8DuXEzgXT+OZ8ZjmY45OJkAEbB1Wh4ZkQ9CloSEziWIO32DVMtypVGI4FUsZJooBwONyYF1foObQ0H8+fw5PvzaHw+drC2mWBedq9wjsKkP9CBH9BvHslkkYYxNcmI4xlgBwBMBaALcDuE9+2H0A3lrL89ZCj9+NTL5k+oLBrfgLZxc07y+VmOYsAk45RyBCQ2oelr0Bp4OU2Gkj8Ka+MzaS85hP5RD2uhD0OPG9585pPubo5CI29gcQVJUe9/rdcDqo63oJsoUixueM5acr4VP/FmrYLJxfSCNXMC4d5YxFa59fvP/YNIDyhd0s5dBQbR6Bz+W0rQz1BwB8G0CWiBaJKEFENZlHIhoDcDmAXwAYZoxNAJKxADCk8zd3EtEBIjowMzNTy8sp8DfB7InF37wXz8Y1709kCygxbXkJQNp1+NwOMa6ygn2HJnHRaARren01fcj1iKel96nRQSbNZD6Vx2DEi1t2jOIHL01obj6OTCSURjKOw0EYCHZXd3G+WMKffu15xJI53HiR5sdbk0gd416ViqEqoSEA2DQQwPhsynQJaSyZw/Nn4gDK08bMMq8ki2vzCLx29QgYY2HGmIMx5mGMReTfI9X+jkNEIQDfAXAXY8y0AWGMfYExtpsxtntwcNDsny2Dy0CY3YXyi9TUYhZTi5kV9xtJUHMkBVLhEXCmFzN49vQ8bt0xgl6/pyk5ggV5+E+7O0WNmE9KujJvu3wtEtkCHj8yvez+VK6A8bnkskQxZzDsNZUjmFrM4Ju/PN20NbeCQrGEu755EI8dmcI9t1+CN10yYvpvefl1LcOdThqojlYyFg1iKVswnY/5yfEZcJsxX6MnO5/Kwe0kBD3Omv7O63Las6GMiB43c5vO37ohGYGvMca+K988RUSj8v2jkDqVWwL3CMxefNQG4wV5J6B1v1ElgNAbWs4jL0+BMeCWHSPoDbibkiPgBtlOSq+SrowH11wwgOGIF997/uyy+1+ZWgJjWFY6yomGvKbKR//9F6fx4e+8hIUmhNdaQanE8JffeREPvTiBv7ptO37/mrGa/r6ehszx2ST8bieGI9VVb8ZqlLHYf3Qa/UEPwj5X7aEhuZmsxoi65BHYSWKCiHxE1A8gSkR9crVPvxzmWVPtieWcwpcAHGGMfVp11wMA3iX//C4A99e9+irwnbvZi088lYfX5YDTQXhRI0/AQxKGHoHfbejaFoolPHF0qis7SbXYd2gSm6NBbBkKoTfgacpFjHtutgoNJSU5AaeDcPuutXjy2AxiqnDCUS4tMbLSIzCrN8Rr7O0YemSM4W/uP4TvPncOH7hpK+687oKanyNSR7J4XC4dNXPB3VTDSMxiieFHr8zg+q2DiIa8dXkEtcpLAFKOIFco6VaetQojj+B9kCQltsvf+df9AD5n4rn3APg9ADcQ0UH56zYAnwBwExEdB3CT/HtLUHIEJt/EeDqHaMiLrcNhvHhupSEwmkXACfvchn0EPzw0iT/4ygG8PNGdjVVq4qkcnn5tDjfvGAERodfvrnlnVUm+WFK0nOziETDGMJ/KKR2rb7t8LQolhgdfPK885sjEIoIeJ9b1raypj4Y9mFvKVd0cnJKP125aVowx3PPgy/j3X5zGH19/Af7shgvrep6w1wWi2jyCkwaqo5Ws6/PD5SBTvQQvnI1jPpXH9duH0Btw15wjiKfq6xTn4ypzxfZ6BUaDaf4BwD8Q0Z8xxv6x1idmjP0UgJ6ZvrHW56sH/kbwnXw1FlJSRdCla3uw7/AkGGPLdhpGswg4YZ8LZw0uULyWfCKewSVrekytq1N57Mg0iiWGW3dIceLegBsL6TxKJWbY/GMEv0iEvC6ciaVQKJZMD/7Q4tRcEmdiac37to6EMBTW71blpPNFZAvlOdYXjUawfSSM7z1/TgmPHJlMYPtoRPO4B0Ne5IolLKYLhhO0uGSDnQxBvljC3z/yCr781Djes2cMf3nztprDIRyHgxDymh/3WiiWcCaWws0m8xAupwPr+wOmQkNPHp2Gg4DrtkRx//NSM2QtzKdy2DxozkCpUQ+n4UahHZjRGqrZCNiFoMcJVw0li3G5NPTSdT34xi/P4EwsjQ0D5fK3BZPJYqMT+ZWpJQDAdBfWjVey79Ak1vT4cOlayeD1+N0oMan6qp7hI0D5PdixNoKfvxbDxELGlMyxFowx/Mbnf6abPLxyUz++9b5rqj4PDxv0q+QE3nb5WvyPHx7FydkkxgYCODqxiF/fqR1Rjaq6i/UMQSKTx5y8K7W6YbFYYnjmZAzff/E8fvjSBOZTefzOVRvw0V+7uG4jwIn4zA934qJ2G2t4/y8YDOH503HkiyXDRrf9x2ZwxYY+9AY86A14lA2cWXjOqFb4xb/dUtSmJpR1KkSE3oCnhhxBDttHIti5rheA5B5WGgKf22FoqSM+l2GM8xW5wW06UdsOo9NIZgv48fEZ/O5VG5SLA98xL6Tqm0IFlA3BznW9+PlrMZyaM6d3r8V8Ko/ZpRzes2cMt106uuy+zz5xAieml8w9T3KlnMBbdq3BJ/YdxX8+fw7veN16LGYK2K5RMQSomsqWsrhwKKT5GHU+pJZihEKxBKeDGr5AA1IBxf0Hz+Ohl85jajELv9uJmy4exlt2rsEN24ea8hrVcmxq4ooBNn/BvePK9fjD+6Zw/8Hz+M1fWaf5mOlEBi+dW8CHbt4mP7+7phwBY6yuWQSAdeMqu9oQAHI4ooby0Z6AG9tGwvC4HHjxbHzZLi5uIC/BifjdyBVKyBaKyjBqTipXUIZ5d6PImJr9x6aRK5Rwi8pt71WS9zlsQH0Xb24ILl0neRmnYkn8KqJ1PRdPvu65IIrXjfUvu2/rcAi/ODm3IjyoBc97qC9Ioz1+vP6CAfznwXOKR3TRyMqKIUAqHwVgmDBW50PMhobyxRKu+R9P4O5bt+te9Myy79AE/ujfnoPH6cAbtg3iLTvX4MaLhhDwNPcSEvG5TJePmqniq+SG7UO4aDSC//PkCbzt8rWaoy1/dEzqW7p+26Dy/Ol80XS4ZilbQKHE6ksWu60ZYG8quEpEa4no9UR0Hf9q9cKahdkEpWTFJfkIt9OBi0cjKzqMJXkJ45OurDe08mQ+Mb2k1CV3e2ho36FJREMe7FZdYMvlvPWHNrgh2DosGetGKof4324cWGmUBkJeZPIlJHPVd2a8Oqjyg//WXWtxai6Frz8j1f5v0zEEXIHUSGZCfZxmtXgW03nMLmVNezZGvCYnWJ+6+wZ88fd349d3rmm6EQCk8KFpjyBde/cuEeH9ey/EazNJ/PDQhOZjnjw2g6GwFxfLHhw38GYLHRTl0bpCQ9Ilud3dxWb6CD4J4CkAfwPgQ/LXB1u8rqbRG3CbuvAkc0UUSkw5qXau68GhcwvLlBDjqbxhMg8w1hviYwrX9vq72hBk8kXsPzqNmy4eWbbjUgxBA70E6sqtDf2BumSLOfziqhVa4uEaM2Jjeh/8W3aMwOd24PGj01jf71ckSCrpC3jgdJChAunpWBL9QQ8cZN4j4JuRZnRzx5Zy8LudivfSKiJ+8zkCxSOoMcx4y44RbB4M4nP7X11RqZUvlvDj4zPYu60c6uIGngsLVmO+TnkJAEoUwY4ewVsBbGOM3cYY+3X5q2OG1/f4PaY+CIo2iLzjv2xdL1K5Il6bKe+m+HQyIyIGekPHp5fgcTnwurG+rhQZ4zx1YhbJXBG37FhezdHj5zmC+ktIF1SVWxv7A415BLEkRnt8mu7+AN+lmzAE3COoPDfCPjduulj6H1RKS6hxOAj9QY/hyMrx2RQ2DgQQ8ppvWOSPa4YIYiyZa8tAl2rFFmqU4o0aL7hOB+FPrr8QRyYW8cTR5f2sz52aRyJTwN7tZTUDHnoy6xHUKy8B2NgjAPAagPoyezZA8giqv4FKaah8Ul0mx6DV4SEj5VFO2EAv5dhkAhcOhjDc48NMojvHEwJSr0TY58I1mweW3a40+DUYGvK7nfC6nNgwEMDpmHntmEpOG4iiRYPm5aF57kirjPW/XL4WgH5+QHm9Kk1lp2MpjA0EEa5BwoRXFzXDI5hL5hTj2EoifpcUYzdRRz+fzCHgca7IxZnh9l1rsK7Pj3984sSy82f/sRm4HIQ9F5bzTrWHhrRDhWaws0eQAnCQiP6ZiP43/2r1wppFr9+NZK6IXJVyLCXkIF+sNg+GEPQ4lwnQxU1UuxiNqzw+lcC2kTCGwj7kiqWmKHHajXyxhMeOTOGNFw3D41p+enlcDgQ9zoZDQ/w9GBsIIpUr1j3UZXwupZkfAKQmLwCYM2EIYqm87of+2i1RvO+6zXjbFcbJ2mhIX3guWyji/II0BD7kdWEpa+7/t9jM0FAbPQLAXPgrbmJjpofb6cAfveECHDwTx89enVNuf/LYNF431r8sjNerhIZMegQaVWRm4R5Bu6uGzBiCBwD8fwB+huUdxh2BWQXSygoEp4OwY22P4hFkC0Wk88WqcT+9cZWLmTzOL2SwZTiEoXD3TqV65mQM8VR+RViI0xvwNGQA1aNCeWnv6TrCQ8lsAbNLWWzUUa3kFz0zoaF4KqcbBnA5HfjIbRdV7X4dDOkLz52dT4MxKakd9rlqyBFI/+dmSFK0zRDUIEUt5ezqX9Nv/so6DIW9+OwTJwAA5+NpHJ1MLAsLAeXcj9kS0nqH0gA27iNgjN1X7TF2hp8oC+mcYaJLqwJh5/pefOWpceQKJVU80vjEi/i0FRSPy41k24bDih799GJ22ZCSbuCxI1Pwuhy4bou2YmyP340Fk53eWvASXwBKI9GpudSy6iQz8HJMPY/A63Ii4nOZShbHkjkMG8zLNcNg2IuZpaxmueppVXVTyOdapmFkRLOSxYwxzCWzSgK9leh9frRYSOfq9ggA6aJ753Wbce9DR/DsqRiOTUqf0b3blktnu50OhL3m/+/xVA5hn6uujner+giMROe+JX9/iYherPxq3xIbo9dkXFpLPuKydT3IFUs4NplYlqQ0IuiR9FIqPQLeSLZ1OKx4BN3YVPbksRlcc8EA/Dryu2aruPRYVHkE6+Txg6fq0BxSSkf79XfqUYNdupp4Sn9YkVmiIS9yhRISGrt9LomwcSAohYbqSBY3ImKWyhWRyZfa6hGY8WKa8X//nas2oD/owWefOIH9x6axttev2dTXFzQvoV5vVzEAeG3oEfy5/P3X2rGQVmG2dj2eyq3oGlZ3GPM5s9V2IHp6Ka9MJRDwOLG214+UbO27rans5GwSJ2eTePfrx3Qf0xtwKzIb9bCQzmOH/B54XA6s6fXXVULK/2aDjkcAmFcFjSVz6G8gRAGUcxIziawSJ+ecmksh6HFiQJZE1jIWWvCLaYkBS7nCiuc1C98JtzNHYKbSiUvCNELA48If7BnD3z3yCjxOB37rdes0Gwj7Am7ETIeG6lMeBWyYI1BNETul9dW+JTaGMpzGRI6g0oqv6/OjL+DGi2fjpgTnOFIJ3EqPYMtQCA6HNKzC73Z2XS/Bk/JYv0rXWk2Pv7EcQWUJ78aB+kpIT8VS6A24Dd/PAYMELieTl3JH9ZQKqjGaXXw6lsKGgSCIqEaPoPx/bkT+m2scDbTFI5BDQ1U8AsaYLFXS+Jp+//VjCPtcyBVLuuduLR5BvcqjAOBxOkCEtg+nqV+2sUPoUTwC4zcxrtEjQES4dF0vXjy7oBgSMzuQsEab/CtTS0o+gIgwFPF2nSHYf2wGmweDhrtsSYG0uuSyFrlCCalccdn7tKE/WJcc9em5lG6imDMQ8igXQT0a6SJVU9YbWvl6p+aSSj4k7HMjnS8ib6K8Ul251kjCOJaUztN2eATKAPsqOYJ0vohcsdSwRwBIG7c7r92M3oAb11wwoPmYvoDHdI6gkcQ6EcHrcrQ9NNT1hiDsdcFB1RNmCzrxxp3renB8ekkZXVlNYgKQ4pzq3VgsmcNMYnlieCjsxUwX5QhSuQJ+/tqcoTcASKG1fJEhZUK6oRKteRAbBwKIJXM1X+hOxZJVVSujIS/iqbzhRVdPXqJW1MJzaoolhjOxNDZGpbWG5EKDpInwkNoQNJIw5iW0A8HWJ4uDHunzWu39rLerWI/333Ahfnb3DbqyGX0mq90YY5hOZDBkYmKaHl6X0z6hIS3kSWWXtWoxrcDhIPT4qyco4+mc5kX+snW9KJYYfvbqLIjKEhJGRCrGVSqJYlVT0WC4uzyCp1+dQ66g71pzGpGZ0JIBH6ujhDRXKOHcfFq3YogzIF+cjXaCSvNQg7tlLh9RaQgmFzPIFUtKUjtkoGVVSSKTVwoTGuku5sffjoYyh4Ok4U6my72bYwiIyFA7qS/gxlK2ULUfaT6VR77IMNJAFZnPbUOPgIieJKKIPLbyBQBfJqJPV/s7O2FGilqvAmGn3GH8y5PziPjcpgaqhCtyBMdlQ7BtmUfgw8xi9xiC/cemEfA48bpNfYaPU4YF1SEzwQ1BpCI0BNQ2tvJcPI0Sg25XMWfQhMxETOkibewi6XQQ+oMrk9OnlIohOTQkewRmegkSmQLWyhPRGvIIkjl4XQ4EahzEXi8Rf/XhNOWxsa03TkDZ0Fc7bycXJC+/kXJin9ueHkEPY2wRwH8B8GXG2K8AeGNrl9VcJI9A/w1kjEk5Ag1DMBTxYSTiqykeWekRHJtKIOxzLRuwPRj2IpEtIF1HiMRuMMaw/+gM9lwYrdruz135epKXCxozo3k+4lTMfOUQv7iOVWnyGjCI23PKujKN70yjIQ9mKvSGuKezQZUjAMwZgsVMAev6pL9rNDQ0EKx9EHu9mBlOs9Bkj6Aa3NDHqhiCqQQ3BI2Ehhy2lJhwEdEogN8C8GCL19MS+IhEPTL5EnKFkm78n+sOmR2mIunB5JWE6CtTS9g2HF72QeLNbd1QQnpiegnn4umqYSFAPT60/tCQOi4c8roQDXlqCg0pzWQmcgSAsQKpIifQhJ3pYHilRzA+l4LbSVjTK+3sy6Gh6v+/RCaP0R4fnA5qyBDEkln0tyEsxNGququkluKNZsANfTUF0qkmeQR2FJ27B8DDAF5ljP2SiDYDON7aZTWXaomearrmtRqCiN+FEpOkrRljUuloRQdxNzWV7ZfLRvkgDyMU3ZZ6QkM6JbwbB4Km5tByxmdTpiSVeUzcSG9oPpVD2OtaoatUD9GQd8XG4HQsifV9AUXOmyeLq+UIpOFIJUR8rpqGvWghVcG0PlHMifhd5iVh2hUaMqlAOiWHe83MutbDCo/AjMTEtwF8W/X7awB+o5WLajbVQkPVKhAukxvLavEIAClBl8oVEE/lsW14ebciP1G6wSPYf3QG20fCyq7ViEYUSBfki1mk0hD0B/Dz1+a0/kST07EkNvQHqoY6wl4XPE6HYY5gPplDbxPCQkBZeE4tM3FqLrWsHJcXK1QLDXGPIexzy7IejeUINg9qj9BsBVJoqHqOwONyKA1YrcasAunkYgYDQU9DGwOf22mqKqyZmEkWbyai7xPRDBFNE9H9RLSpHYtrFr0BSeO8qNNmXylBXQn3CMy6oeopZa/I+iWVmkKDikfQPkMQS+bw3q8ewOf2n6hLqE2LRCaPX47HcL2JsBAgneQ+t6OuC9NCOo+gx7li6PiGgQAmFjOm3elTBqqjaohIvjgb5wga7SrmRENeZAsl5SLPGJP6Hfo1DEEVj4AnW8M+V8OGoF2Ccxwzc4sX5GmC7cpbmFUgnV7MYKhB3SmpfNR+OYJ/B/AtAKMA1kDyDr7RykU1m15/eYeuBU9C6rmZvQEP7nrjFty+a62p14uoFEi1SkcBqUvT6aC2hoaeOzWPR1+ewv98+Biu+5/7cfvnnsK//OQ1pdKhHp46MYtCiWGvibAQp9dvvktTTTytPTN640AAjAFnYumqz1EqMZyOmTMEgJQwNvQI6hxSrkVlU9l8Ko9EtrCs8c3vdsJB1UNDao8g0oAhSOeKSOWKbSkd5UR8bqRyxk1zzdAZqgWvy4mAx1lVgXQqkcFIA4liAPC6HcjYMEdAjLH/yxgryF//BqBqWygR/avsQRxS3fYxIjpHRAflr9saWbxZqk0YMlOTfNcbt64YcK6HMq5SNgT9Qc8K5UaHg+QqkTZ6BPLxf+POq/GRW7ejUCzh3oeO4JpPPI53/PPTGJ+tXbNn/9EZhH0uXLHRuGxUTb3Cc4tpbdnhcglp9fVPJTLIFkrYUKWrmBMNeTCXNDYEzdotRyuG2I9XlI4CKMtMVA0NlT2CWsY/VsKPvR3yEhyjmR6ceLp5BtgsfQFPVY9gciHbsBKtz+VE1oYewX4iupuIxohoIxH9JYCHiKhf7i3Q4ysAbtG4/X8xxnbJXz+oZ9G10lOlianZFQg8hp3IFPDKVAJbh7Xjq+1uKuMn8aVre/C+N1yAh/6fa/H4X7wBf37jFrx4dgGf23+ipudjjGH/sWlct2VwRbjGiB6/u+6qoR7/yrQWbyoz00tQVh2twSMwGCE5n2zeznSwQm9ILT+txsyUsrJH4KppIHwlZcG59iWLe6p48IDsETSpq9gsfUG3YY4gXyxhLpltPDTkdrS9aqh6myzwDvn7+ypu/wNInsFmrT9ijP2YiMbqX1rzqFa7Hk/l4XE64NeYXVsP3CNYSOdxfGoJb7tCO6Q0FPY1FJaplVhKSrCpG4MuGAzhrjduxfhsEo8emUKhWDKto/7yxCKmE1lT1UJqegNujM/WnqNYSOc1B7z0Bz0IeV2mNIf4xXXMtEfgxVxSe05ATo7nNy1HEF7ewHZqLgUiKL0AHGk4jfGFnecIIqpksdYxVGOujcqjnIjBuFfOQhOUR2ulL+AxVCCVxs+ioa5iwKYeAWNsk8GXphGowvvlmQb/SkS68QQiupOIDhDRgZmZmTpepky5dl3bmi+kc+gJNC/xxE/kV6YSSGQLusNnhuRhJO1iXpZL1jrOW3aMIJ7K45mTMdPP9+Qx6X15Q62GwO/RfS+MqFQe5RARNvQHTJWQnool4XIQ1vSa+7BGQx7ki0yz05XnOXqbdJGU3ptyJdmpWBKjEd8yaXQApgbYJyqSxfkiQ7qObtWYojPU3mQxYCw818zcjFmkMnT985brkTXSTAZIEhO2yxEQUYCI/oaIviD/voWI6p1R8HkAFwDYBWACwN/rPZAx9gXG2G7G2O7BwdouNJVUG07TbDfT53bC43TgwPg8gJUVQ5yhsBdzS1ndaqZmE0vmdTVxrts6CJ/bgX2HJ00/3/6j07h0bU/NNdP15giMZkZvHAiYqoQan0thbZ/ftNczYCAzoXQVN2ln6nI60B/wYEa++FaWjnJCJsZV8tBQyOtS/mf1JIyV0FA7k8VVpKgzeWlQjtly7mbRHzRWIOU9BI3mCLwuJ/JF1rbrAmAuR/BlADkAr5d/Pwvg3npejDE2xRgrMsZKAL4I4Mp6nqdWImYMQZPdzLDPhWPKVDL9HEGJGXeuNhMpsal9nAGPC2/YOoh9hyZNTbOKp3J47vR8TdVCnJ6AG9lCqSY9lUy+iGyhpLsL3DgQxJn5VNUPz+m5VFWNITXl7uKVFwDlItnEnal6GM6puZTmBDUzMwkSmQKCHidcTofiodZjCOaSObidpGgctYNqw2m0VGjbQW9Ays0UdKqZyh5Bg6EhuTeinXkCM4bgAsbYpwDkAYAxlgZQVwxFlqrgvA3AIb3HNhOngxDx6XcrzqdyTRevivjdYEza9etdvAblnXS7EsbzyZyhONqtO0Yxncji+TPxqs/1o1dmUGLA9dvN9Q+oUYYF1eAV8ItCZTMZZ+NAAPkiw8SCcQnpqbmk6fwAUJZe1vIIlNBQMw1BWGoqS2YLmF3KanoEYZ+76pSyRCavNDb2VMmRGTG3lMVA0Nu2en2g+rjKdncVc3ieRK/QYWoxA5eDGg6jlecWty9PYMYQ5IjID7lklIguAFD1ykVEXwfwNIBtRHSWiP4QwKf4DGQAewH8v/UvvTZ6DeJ7rUg88YTxthH94fRcs7xdJaSxKqWOe7cPwe0kPGwiPPTQixPoD3qUcZ61UJaiNp8n0JKgVqMeZK9HPJXDYqZguocAKCdwtbw2Xo7bzEQq9wh44lvLaIV91T2CxXRBOQeVKhyTk83UtLuZDACCHqfhDJGyAW63RyCXoeuEhyYXMxgKe00pFBvhU+YWt88jMOPvfQzAPgDriehrAPYAeE+1P2KM3aFx85dqWl0T6Q3olyy2ohSNu7dbhvQNAS8XbEdTWaFYwkLaeKh2j9+N118Qxb5Dk/jIrdt1d4HHpxJ45OUp/NkNFyoaOLVQLWejRTVDsEFVQrrnQu3nGK9Q8jSDksDVCA01WxMfkM6J2URuhfy0mpDXpUwp0yvbTWTzKwxBvaGhdjaTAVLyX+p90DZc8SrnQqvoV/qRtP+P04uNl44CZUNgK4+AMfYIJAnqdwP4OoDdjLH9LV5X09EbTtOsmbOVlD0CfY0WRWaiDXMJpPLB6rvXW3aM4HQshSMTCd3H/J8nX0XA48R79tSnNFIeH2r+wlRND2q0xw+P02EoR12+uJoPDbmcDvQFPJoewXwyh4DHuaKqpxGiYS/S+aLy/9dMFpuYUpbIFFaGhupMFrfbIwCMFUjbLUHN4a+nlzCeXMw0XDoKqENDNsoRENHjjLE5xthDjLEHGWOzRPR4OxbXTHoDHs0PwmKLdhfcEFSqjqrxuZ3o8bvbUkI6b3KS1k0XD8NBwL5DE5r3n5pL4v6D5/C7V22o+wLBXeyFJoaGnA7Cun6/YeVQpba/WQaCHu1kcco451IPPDn97Kl59AXcimepxsyUMskQuJY9vqMMgd+lmywuqwVblCPQCTFPLWYaLh0F1KEhG3gEROSTO4ej8ojKfvlrDJLmUEfRq6NA2ipd83JoyFi1cTDsbYtHEJN11KtVuERDXrxurF+3jPSffvQqXE4H3nttPS0kEq0IDQFSnmDcwBCciqUwHPHCX+OkrQFZFbSSeCrflIE0aqJyGOb50/O6nkvEhAKpOlnsdBDCPv0Lqx7ZQhFL2UJbewg4kkegExpK5eFyEIJtmpjGMRpOk8oVkMgUMNzTBI/AbS+P4H0AngWwXf7Ov+4H8LnWL6258OE0laWRrapAeMfr1uNv33ap8mHUYyjsbUuOQBmybuLCdcuOEbwytYRXZ5aW3T6xkMZ/PHsW79i9vqFYaMDjhNtJNclMaI2prGTjQBCn55LKQKBKTs0lNcsxqyF1F2uXj7bKI0jmirpJ7ZC3LGGix2KmoBgMAHUpkFohL8HpMdBHisvFHe2sZAIAv0dSztXawCg9BA3MIeDwKX+2MASMsX9gjG0C8EHG2GZVN/FOxthn27bCJtHjd6PEsKLsrlUVCFuGw/idqzZUfVy7uovna6hwufmSEQBYUT30zz96DYwB73tD/d4AICUDe/zGw4IqWUjnEfa6DJPTGwcCSOaKmFzUNqxm5acriYa8iv6PmngLQkPqYTl6ekghxSPQ/v9lC0XkCqVlRtPowqoHD4e1O1kMVM8RtDtRzOkLaDeVNauHAFD3EdggNKRikojCACB3GH+XiK5o8bqaDv/AVtZSW1WBwOGhIb1dbLNQPAITF641vX7sXN+LfYfKhmB2KYtv/PI03nr52hXaN/XQF3DXnCPQmxfB2XNhFG4n4WMPHF7x/0zniphOZOs0BB4ksoUVOzTJI2jueaM21HoKqdWmlKnlJTj1eATcC7IkNOTXn6pmhfIoR68MnRuCkZ7GvSdbeQQq/itjLEFEvwrgZgD3QZKK6Cj0atetqkDgDIV9yBZKddV410KtFS63XDKCF88u4FxcatD60k9PIlso4Y+vv6Ap66lVZkJPZ0jN1uEwPvimbXj48BS+/syZZffxunyz8tNq+BB79U6wUJTes2ZXm7mdDsUYjOkYrWo5gmYZgpgsQW1V1VA6L3k2lVihPMrpD7oNPYLmlI/a0yPgZunNAD7PGLsfgDXmuAF6dUoW4+kcnA5Sdlntpl1NZbVWuNx8yTAA4OFDk1hI5fF/nz6FN186iguaNLKwntCQGa/tvdduxrVborjnwcM4PlUugVW0/WusGALUA2PK7xH3JJsdGpJeT3pOrdJRoHrVEA8Bhb3l/1fEV4dHoAjOtT9HUJZyX7nmeKq6d9gqenXmn08tZhHwOJsixaFUDdnMIzhHRP8M4LcA/ICIvCb/zlZwCYnKBCXfXbQ78cQx01T27KkYzs43NlpyvsYywM2DIWwbDmPf4Ul85WfjWMoW8Kd7dTq16kDyCGoMDZkwBA4H4e/fvhMBjwt/9vXnFfe6VvlpNVpD7OMmy3HrIRryIuBxKudGJXxKmV53saZHEKgvWexykCIC107KwnMrj3EhnW+7vASnP+DRrBqaXMxgOOJrynXErhITvwXgYQC3MMbiAPoBfKiVi2oF3CNYqHgT4yZiz62kmkeQLRTx+196Bp957HhDrxNL6SuP6nHzjhH8cjyGL/30NbzxoiFcNBppaA1qemscTlOLMOBQxIe/e/tlODqZwCd+eBSAJOnc43fX9V5H5R2xOqnPy3GbnSMAgMvW9eLKTf26F5VqU8rUYyo5Pf7ahf5iyRz6gtqy5a1GTygvX5RmQLTi/26GPtmgVoobTjephwCwRmLCTGdxijH2XcbYcfn3CbnbuKPo0aldX7Aw3giUhef0DMEvT84jmSvi3Hz1ebxGSLMIajvOW3eMgDFpV9ZMbwCQDHMqVzR1sjPGsJjOG5aOVnLD9mG8+/Vj+MrPxvHE0am6K4YAtd5QeROhNOi1IDR0963b8ZX3GAvzGk0p0/IIqgm5aTG7lLMkUQyoZxJUfF4tUh7l9AU9YGylgeIeQTNwOQgOsp9H0BW4nQ6EvK6VoSELKxAAKfHncTl0FUj3H5sGUE5G1cu8vLurhe0jYWwZCuENWwdx+QbzM4nN0KN0F1e/MGXyJeSKtevP333rdlw0GsEHv/0ijkwkau4o5gQ8LvjdzmU5gvlk60JDZjCaUsYv9pEKjwAwHv9YSSyZtaR0FNCfUsY3clqzq9tBn6I3VN4UMMYwtdj4rGIOEcHndtrLI+gmevwrZ45aWYEASG/6UNiLaZ0LPTcEk4uZuktMc4USEnWMVCQifPuPrsHn39n8auFq40PVmOkq1sLnduIf79iFVE6SdK4nP8CJhpfrDXHhsWbOIqgFoyll/PaQ2iOoQ2ZCkpdof6IYUOUIKkpIecmxVZ9ZbvjVCqTxVB65QqlphgCQzl3hEbSI3oB7xYVnwcIKBI5eU9mpuSRem0liXZ8fqVyx7hLTRhKbvQEPAp7mJwvL5bytMwQAcOFQGB/9tUsAAJsH6zcEA8Hl3cXzqRy8LkfNchXNwmhKWSJTQKii+a4e4bm5pIWhoSoegWWhIfl11QqkU4nmjKhU43U5bNdH0DVUSlHni9JO2aoKBI6e3hCfCfzbr1sPoP7wUCt08xulluE0Svd3ne/THVeuxzfvvBpvvmy0+oN1iIa8y/I41Yb8tJqwz21QNZRflh8AajcEuUIJiUzBsnMm4HHC5aAVoaz5FknCmKVPYyYBl5dohvIoRwoNCY+gJfT6l3cFWp144gyFfZo5gv3HprE5GsRVmwcAABMLdRqCGrqK20W5r6N6CWkjHgEghbiu2jygdGzWQzTkWeERWJUfAOTQkIFHoGcIjAbCq6lFkqQVKDMJVngE0rqs8uKV0JDqvJ1aaJ68BEd4BC2kspbaajeTMxT2YiGdX5YcSueKePrVOVy/bUjZaUzVaQjmufKojTwC/kE2s0Nt1BA0g4GQpDHDRQvnU3nLShgBKVms1WwF8KE0y9cWqdEjKDeTWVtIsTJHkIeD0NYZymqCHic8TseyXoJyV3ETQ0PCI2gdvfJwGp505YknKy8wQFloTB16+Plrc8gWSti7fVA5wfTE1KoRU3IE1h6nGi4gZyY0ZAdDEA15USwxJbRYTxVWMwl5XcjkS8hrDFJXj6nkuJ0OBD1O84ZAlpcY0GlqawfaHoHUWNjoOMh6ISIpxJwsr2tyMYO+gLshj7MSn/AIWkdvwI1CiSGZk/7BZY/A2p0yv9Crw0P7j03D73biyk398LqcGAh66g4NzdswNCQpkLpNzS1eTOdBhBUXt3bCL4i8cmg+1XzBuVowmlKmnkWgJlKD3lBZgtpKj2ClYqokQW3tedwf9FR4BM0rHeV43U5khEfQGsoJypz83Xj8YbsYqmgqY4zhiaPT2HNhVNllDEd89SeLkzmEfS7d+bZWwT20asTTeUR81u0CgbL+z8xSFsUSw0I6b1npKFA2ilolpFo5AqA24TlbhIb8rhWVcvFUznIPvlIeZTrRvGYyjs/lsJ3WUNdQOSu3VdPJakWZXSwbgldnkjg7n8be7YPKY0Z7fPV7BClrxg1Ww6z+jVmdoVYSVTyCHBbTeZSYtZ5kPYYgUsNMglhSEmO08v+u5REspM1LjbSK/uDymQSTC82Tl+CIHEEL6a1ImC2kciCC5lzYdjIQ9IAImJF3/E/KTWTXbxtSHjPc05hHYKewEMesR2AnQzC7lLW8ogYoTymr7CXI5IvIFUua53RNHoE8a8FKL0wrlGV1AyiwXIG0UCxhdinb1NJRQHgELYXv4NQegZWJJ47L6cBAsNxUtv/YNLYNh7G21688ZjTiQyyZqyuBZFePoDfgMZUjsIMh6PW74SDJI+CGwMqdqd6UMi2dIU4tU8piyazl50zE51ohlBdPWSsJA0hNZXF57O3sUg4l1pw5BGp83ZIjIKJ/JaJpIjqkuq2fiB4louPy9+YK2FShcjiNHXYXHN5UtpQt4JmTMVy/bXDZ/Xwodj2D7ueTeVt6BD21eAQWhwMcDkJ/0Ct5BDYox9ULDSU0dIY4tcwkmFuyfvNQnkkgHWOxxLCYKVi+KegLeFAsMSQyhfJksmYni7uoaugrAG6puO1uAI8zxrYAeFz+vW1UKpBKEtT2uEBKQ+yzeOrELPJFtiwsBJRPtHpKSCXNGHsYPDW9AUlBs6BRAqnGyhm1aqIhD2aXcuVyXCtzBF7tKWXVPIJkrqhZclpJLJmztHQUWCkzsWiTnJ5aeK6Zs4rVdE1nMWPsxwBiFTffDmnUJeTvb23V62vhczvhdzuX5Qjs4hEMhSUJgyePTSPkdWH32HJnaVT2CCYWapOjTueKSOeLlta868E/UEYaSowxW4SGAClPMJfMtnQojVn0ppSVDYFWjoALuVX3CqzUGeJUKqbapbiDe0qxZYagyclilwPFEjNltJtBu3MEw4yxCUCaawBgSO+BRHQnER0gogMzMzNNW4C69CtugwoEzqAsPLf/6Ayu3RJdUerJQ0O1JoyVxKZNPB81ZmQmUrkiCiVmE0PgwexSFrFkHm4nIWiR4BwgTSlzOmiF3tCiMpRGwyMI8B22scxEvliSymMtDw0tn1LWqOZUs1Cft1OLWTgd1HTvqTycpjsNgWkYY19gjO1mjO0eHBys/gcmUcel7ZQjGApLnauTixns3bbSPoa9LgQ9zppLSGMW6+YboYTqDHaoih6UDd6ngZAXc0s5xOX5z1aNNwX0p5QljAyBSZkJvnmw2iNQQkMVHoHV+SLFI0jmMbmYwWDIu0zptRnwAfbtyhO02xBMEdEoAMjfp9v8+ooCqZR4slGOQBVjfMO2lYaPiOoqIbVDqaMevPrDaCaBMojEBoYgGvIilSviXDxti+S71kwC49CQOUNQ7iq2OEdQMVWNnydW/+/L1YdSaIh7682EN5J2qyF4AMC75J/fBeD+Nr8+ev0eLKTySGTyYMweO02g3FR2yZqIbuJpJOLDZL0egQ0uXJXw/33lsCA1dtAZ4vBpXSeml2yh26QlPMfDKCENUTa9OcCVxJbssXkoewSVoSFr//cRn6STFUvKhiDcfIPplT2Cjg8NEdHXATwNYBsRnSWiPwTwCQA3EdFxADfJv7cVySPI2UZ5lDMsy0xohYU4Iz21G4J5G2jG6NFb0emtBb9o1TKvuFVwmYmJhYwtDKteaKhyKA3HrEcwK58zVo2p5PjcDridpHgEcZucC0SEvoAb86k8phazGGmBR8BzBO3yCFqm4sUYu0Pnrhtb9Zpm6AlIOQK7VCBw1vf7ce9bd+C2S/WHp4xEpLkFxRIzHZOMpSTBNjvsqCsJ+9wgMs4RLNrII4iqEoJ2yLmEfa5lMxIAfXkJQH8gfCUxubHR6hwBES2TmYin8spu3Gr6Ah5MLqSxkM43vXQUkKqGgC7wCOxKr9+DbKGESbkMs8fiCgQOEeGdV2803LmP9vhQKLFls3OrMZ+USmTt8OGpxOmQPugLJkJDdjDY6soQK5VHOSGNKWWJTF5XMsXndsLrclQ3BElJesXqDl5guczEgg2URzl9AQ+OTiYASIUezabdHsHqMwTyB/jkbGrZ753AcB1NZTGLJ2lVo3J8aCXxtCR+phXzbjfqHbJdQkOVpaBGHgFgTm9oTtamssPmIeJzLSsftcvntS/oVir4WhEaUjyCNg2wX32GQHaPT80ll/3eCfATrpY8wXwyZ8seAk414bmFtBQOsLJUk+NzO5WOXjsYgrDPpak1ZGQIzMwkkDrRrT8+YLli6rxNOsyB5e9/K0JD5T4C4RG0BF6DPC4bArucWGZQDEEtHoHFk7Sq0RPwVOkjsF5bRk1UDgPYoWpIa0qZ3lAajlmPwFaGIGPD0FCwPYYgIzyC1sC7Ek/NpRD2uuCy2bAWI6JBL1wOqs0jSNnfI6iWI7CTIeDhIbt4BMDyKWWLzQgNLWUtTxRzpGSxKjRkk3OB54h8bgciLZicV04WC4+gJfAY48RCxvIOxVpxOAjDNfQSMMYk5VGbfKi1qJYjWLCRMCBQLqm0gyHgeRPeRMYYM+URVM4BrsReoSEXFjOS5LMdhtJw+Ps/HPG1JGwpPIIWoz6R7HJS1cJwxGs6NJTMSUNK7Kg8yumVd6ilEtO8f8EGownV8BJSOxjXSinqbKGEfJFV9wgMcjLFEkM8nbdceZQT8bmRK5Qwm8yixOwTylUbglbAPQJRNdQi/G4nPHI4yGrxqnoY6fGZNgR2HFpfSU/AA8a0Ry4CPDRkfcUQZ9tIGENhb0vCAbVSOaVsUZlFYJwsTmQLuoZ3PpUDY9b3EHB478OZGK/ys8e6eI6oVYZAiM61GCJSQkKdFhoCgJGIH5MLGTCm/UFWE7NxVzGHx3y1JpUxZo9BJGreedVG/Pgv99qiiilcMaXMSGeIE/G5DA2v3c4ZbtROc0Ngk3OBb65Gmiw/zXE6CG4nCY+glfCTyQ5NQbUy0iMJnyWyxlLCAMoDVGzyodbCSGZiKVtAscRs5bk5HKTs1qymciaB0VAaTjWZibkleyiPcrhHcHpOagC1Szh3MOyFx+nAWDTYstfwuto3nMZ6/9YCuDW30wXGLCM90hzjyYWMbgcpR9EZsok7rUV5fOjKC5OdBOfsSNhbaQiqa/Eow150EsaKR2CxzhCHn+OKR2ATQxD2ubHvrmuxri/Qstfwuds3rnJVegQ8JGSXk6oWlJGVJiqH7DyLgMMlPrSG03AvwWqRMbtSHmDfRI8gKcmX2CU0xPNDp2O878ce6wKAzYMheFytu4R6XU5RNdRKeGioE3eaozU0lc2ncrKej30dP26MtS5MdhKcsyOVU8rKQ2kMPAKD/zdQDg3ZxYus9AhW07ngdTtEH0Er6VU8Anuc7LUwJCenzHkEecsnaVWDf7CPTCRW3CdCQ8ZUTinjjVeGEhNVZhLMLGXRG3DbptGSe4NTi1kEPc6W7sDthk94BK2FG4BODA15XU70Bz3mPIJkztY9BADgdjrwjt3r8fVnTuNffvLasvvspDxqVyThOV41JEmOhzz1hYYYY/jZiVlcuranNYutA5+7fPHvxI1bI7TTI7BvzKCF8A+DXUrRasXspLKYPFvX7vz3t+3AUraAex86Aq/Lgd+7ZgyAakZth75P7SDscymhocVMASGPCw4D1dCAxwmXgzQNwdHJBMbnUnjvdZtbtt56iPjcmJU9ldWEz+Vsm/roqjQE128bxO9etaGlpV+txOykslgyhy1DoTasqDFcTgc+89u7kC0U8V/vPwyv24nf2r0eC+k8XA5CwGOPck07IimQlpPFRmEhQO6jUSl6qtl3aBJEwJsuHmnJWusl4netSkPgdTuUgo9WsypDQ+v6Avjvb7sUbpvEQWtlxOQQ+3mbK4+qcTsd+OzvXIFrt0Tx4e+8iPsPnlME5+yc47AadY6gms4QR0947uHDk3jdxn5lfrZd4HmNTiz3boR2egSdeSVc5YxEfJhL5gzjh6USs73yaCU+txNf+L3duHKsHx/41gv4yfGZjuz+bichn3tZQ1k1jwAAwhqG4ORsEkcnE7h5h728AaCcMF5t54LP7UBGVA0J9OC9BNOL+iMrFzN5lJi9ewi08Huc+NK7X4ed63pwJpYW+YEqhLyusiHI5k31XGiFhh4+PAkAuPmS4eYvskF4+XOn5vTqxSs8AoERfEDNhEGeoKwZ03kfnpDXhS+/50pcsaEXF49GrF6OrVFPKTPrEWiFhn54aBKXretpaadsvXDjttpyBO30CFZlsrjTMTOpbD5lf+VRI3r8bnznj19v9TJsT1g1pcy8IVg+6/h8PI0XzsTxoZu3tXKpdbNqcwRup5CYEOhTnl2c1n1MLCnt+OwiFVAPRCQSxVVQZCYyhZqTxVzB9hE5LHSLDfMDgFQ1BKy+HIHX5UC2UDKlNNwowhB0IGGvCwGPE5ML+jmCTphFIGgcPqVsdilbdSgNp8fvRrHEkMxJu819hyexdTiECwbtWWpc9ghWmSFwO8EYkCu2Pk9gSWiIiMYBJAAUARQYY7utWEenQkQYiRiXkHIJ6k72CATV4Rf+c/G0/Hv1i6VaZiKbL+KZkzG8f++FrVtkg/DhL60aAmNXynOLS/C6WttLY2WOYC9jbNbC1+9oRnp8mDAIDc0nc/C4HKIZq8vhF35eOGBGYFCRmUjl8ZOzcZQYbFk2yrlx+xC+//5f7dgG0Hopzy0uVpWcbxQRGupQJI9APzQUS0o9BCLG3t3w0NCE4hHUYAjSeew7PIn1/X5bV2c5HIRL19lH/6hdKOMq21BCapUhYAAeIaJniehOrQcQ0Z1EdICIDszMzLR5efaHdxcbzZ7ttB4CQe3wZPF52SMwFRqSDcG5eBpPnZjFrTtGxYbBhpRDQ62vHLLKEOxhjF0B4FYAf0pE11U+gDH2BcbYbsbY7sHBwfav0OaM9PhQKDHMJrW9glgHKI8KGodPKTtfh0fwn8+fQ77IcPMl9g0LrWbKoaEu9QgYY+fl79MAvgfgSivW0cnwxNmUTuXQfCovKoZWAStzBOY9gp+emMVwxIvL1/e2bH2C+ulqj4CIgkQU5j8DeBOAQ+1eR6czqnQXayeMJY9AGIJux+d2wOmgmjyCsNcFHgm6+ZIRQ9lqgXW00yOwompoGMD35JikC8C/M8b2WbCOjobrDWmVkBaKJSykhUewGuBTyhbS0lCaoMFQGo7DQYj4pKayW0RYyLb0Bdy4fEOvYhBaSdsNAWPsNQA72/263cZAyAuXgzT1hvhAF+ERrA64IQh5jYfSqJHkvYErN/W3eHWCetkyHMb3/mRPW15LaA11KE4HYSjs1dQbUrqKhSFYFfBwUC215jdsH8JA0GOb2cQCaxGGoIMZ7vHhtZkkMvniMvdRUR4VoaFVATcEZvIDnI+95ZJWLUfQgYjtQAdz8WgEB8/Esfvex/CBbx7E/mPTyBdLZeVRUT66KuBNZbUYAoFAjThzOph7bt+B2y4dxQMHz+OHhybw3efPoS/gxoZ+SVNe5AhWByE5JGSmmUwg0EIYgg7G6SDsuTCKPRdGcc9bL8FPXpnFAy+cx6MvTyHocYqqoVWC8AgEjSLOnC7B63LijRcP440XDyOVKyCRKbSl7ExgPZE6ksUCgRphCLqQgMeFgIl6ckF3IDwCQaOIZLFA0OGElKoh4REI6kMYAoGgwxEegaBRhCEQCDqcsFI1JAyBoD6EIRAIOpx6OosFAjXCEAgEHc4VG/rw3ms3Cd0gQd0IX1Ig6HD8Hif++s0XW70MQQcjPAKBQCBY5QhDIBAIBKscYQgEAoFglSMMgUAgEKxyhCEQCASCVY4wBAKBQLDKEYZAIBAIVjnCEAgEAsEqhxhjVq+hKkQ0A+BUnX8eBTDbxOV0CuK4Vx+r9djFceuzkTE2WO2JOsIQNAIRHWCM7bZ6He1GHPfqY7UeuzjuxhGhIYFAIFjlCEMgEAgEq5zVYAi+YPUCLEIc9+pjtR67OO4G6focgUAgEAiMWQ0egUAgEAgMEIZAIBAIVjldbQiI6BYiOkZEJ4jobqvX0yhE9K9ENE1Eh1S39RPRo0R0XP7ep7rvI/KxHyOim1W3/woRvSTf97+JiNp9LGYhovVEtJ+IjhDRYSL6c/n2bj9uHxE9Q0QvyMf9cfn2rj5uDhE5ieh5InpQ/n21HPe4vOaDRHRAvq31x84Y68ovAE4ArwLYDMAD4AUAF1u9rgaP6ToAVwA4pLrtUwDuln++G8An5Z8vlo/ZC2CT/L9wyvc9A+AaAATghwButfrYDI55FMAV8s9hAK/Ix9btx00AQvLPbgC/AHB1tx+36vg/AODfATy4Gs5z1XGPA4hW3NbyY+9mj+BKACcYY68xxnIAvgHgdovX1BCMsR8DiFXcfDuA++Sf7wPwVtXt32CMZRljJwGcAHAlEY0CiDDGnmbSGfNV1d/YDsbYBGPsOfnnBIAjANai+4+bMcaW5F/d8hdDlx83ABDROgBvBvAvqpu7/rgNaPmxd7MhWAvgjOr3s/Jt3cYwY2wCkC6aAIbk2/WOf638c+XttoeIxgBcDml33PXHLYdHDgKYBvAoY2xVHDeAzwD4SwAl1W2r4bgBydg/QkTPEtGd8m0tP/ZuHl6vFRNbTbWyesffkf8XIgoB+A6AuxhjiwYhz645bsZYEcAuIuoF8D0i2mHw8K44biL6NQDTjLFnieh6M3+icVvHHbeKPYyx80Q0BOBRIjpq8NimHXs3ewRnAaxX/b4OwHmL1tJKpmRXEPL3afl2veM/K/9cebttISI3JCPwNcbYd+Wbu/64OYyxOIAnAdyC7j/uPQDeQkTjkMK5NxDRv6H7jxsAwBg7L3+fBvA9SCHulh97NxuCXwLYQkSbiMgD4LcBPGDxmlrBAwDeJf/8LgD3q27/bSLyEtEmAFsAPCO7lgkiulquJPh91d/YDnmNXwJwhDH2adVd3X7cg7InACLyA3gjgKPo8uNmjH2EMbaOMTYG6TP7BGPsnejy4wYAIgoSUZj/DOBNAA6hHcdudZa8lV8AboNUZfIqgL+2ej1NOJ6vA5gAkIdk9f8QwACAxwEcl7/3qx7/1/KxH4OqagDAbvkEexXAZyF3mNvxC8CvQnJrXwRwUP66bRUc92UAnpeP+xCAj8q3d/VxV/wPrke5aqjrjxtSheML8tdhfs1qx7ELiQmBQCBY5XRzaEggEAgEJhCGQCAQCFY5whAIBALBKkcYAoFAIFjlCEMgEAgEqxxhCAQCHYiol4j+xOD+n5l4jqVqjxEIrEYYAoFAn14AKwwBETkBgDH2+nYvSCBoBd2sNSQQNMonAFwgC7/lASxBaujbBeBiIlpijIVkHaT7AfRBUgn9G8aYrbtYBQI1oqFMINBBVjt9kDG2QxZAewjADiZJ/kJlCFwAAkwSw4sC+DmALYwxxh9j0SEIBKYQHoFAYJ5nuBGogAD8LRFdB0k6eS2AYQCT7VycQFAvwhAIBOZJ6tz+uwAGAfwKYywvK2f62rYqgaBBRLJYINAnAWk8ZjV6IGno54loL4CNrV2WQNBchEcgEOjAGJsjoqeI6BCANIApnYd+DcD35WHjByHJRQsEHYNIFgsEAsEqR4SGBAKBYJUjDIFAIBCscoQhEAgEglWOMAQCgUCwyhGGQCAQCFY5whAIBALBKkcYAoFAIFjl/P/w1wndVk9qEgAAAABJRU5ErkJggg==\n", 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" ] diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index 6ea0b02..65d7d03 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -1,5 +1,8 @@ import pandas as pd -from lcs.agents.xncs import XNCS, Configuration +import numpy as np +import random +from lcs.agents.xncs import XNCS, Configuration, Classifier, ClassifiersList, Effect +from lcs.agents.xcs import Condition def cl_accuracy(cl, cfg): @@ -9,33 +12,31 @@ def cl_accuracy(cl, cfg): return cfg.alpha * pow(1 / (cl.error * cfg.epsilon_0), cfg.v) -def fraction_accuracy(xncs): - action_sets_percentages = [] +def fraction_accuracy(xncs: XNCS): + accuracies = [] for action in range(xncs.cfg.number_of_actions): action_set = xncs.population.generate_action_set(action) - if action_set is not None: - total_accuracy = 0 - most_numerous = action_set[0] - for cl in action_set: - total_accuracy += cl_accuracy(cl, xncs.cfg) - if cl.numerosity > most_numerous.numerosity: - most_numerous = cl - action_sets_percentages.append( - cl_accuracy(most_numerous, xncs.cfg) / total_accuracy - ) + most_numerous = action_set[0] + for cl in action_set: + if cl.numerosity > most_numerous.numerosity: + most_numerous = cl + if most_numerous.queses > 0: + accuracies.append( + (most_numerous.queses - most_numerous.mistakes) / most_numerous.queses + ) else: - action_sets_percentages.append("1") - return sum(action_sets_percentages) / xncs.cfg.number_of_actions + accuracies.append(0) + return sum(accuracies) / xncs.cfg.number_of_actions def specificity(xncs, population): total_specificity = 0 for cl in population: - total_specificity += pow(2, cl.wildcard_number) + total_specificity += pow(2, cl.wildcard_number) * cl.numerosity return total_specificity / xncs.population.numerosity -def xcs_maze_metrics(xncs: XNCS, environment): +def xncs_metrics(xncs: XNCS, environment): return { 'numerosity': xncs.population.numerosity, 'population': len(xncs.population), @@ -43,20 +44,24 @@ def xcs_maze_metrics(xncs: XNCS, environment): 'fraction_accuracy': fraction_accuracy(xncs) } - -def avg_experiment(maze, cfg, number_of_tests=1, explore_trials=3000, exploit_trials=1000): +def avg_experiment(maze, cfg, number_of_tests=1, explore_trials=3000, exploit_trials=1000, pre_generate=False): test_metrics =[] for i in range(number_of_tests): print(f'Executing {i} experiment') + if pre_generate: + population = XNCS_population(maze, cfg) + else: + population = None test_metrics.append(start_single_experiment(maze, cfg, explore_trials, - exploit_trials)) + exploit_trials, + population)) return pd.concat(test_metrics).groupby(['trial']).mean() -def start_single_experiment(maze, cfg, explore_trials, exploit_trials): - agent = XNCS(cfg) +def start_single_experiment(maze, cfg, explore_trials, exploit_trials, population=None): + agent = XNCS(cfg, population) _, explore_metrics = agent.explore(maze, explore_trials) _, exploit_metrics = agent.exploit(maze, exploit_trials) df = parse_results(explore_metrics, cfg) @@ -65,6 +70,31 @@ def start_single_experiment(maze, cfg, explore_trials, exploit_trials): return df +def XNCS_classifier(situation, cfg): + generalized = [] + effect = [] + for i in range(len(situation)): + if np.random.rand() > cfg.covering_wildcard_chance: + generalized.append(cfg.classifier_wildcard) + else: + generalized.append(situation[i]) + effect.append(str(random.choice(situation))) + cl = Classifier(cfg=cfg, + condition=Condition(generalized), + action=random.randrange(0, cfg.number_of_actions), + time_stamp=0, + effect=Effect(effect)) + return cl + + +def XNCS_population(maze, cfg): + classifiers_list = ClassifiersList(cfg) + while classifiers_list.numerosity < cfg.max_population: + situation = maze.reset() + classifiers_list.insert_in_population(XNCS_classifier(situation, cfg)) + return classifiers_list + + def parse_results(metrics, cfg): df = pd.DataFrame(metrics) df['trial'] = df.index * cfg.metrics_trial_frequency diff --git a/XCS_Experiments/utils/xcs_utils.py b/XCS_Experiments/utils/xcs_utils.py index 3d5ee36..74634ca 100644 --- a/XCS_Experiments/utils/xcs_utils.py +++ b/XCS_Experiments/utils/xcs_utils.py @@ -7,14 +7,53 @@ import gym # noinspection PyUnresolvedReferences import gym_maze +import random +import numpy as np +from lcs.agents.xcs import ClassifiersList +from lcs.agents.xcs import Classifier +from lcs.agents.xcs import Condition + + +def cl_accuracy(cl, cfg): + if cl.error < cfg.epsilon_0: + return 1 + else: + return cfg.alpha * pow(1 / (cl.error * cfg.epsilon_0), cfg.v) + + +def specificity(xncs, population): + total_specificity = 0 + for cl in population: + total_specificity += pow(2, cl.wildcard_number) * cl.numerosity + return total_specificity / xncs.population.numerosity def xcs_metrics(xcs: XCS, environment): return { 'population': len(xcs.population), - 'numerosity': sum(cl.numerosity for cl in xcs.population) + 'numerosity': sum(cl.numerosity for cl in xcs.population), + 'average_specificity': specificity(xcs, xcs.population), } +def XCS_classifier(situation, cfg): + generalized = [] + for i in range(len(situation)): + if np.random.rand() > cfg.covering_wildcard_chance: + generalized.append(cfg.classifier_wildcard) + else: + generalized.append(situation[i]) + + return Classifier(condition=Condition(generalized), + action=random.randrange(0, cfg.number_of_actions), + time_stamp=0, + cfg=cfg) + +def XCS_population(maze, cfg): + classifiers_list = ClassifiersList(cfg) + while classifiers_list.numerosity < cfg.max_population: + situation = maze.reset() + classifiers_list.insert_in_population(XCS_classifier(situation, cfg)) + return classifiers_list def parse_results(metrics, cfg): df = pd.DataFrame(metrics) @@ -30,18 +69,21 @@ def parse_results_exploit(metrics, cfg, explore_trials): return df -def avg_experiment(maze, cfg, number_of_tests=1, explore_trials=4000, exploit_metrics=1000): +def avg_experiment(maze, cfg, number_of_tests=1, explore_trials=4000, exploit_trials=1000, pre_generate=False): test_metrics = [] for i in range(number_of_tests): print(f'Executing {i} experiment') - test_metrics.append(start_single_experiment(maze, cfg, explore_trials, exploit_metrics)) + if pre_generate: + population = XCS_population(maze, cfg) + else: + population = None + test_metrics.append(start_single_experiment(maze, cfg, explore_trials, exploit_trials, population)) return pd.concat(test_metrics).groupby(['trial']).mean() -def start_single_experiment(maze, cfg, explore_trials=4000, exploit_metrics=1000): - agent = XCS(cfg) +def start_single_experiment(maze, cfg, explore_trials=4000, exploit_metrics=1000, population=None): + agent = XCS(cfg, population) explore_population, explore_metrics = agent.explore(maze, explore_trials, False) - agent = XCS(cfg=cfg, population=explore_population) exploit_population, exploit_metrics = agent.exploit(maze, exploit_metrics) df = parse_results(explore_metrics, cfg) diff --git a/examples/xncs/XNCS_maze5.py b/examples/xncs/XNCS_maze5.py index efb9b0a..2a1a6d9 100644 --- a/examples/xncs/XNCS_maze5.py +++ b/examples/xncs/XNCS_maze5.py @@ -11,6 +11,7 @@ metrics_trial_frequency=100, covering_wildcard_chance=0.9, mutation_chance=1, + lmc=10, delta=0.1, user_metrics_collector_fcn=xcs_metrics) From 28b1d24691cf1cd3c70826a43f341c91cc431788 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 30 May 2021 13:51:58 +0200 Subject: [PATCH 08/19] Further tests --- .../XCS_XNCS_Maze5_pre_populated.ipynb | 760 ++++++----- XCS_Experiments/XCS_XNCS_Woods1.ipynb | 1153 +++++++++++++++-- XCS_Experiments/XNCS_maze5_avg.ipynb | 246 ++++ .../XNCS_maze5_avg_pre_populated.ipynb | 24 + 4 files changed, 1712 insertions(+), 471 deletions(-) create mode 100644 XCS_Experiments/XNCS_maze5_avg.ipynb diff --git a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb index da7d030..3753b75 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -23,15 +23,15 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '0', '0', '1', '0', '0', '1', '0')\n", + "('0', '1', '1', '0', '0', '1', '1', '1')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -97,14 +97,14 @@ " initial_fitness = 10, # f_i\n", " user_metrics_collector_fcn=xncs_metrics,\n", " lmc=10,\n", - " lem=20)\n" + " lem=100)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -118,16 +118,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 91, 'reward': 1000.0, 'perf_time': 1.182630300000028, 'population': 1511, 'numerosity': 1600, 'average_specificity': 8.2225}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 8, 'reward': 1071.0358873685009, 'perf_time': 0.06847820000007232, 'population': 1187, 'numerosity': 1600, 'average_specificity': 6.52625}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 8, 'reward': 1066.4893540049404, 'perf_time': 0.03988800000001902, 'population': 989, 'numerosity': 1600, 'average_specificity': 6.39625}\n", - 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'average_specificity': 5.999375}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 1, 'reward': 1907.6085990615102, 'perf_time': 0.0035444999994069804, 'population': 627, 'numerosity': 1600, 'average_specificity': 5.635625}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 3, 'reward': 1416.5154669782146, 'perf_time': 0.01350659999934578, 'population': 615, 'numerosity': 1600, 'average_specificity': 5.875}\n" ] }, { @@ -141,16 +141,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.334747100000186, 'population': 1501, 'numerosity': 1600, 'average_specificity': 8.259375}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 2, 'reward': 1601.3036719689326, 'perf_time': 0.010985599999912665, 'population': 1051, 'numerosity': 1600, 'average_specificity': 6.85}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 12, 'reward': 1017.1125914426574, 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'perf_time': 0.10731860000032611, 'population': 799, 'numerosity': 1600, 'average_specificity': 7.603125}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 10, 'reward': 1033.7784800983545, 'perf_time': 0.06304760000057286, 'population': 776, 'numerosity': 1600, 'average_specificity': 6.7425}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 4, 'reward': 1254.11681, 'perf_time': 0.020683000000644824, 'population': 770, 'numerosity': 1600, 'average_specificity': 5.95875}\n" ] }, { @@ -164,16 +164,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.4415217000000666, 'population': 1474, 'numerosity': 1600, 'average_specificity': 8.50375}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 9, 'reward': 1047.9978638786406, 'perf_time': 0.12999459999991814, 'population': 1152, 'numerosity': 1600, 'average_specificity': 7.305}\n", - 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'numerosity': 1600, 'population': 1021, 'average_specificity': 8.676875, 'fraction_accuracy': 0.10416666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 5, 'reward': 1200.254873423201, 'perf_time': 0.05341669999961596, 'numerosity': 1600, 'population': 1038, 'average_specificity': 8.513125, 'fraction_accuracy': 0.10416666666666667}\n" ] }, { @@ -210,16 +210,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.909581000000344, 'numerosity': 1600, 'population': 1494, 'average_specificity': 8.16875, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1403.7597860833218, 'perf_time': 0.0222316000003957, 'numerosity': 1600, 'population': 1111, 'average_specificity': 6.265, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 6, 'reward': 1132.0401457425107, 'perf_time': 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'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1510.6896376499649, 'perf_time': 0.021415600000182167, 'numerosity': 1600, 'population': 989, 'average_specificity': 6.65375, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 8, 'reward': 1068.940815170528, 'perf_time': 0.08006010000008246, 'numerosity': 1600, 'population': 974, 'average_specificity': 7.843125, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 7, 'reward': 1090.95120158391, 'perf_time': 0.09305210000002262, 'numerosity': 1600, 'population': 965, 'average_specificity': 7.901875, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 11, 'reward': 1023.2952084571973, 'perf_time': 0.10027730000001611, 'numerosity': 1600, 'population': 977, 'average_specificity': 7.9225, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 19, 'reward': 1001.5223601969785, 'perf_time': 0.2427440999999817, 'numerosity': 1600, 'population': 1035, 'average_specificity': 7.559375, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 1, 'reward': 1913.8090164141504, 'perf_time': 0.006096500000239757, 'numerosity': 1600, 'population': 1023, 'average_specificity': 7.144375, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 13, 'reward': 1012.0391934624969, 'perf_time': 0.17555749999974068, 'numerosity': 1600, 'population': 1000, 'average_specificity': 7.061875, 'fraction_accuracy': 0.0}\n" ] } ], @@ -269,16 +269,12 @@ " explore_trials=0,\n", " exploit_trials=exploit + explore,\n", " pre_generate=True\n", - " )\n", - "\n", - "\n", - "\n", - "\n" + " )" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -332,353 +328,353 @@ " \n", " \n", " 0\n", - " 97.000000\n", - " 333.333333\n", - " 1.319633\n", - 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5.893333 11.333333 1039.333333 \n", + "1600 6.177917 14.333333 1044.000000 \n", + "1700 6.659583 9.333333 1056.333333 \n", + "1800 6.419167 7.666667 1047.000000 \n", + "1900 6.312292 6.666667 1061.000000 \n", + "2000 6.300625 4.000000 1047.333333 \n", + "2100 6.339375 36.333333 1033.666667 \n", + "2200 6.042083 7.666667 1040.000000 \n", + "2300 5.879375 38.333333 1027.666667 \n", + "2400 5.822917 6.000000 1029.000000 \n", "\n", " numerosity_other average_specificity_other fraction_accuracy_other \n", "trial \n", - "0 1600.0 8.233333 0.000000 \n", - "100 1600.0 7.122917 0.000000 \n", - "200 1600.0 6.810000 0.004630 \n", - "300 1600.0 7.005625 0.000000 \n", - "400 1600.0 6.967708 0.000000 \n", - "500 1600.0 7.246250 0.000000 \n", - "600 1600.0 7.155208 0.000000 \n", - "700 1600.0 7.555625 0.000000 \n", - "800 1600.0 7.781042 0.041228 \n", - "900 1600.0 8.387292 0.000000 \n", - "1000 1600.0 8.903542 0.000000 \n", - "1100 1600.0 9.278958 0.000000 \n", - "1200 1600.0 9.023333 0.000000 \n", - "1300 1600.0 8.268542 0.000000 \n", - "1400 1600.0 8.428750 0.000000 \n", - "1500 1600.0 8.575833 0.000000 \n", - "1600 1600.0 8.375208 0.000000 \n", - "1700 1600.0 7.733958 0.040114 \n", - "1800 1600.0 7.803958 0.000000 \n", - "1900 1600.0 8.635833 0.000000 \n", - "2000 1600.0 8.342917 0.040850 \n", - "2100 1600.0 9.434583 0.000000 \n", - "2200 1600.0 8.622917 0.000000 \n", - "2300 1600.0 8.153542 0.000000 \n", - "2400 1600.0 7.553958 0.000000 " + "0 1600.0 8.261458 0.000000 \n", + "100 1600.0 6.731042 0.000000 \n", + "200 1600.0 6.609167 0.000000 \n", + "300 1600.0 6.729167 0.000000 \n", + "400 1600.0 6.934167 0.000000 \n", + "500 1600.0 7.106042 0.000000 \n", + "600 1600.0 7.282292 0.000000 \n", + "700 1600.0 7.161042 0.000000 \n", + "800 1600.0 7.221250 0.041165 \n", + "900 1600.0 7.412917 0.000000 \n", + "1000 1600.0 7.707083 0.034722 \n", + "1100 1600.0 7.814375 0.000000 \n", + "1200 1600.0 8.094375 0.000000 \n", + "1300 1600.0 8.453125 0.009524 \n", + "1400 1600.0 8.200000 0.000000 \n", + "1500 1600.0 8.174792 0.000000 \n", + "1600 1600.0 7.778333 0.000000 \n", + "1700 1600.0 7.743750 0.000000 \n", + "1800 1600.0 7.604375 0.034722 \n", + "1900 1600.0 8.050625 0.034722 \n", + "2000 1600.0 7.732292 0.034722 \n", + "2100 1600.0 8.096250 0.000000 \n", + "2200 1600.0 7.602500 0.034722 \n", + "2300 1600.0 7.602500 0.034722 \n", + "2400 1600.0 7.323958 0.034722 " ] }, "metadata": {}, @@ -786,22 +782,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -825,22 +821,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -860,16 +856,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -895,22 +891,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -930,22 +926,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -965,15 +961,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "208.33333333333337\n", - "184.7777777777778\n" + "163.22222222222226\n", + "135.77777777777777\n" ] } ], diff --git a/XCS_Experiments/XCS_XNCS_Woods1.ipynb b/XCS_Experiments/XCS_XNCS_Woods1.ipynb index 57ed02a..919c6e0 100644 --- a/XCS_Experiments/XCS_XNCS_Woods1.ipynb +++ b/XCS_Experiments/XCS_XNCS_Woods1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 15, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -23,8 +23,8 @@ "text": [ "This is how maze looks like:\n", "\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,26 @@ " initial_fitness = 10, # f_i\n", " user_metrics_collector_fcn=xcs_metrics)\n", "\n", - "XNCScfg = XNCSConfig(number_of_actions=8,\n", + "XNCScfg200 = XNCSConfig(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=200)\n", + "\n", + "XNCScfg20 = XNCSConfig(number_of_actions=8,\n", " max_population=1800,\n", " learning_rate=0.2,\n", " alpha=0.1,\n", @@ -96,16 +115,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 29, 'reward': 1000.0, 'perf_time': 0.29741850000027625, 'population': 1633, 'numerosity': 1800, 'average_specificity': 8.299444444444445}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 20, 'reward': 1001.0596745660605, 'perf_time': 0.29211729999951785, 'population': 1165, 'numerosity': 1800, 'average_specificity': 7.827222222222222}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 13, 'reward': 1011.8642499190946, 'perf_time': 0.15623279999999795, 'population': 788, 'numerosity': 1800, 'average_specificity': 6.928888888888889}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 1, 'reward': 1964.3092661652215, 'perf_time': 0.002553000000261818, 'population': 496, 'numerosity': 1800, 'average_specificity': 8.965555555555556}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 23, 'reward': 1000.475641795266, 'perf_time': 0.2056257000003825, 'population': 625, 'numerosity': 1800, 'average_specificity': 10.70111111111111}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 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explore_trials=0,\n", + " exploit_trials=exploit+explore,\n", + " pre_generate=True)\n", "\n", - "df_other = XNCSExp(maze=maze,\n", - " cfg=XNCScfg,\n", + "df200 = XNCSExp(maze=maze,\n", + " cfg=XNCScfg200,\n", " number_of_tests=number_of_experiments,\n", " explore_trials=0,\n", - " exploit_trials=exploit+explore)\n" + " exploit_trials=exploit+explore,\n", + " pre_generate=True)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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"df['population_other']=df_other['population']\n", - "df['numerosity_other']=df_other['numerosity']\n", - "df['average_specificity_other']=df_other['average_specificity']\n", - "df['fraction_accuracy_other']=df_other['fraction_accuracy']\n", - "display(df_other)\n", + "df['steps_in_trial_20']=df20['steps_in_trial']\n", + "df['population_20']=df20['population']\n", + "df['numerosity_20']=df20['numerosity']\n", + "df['average_specificity_20']=df20['average_specificity']\n", + "df['fraction_accuracy_20']=df20['fraction_accuracy']\n", + "\n", + "df['steps_in_trial_200']=df200['steps_in_trial']\n", + "df['population_200']=df200['population']\n", + "df['numerosity_200']=df200['numerosity']\n", + "df['average_specificity_200']=df200['average_specificity']\n", + "df['fraction_accuracy_200']=df200['fraction_accuracy']\n", + "\n", "display(df)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", - "ax = df[['average_specificity', \"average_specificity_other\"]].plot()\n", + "ax = df[['average_specificity', \"average_specificity_20\",\"average_specificity_200\"]].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"average_specificity\")\n", - "ax.legend([\"XCS\",\"XNCS\"])\n" + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "ax = df[ \"fraction_accuracy_other\"].plot()\n", + "ax = df[['population', \"population_20\", \"population_200\"]].plot()\n", "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"fraction accuracy\")\n", - "ax.legend([\"XNCS\"])" + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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U9+7dOw/pm5lZS3IqKsm//idKOnw/328TcGayfA6wNjn+YcDvgasj4i9Z79tFUq9kuSvwOWA1ZmbWLuXaUxkLHAX8j6RfSTpfklraQdI8YAlwvKQaSZcBE4AZklYA1wMVyeZXAMcC1zQ6dbgbmTmYlUA1sBG4o3VNNDOztqLWzHlL6kSmt3AbsBu4C/hpRNTlJ719V15eHlVVVYVOw8ysqEhaFhHl+7p/znMqkk4CZgA3kjkL68vAduDxfX1zMzPrWHI6pVjSMuANMmdrTc463fdpSZ/KU25mZlZkcv2dyuiIWJcdkFQaEesj4qI85GVmZkUo1+Gv3+QYMzOzA1iLPRVJJwBDgEMlZfdIegDd85mYmZkVn70Nfx1P5myvw4DPZ8V3kDk92MzMrEGLRSUi5gPzJf1LRCxpo5zMzKxI7W3463sRcQPwFUnjGq+PiH/PW2ZmZlZ09jb89ULy7F8RmpnZXu1t+Ouh5HlufSz5Vf3BEbE9z7mZmVmRyfWCkvdL6iHpIOB5YI2kK/ObmpmZFZtcf6cyOOmZfBH4A9CfFu5rYmZmB6Zci0rX5NLzXwTmR8S7ZG4NbGZm1iDXonI78ApwELBY0gAyF5M0MzNrkNO1vyLiZuDmrNCrks7OT0pmZlascp2oP1TSj+vv/y5pBplei5mZWYNch7/uInNplouTx3bg7nwlZWZmxSnXS98PjIgvZb2eKqk6D/mYmVkRy7Wn8g9Jp9W/SG7M9Y/8pGRmZsUq16LydeDnkl6R9ArwM+DylnaQdJekLZJWZ8XKJC2VVJ3MzYzIWne1pJckrZF0flZ8uKRVybqbJalVLTQzszaz16IiqTPwbxFxMnAScFJEDIuIlXvZdQ4wqlHsBmBqRJQB1yavkTQYGEvm3i2jgFuT9wW4DagABiWPxsc0M7N2Yq9zKhHxvqThyXLOv02JiMWSShqHydzgC+BQYFOyfCHwq4jYBayX9BIwIukV9ai/7L6ke8j8APORXPNorW/dOZJNu7fm6/BmZnl1VKfezPw/iwr2/rlO1C+XtAB4AHirPhgRv23l+30LeFTSTWR6SZ9M4kcDS7O2q0li7ybLjeNNklRBpldD//79W5mamZntr1yLSk+gFjgnKxZAa4tKJTApIh6UdDEwGzgXaGqeJFqINykiZgGzAMrLy/fpMjKFrPBmZsUu11/U/++U3m888M1k+QHgzmS5BuiXtV1fMkNjNcly47iZmbVDuf6i/jhJf6o/k0vSSZK+vw/vtwk4M1k+B1ibLC8AxkrqJqmUzIT8MxGxGdgh6dTkrK9Lgfn78L5mZtYGcj2l+A7gajJzHCRnfo1taQdJ84AlwPGSaiRdBkwAZkhaAVxPMv8REc8B/5fMvVr+CEyMiPeTQ1WS6dG8BLxMHifpzcxs/+Q6p/JPEfFMo5+IvNfSDhHxoXvaJ4Y3s/00YFoT8SrgxBzzNDOzAsq1p/K6pIEkk+SSvgxszltWZmZWlHLtqUwkc1bVCZI2AuuBf8tbVmZmVpRyPftrHXBuco/6ThGxI79pmZlZMcqpqEg6jMyZVyVAl/q5lYj493wlZmZmxSfX4a8/kPnF+ypgd/7SMTOzYpZrUekeEd/OayZmZlb0cj3765eSJkg6UlLP+kdeMzMzs6KTa0/lHeBGYAofXHsrgGPykZSZmRWnXIvKt4FjI+L1fCZjZmbFLdfhr+eAv+czETMzK3659lTeB6olPQHsqg/6lGIzM8uWa1H5r+RhZmbWrFx/UT8334mYmVnxy/UX9etp4o6LEeGzv8zMrEGuw1/lWcvdgdFkbjFsZmbWIKezvyKiNuuxMSJmsuf96s3MzHIe/vp41stOZHouh+QlIzMzK1q5Dn/N4IM5lfeAV8gMgZmZmTXI9cePFwCzgT8BfwE2svd71N8laYuk1VmxX0uqTh6vSKpO4pdkxasl7ZZUlqx7UtKarHV9Wt9MMzNrC635ncobwLPA2znuMwf4GXBPfSAixtQvS5oBvJnE7wPuS+JDgfkRUZ11rEuSe9WbmVk7lmtR6RsRo1pz4IhYLKmkqXXK3OXrYpqe7B8HzGvNe5mZWfuQ6/DXfyc9iLScDrwWEWubWDeGDxeVu5Ohr2tUf9tJMzNrd3ItKqcBy5K5jZWSVklauR/v22RvRNIngL9HxOqs8CURMZRMITod+GpzB5VUIalKUtXWrVv3Iz0zM9sXuQ5/XZDWG0rqAlwEDG9i9VgaFZuI2Jg875B0PzCCrHmaRtvOAmYBlJeXf+gKAGZmll+5Xvvr1RTf81zgxYioyQ5K6kTmNOUzsmJdgMMi4nVJXYHPAY+lmIuZmaUo1+GvVpM0D1gCHC+pRtJlyaoP9UYSZwA1EbEuK9YNeDQZaqsmcyrzHfnK2czM9k+uw1+tFhHjmol/rZn4k8CpjWJv0fQwmZmZtUN566mYmdmBx0XFzMxS46JiZmapcVExM7PUuKiYmVlqXFTMzCw1LipmZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS42LipmZpcZFxczMUuOiYmZmqclbUZF0l6QtklZnxX4tqTp5vCKpOomXSPpH1rpfZO0zXNIqSS9JulmS8pWzmZntny55PPYc4GfAPfWBiBhTvyxpBvBm1vYvR0RZE8e5DagAlgJ/AEYBj6SfrpmZ7a+89VQiYjFQ19S6pLdxMTCvpWNIOhLoERFLIiLIFKgvppyqmZmlpFBzKqcDr0XE2qxYqaTlkp6SdHoSOxqoydqmJok1SVKFpCpJVVu3bk0/azMza1Ghiso49uylbAb6R8Qw4NvA/ZJ6AE3Nn0RzB42IWRFRHhHlvXv3TjVhMzPbu3zOqTRJUhfgImB4fSwidgG7kuVlkl4GjiPTM+mbtXtfYFPbZWtmZq1RiJ7KucCLEdEwrCWpt6TOyfIxwCBgXURsBnZIOjWZh7kUmF+AnM3MLAf5PKV4HrAEOF5SjaTLklVj+fAE/RnASkkrgN8AX4+I+kn+SuBO4CXgZXzml5lZu6XMSVUdT3l5eVRVVRU6DTOzoiJpWUSU7+v+/kW9mZmlxkXFzMxS46JiZmapcVExM7PUuKiYmVlqXFTMzCw1LipmZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS42LipmZpcZFxczMUpPPe9TfJWmLpNVZsV9Lqk4er0iqTuIjJS2TtCp5Pidrnyclrcnar0++cjYzs/3TJY/HngP8DLinPhARY+qXJc0A3kxevg58PiI2SToReBQ4OutYl0SEbzhvZtbO5a2oRMRiSSVNrZMk4GLgnGTb5VmrnwO6S+oWEbvylZ+ZmaWvUHMqpwOvRcTaJtZ9CVjeqKDcnQx9XZMUpCZJqpBUJalq69ataedsZmZ7UaiiMg6Y1zgoaQgwHbg8K3xJRAwlU4hOB77a3EEjYlZElEdEee/evVNO2czM9qbNi4qkLsBFwK8bxfsCvwMujYiX6+MRsTF53gHcD4xou2zNzKw1CtFTORd4MSJq6gOSDgN+D1wdEX/JineR1CtZ7gp8DliNmZm1S/k8pXgesAQ4XlKNpMuSVWP58NDXFcCxwDWNTh3uBjwqaSVQDWwE7shXzmZmtn8UEYXOIS/Ky8ujqspnIZuZtYakZRFRvq/7+xf1ZmaWGhcVMzNLjYuKmZmlxkXFzMxS46JiZmapcVExM7PUuKiYmVlqXFTMzCw1LipmZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS42LipmZpcZFxczMUuOiYmZmqVFEFDqHvJC0FXh1H3fvBbyeYjrF5EBuOxzY7T+Q2w4Hdvuz2z4gInrv64E6bFHZH5KqIqK80HkUwoHcdjiw238gtx0O7Pan2XYPf5mZWWpcVMzMLDUuKk2bVegECuhAbjsc2O0/kNsOB3b7U2u751TMzCw17qmYmVlqXFTMzCw1LipZJI2StEbSS5ImFzqffJH0iqRVkqolVSWxnpIWSVqbPB+etf3VyWeyRtL5hcu89STdJWmLpNVZsVa3VdLw5DN7SdLNktTWbdkXzbT/Okkbk++/WtJnstZ1mPZL6ifpCUkvSHpO0jeTeIf//ltoe/6/+4jwIzOv1Bl4GTgG+AiwAhhc6Lzy1NZXgF6NYjcAk5PlycD0ZHlw8ll0A0qTz6hzodvQiraeAXwcWL0/bQWeAf4FEPAIcEGh27Yf7b8O+G4T23ao9gNHAh9Plg8B/pq0scN//y20Pe/fvXsqHxgBvBQR6yLiHeBXwIUFzqktXQjMTZbnAl/Miv8qInZFxHrgJTKfVVGIiMVAXaNwq9oq6UigR0Qsicz/Zfdk7dOuNdP+5nSo9kfE5oh4NlneAbwAHM0B8P230PbmpNZ2F5UPHA1syHpdQ8tfQjELYKGkZZIqktg/R8RmyPwHCfRJ4h3xc2ltW49OlhvHi9kVklYmw2P1wz8dtv2SSoBhwNMcYN9/o7ZDnr97F5UPNDVO2FHPt/5URHwcuACYKOmMFrY9kD6X5tra0T6D24CBQBmwGZiRxDtk+yUdDDwIfCsitre0aROxom5/E23P+3fvovKBGqBf1uu+wKYC5ZJXEbEped4C/I7McNZrSVeX5HlLsnlH/Fxa29aaZLlxvChFxGsR8X5E7Abu4IPhzA7XfkldyfxRvS8ifpuED4jvv6m2t8V376Lygf8BBkkqlfQRYCywoMA5pU7SQZIOqV8GzgNWk2nr+GSz8cD8ZHkBMFZSN0mlwCAyE3fFrFVtTYZIdkg6NTnz5dKsfYpO/R/UxL+S+f6hg7U/yXU28EJE/DhrVYf//ptre5t894U+S6E9PYDPkDlL4mVgSqHzyVMbjyFzlscK4Ln6dgJHAH8C1ibPPbP2mZJ8Jmto52e9NNHeeWS6+e+S+VfXZfvSVqA8+R/wZeBnJFejaO+PZtr/S2AVsDL5Y3JkR2w/cBqZoZqVQHXy+MyB8P230Pa8f/e+TIuZmaXGw19mZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTFLiaTDJH2jhfX/ncMxdqablVnbclExS89hwIeKiqTOABHxybZOyKytdSl0AmYdyH8CAyVVk/mx4U4yPzwsAwZL2hkRByfXY5oPHA50Bb4fEe36F9pmufKPH81SklwN9uGIOFHSWcDvgRMjcylxsopKF+CfImK7pF7AUmBQRET9NgVqgtl+c0/FLH+eqS8ojQi4Prk69G4ylxL/Z+BvbZmcWT64qJjlz1vNxC8BegPDI+JdSa8A3dssK7M88kS9WXp2kLl1694cCmxJCsrZwID8pmXWdtxTMUtJRNRK+ouk1cA/gNea2fQ+4CFJVWSuHvtiG6VolneeqDczs9R4+MvMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS83/B4fSJ1xfKeWgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "ax = df[['population', \"population_other\"]].plot()\n", + "ax = df[['numerosity', 'numerosity_20', 'numerosity_200']].plot()\n", "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"XCS\",\"XNCS\"])" + "ax.set_ylabel(\"numerosity\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "ax = df[['numerosity', 'numerosity_other']].plot()\n", + "ax = df[['steps_in_trial', 'steps_in_trial_20', 'steps_in_trial_200']].plot()\n", "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"numerosity\")\n", - "ax.legend([\"XCS\", \"XNCS\"])" + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "87.77777777777779\n", + "94.77777777777779\n", + "152.33333333333334\n" + ] + } + ], "source": [ - "ax = df[['steps_in_trial', 'steps_in_trial_other']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"XCS\",\"XNCS\"])" + "print(sum(df['steps_in_trial'])/number_of_experiments)\n", + "print(sum(df['steps_in_trial_20'])/number_of_experiments)\n", + "print(sum(df['steps_in_trial_200'])/number_of_experiments)" ] }, { @@ -288,10 +1266,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "print(sum(df['steps_in_trial']))\n", - "print(sum(df['steps_in_trial_other']))" - ] + "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XNCS_maze5_avg.ipynb b/XCS_Experiments/XNCS_maze5_avg.ipynb new file mode 100644 index 0000000..9036d91 --- /dev/null +++ b/XCS_Experiments/XNCS_maze5_avg.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('1', '1', '9', '0', '1', '1', '0', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xncs import XNCS, Configuration\n", + "from utils.nxcs_utils import *\n", + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " epsilon_0=0.01,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.07768279999982042, 'numerosity': 136, 'population': 116, 'average_specificity': 10.330882352941176, 'fraction_accuracy': 0.0}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 20, 'reward': 1001.0596610576391, 'perf_time': 0.33710759999985385, 'numerosity': 1800, 'population': 1387, 'average_specificity': 20.246111111111112, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 100, 'reward': 2.385089144282033e-42, 'perf_time': 1.8018307000002096, 'numerosity': 1800, 'population': 1422, 'average_specificity': 21.259444444444444, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 8, 'reward': 1064.5753531245762, 'perf_time': 0.17786660000001575, 'numerosity': 1800, 'population': 1410, 'average_specificity': 22.786666666666665, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 4, 'reward': 1255.1808898190625, 'perf_time': 0.08564130000013392, 'numerosity': 1800, 'population': 1458, 'average_specificity': 21.15277777777778, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 70, 'reward': 1000.0000000387333, 'perf_time': 1.3591581000000588, 'numerosity': 1800, 'population': 1477, 'average_specificity': 20.385555555555555, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 42, 'reward': 1000.0005726395076, 'perf_time': 0.5941466000003857, 'numerosity': 1800, 'population': 1417, 'average_specificity': 21.80388888888889, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 100, 'reward': 1.785129569347193e-27, 'perf_time': 1.901586199999656, 'numerosity': 1800, 'population': 1443, 'average_specificity': 19.68722222222222, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 59, 'reward': 1000.0, 'perf_time': 1.1278117000001657, 'numerosity': 1800, 'population': 1453, 'average_specificity': 21.00888888888889, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 1, 'reward': 1890.4229351, 'perf_time': 0.011307799999485724, 'numerosity': 1800, 'population': 1460, 'average_specificity': 21.385555555555555, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1000.0000000000007, 'perf_time': 0.046695099999851664, 'numerosity': 1800, 'population': 1412, 'average_specificity': 21.41722222222222, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 100, 'reward': 2.3850891444653006e-42, 'perf_time': 1.384353800000099, 'numerosity': 1800, 'population': 1445, 'average_specificity': 33.532222222222224, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 62, 'reward': 1000.0000005998154, 'perf_time': 0.7323357000004762, 'numerosity': 1800, 'population': 1409, 'average_specificity': 34.611666666666665, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 73, 'reward': 1000.0000000138631, 'perf_time': 0.9091007999995782, 'numerosity': 1800, 'population': 1396, 'average_specificity': 37.727222222222224, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 100, 'reward': 1.33608741082723e-12, 'perf_time': 1.1410980000000563, 'numerosity': 1800, 'population': 1446, 'average_specificity': 31.512777777777778, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 57, 'reward': 1000.0000033244964, 'perf_time': 0.6541858000000502, 'numerosity': 1800, 'population': 1438, 'average_specificity': 38.705555555555556, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 58, 'reward': 1000.0000040362712, 'perf_time': 0.7143783999999869, 'numerosity': 1800, 'population': 1442, 'average_specificity': 42.580555555555556, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 5, 'reward': 1226.2714723709792, 'perf_time': 0.07154629999968165, 'numerosity': 1800, 'population': 1451, 'average_specificity': 37.928333333333335, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 32, 'reward': 1000.0, 'perf_time': 0.5323422000001301, 'numerosity': 1800, 'population': 1458, 'average_specificity': 34.144444444444446, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 69, 'reward': 1000.0000000545539, 'perf_time': 0.9133171999992555, 'numerosity': 1800, 'population': 1427, 'average_specificity': 40.61666666666667, 'fraction_accuracy': 0.0}\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mnumber_of_tests\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2500\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m 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\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'trial'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in \u001b[0;36mstart_single_experiment\u001b[1;34m(maze, cfg, explore_trials, exploit_trials, population)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[0magent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mXNCS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexploit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexplore_metrics\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\XCS.py\u001b[0m in \u001b[0;36m_run_trial_exploit\u001b[1;34m(self, env, trials, current_trial)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mmetrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_explore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m 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max(prediction_array))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m self._distribute_and_update(action_set,\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\XNCS.py\u001b[0m in \u001b[0;36m_distribute_and_update\u001b[1;34m(self, action_set, current_situation, next_situation, p)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[0mEffect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnext_situation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m )\n\u001b[1;32m---> 54\u001b[1;33m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_distribute_and_update\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction_set\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent_situation\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\GeneticAlgorithm.py\u001b[0m in \u001b[0;36mrun_ga\u001b[1;34m(self, action_set, situation, time_stamp)\u001b[0m\n\u001b[0;32m 46\u001b[0m self._perform_insertion_or_subsumption(\n\u001b[0;32m 47\u001b[0m \u001b[0mchild1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchild2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mparent1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparent2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m )\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\GeneticAlgorithm.py\u001b[0m in \u001b[0;36m_perform_insertion_or_subsumption\u001b[1;34m(self, child1, child2, parent1, parent2)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[1;32min\u001b[0m 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average_fitness)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_deletion_vote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage_fitness\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[0mvote\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction_set_size\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumerosity\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperience\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeletion_threshold\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m 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"source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb index f9403d6..ab15590 100644 --- a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb +++ b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb @@ -972,6 +972,30 @@ "ax.set_ylabel(\"steps in trial\")\n", "ax.legend([\"steps\"])" ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "221.3333333333333\n" + ] + } + ], + "source": [ + "print(sum(df['steps_in_trial'])/3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From aaa6fb6828b6d0155085a33e386f54bdc45af8b9 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 30 May 2021 17:31:21 +0200 Subject: [PATCH 09/19] NXCS fix Woods --- .../XNCS_Woods1_pre_populated.ipynb | 622 ++-- XCS_Experiments/XNCS_maze5_avg.ipynb | 59 +- .../XNCS_maze5_avg_pre_populated-Copy1.ipynb | 254 ++ .../XNCS_maze5_avg_pre_populated.ipynb | 870 +----- XCS_Experiments/XNCS_woods.ipynb | 2651 +++++++++-------- XCS_Experiments/XNCS_woods_avg.ipynb | 882 ++---- XCS_Experiments/utils/nxcs_utils.py | 13 +- 7 files changed, 2206 insertions(+), 3145 deletions(-) create mode 100644 XCS_Experiments/XNCS_maze5_avg_pre_populated-Copy1.ipynb diff --git a/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb index 7b38e4c..0398c02 100644 --- a/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb +++ b/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 42, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -23,13 +23,13 @@ "text": [ "This is how maze looks like\n", "\n", - "['.', '.', '.', 'F', 'O', 'O', '.', '.']\n", + "['.', 'O', 'O', '.', '.', '.', '.', '.']\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n" ] } ], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -96,115 +96,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 30, 'reward': 1000.0, 'perf_time': 0.4908828000006906, 'numerosity': 1800, 'population': 1630, 'average_specificity': 8.407222222222222, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1563.7665263868944, 'perf_time': 0.03216950000023644, 'numerosity': 1800, 'population': 1540, 'average_specificity': 18.010555555555555, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 13, 'reward': 1011.8631483990195, 'perf_time': 0.160649999999805, 'numerosity': 1800, 'population': 1543, 'average_specificity': 20.341666666666665, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 8, 'reward': 1070.0230026698277, 'perf_time': 0.09758580000016082, 'numerosity': 1800, 'population': 1551, 'average_specificity': 21.015555555555554, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 8, 'reward': 1069.0362559493183, 'perf_time': 0.09980910000012955, 'numerosity': 1800, 'population': 1575, 'average_specificity': 19.20888888888889, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 7, 'reward': 1097.1024221614744, 'perf_time': 0.07686180000018794, 'numerosity': 1800, 'population': 1578, 'average_specificity': 19.611666666666668, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 1, 'reward': 2217.4535717217086, 'perf_time': 0.025234700000510202, 'numerosity': 1800, 'population': 1584, 'average_specificity': 19.805555555555557, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 5, 'reward': 1198.3710964017487, 'perf_time': 0.06566600000041944, 'numerosity': 1800, 'population': 1567, 'average_specificity': 20.302222222222223, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 3, 'reward': 1409.5230724117976, 'perf_time': 0.03123330000016722, 'numerosity': 1800, 'population': 1575, 'average_specificity': 20.464444444444446, 'fraction_accuracy': 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"stream", - "text": [ - "Executing 2 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 7, 'reward': 1000.0, 'perf_time': 0.09536780000053113, 'numerosity': 1800, 'population': 1604, 'average_specificity': 8.462777777777777, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 50, 'reward': 1000.0000627680964, 'perf_time': 0.9400621999993746, 'numerosity': 1800, 'population': 1542, 'average_specificity': 14.076666666666666, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 4, 'reward': 1259.4241713276256, 'perf_time': 0.05402999999932945, 'numerosity': 1800, 'population': 1498, 'average_specificity': 23.566111111111113, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1398.873865772583, 'perf_time': 0.035098399999696994, 'numerosity': 1800, 'population': 1493, 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'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 2, 'reward': 1886.4707571494996, 'perf_time': 0.02985010000000088, 'numerosity': 1800, 'population': 1585, 'average_specificity': 8.71111111111111, 'fraction_accuracy': 0.11328125}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 8, 'reward': 1082.2976794931064, 'perf_time': 0.11306730000000442, 'numerosity': 1800, 'population': 1573, 'average_specificity': 10.06611111111111, 'fraction_accuracy': 0.375}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 7, 'reward': 1093.588574743161, 'perf_time': 0.09264579999999967, 'numerosity': 1800, 'population': 1580, 'average_specificity': 11.934444444444445, 'fraction_accuracy': 0.125}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 38, 'reward': 1000.0023853594896, 'perf_time': 0.4457937999999899, 'numerosity': 1800, 'population': 1554, 'average_specificity': 13.135555555555555, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 9, 'reward': 1047.9518200343475, 'perf_time': 0.08730189999999993, 'numerosity': 1800, 'population': 1545, 'average_specificity': 14.772222222222222, 'fraction_accuracy': 0.23863636363636365}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 6, 'reward': 1141.528554666973, 'perf_time': 0.0681583999999873, 'numerosity': 1800, 'population': 1564, 'average_specificity': 14.801666666666666, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 2, 'reward': 1504.1000031547478, 'perf_time': 0.03933630000000221, 'numerosity': 1800, 'population': 1561, 'average_specificity': 14.197222222222223, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 6, 'reward': 1177.1675462768721, 'perf_time': 0.062398400000006404, 'numerosity': 1800, 'population': 1558, 'average_specificity': 14.66, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 4, 'reward': 1418.2266975072803, 'perf_time': 0.04104639999999904, 'numerosity': 1800, 'population': 1561, 'average_specificity': 15.536666666666667, 'fraction_accuracy': 0.125}\n" ] } ], "source": [ "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", - "number_of_experiments = 5\n", + "number_of_experiments = 1\n", "explore = 0\n", "exploit = 2500\n", "\n", @@ -215,14 +123,12 @@ " explore_trials=explore,\n", " exploit_trials=exploit,\n", " pre_generate=True\n", - " )\n", - "\n", - "\n" + " )" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -268,314 +174,314 @@ " \n", " \n", " 0\n", - " 13.4\n", - " 1000.000000\n", - " 0.189316\n", - " 1800.0\n", - " 1623.2\n", - " 8.369556\n", + " 8\n", + " 1.000000e+03\n", + " 0.084815\n", + " 1800\n", + " 1637\n", + " 8.145000\n", " 0.000000\n", " \n", " \n", " 100\n", - " 8.6\n", - " 1099.416725\n", - " 0.121443\n", - " 1800.0\n", - " 1556.8\n", - " 11.567222\n", - " 0.020652\n", + " 5\n", + " 1.250735e+03\n", + " 0.062643\n", + " 1800\n", + " 1597\n", + " 8.130556\n", + " 0.000000\n", " \n", " \n", " 200\n", - " 17.2\n", - " 1287.715999\n", - " 0.284367\n", - " 1800.0\n", - " 1539.2\n", - " 14.321778\n", - " 0.025034\n", + " 2\n", + " 1.886471e+03\n", + " 0.029850\n", + " 1800\n", + " 1585\n", + " 8.711111\n", + " 0.113281\n", " \n", " \n", " 300\n", - " 5.2\n", - " 1286.494031\n", - " 0.076812\n", - " 1800.0\n", - " 1521.0\n", - " 17.568000\n", - " 0.075165\n", + " 8\n", + " 1.069032e+03\n", + " 0.089705\n", + " 1800\n", + " 1593\n", + " 10.185556\n", + " 0.241667\n", " \n", " \n", " 400\n", - " 5.4\n", - " 1307.252671\n", - " 0.073358\n", - " 1800.0\n", - " 1517.4\n", - " 19.637444\n", - " 0.050001\n", + " 3\n", + " 1.707064e+03\n", + " 0.029097\n", + " 1800\n", + " 1584\n", + " 10.250556\n", + " 0.241667\n", " \n", " \n", " 500\n", - " 14.0\n", - " 1111.176320\n", - " 0.178077\n", - " 1800.0\n", - " 1510.8\n", - " 20.665000\n", - " 0.038012\n", + " 8\n", + " 1.082298e+03\n", + " 0.113067\n", + " 1800\n", + " 1573\n", + " 10.066111\n", + " 0.375000\n", " \n", " \n", " 600\n", - " 6.0\n", - " 1172.474595\n", - " 0.082059\n", - " 1800.0\n", - " 1518.4\n", - " 21.490444\n", - " 0.038501\n", + " 9\n", + " 1.049088e+03\n", + " 0.119529\n", + " 1800\n", + " 1582\n", + " 11.092222\n", + " 0.000000\n", " \n", " \n", " 700\n", - " 5.2\n", - " 1142.361088\n", - " 0.062282\n", - " 1800.0\n", - " 1506.4\n", - " 23.247222\n", - " 0.046341\n", + " 7\n", + " 1.093589e+03\n", + " 0.092646\n", + " 1800\n", + " 1580\n", + " 11.934444\n", + " 0.125000\n", " \n", " \n", " 800\n", - " 11.6\n", - " 1164.746613\n", - " 0.149228\n", - " 1800.0\n", - " 1504.8\n", - " 23.817000\n", - " 0.052659\n", + " 10\n", + " 1.071260e+03\n", + " 0.139656\n", + " 1800\n", + " 1575\n", + " 12.199444\n", + " 0.375000\n", " \n", " \n", " 900\n", - " 14.4\n", - " 1159.789922\n", - " 0.204233\n", - " 1800.0\n", - " 1502.6\n", - " 23.252222\n", - " 0.042387\n", + " 5\n", + " 1.238699e+03\n", + " 0.064394\n", + " 1800\n", + " 1576\n", + " 12.867222\n", + " 0.250000\n", " \n", " \n", " 1000\n", - " 4.2\n", - " 1518.219211\n", - " 0.052426\n", - " 1800.0\n", - " 1492.8\n", - " 25.584444\n", - " 0.019927\n", + " 38\n", + " 1.000002e+03\n", + " 0.445794\n", + " 1800\n", + " 1554\n", + " 13.135556\n", + " 0.000000\n", " \n", " \n", " 1100\n", - " 16.0\n", - " 897.824742\n", - " 0.204456\n", - " 1800.0\n", - " 1474.2\n", - " 27.055111\n", - " 0.013034\n", + " 9\n", + " 1.081911e+03\n", + " 0.143781\n", + " 1800\n", + " 1545\n", + " 13.717222\n", + " 0.125000\n", " \n", " \n", " 1200\n", - " 15.4\n", - " 1065.309578\n", - " 0.171381\n", - " 1800.0\n", - " 1456.6\n", - " 29.789444\n", - " 0.020771\n", + " 9\n", + " 1.047952e+03\n", + " 0.087302\n", + " 1800\n", + " 1545\n", + " 14.772222\n", + " 0.238636\n", " \n", " \n", " 1300\n", - " 6.8\n", - " 1168.536337\n", - " 0.090427\n", - " 1800.0\n", - " 1450.2\n", - " 30.880111\n", - " 0.013929\n", + " 9\n", + " 1.050219e+03\n", + " 0.121027\n", + " 1800\n", + " 1549\n", + " 14.645000\n", + " 0.000000\n", " \n", " \n", " 1400\n", - " 5.4\n", - " 1294.956789\n", - " 0.073507\n", - " 1800.0\n", - " 1458.0\n", - " 30.821444\n", - " 0.014106\n", + " 1\n", + " 1.769820e+03\n", + " 0.009838\n", + " 1800\n", + " 1558\n", + " 14.951667\n", + " 0.125000\n", " \n", " \n", " 1500\n", - " 7.0\n", - " 1354.902190\n", - " 0.084415\n", - " 1800.0\n", - " 1452.4\n", - " 31.602556\n", - " 0.013922\n", + " 6\n", + " 1.141529e+03\n", + " 0.068158\n", + " 1800\n", + " 1564\n", + " 14.801667\n", + " 0.000000\n", " \n", " \n", " 1600\n", - " 6.6\n", - " 1114.628386\n", - " 0.074703\n", - " 1800.0\n", - " 1446.6\n", - " 32.647778\n", - " 0.013637\n", + " 1\n", + " 1.811296e+03\n", + " 0.009265\n", + " 1800\n", + " 1558\n", + " 13.833333\n", + " 0.041667\n", " \n", " \n", " 1700\n", - " 5.0\n", - " 1392.394373\n", - " 0.064630\n", - " 1800.0\n", - " 1448.8\n", - " 32.844889\n", - " 0.012886\n", + " 2\n", + " 1.504100e+03\n", + " 0.039336\n", + " 1800\n", + " 1561\n", + " 14.197222\n", + " 0.000000\n", " \n", " \n", " 1800\n", - " 7.4\n", - " 1157.446494\n", - " 0.081245\n", - " 1800.0\n", - " 1449.6\n", - " 33.736778\n", - " 0.012378\n", + " 8\n", + " 1.067196e+03\n", + " 0.102992\n", + " 1800\n", + " 1566\n", + " 15.303889\n", + " 0.000000\n", " \n", " \n", " 1900\n", - " 11.8\n", - " 1239.067146\n", - " 0.129333\n", - " 1800.0\n", - " 1446.2\n", - " 33.532889\n", - " 0.008997\n", + " 6\n", + " 1.135569e+03\n", + " 0.083280\n", + " 1800\n", + " 1561\n", + " 14.877222\n", + " 0.125000\n", " \n", " \n", " 2000\n", - " 2.8\n", - " 1539.534878\n", - " 0.028273\n", - " 1800.0\n", - " 1452.8\n", - " 32.882444\n", - " 0.008764\n", + " 6\n", + " 1.177168e+03\n", + " 0.062398\n", + " 1800\n", + " 1558\n", + " 14.660000\n", + " 0.000000\n", " \n", " \n", " 2100\n", - " 4.8\n", - " 1397.112791\n", - " 0.058439\n", - " 1800.0\n", - " 1453.6\n", - " 32.853000\n", - " 0.012493\n", + " 8\n", + " 1.109383e+03\n", + " 0.111724\n", + " 1800\n", + " 1568\n", + " 15.310556\n", + " 0.125000\n", " \n", " \n", " 2200\n", - " 6.0\n", - " 1185.790393\n", - " 0.068443\n", - " 1800.0\n", - " 1451.6\n", - " 32.758333\n", - " 0.008879\n", + " 4\n", + " 1.418227e+03\n", + " 0.041046\n", + " 1800\n", + " 1561\n", + " 15.536667\n", + " 0.125000\n", " \n", " \n", " 2300\n", - " 7.2\n", - " 1129.381893\n", - " 0.090482\n", - " 1800.0\n", - " 1461.4\n", - " 32.383444\n", - " 0.033714\n", + " 7\n", + " 1.094200e+03\n", + " 0.099101\n", + " 1800\n", + " 1547\n", + " 15.074444\n", + " 0.284914\n", " \n", " \n", " 2400\n", - " 5.4\n", - " 1423.324405\n", - " 0.069817\n", - " 1800.0\n", - " 1461.2\n", - " 32.737333\n", - " 0.029473\n", + " 50\n", + " 2.013565e-12\n", + " 0.638773\n", + " 1800\n", + " 1542\n", + " 15.373889\n", + " 0.005435\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial reward perf_time numerosity population \\\n", - "trial \n", - "0 13.4 1000.000000 0.189316 1800.0 1623.2 \n", - "100 8.6 1099.416725 0.121443 1800.0 1556.8 \n", - "200 17.2 1287.715999 0.284367 1800.0 1539.2 \n", - "300 5.2 1286.494031 0.076812 1800.0 1521.0 \n", - "400 5.4 1307.252671 0.073358 1800.0 1517.4 \n", - "500 14.0 1111.176320 0.178077 1800.0 1510.8 \n", - "600 6.0 1172.474595 0.082059 1800.0 1518.4 \n", - "700 5.2 1142.361088 0.062282 1800.0 1506.4 \n", - "800 11.6 1164.746613 0.149228 1800.0 1504.8 \n", - "900 14.4 1159.789922 0.204233 1800.0 1502.6 \n", - "1000 4.2 1518.219211 0.052426 1800.0 1492.8 \n", - "1100 16.0 897.824742 0.204456 1800.0 1474.2 \n", - "1200 15.4 1065.309578 0.171381 1800.0 1456.6 \n", - "1300 6.8 1168.536337 0.090427 1800.0 1450.2 \n", - "1400 5.4 1294.956789 0.073507 1800.0 1458.0 \n", - "1500 7.0 1354.902190 0.084415 1800.0 1452.4 \n", - "1600 6.6 1114.628386 0.074703 1800.0 1446.6 \n", - "1700 5.0 1392.394373 0.064630 1800.0 1448.8 \n", - "1800 7.4 1157.446494 0.081245 1800.0 1449.6 \n", - "1900 11.8 1239.067146 0.129333 1800.0 1446.2 \n", - "2000 2.8 1539.534878 0.028273 1800.0 1452.8 \n", - "2100 4.8 1397.112791 0.058439 1800.0 1453.6 \n", - "2200 6.0 1185.790393 0.068443 1800.0 1451.6 \n", - "2300 7.2 1129.381893 0.090482 1800.0 1461.4 \n", - "2400 5.4 1423.324405 0.069817 1800.0 1461.2 \n", + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 8 1.000000e+03 0.084815 1800 1637 \n", + "100 5 1.250735e+03 0.062643 1800 1597 \n", + "200 2 1.886471e+03 0.029850 1800 1585 \n", + "300 8 1.069032e+03 0.089705 1800 1593 \n", + "400 3 1.707064e+03 0.029097 1800 1584 \n", + "500 8 1.082298e+03 0.113067 1800 1573 \n", + "600 9 1.049088e+03 0.119529 1800 1582 \n", + "700 7 1.093589e+03 0.092646 1800 1580 \n", + "800 10 1.071260e+03 0.139656 1800 1575 \n", + "900 5 1.238699e+03 0.064394 1800 1576 \n", + "1000 38 1.000002e+03 0.445794 1800 1554 \n", + "1100 9 1.081911e+03 0.143781 1800 1545 \n", + "1200 9 1.047952e+03 0.087302 1800 1545 \n", + "1300 9 1.050219e+03 0.121027 1800 1549 \n", + "1400 1 1.769820e+03 0.009838 1800 1558 \n", + "1500 6 1.141529e+03 0.068158 1800 1564 \n", + "1600 1 1.811296e+03 0.009265 1800 1558 \n", + "1700 2 1.504100e+03 0.039336 1800 1561 \n", + "1800 8 1.067196e+03 0.102992 1800 1566 \n", + "1900 6 1.135569e+03 0.083280 1800 1561 \n", + "2000 6 1.177168e+03 0.062398 1800 1558 \n", + "2100 8 1.109383e+03 0.111724 1800 1568 \n", + "2200 4 1.418227e+03 0.041046 1800 1561 \n", + "2300 7 1.094200e+03 0.099101 1800 1547 \n", + "2400 50 2.013565e-12 0.638773 1800 1542 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 8.369556 0.000000 \n", - "100 11.567222 0.020652 \n", - "200 14.321778 0.025034 \n", - "300 17.568000 0.075165 \n", - "400 19.637444 0.050001 \n", - "500 20.665000 0.038012 \n", - "600 21.490444 0.038501 \n", - "700 23.247222 0.046341 \n", - "800 23.817000 0.052659 \n", - "900 23.252222 0.042387 \n", - "1000 25.584444 0.019927 \n", - "1100 27.055111 0.013034 \n", - "1200 29.789444 0.020771 \n", - "1300 30.880111 0.013929 \n", - "1400 30.821444 0.014106 \n", - "1500 31.602556 0.013922 \n", - "1600 32.647778 0.013637 \n", - "1700 32.844889 0.012886 \n", - "1800 33.736778 0.012378 \n", - "1900 33.532889 0.008997 \n", - "2000 32.882444 0.008764 \n", - "2100 32.853000 0.012493 \n", - "2200 32.758333 0.008879 \n", - "2300 32.383444 0.033714 \n", - "2400 32.737333 0.029473 " + "0 8.145000 0.000000 \n", + "100 8.130556 0.000000 \n", + "200 8.711111 0.113281 \n", + "300 10.185556 0.241667 \n", + "400 10.250556 0.241667 \n", + "500 10.066111 0.375000 \n", + "600 11.092222 0.000000 \n", + "700 11.934444 0.125000 \n", + "800 12.199444 0.375000 \n", + "900 12.867222 0.250000 \n", + "1000 13.135556 0.000000 \n", + "1100 13.717222 0.125000 \n", + "1200 14.772222 0.238636 \n", + "1300 14.645000 0.000000 \n", + "1400 14.951667 0.125000 \n", + "1500 14.801667 0.000000 \n", + "1600 13.833333 0.041667 \n", + "1700 14.197222 0.000000 \n", + "1800 15.303889 0.000000 \n", + "1900 14.877222 0.125000 \n", + "2000 14.660000 0.000000 \n", + "2100 15.310556 0.125000 \n", + "2200 15.536667 0.125000 \n", + "2300 15.074444 0.284914 \n", + "2400 15.373889 0.005435 " ] }, "metadata": {}, @@ -588,22 +494,22 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -626,22 +532,22 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 48, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -661,22 +567,22 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -696,22 +602,22 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 50, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -731,14 +637,14 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "42.56000000000002\n" + "229.0\n" ] } ], diff --git a/XCS_Experiments/XNCS_maze5_avg.ipynb b/XCS_Experiments/XNCS_maze5_avg.ipynb index 9036d91..3c8bb7b 100644 --- a/XCS_Experiments/XNCS_maze5_avg.ipynb +++ b/XCS_Experiments/XNCS_maze5_avg.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -23,10 +23,10 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '1', '9', '0', '1', '1', '0', '1')\n", + "('1', '1', '0', '1', '0', '1', '0', '1')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "scrolled": false }, @@ -92,7 +92,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.07768279999982042, 'numerosity': 136, 'population': 116, 'average_specificity': 10.330882352941176, 'fraction_accuracy': 0.0}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.0656737999999999, 'numerosity': 154, 'population': 140, 'average_specificity': 8.37012987012987, 'fraction_accuracy': 0.0}\n" ] }, { @@ -106,48 +106,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 20, 'reward': 1001.0596610576391, 'perf_time': 0.33710759999985385, 'numerosity': 1800, 'population': 1387, 'average_specificity': 20.246111111111112, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 100, 'reward': 2.385089144282033e-42, 'perf_time': 1.8018307000002096, 'numerosity': 1800, 'population': 1422, 'average_specificity': 21.259444444444444, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 8, 'reward': 1064.5753531245762, 'perf_time': 0.17786660000001575, 'numerosity': 1800, 'population': 1410, 'average_specificity': 22.786666666666665, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 4, 'reward': 1255.1808898190625, 'perf_time': 0.08564130000013392, 'numerosity': 1800, 'population': 1458, 'average_specificity': 21.15277777777778, 'fraction_accuracy': 0.0}\n", - 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'population': 1451, 'average_specificity': 37.928333333333335, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 32, 'reward': 1000.0, 'perf_time': 0.5323422000001301, 'numerosity': 1800, 'population': 1458, 'average_specificity': 34.144444444444446, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 69, 'reward': 1000.0000000545539, 'perf_time': 0.9133171999992555, 'numerosity': 1800, 'population': 1427, 'average_specificity': 40.61666666666667, 'fraction_accuracy': 0.0}\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m 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\"\"\"\n\u001b[1;32m---> 66\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_exploit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexplore_exploit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\Agent.py\u001b[0m in \u001b[0;36m_evaluate\u001b[1;34m(self, env, n_trials, func, decay)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mcurrent_trial\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[0mstart_ts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m \u001b[0msteps_in_trial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent_trial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 126\u001b[0m \u001b[0mend_ts\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime_stamp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 105\u001b[0m )\n\u001b[0;32m 106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\GeneticAlgorithm.py\u001b[0m in \u001b[0;36mrun_ga\u001b[1;34m(self, action_set, situation, time_stamp)\u001b[0m\n\u001b[0;32m 46\u001b[0m self._perform_insertion_or_subsumption(\n\u001b[0;32m 47\u001b[0m \u001b[0mchild1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchild2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mparent1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparent2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m )\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\GeneticAlgorithm.py\u001b[0m in \u001b[0;36m_perform_insertion_or_subsumption\u001b[1;34m(self, child1, child2, parent1, parent2)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mchild1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchild2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert_in_population\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchild\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m 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+1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '0', '1', '0', '0', '1', '1', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m 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"cfg = Configuration(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " mutation_chance=0.08,\n", + " chi=0.8,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " delta=0.1,\n", + " initial_error=0.01,\n", + " metrics_trial_frequency=50,\n", + " covering_wildcard_chance=0.9,\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=200\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.2289368000000005, 'numerosity': 1800, 'population': 1181, 'average_specificity': 2.167777777777778, 'fraction_accuracy': 0.0035211267605633804}\n", + "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 47, 'reward': 1000.0001386869448, 'perf_time': 0.5891079999999818, 'numerosity': 1800, 'population': 1398, 'average_specificity': 10.25388888888889, 'fraction_accuracy': 0.019970266709397142}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 28, 'reward': 1000.0929716237177, 'perf_time': 0.33813449999999534, 'numerosity': 1800, 'population': 1440, 'average_specificity': 14.006666666666666, 'fraction_accuracy': 0.028447078738155947}\n", + "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 6, 'reward': 1180.4579753392954, 'perf_time': 0.08292820000002621, 'numerosity': 1800, 'population': 1454, 'average_specificity': 14.765555555555556, 'fraction_accuracy': 0.015276193121876851}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 5, 'reward': 1184.61030663137, 'perf_time': 0.05487260000001015, 'numerosity': 1800, 'population': 1519, 'average_specificity': 15.879444444444445, 'fraction_accuracy': 0.019782387034453147}\n", + "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 22, 'reward': 1000.610834890912, 'perf_time': 0.33302090000000817, 'numerosity': 1800, 'population': 1504, 'average_specificity': 17.66722222222222, 'fraction_accuracy': 0.01843609015051703}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 2, 'reward': 1626.6065922745815, 'perf_time': 0.022644099999979517, 'numerosity': 1800, 'population': 1546, 'average_specificity': 15.996666666666666, 'fraction_accuracy': 0.010403897532610404}\n", + "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 19, 'reward': 1001.6837660118813, 'perf_time': 0.35801870000000235, 'numerosity': 1800, 'population': 1504, 'average_specificity': 15.512777777777778, 'fraction_accuracy': 0.011062158068146092}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 9, 'reward': 1052.0190263148272, 'perf_time': 0.14770529999998416, 'numerosity': 1800, 'population': 1522, 'average_specificity': 15.075555555555555, 'fraction_accuracy': 0.008008278271436166}\n", + "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 3, 'reward': 1497.2441413864253, 'perf_time': 0.03906779999999799, 'numerosity': 1800, 'population': 1498, 'average_specificity': 14.955555555555556, 'fraction_accuracy': 0.00947121692916369}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 1 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.150392399999987, 'numerosity': 1800, 'population': 1194, 'average_specificity': 2.1733333333333333, 'fraction_accuracy': 0.0018050541516245488}\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\Agent.py\u001b[0m in \u001b[0;36mexploit\u001b[1;34m(self, env, trials)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[0mpopulation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mclassifiers\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \"\"\"\n\u001b[1;32m---> 66\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_exploit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mdef\u001b[0m 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\XCS.py\u001b[0m in \u001b[0;36m_run_trial_explore\u001b[1;34m(self, env, trials, current_trial)\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[0mprev_state\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m prev_reward + self.cfg.gamma * max(prediction_array))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m self._distribute_and_update(action_set,\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\XNCS.py\u001b[0m in \u001b[0;36m_distribute_and_update\u001b[1;34m(self, action_set, current_situation, next_situation, p)\u001b[0m\n\u001b[0;32m 50\u001b[0m self.back_propagation.update_cycle(\n\u001b[0;32m 51\u001b[0m \u001b[0maction_set\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m \u001b[0mEffect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnext_situation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 53\u001b[0m )\n\u001b[0;32m 54\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_distribute_and_update\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction_set\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent_situation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnext_situation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\Backpropagation.py\u001b[0m in \u001b[0;36mupdate_cycle\u001b[1;34m(self, action_set, next_vector)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meffect\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mnext_vector\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmistakes\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcl\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0minside\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minside\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers_for_update\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m self.classifiers_for_update.append(\n\u001b[0;32m 25\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnext_vector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlmc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\Backpropagation.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meffect\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mnext_vector\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmistakes\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcl\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0minside\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minside\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers_for_update\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m self.classifiers_for_update.append(\n\u001b[0;32m 25\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnext_vector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlmc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\Classifier.py\u001b[0m in \u001b[0;36m__eq__\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__eq__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcondition\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcondition\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "\n", + "df = avg_experiment(maze=maze,\n", + " cfg=cfg,\n", + " number_of_tests=3,\n", + " explore_trials=0,\n", + " exploit_trials=2500,\n", + " pre_generate=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(sum(df['steps_in_trial'])/3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb index ab15590..30f8a42 100644 --- a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb +++ b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -23,16 +23,16 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '1', '0', '0', '0', '1', '1', '1')\n", + "('0', '1', '1', '1', '1', '1', '1', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m 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"\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] } @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -95,62 +95,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.615795999999989, 'numerosity': 1800, 'population': 1206, 'average_specificity': 2.2794444444444446, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 6, 'reward': 1129.2076241010484, 'perf_time': 0.10484950000000026, 'numerosity': 1800, 'population': 1368, 'average_specificity': 9.976666666666667, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 2, 'reward': 1664.7982218802304, 'perf_time': 0.031416800000044987, 'numerosity': 1800, 'population': 1425, 'average_specificity': 13.872777777777777, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 5, 'reward': 1180.6411262422744, 'perf_time': 0.05660550000004605, 'numerosity': 1800, 'population': 1454, 'average_specificity': 14.497777777777777, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 5, 'reward': 1196.8327617638906, 'perf_time': 0.09974440000002005, 'numerosity': 1800, 'population': 1488, 'average_specificity': 15.306666666666667, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 9, 'reward': 1058.7215841651364, 'perf_time': 0.17037219999997433, 'numerosity': 1800, 'population': 1517, 'average_specificity': 16.550555555555555, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 5, 'reward': 1234.5640858903512, 'perf_time': 0.07221839999999702, 'numerosity': 1800, 'population': 1527, 'average_specificity': 17.31388888888889, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 9, 'reward': 1050.473419177287, 'perf_time': 0.13173100000005888, 'numerosity': 1800, 'population': 1537, 'average_specificity': 18.251666666666665, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 7, 'reward': 1148.834268552228, 'perf_time': 0.09595010000009552, 'numerosity': 1800, 'population': 1543, 'average_specificity': 17.493333333333332, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 23, 'reward': 1000.3798819526679, 'perf_time': 0.4243284000000358, 'numerosity': 1800, 'population': 1530, 'average_specificity': 17.275, 'fraction_accuracy': 0.0}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.1664925999999696, 'numerosity': 1800, 'population': 1175, 'average_specificity': 2.236111111111111, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 23, 'reward': 1000.5154234374991, 'perf_time': 0.2903112000000192, 'numerosity': 1800, 'population': 1305, 'average_specificity': 7.065, 'fraction_accuracy': 0.06944444444444445}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 9, 'reward': 1049.8776464610858, 'perf_time': 0.13190950000000612, 'numerosity': 1800, 'population': 1429, 'average_specificity': 12.384444444444444, 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"output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m pre_generate=True)\n\u001b[0m", + "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in \u001b[0;36mavg_experiment\u001b[1;34m(maze, cfg, number_of_tests, explore_trials, exploit_trials, pre_generate)\u001b[0m\n\u001b[0;32m 57\u001b[0m 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\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mmetrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_explore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent_trial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\XCS.py\u001b[0m in \u001b[0;36m_run_trial_explore\u001b[1;34m(self, env, trials, current_trial)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'duplicates found'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete_from_population\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;31m# We are in t+1 here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\Classifier.py\u001b[0m in \u001b[0;36m__hash__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mhash\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcondition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meffect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -158,706 +123,26 @@ "\n", "df = avg_experiment(maze=maze,\n", " cfg=cfg,\n", - " number_of_tests=3,\n", + " number_of_tests=1,\n", " explore_trials=0,\n", - " exploit_trials=2500,\n", + " exploit_trials=10000,\n", " pre_generate=True)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1800.0 1452.333333 \n", - "750 2.666667 1466.945412 0.030099 1800.0 1461.666667 \n", - "800 6.000000 1364.889884 0.067996 1800.0 1462.000000 \n", - "850 22.333333 1103.846872 0.370494 1800.0 1475.666667 \n", - "900 11.333333 1125.011521 0.162018 1800.0 1481.333333 \n", - "950 9.666667 1408.274754 0.186085 1800.0 1484.000000 \n", - "1000 10.333333 1185.041206 0.160534 1800.0 1482.666667 \n", - "1050 5.666667 1317.207248 0.116124 1800.0 1485.333333 \n", - "1100 27.333333 1033.386428 0.456564 1800.0 1491.666667 \n", - "1150 19.000000 1059.780092 0.277254 1800.0 1502.000000 \n", - "1200 18.333333 1068.761626 0.250548 1800.0 1497.000000 \n", - "1250 6.333333 1200.015695 0.110668 1800.0 1506.000000 \n", - "1300 8.000000 1113.213724 0.108945 1800.0 1516.333333 \n", - "1350 9.666667 1074.795225 0.180158 1800.0 1528.333333 \n", - "1400 8.333333 1322.867820 0.179142 1800.0 1515.666667 \n", - "1450 9.666667 1109.909677 0.147886 1800.0 1508.666667 \n", - "1500 4.666667 1245.930758 0.064060 1800.0 1506.666667 \n", - "1550 10.333333 1172.786971 0.194062 1800.0 1510.000000 \n", - "1600 5.333333 1207.903146 0.102350 1800.0 1515.000000 \n", - "1650 7.000000 1262.517285 0.128104 1800.0 1516.666667 \n", - "1700 5.666667 1222.037680 0.096815 1800.0 1521.333333 \n", - "1750 14.000000 1262.082054 0.192914 1800.0 1524.000000 \n", - "1800 4.333333 1314.071792 0.057903 1800.0 1506.333333 \n", - "1850 11.333333 1133.700494 0.180420 1800.0 1508.000000 \n", - "1900 4.666667 1315.906961 0.079295 1800.0 1501.333333 \n", - "1950 12.333333 1462.162506 0.217254 1800.0 1515.000000 \n", - "2000 5.666667 1398.695481 0.091408 1800.0 1510.333333 \n", - "2050 6.666667 1209.343102 0.127033 1800.0 1514.666667 \n", - "2100 6.666667 1119.324681 0.133224 1800.0 1520.000000 \n", - "2150 7.666667 1159.363460 0.135207 1800.0 1519.666667 \n", - "2200 8.333333 1189.460528 0.133010 1800.0 1512.333333 \n", - "2250 19.666667 1005.764171 0.289981 1800.0 1519.333333 \n", - "2300 22.000000 1128.606459 0.323922 1800.0 1499.666667 \n", - "2350 16.000000 1171.498859 0.206480 1800.0 1492.000000 \n", - "2400 5.666667 1448.225686 0.080992 1800.0 1498.666667 \n", - "2450 9.000000 1180.880447 0.163634 1800.0 1510.000000 \n", - "\n", - " average_specificity fraction_accuracy \n", - "trial \n", - "0 2.248704 0.000000 \n", - "50 4.487407 0.000000 \n", - "100 5.220370 0.000000 \n", - "150 6.278519 0.000000 \n", - "200 8.036111 0.000000 \n", - "250 8.440556 0.023148 \n", - "300 9.887222 0.000000 \n", - "350 10.446111 0.000000 \n", - "400 10.607778 0.000000 \n", - "450 11.352407 0.000000 \n", - "500 12.433148 0.000000 \n", - "550 12.730741 0.000000 \n", - "600 12.941852 0.000000 \n", - "650 12.752778 0.000000 \n", - "700 13.611481 0.000000 \n", - "750 13.494444 0.000000 \n", - "800 14.060370 0.000000 \n", - "850 14.688333 0.000000 \n", - "900 14.368889 0.000000 \n", - "950 15.456296 0.000000 \n", - "1000 15.586667 0.000000 \n", - "1050 15.431667 0.000000 \n", - "1100 15.957593 0.000000 \n", - "1150 15.723333 0.000000 \n", - "1200 15.777778 0.000000 \n", - "1250 16.243333 0.000000 \n", - "1300 16.437593 0.000000 \n", - "1350 16.011296 0.000000 \n", - "1400 16.402778 0.000000 \n", - "1450 16.538889 0.000000 \n", - "1500 16.692407 0.000000 \n", - "1550 16.329259 0.000000 \n", - "1600 16.735741 0.000000 \n", - "1650 17.094444 0.000000 \n", - "1700 17.108704 0.005952 \n", - "1750 17.226296 0.000000 \n", - "1800 16.381296 0.041667 \n", - "1850 16.810370 0.000000 \n", - "1900 16.398704 0.000000 \n", - "1950 16.132778 0.000000 \n", - "2000 16.217778 0.000000 \n", - "2050 16.016667 0.000000 \n", - "2100 16.417037 0.000000 \n", - "2150 16.546296 0.000000 \n", - "2200 16.411481 0.000000 \n", - "2250 16.796852 0.000000 \n", - "2300 17.072593 0.000000 \n", - "2350 16.839815 0.000000 \n", - "2400 16.844259 0.000000 \n", - "2450 16.914259 0.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(df)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df['fraction_accuracy'].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -905,32 +167,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df[['numerosity', 'population']].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -940,32 +179,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df['steps_in_trial'].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -975,17 +191,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "221.3333333333333\n" - ] - } - ], + "outputs": [], "source": [ "print(sum(df['steps_in_trial'])/3)" ] diff --git a/XCS_Experiments/XNCS_woods.ipynb b/XCS_Experiments/XNCS_woods.ipynb index d7ff931..5c9b008 100644 --- a/XCS_Experiments/XNCS_woods.ipynb +++ b/XCS_Experiments/XNCS_woods.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -24,7 +24,7 @@ "This is how maze looks like:\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -79,34 +79,45 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 19, 'reward': 1000.0, 'perf_time': 0.014679899999990198, 'numerosity': 88, 'population': 88, 'average_specificity': 2.227272727272727, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 7, 'reward': 1090.9557755093135, 'perf_time': 0.06831400000000087, 'numerosity': 1650, 'population': 957, 'average_specificity': 18.73090909090909, 'fraction_accuracy': 0.017783101045296165}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1628.1335076814366, 'perf_time': 0.033511500000003025, 'numerosity': 1800, 'population': 1146, 'average_specificity': 21.773888888888887, 'fraction_accuracy': 0.027564102564102563}\n", - "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 2, 'reward': 1775.4082438248972, 'perf_time': 0.024715899999989688, 'numerosity': 1800, 'population': 1173, 'average_specificity': 22.769444444444446, 'fraction_accuracy': 0.05624485696961823}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 1, 'reward': 2485.5120197202286, 'perf_time': 0.00967719999999872, 'numerosity': 1800, 'population': 1202, 'average_specificity': 24.182777777777776, 'fraction_accuracy': 0.05673272357723578}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 1, 'reward': 1712.9694491357925, 'perf_time': 0.009268800000000965, 'numerosity': 1800, 'population': 1189, 'average_specificity': 24.768333333333334, 'fraction_accuracy': 0.16367382617382614}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 30, 'reward': 1000.0642385171988, 'perf_time': 0.3304250000000195, 'numerosity': 1800, 'population': 1163, 'average_specificity': 29.625, 'fraction_accuracy': 0.05153715868169495}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 2, 'reward': 1913.9094897297305, 'perf_time': 0.022648800000013125, 'numerosity': 1800, 'population': 1188, 'average_specificity': 31.697222222222223, 'fraction_accuracy': 0.0561127618315661}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 9, 'reward': 1046.700518721966, 'perf_time': 0.15424020000000382, 'numerosity': 1800, 'population': 1145, 'average_specificity': 37.638888888888886, 'fraction_accuracy': 0.02087473652514303}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 4, 'reward': 1279.408090436091, 'perf_time': 0.03775390000001266, 'numerosity': 1800, 'population': 1137, 'average_specificity': 39.653333333333336, 'fraction_accuracy': 0.020817103134176305}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 24, 'reward': 1000.0, 'perf_time': 0.01964560000000004, 'numerosity': 83, 'population': 83, 'average_specificity': 2.0843373493975905, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 16, 'reward': 1004.2204271968708, 'perf_time': 0.12402200000000008, 'numerosity': 1348, 'population': 812, 'average_specificity': 18.11053412462908, 'fraction_accuracy': 0.12535714285714286}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 7, 'reward': 1107.3640216671884, 'perf_time': 0.10553249999999892, 'numerosity': 1800, 'population': 1154, 'average_specificity': 14.893888888888888, 'fraction_accuracy': 0.6790994331755201}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 7, 'reward': 1107.3653099850317, 'perf_time': 0.08541469999999407, 'numerosity': 1800, 'population': 1151, 'average_specificity': 15.722222222222221, 'fraction_accuracy': 0.36458333333333337}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 6, 'reward': 1128.1856488921549, 'perf_time': 0.0677195000000026, 'numerosity': 1800, 'population': 1238, 'average_specificity': 18.145555555555557, 'fraction_accuracy': 0.3285573122529644}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 2, 'reward': 1595.0543904848719, 'perf_time': 0.04015440000000581, 'numerosity': 1800, 'population': 1265, 'average_specificity': 17.79388888888889, 'fraction_accuracy': 0.5899531024531024}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 8, 'reward': 1098.9665294341632, 'perf_time': 0.10475429999999619, 'numerosity': 1800, 'population': 1277, 'average_specificity': 20.392777777777777, 'fraction_accuracy': 0.3670673076923077}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 9, 'reward': 1101.2224448360037, 'perf_time': 0.15706609999999444, 'numerosity': 1800, 'population': 1311, 'average_specificity': 19.887777777777778, 'fraction_accuracy': 0.432719964452659}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 23, 'reward': 1000.3798142858667, 'perf_time': 0.33525269999999807, 'numerosity': 1800, 'population': 1300, 'average_specificity': 19.254444444444445, 'fraction_accuracy': 0.47659489633173846}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 2, 'reward': 1513.2726521766972, 'perf_time': 0.026904799999982743, 'numerosity': 1800, 'population': 1291, 'average_specificity': 19.108888888888888, 'fraction_accuracy': 0.35739746364746366}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 12, 'reward': 1019.0460203362976, 'perf_time': 0.12114850000000388, 'numerosity': 1800, 'population': 1289, 'average_specificity': 21.624444444444446, 'fraction_accuracy': 0.4538002202475886}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 4, 'reward': 1284.213174925176, 'perf_time': 0.03457489999999552, 'numerosity': 1800, 'population': 1283, 'average_specificity': 20.85333333333333, 'fraction_accuracy': 0.40662202380952384}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': 1252.85645645612, 'perf_time': 0.05369689999997718, 'numerosity': 1800, 'population': 1292, 'average_specificity': 19.458333333333332, 'fraction_accuracy': 0.7439362108479756}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 5, 'reward': 1237.6671805444505, 'perf_time': 0.06959109999999669, 'numerosity': 1800, 'population': 1290, 'average_specificity': 18.91722222222222, 'fraction_accuracy': 0.6325285663948453}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 3, 'reward': 1465.2940928649064, 'perf_time': 0.03880730000000199, 'numerosity': 1800, 'population': 1271, 'average_specificity': 17.692777777777778, 'fraction_accuracy': 0.5604482323232323}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 3, 'reward': 1577.341920079401, 'perf_time': 0.02733839999999077, 'numerosity': 1800, 'population': 1268, 'average_specificity': 18.859444444444446, 'fraction_accuracy': 0.5311915199483598}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 1, 'reward': 2217.089632878987, 'perf_time': 0.017005099999977347, 'numerosity': 1800, 'population': 1256, 'average_specificity': 18.096666666666668, 'fraction_accuracy': 0.5948841698841698}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 2, 'reward': 1601.4475353414186, 'perf_time': 0.017518700000010767, 'numerosity': 1800, 'population': 1281, 'average_specificity': 18.470555555555556, 'fraction_accuracy': 0.611452853407168}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 4, 'reward': 1318.466730313819, 'perf_time': 0.04233250000001476, 'numerosity': 1800, 'population': 1291, 'average_specificity': 17.467777777777776, 'fraction_accuracy': 0.5738495846730142}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 2, 'reward': 1799.9598259258335, 'perf_time': 0.01777559999999312, 'numerosity': 1800, 'population': 1317, 'average_specificity': 19.15277777777778, 'fraction_accuracy': 0.6105944914398153}\n" ] } ], "source": [ "agent = XNCS(cfg)\n", - "explore_population, explore_metrics = agent.explore(maze, 1000, True)" + "explore_population, explore_metrics = agent.explore(maze, 1000, False)\n", + "explore_population, exploit_metrics = agent.exploit(maze, 1000)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -115,1137 +126,1327 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cond:....FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.002, exp: 50.00, pred: 353.055, error:6337.943477712033]acc: 0.54\n", - "Cond:.#..FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 182.00, pred: 496.703, error:4613.81658032312]acc: 0.6593406593406593\n", - "Cond:#....OF. - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 90.00, pred: 297.611, error:2670.2316972579392]acc: 0.0\n", - "Cond:..#O.... - Act:1 - effect:........ - Num:2 [fit: 0.001, exp: 28.00, pred: 449.250, error:10524.783774084593]acc: 0.0\n", - "Cond:...O.... - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 83.00, pred: 544.878, error:4259.89056855382]acc: 0.5301204819277109\n", - "Cond:#...#F.. - Act:1 - effect:.F...F.. - Num:6 [fit: 0.004, exp: 17.00, pred: 399.260, error:9865.895799661299]acc: 0.0\n", - "Cond:O......O - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 45.00, pred: 571.826, error:7137.170298870321]acc: 0.0\n", - "Cond:O......O - Act:2 - effect:.O....OO - Num:1 [fit: 0.009, exp: 13.00, pred: 520.655, error:10075.382209840245]acc: 0.0\n", - "Cond:O......O - Act:3 - effect:..O..O.O - Num:1 [fit: 0.001, exp: 26.00, pred: 405.061, error:11348.755852271704]acc: 0.0\n", - "Cond:#O.....O - Act:0 - effect:O.OOOOOO - Num:4 [fit: 0.022, exp: 9.00, pred: 499.453, error:6266.4934723170445]acc: 0.0\n", - "Cond:#O#....O - Act:2 - effect:.O.O.... - Num:1 [fit: 0.008, exp: 13.00, pred: 490.633, error:9097.96850556846]acc: 0.0\n", - "Cond:OO...... - Act:1 - effect:...O.... - Num:3 [fit: 0.002, exp: 23.00, pred: 465.361, error:7687.653083812652]acc: 0.0\n", - "Cond:OO...... - Act:2 - effect:...O.... - Num:1 [fit: 0.001, exp: 20.00, pred: 530.600, error:10584.571505347523]acc: 0.0\n", - "Cond:.O...... - Act:1 - effect:O.O....O - Num:5 [fit: 0.011, exp: 41.00, pred: 373.449, error:2837.6100851686674]acc: 0.0\n", - "Cond:.O...... - Act:2 - effect:.O...... - Num:2 [fit: 0.002, exp: 23.00, pred: 471.095, error:10553.639474215453]acc: 0.043478260869565216\n", - "Cond:.#OO.... - Act:1 - effect:OO.O...O - Num:4 [fit: 0.005, exp: 19.00, pred: 430.562, error:6595.173042392595]acc: 0.0\n", - "Cond:.#.#.OOF - Act:5 - effect:......O. - Num:4 [fit: 0.020, exp: 124.00, pred: 591.861, error:6424.406081388435]acc: 0.0\n", - "Cond:.....OOF - Act:6 - effect:.OF..O.. - Num:3 [fit: 0.026, exp: 127.00, pred: 278.127, error:895.2348863473378]acc: 0.0\n", - "Cond:...FOO.. - Act:0 - effect:O....OOO - Num:1 [fit: 0.000, exp: 36.00, pred: 523.670, error:9727.334891880051]acc: 0.0\n", - "Cond:...FO#.. - Act:5 - effect:O....F.. - Num:3 [fit: 0.027, exp: 114.00, pred: 596.498, error:2275.633615268892]acc: 0.0\n", - "Cond:..#.FO.. - Act:1 - effect:....FO.. - Num:5 [fit: 0.009, exp: 49.00, pred: 353.057, error:4918.1454716668095]acc: 0.5510204081632653\n", - "Cond:O..#...O - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 43.00, pred: 571.831, error:5911.4023850470285]acc: 0.0\n", - "Cond:O.#...#O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 43.00, pred: 571.831, error:6362.6815813585445]acc: 0.0\n", - "Cond:....F#.# - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 182.00, pred: 496.703, error:3981.4632590278857]acc: 0.34065934065934067\n", - "Cond:#.#.#F.. - Act:1 - effect:.F...F.. - Num:2 [fit: 0.003, exp: 16.00, pred: 403.872, error:9610.638275638881]acc: 0.0\n", - "Cond:.OO..#.. - Act:1 - effect:OO.O.... - Num:2 [fit: 0.023, exp: 8.00, pred: 333.509, error:5004.856920830573]acc: 0.0\n", - "Cond:....F#.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.003, exp: 48.00, pred: 353.065, error:5568.61262428654]acc: 0.5625\n", - "Cond:.#..#O.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 48.00, pred: 353.065, error:4918.410473251848]acc: 0.5625\n", - "Cond:#..##F.. - Act:1 - effect:.F...F.. - Num:3 [fit: 0.004, exp: 15.00, pred: 415.398, error:9480.03334319077]acc: 0.0\n", - "Cond:O#.....O - Act:2 - effect:.......O - Num:5 [fit: 0.001, exp: 25.00, pred: 432.290, error:9826.476675593618]acc: 0.08\n", - "Cond:O..##..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 41.00, pred: 571.844, error:5298.021750311862]acc: 0.0\n", - "Cond:O......# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 41.00, pred: 571.844, error:6714.8405197902575]acc: 0.0\n", - "Cond:..#.#..O - Act:1 - effect:........ - Num:1 [fit: 0.000, exp: 34.00, pred: 650.216, error:8131.760368723735]acc: 0.0\n", - "Cond:..#.O### - Act:1 - effect:OO.O.... - Num:1 [fit: 0.021, exp: 0.00, pred: 525.647, error:347.1414781693665]acc: 0\n", - "Cond:...FOO#. - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 33.00, pred: 523.551, error:9156.453988333025]acc: 0.0\n", - "Cond:OO.....O - Act:7 - effect:.......O - Num:3 [fit: 0.010, exp: 16.00, pred: 446.449, error:7981.043445140795]acc: 0.0\n", - "Cond:O#..#..O - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 24.00, pred: 432.353, error:9681.644302688486]acc: 0.08333333333333333\n", - "Cond:O.#....# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 39.00, pred: 571.896, error:5714.3103867511145]acc: 0.0\n", - "Cond:..OO..#. - Act:2 - effect:.......O - Num:1 [fit: 0.005, exp: 19.00, pred: 583.076, error:8670.392565902705]acc: 0.0\n", - "Cond:..OO#... - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 51.00, pred: 333.881, error:4712.814784746106]acc: 0.0\n", - "Cond:.....OOF - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 123.00, pred: 591.861, error:5209.092778309505]acc: 0.0\n", - "Cond:#OO..#.. - Act:1 - effect:OO.O.... - Num:1 [fit: 0.004, exp: 7.00, pred: 323.868, error:3677.847246191934]acc: 0.0\n", - "Cond:OO...#.O - Act:7 - effect:.......O - Num:4 [fit: 0.004, exp: 14.00, pred: 453.018, error:7096.441229296728]acc: 0.0\n", - "Cond:..#FOO#. - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 27.00, pred: 523.361, error:9057.722346704897]acc: 0.0\n", - "Cond:O..#...O - Act:3 - effect:...OO... - Num:1 [fit: 0.001, exp: 19.00, pred: 407.115, error:9364.707706747295]acc: 0.0\n", - "Cond:.#..FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.003, exp: 47.00, pred: 353.072, error:5885.377964047417]acc: 0.574468085106383\n", - "Cond:..#.#... - Act:0 - effect:.O..O... - Num:1 [fit: 0.014, exp: 0.00, pred: 818.390, error:462.3555041188563]acc: 0\n", - "Cond:..O#..#. - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 18.00, pred: 584.573, error:7300.06082352122]acc: 0.0\n", - "Cond:....O##. - Act:6 - effect:OO.O.... - Num:1 [fit: 0.005, exp: 0.00, pred: 462.466, error:592.0031570377164]acc: 0\n", - "Cond:.O#O#..# - Act:3 - effect:...O.... - Num:1 [fit: 0.011, exp: 45.00, pred: 495.556, error:2148.4343584777043]acc: 0.0\n", - "Cond:#O.....O - Act:7 - effect:.......O - Num:1 [fit: 0.003, exp: 11.00, pred: 435.347, error:7456.636553044949]acc: 0.0\n", - "Cond:O#.#O..# - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:O#.####. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 19.00, pred: 530.441, error:10074.077212800548]acc: 0.0\n", - "Cond:OO..##.# - Act:1 - effect:.....O.. - Num:1 [fit: 0.000, exp: 35.00, pred: 362.415, error:5999.4905704979365]acc: 0.0\n", - "Cond:O#..##.. - Act:1 - effect:.....O.. - Num:2 [fit: 0.006, exp: 21.00, pred: 464.825, error:6146.43610478317]acc: 0.0\n", - "Cond:O#.##... - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 18.00, pred: 530.100, error:9520.127168511372]acc: 0.0\n", - "Cond:OO#...#. - Act:1 - effect:.....O.. - Num:2 [fit: 0.005, exp: 13.00, pred: 448.523, error:7538.032092309675]acc: 0.0\n", - "Cond:O###.#.. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 17.00, pred: 533.208, error:9339.22729942304]acc: 0.0\n", - "Cond:O##.##.O - Act:1 - effect:.....O.. - Num:1 [fit: 0.000, exp: 32.00, pred: 380.280, error:8335.640163793965]acc: 0.0\n", - "Cond:O.##.#.# - Act:3 - effect:...OO... - Num:2 [fit: 0.000, exp: 18.00, pred: 407.178, error:9633.554050330706]acc: 0.0\n", - "Cond:.O.#..#. - Act:1 - effect:O.O....O - Num:1 [fit: 0.003, exp: 40.00, pred: 373.476, error:2551.842039236233]acc: 0.0\n", - "Cond:..#O#O#. - Act:2 - effect:O...O... - Num:1 [fit: 0.009, exp: 0.00, pred: 689.021, error:1047.596900369857]acc: 0\n", - "Cond:.#O#.O.. - Act:7 - effect:.O.O..O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1053.139, error:234.83598116026235]acc: 0\n", - "Cond:O.#....# - Act:1 - effect:O..O.... - Num:2 [fit: 0.000, exp: 20.00, pred: 467.997, error:8917.596295279574]acc: 0.0\n", - "Cond:O......O - Act:7 - effect:.......O - Num:1 [fit: 0.002, exp: 43.00, pred: 398.207, error:4022.1719304523012]acc: 0.023255813953488372\n", - "Cond:.......# - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 19.00, pred: 622.574, error:9516.631539035749]acc: 0.0\n", - "Cond:.#..##.O - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 19.00, pred: 622.574, error:8913.883526082147]acc: 0.10526315789473684\n", - "Cond:...O.... - Act:1 - effect:........ - Num:1 [fit: 0.001, exp: 19.00, pred: 464.128, error:8859.430762072749]acc: 0.0\n", - "Cond:O#..#... - Act:1 - effect:.....O.. - Num:3 [fit: 0.037, exp: 10.00, pred: 448.691, error:5686.635651199907]acc: 0.0\n", - "Cond:OO..##.. - Act:1 - effect:.....O.. - Num:4 [fit: 0.020, exp: 10.00, pred: 448.691, error:6238.277251199907]acc: 0.0\n", - "Cond:.#.OO... - Act:0 - effect:........ - Num:1 [fit: 0.000, exp: 25.00, pred: 574.879, error:8578.260380881457]acc: 0.0\n", - "Cond:#O#....F - Act:7 - effect:...O.OO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#...OO# - Act:6 - effect:.O...... - Num:1 [fit: 0.009, exp: 122.00, pred: 278.127, error:1092.5495480423253]acc: 0.0\n", - "Cond:##OO..## - Act:1 - effect:...O..OO - Num:1 [fit: 0.000, exp: 17.00, pred: 441.085, error:6025.867898627779]acc: 0.0\n", - "Cond:.#..OO#F - Act:7 - effect:..O..FF. - Num:1 [fit: 0.002, exp: 0.00, pred: 921.678, error:1263.4849207381756]acc: 0\n", - "Cond:.#.#O... - Act:0 - effect:........ - Num:1 [fit: 0.000, exp: 24.00, pred: 575.240, error:9459.694031312512]acc: 0.0\n", - "Cond:##O##.## - Act:1 - effect:...O.... - Num:3 [fit: 0.000, exp: 24.00, pred: 305.462, error:6021.623058693252]acc: 0.0\n", - "Cond:##..O#.. - Act:7 - effect:O....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:..##OO## - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 23.00, pred: 522.943, error:9186.726118856637]acc: 0.0\n", - "Cond:###F##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 522.943, error:8918.868471755985]acc: 0.0\n", - "Cond:OO.###.. - Act:1 - effect:.....O.. - Num:1 [fit: 0.006, exp: 8.00, pred: 463.842, error:5093.599988402337]acc: 0.0\n", - "Cond:#..FO#.. - Act:5 - effect:O....F.. - Num:1 [fit: 0.013, exp: 112.00, pred: 596.498, error:3185.7110160511634]acc: 0.0\n", - "Cond:O......# - Act:3 - effect:..OOO.O. - Num:2 [fit: 0.000, exp: 17.00, pred: 410.683, error:9045.182060448225]acc: 0.0\n", - "Cond:#.OO..## - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 45.00, pred: 333.890, error:3957.0801520373807]acc: 0.0\n", - "Cond:..OO.### - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 45.00, pred: 333.890, error:4192.436673411008]acc: 0.0\n", - "Cond:..OO#... - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 11.00, pred: 619.222, error:7159.218141919737]acc: 0.0\n", - "Cond:OO#..#.O - Act:7 - effect:.......O - Num:1 [fit: 0.007, exp: 9.00, pred: 437.253, error:6686.121235567013]acc: 0.0\n", - "Cond:#O.FOO.. - Act:7 - effect:..O....O - Num:1 [fit: 0.005, exp: 0.00, pred: 387.950, error:634.3292197483906]acc: 0\n", - "Cond:O#...#.O - Act:2 - effect:.......O - Num:3 [fit: 0.001, exp: 20.00, pred: 435.794, error:9153.83308925144]acc: 0.1\n", - "Cond:OO#....O - Act:2 - effect:.O.O.... - Num:2 [fit: 0.001, exp: 9.00, pred: 581.029, error:8982.962343868741]acc: 0.0\n", - "Cond:O...F#.O - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 493.170, error:1341.6924102555245]acc: 0\n", - "Cond:...FOO## - Act:0 - effect:O....OOO - Num:1 [fit: 0.000, exp: 21.00, pred: 527.539, error:9334.236872492673]acc: 0.0\n", - "Cond:..##OO#. - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 21.00, pred: 527.539, error:8941.900926083099]acc: 0.0\n", - "Cond:...#FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.002, exp: 46.00, pred: 353.085, error:4951.564378704332]acc: 0.5869565217391305\n", - "Cond:.#.F.##. - Act:7 - effect:FOO....O - Num:1 [fit: 0.004, exp: 0.00, pred: 373.133, error:673.6872299099866]acc: 0\n", - "Cond:..##...O - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 26.00, pred: 650.666, error:7045.348894668639]acc: 0.34615384615384615\n", - "Cond:##.#.OOF - Act:5 - effect:......O. - Num:2 [fit: 0.010, exp: 120.00, pred: 591.861, error:6321.603940344311]acc: 0.0\n", - "Cond:...#OO## - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 20.00, pred: 532.481, error:9375.223570547509]acc: 0.0\n", - "Cond:....F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.007, exp: 180.00, pred: 496.703, error:4646.1842866706265]acc: 0.5833333333333334\n", - "Cond:..O....O - Act:1 - effect:.......O - Num:1 [fit: 0.004, exp: 0.00, pred: 428.073, error:746.9090835819317]acc: 0\n", - "Cond:O##.##.. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 15.00, pred: 551.383, error:9014.65802655628]acc: 0.0\n", - "Cond:.#.FOO.. - Act:2 - effect:....OO.F - Num:3 [fit: 0.000, exp: 25.00, pred: 934.462, error:6669.367840166487]acc: 0.0\n", - "Cond:..#.##.O - Act:1 - effect:........ - Num:1 [fit: 0.000, exp: 23.00, pred: 654.011, error:7878.826723978138]acc: 0.0\n", - "Cond:O#..OO.. - Act:7 - effect:O....F.. - Num:1 [fit: 0.004, exp: 0.00, pred: 398.233, error:678.6737672231693]acc: 0\n", - "Cond:..O#.### - Act:1 - effect:.......O - Num:1 [fit: 0.002, exp: 8.00, pred: 722.686, error:6322.500651323787]acc: 0.0\n", - "Cond:OO..#... - Act:1 - effect:.....O.. - Num:1 [fit: 0.010, exp: 4.00, pred: 478.147, error:3871.9829623297064]acc: 0.0\n", - "Cond:O.#....O - Act:1 - effect:O..O.... - Num:1 [fit: 0.000, exp: 18.00, pred: 474.682, error:8858.523434652972]acc: 0.0\n", - "Cond:..##..#. - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 25.00, pred: 452.339, error:9024.960386303828]acc: 0.0\n", - "Cond:.....F## - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 659.913, error:8696.762631059104]acc: 0.13636363636363635\n", - "Cond:..O.##F. - Act:3 - effect:.....F.. - Num:1 [fit: 0.004, exp: 0.00, pred: 726.080, error:556.1666191940266]acc: 0\n", - "Cond:.##.O### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 885.769, error:805.999306844893]acc: 0\n", - "Cond:..#.O#.. - Act:1 - effect:OO.O.... - Num:2 [fit: 0.003, exp: 0.00, pred: 644.803, error:1059.8649949242924]acc: 0\n", - "Cond:O...#..O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 37.00, pred: 571.887, error:6522.126372083032]acc: 0.0\n", - "Cond:O..#.#.O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 37.00, pred: 571.887, error:5973.310157826459]acc: 0.0\n", - "Cond:##.#.OO# - Act:5 - effect:.O...... - Num:1 [fit: 0.004, exp: 118.00, pred: 591.861, error:5596.286968621487]acc: 0.0\n", - "Cond:#.##..O# - Act:2 - effect:.....O.O - Num:1 [fit: 0.000, exp: 23.00, pred: 607.849, error:9571.301267637094]acc: 0.0\n", - "Cond:.O####F. - Act:7 - effect:.O.O..O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1012.022, error:740.125350469487]acc: 0\n", - "Cond:..#..FO. - Act:5 - effect:O.....O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1045.385, error:992.3975870070019]acc: 0\n", - "Cond:..##.O## - Act:5 - effect:........ - Num:5 [fit: 0.018, exp: 201.00, pred: 575.159, error:5666.270307430446]acc: 0.0\n", - "Cond:.....#.O - Act:0 - effect:.O...... - Num:2 [fit: 0.001, exp: 16.00, pred: 631.860, error:8348.084344770876]acc: 0.0625\n", - "Cond:O.##..#. - Act:1 - effect:..OOO.O. - Num:1 [fit: 0.012, exp: 0.00, pred: 714.596, error:592.963499385789]acc: 0\n", - "Cond:...#FO## - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 180.00, pred: 496.703, error:3981.4635981063934]acc: 0.6611111111111111\n", - "Cond:..###.O# - Act:2 - effect:.....O.O - Num:1 [fit: 0.000, exp: 23.00, pred: 607.849, error:9210.736426807618]acc: 0.0\n", - "Cond:.....O.# - Act:5 - effect:O.....O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1141.640, error:1578.1259378398556]acc: 0\n", - "Cond:#..O#..# - Act:3 - effect:...O.... - Num:3 [fit: 0.028, exp: 157.00, pred: 512.640, error:5059.64966221754]acc: 0.4012738853503185\n", - "Cond:###...#F - Act:0 - effect:......O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1609.351, error:0.0]acc: 0\n", - "Cond:.....#FF - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 910.461, error:973.7211020861592]acc: 0\n", - "Cond:..##.#O. - Act:2 - effect:.....O.O - Num:1 [fit: 0.002, exp: 0.00, pred: 699.818, error:1154.0500303958465]acc: 0\n", - "Cond:...#.O## - Act:5 - effect:......O. - Num:2 [fit: 0.009, exp: 199.00, pred: 575.159, error:5152.772420245019]acc: 0.0\n", - "Cond:O##.#.#O - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 42.00, pred: 556.418, error:7149.3829943641185]acc: 0.0\n", - "Cond:O##.##.# - Act:1 - effect:O..O.... - Num:1 [fit: 0.000, exp: 29.00, pred: 320.095, error:6717.539796007581]acc: 0.0\n", - "Cond:O.#..##O - Act:1 - effect:.....O.. - Num:1 [fit: 0.000, exp: 17.00, pred: 474.894, error:9665.872107625324]acc: 0.0\n", - "Cond:##O.#.## - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 24.00, pred: 502.989, error:10324.285224091193]acc: 0.0\n", - "Cond:#OO#.O.. - Act:6 - effect:O.O..... - Num:1 [fit: 0.001, exp: 0.00, pred: 877.163, error:833.6618838674718]acc: 0\n", - "Cond:...#..O# - Act:2 - effect:.....O.O - Num:1 [fit: 0.000, exp: 22.00, pred: 607.545, error:9767.595135596894]acc: 0.0\n", - "Cond:#.#..O.. - Act:6 - effect:.....F.. - Num:1 [fit: 0.001, exp: 0.00, pred: 937.800, error:978.2963845425596]acc: 0\n", - "Cond:.....O.F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.001, exp: 0.00, pred: 798.308, error:926.7844360640581]acc: 0\n", - "Cond:...#.##F - Act:6 - effect:...O.... - Num:4 [fit: 0.053, exp: 119.00, pred: 278.127, error:2768.0843709811365]acc: 0.0\n", - "Cond:.#####.O - Act:1 - effect:O.....O. - Num:2 [fit: 0.000, exp: 22.00, pred: 651.818, error:8589.429600835692]acc: 0.0\n", - "Cond:.O.#OO.. - Act:0 - effect:........ - Num:1 [fit: 0.002, exp: 0.00, pred: 766.801, error:1148.0721027462323]acc: 0\n", - "Cond:.O..##.. - Act:2 - effect:.O...... - Num:1 [fit: 0.000, exp: 15.00, pred: 489.584, error:9405.707394314819]acc: 0.06666666666666667\n", - "Cond:O#####.. - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 13.00, pred: 549.192, error:9530.97673561599]acc: 0.0\n", - "Cond:.###OO## - Act:0 - effect:O....OOO - Num:1 [fit: 0.000, exp: 18.00, pred: 531.270, error:9221.91449517896]acc: 0.0\n", - "Cond:...#OO.. - Act:0 - effect:O....OOO - Num:2 [fit: 0.000, exp: 18.00, pred: 531.270, error:8984.458972387536]acc: 0.0\n", - "Cond:.#..O.#. - Act:7 - effect:..O....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1172.832, error:1139.8565547531255]acc: 0\n", - "Cond:O..####. - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1165.389, error:250.69490745098673]acc: 0\n", - "Cond:#.##F##O - Act:4 - effect:.O...... - Num:1 [fit: 0.004, exp: 0.00, pred: 1108.561, error:281.8267606571851]acc: 0\n", - "Cond:.O....#. - Act:2 - effect:........ - Num:1 [fit: 0.000, exp: 15.00, pred: 489.584, error:8930.255772938817]acc: 0.0\n", - "Cond:...O...# - Act:1 - effect:..O.OO.. - Num:1 [fit: 0.001, exp: 17.00, pred: 477.095, error:8567.67732449247]acc: 0.0\n", - "Cond:.#..OO## - Act:6 - effect:...O.F.F - Num:1 [fit: 0.002, exp: 0.00, pred: 1586.301, error:613.9652097552853]acc: 0\n", - "Cond:#.##.OO# - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 117.00, pred: 591.861, error:6030.870856717871]acc: 0.0\n", - "Cond:.#O..### - Act:0 - effect:OO...... - Num:1 [fit: 0.000, exp: 20.00, pred: 505.139, error:8374.36097465027]acc: 0.0\n", - "Cond:#######. - Act:5 - effect:....FO.. - Num:4 [fit: 0.002, exp: 1829.00, pred: 1223.458, error:2031.1534559951656]acc: 0.0683433570256971\n", - "Cond:..#O#O.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#..O#.. - Act:1 - effect:OO.O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 977.954, error:1322.7975716551146]acc: 0\n", - "Cond:.#OO#O.# - Act:6 - effect:O.O..... - Num:1 [fit: 0.000, exp: 0.00, pred: 1209.541, error:874.5607862650131]acc: 0\n", - "Cond:...#.#F# - Act:5 - effect:.O.OOOO. - Num:1 [fit: 0.009, exp: 81.00, pred: 297.611, error:2480.781863054097]acc: 0.0\n", - "Cond:OO#...#. - Act:2 - effect:.O.O.... - Num:1 [fit: 0.001, exp: 12.00, pred: 557.625, error:8297.733676617663]acc: 0.0\n", - "Cond:.OOO.#F. - Act:7 - effect:.O.O..O. - Num:1 [fit: 0.005, exp: 0.00, pred: 1086.044, error:1222.6600975588067]acc: 0\n", - "Cond:.OO..#.# - Act:1 - effect:O.O..... - Num:1 [fit: 0.004, exp: 7.00, pred: 323.868, error:3677.847246191934]acc: 0.0\n", - "Cond:.....#.F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 1028.187, error:1645.4258553781983]acc: 0\n", - "Cond:..###..# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 220.00, pred: 481.467, error:5487.271805117372]acc: 0.29545454545454547\n", - "Cond:...F.O.. - Act:7 - effect:FOO....O - Num:1 [fit: 0.001, exp: 0.00, pred: 619.622, error:1684.3817246586636]acc: 0\n", - "Cond:OO##..## - Act:0 - effect:......O. - Num:2 [fit: 0.001, exp: 34.00, pred: 313.077, error:5835.651217844026]acc: 0.0\n", - "Cond:O##.#... - Act:2 - effect:.....O.O - Num:1 [fit: 0.001, exp: 11.00, pred: 585.373, error:9509.71963109912]acc: 0.0\n", - "Cond:..#.#.O. - Act:1 - effect:..O..FF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1171.668, error:857.85087288033]acc: 0\n", - "Cond:###O.#O# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1061.941, error:816.8595806231785]acc: 0\n", - "Cond:....#OOF - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.012, exp: 117.00, pred: 278.127, error:735.4195057757368]acc: 0.0\n", - "Cond:...#.O.F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#...O.. - Act:7 - effect:....O..O - Num:1 [fit: 0.004, exp: 0.00, pred: 553.104, error:1095.300615351723]acc: 0\n", - "Cond:....OO#. - Act:5 - effect:O.....O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:###.#OO# - Act:6 - effect:FOO....O - Num:1 [fit: 0.009, exp: 117.00, pred: 278.127, error:1097.6824027245507]acc: 0.0\n", - "Cond:##..O... - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:O#..#..# - Act:2 - effect:.....O.. - Num:6 [fit: 0.004, exp: 23.00, pred: 372.033, error:7311.767484177768]acc: 0.0\n", - "Cond:#O.O..#. - Act:5 - effect:.O.O.... - Num:1 [fit: 0.002, exp: 0.00, pred: 1023.109, error:640.6608834805902]acc: 0\n", - "Cond:.#.O..## - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 18.00, pred: 710.421, error:7809.517807394174]acc: 0.0\n", - "Cond:O...#.#O - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 35.00, pred: 571.922, error:5782.698143639375]acc: 0.0\n", - "Cond:O##.#..# - Act:1 - effect:O..O.... - Num:1 [fit: 0.000, exp: 26.00, pred: 320.033, error:6904.070537158301]acc: 0.0\n", - "Cond:###..OO. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:..#...#. - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1034.909, error:1016.9640257142541]acc: 0\n", - "Cond:..#..#.# - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 28.00, pred: 523.371, error:8101.894724442725]acc: 0.14285714285714285\n", - "Cond:#...FO.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 45.00, pred: 353.091, error:5434.3116534327755]acc: 0.6\n", - "Cond:##.OO#.. - Act:0 - effect:........ - Num:1 [fit: 0.000, exp: 17.00, pred: 583.353, error:8499.885856641697]acc: 0.0\n", - "Cond:....##O. - Act:1 - effect:O.....O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1447.035, error:1021.2983724191758]acc: 0\n", - "Cond:.##..O.. - Act:5 - effect:..O.OO.. - Num:1 [fit: 0.007, exp: 0.00, pred: 1317.925, error:1200.4001466835905]acc: 0\n", - "Cond:O#####.. - Act:0 - effect:O..O.... - Num:2 [fit: 0.001, exp: 26.00, pred: 400.116, error:6475.23889139357]acc: 0.0\n", - "Cond:O##.##.. - Act:1 - effect:O..O.... - Num:1 [fit: 0.106, exp: 2.00, pred: 211.654, error:2501.99040667384]acc: 0.0\n", - "Cond:.###OFO. - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:..#F.O.. - Act:7 - effect:FOO....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:...#..F# - Act:5 - effect:...O..OO - Num:1 [fit: 0.001, exp: 0.00, pred: 979.638, error:569.264858588701]acc: 0\n", - "Cond:......F. - Act:3 - effect:...OF... - Num:1 [fit: 0.003, exp: 0.00, pred: 1149.718, error:954.3909187689369]acc: 0\n", - "Cond:...#.... - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 80.00, pred: 544.878, error:3657.415947561034]acc: 0.475\n", - "Cond:...O##OO - Act:1 - effect:....O..O - Num:1 [fit: 0.000, exp: 0.00, pred: 1138.897, error:1429.4210671856476]acc: 0\n", - "Cond:#####.#F - Act:6 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#O...## - Act:3 - effect:.OO..OO. - Num:1 [fit: 0.000, exp: 22.00, pred: 474.567, error:7522.249874251819]acc: 0.0\n", - "Cond:.#OO##.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 43.00, pred: 763.512, error:6650.574831572045]acc: 0.023255813953488372\n", - "Cond:##....#. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 20.00, pred: 363.230, error:6972.925625261002]acc: 0.25\n", - "Cond:#..FO##. - Act:1 - effect:..O...F. - Num:1 [fit: 0.000, exp: 23.00, pred: 395.165, error:7352.981700699038]acc: 0.0\n", - "Cond:.#.#.##F - Act:6 - effect:.O...... - Num:1 [fit: 0.010, exp: 113.00, pred: 278.127, error:1479.971534652448]acc: 0.0\n", - "Cond:.#.F###. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 20.00, pred: 934.940, error:7724.784789156964]acc: 0.0\n", - "Cond:...#.OOF - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 115.00, pred: 591.861, error:5465.156334326693]acc: 0.0\n", - "Cond:#..F#OO. - Act:3 - effect:..O...F. - Num:1 [fit: 0.001, exp: 0.00, pred: 1361.214, error:1105.2169014662325]acc: 0\n", - "Cond:..#.FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 179.00, pred: 496.703, error:5013.809343364639]acc: 0.5810055865921788\n", - "Cond:..##F### - Act:0 - effect:.......F - Num:2 [fit: 0.000, exp: 27.00, pred: 501.558, error:8196.98208577534]acc: 0.0\n", - "Cond:O#.#FO## - Act:6 - effect:.....O.O - Num:1 [fit: 0.000, exp: 0.00, pred: 1455.523, error:1090.9086930498254]acc: 0\n", - "Cond:#....FO. - Act:5 - effect:O.....O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.##.#... - Act:2 - effect:.O...... - Num:1 [fit: 0.004, exp: 20.00, pred: 365.780, error:7793.7487469393745]acc: 0.05\n", - "Cond:#O.....# - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 25.00, pred: 367.263, error:6056.625259449768]acc: 0.2\n", - "Cond:#..#F.## - Act:7 - effect:.......F - Num:1 [fit: 0.004, exp: 0.00, pred: 1326.152, error:703.0549951380294]acc: 0\n", - "Cond:OO#.#..# - Act:1 - effect:.....O.. - Num:1 [fit: 0.044, exp: 9.00, pred: 371.756, error:4762.2090882854145]acc: 0.0\n", - "Cond:...OO#.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 663.429, error:4738.355922102305]acc: 0.0\n", - "Cond:.##..#O. - Act:5 - effect:......O. - Num:1 [fit: 0.003, exp: 0.00, pred: 1351.551, error:799.5216151326937]acc: 0\n", - "Cond:.###OF#. - Act:5 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 1351.551, error:799.5216151326937]acc: 0\n", - "Cond:..##.OF. - Act:5 - effect:..O..O.. - Num:1 [fit: 0.014, exp: 80.00, pred: 297.611, error:2182.3884262221527]acc: 0.0\n", - "Cond:#OO.##.. - Act:1 - effect:.O...... - Num:1 [fit: 0.008, exp: 7.00, pred: 323.868, error:4810.647246191935]acc: 0.0\n", - "Cond:O....#.# - Act:3 - effect:..O..O.O - Num:2 [fit: 0.001, exp: 14.00, pred: 429.453, error:9296.244248507992]acc: 0.0\n", - "Cond:#..FOO.# - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 18.00, pred: 938.496, error:6736.3508350884185]acc: 0.0\n", - "Cond:O...#..O - Act:7 - effect:O..O.... - Num:1 [fit: 0.002, exp: 35.00, pred: 398.199, error:4710.856138125791]acc: 0.0\n", - "Cond:####.OOF - Act:5 - effect:......O. - Num:3 [fit: 0.008, exp: 113.00, pred: 591.861, error:6469.6475834343455]acc: 0.0\n", - "Cond:#O.O###F - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1127.096, error:1360.6018909333363]acc: 0\n", - "Cond:#O..#O## - Act:5 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#O#.##O# - Act:7 - effect:.......O - Num:2 [fit: 0.020, exp: 0.00, pred: 1010.378, error:42.428467478511834]acc: 0\n", - "Cond:.#.FO##. - Act:1 - effect:..O...F. - Num:1 [fit: 0.000, exp: 17.00, pred: 392.958, error:6974.27593295062]acc: 0.0\n", - "Cond:#.#.OO## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1116.335, error:564.012229283444]acc: 0\n", - "Cond:###.#F.. - Act:1 - effect:.......O - Num:1 [fit: 0.002, exp: 11.00, pred: 427.548, error:8477.167280708258]acc: 0.0\n", - "Cond:.##O##.. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 216.00, pred: 481.614, error:5548.695761950081]acc: 0.2037037037037037\n", - "Cond:..#.#O.# - Act:0 - effect:..O..O.. - Num:2 [fit: 0.000, exp: 18.00, pred: 503.141, error:7574.780093615441]acc: 0.0\n", - "Cond:..###O## - Act:5 - effect:....FO.. - Num:2 [fit: 0.013, exp: 477.00, pred: 715.928, error:4441.088962206786]acc: 0.2557651991614256\n", - "Cond:.#....O# - Act:1 - effect:........ - Num:2 [fit: 0.000, exp: 22.00, pred: 667.257, error:9111.363028440863]acc: 0.0\n", - "Cond:###OO##. - Act:4 - effect:.......O - Num:2 [fit: 0.008, exp: 44.00, pred: 609.394, error:5604.508906454034]acc: 0.06818181818181818\n", - "Cond:#..#..#. - Act:7 - effect:.......O - Num:3 [fit: 0.001, exp: 25.00, pred: 406.737, error:6133.304751365514]acc: 0.04\n", - "Cond:##OO#... - Act:1 - effect:OO.O...O - Num:4 [fit: 0.010, exp: 16.00, pred: 431.295, error:5947.311541610065]acc: 0.0\n", - "Cond:..#O..## - Act:1 - effect:.......O - Num:2 [fit: 0.001, exp: 23.00, pred: 451.462, error:8792.72463556831]acc: 0.0\n", - "Cond:...###O. - Act:1 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 1642.364, error:700.0339329461576]acc: 0\n", - "Cond:#.##.OFF - Act:6 - effect:.O...... - Num:1 [fit: 0.002, exp: 0.00, pred: 1445.585, error:118.70060570467754]acc: 0\n", - "Cond:.#.###O. - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1445.585, error:118.70060570467754]acc: 0\n", - "Cond:...FOO.# - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 15.00, pred: 515.125, error:8935.28964472972]acc: 0.0\n", - "Cond:..#F#O#. - Act:2 - effect:....OO.F - Num:2 [fit: 0.000, exp: 17.00, pred: 938.399, error:6795.512619369188]acc: 0.0\n", - "Cond:.###..#F - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1187.890, error:548.2118457740978]acc: 0\n", - "Cond:....##OF - Act:6 - effect:.OF..O.. - Num:2 [fit: 0.027, exp: 112.00, pred: 278.127, error:2368.703319091791]acc: 0.0\n", - "Cond:#..#.O#F - Act:6 - effect:.F.O...O - Num:1 [fit: 0.009, exp: 112.00, pred: 278.127, error:1094.9129189627897]acc: 0.0\n", - "Cond:#..##O## - Act:5 - effect:....FO.. - Num:2 [fit: 0.014, exp: 474.00, pred: 715.928, error:5907.094814130806]acc: 0.21940928270042195\n", - "Cond:.....#F. - Act:1 - effect:..F..... - Num:3 [fit: 0.001, exp: 25.00, pred: 382.834, error:7569.8258370587055]acc: 0.0\n", - "Cond:.###.O## - Act:5 - effect:......O. - Num:3 [fit: 0.008, exp: 185.00, pred: 575.159, error:5746.736892729852]acc: 0.0\n", - "Cond:#.##.OOF - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 108.00, pred: 591.861, error:5868.669275707169]acc: 0.0\n", - "Cond:........ - Act:7 - effect:.......O - Num:2 [fit: 0.014, exp: 0.00, pred: 1160.936, error:707.3455795776557]acc: 0\n", - "Cond:##.F.#.# - Act:7 - effect:FOO....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1105.720, error:120.78234019162151]acc: 0\n", - "Cond:O##.#.#O - Act:1 - effect:.....O.. - Num:2 [fit: 0.000, exp: 19.00, pred: 380.365, error:7809.520204502078]acc: 0.0\n", - "Cond:..#..#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 29.00, pred: 377.816, error:10230.152938838537]acc: 0.0\n", - "Cond:##OO#..# - Act:3 - effect:.OOOOO.. - Num:2 [fit: 0.009, exp: 63.00, pred: 321.804, error:3701.378056040754]acc: 0.0\n", - "Cond:O...##.O - Act:3 - effect:..O..O.O - Num:1 [fit: 0.000, exp: 13.00, pred: 426.451, error:8982.803301749775]acc: 0.0\n", - "Cond:####..#O - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 77.00, pred: 384.090, error:5458.683205953437]acc: 0.12987012987012986\n", - "Cond:.#...... - Act:1 - effect:O.O....O - Num:1 [fit: 0.002, exp: 38.00, pred: 373.412, error:2937.3939931076056]acc: 0.0\n", - "Cond:..#.#### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 440.00, pred: 1655.017, error:7284.710455599785]acc: 0.15\n", - "Cond:OO##.#.O - Act:1 - effect:.....O.. - Num:1 [fit: 0.020, exp: 5.00, pred: 407.976, error:3282.844748523742]acc: 0.0\n", - "Cond:....##F. - Act:1 - effect:..F..... - Num:2 [fit: 0.002, exp: 24.00, pred: 381.469, error:7023.003084408075]acc: 0.0\n", - "Cond:##...#.# - Act:1 - effect:.O...... - Num:1 [fit: 0.000, exp: 79.00, pred: 245.224, error:4576.776555035348]acc: 0.26582278481012656\n", - "Cond:O#..#.#O - Act:2 - effect:.....O.. - Num:1 [fit: 0.003, exp: 11.00, pred: 417.503, error:8368.852584064824]acc: 0.0\n", - "Cond:....O### - Act:5 - effect:....OO.F - Num:1 [fit: 0.011, exp: 0.00, pred: 1085.685, error:505.6652056526437]acc: 0\n", - "Cond:.###.O#F - Act:6 - effect:...O.... - Num:1 [fit: 0.011, exp: 110.00, pred: 278.127, error:741.3711930910256]acc: 0.0\n", - "Cond:#...#OO# - Act:6 - effect:FOO....O - Num:1 [fit: 0.009, exp: 110.00, pred: 278.127, error:892.6861869596568]acc: 0.0\n", - "Cond:#.##.OOF - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 23.00, pred: 809.785, error:9453.63069956985]acc: 0.0\n", - "Cond:.#..#.#O - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 38.00, pred: 301.236, error:3972.580036003398]acc: 0.15789473684210525\n", - "Cond:#O#O###. - Act:2 - effect:.......O - Num:1 [fit: 0.014, exp: 14.00, pred: 710.377, error:5857.038785561664]acc: 0.0\n", - "Cond:.#.#OO.. - Act:0 - effect:O....OOO - Num:1 [fit: 0.001, exp: 13.00, pred: 520.926, error:8489.520756534312]acc: 0.0\n", - "Cond:#O....#O - Act:0 - effect:O.OOOOOO - Num:1 [fit: 0.069, exp: 4.00, pred: 368.901, error:2444.9819578950837]acc: 0.0\n", - "Cond:...F#O#. - Act:2 - effect:..O...O. - Num:2 [fit: 0.003, exp: 16.00, pred: 949.903, error:6519.936622501989]acc: 0.0\n", - "Cond:..#FOO#. - Act:2 - effect:....OO.F - Num:1 [fit: 0.001, exp: 16.00, pred: 949.903, error:7602.505184780391]acc: 0.0\n", - "Cond:.....O#F - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.013, exp: 104.00, pred: 278.127, error:2568.0843593325085]acc: 0.0\n", - "Cond:.#####OF - Act:6 - effect:.F.O...O - Num:1 [fit: 0.012, exp: 104.00, pred: 278.127, error:735.4567760476085]acc: 0.0\n", - "Cond:.....#F# - Act:1 - effect:..F..... - Num:4 [fit: 0.001, exp: 21.00, pred: 377.976, error:8174.237787296627]acc: 0.0\n", - "Cond:O....#.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.005, exp: 33.00, pred: 572.026, error:6425.600889925429]acc: 0.0\n", - "Cond:O#..#.## - Act:2 - effect:.....O.. - Num:4 [fit: 0.004, exp: 17.00, pred: 368.144, error:8106.861134472726]acc: 0.0\n", - "Cond:O#.##..# - Act:2 - effect:.....O.. - Num:1 [fit: 0.004, exp: 17.00, pred: 368.144, error:7334.5506005744855]acc: 0.0\n", - "Cond:#..##..# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 193.00, pred: 512.133, error:4505.418757555612]acc: 0.33678756476683935\n", - "Cond:#.#####F - Act:0 - effect:..O..O.. - Num:2 [fit: 0.000, exp: 23.00, pred: 809.785, error:9965.056958171037]acc: 0.0\n", - "Cond:...O###F - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 892.435, error:1252.516038205788]acc: 0\n", - "Cond:..###.#F - Act:2 - effect:..O..FF. - Num:2 [fit: 0.000, exp: 0.00, pred: 892.435, error:1252.516038205788]acc: 0\n", - "Cond:.##.#O#F - Act:6 - effect:.F.O...O - Num:1 [fit: 0.009, exp: 102.00, pred: 278.127, error:897.6828808207779]acc: 0.0\n", - "Cond:##.#.OO# - Act:6 - effect:FOO....O - Num:2 [fit: 0.019, exp: 102.00, pred: 278.127, error:1687.869211410583]acc: 0.0\n", - "Cond:##..#.OF - Act:3 - effect:O.....O. - Num:1 [fit: 0.002, exp: 0.00, pred: 840.076, error:1335.176981049846]acc: 0\n", - "Cond:#OO#.##. - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 331.713, error:6318.894697554077]acc: 0.0\n", - "Cond:##O..### - Act:2 - effect:.......O - Num:2 [fit: 0.068, exp: 7.00, pred: 442.930, error:3040.2692103956697]acc: 0.0\n", - "Cond:##O##F## - Act:7 - effect:.......O - Num:1 [fit: 0.007, exp: 0.00, pred: 807.327, error:646.0636550177974]acc: 0\n", - "Cond:.O#..... - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 43.00, pred: 290.181, error:4262.099099921874]acc: 0.0\n", - "Cond:.#..#.#. - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 12.00, pred: 394.899, error:8444.590532575156]acc: 0.0\n", - "Cond:#O#.##.# - Act:1 - effect:.O...... - Num:1 [fit: 0.002, exp: 48.00, pred: 259.171, error:5058.6255576803505]acc: 0.5416666666666666\n", - "Cond:####F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 177.00, pred: 496.703, error:3981.4705217001365]acc: 0.6101694915254238\n", - "Cond:....F#.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.006, exp: 41.00, pred: 353.067, error:5349.645151495246]acc: 0.6585365853658537\n", - "Cond:....FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.004, exp: 41.00, pred: 353.067, error:5252.618541716733]acc: 0.6585365853658537\n", - "Cond:######## - Act:5 - effect:........ - Num:68 [fit: 0.043, exp: 2334.00, pred: 1210.169, error:1714.0389427135808]acc: 0.0\n", - "Cond:#..FO#.# - Act:5 - effect:........ - Num:1 [fit: 0.010, exp: 108.00, pred: 596.498, error:2068.6618017569153]acc: 0.0\n", - "Cond:#..FOO.. - Act:5 - effect:O....F.. - Num:2 [fit: 0.027, exp: 108.00, pred: 596.498, error:2210.3366856089747]acc: 0.0\n", - "Cond:.#.##O#O - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#OO###.. - Act:2 - effect:.......O - Num:3 [fit: 0.008, exp: 17.00, pred: 640.363, error:5931.649484248948]acc: 0.0\n", - "Cond:###FO#.# - Act:4 - effect:.O...... - Num:4 [fit: 0.027, exp: 116.00, pred: 488.760, error:3869.797046761752]acc: 0.0\n", - "Cond:..##.O.# - Act:5 - effect:......O. - Num:1 [fit: 0.003, exp: 0.00, pred: 1385.514, error:457.9784365998413]acc: 0\n", - "Cond:.#....#. - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 25.00, pred: 637.482, error:5551.218701334168]acc: 0.04\n", - "Cond:#####O#F - Act:5 - effect:......O. - Num:1 [fit: 0.005, exp: 104.00, pred: 591.861, error:5334.329935574037]acc: 0.0\n", - "Cond:###.O#.. - Act:5 - effect:......O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:####.#OO - Act:6 - effect:.O...... - Num:1 [fit: 0.002, exp: 68.00, pred: 373.857, error:3881.6822917453333]acc: 0.0\n", - "Cond:.######O - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 117.00, pred: 336.932, error:5364.967631546808]acc: 0.017094017094017096\n", - "Cond:.#####.F - Act:5 - effect:......O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1423.306, error:938.5080153743152]acc: 0\n", - "Cond:#O...#.# - Act:2 - effect:...O.... - Num:2 [fit: 0.002, exp: 22.00, pred: 364.071, error:5955.988703519521]acc: 0.13636363636363635\n", - "Cond:#...#.#. - Act:2 - effect:.......O - Num:1 [fit: 0.007, exp: 0.00, pred: 1254.420, error:883.9785429329164]acc: 0\n", - "Cond:##OO...# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 25.00, pred: 511.057, error:7159.913096518207]acc: 0.0\n", - "Cond:####.O## - Act:5 - effect:.O...... - Num:1 [fit: 0.005, exp: 180.00, pred: 575.159, error:5615.660009462372]acc: 0.0\n", - "Cond:###.#### - Act:5 - effect:....FO.. - Num:4 [fit: 0.001, exp: 1285.00, pred: 1582.586, error:4047.1964077835983]acc: 0.11673151750972763\n", - "Cond:..#####. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 663.00, pred: 1501.499, error:2541.895310687191]acc: 0.09502262443438914\n", - "Cond:###F#### - Act:4 - effect:.O...... - Num:3 [fit: 0.008, exp: 112.00, pred: 488.760, error:3644.2827655667184]acc: 0.0\n", - "Cond:##.##O#. - Act:5 - effect:.O...... - Num:1 [fit: 0.010, exp: 340.00, pred: 597.351, error:1870.2967593637395]acc: 0.0\n", - "Cond:#O#O#### - Act:2 - effect:.......O - Num:1 [fit: 0.003, exp: 9.00, pred: 674.217, error:4891.290796572414]acc: 0.0\n", - "Cond:#.#..##O - Act:1 - effect:.....F.. - Num:3 [fit: 0.003, exp: 24.00, pred: 352.798, error:8569.491524636658]acc: 0.0\n", - "Cond:#....... - Act:7 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1049.813, error:538.7368909740012]acc: 0\n", - "Cond:..###OO# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 22.00, pred: 811.207, error:9792.035938324832]acc: 0.0\n", - "Cond:#OOO..## - Act:0 - effect:O..O.... - Num:3 [fit: 0.003, exp: 9.00, pred: 420.011, error:7445.55052754914]acc: 0.0\n", - "Cond:.#OO.### - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 22.00, pred: 513.862, error:7048.307883108745]acc: 0.0\n", - "Cond:#.####O. - Act:6 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 0.00, pred: 942.618, error:718.766108231561]acc: 0\n", - "Cond:O..O##.# - Act:1 - effect:.....F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1170.649, error:123.62884893330869]acc: 0\n", - "Cond:..O..O## - Act:1 - effect:.....F.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1008.226, error:779.4421561141331]acc: 0\n", - "Cond:##O##... - Act:1 - effect:OO.O...O - Num:2 [fit: 0.000, exp: 19.00, pred: 301.738, error:5599.2198561562145]acc: 0.0\n", - "Cond:#.#OO##. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 24.00, pred: 315.988, error:6647.986375771467]acc: 0.041666666666666664\n", - "Cond:##.#..#O - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 230.00, pred: 311.393, error:3969.132861207423]acc: 0.0\n", - "Cond:..O..### - Act:5 - effect:......O. - Num:1 [fit: 0.014, exp: 0.00, pred: 876.723, error:421.45930910212934]acc: 0\n", - "Cond:....#.#O - Act:2 - effect:.......O - Num:2 [fit: 0.000, exp: 24.00, pred: 433.304, error:7672.325983248076]acc: 0.25\n", - "Cond:##.FOO## - Act:5 - effect:.O...... - Num:1 [fit: 0.013, exp: 95.00, pred: 596.498, error:2031.7575922978199]acc: 0.0\n", - "Cond:##O#.O## - Act:7 - effect:.......O - Num:1 [fit: 0.002, exp: 0.00, pred: 887.140, error:1272.497726707377]acc: 0\n", - "Cond:.#....## - Act:1 - effect:.O...... - Num:2 [fit: 0.003, exp: 43.00, pred: 323.006, error:5493.457485625134]acc: 0.6046511627906976\n", - "Cond:.O#....# - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 40.00, pred: 290.185, error:4558.639127618495]acc: 0.6\n", - "Cond:#.#####O - Act:1 - effect:.....F.. - Num:2 [fit: 0.001, exp: 14.00, pred: 343.470, error:9161.65053486605]acc: 0.0\n", - "Cond:##O#.#F# - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 0.00, pred: 871.722, error:1207.5148281504673]acc: 0\n", - "Cond:##O#.### - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 31.00, pred: 478.628, error:5775.434261014508]acc: 0.06451612903225806\n", - "Cond:..###OO. - Act:6 - effect:.....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 1001.471, error:1389.208260695104]acc: 0\n", - "Cond:.##.#..# - Act:1 - effect:.O...... - Num:6 [fit: 0.046, exp: 41.00, pred: 277.005, error:5362.820858610951]acc: 0.5365853658536586\n", - "Cond:#.#.###F - Act:0 - effect:..O..O.. - Num:5 [fit: 0.002, exp: 21.00, pred: 810.828, error:10688.920098385375]acc: 0.0\n", - "Cond:.###.O.. - Act:5 - effect:......O. - Num:1 [fit: 0.001, exp: 0.00, pred: 1021.983, error:410.669088037938]acc: 0\n", - "Cond:..#.##.. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 20.00, pred: 429.482, error:7655.435370677475]acc: 0.0\n", - "Cond:#.OO.... - Act:2 - effect:...O.... - Num:1 [fit: 0.003, exp: 5.00, pred: 483.574, error:5209.847874209787]acc: 0.0\n", - "Cond:##.OO##. - Act:4 - effect:.O...... - Num:1 [fit: 0.006, exp: 30.00, pred: 608.991, error:6046.540885747135]acc: 0.0\n", - "Cond:#.#OO... - Act:4 - effect:.O...... - Num:2 [fit: 0.019, exp: 30.00, pred: 608.991, error:4968.915880237747]acc: 0.0\n", - "Cond:##..##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 84.00, pred: 501.213, error:7321.051773682124]acc: 0.0\n", - "Cond:###F#..# - Act:3 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 957.493, error:662.954673892797]acc: 0\n", - "Cond:.O#..### - Act:1 - effect:.O...... - Num:1 [fit: 0.004, exp: 35.00, pred: 290.221, error:4467.559677450754]acc: 0.5714285714285714\n", - "Cond:O#..##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 39.00, pred: 497.818, error:7250.177165025325]acc: 0.0\n", - "Cond:#.#.##.. - Act:0 - effect:......O. - Num:1 [fit: 0.000, exp: 17.00, pred: 418.873, error:8132.343727729604]acc: 0.0\n", - "Cond:#.###..# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 218.00, pred: 481.374, error:5022.566696835398]acc: 0.3211009174311927\n", - "Cond:#...##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 47.00, pred: 565.915, error:7454.920115659406]acc: 0.0\n", - "Cond:##O#.### - Act:2 - effect:.......O - Num:1 [fit: 0.003, exp: 18.00, pred: 736.031, error:6617.280601435991]acc: 0.0\n", - "Cond:#..O.### - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 79.00, pred: 544.878, error:3856.090012953712]acc: 0.7215189873417721\n", - "Cond:..###O#F - Act:6 - effect:.F.O...O - Num:1 [fit: 0.013, exp: 99.00, pred: 278.127, error:2368.3938326927264]acc: 0.0\n", - "Cond:.#####OF - Act:5 - effect:.F.O...O - Num:1 [fit: 0.005, exp: 103.00, pred: 591.861, error:6327.55686961147]acc: 0.0\n", - "Cond:..#.##.# - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 19.00, pred: 398.185, error:7918.390553515605]acc: 0.0\n", - "Cond:##.#FO## - Act:5 - effect:....FO.. - Num:1 [fit: 0.020, exp: 154.00, pred: 496.703, error:4442.491574836091]acc: 0.6233766233766234\n", - "Cond:..#O..#. - Act:1 - effect:OO.O.... - Num:1 [fit: 0.000, exp: 17.00, pred: 443.100, error:8756.975315099824]acc: 0.0\n", - "Cond:..##.#F. - Act:2 - effect:...O..O. - Num:1 [fit: 0.000, exp: 22.00, pred: 755.918, error:7761.244310375663]acc: 0.0\n", - "Cond:##.#.OOF - Act:3 - effect:......O. - Num:1 [fit: 0.000, exp: 22.00, pred: 440.994, error:9643.159346139673]acc: 0.0\n", - "Cond:.....#.. - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 22.00, pred: 746.197, error:10556.498193661186]acc: 0.045454545454545456\n", - "Cond:#.###### - Act:7 - effect:.O...... - Num:2 [fit: 0.001, exp: 1015.00, pred: 1343.672, error:2075.5516091641402]acc: 0.008866995073891626\n", - "Cond:#...#O## - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 315.00, pred: 676.251, error:6653.3806006171835]acc: 0.3238095238095238\n", - "Cond:###F###. - Act:4 - effect:.O...... - Num:2 [fit: 0.012, exp: 104.00, pred: 488.760, error:3572.8135521330214]acc: 0.0\n", - "Cond:#.#.#O.. - Act:2 - effect:OO...... - Num:1 [fit: 0.000, exp: 22.00, pred: 888.344, error:9888.334146479247]acc: 0.0\n", - "Cond:.#.#FO.. - Act:0 - effect:.......O - Num:2 [fit: 0.003, exp: 8.00, pred: 428.207, error:6101.589986358933]acc: 0.0\n", - "Cond:O#.O###. - Act:5 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1182.514, error:407.1922349318695]acc: 0\n", - "Cond:#####O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.007, exp: 316.00, pred: 597.351, error:2495.189789320173]acc: 0.22468354430379747\n", - "Cond:###..F.O - Act:7 - effect:.......O - Num:1 [fit: 0.002, exp: 0.00, pred: 943.947, error:605.5508845908984]acc: 0\n", - "Cond:#..##O.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.018, exp: 243.00, pred: 597.351, error:768.5579449698157]acc: 0.42386831275720166\n", - "Cond:###F##.# - Act:4 - effect:.O...... - Num:3 [fit: 0.019, exp: 102.00, pred: 488.760, error:3643.7199522565293]acc: 0.0\n", - "Cond:O##...## - Act:6 - effect:O....O.O - Num:2 [fit: 0.001, exp: 172.00, pred: 284.895, error:4344.240101852718]acc: 0.0\n", - "Cond:.##...## - Act:1 - effect:.O...... - Num:1 [fit: 0.005, exp: 26.00, pred: 269.621, error:5324.595181816107]acc: 0.4230769230769231\n", - "Cond:.O#..##. - Act:1 - effect:.O...... - Num:1 [fit: 0.015, exp: 21.00, pred: 287.200, error:4207.58234537588]acc: 0.5238095238095238\n", - "Cond:#.O#.### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 22.00, pred: 396.058, error:6367.178997922822]acc: 0.0\n", - "Cond:##..##FF - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 793.315, error:623.7827062017626]acc: 0\n", - "Cond:###O###. - Act:4 - effect:.O...... - Num:2 [fit: 0.001, exp: 88.00, pred: 743.895, error:6562.428185488546]acc: 0.2159090909090909\n", - "Cond:.####### - Act:5 - effect:.O...... - Num:22 [fit: 0.006, exp: 1687.00, pred: 1206.937, error:2163.408786861434]acc: 0.004149377593360996\n", - "Cond:....#F.# - Act:7 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 23.00, pred: 793.041, error:9187.295147820674]acc: 0.0\n", - "Cond:O#..#..# - Act:0 - effect:O..O.... - Num:2 [fit: 0.001, exp: 36.00, pred: 497.740, error:7184.502136854692]acc: 0.0\n", - "Cond:O.##.### - Act:3 - effect:...OO... - Num:3 [fit: 0.004, exp: 9.00, pred: 390.022, error:7953.8481713361525]acc: 0.0\n", - "Cond:##.##F.. - Act:1 - effect:.F...F.. - Num:2 [fit: 0.004, exp: 6.00, pred: 271.122, error:6137.160101761207]acc: 0.0\n", - "Cond:#OO..### - Act:6 - effect:.......O - Num:3 [fit: 0.009, exp: 16.00, pred: 547.587, error:8464.960499984114]acc: 0.125\n", - "Cond:O...#### - Act:7 - effect:O..O.... - Num:1 [fit: 0.008, exp: 22.00, pred: 401.037, error:4490.697494662628]acc: 0.0\n", - "Cond:.O.##O.. - Act:7 - effect:....O..O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:##.O#### - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.002, exp: 41.00, pred: 622.380, error:7228.292953677383]acc: 0.0\n", - "Cond:#..O##.# - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 147.00, pred: 512.640, error:4423.341646539215]acc: 0.4217687074829932\n", - "Cond:###O#### - Act:4 - effect:.O...... - Num:3 [fit: 0.000, exp: 85.00, pred: 743.895, error:6787.203658618655]acc: 0.2235294117647059\n", - "Cond:#.O.###. - Act:7 - effect:...O.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1023.067, error:1162.5038570294116]acc: 0\n", - "Cond:.#O#.### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 56.00, pred: 406.105, error:9301.344391667684]acc: 0.0\n", - "Cond:O###.##. - Act:3 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 50.00, pred: 673.006, error:9213.482043637343]acc: 0.0\n", - "Cond:..#..#F. - Act:1 - effect:..F..... - Num:1 [fit: 0.004, exp: 17.00, pred: 377.319, error:7221.28743867438]acc: 0.0\n", - "Cond:#.OO.##. - Act:0 - effect:OO..O... - Num:1 [fit: 0.002, exp: 10.00, pred: 628.857, error:8639.076983377097]acc: 0.0\n", - "Cond:..#..O#. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 19.00, pred: 372.623, error:9617.240457646203]acc: 0.0\n", - "Cond:O#.####. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.020, exp: 7.00, pred: 295.142, error:5264.487144757223]acc: 0.0\n", - "Cond:###.O### - Act:4 - effect:.O...... - Num:1 [fit: 0.006, exp: 0.00, pred: 899.875, error:734.1128744461657]acc: 0\n", - "Cond:.####..# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 268.00, pred: 477.463, error:6728.467761293084]acc: 0.291044776119403\n", - "Cond:OO.##..# - Act:7 - effect:......O. - Num:1 [fit: 0.003, exp: 20.00, pred: 471.381, error:8601.810212912911]acc: 0.0\n", - "Cond:##O.#.#. - Act:7 - effect:.......O - Num:1 [fit: 0.012, exp: 2.00, pred: 394.415, error:2980.7332048225508]acc: 0.0\n", - "Cond:#.OO.#.. - Act:2 - effect:.......O - Num:1 [fit: 0.005, exp: 5.00, pred: 483.574, error:5249.847874209787]acc: 0.0\n", - "Cond:#..#.O## - Act:5 - effect:..O....O - Num:1 [fit: 0.005, exp: 163.00, pred: 575.159, error:5907.932418780404]acc: 0.0\n", - "Cond:.#####.# - Act:5 - effect:....FO.. - Num:2 [fit: 0.001, exp: 1410.00, pred: 1206.758, error:1832.3391111770547]acc: 0.15319148936170213\n", - "Cond:..###O## - Act:4 - effect:.......O - Num:2 [fit: 0.000, exp: 268.00, pred: 591.450, error:6135.422404710654]acc: 0.15298507462686567\n", - "Cond:..O..O## - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#O###.# - Act:3 - effect:...O.... - Num:1 [fit: 0.006, exp: 65.00, pred: 299.353, error:3399.8632238621312]acc: 0.03076923076923077\n", - "Cond:....##OF - Act:1 - effect:...OOO.. - Num:1 [fit: 0.000, exp: 35.00, pred: 315.648, error:8346.192379523967]acc: 0.0\n", - "Cond:#.#.O#.# - Act:5 - effect:..O....O - Num:1 [fit: 0.001, exp: 0.00, pred: 860.468, error:1193.2309308825807]acc: 0\n", - "Cond:.##..#.. - Act:0 - effect:O..O.... - Num:2 [fit: 0.014, exp: 14.00, pred: 362.104, error:6195.293094330063]acc: 0.0\n", - "Cond:#.###O## - Act:5 - effect:....FO.. - Num:3 [fit: 0.021, exp: 404.00, pred: 715.928, error:4564.867446431187]acc: 0.04950495049504951\n", - "Cond:#OO#.##. - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 47.00, pred: 630.663, error:6253.572662395977]acc: 0.02127659574468085\n", - "Cond:#O#O###. - Act:4 - effect:.O...... - Num:2 [fit: 0.040, exp: 23.00, pred: 682.470, error:4004.743693169525]acc: 0.8260869565217391\n", - "Cond:.#.####O - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 27.00, pred: 300.968, error:4130.728302811267]acc: 0.2222222222222222\n", - "Cond:..O##O#F - Act:1 - effect:......O. - Num:1 [fit: 0.000, exp: 0.00, pred: 787.871, error:906.996416750163]acc: 0\n", - "Cond:O####... - Act:3 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 47.00, pred: 673.006, error:9606.760246992913]acc: 0.0\n", - "Cond:#...O### - Act:5 - effect:..O....O - Num:1 [fit: 0.000, exp: 0.00, pred: 999.128, error:1314.4362254009297]acc: 0\n", - "Cond:##OO###. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 15.00, pred: 510.292, error:6228.342815664939]acc: 0.0\n", - "Cond:.##.#O## - Act:5 - effect:....FO.. - Num:2 [fit: 0.005, exp: 300.00, pred: 676.251, error:7102.905171768876]acc: 0.15\n", - "Cond:.#.#...# - Act:3 - effect:...O.... - Num:2 [fit: 0.000, exp: 143.00, pred: 552.013, error:7345.168721930051]acc: 0.5034965034965035\n", - "Cond:.O.###O# - Act:3 - effect:...O.... - Num:1 [fit: 0.006, exp: 0.00, pred: 996.030, error:287.5143249372769]acc: 0\n", - "Cond:####.##F - Act:6 - effect:.......O - Num:1 [fit: 0.009, exp: 99.00, pred: 278.127, error:893.2510474953638]acc: 0.0\n", - "Cond:##.#.#O# - Act:6 - effect:FOO....O - Num:1 [fit: 0.002, exp: 160.00, pred: 373.857, error:2945.569533035551]acc: 0.0\n", - "Cond:##.###.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.000, exp: 75.00, pred: 490.912, error:8215.341771104793]acc: 0.0\n", - "Cond:..##.#F. - Act:1 - effect:..F..... - Num:3 [fit: 0.013, exp: 13.00, pred: 375.287, error:7346.92204780589]acc: 0.0\n", - "Cond:...#.#F# - Act:1 - effect:..F..... - Num:1 [fit: 0.014, exp: 13.00, pred: 375.287, error:7056.721983805889]acc: 0.0\n", - "Cond:.##.#.O# - Act:3 - effect:...O.... - Num:3 [fit: 0.015, exp: 14.00, pred: 580.603, error:8183.555262193819]acc: 0.2857142857142857\n", - "Cond:.#..##F# - Act:0 - effect:..O..O.. - Num:2 [fit: 0.005, exp: 14.00, pred: 386.022, error:9720.764899560123]acc: 0.0\n", - "Cond:.##..#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.005, exp: 14.00, pred: 386.022, error:9005.008335080121]acc: 0.0\n", - "Cond:.##.F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 149.00, pred: 496.703, error:4383.3117407324735]acc: 0.8859060402684564\n", - "Cond:.#.....O - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 5.00, pred: 461.863, error:5492.662438411722]acc: 0.0\n", - "Cond:O##O#.#. - Act:5 - effect:........ - Num:1 [fit: 0.002, exp: 0.00, pred: 934.723, error:1297.2513999816692]acc: 0\n", - "Cond:..###..# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 21.00, pred: 519.476, error:8847.729912412618]acc: 0.0\n", - "Cond:.#.#..#. - Act:2 - effect:.OO..... - Num:1 [fit: 0.000, exp: 23.00, pred: 460.774, error:4172.713641162449]acc: 0.043478260869565216\n", - "Cond:.O.FO#.. - Act:7 - effect:FOO....O - Num:1 [fit: 0.000, exp: 0.00, pred: 981.285, error:1552.166260721175]acc: 0\n", - "Cond:..##.### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 275.00, pred: 1653.047, error:8179.074374008822]acc: 0.12363636363636364\n", - "Cond:.#.##### - Act:4 - effect:..O.OO.. - Num:2 [fit: 0.000, exp: 431.00, pred: 1479.746, error:7384.564715543683]acc: 0.0069605568445475635\n", - "Cond:#.....#O - Act:2 - effect:..O..FF. - Num:1 [fit: 0.000, exp: 20.00, pred: 385.409, error:8218.715837919914]acc: 0.0\n", - "Cond:##..#O.. - Act:2 - effect:OO...... - Num:1 [fit: 0.003, exp: 17.00, pred: 905.563, error:9914.426924782434]acc: 0.0\n", - "Cond:##.##### - Act:5 - effect:........ - Num:9 [fit: 0.002, exp: 1624.00, pred: 1320.994, error:2613.878512108236]acc: 0.014778325123152709\n", - "Cond:####..#F - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#..#O## - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 297.00, pred: 676.251, error:6583.786001041673]acc: 0.3468013468013468\n", - "Cond:...FO.## - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.004, exp: 0.00, pred: 1503.755, error:530.8077017923646]acc: 0\n", - "Cond:##O.#.#. - Act:0 - effect:.O...... - Num:1 [fit: 0.002, exp: 6.00, pred: 378.027, error:6958.899050422108]acc: 0.0\n", - "Cond:#...#F#. - Act:2 - effect:...O.... - Num:1 [fit: 0.002, exp: 12.00, pred: 735.276, error:8662.148593033493]acc: 0.0\n", - "Cond:.##O.#OF - Act:6 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#O..#O#. - Act:4 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#O##... - Act:1 - effect:OO.O...O - Num:1 [fit: 0.008, exp: 17.00, pred: 302.315, error:5848.835933227193]acc: 0.0\n", - "Cond:.#.##... - Act:2 - effect:OO...... - Num:1 [fit: 0.000, exp: 33.00, pred: 538.321, error:3848.7563493506855]acc: 0.24242424242424243\n", - "Cond:##..#..# - Act:0 - effect:O..O.... - Num:2 [fit: 0.002, exp: 47.00, pred: 504.190, error:7603.740006362076]acc: 0.0\n", - "Cond:#O..#O.. - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1021.076, error:160.06554608447567]acc: 0\n", - "Cond:.##..O#. - Act:0 - effect:..O..O.. - Num:2 [fit: 0.002, exp: 11.00, pred: 357.769, error:8633.561988580816]acc: 0.0\n", - "Cond:.#.##.#. - Act:2 - effect:OO...... - Num:1 [fit: 0.000, exp: 32.00, pred: 538.248, error:3563.93608336706]acc: 0.125\n", - "Cond:###.#O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.034, exp: 218.00, pred: 483.016, error:4700.323736154113]acc: 0.44495412844036697\n", - "Cond:.##..O#F - Act:5 - effect:.O...... - Num:3 [fit: 0.016, exp: 77.00, pred: 591.861, error:5854.14559437308]acc: 0.0\n", - "Cond:..O#..## - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 18.00, pred: 396.939, error:7002.051698185215]acc: 0.0\n", - "Cond:O#.###.. - Act:0 - effect:O..O.... - Num:1 [fit: 0.021, exp: 7.00, pred: 295.142, error:4874.087144757223]acc: 0.0\n", - "Cond:.....F## - Act:7 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 21.00, pred: 797.849, error:9287.086527396166]acc: 0.0\n", - "Cond:.O..F### - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1221.548, error:554.6220856208612]acc: 0\n", - "Cond:.#...O.# - Act:5 - effect:..O....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1631.061, error:297.0688674537016]acc: 0\n", - "Cond:....##.O - Act:7 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 9.00, pred: 344.626, error:4667.656188194025]acc: 0.0\n", - "Cond:....O##. - Act:3 - effect:....OO.F - Num:1 [fit: 0.000, exp: 0.00, pred: 1444.239, error:637.1258636088164]acc: 0\n", - "Cond:.##...O# - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.003, exp: 18.00, pred: 338.679, error:3574.3694669696256]acc: 0.0\n", - "Cond:.O#..##. - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 13.00, pred: 388.276, error:8530.86294394073]acc: 0.0\n", - "Cond:O..##### - Act:2 - effect:.O.OF..O - Num:1 [fit: 0.008, exp: 3.00, pred: 314.479, error:3603.3674673742876]acc: 0.0\n", - "Cond:.#.###.# - Act:2 - effect:.O...... - Num:1 [fit: 0.000, exp: 57.00, pred: 760.164, error:5786.234266702058]acc: 0.10526315789473684\n", - "Cond:..OO##.F - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1659.907, error:1009.4067550339951]acc: 0\n", - "Cond:....F..O - Act:5 - effect:.OO..... - Num:1 [fit: 0.009, exp: 0.00, pred: 1080.827, error:426.79126173403404]acc: 0\n", - "Cond:.#..F#.# - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 149.00, pred: 496.703, error:4111.311692272174]acc: 0.6845637583892618\n", - "Cond:#.O#.### - Act:2 - effect:........ - Num:1 [fit: 0.005, exp: 5.00, pred: 483.574, error:4769.847874209787]acc: 0.0\n", - "Cond:.#..#... - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 11.00, pred: 387.500, error:8414.544477878724]acc: 0.0\n", - "Cond:...#FO.# - Act:1 - effect:....FO.. - Num:6 [fit: 0.022, exp: 36.00, pred: 353.044, error:5539.574500283724]acc: 0.7222222222222222\n", - "Cond:.#..#O.# - Act:2 - effect:..O..FF. - Num:1 [fit: 0.002, exp: 8.00, pred: 775.557, error:8347.69055930361]acc: 0.0\n", - "Cond:#..###.. - Act:2 - effect:OO...... - Num:1 [fit: 0.001, exp: 40.00, pred: 765.035, error:5626.406735164864]acc: 0.2\n", - "Cond:.#...O#. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 9.00, pred: 338.183, error:8116.758128362655]acc: 0.0\n", - "Cond:#O.###.# - Act:2 - effect:.....F.. - Num:2 [fit: 0.000, exp: 17.00, pred: 360.421, error:7929.978367977126]acc: 0.0\n", - "Cond:..#O.### - Act:2 - effect:........ - Num:1 [fit: 0.000, exp: 18.00, pred: 490.781, error:6147.092872631598]acc: 0.0\n", - "Cond:....O### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#..##O.. - Act:2 - effect:OO...... - Num:1 [fit: 0.004, exp: 8.00, pred: 758.366, error:7965.867722013965]acc: 0.0\n", - "Cond:.#.###.. - Act:2 - effect:.O...... - Num:3 [fit: 0.002, exp: 45.00, pred: 761.974, error:5328.024100964356]acc: 0.15555555555555556\n", - "Cond:.###.#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.005, exp: 8.00, pred: 322.173, error:7982.162480079689]acc: 0.0\n", - "Cond:...#..#O - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 11.00, pred: 403.655, error:7054.291908154859]acc: 0.09090909090909091\n", - "Cond:#...FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.016, exp: 26.00, pred: 353.516, error:5174.758692774016]acc: 0.6923076923076923\n", - "Cond:.##..#.# - Act:1 - effect:...O.... - Num:4 [fit: 0.004, exp: 28.00, pred: 249.354, error:6660.6000231897115]acc: 0.03571428571428571\n", - "Cond:##.#.... - Act:0 - effect:O..O.... - Num:1 [fit: 0.004, exp: 14.00, pred: 266.731, error:7144.291419714787]acc: 0.0\n", - "Cond:.##..#.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 17.00, pred: 385.693, error:6466.448170716505]acc: 0.0\n", - "Cond:##O###.. - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 16.00, pred: 734.013, error:5789.181318241539]acc: 0.0\n", - "Cond:#OO##... - Act:2 - effect:.......O - Num:1 [fit: 0.018, exp: 11.00, pred: 618.754, error:5103.286795335611]acc: 0.0\n", - "Cond:#O#..O## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 841.818, error:1112.50008423622]acc: 0\n", - "Cond:#.#.#OO# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 15.00, pred: 445.767, error:8807.30927092852]acc: 0.0\n", - "Cond:.##.##.. - Act:2 - effect:.O...... - Num:1 [fit: 0.000, exp: 24.00, pred: 802.261, error:7499.688391408275]acc: 0.0\n", - "Cond:..##O#O# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1197.896, error:788.1111123008943]acc: 0\n", - "Cond:#..#F#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.027, exp: 25.00, pred: 352.897, error:5425.191227136094]acc: 0.68\n", - "Cond:##O#..#. - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 19.00, pred: 478.997, error:6434.270032392207]acc: 0.0\n", - "Cond:#OOO...# - Act:0 - effect:O..O.... - Num:1 [fit: 0.006, exp: 5.00, pred: 319.336, error:4782.484491451696]acc: 0.0\n", - "Cond:.....#F# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.004, exp: 7.00, pred: 309.722, error:7005.050361913452]acc: 0.0\n", - "Cond:.#.OO... - Act:7 - effect:....O..O - Num:2 [fit: 0.000, exp: 21.00, pred: 316.518, error:6164.649007456267]acc: 0.0\n", - "Cond:.#....#. - Act:2 - effect:...O.... - Num:1 [fit: 0.002, exp: 5.00, pred: 326.296, error:4219.765375790463]acc: 0.2\n", - "Cond:O####O#. - Act:3 - effect:..OOO.O. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:##..FO.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.041, exp: 19.00, pred: 353.753, error:6195.988236233408]acc: 0.5789473684210527\n", - "Cond:##.#...O - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 33.00, pred: 388.045, error:6479.957717130735]acc: 0.15151515151515152\n", - "Cond:#....#.# - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 32.00, pred: 340.758, error:6122.56362511412]acc: 0.15625\n", - "Cond:O#...#.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.002, exp: 32.00, pred: 497.659, error:6978.106079872781]acc: 0.0\n", - "Cond:O...#.## - Act:2 - effect:.O....OO - Num:1 [fit: 0.010, exp: 2.00, pred: 233.216, error:2706.9458102429157]acc: 0.0\n", - "Cond:O#...##O - Act:2 - effect:.....O.. - Num:1 [fit: 0.002, exp: 7.00, pred: 348.461, error:7575.496963640455]acc: 0.0\n", - "Cond:.OO#.### - Act:6 - effect:.......O - Num:1 [fit: 0.003, exp: 22.00, pred: 660.479, error:8532.059353393699]acc: 0.045454545454545456\n", - "Cond:##.#.F.# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 30.00, pred: 596.760, error:10532.493611895115]acc: 0.0\n", - "Cond:.#O##... - Act:3 - effect:.OO..OO. - Num:6 [fit: 0.019, exp: 59.00, pred: 299.353, error:2988.393877525843]acc: 0.0\n", - "Cond:...#.#.# - Act:0 - effect:..O..O.. - Num:1 [fit: 0.002, exp: 8.00, pred: 422.564, error:5614.700362304513]acc: 0.0\n", - "Cond:.##O#.## - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 49.00, pred: 301.014, error:6658.557837769128]acc: 0.40816326530612246\n", - "Cond:O#...#.# - Act:2 - effect:.....O.. - Num:1 [fit: 0.003, exp: 11.00, pred: 350.940, error:7092.818708623942]acc: 0.0\n", - "Cond:#O.##.## - Act:2 - effect:...O.... - Num:2 [fit: 0.015, exp: 14.00, pred: 355.839, error:5452.945129336869]acc: 0.07142857142857142\n", - "Cond:OO###..# - Act:1 - effect:.....O.. - Num:2 [fit: 0.259, exp: 4.00, pred: 212.203, error:2954.800967022076]acc: 0.0\n", - "Cond:.O#.##.. - Act:1 - effect:..O.OO.. - Num:1 [fit: 0.008, exp: 18.00, pred: 286.523, error:4699.02858282333]acc: 0.0\n", - "Cond:....#.#. - Act:0 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 0.00, pred: 563.315, error:1093.333466498043]acc: 0\n", - "Cond:.##.#.#. - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 12.00, pred: 380.633, error:8124.322451598014]acc: 0.0\n", - "Cond:......#O - Act:2 - effect:OO...... - Num:2 [fit: 0.002, exp: 8.00, pred: 406.642, error:4598.827279185254]acc: 0.25\n", - "Cond:#..##### - Act:2 - effect:OO...... - Num:4 [fit: 0.003, exp: 52.00, pred: 760.583, error:6324.026647679455]acc: 0.19230769230769232\n", - "Cond:##..#.## - Act:0 - effect:..O..O.. - Num:1 [fit: 0.000, exp: 78.00, pred: 492.038, error:6629.52454864763]acc: 0.0\n", - "Cond:.######O - Act:1 - effect:.......O - Num:1 [fit: 0.010, exp: 4.00, pred: 328.348, error:4754.073320722432]acc: 0.0\n", - "Cond:#.#.#.#O - Act:1 - effect:.....F.. - Num:3 [fit: 0.006, exp: 8.00, pred: 322.639, error:7284.331142732702]acc: 0.0\n", - "Cond:#..#..#O - Act:1 - effect:..O...F. - Num:1 [fit: 0.002, exp: 8.00, pred: 322.639, error:6770.731142732701]acc: 0.0\n", - "Cond:#OO#.#.# - Act:6 - effect:..O..O.. - Num:1 [fit: 0.004, exp: 19.00, pred: 658.141, error:9575.554664369201]acc: 0.0\n", - "Cond:.#.##.O# - Act:3 - effect:...O.... - Num:1 [fit: 0.004, exp: 9.00, pred: 606.196, error:8146.629432549256]acc: 0.2222222222222222\n", - "Cond:.#####.. - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 282.00, pred: 771.025, error:4754.648434958876]acc: 0.0673758865248227\n", - "Cond:#O#.##.# - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.000, exp: 126.00, pred: 1346.367, error:3408.902814211071]acc: 0.0\n", - "Cond:##.###.. - Act:2 - effect:OO...... - Num:2 [fit: 0.002, exp: 42.00, pred: 761.870, error:6066.864712613375]acc: 0.21428571428571427\n", - "Cond:#..###.# - Act:2 - effect:OO...... - Num:2 [fit: 0.001, exp: 42.00, pred: 762.474, error:5305.9131113819]acc: 0.23809523809523808\n", - "Cond:.##F###. - Act:4 - effect:.......O - Num:1 [fit: 0.005, exp: 100.00, pred: 488.760, error:3777.5534281358823]acc: 0.0\n", - "Cond:.######. - Act:5 - effect:....FO.. - Num:1 [fit: 0.001, exp: 1206.00, pred: 1206.728, error:1646.225456448609]acc: 0.17827529021558872\n", - "Cond:#...#.## - Act:2 - effect:O..O.... - Num:2 [fit: 0.003, exp: 9.00, pred: 365.875, error:6738.529417947816]acc: 0.0\n", - "Cond:.###.##O - Act:0 - effect:.O...... - Num:2 [fit: 0.002, exp: 41.00, pred: 328.063, error:4181.158531457421]acc: 0.0\n", - "Cond:.#..#OOF - Act:1 - effect:...OOO.. - Num:1 [fit: 0.000, exp: 28.00, pred: 315.724, error:8640.014290323408]acc: 0.0\n", - "Cond:..#.#... - Act:7 - effect:....O..O - Num:1 [fit: 0.001, exp: 0.00, pred: 569.620, error:936.4555205331078]acc: 0\n", - "Cond:....#O#. - Act:1 - effect:....FO.. - Num:1 [fit: 0.000, exp: 24.00, pred: 292.652, error:8603.61728212125]acc: 0.041666666666666664\n", - "Cond:..##FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 10.00, pred: 341.562, error:4560.556699805004]acc: 0.3\n", - "Cond:.####O.. - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 130.00, pred: 1051.486, error:8304.801885327211]acc: 0.0\n", - "Cond:...FOO.. - Act:1 - effect:....OO.F - Num:3 [fit: 0.005, exp: 11.00, pred: 353.575, error:6222.790795805741]acc: 0.0\n", - "Cond:..####.# - Act:5 - effect:....FO.. - Num:3 [fit: 0.001, exp: 1084.00, pred: 1205.989, error:2010.6054677850102]acc: 0.16512915129151293\n", - "Cond:...#O#.# - Act:4 - effect:...O.... - Num:2 [fit: 0.004, exp: 102.00, pred: 653.345, error:4831.958339724557]acc: 0.0\n", - "Cond:#..O###. - Act:3 - effect:...O.... - Num:1 [fit: 0.005, exp: 98.00, pred: 512.640, error:4265.49446193219]acc: 0.5510204081632653\n", - "Cond:.#.#.... - Act:1 - effect:..F..... - Num:1 [fit: 0.007, exp: 18.00, pred: 388.235, error:7033.770579177785]acc: 0.0\n", - "Cond:##O#..#. - Act:2 - effect:.......O - Num:1 [fit: 0.007, exp: 15.00, pred: 731.973, error:6221.815934399172]acc: 0.0\n", - "Cond:..OO.### - Act:2 - effect:.......O - Num:2 [fit: 0.135, exp: 4.00, pred: 481.264, error:3964.4416708919866]acc: 0.0\n", - "Cond:.###O... - Act:7 - effect:....O..O - Num:2 [fit: 0.004, exp: 19.00, pred: 316.540, error:7256.78662008158]acc: 0.0\n", - "Cond:...O#..# - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 21.00, pred: 544.615, error:4159.557008833368]acc: 0.0\n", - "Cond:#..O.#.# - Act:0 - effect:...O.... - Num:1 [fit: 0.012, exp: 2.00, pred: 368.383, error:3229.4181650642663]acc: 0.0\n", - "Cond:######.# - Act:6 - effect:.O...... - Num:6 [fit: 0.066, exp: 656.00, pred: 271.508, error:2804.2657259436173]acc: 0.07317073170731707\n", - "Cond:##..FO#. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 668.095, error:9266.358210736316]acc: 0.043478260869565216\n", - "Cond:###O..## - Act:1 - effect:OO.O...O - Num:1 [fit: 0.004, exp: 14.00, pred: 398.417, error:7735.99150520128]acc: 0.0\n", - "Cond:.###O.#. - Act:7 - effect:....O..O - Num:2 [fit: 0.014, exp: 12.00, pred: 297.731, error:7890.008744187779]acc: 0.0\n", - "Cond:.#.O###. - Act:3 - effect:...O.... - Num:2 [fit: 0.018, exp: 82.00, pred: 512.640, error:5046.879778936711]acc: 0.524390243902439\n", - "Cond:O#.####. - Act:3 - effect:...O.... - Num:3 [fit: 0.001, exp: 38.00, pred: 672.969, error:9102.347198350044]acc: 0.0\n", - "Cond:.#.#F#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.003, exp: 9.00, pred: 348.271, error:4975.818935275545]acc: 0.3333333333333333\n", - "Cond:O#.##.## - Act:2 - effect:.....O.. - Num:4 [fit: 0.053, exp: 8.00, pred: 333.724, error:5915.294428793219]acc: 0.0\n", - "Cond:#O.##..# - Act:2 - effect:...O.... - Num:2 [fit: 0.036, exp: 10.00, pred: 345.389, error:6084.648701783818]acc: 0.1\n", - "Cond:.#O..#.# - Act:1 - effect:...O.... - Num:2 [fit: 0.096, exp: 6.00, pred: 290.307, error:3797.0896929461996]acc: 0.0\n", - "Cond:#O#.O.#. - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.009, exp: 0.00, pred: 482.261, error:947.7665682788496]acc: 0\n", - "Cond:.##O#### - Act:0 - effect:OO.O.... - Num:1 [fit: 0.000, exp: 18.00, pred: 465.319, error:7837.479118404401]acc: 0.0\n", - "Cond:..O#..#. - Act:1 - effect:.......O - Num:1 [fit: 0.006, exp: 2.00, pred: 357.315, error:3106.8287160270793]acc: 0.0\n", - "Cond:O#..#... - Act:7 - effect:O....O.O - Num:1 [fit: 0.005, exp: 5.00, pred: 424.589, error:5664.402346227356]acc: 0.0\n", - "Cond:###..#.. - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 16.00, pred: 268.496, error:6707.8685744458835]acc: 0.0\n", - "Cond:.####.O# - Act:3 - effect:...O.... - Num:2 [fit: 0.084, exp: 6.00, pred: 524.214, error:5969.095133624445]acc: 0.16666666666666666\n", - "Cond:...#.##O - Act:7 - effect:.O.OF..O - Num:3 [fit: 0.002, exp: 22.00, pred: 300.309, error:3682.9896599305557]acc: 0.0\n", - "Cond:.###.#.# - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 169.00, pred: 1669.973, error:8190.539540751133]acc: 0.023668639053254437\n", - "Cond:#..#.F.. - Act:1 - effect:.F...F.. - Num:1 [fit: 0.004, exp: 5.00, pred: 263.544, error:6060.39877761789]acc: 0.0\n", - "Cond:OO#.#.## - Act:2 - effect:.....O.. - Num:1 [fit: 0.012, exp: 4.00, pred: 342.443, error:3582.7024760136796]acc: 0.0\n", - "Cond:O#.###.# - Act:2 - effect:...O.... - Num:1 [fit: 0.005, exp: 6.00, pred: 318.007, error:5362.4872973573]acc: 0.0\n", - "Cond:##.FO#.# - Act:4 - effect:...O.... - Num:2 [fit: 0.007, exp: 75.00, pred: 488.760, error:3322.799408107521]acc: 0.0\n", - "Cond:...####. - Act:2 - effect:OO...... - Num:4 [fit: 0.003, exp: 34.00, pred: 764.849, error:5550.704033584911]acc: 0.23529411764705882\n", - "Cond:#.#.##.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.000, exp: 16.00, pred: 260.216, error:6533.490802897789]acc: 0.1875\n", - "Cond:##.#.##O - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 219.00, pred: 311.393, error:2078.6359662951977]acc: 0.0365296803652968\n", - "Cond:......## - Act:1 - effect:.....F.. - Num:1 [fit: 0.007, exp: 2.00, pred: 250.555, error:3310.433184890004]acc: 0.0\n", - "Cond:###.#.#O - Act:1 - effect:.O...... - Num:2 [fit: 0.008, exp: 4.00, pred: 269.280, error:3178.6642257235344]acc: 0.25\n", - "Cond:...#F#.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.059, exp: 7.00, pred: 284.915, error:4268.138253189593]acc: 0.42857142857142855\n", - "Cond:##..F#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.056, exp: 7.00, pred: 284.915, error:3456.9382531895935]acc: 0.42857142857142855\n", - "Cond:.O#..#.# - Act:1 - effect:..O.OO.. - Num:1 [fit: 0.055, exp: 14.00, pred: 285.774, error:4866.6865262784795]acc: 0.0\n", - "Cond:O##.#.## - Act:1 - effect:.....F.. - Num:1 [fit: 0.007, exp: 4.00, pred: 249.829, error:3434.651170605971]acc: 0.0\n", - "Cond:#O.#..#O - Act:6 - effect:.O...... - Num:2 [fit: 0.048, exp: 58.00, pred: 342.659, error:2168.4901885057616]acc: 0.7241379310344828\n", - "Cond:##.#.##. - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 17.00, pred: 256.920, error:9107.422910730182]acc: 0.0\n", - "Cond:######.# - Act:4 - effect:.......O - Num:4 [fit: 0.000, exp: 403.00, pred: 1356.819, error:3091.1545087210197]acc: 0.007444168734491315\n", - "Cond:.#.#O... - Act:7 - effect:....O..O - Num:3 [fit: 0.081, exp: 9.00, pred: 312.862, error:5673.730932168854]acc: 0.0\n", - "Cond:#OO##### - Act:2 - effect:.......O - Num:1 [fit: 0.004, exp: 7.00, pred: 604.850, error:4960.908246234974]acc: 0.0\n", - "Cond:.#O#..#. - Act:2 - effect:...O.... - Num:1 [fit: 0.002, exp: 11.00, pred: 727.021, error:5866.288918309882]acc: 0.0\n", - "Cond:.O#.#.#O - Act:6 - effect:.......O - Num:1 [fit: 0.004, exp: 0.00, pred: 377.141, error:812.8806537563833]acc: 0\n", - "Cond:...##### - Act:2 - effect:.O...... - Num:3 [fit: 0.003, exp: 41.00, pred: 760.627, error:5377.135235642303]acc: 0.0975609756097561\n", - "Cond:.##...## - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 47.00, pred: 328.010, error:4297.426235795771]acc: 0.0\n", - "Cond:.#O###.. - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 17.00, pred: 475.389, error:5883.451203586955]acc: 0.0\n", - "Cond:#####..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 50.00, pred: 639.364, error:8040.703247092034]acc: 0.0\n", - "Cond:.###.#.# - Act:0 - effect:..O..O.. - Num:3 [fit: 0.003, exp: 24.00, pred: 524.112, error:7075.444559868296]acc: 0.0\n", - "Cond:.##.#.## - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 45.00, pred: 328.003, error:5223.936168516289]acc: 0.0\n", - "Cond:#O###..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 15.00, pred: 229.775, error:6208.474922598765]acc: 0.0\n", - "Cond:..##.##O - Act:0 - effect:.......F - Num:2 [fit: 0.002, exp: 39.00, pred: 328.033, error:4503.890770741183]acc: 0.0\n", - "Cond:####F### - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 5.00, pred: 319.336, error:4454.503356007851]acc: 0.0\n", - "Cond:#..###.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 30.00, pred: 548.918, error:7681.5847094120945]acc: 0.0\n", - "Cond:O..##.## - Act:0 - effect:O..O.... - Num:3 [fit: 0.045, exp: 16.00, pred: 570.990, error:6368.265216783599]acc: 0.0\n", - "Cond:.###...# - Act:1 - effect:...O.... - Num:2 [fit: 0.001, exp: 14.00, pred: 346.045, error:8240.256395765085]acc: 0.0\n", - "Cond:.#..##.F - Act:1 - effect:...OOO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 601.081, error:719.1799693328664]acc: 0\n", - "Cond:##....#O - Act:2 - effect:OO...... - Num:1 [fit: 0.003, exp: 9.00, pred: 360.132, error:5797.152688986372]acc: 0.2222222222222222\n", - "Cond:.###.#.. - Act:0 - effect:..O..O.. - Num:2 [fit: 0.002, exp: 16.00, pred: 494.539, error:7232.725623493198]acc: 0.0\n", - "Cond:##.#FO## - Act:3 - effect:...O.... - Num:3 [fit: 0.001, exp: 22.00, pred: 668.717, error:9815.728236010857]acc: 0.045454545454545456\n", - "Cond:.####O.F - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 852.141, error:333.6233280084333]acc: 0\n", - "Cond:.##F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 135.00, pred: 1431.491, error:5142.002156827013]acc: 0.5259259259259259\n", - "Cond:#..#FO## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 19.00, pred: 669.410, error:9155.030228919488]acc: 0.05263157894736842\n", - "Cond:###..### - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 782.00, pred: 1582.586, error:4594.8011443380365]acc: 0.0\n", - "Cond:..#O##F# - Act:6 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1154.507, error:314.3767441221915]acc: 0\n", - "Cond:.#.#..#. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 81.00, pred: 552.056, error:8858.927377431834]acc: 0.5802469135802469\n", - "Cond:######## - Act:6 - effect:...O.... - Num:4 [fit: 0.021, exp: 812.00, pred: 275.537, error:3396.2269400353703]acc: 0.06527093596059114\n", - "Cond:##.#F#F. - Act:3 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1114.205, error:1026.6930939616996]acc: 0\n", - "Cond:..#.#.## - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 22.00, pred: 300.309, error:4309.447124394454]acc: 0.0\n", - "Cond:#.#..OF. - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 15.00, pred: 572.702, error:8892.366517832546]acc: 0.0\n", - "Cond:##.##### - Act:6 - effect:.O...... - Num:3 [fit: 0.048, exp: 731.00, pred: 275.651, error:3680.330253216206]acc: 0.06155950752393981\n", - "Cond:.#####O. - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 768.152, error:393.26430400777997]acc: 0\n", - "Cond:.#.##OOF - Act:1 - effect:...OOO.. - Num:1 [fit: 0.001, exp: 23.00, pred: 315.944, error:8130.2429661707765]acc: 0.0\n", - "Cond:...##.## - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 63.00, pred: 657.384, error:5324.469114154322]acc: 0.5238095238095238\n", - "Cond:.####.O. - Act:3 - effect:...O.... - Num:1 [fit: 0.020, exp: 0.00, pred: 462.534, error:42.61522724851508]acc: 0\n", - "Cond:##.###.# - Act:6 - effect:.O...... - Num:1 [fit: 0.009, exp: 517.00, pred: 271.650, error:3985.427803416029]acc: 0.08897485493230174\n", - "Cond:.#..#O#. - Act:5 - effect:....FO.. - Num:3 [fit: 0.042, exp: 158.00, pred: 483.016, error:4832.67241128591]acc: 0.569620253164557\n", - "Cond:.#..#O#F - Act:1 - effect:...OOO.. - Num:8 [fit: 0.012, exp: 22.00, pred: 317.322, error:7880.945643824181]acc: 0.0\n", - "Cond:....##F# - Act:0 - effect:..O..O.. - Num:3 [fit: 0.005, exp: 6.00, pred: 305.673, error:6726.752603934766]acc: 0.0\n", - "Cond:##OO.... - Act:3 - effect:.OOOOO.. - Num:1 [fit: 0.005, exp: 43.00, pred: 321.812, error:4028.280504535723]acc: 0.0\n", - "Cond:.#O##..# - Act:3 - effect:.OO..OO. - Num:1 [fit: 0.004, exp: 45.00, pred: 299.352, error:3000.8149288994987]acc: 0.0\n", - "Cond:..##.#.. - Act:7 - effect:.O...... - Num:2 [fit: 0.006, exp: 15.00, pred: 352.755, error:6570.322759971672]acc: 0.06666666666666667\n", - "Cond:....##F# - Act:1 - effect:..F..... - Num:2 [fit: 0.027, exp: 12.00, pred: 367.102, error:7077.39540922497]acc: 0.0\n", - "Cond:.####### - Act:7 - effect:.O...... - Num:9 [fit: 0.012, exp: 818.00, pred: 1343.672, error:1833.8170765267917]acc: 0.011002444987775062\n", - "Cond:.#.#.... - Act:0 - effect:...O.... - Num:1 [fit: 0.008, exp: 1.00, pred: 309.547, error:2000.0]acc: 0.0\n", - "Cond:...#..#. - Act:1 - effect:..F..... - Num:1 [fit: 0.004, exp: 4.00, pred: 418.213, error:5131.572679117815]acc: 0.0\n", - "Cond:##..#### - Act:6 - effect:...O.... - Num:3 [fit: 0.023, exp: 531.00, pred: 275.083, error:2845.202835512542]acc: 0.005649717514124294\n", - "Cond:.##F.##. - Act:7 - effect:...O.... - Num:2 [fit: 0.072, exp: 0.00, pred: 750.521, error:69.16038788515706]acc: 0\n", - "Cond:.O#F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.072, exp: 0.00, pred: 750.521, error:69.16038788515706]acc: 0\n", - "Cond:##....## - Act:6 - effect:.O...... - Num:1 [fit: 0.007, exp: 311.00, pred: 266.063, error:3510.7268650086294]acc: 0.1189710610932476\n", - "Cond:..#####O - Act:7 - effect:.O...... - Num:1 [fit: 0.001, exp: 22.00, pred: 300.309, error:3669.780263301504]acc: 0.045454545454545456\n", - "Cond:O..##O#. - Act:3 - effect:...O.... - Num:1 [fit: 0.016, exp: 0.00, pred: 401.704, error:369.2498571308222]acc: 0\n", - "Cond:.#O###.. - Act:1 - effect:OO.O...O - Num:1 [fit: 0.045, exp: 6.00, pred: 255.140, error:3898.1837524720786]acc: 0.0\n", - "Cond:.#.#.##. - Act:1 - effect:...O.... - Num:2 [fit: 0.003, exp: 14.00, pred: 324.876, error:7654.032388594469]acc: 0.07142857142857142\n", - "Cond:.##O###O - Act:1 - effect:...O.... - Num:1 [fit: 0.009, exp: 0.00, pred: 421.070, error:257.1958862806991]acc: 0\n", - "Cond:.#..O### - Act:7 - effect:.O...... - Num:1 [fit: 0.011, exp: 0.00, pred: 520.148, error:487.8244243263322]acc: 0\n", - "Cond:.#..#O.# - Act:5 - effect:....FO.. - Num:1 [fit: 0.006, exp: 87.00, pred: 496.703, error:4089.629122496869]acc: 0.896551724137931\n", - "Cond:.###.#O# - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 154.00, pred: 373.857, error:2827.5249537875056]acc: 0.0\n", - "Cond:#.###O#F - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 9.00, pred: 780.756, error:8047.045071643977]acc: 0.0\n", - "Cond:#O....## - Act:6 - effect:O....O.O - Num:2 [fit: 0.001, exp: 144.00, pred: 274.734, error:3071.561730561595]acc: 0.0\n", - "Cond:.######O - Act:7 - effect:.......O - Num:2 [fit: 0.001, exp: 22.00, pred: 300.309, error:4376.624530327978]acc: 0.22727272727272727\n", - "Cond:#######F - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 8.00, pred: 765.368, error:8384.089925643155]acc: 0.0\n", - "Cond:....O##. - Act:5 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 1290.835, error:321.8459485751058]acc: 0\n", - "Cond:..#O#O## - Act:6 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 775.342, error:374.7828915131181]acc: 0\n", - "Cond:######F# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 21.00, pred: 1177.061, error:8537.578097611564]acc: 0.0\n", - "Cond:##..#O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.025, exp: 126.00, pred: 483.016, error:4901.201995289743]acc: 0.5\n", - "Cond:#O...### - Act:6 - effect:O....O.O - Num:1 [fit: 0.000, exp: 136.00, pred: 274.734, error:4749.925607632031]acc: 0.0\n", - "Cond:.#.###F# - Act:5 - effect:.O...... - Num:1 [fit: 0.014, exp: 50.00, pred: 297.622, error:2581.70030422921]acc: 0.0\n", - "Cond:...#.OOF - Act:0 - effect:......O. - Num:2 [fit: 0.003, exp: 8.00, pred: 765.368, error:7583.769925643154]acc: 0.0\n", - "Cond:.#..#O.F - Act:1 - effect:...OOO.. - Num:2 [fit: 0.014, exp: 0.00, pred: 653.208, error:978.5352812008947]acc: 0\n", - "Cond:##..#O#F - Act:1 - effect:...OOO.. - Num:2 [fit: 0.003, exp: 11.00, pred: 299.938, error:7514.9296982767155]acc: 0.0\n", - "Cond:#.#####F - Act:6 - effect:...O.... - Num:1 [fit: 0.009, exp: 47.00, pred: 278.137, error:1298.5975146746619]acc: 0.0\n", - "Cond:..####OF - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.009, exp: 47.00, pred: 278.137, error:1493.718691537472]acc: 0.0\n", - "Cond:..####F# - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 11.00, pred: 1160.305, error:7760.0416130393805]acc: 0.0\n", - "Cond:O#.#O### - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 0.00, pred: 595.109, error:693.9974767453382]acc: 0\n", - "Cond:##...O#. - Act:0 - effect:....FO.. - Num:1 [fit: 0.015, exp: 5.00, pred: 284.376, error:5837.513036683071]acc: 0.0\n", - "Cond:.##.FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.005, exp: 68.00, pred: 496.703, error:4219.469126114722]acc: 0.8823529411764706\n", - "Cond:....##F. - Act:0 - effect:....FO.. - Num:1 [fit: 0.008, exp: 4.00, pred: 258.577, error:4517.218119971063]acc: 0.0\n", - "Cond:.#...O#F - Act:1 - effect:...OOO.. - Num:3 [fit: 0.011, exp: 9.00, pred: 279.804, error:6540.977102045524]acc: 0.0\n", - "Cond:##.##.## - Act:6 - effect:...O.... - Num:2 [fit: 0.013, exp: 345.00, pred: 266.294, error:3634.8916219078683]acc: 0.02608695652173913\n", - "Cond:.##F###. - Act:2 - effect:...O.... - Num:1 [fit: 0.024, exp: 0.00, pred: 608.111, error:849.7216848178907]acc: 0\n", - "Cond:##.#...# - Act:6 - effect:O....O.O - Num:2 [fit: 0.013, exp: 254.00, pred: 266.082, error:3679.4884268190917]acc: 0.0\n", - "Cond:#####.#. - Act:5 - effect:........ - Num:3 [fit: 0.001, exp: 819.00, pred: 1215.005, error:2294.985237649877]acc: 0.0\n", - "Cond:##O##.#. - Act:2 - effect:.......O - Num:6 [fit: 0.240, exp: 8.00, pred: 723.332, error:5444.782682022333]acc: 0.0\n", - "Cond:...O.#F# - Act:1 - effect:..F..... - Num:1 [fit: 0.003, exp: 0.00, pred: 482.812, error:701.0958461641058]acc: 0\n", - "Cond:#..#.OO# - Act:6 - effect:.O...... - Num:1 [fit: 0.014, exp: 27.00, pred: 277.677, error:2563.8288459991772]acc: 0.0\n", - "Cond:###O...# - Act:0 - effect:O..O.... - Num:4 [fit: 0.004, exp: 11.00, pred: 481.257, error:5812.148501554744]acc: 0.0\n", - "Cond:.#####.. - Act:0 - effect:..O..O.. - Num:2 [fit: 0.000, exp: 24.00, pred: 488.393, error:8129.269716786038]acc: 0.0\n", - "Cond:.###O..# - Act:7 - effect:....O..O - Num:1 [fit: 0.018, exp: 4.00, pred: 272.156, error:3976.0308854861705]acc: 0.0\n", - "Cond:.#.##... - Act:7 - effect:....O..O - Num:3 [fit: 0.000, exp: 21.00, pred: 284.451, error:7068.259949686054]acc: 0.0\n", - "Cond:#.##.#O# - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 8.00, pred: 471.303, error:7590.281594796087]acc: 0.0\n", - "Cond:#...#O## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 550.935, error:8173.272515728394]acc: 0.0\n", - "Cond:##O..#.# - Act:6 - effect:.O...... - Num:1 [fit: 0.003, exp: 8.00, pred: 522.883, error:8020.059834009292]acc: 0.0\n", - "Cond:.#####O# - Act:3 - effect:...O.... - Num:3 [fit: 0.005, exp: 7.00, pred: 468.021, error:7359.179029065638]acc: 0.0\n", - "Cond:.##.#.#. - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 18.00, pred: 340.837, error:5452.803823930583]acc: 0.2222222222222222\n", - "Cond:##.#FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.085, exp: 4.00, pred: 274.133, error:3787.8249570336934]acc: 0.5\n", - "Cond:.#..F#.# - Act:1 - effect:....FO.. - Num:3 [fit: 0.086, exp: 4.00, pred: 274.133, error:3087.8249570336934]acc: 0.5\n", - "Cond:...####. - Act:0 - effect:OO...... - Num:1 [fit: 0.000, exp: 16.00, pred: 304.327, error:6963.50601868045]acc: 0.0\n", - "Cond:...##.#. - Act:2 - effect:OO...... - Num:1 [fit: 0.012, exp: 15.00, pred: 554.276, error:3396.1824230415186]acc: 0.5333333333333333\n", - "Cond:...###.. - Act:2 - effect:OO...... - Num:3 [fit: 0.005, exp: 25.00, pred: 764.657, error:5942.8744599945485]acc: 0.32\n", - "Cond:##O#.#.# - Act:4 - effect:..O.OO.. - Num:3 [fit: 0.002, exp: 63.00, pred: 597.239, error:5357.799740204838]acc: 0.0\n", - "Cond:#OO#.#.. - Act:2 - effect:.......O - Num:1 [fit: 0.004, exp: 4.00, pred: 663.227, error:3888.002921669111]acc: 0.0\n", - "Cond:.OO##..# - Act:2 - effect:...O.... - Num:1 [fit: 0.013, exp: 4.00, pred: 663.227, error:4588.002921669111]acc: 0.0\n", - "Cond:.##O..#. - Act:1 - effect:.O...... - Num:1 [fit: 0.005, exp: 4.00, pred: 418.213, error:3331.572679117815]acc: 0.25\n", - "Cond:###....# - Act:6 - effect:.O...... - Num:2 [fit: 0.017, exp: 232.00, pred: 266.088, error:3905.5176590669953]acc: 0.16810344827586207\n", - "Cond:..###..# - Act:7 - effect:.O...... - Num:3 [fit: 0.006, exp: 18.00, pred: 259.401, error:5719.016882837783]acc: 0.1111111111111111\n", - "Cond:..##O.## - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 9.00, pred: 309.986, error:3710.0466784191676]acc: 0.1111111111111111\n", - "Cond:#.####.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.000, exp: 22.00, pred: 687.614, error:8330.900721585684]acc: 0.0\n", - "Cond:.#.F###. - Act:5 - effect:........ - Num:1 [fit: 0.012, exp: 70.00, pred: 596.498, error:1851.8274063047402]acc: 0.0\n", - "Cond:.####OOF - Act:5 - effect:........ - Num:1 [fit: 0.004, exp: 51.00, pred: 591.863, error:6721.233364844495]acc: 0.0\n", - "Cond:.....OO# - Act:5 - effect:........ - Num:1 [fit: 0.004, exp: 51.00, pred: 591.863, error:6373.29279005755]acc: 0.0\n", - "Cond:##..###F - Act:0 - effect:..O..O.. - Num:1 [fit: 0.003, exp: 7.00, pred: 746.118, error:7641.310595509452]acc: 0.0\n", - "Cond:.#..#O.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.064, exp: 3.00, pred: 265.654, error:3237.5346596303316]acc: 0.3333333333333333\n", - "Cond:#..#FO.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.064, exp: 3.00, pred: 265.654, error:2904.2013262969986]acc: 0.3333333333333333\n", - "Cond:##.##### - Act:4 - effect:.......O - Num:2 [fit: 0.000, exp: 487.00, pred: 1356.819, error:3682.393791142411]acc: 0.07597535934291581\n", - "Cond:###.#### - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 64.00, pred: 859.870, error:8312.177845336526]acc: 0.0\n", - "Cond:#.#..### - Act:1 - effect:.......O - Num:1 [fit: 0.001, exp: 14.00, pred: 251.094, error:5844.6802219438905]acc: 0.0\n", - "Cond:##..##.O - Act:0 - effect:O..O.... - Num:1 [fit: 0.002, exp: 8.00, pred: 549.792, error:5837.095124292826]acc: 0.0\n", - "Cond:##..#.## - Act:6 - effect:.O...... - Num:2 [fit: 0.007, exp: 281.00, pred: 266.063, error:3716.832887325856]acc: 0.1387900355871886\n", - "Cond:O###.### - Act:6 - effect:.O...... - Num:1 [fit: 0.001, exp: 142.00, pred: 284.895, error:4155.5472814130135]acc: 0.2746478873239437\n", - "Cond:....#O.# - Act:3 - effect:...O.... - Num:1 [fit: 0.003, exp: 6.00, pred: 660.289, error:6314.822394680403]acc: 0.0\n", - "Cond:##O#.#.O - Act:6 - effect:..O..O.. - Num:1 [fit: 0.006, exp: 0.00, pred: 356.791, error:940.6782499223368]acc: 0\n", - "Cond:..#..##O - Act:0 - effect:.......F - Num:1 [fit: 0.001, exp: 35.00, pred: 328.000, error:4439.122810545058]acc: 0.0\n", - "Cond:.#O#.#.. - Act:7 - effect:.OO..OO. - Num:2 [fit: 0.006, exp: 6.00, pred: 395.592, error:6380.48726795551]acc: 0.0\n", - "Cond:#.###.## - Act:2 - effect:O..O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 537.338, error:3977.8448535756547]acc: 0.0\n", - "Cond:#..###OF - Act:6 - effect:.OF..O.. - Num:1 [fit: 0.018, exp: 23.00, pred: 278.213, error:1838.791156681884]acc: 0.0\n", - "Cond:####.OO# - Act:6 - effect:.O...... - Num:1 [fit: 0.019, exp: 23.00, pred: 278.213, error:708.4314532154426]acc: 0.0\n", - "Cond:#.###### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 113.00, pred: 627.264, error:6213.256063750292]acc: 0.1592920353982301\n", - "Cond:###.F#.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 40.00, pred: 496.683, error:4183.186324897397]acc: 0.875\n", - "Cond:.#..F### - Act:1 - effect:....FO.. - Num:1 [fit: 0.006, exp: 2.00, pred: 221.611, error:3702.4975696719002]acc: 0.0\n", - "Cond:#.#.##.# - Act:0 - effect:O..O.... - Num:1 [fit: 0.001, exp: 11.00, pred: 554.758, error:6800.672033110737]acc: 0.0\n", - "Cond:#....#.# - Act:3 - effect:.......O - Num:1 [fit: 0.002, exp: 11.00, pred: 405.809, error:6063.530977668383]acc: 0.2727272727272727\n", - "Cond:O#.##O## - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:O#.###.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.001, exp: 8.00, pred: 484.619, error:5799.854745877408]acc: 0.0\n", - "Cond:##.##..# - Act:0 - effect:O..O.... - Num:2 [fit: 0.010, exp: 12.00, pred: 483.814, error:7824.5859311902805]acc: 0.0\n", - "Cond:...FOO#. - Act:1 - effect:....OO.F - Num:1 [fit: 0.017, exp: 9.00, pred: 364.277, error:6713.7791979318035]acc: 0.0\n", - "Cond:..###### - Act:0 - effect:.......F - Num:2 [fit: 0.000, exp: 54.00, pred: 862.033, error:8814.861816729974]acc: 0.0\n", - "Cond:...#.#OF - Act:0 - effect:......O. - Num:1 [fit: 0.005, exp: 5.00, pred: 838.329, error:6125.3965625392975]acc: 0.0\n", - "Cond:.###.O## - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 8.00, pred: 736.462, error:8046.440484883746]acc: 0.0\n", - "Cond:#.#..##. - Act:1 - effect:.......O - Num:1 [fit: 0.156, exp: 3.00, pred: 233.792, error:2804.557527580674]acc: 0.0\n", - "Cond:....#### - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 46.00, pred: 765.924, error:7246.222157875497]acc: 0.0\n", - "Cond:###F##.. - Act:4 - effect:..O.OO.. - Num:3 [fit: 0.012, exp: 73.00, pred: 488.760, error:4614.8196076623835]acc: 0.0\n", - "Cond:.#.#F#.. - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 5.00, pred: 593.691, error:6610.805831522767]acc: 0.0\n", - "Cond:.#O#.### - Act:2 - effect:..O..O.. - Num:1 [fit: 0.126, exp: 5.00, pred: 730.384, error:4453.7561139629215]acc: 0.0\n", - "Cond:#..##.## - Act:3 - effect:...O.... - Num:2 [fit: 0.002, exp: 41.00, pred: 657.207, error:7030.415409510439]acc: 0.5365853658536586\n", - "Cond:.#.O#..# - Act:3 - effect:...O.... - Num:3 [fit: 0.004, exp: 35.00, pred: 512.635, error:4447.426050795117]acc: 0.6285714285714286\n", - "Cond:#.###.## - Act:7 - effect:.O...... - Num:2 [fit: 0.000, exp: 35.00, pred: 252.625, error:5812.840353839525]acc: 0.0\n", - "Cond:.####..O - Act:7 - effect:.......O - Num:1 [fit: 0.028, exp: 4.00, pred: 296.246, error:2739.7830277372695]acc: 0.5\n", - "Cond:#.#.#.#O - Act:2 - effect:OO...... - Num:1 [fit: 0.006, exp: 4.00, pred: 327.015, error:3410.99512078877]acc: 0.25\n", - "Cond:##....## - Act:2 - effect:O..O.... - Num:1 [fit: 0.001, exp: 10.00, pred: 359.740, error:4645.16987342024]acc: 0.0\n", - "Cond:.###.... - Act:7 - effect:....O..O - Num:2 [fit: 0.000, exp: 21.00, pred: 300.122, error:7660.0312805988615]acc: 0.0\n", - "Cond:#O#.#..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.009, exp: 4.00, pred: 210.242, error:3411.668626919173]acc: 0.0\n", - "Cond:.####OF# - Act:5 - effect:.O...... - Num:1 [fit: 0.021, exp: 22.00, pred: 301.074, error:3076.4313024023495]acc: 0.0\n", - "Cond:##....## - Act:1 - effect:.O...... - Num:1 [fit: 0.132, exp: 4.00, pred: 223.200, error:2508.3315372434317]acc: 0.25\n", - "Cond:#.#.#O## - Act:3 - effect:.......O - Num:4 [fit: 0.009, exp: 17.00, pred: 545.981, error:7386.353416992961]acc: 0.0\n", - "Cond:.#O##### - Act:1 - effect:...O.... - Num:1 [fit: 0.212, exp: 2.00, pred: 226.173, error:1500.1820045954219]acc: 0.0\n", - "Cond:..##.O#O - Act:0 - effect:.......F - Num:1 [fit: 0.004, exp: 0.00, pred: 311.352, error:570.982647472199]acc: 0\n", - "Cond:.#..#O## - Act:1 - effect:...OOO.. - Num:1 [fit: 0.001, exp: 7.00, pred: 252.165, error:4576.260654926502]acc: 0.0\n", - "Cond:###.#.## - Act:6 - effect:.O...... - Num:4 [fit: 0.022, exp: 284.00, pred: 266.060, error:3538.4329406932234]acc: 0.13732394366197184\n", - "Cond:#.##.##. - Act:1 - effect:...O.... - Num:6 [fit: 0.084, exp: 6.00, pred: 349.855, error:6077.5566651442105]acc: 0.0\n", - "Cond:##..#F#. - Act:0 - effect:.F...F.. - Num:1 [fit: 0.005, exp: 1.00, pred: 474.386, error:1800.0]acc: 0.0\n", - "Cond:##.###.# - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.000, exp: 335.00, pred: 1356.819, error:2897.412594323568]acc: 0.0\n", - "Cond:#####..# - Act:4 - effect:..O.OO.. - Num:4 [fit: 0.001, exp: 263.00, pred: 1356.914, error:2893.475468629773]acc: 0.0\n", - "Cond:###O.... - Act:0 - effect:O..O.... - Num:2 [fit: 0.006, exp: 2.00, pred: 728.562, error:2959.186424245373]acc: 0.0\n", - "Cond:.##..#F. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.005, exp: 2.00, pred: 255.516, error:3614.792401476874]acc: 0.0\n", - "Cond:#.#..O#. - Act:3 - effect:.......O - Num:1 [fit: 0.003, exp: 8.00, pred: 654.178, error:8034.581192911398]acc: 0.0\n", - "Cond:#.#.#OF. - Act:3 - effect:...O.... - Num:2 [fit: 0.003, exp: 8.00, pred: 654.178, error:7091.221192911397]acc: 0.0\n", - "Cond:O#..#### - Act:7 - effect:.....F.. - Num:3 [fit: 0.012, exp: 12.00, pred: 428.006, error:6855.834284632549]acc: 0.08333333333333333\n", - "Cond:..#.#.#O - Act:1 - effect:.....F.. - Num:1 [fit: 0.001, exp: 1.00, pred: 229.689, error:2000.0]acc: 0.0\n", - "Cond:.####### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 122.00, pred: 624.761, error:5865.815530086743]acc: 0.18032786885245902\n", - "Cond:#....##. - Act:3 - effect:.......O - Num:1 [fit: 0.000, exp: 13.00, pred: 525.559, error:6330.537331980472]acc: 0.0\n", - "Cond:#.#O###. - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.000, exp: 53.00, pred: 603.374, error:6551.140380742362]acc: 0.0\n", - "Cond:###O.### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 53.00, pred: 600.987, error:2807.2345124648123]acc: 0.05660377358490566\n", - "Cond:.##.#.#. - Act:1 - effect:.O...... - Num:1 [fit: 0.005, exp: 0.00, pred: 280.905, error:951.4836990579992]acc: 0\n", - "Cond:####.#.# - Act:6 - effect:.OO.OO.. - Num:3 [fit: 0.021, exp: 269.00, pred: 266.100, error:3000.4837999486085]acc: 0.0\n", - "Cond:#.#.##.O - Act:7 - effect:.......O - Num:1 [fit: 0.016, exp: 6.00, pred: 293.197, error:3853.93893820953]acc: 0.3333333333333333\n", - "Cond:O...#### - Act:2 - effect:.O.OF..O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:###.#O## - Act:5 - effect:........ - Num:2 [fit: 0.002, exp: 42.00, pred: 676.284, error:6852.498931210781]acc: 0.0\n", - "Cond:..##.#.. - Act:2 - effect:OO...... - Num:1 [fit: 0.004, exp: 11.00, pred: 553.518, error:4629.6251533727755]acc: 0.0\n", - "Cond:.#.####. - Act:2 - effect:OO...... - Num:1 [fit: 0.002, exp: 23.00, pred: 765.835, error:5562.023404521855]acc: 0.391304347826087\n", - "Cond:.#..#O.. - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 19.00, pred: 498.672, error:4076.3738973027607]acc: 0.0\n", - "Cond:.###F#.. - Act:5 - effect:........ - Num:1 [fit: 0.005, exp: 19.00, pred: 498.672, error:4584.142606422761]acc: 0.0\n", - "Cond:.....O#. - Act:0 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 1.00, pred: 225.931, error:1600.0]acc: 0.0\n", - "Cond:###.##F# - Act:0 - effect:....FO.. - Num:1 [fit: 0.001, exp: 1.00, pred: 225.931, error:1800.0]acc: 0.0\n", - "Cond:..##.O#F - Act:5 - effect:.O...... - Num:1 [fit: 0.039, exp: 17.00, pred: 593.392, error:5698.164873173553]acc: 0.0\n", - "Cond:.#...O.F - Act:1 - effect:...OOO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 330.572, error:1404.3298237047493]acc: 0\n", - "Cond:.####O#F - Act:5 - effect:.O...... - Num:1 [fit: 0.007, exp: 3.00, pred: 883.000, error:4178.739927391851]acc: 0.0\n", - "Cond:.#.#.O## - Act:0 - effect:....FO.. - Num:2 [fit: 0.120, exp: 5.00, pred: 837.852, error:925.5317760514256]acc: 0.6\n", - "Cond:..#.#O## - Act:7 - effect:...O.... - Num:4 [fit: 0.001, exp: 620.00, pred: 1375.127, error:2152.2330599723864]acc: 0.0\n", - "Cond:.#..F#.O - Act:1 - effect:....FO.. - Num:1 [fit: 0.006, exp: 0.00, pred: 313.943, error:635.4766491583878]acc: 0\n", - "Cond:###...## - Act:1 - effect:.....O.. - Num:2 [fit: 0.208, exp: 2.00, pred: 211.654, error:1701.9904066738402]acc: 0.0\n", - "Cond:..#.#..# - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.024, exp: 4.00, pred: 296.246, error:3139.7830277372695]acc: 0.0\n", - "Cond:.##.##O# - Act:6 - effect:.O...... - Num:3 [fit: 0.004, exp: 80.00, pred: 373.857, error:3552.513832628453]acc: 0.0\n", - "Cond:..OO.##. - Act:0 - effect:.......O - Num:1 [fit: 0.019, exp: 1.00, pred: 1246.935, error:1800.0]acc: 0.0\n", - "Cond:.#O###.# - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 60.00, pred: 597.241, error:5005.895875262972]acc: 0.06666666666666667\n", - "Cond:####.#.# - Act:0 - effect:.......F - Num:1 [fit: 0.001, exp: 10.00, pred: 681.142, error:7858.862964964883]acc: 0.0\n", - "Cond:#OOO.... - Act:0 - effect:O..O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#OO.##.# - Act:6 - effect:.......O - Num:1 [fit: 0.004, exp: 5.00, pred: 682.280, error:6386.453634859473]acc: 0.0\n", - "Cond:#OO#.### - Act:6 - effect:..O..O.. - Num:1 [fit: 0.001, exp: 11.00, pred: 700.604, error:8075.36211630203]acc: 0.0\n", - "Cond:####.O#. - Act:0 - effect:....FO.. - Num:1 [fit: 0.011, exp: 0.00, pred: 254.256, error:542.3013413686936]acc: 0\n", - "Cond:.##O#### - Act:3 - effect:...O.... - Num:1 [fit: 0.004, exp: 67.00, pred: 481.614, error:5922.990764603229]acc: 0.26865671641791045\n", - "Cond:###.#O## - Act:3 - effect:...O.... - Num:1 [fit: 0.002, exp: 10.00, pred: 661.625, error:6841.861468512228]acc: 0.0\n", - "Cond:#####.## - Act:6 - effect:.O...... - Num:3 [fit: 0.017, exp: 328.00, pred: 266.277, error:3627.454456673906]acc: 0.11585365853658537\n", - "Cond:####.### - Act:6 - effect:.O...... - Num:2 [fit: 0.023, exp: 327.00, pred: 271.381, error:2804.4904943710667]acc: 0.1162079510703364\n", - "Cond:O##.#..# - Act:2 - effect:.O...... - Num:1 [fit: 0.013, exp: 4.00, pred: 342.443, error:3682.702476013679]acc: 0.0\n", - "Cond:####.### - Act:4 - effect:...O.... - Num:2 [fit: 0.000, exp: 415.00, pred: 1357.120, error:3081.9626010409465]acc: 0.02650602409638554\n", - "Cond:##.FO... - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#####... - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 95.00, pred: 477.965, error:5520.12829267047]acc: 0.23157894736842105\n", - "Cond:#.#F#... - Act:7 - effect:.O...... - Num:1 [fit: 0.005, exp: 0.00, pred: 392.971, error:343.0735553561427]acc: 0\n", - "Cond:##.#OO## - Act:3 - effect:...O.... - Num:2 [fit: 0.005, exp: 15.00, pred: 1263.542, error:7184.668610164109]acc: 0.0\n", - "Cond:#...#OO. - Act:2 - effect:.......O - Num:1 [fit: 0.002, exp: 0.00, pred: 498.418, error:1048.1246168154105]acc: 0\n", - "Cond:O#####.# - Act:6 - effect:.O...... - Num:2 [fit: 0.002, exp: 128.00, pred: 284.895, error:3200.679190799642]acc: 0.2734375\n", - "Cond:##..#.#O - Act:2 - effect:.O...... - Num:1 [fit: 0.006, exp: 5.00, pred: 396.847, error:4865.498547695175]acc: 0.0\n", - "Cond:O#..##.# - Act:7 - effect:.....F.. - Num:3 [fit: 0.004, exp: 9.00, pred: 467.974, error:5832.041063146508]acc: 0.1111111111111111\n", - "Cond:.##..##O - Act:0 - effect:.......F - Num:4 [fit: 0.003, exp: 31.00, pred: 328.628, error:4114.827269068202]acc: 0.0\n", - "Cond:##.#OO#. - Act:3 - effect:...O.... - Num:2 [fit: 0.002, exp: 12.00, pred: 1266.426, error:6982.226943068137]acc: 0.0\n", - "Cond:####OO## - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 12.00, pred: 1266.426, error:8126.633983068137]acc: 0.0\n", - "Cond:O####..# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 120.00, pred: 284.895, error:4201.220211374433]acc: 0.275\n", - "Cond:#O.##..# - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 112.00, pred: 274.734, error:4665.490315287242]acc: 0.0\n", - "Cond:#####.## - Act:5 - effect:...O.... - Num:5 [fit: 0.003, exp: 1147.00, pred: 1204.687, error:2158.515558207246]acc: 0.0026155187445510027\n", - "Cond:....O### - Act:6 - effect:.OO.OO.. - Num:1 [fit: 0.008, exp: 0.00, pred: 1205.182, error:521.3488597847445]acc: 0\n", - "Cond:O#...##. - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.014, exp: 2.00, pred: 378.944, error:2524.3163386332635]acc: 0.0\n", - "Cond:###.##.# - Act:6 - effect:.O...... - Num:1 [fit: 0.006, exp: 201.00, pred: 270.836, error:3169.6817965033383]acc: 0.14925373134328357\n", - "Cond:..#.OO## - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.O.##..# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 27.00, pred: 430.132, error:3860.3758184358203]acc: 0.0\n", - "Cond:##..#..# - Act:6 - effect:.O...... - Num:3 [fit: 0.042, exp: 165.00, pred: 266.101, error:3728.475341877708]acc: 0.18181818181818182\n", - "Cond:####.O## - Act:6 - effect:.O...... - Num:1 [fit: 0.001, exp: 4.00, pred: 1178.220, error:5092.578858917577]acc: 0.0\n", - "Cond:##.####. - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 68.00, pred: 504.266, error:7778.532816199033]acc: 0.3088235294117647\n", - "Cond:.#OO..#. - Act:3 - effect:...O.... - Num:1 [fit: 0.018, exp: 23.00, pred: 322.786, error:3816.8384015040215]acc: 0.0\n", - "Cond:#O..#..# - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 71.00, pred: 274.734, error:4744.903659527759]acc: 0.30985915492957744\n", - "Cond:.###.#OF - Act:0 - effect:.O...... - Num:1 [fit: 0.007, exp: 3.00, pred: 1246.996, error:3828.21272383193]acc: 0.0\n", - "Cond:####O### - Act:3 - effect:...O.... - Num:1 [fit: 0.005, exp: 6.00, pred: 453.662, error:3326.119856612714]acc: 0.16666666666666666\n", - "Cond:#..####. - Act:3 - effect:...O.... - Num:4 [fit: 0.002, exp: 36.00, pred: 501.018, error:5412.330591853073]acc: 0.5\n", - "Cond:#O###.## - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 71.00, pred: 274.346, error:4523.6116007688215]acc: 0.014084507042253521\n", - "Cond:OO.....# - Act:7 - effect:.....F.. - Num:1 [fit: 0.009, exp: 4.00, pred: 432.825, error:3991.931229796069]acc: 0.0\n", - "Cond:#..#O#O# - Act:7 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1066.259, error:576.189615324252]acc: 0\n", - "Cond:...####. - Act:3 - effect:...O.... - Num:2 [fit: 0.006, exp: 31.00, pred: 500.708, error:6231.1788061238985]acc: 0.5806451612903226\n", - "Cond:...O#.#. - Act:3 - effect:...O.... - Num:1 [fit: 0.003, exp: 26.00, pred: 513.514, error:4382.7438542627115]acc: 0.6923076923076923\n", - "Cond:.#OO.### - Act:3 - effect:...O.... - Num:1 [fit: 0.229, exp: 5.00, pred: 274.280, error:3149.3698546419264]acc: 0.0\n", - "Cond:..##...# - Act:1 - effect:....O..O - Num:1 [fit: 0.008, exp: 3.00, pred: 476.204, error:3893.3617072801035]acc: 0.0\n", - "Cond:.###.### - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 31.00, pred: 879.837, error:7795.068664429532]acc: 0.0\n", - "Cond:#.#F#.## - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.##..#.. - Act:7 - effect:...O.... - Num:1 [fit: 0.003, exp: 7.00, pred: 367.691, error:7074.387036745308]acc: 0.0\n", - "Cond:.####### - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 32.00, pred: 861.818, error:8741.609841461355]acc: 0.0\n", - "Cond:..##.#O# - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 25.00, pred: 784.153, error:6803.795911007033]acc: 0.0\n", - "Cond:..###..O - Act:7 - effect:.O...... - Num:1 [fit: 0.028, exp: 4.00, pred: 296.246, error:2539.7830277372695]acc: 0.0\n", - "Cond:.####.## - Act:6 - effect:.O...... - Num:1 [fit: 0.000, exp: 41.00, pred: 318.749, error:3939.5446416291643]acc: 0.0\n", - "Cond:###.#### - Act:6 - effect:...O.... - Num:4 [fit: 0.032, exp: 81.00, pred: 275.082, error:2991.9542987414197]acc: 0.0\n", - "Cond:.##..#.# - Act:7 - effect:...O.... - Num:3 [fit: 0.014, exp: 8.00, pred: 271.725, error:5888.816898170148]acc: 0.0\n", - "Cond:..#..#O# - Act:0 - effect:.......F - Num:2 [fit: 0.002, exp: 23.00, pred: 784.365, error:7356.541435883018]acc: 0.0\n", - "Cond:..#.##O# - Act:0 - effect:.......F - Num:1 [fit: 0.001, exp: 23.00, pred: 784.365, error:6966.139819866035]acc: 0.0\n", - "Cond:##..##.# - Act:6 - effect:.O...... - Num:3 [fit: 0.019, exp: 47.00, pred: 270.840, error:3367.7345061366786]acc: 0.14893617021276595\n", - "Cond:O#...#.# - Act:7 - effect:.....F.. - Num:2 [fit: 0.106, exp: 5.00, pred: 378.918, error:3867.0241894751084]acc: 0.2\n", - "Cond:O#..##.. - Act:7 - effect:.O.OF..O - Num:1 [fit: 0.001, exp: 0.00, pred: 462.633, error:490.8590726690501]acc: 0\n", - "Cond:...#.#OO - Act:7 - effect:...O.... - Num:1 [fit: 0.010, exp: 7.00, pred: 312.214, error:2915.0566339403426]acc: 0.0\n", - "Cond:..#.#### - Act:3 - effect:.......O - Num:2 [fit: 0.391, exp: 5.00, pred: 502.501, error:3540.6948088512354]acc: 0.6\n", - "Cond:#####O## - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 530.619, error:930.5427752379929]acc: 0\n", - "Cond:..#.#..O - Act:7 - effect:.O...... - Num:1 [fit: 0.103, exp: 2.00, pred: 234.551, error:1504.009284895448]acc: 0.0\n", - "Cond:####...# - Act:6 - effect:.O...... - Num:3 [fit: 0.028, exp: 39.00, pred: 266.081, error:3289.6819528601013]acc: 0.15384615384615385\n", - "Cond:..#..### - Act:0 - effect:.......F - Num:3 [fit: 0.003, exp: 22.00, pred: 783.877, error:8020.226459940629]acc: 0.0\n", - "Cond:.##.#..# - Act:7 - effect:.O...... - Num:1 [fit: 0.008, exp: 4.00, pred: 253.460, error:3267.135232297987]acc: 0.0\n", - "Cond:####O#.# - Act:4 - effect:..O.OO.. - Num:2 [fit: 0.006, exp: 94.00, pred: 653.345, error:4435.850458268377]acc: 0.0\n", - "Cond:####..#. - Act:6 - effect:.OO..OO. - Num:1 [fit: 0.004, exp: 15.00, pred: 288.919, error:3975.0549489287823]acc: 0.0\n", - "Cond:...O###. - Act:3 - effect:...O.... - Num:1 [fit: 0.011, exp: 22.00, pred: 512.223, error:5040.591536453556]acc: 0.7727272727272727\n", - "Cond:##.#.#.# - Act:6 - effect:.O...... - Num:1 [fit: 0.008, exp: 34.00, pred: 266.101, error:3314.726891379076]acc: 0.14705882352941177\n", - "Cond:O#..#..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.166, exp: 3.00, pred: 434.574, error:3601.6644910367277]acc: 0.0\n", - "Cond:#..##### - Act:3 - effect:...O.... - Num:3 [fit: 0.159, exp: 20.00, pred: 650.633, error:6682.8361312929865]acc: 0.7\n", - "Cond:##.#..#. - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 35.00, pred: 551.695, error:8775.587843096211]acc: 0.42857142857142855\n", - "Cond:.##.#### - Act:6 - effect:...O.... - Num:2 [fit: 0.004, exp: 14.00, pred: 408.614, error:2846.849213297319]acc: 0.0\n", - "Cond:#..###.# - Act:3 - effect:...O.... - Num:1 [fit: 0.080, exp: 6.00, pred: 469.935, error:5338.935393121659]acc: 0.16666666666666666\n", - "Cond:#.##.### - Act:1 - effect:...O.... - Num:1 [fit: 0.015, exp: 3.00, pred: 250.115, error:3699.967140687397]acc: 0.0\n", - "Cond:.##.##O# - Act:0 - effect:.......F - Num:1 [fit: 0.008, exp: 19.00, pred: 788.413, error:6714.912957134097]acc: 0.0\n", - "Cond:.....OO# - Act:0 - effect:.......F - Num:1 [fit: 0.010, exp: 3.00, pred: 1246.996, error:3828.21272383193]acc: 0.0\n", - "Cond:.####### - Act:4 - effect:.......O - Num:8 [fit: 0.001, exp: 420.00, pred: 1488.579, error:7875.997225382572]acc: 0.09047619047619047\n", - "Cond:.####O## - Act:7 - effect:.......O - Num:6 [fit: 0.007, exp: 690.00, pred: 1343.672, error:1829.0745094907559]acc: 0.0\n", - "Cond:.##.#### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 269.00, pred: 1653.753, error:7285.451884660942]acc: 0.11895910780669144\n", - "Cond:.....### - Act:0 - effect:.......F - Num:1 [fit: 0.011, exp: 17.00, pred: 785.679, error:7294.85941152117]acc: 0.0\n", - "Cond:..#F##.# - Act:4 - effect:...O.... - Num:1 [fit: 0.006, exp: 70.00, pred: 488.760, error:3375.233913687388]acc: 0.0\n", - "Cond:##.#F#.# - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 624.330, error:907.6888334002822]acc: 0\n", - "Cond:#.#..### - Act:0 - effect:.......F - Num:3 [fit: 0.006, exp: 20.00, pred: 899.768, error:7864.742188610059]acc: 0.0\n", - "Cond:..#.#### - Act:0 - effect:.......F - Num:3 [fit: 0.011, exp: 18.00, pred: 765.440, error:7379.466460116797]acc: 0.0\n", - "Cond:.#.##O#F - Act:1 - effect:...OOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 409.804, error:1291.3154108143]acc: 0\n", - "Cond:###O#### - Act:7 - effect:.O...... - Num:1 [fit: 0.019, exp: 6.00, pred: 263.268, error:4513.933547229262]acc: 0.0\n", - "Cond:.###...# - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 16.00, pred: 494.177, error:6202.024495247502]acc: 0.0625\n", - "Cond:.####O## - Act:5 - effect:.O...... - Num:5 [fit: 0.020, exp: 76.00, pred: 715.928, error:4672.652545238236]acc: 0.0\n", - "Cond:.O###O## - Act:7 - effect:.......O - Num:1 [fit: 0.055, exp: 0.00, pred: 986.412, error:61.168073879706284]acc: 0\n", - "Cond:#O...#.O - Act:2 - effect:.O.O.... - Num:1 [fit: 0.008, exp: 2.00, pred: 416.670, error:2594.349759262478]acc: 0.0\n", - "Cond:#O#..#.# - Act:0 - effect:...O.... - Num:1 [fit: 0.166, exp: 3.00, pred: 197.359, error:2801.024893355541]acc: 0.0\n", - "Cond:#.####.# - Act:2 - effect:.......O - Num:1 [fit: 0.001, exp: 23.00, pred: 769.508, error:5424.0563911936515]acc: 0.0\n", - "Cond:######.# - Act:0 - effect:..O.OO.. - Num:2 [fit: 0.001, exp: 12.00, pred: 649.381, error:7805.7213447047825]acc: 0.0\n", - "Cond:##O.##.# - Act:4 - effect:..O.OO.. - Num:2 [fit: 0.001, exp: 19.00, pred: 504.988, error:6433.80468221176]acc: 0.0\n", - "Cond:.OO###.# - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 40.00, pred: 630.646, error:2288.0916137575764]acc: 0.075\n", - "Cond:#####.#. - Act:1 - effect:.O...... - Num:1 [fit: 0.000, exp: 23.00, pred: 269.652, error:5212.190483197499]acc: 0.0\n", - "Cond:.O#####. - Act:7 - effect:.......O - Num:1 [fit: 0.014, exp: 2.00, pred: 272.369, error:2510.873779579957]acc: 0.0\n", - "Cond:#O#O#### - Act:4 - effect:.O...... - Num:3 [fit: 0.190, exp: 16.00, pred: 679.679, error:3905.3230637507713]acc: 0.8125\n", - "Cond:.#O.##.# - Act:4 - effect:..O.OO.. - Num:2 [fit: 0.006, exp: 13.00, pred: 498.601, error:6532.73955264841]acc: 0.0\n", - "Cond:.#..##.. - Act:3 - effect:...O.... - Num:2 [fit: 0.001, exp: 10.00, pred: 340.785, error:7663.945745234387]acc: 0.0\n", - "Cond:.#.##### - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 29.00, pred: 318.498, error:6378.470638790612]acc: 0.0\n", - "Cond:.###O#.. - Act:4 - effect:.......O - Num:1 [fit: 0.003, exp: 35.00, pred: 653.388, error:5414.747290516603]acc: 0.0\n", - "Cond:..#...## - Act:7 - effect:...O.... - Num:1 [fit: 0.106, exp: 2.00, pred: 234.551, error:2504.009284895448]acc: 0.0\n", - "Cond:###.O### - Act:6 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 546.597, error:817.172652934662]acc: 0\n", - "Cond:...#O#.# - Act:1 - effect:...O.... - Num:1 [fit: 0.000, exp: 27.00, pred: 333.395, error:5907.038374327226]acc: 0.0\n", - "Cond:.#.FOO#. - Act:1 - effect:....OO.F - Num:2 [fit: 0.005, exp: 8.00, pred: 356.757, error:5650.729104522328]acc: 0.0\n", - "Cond:...F#O.# - Act:1 - effect:....OO.F - Num:1 [fit: 0.007, exp: 8.00, pred: 356.757, error:6363.689104522328]acc: 0.0\n", - "Cond:##.#O.#. - Act:6 - effect:.O...... - Num:1 [fit: 0.005, exp: 1.00, pred: 331.537, error:1600.0]acc: 0.0\n", - "Cond:#..#O#.# - Act:0 - effect:..O.OO.. - Num:1 [fit: 0.008, exp: 1.00, pred: 216.934, error:1600.0]acc: 0.0\n", - "Cond:.####O#. - Act:0 - effect:O....OOO - Num:1 [fit: 0.031, exp: 2.00, pred: 312.816, error:1109.0063813740094]acc: 0.0\n", - "Cond:.#.##O## - Act:7 - effect:.......O - Num:2 [fit: 0.002, exp: 673.00, pred: 1343.672, error:1921.3542874483023]acc: 0.0\n", - "Cond:..#FO#.. - Act:4 - effect:.O...... - Num:2 [fit: 0.020, exp: 20.00, pred: 490.052, error:3855.483763280707]acc: 0.0\n", - "Cond:###F###. - Act:1 - effect:...O.... - Num:1 [fit: 0.006, exp: 6.00, pred: 344.528, error:4979.359356814311]acc: 0.0\n", - "Cond:..##OO#. - Act:1 - effect:....OO.F - Num:1 [fit: 0.008, exp: 6.00, pred: 344.528, error:4731.359356814311]acc: 0.0\n", - "Cond:###.OO## - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 518.395, error:909.8364251726756]acc: 0\n", - "Cond:.####.## - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 793.00, pred: 1196.527, error:2552.653513229712]acc: 0.0025220680958385876\n", - "Cond:##...### - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 246.00, pred: 1357.120, error:3271.0759409932725]acc: 0.08943089430894309\n", - "Cond:####O#.. - Act:4 - effect:.......O - Num:1 [fit: 0.067, exp: 7.00, pred: 634.503, error:4282.224515257742]acc: 0.0\n", - "Cond:#..#..#. - Act:3 - effect:...O.... - Num:1 [fit: 0.004, exp: 1.00, pred: 1339.698, error:2400.0]acc: 0.0\n", - "Cond:.#.#..## - Act:1 - effect:.......O - Num:1 [fit: 0.007, exp: 1.00, pred: 202.454, error:1800.0]acc: 0.0\n", - "Cond:.....### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 134.00, pred: 1654.385, error:7204.682933523317]acc: 0.15671641791044777\n", - "Cond:#####... - Act:6 - effect:...O.... - Num:1 [fit: 0.002, exp: 9.00, pred: 287.866, error:3696.631667507477]acc: 0.0\n", - "Cond:...#.##F - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 40.00, pred: 477.163, error:2662.8122782994146]acc: 0.5\n", - "Cond:..#..### - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 131.00, pred: 1654.385, error:8134.0225903193095]acc: 0.15267175572519084\n", - "Cond:..#FOO.. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 30.00, pred: 360.797, error:6775.334927439636]acc: 0.03333333333333333\n", - "Cond:#..F##.. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 30.00, pred: 360.797, error:7301.454230938074]acc: 0.03333333333333333\n", - "Cond:...#..#F - Act:4 - effect:.......O - Num:1 [fit: 0.048, exp: 0.00, pred: 607.549, error:67.05954631761213]acc: 0\n", - "Cond:..O##### - Act:4 - effect:.......O - Num:1 [fit: 0.002, exp: 9.00, pred: 434.463, error:7021.68139944434]acc: 0.0\n", - "Cond:.O###### - Act:4 - effect:.......O - Num:2 [fit: 0.002, exp: 23.00, pred: 624.600, error:2729.9182768484197]acc: 0.13043478260869565\n", - "Cond:..#.##.# - Act:2 - effect:.O...... - Num:1 [fit: 0.027, exp: 7.00, pred: 994.734, error:6367.960361361349]acc: 0.0\n", - "Cond:..#..##F - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 32.00, pred: 477.085, error:3513.8673583767268]acc: 0.53125\n", - "Cond:.#.##### - Act:2 - effect:..O.OO.. - Num:2 [fit: 0.001, exp: 28.00, pred: 760.123, error:5720.933330411987]acc: 0.0\n", - "Cond:..#FOO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 24.00, pred: 363.374, error:7102.068650733292]acc: 0.041666666666666664\n", - "Cond:#.#F##.. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 24.00, pred: 363.374, error:6796.247937062478]acc: 0.041666666666666664\n", - "Cond:.###.### - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 13.00, pred: 639.293, error:6327.61005049589]acc: 0.07692307692307693\n", - "Cond:.#O..#.# - Act:4 - effect:.......O - Num:1 [fit: 0.002, exp: 7.00, pred: 491.656, error:4712.876179924646]acc: 0.0\n", - "Cond:.##OO.## - Act:3 - effect:...O.... - Num:1 [fit: 0.202, exp: 2.00, pred: 235.514, error:1502.3710324937852]acc: 0.0\n", - "Cond:.#.##.## - Act:1 - effect:....FO.. - Num:4 [fit: 0.049, exp: 13.00, pred: 308.547, error:6065.697095110263]acc: 0.46153846153846156\n", - "Cond:.O#O#### - Act:1 - effect:.......O - Num:1 [fit: 0.001, exp: 0.00, pred: 513.702, error:363.93827822945883]acc: 0\n", - "Cond:O##F#... - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 817.701, error:1678.4490241463577]acc: 0\n", - "Cond:##...##F - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 23.00, pred: 479.018, error:3899.3019428460148]acc: 0.391304347826087\n", - "Cond:.####.## - Act:1 - effect:....FO.. - Num:5 [fit: 0.200, exp: 11.00, pred: 284.923, error:6837.98580305198]acc: 0.36363636363636365\n", - "Cond:.##.###O - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 32.00, pred: 1657.392, error:7715.8424462059975]acc: 0.0\n", - "Cond:..#FOO.# - Act:6 - effect:...O.... - Num:2 [fit: 0.001, exp: 17.00, pred: 358.145, error:6880.028077474584]acc: 0.058823529411764705\n", - "Cond:#.#FOO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 17.00, pred: 358.145, error:7424.662018671383]acc: 0.058823529411764705\n", - "Cond:#.#.##O# - Act:4 - effect:.......O - Num:1 [fit: 0.002, exp: 23.00, pred: 425.737, error:3372.574644771581]acc: 0.30434782608695654\n", - "Cond:.#..###O - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 29.00, pred: 1657.045, error:8032.669562925308]acc: 0.0\n", - "Cond:###..### - Act:4 - effect:.......O - Num:3 [fit: 0.000, exp: 161.00, pred: 1357.120, error:2926.4373127893077]acc: 0.031055900621118012\n", - "Cond:..#FO### - Act:6 - effect:...O.... - Num:2 [fit: 0.002, exp: 15.00, pred: 353.343, error:5954.274211305913]acc: 0.06666666666666667\n", - "Cond:O.#...## - Act:0 - effect:.......F - Num:1 [fit: 0.058, exp: 3.00, pred: 678.442, error:3616.2740146824112]acc: 0.0\n", - "Cond:.##...## - Act:3 - effect:.......O - Num:1 [fit: 0.002, exp: 8.00, pred: 466.819, error:7065.305137777038]acc: 0.0\n", - "Cond:.O###.## - Act:1 - effect:....FO.. - Num:1 [fit: 0.041, exp: 0.00, pred: 456.814, error:1.4680196347900676]acc: 0\n", - "Cond:####.### - Act:5 - effect:........ - Num:3 [fit: 0.001, exp: 858.00, pred: 1316.268, error:2817.029089179964]acc: 0.002331002331002331\n", - "Cond:..##O### - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 13.00, pred: 360.018, error:6393.030765743157]acc: 0.07692307692307693\n", - "Cond:.##O.### - Act:4 - effect:.......O - Num:1 [fit: 0.004, exp: 20.00, pred: 602.011, error:3013.989618930968]acc: 0.15\n", - "Cond:#OO##### - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 16.00, pred: 628.359, error:2830.06563254327]acc: 0.1875\n", - "Cond:.###.### - Act:4 - effect:.O...... - Num:2 [fit: 0.000, exp: 57.00, pred: 1669.781, error:7372.4479959981045]acc: 0.12280701754385964\n", - "Cond:.##.#### - Act:1 - effect:....FO.. - Num:1 [fit: 0.043, exp: 1.00, pred: 221.705, error:1600.0]acc: 0.0\n", - "Cond:..##..## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 28.00, pred: 1653.556, error:7918.62817880637]acc: 0.03571428571428571\n", - "Cond:..#F#.## - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 635.121, error:982.9002276237992]acc: 0\n", - "Cond:..#F#O.# - Act:6 - effect:...O.... - Num:3 [fit: 0.008, exp: 9.00, pred: 333.340, error:6205.095335966489]acc: 0.1111111111111111\n", - "Cond:.#.#O### - Act:4 - effect:.......O - Num:1 [fit: 0.104, exp: 5.00, pred: 715.357, error:3533.6645634313827]acc: 0.0\n", - "Cond:.O#O#### - Act:4 - effect:.O...... - Num:1 [fit: 0.380, exp: 6.00, pred: 634.016, error:4076.7763207087055]acc: 0.6666666666666666\n", - "Cond:.#..O##. - Act:3 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 577.747, error:1654.9793529686635]acc: 0\n", - "Cond:##.##.## - Act:1 - effect:....FO.. - Num:2 [fit: 0.048, exp: 9.00, pred: 244.612, error:3775.921322567716]acc: 0.1111111111111111\n", - "Cond:.##..#O# - Act:4 - effect:.......O - Num:1 [fit: 0.008, exp: 5.00, pred: 407.505, error:1832.2231195902891]acc: 0.2\n", - "Cond:..###### - Act:5 - effect:.......O - Num:5 [fit: 0.002, exp: 777.00, pred: 1206.332, error:2137.4352088268065]acc: 0.0\n", - "Cond:###.##O# - Act:4 - effect:.......O - Num:1 [fit: 0.203, exp: 2.00, pred: 316.840, error:2103.122229708746]acc: 0.5\n", - "Cond:.#..#O## - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 564.00, pred: 1375.127, error:1954.7898328808528]acc: 0.0\n", - "Cond:#..F#O.# - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 3.00, pred: 238.167, error:4812.625377514138]acc: 0.0\n", - "Cond:OO..#### - Act:3 - effect:........ - Num:2 [fit: 0.014, exp: 3.00, pred: 269.078, error:3743.273987036965]acc: 0.0\n", - "Cond:OO..F### - Act:3 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:..#.#.## - Act:3 - effect:.......O - Num:1 [fit: 0.006, exp: 1.00, pred: 1212.005, error:1800.0]acc: 0.0\n", - "Cond:O#####.# - Act:0 - effect:O..O.... - Num:2 [fit: 0.045, exp: 4.00, pred: 508.831, error:4497.137491770538]acc: 0.0\n", - "Cond:..#OO#.. - Act:4 - effect:.O...... - Num:1 [fit: 0.011, exp: 2.00, pred: 1059.750, error:2807.184160187796]acc: 0.0\n", - "Cond:####O.#. - Act:4 - effect:.......O - Num:1 [fit: 0.010, exp: 2.00, pred: 1059.750, error:2807.184160187796]acc: 0.0\n", - "Cond:.######. - Act:4 - effect:.......O - Num:2 [fit: 0.090, exp: 7.00, pred: 793.162, error:3309.57393467238]acc: 0.2857142857142857\n", - "Cond:####.### - Act:0 - effect:.......F - Num:2 [fit: 0.019, exp: 8.00, pred: 954.755, error:7664.061316965305]acc: 0.0\n", - "Cond:#O###### - Act:3 - effect:........ - Num:1 [fit: 0.009, exp: 3.00, pred: 229.233, error:3874.926358033689]acc: 0.0\n", - "Cond:##..##.. - Act:3 - effect:...O.... - Num:1 [fit: 0.004, exp: 3.00, pred: 237.294, error:3802.8857178226895]acc: 0.0\n", - "Cond:.##FOO#. - Act:1 - effect:....OO.F - Num:2 [fit: 0.007, exp: 4.00, pred: 364.386, error:3125.3462967891246]acc: 0.0\n", - "Cond:O.###.#O - Act:0 - effect:O..O.... - Num:1 [fit: 0.009, exp: 1.00, pred: 1524.972, error:1600.0]acc: 0.0\n", - "Cond:#..F.### - Act:7 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 366.323, error:599.7059354379264]acc: 0\n", - "Cond:..O#.#.. - Act:7 - effect:.O...... - Num:1 [fit: 0.006, exp: 1.00, pred: 229.858, error:1600.0]acc: 0.0\n", - "Cond:.#O#.##. - Act:7 - effect:.OO..OO. - Num:1 [fit: 0.006, exp: 1.00, pred: 229.858, error:2000.0]acc: 0.0\n", - "Cond:#..F#### - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 70.00, pred: 596.498, error:2273.691645068624]acc: 0.0\n", - "Cond:#.##O### - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 1.00, pred: 219.560, error:1600.0]acc: 0.0\n", - "Cond:..#F#O.. - Act:6 - effect:...O.... - Num:1 [fit: 0.005, exp: 1.00, pred: 219.560, error:1800.0]acc: 0.0\n", - "Cond:O#.....# - Act:7 - effect:.....F.. - Num:1 [fit: 0.013, exp: 1.00, pred: 837.458, error:1600.0]acc: 0.0\n", - "Cond:#O.#...# - Act:2 - effect:...O.... - Num:2 [fit: 0.015, exp: 3.00, pred: 402.250, error:2137.123730734622]acc: 0.3333333333333333\n", - "Cond:#O..#..# - Act:2 - effect:...O.... - Num:1 [fit: 0.007, exp: 3.00, pred: 402.250, error:1937.1237307346219]acc: 0.3333333333333333\n", - "Cond:#.##.##. - Act:4 - effect:.O...... - Num:1 [fit: 0.102, exp: 2.00, pred: 283.680, error:1716.4458683884743]acc: 0.0\n", - "Cond:###.F#.# - Act:4 - effect:O....O.O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:O..#F##. - Act:7 - effect:.O...... - Num:1 [fit: 0.017, exp: 0.00, pred: 311.704, error:275.9491727128152]acc: 0\n", - "Cond:#O.##..# - Act:0 - effect:O..O.... - Num:1 [fit: 0.006, exp: 1.00, pred: 0.000, error:1600.0]acc: 0.0\n", - "Cond:.##O#### - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 4.00, pred: 994.568, error:5211.976502663918]acc: 0.0\n", - "Cond:.#O#..## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 2.00, pred: 929.387, error:2934.449080344359]acc: 0.0\n", - "Cond:.OO#.### - Act:4 - effect:.O...... - Num:1 [fit: 0.031, exp: 1.00, pred: 1598.285, error:2200.0]acc: 0.0\n", - "Cond:.##.##.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 312.788, error:1001.3297844548603]acc: 0\n", - "Cond:#.#.#F## - Act:4 - effect:.F...F.. - Num:1 [fit: 0.002, exp: 0.00, pred: 312.788, error:1001.3297844548603]acc: 0\n", - "Cond:#..##O.. - Act:6 - effect:...O.... - Num:1 [fit: 0.007, exp: 1.00, pred: 935.716, error:1600.0]acc: 0.0\n", - "Cond:###.#.## - Act:1 - effect:.....O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 362.290, error:637.2549862598926]acc: 0\n", - "Cond:O#...#.. - Act:1 - effect:.....O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 362.290, error:637.2549862598926]acc: 0\n", - "Cond:###.##.. - Act:0 - effect:O..O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 209.556, error:711.285334851536]acc: 0\n", - "Cond:#O.#.### - Act:0 - effect:.......F - Num:1 [fit: 0.003, exp: 0.00, pred: 209.556, error:711.285334851536]acc: 0\n", - "Cond:#O##.### - Act:3 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#####.# - Act:3 - effect:...O.... - Num:3 [fit: 0.443, exp: 4.00, pred: 501.664, error:3012.906544172839]acc: 0.0\n", - "Cond:.###..## - Act:1 - effect:....FO.. - Num:1 [fit: 0.014, exp: 0.00, pred: 317.389, error:1164.4428085245238]acc: 0\n", - "Cond:####..F# - Act:6 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 298.181, error:515.9294345364439]acc: 0\n", - "Cond:.#.#OO## - Act:1 - effect:....OO.F - Num:1 [fit: 0.194, exp: 2.00, pred: 257.372, error:1707.446025990852]acc: 0.0\n", - "Cond:##.##..# - Act:6 - effect:.O...... - Num:2 [fit: 0.141, exp: 13.00, pred: 269.681, error:3566.8609527074714]acc: 0.15384615384615385\n", - "Cond:...##..# - Act:6 - effect:.O...... - Num:1 [fit: 0.019, exp: 0.00, pred: 357.402, error:197.03067495873]acc: 0\n", - "Cond:#.#....# - Act:7 - effect:...O.... - Num:1 [fit: 0.002, exp: 0.00, pred: 281.027, error:162.05913284239492]acc: 0\n", - "Cond:#####O## - Act:5 - effect:....FO.. - Num:5 [fit: 0.021, exp: 73.00, pred: 715.928, error:4753.878483483094]acc: 0.0136986301369863\n", - "Cond:.##F#O#. - Act:1 - effect:....OO.F - Num:1 [fit: 0.001, exp: 1.00, pred: 242.480, error:2400.0]acc: 0.0\n", - "Cond:##.F###. - Act:7 - effect:...O.... - Num:3 [fit: 0.000, exp: 104.00, pred: 1431.491, error:4551.723198512714]acc: 0.6346153846153846\n", - "Cond:#..##### - Act:7 - effect:.O...... - Num:3 [fit: 0.003, exp: 665.00, pred: 1343.672, error:1848.4127209674325]acc: 0.0\n", - "Cond:..#..#O# - Act:2 - effect:...O.... - Num:3 [fit: 0.029, exp: 4.00, pred: 714.424, error:5000.4295844354]acc: 0.0\n", - "Cond:######.# - Act:5 - effect:........ - Num:8 [fit: 0.004, exp: 1046.00, pred: 1210.129, error:2368.1646793496975]acc: 0.0\n", - "Cond:#####O## - Act:7 - effect:.......O - Num:6 [fit: 0.006, exp: 663.00, pred: 1343.672, error:1582.3094434824782]acc: 0.0\n", - "Cond:.##.#O## - Act:7 - effect:...O.... - Num:8 [fit: 0.005, exp: 558.00, pred: 1375.127, error:2227.9561613954756]acc: 0.0\n", - "Cond:#O.O...O - Act:5 - effect:.OO..... - Num:1 [fit: 0.010, exp: 0.00, pred: 379.224, error:812.4755573704872]acc: 0\n", - "Cond:#.###### - Act:5 - effect:........ - Num:5 [fit: 0.001, exp: 753.00, pred: 1206.081, error:2340.740952971882]acc: 0.0\n", - "Cond:.####O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.010, exp: 27.00, pred: 598.630, error:1867.3931966943296]acc: 0.037037037037037035\n", - "Cond:###.#O## - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 549.00, pred: 1375.127, error:2443.7219353497776]acc: 0.0\n", - "Cond:###F.##. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#.####.# - Act:5 - effect:........ - Num:2 [fit: 0.000, exp: 706.00, pred: 1205.812, error:2275.6211367705996]acc: 0.0\n", - "Cond:.##.#### - Act:3 - effect:.......O - Num:1 [fit: 0.030, exp: 0.00, pred: 490.433, error:765.246696155309]acc: 0\n", - "Cond:#...#### - Act:3 - effect:...O.... - Num:1 [fit: 0.030, exp: 0.00, pred: 490.433, error:765.246696155309]acc: 0\n", - "Cond:#O..#.## - Act:2 - effect:........ - Num:1 [fit: 0.007, exp: 1.00, pred: 385.746, error:1600.0]acc: 0.0\n", - "Cond:###F###. - Act:7 - effect:...O.... - Num:2 [fit: 0.000, exp: 98.00, pred: 1431.491, error:4756.768247612424]acc: 0.6122448979591837\n", - "Cond:.####..# - Act:1 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1313.966, error:402.3435338779427]acc: 0\n", - "Cond:OO..#.#. - Act:7 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1302.291, error:584.0409062776037]acc: 0\n", - "Cond:#..#F##O - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1350.767, error:846.37293211164]acc: 0\n", - "Cond:#####OO# - Act:5 - effect:........ - Num:1 [fit: 0.008, exp: 2.00, pred: 949.184, error:2901.0426637134656]acc: 0.0\n", - "Cond:###F##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 96.00, pred: 1431.491, error:4576.196685113501]acc: 0.6041666666666666\n", - "Cond:O##.#### - Act:7 - effect:.O...... - Num:1 [fit: 0.006, exp: 1.00, pred: 837.458, error:1800.0]acc: 0.0\n", - "Cond:.######. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 358.00, pred: 1501.499, error:2922.320013113878]acc: 0.002793296089385475\n", - "Cond:#O.F##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1626.518, error:8.265376374502807]acc: 0\n", - "Cond:######## - Act:7 - effect:.......O - Num:5 [fit: 0.004, exp: 579.00, pred: 1343.672, error:1895.70170036745]acc: 0.0\n", - "Cond:...##O## - Act:7 - effect:.O...... - Num:4 [fit: 0.006, exp: 578.00, pred: 1343.672, error:1816.3871096888995]acc: 0.0\n", - "Cond:#..##O## - Act:7 - effect:.O...... - Num:8 [fit: 0.008, exp: 551.00, pred: 1343.672, error:1499.6091153772782]acc: 0.0\n", - "Cond:###F#### - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 90.00, pred: 1431.491, error:4873.00029774006]acc: 0.5777777777777777\n", - "Cond:.###.#.# - Act:3 - effect:...O.... - Num:1 [fit: 0.044, exp: 0.00, pred: 501.664, error:353.2266360432097]acc: 0\n", - "Cond:.##FO.## - Act:6 - effect:...O.... - Num:1 [fit: 0.037, exp: 0.00, pred: 1666.388, error:71.85256688430697]acc: 0\n", - "Cond:###F.##. - Act:7 - effect:...O.... - Num:1 [fit: 0.037, exp: 0.00, pred: 1666.388, error:71.85256688430697]acc: 0\n", - "Cond:.####### - Act:6 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1186.755, error:549.8001989838074]acc: 0\n", - "Cond:#O#F#.#. - Act:7 - effect:...O.... - Num:1 [fit: 0.065, exp: 0.00, pred: 1623.942, error:39.46462947190855]acc: 0\n", - "Cond:##.###.# - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 531.00, pred: 1320.530, error:2689.6704798631527]acc: 0.0\n", - "Cond:.##.#### - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 404.00, pred: 1375.127, error:1828.5268019817704]acc: 0.0\n", - "Cond:##.##### - Act:7 - effect:.O...... - Num:1 [fit: 0.001, exp: 487.00, pred: 1343.672, error:1886.1621058547494]acc: 0.0\n", - "Cond:####F#.# - Act:5 - effect:........ - Num:1 [fit: 0.205, exp: 2.00, pred: 447.729, error:1707.4789466037957]acc: 0.0\n", - "Cond:.##.#### - Act:2 - effect:.O...... - Num:2 [fit: 0.011, exp: 12.00, pred: 850.799, error:6942.094720945614]acc: 0.0\n", - "Cond:#O.F###. - Act:7 - effect:...O.... - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:O##F#.## - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:##OO###. - Act:1 - effect:.......O - Num:1 [fit: 0.022, exp: 0.00, pred: 449.472, error:551.8461007980261]acc: 0\n", - "Cond:..O#.#.. - Act:2 - effect:.......O - Num:1 [fit: 0.022, exp: 0.00, pred: 449.472, error:551.8461007980261]acc: 0\n", - "Cond:.#..#OO. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1265.165, error:590.068061242368]acc: 0\n", - "Cond:..####.. - Act:2 - effect:OO...... - Num:1 [fit: 0.001, exp: 20.00, pred: 768.407, error:4825.495497962967]acc: 0.4\n", - "Cond:.#..###. - Act:2 - effect:OO...... - Num:1 [fit: 0.014, exp: 5.00, pred: 709.228, error:5044.084267627385]acc: 0.0\n", - "Cond:.#.##F#. - Act:2 - effect:OO...... - Num:1 [fit: 0.012, exp: 1.00, pred: 396.077, error:2000.0]acc: 0.0\n", - "Cond:.######. - Act:2 - effect:.O...... - Num:2 [fit: 0.003, exp: 21.00, pred: 998.582, error:6707.384470257394]acc: 0.0\n", - "Cond:#O#F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.056, exp: 0.00, pred: 1556.361, error:56.899609112875766]acc: 0\n", - "Cond:..##.##. - Act:2 - effect:OO...... - Num:1 [fit: 0.004, exp: 7.00, pred: 468.390, error:3514.639910080141]acc: 0.0\n", - "Cond:...###.# - Act:2 - effect:OO...... - Num:1 [fit: 0.006, exp: 18.00, pred: 760.881, error:5518.464293654875]acc: 0.4444444444444444\n", - "Cond:#.###.#. - Act:2 - effect:.......O - Num:1 [fit: 0.011, exp: 13.00, pred: 549.915, error:3718.67793823691]acc: 0.0\n", - "Cond:..O#.### - Act:2 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 714.424, error:800.10739610885]acc: 0\n", - "Cond:.O..#..# - Act:2 - effect:.O...... - Num:1 [fit: 0.004, exp: 0.00, pred: 517.254, error:498.8690656223327]acc: 0\n", - "Cond:######## - Act:2 - effect:...O.... - Num:2 [fit: 0.023, exp: 15.00, pred: 994.244, error:6268.989638630434]acc: 0.06666666666666667\n", - "Cond:..####.# - Act:2 - effect:OO...... - Num:3 [fit: 0.096, exp: 9.00, pred: 752.970, error:5449.241461763329]acc: 0.2222222222222222\n", - "Cond:#######O - Act:7 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 756.885, error:786.2527443021846]acc: 0\n", - "Cond:#.#.F#.. - Act:6 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 504.132, error:771.7111561781936]acc: 0\n", - "Cond:.O#####. - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:###FO### - Act:5 - effect:........ - Num:1 [fit: 0.017, exp: 22.00, pred: 598.152, error:2592.9029962077434]acc: 0.0\n", - "Cond:###F#O## - Act:5 - effect:.O...... - Num:1 [fit: 0.016, exp: 22.00, pred: 598.152, error:1864.1630892299515]acc: 0.0\n", - "Cond:.##.OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1110.142, error:529.2566857852762]acc: 0\n", - "Cond:##.F##.. - Act:7 - effect:...O.... - Num:5 [fit: 0.000, exp: 63.00, pred: 1431.492, error:4475.563803113068]acc: 0.3968253968253968\n", - "Cond:#...##.# - Act:1 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 936.455, error:435.92523588126096]acc: 0\n", - "Cond:.####### - Act:2 - effect:OO...... - Num:1 [fit: 0.005, exp: 2.00, pred: 1890.365, error:3427.7857053476973]acc: 0.0\n", - "Cond:O#.F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.036, exp: 0.00, pred: 1079.539, error:9.054931690271541]acc: 0\n", - "Cond:###.#### - Act:7 - effect:.O...... - Num:2 [fit: 0.002, exp: 308.00, pred: 1375.127, error:2147.860452219234]acc: 0.0\n", - "Cond:###F##.. - Act:7 - effect:...O.... - Num:2 [fit: 0.000, exp: 56.00, pred: 1431.494, error:4920.883903827482]acc: 0.32142857142857145\n", - "Cond:#O.F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.054, exp: 0.00, pred: 1243.262, error:52.33005485402675]acc: 0\n", - "Cond:#.###.## - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 346.00, pred: 1195.448, error:2436.5756414308225]acc: 0.0\n", - "Cond:##.#.O## - Act:0 - effect:.......F - Num:1 [fit: 0.014, exp: 2.00, pred: 1222.939, error:2590.869919697658]acc: 0.0\n", - "Cond:####.##. - Act:0 - effect:....FO.. - Num:1 [fit: 0.013, exp: 1.00, pred: 1246.935, error:2600.0]acc: 0.0\n", - "Cond:#######F - Act:5 - effect:........ - Num:1 [fit: 0.011, exp: 2.00, pred: 949.184, error:2701.0426637134656]acc: 0.0\n", - "Cond:#####OO. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1295.178, error:786.5683099539451]acc: 0\n", - "Cond:.O#.###. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1515.358, error:623.442897245877]acc: 0\n", - "Cond:#.#####. - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 291.00, pred: 1205.883, error:2358.391699630094]acc: 0.0\n", - "Cond:######## - Act:4 - effect:........ - Num:4 [fit: 0.001, exp: 109.00, pred: 1356.819, error:3201.5923103753744]acc: 0.0\n", - "Cond:###F##.. - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#O.F###. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:##..#### - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 233.00, pred: 1375.127, error:2666.3484267425592]acc: 0.0\n", - "Cond:#.###.## - Act:4 - effect:........ - Num:2 [fit: 0.001, exp: 28.00, pred: 1625.164, error:7503.286215393206]acc: 0.0\n", - "Cond:##.#.### - Act:4 - effect:........ - Num:5 [fit: 0.000, exp: 103.00, pred: 1357.120, error:3208.79300242492]acc: 0.0\n", - "Cond:.##FO##. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 48.00, pred: 1431.495, error:4368.240966153006]acc: 0.25\n", - "Cond:#######. - Act:0 - effect:.......F - Num:2 [fit: 0.005, exp: 1.00, pred: 1246.935, error:1600.0]acc: 0.0\n", - "Cond:#####O## - Act:4 - effect:........ - Num:1 [fit: 0.024, exp: 1.00, pred: 940.330, error:2600.0]acc: 0.0\n", - "Cond:.##F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 44.00, pred: 1431.480, error:5028.724546209979]acc: 0.25\n", - "Cond:##.FO##. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 44.00, pred: 1431.480, error:4720.622471236122]acc: 0.25\n", - "Cond:#####..# - Act:5 - effect:........ - Num:2 [fit: 0.001, exp: 327.00, pred: 1204.636, error:2319.8492440901687]acc: 0.0\n", - "Cond:#.#.#.## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 22.00, pred: 1704.624, error:5771.894052773212]acc: 0.0\n", - "Cond:..###.## - Act:4 - effect:........ - Num:3 [fit: 0.001, exp: 13.00, pred: 1607.265, error:8718.216065062403]acc: 0.0\n", - "Cond:.#.##O## - Act:1 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#.F##.. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 39.00, pred: 1431.489, error:4723.362415386215]acc: 0.28205128205128205\n", - "Cond:#######. - Act:6 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1305.647, error:634.5961685684134]acc: 0\n", - "Cond:..###O## - Act:7 - effect:...O.... - Num:5 [fit: 0.004, exp: 236.00, pred: 1343.672, error:1974.9066731342975]acc: 0.046610169491525424\n", - "Cond:O#.F###. - Act:7 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1524.702, error:1112.6609078471674]acc: 0\n", - "Cond:.####..# - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.001, exp: 13.00, pred: 1607.265, error:7668.729421862402]acc: 0.0\n", - "Cond:##..#### - Act:3 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 1675.136, error:644.4636835808452]acc: 0\n", - "Cond:.#.#OO## - Act:5 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1445.184, error:655.3313232282262]acc: 0\n", - "Cond:.####O## - Act:0 - effect:....FO.. - Num:1 [fit: 0.004, exp: 1.00, pred: 1404.679, error:0.0]acc: 1.0\n", - "Cond:#.#.F.## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:###..### - Act:3 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1462.687, error:749.920479389614]acc: 0\n", - "Cond:#.##..## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 17.00, pred: 1710.652, error:6363.960086726091]acc: 0.0\n", - "Cond:####O### - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.004, exp: 3.00, pred: 1019.944, error:1847.1519759087978]acc: 0.0\n", - "Cond:##.#...# - Act:4 - effect:........ - Num:2 [fit: 0.000, exp: 60.00, pred: 1357.121, error:3310.3994094567634]acc: 0.0\n", - "Cond:#.#.#O## - Act:5 - effect:........ - Num:1 [fit: 0.002, exp: 1.00, pred: 747.099, error:1600.0]acc: 0.0\n", - "Cond:..###O## - Act:0 - effect:...O.... - Num:1 [fit: 0.004, exp: 1.00, pred: 1404.679, error:2600.0]acc: 0.0\n", - "Cond:##.##... - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 23.00, pred: 1245.336, error:5983.208906437757]acc: 0.0\n", - "Cond:.####.## - Act:4 - effect:..O.OO.. - Num:1 [fit: 0.002, exp: 11.00, pred: 1624.658, error:7179.014095472662]acc: 0.0\n", - "Cond:..###..# - Act:4 - effect:........ - Num:1 [fit: 0.003, exp: 11.00, pred: 1624.658, error:8029.612495472662]acc: 0.0\n", - "Cond:#.###.#. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:####..## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 49.00, pred: 1357.129, error:2472.5334686754136]acc: 0.0\n", - "Cond:.####O.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 1835.107, error:399.4585087850263]acc: 0\n", - "Cond:#######. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 80.00, pred: 1501.499, error:2062.42186162631]acc: 0.05\n", - "Cond:.#.F#### - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 24.00, pred: 1432.094, error:5995.129722059982]acc: 0.0\n", - "Cond:#####.## - Act:4 - effect:........ - Num:5 [fit: 0.000, exp: 45.00, pred: 1356.906, error:3435.520478155384]acc: 0.0\n", - "Cond:##.#..## - Act:4 - effect:........ - Num:2 [fit: 0.000, exp: 43.00, pred: 1357.108, error:3066.1611466337417]acc: 0.0\n", - "Cond:.######F - Act:5 - effect:.O...... - Num:1 [fit: 0.002, exp: 1.00, pred: 747.099, error:2000.0]acc: 0.0\n", - "Cond:##.###.. - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 19.00, pred: 1233.229, error:6106.644950719108]acc: 0.0\n", - "Cond:#O#F##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1955.717, error:718.7898186463824]acc: 0\n", - "Cond:#####.#. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1955.717, error:718.7898186463824]acc: 0\n", - "Cond:##.##.## - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 36.00, pred: 1356.796, error:2612.0963425235773]acc: 0.0\n", - "Cond:#.#.#### - Act:5 - effect:........ - Num:2 [fit: 0.000, exp: 31.00, pred: 1838.357, error:5412.987160626485]acc: 0.0\n", - "Cond:O.###O## - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 1670.538, error:568.8650421987163]acc: 0\n", - "Cond:..###O## - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:.#.##### - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 103.00, pred: 1343.672, error:1469.7055008019365]acc: 0.0\n", - "Cond:.#.F#O## - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 18.00, pred: 1422.015, error:6343.660715644108]acc: 0.0\n", - "Cond:######.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 105.00, pred: 1223.458, error:1801.7313824115113]acc: 0.13333333333333333\n", - "Cond:#..##.## - Act:4 - effect:........ - Num:1 [fit: 0.003, exp: 7.00, pred: 1569.606, error:4497.6588507993465]acc: 0.0\n", - "Cond:##.#.#.# - Act:4 - effect:........ - Num:2 [fit: 0.000, exp: 31.00, pred: 1356.482, error:3011.6344445831337]acc: 0.0\n", - "Cond:..#O##.. - Act:4 - effect:........ - Num:1 [fit: 0.003, exp: 1.00, pred: 845.382, error:1800.0]acc: 0.0\n", - "Cond:O##.#### - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 25.00, pred: 1532.172, error:3704.857436263127]acc: 0.0\n", - "Cond:.#.###.# - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 36.00, pred: 1475.793, error:3356.736751092681]acc: 0.0\n", - "Cond:##.F.#.# - Act:4 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: 0\n", - "Cond:#..#.#.# - Act:4 - effect:........ - Num:1 [fit: 0.006, exp: 5.00, pred: 1704.068, error:3856.9752558330542]acc: 0.0\n", - "Cond:.######O - Act:2 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1486.690, error:495.8040119370071]acc: 0\n", - "Cond:######## - Act:1 - effect:........ - Num:1 [fit: 0.006, exp: 0.00, pred: 1637.381, error:365.2649736286069]acc: 0\n", - "Cond:...##O## - Act:4 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1291.702, error:563.3519985533375]acc: 0\n", - "Cond:#.#####. - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 31.00, pred: 1502.410, error:2957.059618469304]acc: 0.0\n", - "Cond:.#.#..#O - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 1577.031, error:1073.409693174345]acc: 0\n", - "Cond:#.###O## - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 31.00, pred: 1343.607, error:1625.1953870233979]acc: 0.0\n", - "Cond:###O#..# - Act:7 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 1535.768, error:1129.0716800764071]acc: 0\n", - "Cond:.####O#. - Act:7 - effect:.......O - Num:1 [fit: 0.013, exp: 16.00, pred: 1511.414, error:2474.320808728021]acc: 0.0\n", - "Cond:#..##O#. - Act:7 - effect:.O...... - Num:1 [fit: 0.037, exp: 12.00, pred: 1476.304, error:1983.0638386156036]acc: 0.0\n", - "Cond:#.###O## - Act:0 - effect:.......F - Num:1 [fit: 0.000, exp: 0.00, pred: 1152.223, error:733.0927735762574]acc: 0\n", - "Cond:#..#F#.. - Act:7 - effect:.O...... - Num:1 [fit: 0.017, exp: 2.00, pred: 1629.194, error:947.154082762917]acc: 0.0\n" + "Cond:..#O.##O - Act:7 - effect:OO.O.O.O - Num:1 [fit: 0.011, exp: 0.00, pred: 486.429, error:0.0]acc: None\n", + "Cond:#O#F..## - Act:0 - effect:......O. - Num:1 [fit: 0.006, exp: 0.00, pred: 675.531, error:787.1840290737638]acc: None\n", + "Cond:.#..FOF. - Act:3 - effect:.....FF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##O##.#O - Act:1 - effect:......O. - Num:1 [fit: 0.005, exp: 0.00, pred: 798.521, error:747.3180357670524]acc: None\n", + "Cond:.OOO#..F - Act:6 - effect:....O... - Num:1 [fit: 0.006, exp: 0.00, pred: 968.714, error:960.9493884141996]acc: None\n", + "Cond:##..O... - Act:0 - effect:.....OO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1317.461, error:620.428793052614]acc: None\n", + "Cond:##.O#O.. - Act:6 - effect:.O..O... - Num:1 [fit: 0.002, exp: 0.00, pred: 868.895, error:742.4956394789167]acc: None\n", + "Cond:.#.#O#O. - Act:3 - effect:.O...O.F - Num:2 [fit: 0.004, exp: 0.00, pred: 1290.917, error:498.0350006750616]acc: None\n", + "Cond:.#OO#O.# - Act:5 - effect:........ - Num:1 [fit: 0.014, exp: 0.00, pred: 1366.593, error:449.46948099180383]acc: None\n", + "Cond:.##O.#F# - Act:5 - effect:OO.....O - Num:1 [fit: 0.007, exp: 0.00, pred: 1082.773, error:1625.0079750798154]acc: None\n", + "Cond:.#OO.O.# - Act:5 - effect:......O. - Num:1 [fit: 0.010, exp: 0.00, pred: 1235.692, error:947.6033018956205]acc: None\n", + "Cond:#.#FFO.. - Act:6 - effect:.O..O... - Num:1 [fit: 0.007, exp: 0.00, pred: 853.563, error:937.0636316649458]acc: None\n", + "Cond:#O#OO..# - Act:1 - effect:........ - Num:1 [fit: 0.006, exp: 0.00, pred: 908.339, error:696.0646724637395]acc: None\n", + "Cond:.##.F.## - Act:0 - effect:........ - Num:1 [fit: 0.005, exp: 0.00, pred: 952.004, error:800.679354339998]acc: None\n", + "Cond:.###.OO. - Act:0 - effect:......OO - Num:1 [fit: 0.023, exp: 0.00, pred: 1295.018, error:458.69189439857325]acc: None\n", + "Cond:.#OO.O.. - Act:5 - effect:OO.....O - Num:1 [fit: 0.009, exp: 0.00, pred: 1173.026, error:1308.483127834058]acc: None\n", + "Cond:.O.#.O## - Act:3 - effect:....O... - Num:1 [fit: 0.010, exp: 0.00, pred: 1105.060, error:606.1126581212585]acc: None\n", + "Cond:.###.O.# - Act:3 - effect:....OOO. - Num:2 [fit: 0.024, exp: 0.00, pred: 1052.038, error:442.73889339713037]acc: None\n", + "Cond:.##.#O#F - Act:7 - effect:....OOO. - Num:3 [fit: 0.032, exp: 441.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:##...#FF - Act:0 - effect:.....OOF - Num:1 [fit: 0.009, exp: 0.00, pred: 1157.490, error:572.6397250619132]acc: None\n", + "Cond:#..#F..# - Act:5 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1310.441, error:275.3569583937879]acc: None\n", + "Cond:#.O#..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1055.235, error:577.6776395920742]acc: None\n", + "Cond:.#O#...O - Act:4 - effect:.OO..... - Num:1 [fit: 0.009, exp: 0.00, pred: 957.239, error:1100.6055931132682]acc: None\n", + "Cond:#.#.O### - Act:7 - effect:....OOO. - Num:1 [fit: 0.053, exp: 0.00, pred: 1051.325, error:236.17931422648815]acc: None\n", + "Cond:#O#.#OF. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1173.101, error:1012.6138267928708]acc: None\n", + "Cond:#O..#O.# - Act:3 - effect:OO...... - Num:1 [fit: 0.003, exp: 0.00, pred: 934.448, error:1074.286204171456]acc: None\n", + "Cond:.##.#OOO - Act:1 - effect:.OO..... - Num:1 [fit: 0.020, exp: 0.00, pred: 1278.572, error:131.82882220682393]acc: None\n", + "Cond:......OF - Act:2 - effect:.O...F.F - Num:1 [fit: 0.005, exp: 0.00, pred: 1011.372, error:236.2785042919653]acc: None\n", + "Cond:....O#.. - Act:0 - effect:.....OO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#OO...#O - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.#.#O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:..#.#... - Act:7 - effect:.OO..O.. - Num:1 [fit: 0.002, exp: 0.00, pred: 917.411, error:1249.192258730387]acc: None\n", + "Cond:.#O...O# - Act:4 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O.#O#.## - Act:0 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..#OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 409.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", + "Cond:#...#O.. - Act:4 - effect:....OOO. - Num:9 [fit: 0.342, exp: 271.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:#.O.#### - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1371.904, error:591.6731476361124]acc: None\n", + "Cond:.#O#...O - Act:1 - effect:.OO..... - Num:1 [fit: 0.002, exp: 0.00, pred: 870.510, error:1647.7144892413003]acc: None\n", + "Cond:#.O.#..# - Act:7 - effect:....FO.. - Num:1 [fit: 0.016, exp: 0.00, pred: 961.244, error:130.18274558382836]acc: None\n", + "Cond:##..#OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.#OOO. - Act:3 - effect:....OOO. - Num:2 [fit: 0.029, exp: 0.00, pred: 1712.811, error:77.84018656925448]acc: None\n", + "Cond:#.#.#OO# - Act:7 - effect:....OOO. - Num:3 [fit: 0.032, exp: 406.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:###F#.#. - Act:3 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 1246.242, error:829.9372958520112]acc: None\n", + "Cond:#..#O#O. - Act:3 - effect:....OOO. - Num:3 [fit: 0.022, exp: 0.00, pred: 1413.811, error:318.58411037510245]acc: None\n", + "Cond:#...OO.. - Act:4 - effect:....OOO. - Num:3 [fit: 0.064, exp: 0.00, pred: 1221.388, error:321.40051658332544]acc: None\n", + "Cond:###O.O.# - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1375.992, error:558.4261778114585]acc: None\n", + "Cond:###.#.O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1225.703, error:703.5383346038936]acc: None\n", + "Cond:O...O..O - Act:2 - effect:O.O.O..O - Num:1 [fit: 0.001, exp: 0.00, pred: 1130.448, error:382.01736334774284]acc: None\n", + "Cond:O###.O.O - Act:5 - effect:O.O.O..O - Num:1 [fit: 0.001, exp: 0.00, pred: 1130.448, error:382.01736334774284]acc: None\n", + "Cond:..#.#O#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 0.00, pred: 1453.142, error:87.40187023957661]acc: None\n", + "Cond:....F.#O - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 1124.319, error:1122.58915243914]acc: None\n", + "Cond:##..OO.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.290, exp: 0.00, pred: 1239.699, error:53.818468015523706]acc: None\n", + "Cond:##.O#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1257.095, error:870.6672815606748]acc: None\n", + "Cond:#...O#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.#FF## - Act:5 - effect:F....... - Num:1 [fit: 0.001, exp: 0.00, pred: 1037.969, error:1460.975763111683]acc: None\n", + "Cond:.O...O.F - Act:3 - effect:..FFF.O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1398.075, error:788.4620672090857]acc: None\n", + "Cond:.##.O.## - Act:0 - effect:.....OO. - Num:2 [fit: 0.015, exp: 0.00, pred: 915.030, error:251.8504176773527]acc: None\n", + "Cond:..#O.#O# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#O..O... - Act:4 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 1078.272, error:1932.0009957563316]acc: None\n", + "Cond:#.#..O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.065, exp: 0.00, pred: 1120.871, error:538.5933767760804]acc: None\n", + "Cond:##...#O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##..OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#...OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.086, exp: 0.00, pred: 1712.398, error:52.2968174983977]acc: None\n", + "Cond:O##F...O - Act:5 - effect:O.O.O..O - Num:1 [fit: 0.002, exp: 0.00, pred: 1109.204, error:943.3406478652846]acc: None\n", + "Cond:...#OO#. - Act:3 - effect:....OOO. - Num:7 [fit: 0.173, exp: 396.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", + "Cond:.OO..O#. - Act:7 - effect:.O.O...O - Num:1 [fit: 0.009, exp: 0.00, pred: 921.690, error:1012.2129429194255]acc: None\n", + "Cond:.#.#OO#. - Act:3 - effect:....OOO. - Num:3 [fit: 0.078, exp: 391.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", + "Cond:....OO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1292.545, error:588.7423004793769]acc: None\n", + "Cond:###.O.## - Act:6 - effect:..OO.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1307.561, error:890.4985991170431]acc: None\n", + "Cond:#.##..F# - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:..##..F# - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..O#O## - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.O.#O.## - Act:0 - effect:OO...... - Num:2 [fit: 0.020, exp: 0.00, pred: 1100.405, error:28.686164794740478]acc: None\n", + "Cond:O##.O#.# - Act:3 - effect:.....O.. - Num:1 [fit: 0.007, exp: 0.00, pred: 1116.517, error:654.6447521276302]acc: None\n", + "Cond:.O####O. - Act:6 - effect:OO...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1245.488, error:749.8974713237016]acc: None\n", + "Cond:....OO.. - Act:1 - effect:......OO - Num:1 [fit: 0.021, exp: 0.00, pred: 1322.993, error:25.376463183913188]acc: None\n", + "Cond:..OO.F.# - Act:7 - effect:.......O - Num:1 [fit: 0.046, exp: 0.00, pred: 1197.124, error:732.8842404229987]acc: None\n", + "Cond:.#O#..F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1154.148, error:811.2300189020247]acc: None\n", + "Cond:.#..O##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1035.241, error:406.07397382479]acc: None\n", + "Cond:.####.O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1070.964, error:1099.353045352575]acc: None\n", + "Cond:#O..#.O# - Act:7 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##.#.O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1023.601, error:1212.0691337709113]acc: None\n", + "Cond:....#OOO - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:###O#O.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1093.278, error:305.6171029783959]acc: None\n", + "Cond:..#.#O.F - Act:3 - effect:.O...O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 986.143, error:1149.0428031010574]acc: None\n", + "Cond:####O#.O - Act:5 - effect:.O...... - Num:1 [fit: 0.004, exp: 0.00, pred: 1010.037, error:862.615517691796]acc: None\n", + "Cond:O...O#.O - Act:5 - effect:O.O.O..O - Num:1 [fit: 0.004, exp: 0.00, pred: 1010.037, error:862.615517691796]acc: None\n", + "Cond:..O#..F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1046.988, error:802.7140270558417]acc: None\n", + "Cond:##...O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 336.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:.####O.O - Act:5 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:###F##.O - Act:5 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 1017.279, error:992.3148403110738]acc: None\n", + "Cond:..O...#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 972.342, error:526.8414023106957]acc: None\n", + "Cond:#OOO.O.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1513.390, error:316.2505744698374]acc: None\n", + "Cond:..##.O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1341.420, error:856.740066900612]acc: None\n", + "Cond:#.#..O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 327.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:#..O.F.. - Act:3 - effect:.....OF. - Num:1 [fit: 0.006, exp: 0.00, pred: 1176.996, error:966.2011405236863]acc: None\n", + "Cond:#....... - Act:3 - effect:..OO.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1033.867, error:20.978531211787185]acc: None\n", + "Cond:O.###.#. - Act:3 - effect:.....OF. - Num:1 [fit: 0.021, exp: 0.00, pred: 1013.828, error:550.1928835185141]acc: None\n", + "Cond:#OOO#O#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:###.OO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.052, exp: 0.00, pred: 1056.944, error:22.50983175248189]acc: None\n", + "Cond:O..###O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 940.272, error:714.790654298766]acc: None\n", + "Cond:###O#O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1034.293, error:224.3069038578633]acc: None\n", + "Cond:.##O#.OO - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:###O##O# - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O#..#.O# - Act:1 - effect:OO.....O - Num:1 [fit: 0.003, exp: 0.00, pred: 862.805, error:469.37156404507925]acc: None\n", + "Cond:..###.O. - Act:6 - effect:.O...... - Num:1 [fit: 0.009, exp: 0.00, pred: 871.096, error:727.4231646460298]acc: None\n", + "Cond:#.OO.F.# - Act:7 - effect:.......O - Num:1 [fit: 0.005, exp: 0.00, pred: 1073.136, error:1253.3634244906325]acc: None\n", + "Cond:.O##FO.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.020, exp: 0.00, pred: 1381.419, error:447.1885461893281]acc: None\n", + "Cond:#...O##. - Act:2 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1340.913, error:65.86836408342623]acc: None\n", + "Cond:..O..#.# - Act:4 - effect:.....OF. - Num:1 [fit: 0.021, exp: 0.00, pred: 1142.244, error:10.777683666177097]acc: None\n", + "Cond:##.#O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##.O#.. - Act:5 - effect:.......O - Num:1 [fit: 0.012, exp: 0.00, pred: 1026.491, error:473.1368560343111]acc: None\n", + "Cond:.#.F.##. - Act:3 - effect:.....OF. - Num:1 [fit: 0.004, exp: 0.00, pred: 1028.913, error:730.1889282368737]acc: None\n", + "Cond:..O#.F.# - Act:7 - effect:.....OF. - Num:1 [fit: 0.056, exp: 0.00, pred: 1043.703, error:56.941928141147905]acc: None\n", + "Cond:..O...## - Act:7 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 937.254, error:43.25051690418307]acc: None\n", + "Cond:.##.FO#O - Act:1 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1056.651, error:922.2291868076169]acc: None\n", + "Cond:##..F..O - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1022.303, error:645.7145851666032]acc: None\n", + "Cond:#.#.O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##OO##F. - Act:7 - effect:...OO... - Num:1 [fit: 0.001, exp: 0.00, pred: 1236.669, error:279.96683656446635]acc: None\n", + "Cond:#...#F.# - Act:1 - effect:.OO..... - Num:1 [fit: 0.000, exp: 23.00, pred: 1032.311, error:6356.178042186581]acc: 0.2608695652173913\n", + "Cond:#.....#. - Act:3 - effect:OO.....O - Num:1 [fit: 0.005, exp: 0.00, pred: 1004.568, error:1088.9644399025865]acc: None\n", + "Cond:OO..O..O - Act:5 - effect:OO...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1004.292, error:1151.631820108515]acc: None\n", + "Cond:#O.O.##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 931.790, error:749.3639915320946]acc: None\n", + "Cond:O###O#.# - Act:0 - effect:OO...... - Num:1 [fit: 0.011, exp: 0.00, pred: 750.301, error:482.433824467105]acc: None\n", + "Cond:.#...O.# - Act:2 - effect:.O...F.F - Num:1 [fit: 0.000, exp: 0.00, pred: 1190.696, error:1695.5100590454604]acc: None\n", + "Cond:.##.#OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1016.031, error:214.6011986904296]acc: None\n", + "Cond:..#..FF# - Act:4 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 976.558, error:787.2098183061604]acc: None\n", + "Cond:###O#.#O - Act:5 - effect:.....OOF - Num:1 [fit: 0.013, exp: 0.00, pred: 931.625, error:805.1566168038503]acc: None\n", + "Cond:..O..#.. - Act:7 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O.#..##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1045.330, error:502.0952265244017]acc: None\n", + "Cond:#OOO.#F# - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 872.772, error:249.32361657150005]acc: None\n", + "Cond:.O##..O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 955.580, error:1325.592537203126]acc: None\n", + "Cond:.##.O.## - Act:3 - effect:.O...... - Num:1 [fit: 0.020, exp: 0.00, pred: 733.325, error:28.278326875062632]acc: None\n", + "Cond:.###OOO. - Act:3 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:....OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:###.O### - Act:0 - effect:OO...... - Num:1 [fit: 0.004, exp: 0.00, pred: 962.613, error:1127.1987578896076]acc: None\n", + "Cond:O.#OO... - Act:7 - effect:...OO... - Num:1 [fit: 0.005, exp: 0.00, pred: 984.728, error:1544.7584297438507]acc: None\n", + "Cond:##..OO.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 1064.892, error:12.166061764886987]acc: None\n", + "Cond:#.#..... - Act:0 - effect:OO...... - Num:1 [fit: 0.009, exp: 0.00, pred: 805.080, error:131.90583494005463]acc: None\n", + "Cond:.#..#OO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#..#O.. - Act:4 - effect:....OOO. - Num:4 [fit: 0.156, exp: 229.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:.##....F - Act:7 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 838.451, error:164.77474804282448]acc: None\n", + "Cond:#..#.#O. - Act:0 - effect:.....OF. - Num:1 [fit: 0.003, exp: 0.00, pred: 915.151, error:764.5866828411978]acc: None\n", + "Cond:.#...OOO - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#O..F## - Act:7 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.##O#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 929.654, error:514.1286320878355]acc: None\n", + "Cond:..O..##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 818.559, error:517.9644260116796]acc: None\n", + "Cond:#..#OOO# - Act:3 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1134.909, error:43.4023400593814]acc: None\n", + "Cond:#.O##O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 964.712, error:709.1677494475464]acc: None\n", + "Cond:..#.##O. - Act:0 - effect:.....OF. - Num:1 [fit: 0.009, exp: 0.00, pred: 971.716, error:371.8659699068131]acc: None\n", + "Cond:.##.#O.. - Act:4 - effect:....OOO. - Num:2 [fit: 0.111, exp: 227.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:##O#.O## - Act:6 - effect:....OOO. - Num:2 [fit: 0.014, exp: 0.00, pred: 863.289, error:533.4398996516292]acc: None\n", + "Cond:###O#O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.005, exp: 0.00, pred: 1092.244, error:868.6700609672657]acc: None\n", + "Cond:#O##.O## - Act:7 - effect:OO...... - Num:1 [fit: 0.005, exp: 0.00, pred: 1120.782, error:818.0517726401399]acc: None\n", + "Cond:..##.O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1161.680, error:164.01551824108128]acc: None\n", + "Cond:..O##F## - Act:4 - effect:.O...... - Num:1 [fit: 0.008, exp: 0.00, pred: 1366.903, error:470.1862232563487]acc: None\n", + "Cond:.#..O#.# - Act:0 - effect:.OO..... - Num:2 [fit: 0.072, exp: 0.00, pred: 1030.397, error:49.11370807180829]acc: None\n", + "Cond:#.#O.O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1253.428, error:664.2824684019367]acc: None\n", + "Cond:##.O.O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.O#.O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1266.500, error:941.5112091758672]acc: None\n", + "Cond:..#.#.#. - Act:1 - effect:...O.... - Num:1 [fit: 0.021, exp: 0.00, pred: 403.308, error:75.41351828942646]acc: None\n", + "Cond:###.OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1513.666, error:95.49879982542083]acc: None\n", + "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:.#..#OO. - Act:6 - effect:....OOO. - Num:2 [fit: 0.024, exp: 0.00, pred: 1447.589, error:119.16049898953861]acc: None\n", + "Cond:.#OO##O# - Act:6 - effect:.OOO.... - Num:2 [fit: 0.021, exp: 0.00, pred: 979.937, error:37.96349960615717]acc: None\n", + "Cond:##...F.# - Act:1 - effect:.....F.. - Num:1 [fit: 0.004, exp: 20.00, pred: 1029.884, error:8515.654258146047]acc: 0.25\n", + "Cond:..#.#.#. - Act:4 - effect:OO...... - Num:1 [fit: 0.036, exp: 0.00, pred: 930.431, error:58.07335962502272]acc: None\n", + "Cond:##...#O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1136.997, error:30.99607865271706]acc: None\n", + "Cond:.#...OOO - Act:0 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 893.857, error:571.3297931467508]acc: None\n", + "Cond:.##.#OOF - Act:1 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 22.00, pred: 980.488, error:6061.912172086413]acc: 0.045454545454545456\n", + "Cond:###.OO#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1300.412, error:57.42400185589459]acc: None\n", + "Cond:#.#.#... - Act:4 - effect:..OO.... - Num:1 [fit: 0.009, exp: 0.00, pred: 928.978, error:830.4180045982635]acc: None\n", + "Cond:##.#OOO# - Act:3 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1381.904, error:58.24584308313]acc: None\n", + "Cond:.O.#O..# - Act:1 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##O#O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.039, exp: 0.00, pred: 1276.719, error:457.35769574422096]acc: None\n", + "Cond:.O###O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.039, exp: 0.00, pred: 1276.719, error:457.35769574422096]acc: None\n", + "Cond:.##.O..# - Act:0 - effect:.OO..... - Num:2 [fit: 0.014, exp: 0.00, pred: 1042.561, error:294.0139442634239]acc: None\n", + "Cond:#.###O#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.011, exp: 0.00, pred: 903.428, error:173.00392343184922]acc: None\n", + "Cond:#....... - Act:5 - effect:.....OF. - Num:1 [fit: 0.005, exp: 0.00, pred: 974.418, error:1263.1907452281937]acc: None\n", + "Cond:#O.F.##. - Act:4 - effect:..OO.... - Num:1 [fit: 0.013, exp: 0.00, pred: 1486.255, error:534.7820983971193]acc: None\n", + "Cond:##O.#..O - Act:5 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 910.770, error:511.65774802786757]acc: None\n", + "Cond:.#..OO.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1223.005, error:246.92583611372223]acc: None\n", + "Cond:O..#.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1701.050, error:157.55757826416934]acc: None\n", + "Cond:..O##O## - Act:2 - effect:.......O - Num:1 [fit: 0.024, exp: 0.00, pred: 1008.903, error:448.2334731752781]acc: None\n", + "Cond:.#O#.#F# - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1093.221, error:670.5491305483995]acc: None\n", + "Cond:###O##OF - Act:2 - effect:......OO - Num:1 [fit: 0.026, exp: 0.00, pred: 1039.096, error:62.98328176762753]acc: None\n", + "Cond:OO..#O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 898.662, error:1194.554506363608]acc: None\n", + "Cond:#.####O. - Act:3 - effect:...O.... - Num:1 [fit: 0.035, exp: 0.00, pred: 977.343, error:77.2621594002525]acc: None\n", + "Cond:.#OO#O## - Act:6 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1168.518, error:1250.5173020420696]acc: None\n", + "Cond:.##O##F. - Act:6 - effect:...O.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1353.501, error:704.3150709895405]acc: None\n", + "Cond:##O#.OO# - Act:2 - effect:......OO - Num:1 [fit: 0.007, exp: 0.00, pred: 1109.436, error:536.0384564808498]acc: None\n", + "Cond:..##.#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 351.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:###O##F# - Act:7 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1165.517, error:829.8050736029929]acc: None\n", + "Cond:#.#.#O.. - Act:4 - effect:....OOO. - Num:11 [fit: 0.437, exp: 216.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:##..OO.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O#O###.. - Act:3 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 0.00, pred: 998.944, error:763.5809422360993]acc: None\n", + "Cond:#.##O.#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.006, exp: 0.00, pred: 972.454, error:1209.2678892441409]acc: None\n", + "Cond:####OO#. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 69.00, pred: 1047.171, error:4583.402553013802]acc: 0.4492753623188406\n", + "Cond:..#.#O.. - Act:4 - effect:....OOO. - Num:2 [fit: 0.078, exp: 211.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:..###OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 342.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:.#..O### - Act:0 - effect:.OO..... - Num:1 [fit: 0.003, exp: 0.00, pred: 1009.244, error:945.8120367414008]acc: None\n", + "Cond:.#.#.O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1268.151, error:703.36509256923]acc: None\n", + "Cond:#...O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1197.999, error:825.167592832555]acc: None\n", + "Cond:#O.##O.# - Act:3 - effect:OO.....O - Num:1 [fit: 0.003, exp: 0.00, pred: 1415.370, error:228.17216904426186]acc: None\n", + "Cond:####O.#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#O.#O..# - Act:3 - effect:OO.....O - Num:2 [fit: 0.002, exp: 0.00, pred: 1181.242, error:415.0973867984543]acc: None\n", + "Cond:####F..# - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##...F#. - Act:1 - effect:.....F.. - Num:1 [fit: 0.001, exp: 15.00, pred: 1029.068, error:9719.508217033717]acc: 0.13333333333333333\n", + "Cond:..#.#OOF - Act:1 - effect:.O...O.. - Num:1 [fit: 0.000, exp: 21.00, pred: 979.546, error:5094.131113166537]acc: 0.0\n", + "Cond:.###.O.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:####.#O. - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 0.00, pred: 972.120, error:550.7469166679264]acc: None\n", + "Cond:O##F#### - Act:3 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.#.OO#. - Act:6 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 1247.081, error:893.0958731850169]acc: None\n", + "Cond:.#.#O#.O - Act:0 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##.##O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1218.710, error:36.0999348841262]acc: None\n", + "Cond:O####O.. - Act:3 - effect:...OO... - Num:1 [fit: 0.061, exp: 0.00, pred: 1506.588, error:410.06151698988685]acc: None\n", + "Cond:##.#O..O - Act:1 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1181.571, error:1107.3681277358278]acc: None\n", + "Cond:#O.#O.#O - Act:4 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 1021.800, error:1269.2494760894838]acc: None\n", + "Cond:.##.OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O###OO.# - Act:3 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.FO#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 32.00, pred: 1076.073, error:1089.2917565981948]acc: 0.15625\n", + "Cond:.#..OO.# - Act:7 - effect:.O..OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1019.834, error:1183.851075920574]acc: None\n", + "Cond:###..O#F - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 20.00, pred: 980.531, error:5265.246965455828]acc: 0.0\n", + "Cond:#.##O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1186.953, error:37.860823204333464]acc: None\n", + "Cond:.###.##F - Act:7 - effect:....OOO. - Num:2 [fit: 0.018, exp: 322.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:#.#..F.O - Act:3 - effect:....OOO. - Num:1 [fit: 0.030, exp: 0.00, pred: 905.262, error:286.27934222656575]acc: None\n", + "Cond:.OOO#... - Act:2 - effect:OO...... - Num:1 [fit: 0.007, exp: 21.00, pred: 940.345, error:9551.143908792434]acc: 0.0\n", + "Cond:##O###F# - Act:7 - effect:.OOO.... - Num:1 [fit: 0.011, exp: 0.00, pred: 1057.184, error:759.1516880929711]acc: None\n", + "Cond:##..OO#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1137.403, error:34.573048125231956]acc: None\n", + "Cond:###F.### - Act:5 - effect:OO.....O - Num:1 [fit: 0.007, exp: 0.00, pred: 988.926, error:576.6385820120499]acc: None\n", + "Cond:.###OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.052, exp: 300.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", + "Cond:...O#F## - Act:6 - effect:..OO.... - Num:1 [fit: 0.018, exp: 0.00, pred: 1069.607, error:46.812761319223604]acc: None\n", + "Cond:.O##O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 775.654, error:119.90554646370725]acc: None\n", + "Cond:.##O.F## - Act:7 - effect:.....F.. - Num:1 [fit: 0.002, exp: 0.00, pred: 945.915, error:1083.1149867686468]acc: None\n", + "Cond:....##F# - Act:4 - effect:......OO - Num:1 [fit: 0.003, exp: 103.00, pred: 1368.534, error:3153.2862143935286]acc: 0.47572815533980584\n", + "Cond:.#####.F - Act:7 - effect:.......O - Num:1 [fit: 0.019, exp: 0.00, pred: 1341.929, error:234.80242710497535]acc: None\n", + "Cond:.#...OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 318.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:####.OF# - Act:6 - effect:....OOO. - Num:2 [fit: 0.022, exp: 249.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:##...O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1213.371, error:1164.1810628654935]acc: None\n", + "Cond:###..OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1290.790, error:74.41226859335936]acc: None\n", + "Cond:#O##O.## - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##...O.O - Act:4 - effect:....OOO. - Num:2 [fit: 0.001, exp: 0.00, pred: 1136.548, error:955.4343547648631]acc: None\n", + "Cond:O#.F###. - Act:7 - effect:...OO... - Num:1 [fit: 0.002, exp: 0.00, pred: 1117.402, error:1211.5034252941382]acc: None\n", + "Cond:..O####. - Act:2 - effect:.....F.. - Num:1 [fit: 0.000, exp: 25.00, pred: 971.160, error:6393.7121924022285]acc: 0.08\n", + "Cond:O##.#O.O - Act:4 - effect:O......O - Num:1 [fit: 0.002, exp: 0.00, pred: 1104.331, error:629.8255807138653]acc: None\n", + "Cond:.....O#. - Act:1 - effect:.O...... - Num:3 [fit: 0.002, exp: 22.00, pred: 926.395, error:9731.598541633895]acc: 0.13636363636363635\n", + "Cond:.O.#O### - Act:3 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:..#..#.. - Act:5 - effect:....OOO. - Num:7 [fit: 0.365, exp: 325.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:###..OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 238.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:...#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1149.751, error:39.32850747625649]acc: None\n", + "Cond:.#...OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1085.168, error:431.45322213678213]acc: None\n", + "Cond:O#.O#.#. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.061, exp: 0.00, pred: 1159.109, error:439.64233462125304]acc: None\n", + "Cond:.####OO# - Act:6 - effect:.....OOF - Num:1 [fit: 0.095, exp: 49.00, pred: 933.318, error:3236.1384018544727]acc: 0.6122448979591837\n", + "Cond:#.O..### - Act:5 - effect:....FO.. - Num:1 [fit: 0.020, exp: 0.00, pred: 1770.615, error:42.92531866578943]acc: None\n", + "Cond:#O.OO.#. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.049, exp: 0.00, pred: 1232.650, error:766.4221524342124]acc: None\n", + "Cond:.#...O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1051.819, error:1081.5533476689016]acc: None\n", + "Cond:#.#.O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1253.513, error:259.8020440745543]acc: None\n", + "Cond:..OO##.# - Act:2 - effect:.....OF. - Num:3 [fit: 0.003, exp: 22.00, pred: 969.211, error:5476.468524347716]acc: 0.09090909090909091\n", + "Cond:##.#.F## - Act:3 - effect:.....OOF - Num:1 [fit: 0.000, exp: 23.00, pred: 1252.601, error:2371.4969366342984]acc: 0.21739130434782608\n", + "Cond:#OO##O## - Act:2 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 887.361, error:760.5945495509885]acc: None\n", + "Cond:#O#O#... - Act:0 - effect:.OOO.... - Num:2 [fit: 0.000, exp: 25.00, pred: 1030.815, error:4411.786000313852]acc: 0.2\n", + "Cond:O....##. - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 936.109, error:841.7903084571736]acc: None\n", + "Cond:O###O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 899.179, error:2.037945717961648]acc: None\n", + "Cond:.#.#OO.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.052, exp: 284.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", + "Cond:##O#O#.# - Act:5 - effect:...O.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1349.388, error:860.338571438274]acc: None\n", + "Cond:O.#####. - Act:5 - effect:.O...... - Num:1 [fit: 0.005, exp: 0.00, pred: 1079.526, error:1455.0142491802617]acc: None\n", + "Cond:.O##.#O# - Act:7 - effect:....FO.. - Num:1 [fit: 0.010, exp: 0.00, pred: 1062.789, error:908.1755681207281]acc: None\n", + "Cond:###O#.#O - Act:1 - effect:O......O - Num:1 [fit: 0.021, exp: 0.00, pred: 822.781, error:26.120633460353872]acc: None\n", + "Cond:##O#..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1021.601, error:262.6330811729328]acc: None\n", + "Cond:#.O#..#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 14.00, pred: 968.009, error:7636.359034938088]acc: 0.0\n", + "Cond:###O##O# - Act:2 - effect:......OO - Num:1 [fit: 0.009, exp: 0.00, pred: 974.747, error:888.9318642454855]acc: None\n", + "Cond:.O#O#.#. - Act:2 - effect:.OOO.... - Num:5 [fit: 0.025, exp: 15.00, pred: 945.849, error:8328.366070324544]acc: 0.26666666666666666\n", + "Cond:.OOO..## - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 25.00, pred: 1128.044, error:9034.44271686875]acc: 0.24\n", + "Cond:####.#F# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 231.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:.OOO.### - Act:4 - effect:......OO - Num:2 [fit: 0.000, exp: 24.00, pred: 1128.847, error:3570.5421240399155]acc: 0.4166666666666667\n", + "Cond:.#O##O## - Act:3 - effect:OO.....O - Num:2 [fit: 0.024, exp: 0.00, pred: 1133.023, error:84.88954122388782]acc: None\n", + "Cond:.#O#...O - Act:6 - effect:.OOO.... - Num:1 [fit: 0.009, exp: 0.00, pred: 916.294, error:289.5283432959402]acc: None\n", + "Cond:.O##.O## - Act:7 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 942.460, error:1309.5593472876953]acc: None\n", + "Cond:....#..# - Act:7 - effect:.......O - Num:1 [fit: 0.007, exp: 19.00, pred: 909.151, error:8717.296543212979]acc: 0.15789473684210525\n", + "Cond:......#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1063.407, error:1045.7580299090591]acc: None\n", + "Cond:#....#.. - Act:5 - effect:....OOO. - Num:5 [fit: 0.325, exp: 314.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:##.#.O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 229.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:###.#F#O - Act:6 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O...###. - Act:7 - effect:OO.....O - Num:1 [fit: 0.001, exp: 0.00, pred: 984.142, error:760.3215295171951]acc: None\n", + "Cond:.#.#.F#. - Act:1 - effect:...OO... - Num:1 [fit: 0.001, exp: 12.00, pred: 1049.342, error:8029.785487746452]acc: 0.08333333333333333\n", + "Cond:#..#.... - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 17.00, pred: 1036.303, error:10134.2562084032]acc: 0.0\n", + "Cond:#.#.OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1307.307, error:379.1873498518728]acc: None\n", + "Cond:###.#O#O - Act:4 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 1618.104, error:114.78010332816353]acc: None\n", + "Cond:##.#OO.# - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 24.00, pred: 1012.854, error:4167.558598157433]acc: 0.2916666666666667\n", + "Cond:.....O.# - Act:5 - effect:....OOO. - Num:3 [fit: 0.423, exp: 0.00, pred: 1422.851, error:83.66002322219938]acc: None\n", + "Cond:........ - Act:1 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 1148.456, error:0.0]acc: None\n", + "Cond:O#.##O#. - Act:0 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1073.559, error:1014.9064474411622]acc: None\n", + "Cond:###.OO.O - Act:4 - effect:..OO.... - Num:1 [fit: 0.005, exp: 0.00, pred: 1032.180, error:201.3285240770358]acc: None\n", + "Cond:.O#.#O.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.005, exp: 0.00, pred: 1071.241, error:688.314472379484]acc: None\n", + "Cond:.O#..##O - Act:0 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1176.583, error:639.3652594239725]acc: None\n", + "Cond:##..O.#. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1301.807, error:1160.6155376280176]acc: None\n", + "Cond:#...#F#. - Act:2 - effect:.....F.. - Num:1 [fit: 0.012, exp: 15.00, pred: 1026.661, error:8358.672035887259]acc: 0.3333333333333333\n", + "Cond:.#..OO.# - Act:1 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 962.685, error:1191.8858311548393]acc: None\n", + "Cond:O#.OO#.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 904.868, error:279.65432478616634]acc: None\n", + "Cond:O#O#.##. - Act:7 - effect:.......O - Num:1 [fit: 0.016, exp: 0.00, pred: 1088.227, error:352.2148094817006]acc: None\n", + "Cond:##..OO.. - Act:3 - effect:....OOO. - Num:3 [fit: 0.008, exp: 0.00, pred: 1239.852, error:67.33541676300119]acc: None\n", + "Cond:#.O..O#. - Act:1 - effect:.O...O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 912.289, error:898.5363880938601]acc: None\n", + "Cond:..#..OO. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1422.601, error:83.83675611846584]acc: None\n", + "Cond:..OO#### - Act:3 - effect:.....OOF - Num:1 [fit: 0.000, exp: 25.00, pred: 1147.681, error:2371.6761143163076]acc: 0.08\n", + "Cond:.#..#O.. - Act:1 - effect:...OO... - Num:2 [fit: 0.001, exp: 15.00, pred: 874.968, error:8489.480539792336]acc: 0.0\n", + "Cond:.O.##O.. - Act:7 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#O##.F# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#..#F## - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 21.00, pred: 1063.803, error:7576.750395072394]acc: 0.14285714285714285\n", + "Cond:####.O.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1176.870, error:267.18385858058247]acc: None\n", + "Cond:#####O.O - Act:7 - effect:OO.....O - Num:1 [fit: 0.021, exp: 0.00, pred: 775.400, error:27.630181577521896]acc: None\n", + "Cond:O###O.## - Act:5 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#O#O.##O - Act:5 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.#OO## - Act:2 - effect:.....OF. - Num:1 [fit: 0.006, exp: 21.00, pred: 1014.009, error:6506.79511269634]acc: 0.19047619047619047\n", + "Cond:O##.#.#. - Act:0 - effect:OO...... - Num:1 [fit: 0.002, exp: 24.00, pred: 857.688, error:6821.283418738479]acc: 0.375\n", + "Cond:#####O#F - Act:2 - effect:......OO - Num:3 [fit: 0.009, exp: 13.00, pred: 946.391, error:6025.758044946096]acc: 0.23076923076923078\n", + "Cond:.O#O#.## - Act:4 - effect:.......O - Num:1 [fit: 0.003, exp: 16.00, pred: 1117.235, error:8171.291984742688]acc: 0.1875\n", + "Cond:##..OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1001.980, error:152.71141754622786]acc: None\n", + "Cond:#.#.#.#. - Act:3 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#O#F#### - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##..##O. - Act:0 - effect:....OOO. - Num:1 [fit: 0.102, exp: 0.00, pred: 1186.670, error:88.59267966867945]acc: None\n", + "Cond:.OO.#.## - Act:2 - effect:.OO..... - Num:2 [fit: 0.014, exp: 21.00, pred: 953.744, error:6467.789179675442]acc: 0.2857142857142857\n", + "Cond:.....#.. - Act:2 - effect:OO...... - Num:1 [fit: 0.002, exp: 10.00, pred: 1009.894, error:8560.897664249467]acc: 0.0\n", + "Cond:#O##O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1131.787, error:929.218849854554]acc: None\n", + "Cond:###..O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 226.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:...###OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 11.00, pred: 920.050, error:8733.98821177772]acc: 0.0\n", + "Cond:..#.#OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 276.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:.O.#.... - Act:0 - effect:...OO... - Num:3 [fit: 0.000, exp: 20.00, pred: 936.856, error:9685.483228709065]acc: 0.0\n", + "Cond:.#...F## - Act:2 - effect:OO...... - Num:1 [fit: 0.003, exp: 9.00, pred: 1027.070, error:8234.678884458044]acc: 0.0\n", + "Cond:.###O.#. - Act:1 - effect:OO...... - Num:2 [fit: 0.001, exp: 19.00, pred: 1063.534, error:8723.74925557507]acc: 0.10526315789473684\n", + "Cond:#.###F.. - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 19.00, pred: 1066.536, error:6777.381512603277]acc: 0.05263157894736842\n", + "Cond:#O.#O... - Act:6 - effect:...OO... - Num:1 [fit: 0.005, exp: 0.00, pred: 1056.789, error:1357.7561687820216]acc: None\n", + "Cond:.#..OO.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1284.352, error:71.47181342268837]acc: None\n", + "Cond:..OO..#. - Act:3 - effect:.......O - Num:1 [fit: 0.003, exp: 24.00, pred: 1146.215, error:5886.829756550145]acc: 0.0\n", + "Cond:.##FO##. - Act:2 - effect:OO.....O - Num:3 [fit: 0.031, exp: 18.00, pred: 1018.944, error:9187.832221158535]acc: 0.16666666666666666\n", + "Cond:O#.#O#.# - Act:5 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 1006.093, error:1066.3295755822382]acc: None\n", + "Cond:.O..###O - Act:4 - effect:..OO.... - Num:1 [fit: 0.016, exp: 0.00, pred: 1034.413, error:360.2779666190165]acc: None\n", + "Cond:......#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:..OO#... - Act:3 - effect:.......O - Num:1 [fit: 0.003, exp: 22.00, pred: 1146.519, error:5119.998679017744]acc: 0.0\n", + "Cond:#######F - Act:2 - effect:......OO - Num:1 [fit: 0.004, exp: 9.00, pred: 902.003, error:6056.060058693509]acc: 0.2222222222222222\n", + "Cond:OO##.#.. - Act:0 - effect:OO...... - Num:1 [fit: 0.001, exp: 19.00, pred: 850.793, error:5142.9794233053]acc: 0.42105263157894735\n", + "Cond:#O.##O#F - Act:2 - effect:......OO - Num:1 [fit: 0.004, exp: 0.00, pred: 1094.524, error:971.1088582731361]acc: None\n", + "Cond:.O#O#.#. - Act:0 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 19.00, pred: 1029.243, error:3708.798639406525]acc: 0.3157894736842105\n", + "Cond:.#.#.O#. - Act:0 - effect:....OOO. - Num:2 [fit: 0.012, exp: 67.00, pred: 1070.650, error:2160.9847580697715]acc: 0.6567164179104478\n", + "Cond:.#.#.O.# - Act:0 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#...#... - Act:7 - effect:.O...... - Num:1 [fit: 0.008, exp: 0.00, pred: 1069.668, error:1508.9817266422792]acc: None\n", + "Cond:.O#O###. - Act:2 - effect:.....OOF - Num:1 [fit: 0.084, exp: 6.00, pred: 894.728, error:5770.205625715598]acc: 0.3333333333333333\n", + "Cond:OO#.##.. - Act:0 - effect:OO...... - Num:1 [fit: 0.002, exp: 18.00, pred: 850.500, error:5722.201222666856]acc: 0.4444444444444444\n", + "Cond:OO#...#. - Act:0 - effect:.....OOF - Num:1 [fit: 0.002, exp: 18.00, pred: 850.500, error:6861.22483209687]acc: 0.0\n", + "Cond:.#.#.O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.103, exp: 46.00, pred: 933.324, error:3506.6951633507415]acc: 0.4782608695652174\n", + "Cond:#..#.O#. - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 12.00, pred: 924.291, error:8854.906396180486]acc: 0.0\n", + "Cond:O#..#O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 896.143, error:244.6402517932314]acc: None\n", + "Cond:....OO#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.O.O.#.. - Act:0 - effect:OO.....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1060.214, error:1397.0872476820637]acc: None\n", + "Cond:..#FO### - Act:2 - effect:OO.....O - Num:1 [fit: 0.001, exp: 16.00, pred: 1015.376, error:9051.084453573203]acc: 0.0625\n", + "Cond:.....#.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.023, exp: 12.00, pred: 1049.342, error:9618.194319746453]acc: 0.0\n", + "Cond:..###O#O - Act:7 - effect:...OO... - Num:1 [fit: 0.015, exp: 0.00, pred: 1117.466, error:766.6614226048357]acc: None\n", + "Cond:...OO..# - Act:7 - effect:.....F.. - Num:3 [fit: 0.376, exp: 73.00, pred: 1113.593, error:269.6657456943022]acc: 0.821917808219178\n", + "Cond:###O.##O - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 982.327, error:782.5501071232829]acc: None\n", + "Cond:#..###O. - Act:4 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1165.763, error:293.6334388110495]acc: None\n", + "Cond:.#...O.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.426, exp: 0.00, pred: 1118.494, error:31.556817384914076]acc: None\n", + "Cond:.#O##O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##...O#O - Act:0 - effect:OO.....O - Num:1 [fit: 0.015, exp: 0.00, pred: 854.738, error:470.99578246073844]acc: None\n", + "Cond:.O###.#O - Act:7 - effect:.O...... - Num:1 [fit: 0.006, exp: 0.00, pred: 956.566, error:776.3966998598792]acc: None\n", + "Cond:..OO...# - Act:4 - effect:......OO - Num:3 [fit: 0.064, exp: 48.00, pred: 1151.838, error:4817.885635846153]acc: 0.5833333333333334\n", + "Cond:##.###O# - Act:2 - effect:OO...... - Num:2 [fit: 0.000, exp: 18.00, pred: 964.871, error:6325.735235005553]acc: 0.16666666666666666\n", + "Cond:#.#.##F# - Act:5 - effect:.....OF. - Num:1 [fit: 0.030, exp: 95.00, pred: 948.221, error:4802.898969221626]acc: 0.6631578947368421\n", + "Cond:.##.#O.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.025, exp: 6.00, pred: 1024.032, error:2441.176714447477]acc: 0.5\n", + "Cond:##.O.##. - Act:2 - effect:...OO... - Num:1 [fit: 0.002, exp: 13.00, pred: 1007.329, error:6254.719680946342]acc: 0.07692307692307693\n", + "Cond:.###.OO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 841.398, error:599.0736656223875]acc: None\n", + "Cond:O#..###. - Act:0 - effect:OO.....O - Num:1 [fit: 0.001, exp: 16.00, pred: 857.303, error:6853.395773564696]acc: 0.0625\n", + "Cond:O.#.#..# - Act:0 - effect:O......O - Num:1 [fit: 0.019, exp: 37.00, pred: 1028.748, error:3847.9798068190203]acc: 0.40540540540540543\n", + "Cond:##.#OO## - Act:4 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 57.00, pred: 1047.170, error:3907.877596548241]acc: 0.543859649122807\n", + "Cond:.O.#.##. - Act:0 - effect:.......O - Num:1 [fit: 0.008, exp: 16.00, pred: 937.529, error:5525.74347549681]acc: 0.3125\n", + "Cond:##...OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.033, exp: 223.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:.##O##O# - Act:6 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 791.906, error:965.327013681102]acc: None\n", + "Cond:.....##F - Act:2 - effect:.OOO.... - Num:1 [fit: 0.106, exp: 5.00, pred: 1010.040, error:5384.854890251749]acc: 0.2\n", + "Cond:.#####OF - Act:2 - effect:......OO - Num:1 [fit: 0.003, exp: 5.00, pred: 1010.040, error:3224.854890251748]acc: 0.4\n", + "Cond:#O#...#F - Act:7 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##.OO#F - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1052.891, error:16.301000973399304]acc: None\n", + "Cond:.....O.. - Act:5 - effect:....OOO. - Num:5 [fit: 0.363, exp: 0.00, pred: 1176.793, error:32.512296442912294]acc: None\n", + "Cond:O#.#O#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.016, exp: 0.00, pred: 897.449, error:441.0635530135107]acc: None\n", + "Cond:.#.F###. - Act:2 - effect:...FOO.. - Num:2 [fit: 0.043, exp: 9.00, pred: 1009.545, error:7765.241836811662]acc: 0.1111111111111111\n", + "Cond:.#.FO### - Act:2 - effect:OO.....O - Num:1 [fit: 0.004, exp: 9.00, pred: 1009.545, error:6560.953836811662]acc: 0.1111111111111111\n", + "Cond:##...O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 1151.187, error:132.66421236771774]acc: None\n", + "Cond:#..###F# - Act:5 - effect:.....OF. - Num:2 [fit: 0.009, exp: 90.00, pred: 948.221, error:4493.86813516331]acc: 0.5888888888888889\n", + "Cond:.#.O.O#. - Act:1 - effect:..OO.... - Num:1 [fit: 0.007, exp: 0.00, pred: 940.499, error:342.8949450838872]acc: None\n", + "Cond:O..####. - Act:0 - effect:.....F.. - Num:1 [fit: 0.017, exp: 0.00, pred: 1229.109, error:209.3114901886919]acc: None\n", + "Cond:.O#O..## - Act:1 - effect:..OO.... - Num:1 [fit: 0.086, exp: 6.00, pred: 893.929, error:4770.082504109861]acc: 0.16666666666666666\n", + "Cond:O.#.#.#O - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 13.00, pred: 884.097, error:8209.072561856661]acc: 0.0\n", + "Cond:##O..... - Act:1 - effect:.OO..... - Num:1 [fit: 0.002, exp: 24.00, pred: 957.386, error:2965.3242697984847]acc: 0.5833333333333334\n", + "Cond:#..#..O# - Act:7 - effect:......OO - Num:1 [fit: 0.005, exp: 31.00, pred: 1150.489, error:3009.5830275008134]acc: 0.25806451612903225\n", + "Cond:.##O.#.. - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 24.00, pred: 900.959, error:7587.751645673359]acc: 0.041666666666666664\n", + "Cond:##.#.OF. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 221.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:##.O.... - Act:2 - effect:...O.... - Num:2 [fit: 0.081, exp: 11.00, pred: 1006.733, error:8579.125112063844]acc: 0.2727272727272727\n", + "Cond:####.O#F - Act:3 - effect:.......O - Num:3 [fit: 0.017, exp: 75.00, pred: 1140.138, error:4443.863756411878]acc: 0.6\n", + "Cond:.OO##.#. - Act:2 - effect:OO...... - Num:1 [fit: 0.010, exp: 16.00, pred: 942.001, error:6265.789508399891]acc: 0.25\n", + "Cond:#.#.###. - Act:2 - effect:.....OF. - Num:1 [fit: 0.005, exp: 36.00, pred: 1045.850, error:4067.5352741501056]acc: 0.3055555555555556\n", + "Cond:#.#.O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1207.863, error:39.195610020814875]acc: None\n", + "Cond:O.####.# - Act:3 - effect:.....OF. - Num:1 [fit: 0.001, exp: 43.00, pred: 1261.457, error:4070.0098377442073]acc: 0.4883720930232558\n", + "Cond:O#..#F.# - Act:3 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.##.O.# - Act:1 - effect:.OO..... - Num:1 [fit: 0.003, exp: 0.00, pred: 1215.669, error:1531.1879395285355]acc: None\n", + "Cond:#..#OOO. - Act:3 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1250.563, error:45.139962864878655]acc: None\n", + "Cond:#O#O..## - Act:7 - effect:.....OOF - Num:1 [fit: 0.001, exp: 27.00, pred: 2049.631, error:1678.8381281427182]acc: 0.25925925925925924\n", + "Cond:.O.#.#.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.009, exp: 67.00, pred: 1894.529, error:550.1482358605605]acc: 0.7014925373134329\n", + "Cond:.#.###.O - Act:3 - effect:OO...... - Num:1 [fit: 0.000, exp: 22.00, pred: 1212.852, error:5746.761413158551]acc: 0.045454545454545456\n", + "Cond:#.####OO - Act:6 - effect:.....OOF - Num:1 [fit: 0.005, exp: 36.00, pred: 978.032, error:2830.8684347192475]acc: 0.4166666666666667\n", + "Cond:..#.#..# - Act:0 - effect:.OOO.... - Num:1 [fit: 0.008, exp: 20.00, pred: 1245.637, error:6765.218248784539]acc: 0.1\n", + "Cond:.#...#.. - Act:5 - effect:....OOO. - Num:10 [fit: 0.692, exp: 417.00, pred: 1225.169, error:248.2805850823554]acc: 0.9256594724220624\n", + "Cond:#.#..#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 283.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:.#..OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1111.853, error:37.90975355208421]acc: None\n", + "Cond:###..O#. - Act:1 - effect:.....OF. - Num:1 [fit: 0.011, exp: 8.00, pred: 879.937, error:8136.584696373757]acc: 0.125\n", + "Cond:..#..O.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.015, exp: 0.00, pred: 986.386, error:1198.3295749210704]acc: None\n", + "Cond:.O...##. - Act:0 - effect:.......O - Num:1 [fit: 0.033, exp: 12.00, pred: 931.021, error:5575.988477887693]acc: 0.25\n", + "Cond:.#..#... - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 12.00, pred: 1156.305, error:7780.24041537731]acc: 0.0\n", + "Cond:.###O#O# - Act:3 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1088.083, error:44.77071679352069]acc: None\n", + "Cond:#..##O.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 25.00, pred: 960.622, error:3421.626114607917]acc: 0.36\n", + "Cond:#.O.###. - Act:2 - effect:.....F.. - Num:2 [fit: 0.001, exp: 0.00, pred: 1231.565, error:740.9353479898393]acc: None\n", + "Cond:.#O#.O## - Act:1 - effect:.OOO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 1032.501, error:522.6503829218943]acc: None\n", + "Cond:#..O#F#. - Act:2 - effect:.....F.. - Num:1 [fit: 0.002, exp: 0.00, pred: 1144.525, error:1222.5213098990876]acc: None\n", + "Cond:.##O.F#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:...#.F## - Act:0 - effect:.......O - Num:1 [fit: 0.002, exp: 9.00, pred: 1105.135, error:8027.867406869053]acc: 0.0\n", + "Cond:.O..#.#. - Act:0 - effect:.......O - Num:1 [fit: 0.004, exp: 10.00, pred: 908.670, error:5670.271644041398]acc: 0.2\n", + "Cond:.#O.#..# - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 7.00, pred: 858.255, error:7110.2418365759895]acc: 0.0\n", + "Cond:#.O#.O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1570.376, error:234.89592299834823]acc: None\n", + "Cond:#.O#.O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1146.145, error:454.1831616467405]acc: None\n", + "Cond:####O### - Act:4 - effect:OO.....O - Num:1 [fit: 0.005, exp: 74.00, pred: 1026.373, error:3875.197274471728]acc: 0.10810810810810811\n", + "Cond:O..#.F.# - Act:3 - effect:O......O - Num:1 [fit: 0.004, exp: 0.00, pred: 1092.201, error:306.4644654474623]acc: None\n", + "Cond:#OO..... - Act:1 - effect:.OO..... - Num:1 [fit: 0.006, exp: 20.00, pred: 957.265, error:2506.545444965662]acc: 0.65\n", + "Cond:.OO..### - Act:2 - effect:OO...... - Num:2 [fit: 0.002, exp: 12.00, pred: 933.967, error:6134.522638733617]acc: 0.16666666666666666\n", + "Cond:O...#.#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1212.195, error:549.264601459175]acc: None\n", + "Cond:..#F###. - Act:2 - effect:OO.....O - Num:1 [fit: 0.005, exp: 7.00, pred: 989.777, error:7013.11585548437]acc: 0.0\n", + "Cond:.#..##.O - Act:7 - effect:.OO..... - Num:1 [fit: 0.015, exp: 6.00, pred: 959.286, error:3714.1502632249276]acc: 0.3333333333333333\n", + "Cond:#..F#O.. - Act:5 - effect:....OOO. - Num:4 [fit: 0.052, exp: 17.00, pred: 1074.928, error:2421.304164406746]acc: 0.23529411764705882\n", + "Cond:O##.#F## - Act:7 - effect:...FOO.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1027.066, error:682.5119003527898]acc: None\n", + "Cond:.#.O.### - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 9.00, pred: 1033.085, error:8195.347572017981]acc: 0.0\n", + "Cond:.O#..##. - Act:2 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 16.00, pred: 909.757, error:7451.677856406234]acc: 0.0625\n", + "Cond:O...O... - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1065.197, error:643.162488784322]acc: None\n", + "Cond:.O#O..#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.012, exp: 1.00, pred: 841.502, error:1800.0]acc: 0.0\n", + "Cond:.O#O#... - Act:2 - effect:.....OOF - Num:1 [fit: 0.012, exp: 1.00, pred: 841.502, error:1600.0]acc: 0.0\n", + "Cond:..##OO.. - Act:4 - effect:OO...... - Num:1 [fit: 0.003, exp: 56.00, pred: 1047.171, error:2781.8296713777454]acc: 0.23214285714285715\n", + "Cond:O##.F#.# - Act:3 - effect:O......O - Num:1 [fit: 0.003, exp: 0.00, pred: 1103.218, error:648.7735131315318]acc: None\n", + "Cond:##.#.F.# - Act:1 - effect:.....F.. - Num:1 [fit: 0.009, exp: 5.00, pred: 1008.599, error:5876.987299821381]acc: 0.0\n", + "Cond:O.#.#... - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1076.793, error:754.0843658338299]acc: None\n", + "Cond:O#.####O - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 24.00, pred: 977.785, error:5479.632449687706]acc: 0.041666666666666664\n", + "Cond:.O#.#.## - Act:2 - effect:OO...... - Num:3 [fit: 0.015, exp: 14.00, pred: 908.246, error:6550.69023223451]acc: 0.14285714285714285\n", + "Cond:O#.#.O.# - Act:3 - effect:O......O - Num:1 [fit: 0.005, exp: 0.00, pred: 1159.774, error:555.6545574767681]acc: None\n", + "Cond:.....#.# - Act:7 - effect:....FO.. - Num:1 [fit: 0.002, exp: 52.00, pred: 1268.250, error:6795.152125820821]acc: 0.3269230769230769\n", + "Cond:###O.O#. - Act:2 - effect:......OO - Num:1 [fit: 0.009, exp: 0.00, pred: 1122.699, error:869.3543599420655]acc: None\n", + "Cond:#.###... - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 16.00, pred: 948.858, error:7161.262966456474]acc: 0.0625\n", + "Cond:..#O#... - Act:3 - effect:...OO... - Num:2 [fit: 0.151, exp: 58.00, pred: 1234.455, error:2250.1608840581725]acc: 0.5344827586206896\n", + "Cond:#O...##. - Act:0 - effect:.......O - Num:2 [fit: 0.000, exp: 21.00, pred: 878.038, error:6214.440586184976]acc: 0.09523809523809523\n", + "Cond:.O..###. - Act:7 - effect:.OO..... - Num:1 [fit: 0.000, exp: 19.00, pred: 1076.732, error:8187.071025752364]acc: 0.0\n", + "Cond:...##..# - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 14.00, pred: 920.966, error:9224.092882824807]acc: 0.0\n", + "Cond:#O.#O.#. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 989.542, error:1503.3375897588335]acc: None\n", + "Cond:#.#..#.# - Act:0 - effect:O......O - Num:1 [fit: 0.004, exp: 61.00, pred: 1086.034, error:4483.733345606549]acc: 0.4098360655737705\n", + "Cond:..##.F## - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 19.00, pred: 1162.723, error:4799.6932441188055]acc: 0.21052631578947367\n", + "Cond:O#..#.#O - Act:2 - effect:......OO - Num:1 [fit: 0.002, exp: 22.00, pred: 975.885, error:5884.888119189976]acc: 0.045454545454545456\n", + "Cond:..#.OO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.064, exp: 0.00, pred: 1146.172, error:478.69331607371066]acc: None\n", + "Cond:..###..O - Act:1 - effect:...O.... - Num:1 [fit: 0.006, exp: 6.00, pred: 1049.240, error:6931.501353723165]acc: 0.0\n", + "Cond:.....F.# - Act:7 - effect:....FO.. - Num:1 [fit: 0.002, exp: 45.00, pred: 1268.261, error:5839.2376780789]acc: 0.37777777777777777\n", + "Cond:##O.##O# - Act:2 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.....##. - Act:5 - effect:....OOO. - Num:2 [fit: 0.179, exp: 364.00, pred: 1226.728, error:249.8057188608285]acc: 0.7939560439560439\n", + "Cond:O.##...O - Act:1 - effect:.....OOF - Num:1 [fit: 0.032, exp: 10.00, pred: 1050.830, error:4119.098536581365]acc: 0.0\n", + "Cond:OO.###.. - Act:2 - effect:......OO - Num:2 [fit: 0.007, exp: 14.00, pred: 966.085, error:9216.000494178003]acc: 0.0\n", + "Cond:.#.F#.#. - Act:0 - effect:OO.....O - Num:2 [fit: 0.021, exp: 0.00, pred: 911.826, error:42.47580794201477]acc: None\n", + "Cond:.##O..#. - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 19.00, pred: 902.597, error:7492.128699164037]acc: 0.0\n", + "Cond:###O...# - Act:1 - effect:.....OF. - Num:1 [fit: 0.000, exp: 19.00, pred: 902.597, error:4015.566950193336]acc: 0.15789473684210525\n", + "Cond:####O#.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.041, exp: 9.00, pred: 1003.786, error:4959.16399310077]acc: 0.3333333333333333\n", + "Cond:..##OO#. - Act:2 - effect:OO.....O - Num:1 [fit: 0.009, exp: 4.00, pred: 1008.919, error:4786.2214770416695]acc: 0.0\n", + "Cond:.O#.#.O# - Act:7 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 912.433, error:1047.5437655435983]acc: None\n", + "Cond:....#..O - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 5.00, pred: 1077.739, error:5594.286819264981]acc: 0.0\n", + "Cond:O#..##.. - Act:0 - effect:OO.....O - Num:3 [fit: 0.108, exp: 12.00, pred: 871.049, error:6264.20831665201]acc: 0.08333333333333333\n", + "Cond:O#.F.O.# - Act:3 - effect:O......O - Num:1 [fit: 0.004, exp: 0.00, pred: 1009.751, error:1006.1290705479719]acc: None\n", + "Cond:.#..OO#. - Act:7 - effect:.O...... - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:..#.#O.O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.008, exp: 0.00, pred: 988.863, error:946.8702685718273]acc: None\n", + "Cond:O.#.###. - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 988.863, error:946.8702685718273]acc: None\n", + "Cond:#..#.#.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.002, exp: 15.00, pred: 906.610, error:7805.619315124308]acc: 0.0\n", + "Cond:..###OOF - Act:2 - effect:......OO - Num:1 [fit: 0.458, exp: 4.00, pred: 1042.932, error:1943.839488949075]acc: 0.75\n", + "Cond:######.O - Act:2 - effect:.....F.. - Num:1 [fit: 0.004, exp: 20.00, pred: 978.264, error:4940.531397711445]acc: 0.2\n", + "Cond:#O.###.# - Act:4 - effect:..OO.... - Num:4 [fit: 0.004, exp: 21.00, pred: 1005.876, error:8283.633918014073]acc: 0.0\n", + "Cond:###OO### - Act:4 - effect:.....OF. - Num:2 [fit: 0.014, exp: 14.00, pred: 897.282, error:6127.372875639444]acc: 0.0\n", + "Cond:#...###F - Act:2 - effect:.OOO.... - Num:1 [fit: 0.011, exp: 3.00, pred: 1031.202, error:3920.435596727855]acc: 0.0\n", + "Cond:.#...##F - Act:0 - effect:.....F.. - Num:1 [fit: 0.005, exp: 57.00, pred: 1086.208, error:4224.416096361533]acc: 0.5614035087719298\n", + "Cond:O##..##O - Act:0 - effect:OO.....O - Num:5 [fit: 0.018, exp: 42.00, pred: 1028.099, error:3143.890364783095]acc: 0.21428571428571427\n", + "Cond:##.O###O - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1076.826, error:776.4757819035192]acc: None\n", + "Cond:O#..#... - Act:0 - effect:OO.....O - Num:2 [fit: 0.098, exp: 9.00, pred: 822.498, error:6607.248766658174]acc: 0.0\n", + "Cond:O#..#.#. - Act:7 - effect:OO...... - Num:1 [fit: 0.002, exp: 13.00, pred: 1202.255, error:8735.888047721826]acc: 0.07692307692307693\n", + "Cond:##.##O.O - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 975.410, error:784.4140408532026]acc: None\n", + "Cond:....#O.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 90.00, pred: 1073.741, error:5503.268271681289]acc: 0.6666666666666666\n", + "Cond:##.#OO#. - Act:7 - effect:...O.... - Num:6 [fit: 0.033, exp: 117.00, pred: 1135.011, error:1793.5468918461547]acc: 0.6495726495726496\n", + "Cond:#O.##..# - Act:4 - effect:..OO.... - Num:1 [fit: 0.004, exp: 16.00, pred: 1004.460, error:8612.367884582563]acc: 0.0\n", + "Cond:.O.##### - Act:4 - effect:..OO.... - Num:1 [fit: 0.015, exp: 3.00, pred: 852.148, error:4092.6958949227255]acc: 0.0\n", + "Cond:...O##.# - Act:1 - effect:OO...... - Num:2 [fit: 0.009, exp: 16.00, pred: 1044.056, error:8258.774526447933]acc: 0.1875\n", + "Cond:.O.F##.# - Act:4 - effect:..OO.... - Num:1 [fit: 0.010, exp: 0.00, pred: 1077.228, error:597.6797520242822]acc: None\n", + "Cond:...#.##. - Act:0 - effect:....OOO. - Num:2 [fit: 0.012, exp: 72.00, pred: 1070.646, error:1783.4359982262492]acc: 0.5555555555555556\n", + "Cond:#..OOO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 982.436, error:761.9897994496507]acc: None\n", + "Cond:#.##.#.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.#OOF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..##O.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 19.00, pred: 967.871, error:4641.154852079746]acc: 0.3157894736842105\n", + "Cond:..##.O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1719.392, error:80.35469097515721]acc: None\n", + "Cond:#...OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1312.156, error:74.1541690018642]acc: None\n", + "Cond:.##..O#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 20.00, pred: 1057.940, error:3272.423683307851]acc: 0.1\n", + "Cond:.####... - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 900.694, error:3475.9274737327464]acc: 0.18181818181818182\n", + "Cond:.###O##. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 62.00, pred: 1026.373, error:3691.580133828454]acc: 0.3709677419354839\n", + "Cond:...O.#.# - Act:1 - effect:OO...... - Num:1 [fit: 0.003, exp: 5.00, pred: 1047.809, error:4641.506856360296]acc: 0.2\n", + "Cond:##.F.### - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1029.965, error:1231.1329294173497]acc: None\n", + "Cond:....#F## - Act:4 - effect:....FO.. - Num:5 [fit: 0.014, exp: 56.00, pred: 1414.391, error:6763.535623326024]acc: 0.4642857142857143\n", + "Cond:....FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.016, exp: 86.00, pred: 1073.741, error:4619.235431506471]acc: 0.20930232558139536\n", + "Cond:###O##.. - Act:1 - effect:.OOO.... - Num:2 [fit: 0.004, exp: 20.00, pred: 905.216, error:7565.299565666998]acc: 0.1\n", + "Cond:##..F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 177.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:.###O#.. - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 16.00, pred: 967.129, error:8338.701199802417]acc: 0.0\n", + "Cond:O##.##.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 11.00, pred: 1091.139, error:4774.5523888713415]acc: 0.0\n", + "Cond:##O..##. - Act:1 - effect:OO...... - Num:1 [fit: 0.003, exp: 13.00, pred: 948.155, error:905.0891072731959]acc: 0.07692307692307693\n", + "Cond:.#.F.##. - Act:0 - effect:OO.....O - Num:1 [fit: 0.018, exp: 0.00, pred: 958.195, error:227.98786814759765]acc: None\n", + "Cond:#O#O..## - Act:2 - effect:...O.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1011.534, error:940.8151651409147]acc: None\n", + "Cond:###.##O# - Act:0 - effect:.....F.. - Num:3 [fit: 0.020, exp: 104.00, pred: 1086.208, error:4255.928804071957]acc: 0.5576923076923077\n", + "Cond:.OO##.## - Act:4 - effect:...O.... - Num:1 [fit: 0.166, exp: 3.00, pred: 764.515, error:2882.0099067161586]acc: 0.3333333333333333\n", + "Cond:#.O#.#.# - Act:3 - effect:.....OF. - Num:1 [fit: 0.003, exp: 13.00, pred: 1148.873, error:5036.676194628255]acc: 0.0\n", + "Cond:..#O..#. - Act:0 - effect:...OO... - Num:1 [fit: 0.008, exp: 17.00, pred: 1166.454, error:6096.253578777854]acc: 0.0\n", + "Cond:##.#.F## - Act:1 - effect:...FOO.. - Num:1 [fit: 0.012, exp: 3.00, pred: 1151.201, error:3629.1161775863507]acc: 0.0\n", + "Cond:..#..#.O - Act:0 - effect:O......O - Num:1 [fit: 0.009, exp: 16.00, pred: 1257.893, error:5745.895320287297]acc: 0.25\n", + "Cond:##.#.F#. - Act:0 - effect:.....F.. - Num:1 [fit: 0.004, exp: 6.00, pred: 1092.346, error:4388.764028499629]acc: 0.16666666666666666\n", + "Cond:##...#.O - Act:2 - effect:OO.....O - Num:1 [fit: 0.002, exp: 14.00, pred: 977.503, error:5841.239385966655]acc: 0.0\n", + "Cond:#O####.O - Act:0 - effect:OO.....O - Num:2 [fit: 0.371, exp: 7.00, pred: 847.099, error:2117.3821333132382]acc: 0.8571428571428571\n", + "Cond:###....O - Act:0 - effect:O......O - Num:5 [fit: 0.019, exp: 50.00, pred: 1086.019, error:4630.545089317917]acc: 0.54\n", + "Cond:.O.#.... - Act:1 - effect:...O.... - Num:3 [fit: 0.078, exp: 9.00, pred: 1034.946, error:5065.751231928055]acc: 0.1111111111111111\n", + "Cond:#..##..O - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 12.00, pred: 1043.901, error:4054.031571005369]acc: 0.0\n", + "Cond:####O#.. - Act:1 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 13.00, pred: 968.905, error:7538.006398373851]acc: 0.0\n", + "Cond:#..###.O - Act:7 - effect:.....F.. - Num:1 [fit: 0.000, exp: 18.00, pred: 1127.283, error:5240.581048347089]acc: 0.05555555555555555\n", + "Cond:..#FO#.. - Act:2 - effect:.....OF. - Num:1 [fit: 0.165, exp: 3.00, pred: 1062.991, error:1202.7419032485395]acc: 0.6666666666666666\n", + "Cond:#..###.. - Act:2 - effect:.....F.. - Num:1 [fit: 0.005, exp: 8.00, pred: 923.684, error:7476.630921114324]acc: 0.0\n", + "Cond:#....O.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.043, exp: 0.00, pred: 1212.607, error:56.62081362382797]acc: None\n", + "Cond:O#.F.#.. - Act:3 - effect:O......O - Num:1 [fit: 0.002, exp: 0.00, pred: 1008.738, error:980.0350435429482]acc: None\n", + "Cond:.#..#O## - Act:1 - effect:.....OOF - Num:2 [fit: 0.008, exp: 20.00, pred: 963.343, error:2707.792578440547]acc: 0.6\n", + "Cond:###O..#. - Act:0 - effect:...O.... - Num:3 [fit: 0.002, exp: 25.00, pred: 1010.035, error:5781.626516676305]acc: 0.16\n", + "Cond:#O####.. - Act:2 - effect:OO.....O - Num:1 [fit: 0.014, exp: 14.00, pred: 906.539, error:5763.436826387691]acc: 0.35714285714285715\n", + "Cond:.O#..#.# - Act:2 - effect:.OO..... - Num:1 [fit: 0.147, exp: 7.00, pred: 882.454, error:5723.604513084536]acc: 0.42857142857142855\n", + "Cond:.OO#.#.. - Act:3 - effect:......OO - Num:2 [fit: 0.007, exp: 21.00, pred: 1067.431, error:1328.6415100493077]acc: 0.14285714285714285\n", + "Cond:#.##.... - Act:2 - effect:...O.... - Num:1 [fit: 0.006, exp: 7.00, pred: 998.519, error:4586.515230589453]acc: 0.14285714285714285\n", + "Cond:.#O.#OOF - Act:7 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.###.O# - Act:3 - effect:...OO... - Num:1 [fit: 0.000, exp: 19.00, pred: 1333.707, error:5101.386601760429]acc: 0.10526315789473684\n", + "Cond:..#.#.## - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 13.00, pred: 1068.220, error:3486.0740892962704]acc: 0.0\n", + "Cond:O#.####. - Act:1 - effect:OO...... - Num:1 [fit: 0.059, exp: 11.00, pred: 1091.139, error:4380.565828871341]acc: 0.45454545454545453\n", + "Cond:.###O.## - Act:3 - effect:...OO... - Num:1 [fit: 0.139, exp: 38.00, pred: 1235.095, error:2571.214307763053]acc: 0.5789473684210527\n", + "Cond:##..##.# - Act:2 - effect:.O...... - Num:4 [fit: 0.006, exp: 21.00, pred: 941.240, error:4093.077358903058]acc: 0.09523809523809523\n", + "Cond:O....#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1035.806, error:1461.4433260220553]acc: None\n", + "Cond:O#..###O - Act:1 - effect:OO...... - Num:1 [fit: 0.000, exp: 21.00, pred: 1008.185, error:3076.1872702948795]acc: 0.09523809523809523\n", + "Cond:.#.#.O## - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 22.00, pred: 1047.888, error:3415.3476026007957]acc: 0.2727272727272727\n", + "Cond:#..O.#.. - Act:2 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 1006.733, error:1544.7812780159609]acc: None\n", + "Cond:.#...##F - Act:7 - effect:....OOO. - Num:2 [fit: 0.027, exp: 244.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:#O#.##O# - Act:2 - effect:.OO..... - Num:1 [fit: 0.009, exp: 0.00, pred: 1164.692, error:1094.0789501273748]acc: None\n", + "Cond:O#.####. - Act:0 - effect:....FO.. - Num:1 [fit: 0.007, exp: 5.00, pred: 887.521, error:5272.50087847382]acc: 0.0\n", + "Cond:..#..#.# - Act:2 - effect:...FOO.. - Num:1 [fit: 0.010, exp: 3.00, pred: 888.421, error:3504.521271782695]acc: 0.0\n", + "Cond:.....#.O - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 15.00, pred: 1270.476, error:3380.3665805145647]acc: 0.26666666666666666\n", + "Cond:#.####.O - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 12.00, pred: 1043.901, error:4447.36277100537]acc: 0.0\n", + "Cond:OO..##.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.021, exp: 12.00, pred: 981.443, error:7317.262391732848]acc: 0.25\n", + "Cond:###.##.. - Act:1 - effect:.OO..... - Num:1 [fit: 0.007, exp: 35.00, pred: 971.009, error:2251.3704472450604]acc: 0.34285714285714286\n", + "Cond:OO..#### - Act:1 - effect:....FO.. - Num:1 [fit: 0.000, exp: 21.00, pred: 1005.193, error:6116.4141812417065]acc: 0.0\n", + "Cond:##.....O - Act:0 - effect:O......O - Num:1 [fit: 0.015, exp: 47.00, pred: 1086.025, error:5349.287198516267]acc: 0.5531914893617021\n", + "Cond:##..##.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.002, exp: 23.00, pred: 1019.726, error:6139.922608540119]acc: 0.13043478260869565\n", + "Cond:#...##.. - Act:1 - effect:.OOO.... - Num:1 [fit: 0.007, exp: 5.00, pred: 994.600, error:5272.605776743201]acc: 0.0\n", + "Cond:#.#.#F.O - Act:7 - effect:.....F.. - Num:1 [fit: 0.008, exp: 0.00, pred: 986.868, error:872.337430607437]acc: None\n", + "Cond:##.#O.#. - Act:3 - effect:...OO... - Num:2 [fit: 0.118, exp: 37.00, pred: 1235.090, error:2236.4349970935136]acc: 0.5405405405405406\n", + "Cond:##..###. - Act:1 - effect:.....F.. - Num:2 [fit: 0.000, exp: 23.00, pred: 999.918, error:6739.216295531036]acc: 0.08695652173913043\n", + "Cond:#..#O#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.003, exp: 9.00, pred: 996.872, error:7106.856031038244]acc: 0.0\n", + "Cond:..###.## - Act:1 - effect:...O.... - Num:1 [fit: 0.000, exp: 21.00, pred: 1059.340, error:4195.548892067742]acc: 0.047619047619047616\n", + "Cond:#O##.##. - Act:0 - effect:....FO.. - Num:1 [fit: 0.000, exp: 24.00, pred: 1113.557, error:7013.043645619518]acc: 0.0\n", + "Cond:#OO..#.. - Act:1 - effect:.OO..... - Num:1 [fit: 0.809, exp: 10.00, pred: 1009.578, error:2542.189272565551]acc: 0.9\n", + "Cond:O#.F.##. - Act:0 - effect:....FO.. - Num:1 [fit: 0.009, exp: 0.00, pred: 1106.777, error:868.6544488779643]acc: None\n", + "Cond:O###.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 26.00, pred: 1743.771, error:932.6812699260779]acc: 0.46153846153846156\n", + "Cond:##.##.#O - Act:2 - effect:OO.....O - Num:3 [fit: 0.007, exp: 15.00, pred: 971.036, error:6248.841194270313]acc: 0.0\n", + "Cond:O#...#.# - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 15.00, pred: 952.909, error:4482.9292181243145]acc: 0.06666666666666667\n", + "Cond:.O#.###. - Act:0 - effect:...O.... - Num:1 [fit: 0.003, exp: 8.00, pred: 1422.420, error:8501.700989900859]acc: 0.0\n", + "Cond:..#.#OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.022, exp: 242.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:.#.#.OF. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 195.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:.#.#..O# - Act:2 - effect:......OO - Num:3 [fit: 0.031, exp: 4.00, pred: 903.104, error:4606.124207292221]acc: 0.0\n", + "Cond:#O###.#O - Act:2 - effect:OO.....O - Num:1 [fit: 0.006, exp: 5.00, pred: 1010.935, error:6141.271308624043]acc: 0.0\n", + "Cond:.OO##.## - Act:2 - effect:...O.... - Num:1 [fit: 0.011, exp: 3.00, pred: 1016.994, error:2361.3938799130956]acc: 0.0\n", + "Cond:.##...## - Act:2 - effect:...FOO.. - Num:1 [fit: 0.020, exp: 9.00, pred: 892.973, error:6238.063231174558]acc: 0.0\n", + "Cond:O.##.### - Act:1 - effect:OO...... - Num:2 [fit: 0.015, exp: 8.00, pred: 1063.714, error:3073.570766999506]acc: 0.0\n", + "Cond:O#.##### - Act:1 - effect:OO.....O - Num:2 [fit: 0.003, exp: 21.00, pred: 998.660, error:4104.086075034141]acc: 0.19047619047619047\n", + "Cond:.O.F###. - Act:0 - effect:OO.....O - Num:1 [fit: 0.005, exp: 0.00, pred: 986.311, error:1207.1240411617118]acc: None\n", + "Cond:..##O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 201.00, pred: 1154.868, error:139.8387303607511]acc: 0.8208955223880597\n", + "Cond:OO.###.# - Act:1 - effect:OO.....O - Num:1 [fit: 0.006, exp: 11.00, pred: 1042.904, error:3072.1316455258234]acc: 0.2727272727272727\n", + "Cond:#.##..O. - Act:7 - effect:..OO.... - Num:1 [fit: 0.011, exp: 0.00, pred: 903.753, error:605.6001788126813]acc: None\n", + "Cond:O..#.### - Act:1 - effect:.....OOF - Num:1 [fit: 0.006, exp: 7.00, pred: 1037.894, error:3592.751789279163]acc: 0.0\n", + "Cond:..##.##F - Act:2 - effect:......OO - Num:1 [fit: 0.020, exp: 1.00, pred: 861.972, error:1600.0]acc: 0.0\n", + "Cond:#..##.O# - Act:3 - effect:......OO - Num:1 [fit: 0.013, exp: 18.00, pred: 1326.100, error:6438.933752313971]acc: 0.1111111111111111\n", + "Cond:.#.#.... - Act:5 - effect:....FO.. - Num:1 [fit: 0.001, exp: 341.00, pred: 2349.787, error:7533.889525113581]acc: 0.6832844574780058\n", + "Cond:..#..O.# - Act:6 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.####OOF - Act:6 - effect:.....OOF - Num:1 [fit: 0.081, exp: 38.00, pred: 933.269, error:3439.157332231989]acc: 0.7368421052631579\n", + "Cond:...O..#. - Act:0 - effect:...OO... - Num:1 [fit: 0.015, exp: 4.00, pred: 1131.811, error:5513.474272304136]acc: 0.0\n", + "Cond:.O#.#OF. - Act:3 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 954.489, error:612.3097437265495]acc: None\n", + "Cond:#####.O# - Act:3 - effect:.....F.. - Num:1 [fit: 0.012, exp: 17.00, pred: 1330.199, error:3892.148642867151]acc: 0.47058823529411764\n", + "Cond:O#.##..# - Act:1 - effect:OO.....O - Num:1 [fit: 0.002, exp: 16.00, pred: 1011.678, error:3965.2640239391685]acc: 0.1875\n", + "Cond:.OOO..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 25.00, pred: 2049.926, error:8829.359428261072]acc: 0.24\n", + "Cond:#O###O.. - Act:7 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#....O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 192.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:O#...### - Act:2 - effect:.O...... - Num:1 [fit: 0.004, exp: 9.00, pred: 934.872, error:3624.8390441050356]acc: 0.0\n", + "Cond:#..#.##O - Act:7 - effect:......OO - Num:1 [fit: 0.008, exp: 32.00, pred: 1121.057, error:3790.3966765243895]acc: 0.4375\n", + "Cond:###O..#. - Act:1 - effect:...OO... - Num:2 [fit: 0.090, exp: 6.00, pred: 934.146, error:3448.1987537499813]acc: 0.5\n", + "Cond:..#O.O.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.022, exp: 0.00, pred: 954.235, error:25.64789396802189]acc: None\n", + "Cond:..#O.#.. - Act:0 - effect:.....OF. - Num:2 [fit: 0.003, exp: 13.00, pred: 1188.087, error:3113.6198611430964]acc: 0.07692307692307693\n", + "Cond:.#...O## - Act:1 - effect:.....OOF - Num:1 [fit: 0.345, exp: 12.00, pred: 1025.754, error:2010.412465109011]acc: 0.9166666666666666\n", + "Cond:..##...F - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 854.407, error:0.0]acc: None\n", + "Cond:.#...O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.130, exp: 33.00, pred: 933.394, error:3102.7885811142933]acc: 0.6060606060606061\n", + "Cond:..OO#OOF - Act:2 - effect:......OO - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O###.##. - Act:1 - effect:OO...... - Num:1 [fit: 0.017, exp: 0.00, pred: 1040.946, error:170.9246408457614]acc: None\n", + "Cond:#O###... - Act:2 - effect:OO...... - Num:1 [fit: 0.104, exp: 5.00, pred: 985.438, error:3626.3182561509666]acc: 0.2\n", + "Cond:O.##.##. - Act:3 - effect:O......O - Num:1 [fit: 0.002, exp: 0.00, pred: 974.120, error:647.8050227623039]acc: None\n", + "Cond:##.#OO.. - Act:3 - effect:....OOO. - Num:3 [fit: 0.082, exp: 163.00, pred: 1158.783, error:143.70590038414701]acc: 1.0\n", + "Cond:.####.O# - Act:6 - effect:......OO - Num:3 [fit: 0.013, exp: 27.00, pred: 979.212, error:3316.023485212587]acc: 0.8148148148148148\n", + "Cond:.#.#.#O# - Act:2 - effect:......OO - Num:1 [fit: 0.009, exp: 1.00, pred: 949.513, error:1800.0]acc: 0.0\n", + "Cond:##.#.##O - Act:2 - effect:OO.....O - Num:1 [fit: 0.003, exp: 7.00, pred: 962.901, error:4960.096529528123]acc: 0.0\n", + "Cond:#....### - Act:2 - effect:.....F.. - Num:1 [fit: 0.001, exp: 25.00, pred: 1042.737, error:3220.7400207634764]acc: 0.12\n", + "Cond:.##O.O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1074.268, error:973.1937445573589]acc: None\n", + "Cond:#O###### - Act:4 - effect:....OOO. - Num:5 [fit: 0.354, exp: 5.00, pred: 993.941, error:2445.4314661796543]acc: 0.4\n", + "Cond:##OO..#. - Act:0 - effect:...O.... - Num:1 [fit: 0.001, exp: 17.00, pred: 1021.471, error:6219.044090702826]acc: 0.17647058823529413\n", + "Cond:#...##.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.059, exp: 3.00, pred: 936.340, error:2640.7096690387702]acc: 0.0\n", + "Cond:#.##O##. - Act:3 - effect:....OOO. - Num:3 [fit: 0.087, exp: 195.00, pred: 1154.868, error:137.91649950608277]acc: 0.8256410256410256\n", + "Cond:##.##.#. - Act:0 - effect:......OO - Num:2 [fit: 0.014, exp: 14.00, pred: 975.344, error:6625.179926055554]acc: 0.14285714285714285\n", + "Cond:OO...#.# - Act:0 - effect:OO.....O - Num:1 [fit: 0.000, exp: 1.00, pred: 780.853, error:2600.0]acc: 0.0\n", + "Cond:#.#...#. - Act:2 - effect:.......O - Num:1 [fit: 0.014, exp: 0.00, pred: 1231.601, error:640.6518201512376]acc: None\n", + "Cond:##.####O - Act:2 - effect:.....F.. - Num:1 [fit: 0.288, exp: 4.00, pred: 1031.716, error:3195.626481288689]acc: 0.25\n", + "Cond:#.#..F.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.007, exp: 1.00, pred: 771.158, error:2600.0]acc: 0.0\n", + "Cond:.##FO### - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.###.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.214, exp: 2.00, pred: 812.028, error:2104.411165451589]acc: 0.5\n", + "Cond:OO#.#..# - Act:7 - effect:OO.....O - Num:2 [fit: 0.008, exp: 16.00, pred: 990.750, error:5791.336068242492]acc: 0.25\n", + "Cond:OO.###.# - Act:7 - effect:OO.....O - Num:3 [fit: 0.008, exp: 16.00, pred: 990.750, error:5585.919029599292]acc: 0.25\n", + "Cond:O#.##.## - Act:1 - effect:O......O - Num:1 [fit: 0.405, exp: 6.00, pred: 1049.159, error:1848.5416676934208]acc: 0.6666666666666666\n", + "Cond:#O##.#.. - Act:2 - effect:OO...... - Num:1 [fit: 0.081, exp: 2.00, pred: 744.253, error:1902.3279862212667]acc: 0.0\n", + "Cond:....#.O# - Act:3 - effect:......OO - Num:1 [fit: 0.003, exp: 15.00, pred: 1342.165, error:6443.7731745562105]acc: 0.0\n", + "Cond:#.##..O# - Act:3 - effect:......OO - Num:1 [fit: 0.004, exp: 15.00, pred: 1342.165, error:6692.1456647962095]acc: 0.0\n", + "Cond:#OO##.#. - Act:0 - effect:......OO - Num:1 [fit: 0.005, exp: 11.00, pred: 1162.581, error:5313.397773074591]acc: 0.18181818181818182\n", + "Cond:..#..O#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.007, exp: 17.00, pred: 1045.506, error:3567.0064414416106]acc: 0.058823529411764705\n", + "Cond:#.#O.### - Act:2 - effect:.......O - Num:1 [fit: 0.073, exp: 5.00, pred: 915.880, error:3810.9382048578827]acc: 0.0\n", + "Cond:##..###O - Act:2 - effect:.....F.. - Num:1 [fit: 0.029, exp: 0.00, pred: 1031.716, error:348.9066203221722]acc: None\n", + "Cond:##....#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.036, exp: 0.00, pred: 975.171, error:57.439108031317886]acc: None\n", + "Cond:##..##.# - Act:1 - effect:.....F.. - Num:2 [fit: 0.006, exp: 6.00, pred: 1049.159, error:2816.541667693421]acc: 0.0\n", + "Cond:#....#.. - Act:1 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 0.00, pred: 942.547, error:566.5342313653545]acc: None\n", + "Cond:.OOO##.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 19.00, pred: 2047.304, error:7360.522609997426]acc: 0.15789473684210525\n", + "Cond:.###.O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 184.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:#.####.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.005, exp: 12.00, pred: 1018.717, error:4098.311730316056]acc: 0.08333333333333333\n", + "Cond:##.#O#.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.013, exp: 0.00, pred: 907.907, error:432.9468948190449]acc: None\n", + "Cond:#####O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 228.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:.##.#### - Act:1 - effect:......OO - Num:1 [fit: 0.010, exp: 33.00, pred: 973.581, error:4495.954911046914]acc: 0.3333333333333333\n", + "Cond:.##.#.O# - Act:6 - effect:......OO - Num:1 [fit: 0.062, exp: 19.00, pred: 977.201, error:3151.3074548334425]acc: 0.8421052631578947\n", + "Cond:#.##.#.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.002, exp: 21.00, pred: 1272.687, error:6439.980334374316]acc: 0.0\n", + "Cond:.##..#O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1175.087, error:1241.4988319845436]acc: None\n", + "Cond:##O..### - Act:5 - effect:....FO.. - Num:1 [fit: 0.032, exp: 83.00, pred: 1107.575, error:5206.929315359281]acc: 0.4939759036144578\n", + "Cond:..##.O.. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1346.998, error:773.2570564174982]acc: None\n", + "Cond:#.O..##. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1346.998, error:773.2570564174982]acc: None\n", + "Cond:.....OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 181.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", + "Cond:.OOO##.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.215, exp: 2.00, pred: 899.934, error:2541.2018495014827]acc: 0.5\n", + "Cond:##.F##.# - Act:5 - effect:...FOO.. - Num:2 [fit: 0.214, exp: 12.00, pred: 1089.165, error:2658.648404269124]acc: 0.8333333333333334\n", + "Cond:O..#O#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1204.064, error:839.1751626761068]acc: None\n", + "Cond:..#..O.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.040, exp: 0.00, pred: 1172.958, error:44.2814138798604]acc: None\n", + "Cond:###.#.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 33.00, pred: 2350.879, error:2794.8241027205604]acc: 0.5454545454545454\n", + "Cond:.#...#.. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 49.00, pred: 1268.090, error:5609.838198394471]acc: 0.3469387755102041\n", + "Cond:.#.#.O.# - Act:5 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#...#.# - Act:0 - effect:.....F.. - Num:2 [fit: 0.011, exp: 15.00, pred: 1268.851, error:6339.839921960003]acc: 0.06666666666666667\n", + "Cond:..#####O - Act:1 - effect:...O.... - Num:1 [fit: 0.002, exp: 11.00, pred: 1116.698, error:2980.6374227047777]acc: 0.0\n", + "Cond:..#O#.#. - Act:1 - effect:...O.... - Num:1 [fit: 0.010, exp: 1.00, pred: 842.089, error:1800.0]acc: 0.0\n", + "Cond:.##.O##. - Act:7 - effect:...O.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1199.748, error:1097.0121975981342]acc: None\n", + "Cond:#.#####F - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 222.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:##OO..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 35.00, pred: 1575.636, error:7358.840028380428]acc: 0.3142857142857143\n", + "Cond:.O#O..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 15.00, pred: 2048.571, error:7115.5650135736805]acc: 0.2\n", + "Cond:.#.O##.# - Act:0 - effect:.....OF. - Num:1 [fit: 0.018, exp: 7.00, pred: 992.646, error:5717.78281537583]acc: 0.0\n", + "Cond:.#..O#.# - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..###.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:...#.... - Act:6 - effect:O......O - Num:1 [fit: 0.000, exp: 24.00, pred: 1370.164, error:2605.652050381606]acc: 0.25\n", + "Cond:##....#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 10.00, pred: 1156.730, error:6539.6831235479085]acc: 0.0\n", + "Cond:.#..#.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 4.00, pred: 1234.022, error:4575.737153208211]acc: 0.0\n", + "Cond:##.#OO#. - Act:3 - effect:....OOO. - Num:3 [fit: 0.078, exp: 158.00, pred: 1158.783, error:143.70590038414798]acc: 1.0\n", + "Cond:###F##.# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.194, exp: 12.00, pred: 1089.165, error:2191.539540269124]acc: 0.8333333333333334\n", + "Cond:##.##O.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.026, exp: 2.00, pred: 929.043, error:2703.1278750796173]acc: 0.0\n", + "Cond:.#.#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1133.445, error:32.69617932590974]acc: None\n", + "Cond:.##O..#. - Act:0 - effect:.....OF. - Num:1 [fit: 0.005, exp: 16.00, pred: 1033.758, error:4780.7164645751745]acc: 0.0625\n", + "Cond:#.#O.#.. - Act:0 - effect:...OO... - Num:1 [fit: 0.019, exp: 9.00, pred: 1290.559, error:4013.660929514153]acc: 0.0\n", + "Cond:###.#.## - Act:7 - effect:......OO - Num:1 [fit: 0.018, exp: 46.00, pred: 1134.521, error:4662.994326761784]acc: 0.30434782608695654\n", + "Cond:.O###### - Act:2 - effect:OO...... - Num:1 [fit: 0.002, exp: 0.00, pred: 949.341, error:1363.5951536290495]acc: None\n", + "Cond:.O#.#... - Act:2 - effect:OO.....O - Num:1 [fit: 0.002, exp: 0.00, pred: 949.341, error:1363.5951536290495]acc: None\n", + "Cond:##O##.#. - Act:0 - effect:......OO - Num:4 [fit: 0.041, exp: 19.00, pred: 1044.011, error:5684.5284485501925]acc: 0.10526315789473684\n", + "Cond:.##.#.F# - Act:7 - effect:...O.... - Num:1 [fit: 0.071, exp: 0.00, pred: 987.482, error:247.31087635730563]acc: None\n", + "Cond:#.#..O## - Act:2 - effect:.....F.. - Num:1 [fit: 0.007, exp: 3.00, pred: 1053.422, error:1522.1952852355257]acc: 0.0\n", + "Cond:..#..##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 210.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:..O#.##. - Act:2 - effect:O......O - Num:1 [fit: 0.023, exp: 3.00, pred: 925.528, error:2437.4551618976143]acc: 0.0\n", + "Cond:...#O.#. - Act:3 - effect:..OO.... - Num:1 [fit: 0.019, exp: 31.00, pred: 1235.309, error:975.2334001088934]acc: 0.22580645161290322\n", + "Cond:.#.O#..# - Act:0 - effect:.....OF. - Num:1 [fit: 0.014, exp: 3.00, pred: 873.708, error:3851.6826563252234]acc: 0.0\n", + "Cond:.##O##.# - Act:0 - effect:.....OF. - Num:1 [fit: 0.001, exp: 19.00, pred: 999.129, error:5548.393956209442]acc: 0.05263157894736842\n", + "Cond:##..#OF# - Act:5 - effect:.....OF. - Num:1 [fit: 0.020, exp: 75.00, pred: 948.221, error:5043.350493758598]acc: 0.8266666666666667\n", + "Cond:O######. - Act:3 - effect:O......O - Num:3 [fit: 0.002, exp: 23.00, pred: 1745.641, error:8915.19825467468]acc: 0.0\n", + "Cond:...#O##. - Act:2 - effect:.....F.. - Num:1 [fit: 0.037, exp: 0.00, pred: 1104.508, error:70.92704177434082]acc: None\n", + "Cond:..O###O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 1232.826, error:6.014264340211241]acc: None\n", + "Cond:#####OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 209.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:#O.O.#.# - Act:5 - effect:OO.....O - Num:1 [fit: 0.011, exp: 0.00, pred: 1214.059, error:37.58310461393346]acc: None\n", + "Cond:.....#.. - Act:0 - effect:....OOO. - Num:2 [fit: 0.352, exp: 1.00, pred: 914.254, error:2200.0]acc: 0.0\n", + "Cond:#....#F. - Act:5 - effect:.....OF. - Num:1 [fit: 0.011, exp: 69.00, pred: 948.221, error:4384.319382073632]acc: 0.8405797101449275\n", + "Cond:.....#O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.008, exp: 211.00, pred: 1215.957, error:605.3768497723952]acc: 0.9241706161137441\n", + "Cond:..###OO# - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##O##.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.012, exp: 53.00, pred: 1233.898, error:734.5857520755062]acc: 0.2641509433962264\n", + "Cond:##.#.#OF - Act:1 - effect:.....OOF - Num:1 [fit: 0.345, exp: 12.00, pred: 1025.754, error:2010.412465109011]acc: 0.9166666666666666\n", + "Cond:##..###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 188.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:.####OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.014, exp: 164.00, pred: 1158.175, error:194.15050445662376]acc: 1.0\n", + "Cond:....#OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 183.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:#.#.#O.. - Act:7 - effect:...OO... - Num:2 [fit: 0.005, exp: 68.00, pred: 1463.126, error:2037.406646449693]acc: 0.6617647058823529\n", + "Cond:.#.O#..# - Act:4 - effect:.....OOF - Num:1 [fit: 0.001, exp: 21.00, pred: 1237.976, error:5425.797232369607]acc: 0.047619047619047616\n", + "Cond:#.##.##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 177.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:...#O### - Act:0 - effect:...OO... - Num:8 [fit: 0.280, exp: 32.00, pred: 1145.166, error:5615.340915401308]acc: 0.75\n", + "Cond:#O.###.# - Act:0 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 2051.626, error:610.5731082833926]acc: None\n", + "Cond:O#.###.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.003, exp: 15.00, pred: 1141.116, error:5652.195024302015]acc: 0.06666666666666667\n", + "Cond:##.OO..# - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.O##O##. - Act:7 - effect:...O.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1388.697, error:438.6370570556502]acc: None\n", + "Cond:##.#.OF# - Act:6 - effect:....OOO. - Num:3 [fit: 0.024, exp: 151.00, pred: 1158.175, error:194.1505044566229]acc: 1.0\n", + "Cond:##O..### - Act:1 - effect:O......O - Num:1 [fit: 0.003, exp: 10.00, pred: 1009.578, error:851.1172725655512]acc: 0.0\n", + "Cond:O...#O.# - Act:5 - effect:OO...... - Num:2 [fit: 0.047, exp: 0.00, pred: 2160.828, error:115.75711884237765]acc: None\n", + "Cond:.##.#.## - Act:7 - effect:...O.... - Num:5 [fit: 0.008, exp: 28.00, pred: 1247.251, error:1626.1146217216738]acc: 0.0\n", + "Cond:#.#.#O.. - Act:1 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:....#O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 229.00, pred: 1240.161, error:4283.24503580116]acc: 0.29694323144104806\n", + "Cond:.#.#...# - Act:7 - effect:....FO.. - Num:1 [fit: 0.009, exp: 17.00, pred: 1228.091, error:2801.841059860142]acc: 0.6470588235294118\n", + "Cond:O.#.#O.. - Act:7 - effect:...OO... - Num:1 [fit: 0.019, exp: 0.00, pred: 1512.252, error:329.73327805082886]acc: None\n", + "Cond:#.#.#... - Act:7 - effect:...OO... - Num:1 [fit: 0.019, exp: 0.00, pred: 1512.252, error:329.73327805082886]acc: None\n", + "Cond:O...###. - Act:5 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##.F#.O - Act:3 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:...O#... - Act:0 - effect:....OOO. - Num:1 [fit: 0.028, exp: 2.00, pred: 932.725, error:2707.5049593035847]acc: 0.0\n", + "Cond:.#..#OOF - Act:2 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1049.979, error:5.9389717513306834]acc: None\n", + "Cond:#...#O.. - Act:7 - effect:...OO... - Num:3 [fit: 0.011, exp: 49.00, pred: 1463.122, error:1485.508833455836]acc: 0.5918367346938775\n", + "Cond:.#.##..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.007, exp: 57.00, pred: 1139.432, error:4317.678212805482]acc: 0.7719298245614035\n", + "Cond:#O###O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1217.565, error:146.8456593963129]acc: None\n", + "Cond:.O###..# - Act:7 - effect:.....F.. - Num:1 [fit: 0.004, exp: 23.00, pred: 2389.527, error:6887.771289875635]acc: 0.0\n", + "Cond:##..O#.. - Act:7 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 1379.437, error:385.4198882237531]acc: None\n", + "Cond:#..##OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 118.00, pred: 1158.175, error:194.15050445844653]acc: 1.0\n", + "Cond:#.#..O.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#..#..# - Act:1 - effect:.....F.. - Num:1 [fit: 0.009, exp: 0.00, pred: 1339.420, error:588.9062030430584]acc: None\n", + "Cond:#.###OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1290.766, error:49.909352437585]acc: None\n", + "Cond:##.#FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.012, exp: 72.00, pred: 1073.741, error:4695.940217904757]acc: 0.3194444444444444\n", + "Cond:.#.OO..# - Act:7 - effect:.....F.. - Num:1 [fit: 0.092, exp: 36.00, pred: 1113.523, error:269.37796208821567]acc: 1.0\n", + "Cond:.#.OO#.. - Act:6 - effect:.....OF. - Num:1 [fit: 0.000, exp: 25.00, pred: 1420.590, error:2928.679293101617]acc: 0.16\n", + "Cond:###..O#. - Act:5 - effect:.....OF. - Num:1 [fit: 0.009, exp: 69.00, pred: 948.221, error:5128.9291247168085]acc: 0.7681159420289855\n", + "Cond:..#OO..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.251, exp: 32.00, pred: 1113.734, error:269.5549560121449]acc: 1.0\n", + "Cond:##.OO..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.256, exp: 32.00, pred: 1113.734, error:269.5549560121449]acc: 1.0\n", + "Cond:#..#OO.. - Act:6 - effect:...O.... - Num:2 [fit: 0.013, exp: 25.00, pred: 1151.212, error:4018.0744261346667]acc: 0.72\n", + "Cond:###.O#.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1639.590, error:145.60425656953578]acc: None\n", + "Cond:.#.OO... - Act:7 - effect:.....F.. - Num:2 [fit: 0.182, exp: 29.00, pred: 1113.550, error:269.23378330949276]acc: 1.0\n", + "Cond:.#.O#..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.007, exp: 41.00, pred: 1139.432, error:4092.877746000667]acc: 0.7317073170731707\n", + "Cond:##.###F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 111.00, pred: 1158.175, error:194.15050446630084]acc: 1.0\n", + "Cond:..##.#O# - Act:1 - effect:.....F.. - Num:1 [fit: 0.000, exp: 23.00, pred: 982.868, error:5723.068087766148]acc: 0.0\n", + "Cond:##.#O... - Act:6 - effect:.....OF. - Num:2 [fit: 0.018, exp: 24.00, pred: 1421.036, error:1154.3540450101648]acc: 0.7916666666666666\n", + "Cond:#..#.#O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.023, exp: 0.00, pred: 1740.435, error:59.47012174939539]acc: None\n", + "Cond:..##..O# - Act:6 - effect:.....F.. - Num:1 [fit: 0.063, exp: 2.00, pred: 1005.259, error:1511.8546060290998]acc: 0.0\n", + "Cond:.##FOO## - Act:1 - effect:.....OF. - Num:1 [fit: 0.020, exp: 2.00, pred: 929.043, error:2703.1278750796173]acc: 0.0\n", + "Cond:##.OO... - Act:6 - effect:.....OF. - Num:1 [fit: 0.014, exp: 19.00, pred: 1424.181, error:1158.773533980837]acc: 0.7368421052631579\n", + "Cond:#.##O... - Act:6 - effect:.....OF. - Num:1 [fit: 0.013, exp: 19.00, pred: 1424.181, error:1107.0191780640373]acc: 0.7368421052631579\n", + "Cond:##.#OO.. - Act:6 - effect:...O.... - Num:2 [fit: 0.024, exp: 19.00, pred: 1149.791, error:3541.9914979320965]acc: 0.631578947368421\n", + "Cond:.#.#O..# - Act:6 - effect:.....OF. - Num:2 [fit: 0.014, exp: 15.00, pred: 1407.112, error:1119.037051417967]acc: 0.6666666666666666\n", + "Cond:####OO.. - Act:6 - effect:.O...... - Num:2 [fit: 0.042, exp: 15.00, pred: 1141.473, error:4934.568880804928]acc: 0.5333333333333333\n", + "Cond:##.OOO.. - Act:6 - effect:...O.... - Num:1 [fit: 0.034, exp: 0.00, pred: 1997.792, error:465.281563782264]acc: None\n", + "Cond:...OO..# - Act:2 - effect:.....F.. - Num:1 [fit: 0.017, exp: 0.00, pred: 1748.321, error:45.76753873363314]acc: None\n", + "Cond:###.OO.# - Act:6 - effect:...OO... - Num:1 [fit: 0.030, exp: 0.00, pred: 2078.422, error:50.65527652051095]acc: None\n", + "Cond:.#.O#.## - Act:7 - effect:.....F.. - Num:4 [fit: 0.013, exp: 37.00, pred: 1139.280, error:4681.012861031628]acc: 0.7027027027027027\n", + "Cond:###.#F#. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.002, exp: 20.00, pred: 1268.526, error:5746.992324087783]acc: 0.4\n", + "Cond:###.#F.# - Act:7 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 20.00, pred: 1268.526, error:6187.763968808198]acc: 0.4\n", + "Cond:##.##O.# - Act:1 - effect:....OOO. - Num:2 [fit: 0.009, exp: 1.00, pred: 935.299, error:2000.0]acc: 0.0\n", + "Cond:#.##O#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.009, exp: 1.00, pred: 935.299, error:1600.0]acc: 0.0\n", + "Cond:O.#.F### - Act:4 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1232.683, error:627.9552248837119]acc: None\n", + "Cond:##..#O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 163.00, pred: 1810.484, error:304.51039335399423]acc: 1.0\n", + "Cond:....#..# - Act:4 - effect:....OOO. - Num:3 [fit: 0.016, exp: 39.00, pred: 1154.434, error:4630.1092007687785]acc: 0.6666666666666666\n", + "Cond:O#.#OO#. - Act:7 - effect:...O.... - Num:3 [fit: 0.018, exp: 0.00, pred: 1315.496, error:167.51006031233507]acc: None\n", + "Cond:.##..#F# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 82.00, pred: 1158.175, error:194.1505053935631]acc: 1.0\n", + "Cond:#.#..O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.198, exp: 0.00, pred: 1391.066, error:40.097674984365646]acc: None\n", + "Cond:##.#O.#. - Act:6 - effect:.....OF. - Num:2 [fit: 0.088, exp: 3.00, pred: 1099.588, error:123.22591380169827]acc: 1.0\n", + "Cond:O..#.#.. - Act:6 - effect:OO...... - Num:1 [fit: 0.037, exp: 0.00, pred: 2075.394, error:28.40629421452286]acc: None\n", + "Cond:.##.#.## - Act:2 - effect:.....OF. - Num:1 [fit: 0.017, exp: 0.00, pred: 1440.191, error:441.9473279060891]acc: None\n", + "Cond:#...##.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 37.00, pred: 1163.292, error:1509.2127535535124]acc: 0.7837837837837838\n", + "Cond:#...#O## - Act:6 - effect:....OOO. - Num:2 [fit: 0.128, exp: 125.00, pred: 1031.818, error:1525.6267389653076]acc: 0.752\n", + "Cond:....#..# - Act:0 - effect:....OOO. - Num:1 [fit: 0.009, exp: 14.00, pred: 1294.560, error:6681.447742582937]acc: 0.0\n", + "Cond:#..###O# - Act:1 - effect:.......O - Num:1 [fit: 0.001, exp: 23.00, pred: 982.868, error:4654.904962183914]acc: 0.0\n", + "Cond:...#O... - Act:6 - effect:.....OF. - Num:1 [fit: 0.101, exp: 2.00, pred: 954.757, error:7.5996570049145475]acc: 1.0\n", + "Cond:.##O..#. - Act:3 - effect:O......O - Num:2 [fit: 0.018, exp: 21.00, pred: 1084.872, error:4214.015864303169]acc: 0.0\n", + "Cond:.OO#.... - Act:3 - effect:.OOO.... - Num:1 [fit: 0.023, exp: 15.00, pred: 1079.180, error:2482.714134098973]acc: 0.4\n", + "Cond:..###.OO - Act:0 - effect:.OO..... - Num:2 [fit: 0.002, exp: 24.00, pred: 1142.450, error:4021.757995976024]acc: 0.125\n", + "Cond:#.###O.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.036, exp: 32.00, pred: 1215.612, error:231.67064945285105]acc: 0.84375\n", + "Cond:...#OO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.209, exp: 2.00, pred: 876.209, error:2518.3867250865737]acc: 0.5\n", + "Cond:##.#O... - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#...O### - Act:6 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 996.646, error:177.58081115035782]acc: None\n", + "Cond:..O#.#O# - Act:1 - effect:.......O - Num:1 [fit: 0.004, exp: 0.00, pred: 2038.914, error:0.0]acc: None\n", + "Cond:...###O# - Act:1 - effect:.....F.. - Num:1 [fit: 0.000, exp: 22.00, pred: 980.349, error:4754.116903700362]acc: 0.0\n", + "Cond:.#O.#OF. - Act:3 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.#.OF. - Act:3 - effect:......OO - Num:1 [fit: 0.006, exp: 35.00, pred: 1134.620, error:2649.1102837612234]acc: 0.6857142857142857\n", + "Cond:.#O.#### - Act:1 - effect:......OO - Num:1 [fit: 0.011, exp: 10.00, pred: 1009.578, error:1451.117272565551]acc: 0.0\n", + "Cond:##.#O#.. - Act:6 - effect:...O.... - Num:1 [fit: 0.002, exp: 1.00, pred: 839.436, error:1600.0]acc: 0.0\n", + "Cond:.####### - Act:1 - effect:......OO - Num:1 [fit: 0.002, exp: 21.00, pred: 965.364, error:5547.139915442371]acc: 0.0\n", + "Cond:O##F###. - Act:3 - effect:O......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#O.#OO#. - Act:7 - effect:...O.... - Num:1 [fit: 0.216, exp: 0.00, pred: 1109.265, error:323.24979286440873]acc: None\n", + "Cond:.O.#.OF. - Act:3 - effect:......OO - Num:1 [fit: 0.012, exp: 0.00, pred: 1018.414, error:288.9724833716014]acc: None\n", + "Cond:.O##..#. - Act:7 - effect:.....F.. - Num:1 [fit: 0.001, exp: 18.00, pred: 2406.659, error:6637.042639377646]acc: 0.0\n", + "Cond:.#OO#..# - Act:7 - effect:.....OF. - Num:1 [fit: 0.001, exp: 19.00, pred: 1585.960, error:4489.211296213905]acc: 0.15789473684210525\n", + "Cond:...#.O#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.187, exp: 2.00, pred: 927.178, error:1750.9743065948176]acc: 0.5\n", + "Cond:.###..#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.010, exp: 3.00, pred: 925.528, error:2304.121828564281]acc: 0.0\n", + "Cond:..##OO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.O.### - Act:7 - effect:.....F.. - Num:3 [fit: 0.032, exp: 12.00, pred: 1272.488, error:4996.947949204449]acc: 0.08333333333333333\n", + "Cond:..###O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.028, exp: 84.00, pred: 1136.526, error:160.3595426498383]acc: 1.0\n", + "Cond:###..#.O - Act:2 - effect:....OOO. - Num:1 [fit: 0.048, exp: 0.00, pred: 1410.939, error:73.70466975710164]acc: None\n", + "Cond:##...F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.121, exp: 120.00, pred: 1227.238, error:250.1648037622783]acc: 1.0\n", + "Cond:.#..#..# - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 18.00, pred: 1156.999, error:4263.227230469365]acc: 0.2777777777777778\n", + "Cond:....O#O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.O#.## - Act:3 - effect:.....F.. - Num:1 [fit: 0.005, exp: 30.00, pred: 1235.553, error:1485.1190419950399]acc: 0.0\n", + "Cond:###..F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 113.00, pred: 1227.238, error:250.16480376531996]acc: 1.0\n", + "Cond:#O.OO.## - Act:7 - effect:.....F.. - Num:1 [fit: 0.031, exp: 0.00, pred: 1639.656, error:58.23705505079005]acc: None\n", + "Cond:..#O...# - Act:4 - effect:......OO - Num:3 [fit: 0.007, exp: 25.00, pred: 1087.327, error:3895.230122884983]acc: 0.48\n", + "Cond:..#OO... - Act:7 - effect:.....F.. - Num:1 [fit: 0.512, exp: 4.00, pred: 898.981, error:38.34946290007767]acc: 1.0\n", + "Cond:...###F# - Act:4 - effect:......OO - Num:1 [fit: 0.001, exp: 70.00, pred: 1368.534, error:3291.8302629428094]acc: 0.4\n", + "Cond:..###O.# - Act:0 - effect:...FOO.. - Num:1 [fit: 0.005, exp: 49.00, pred: 1192.717, error:8169.438266719706]acc: 0.4897959183673469\n", + "Cond:#..O#### - Act:1 - effect:.....F.. - Num:1 [fit: 0.010, exp: 0.00, pred: 1567.255, error:489.8704905597739]acc: None\n", + "Cond:##.O...# - Act:7 - effect:.....F.. - Num:1 [fit: 0.013, exp: 7.00, pred: 1130.097, error:3803.882547348386]acc: 0.0\n", + "Cond:#..#OO.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.012, exp: 12.00, pred: 1089.165, error:1221.824340269124]acc: 0.16666666666666666\n", + "Cond:...#O..# - Act:0 - effect:...OO... - Num:2 [fit: 0.134, exp: 1.00, pred: 947.735, error:1600.0]acc: 0.0\n", + "Cond:.....F.O - Act:7 - effect:....FO.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1208.894, error:535.1452045621386]acc: None\n", + "Cond:.O#OO..# - Act:7 - effect:.....F.. - Num:1 [fit: 0.032, exp: 0.00, pred: 1187.313, error:65.84129432068677]acc: None\n", + "Cond:.#.#O..# - Act:4 - effect:.....OOF - Num:1 [fit: 0.112, exp: 2.00, pred: 834.046, error:1507.562118843908]acc: 0.0\n", + "Cond:.#.##F## - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 102.00, pred: 1227.238, error:250.16480377109886]acc: 1.0\n", + "Cond:.....#.. - Act:7 - effect:....OOO. - Num:4 [fit: 0.020, exp: 14.00, pred: 1286.805, error:6945.473498041807]acc: 0.0\n", + "Cond:..#.##OF - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 22.00, pred: 935.450, error:3006.622600088295]acc: 0.0\n", + "Cond:....OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.042, exp: 0.00, pred: 1304.085, error:61.541776202365824]acc: None\n", + "Cond:.###.... - Act:4 - effect:......OO - Num:1 [fit: 0.000, exp: 24.00, pred: 1087.259, error:4284.5253639708035]acc: 0.125\n", + "Cond:.#..###F - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 169.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", + "Cond:###.#F.O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 1231.306, error:423.1760136186921]acc: None\n", + "Cond:..OO..## - Act:4 - effect:......OO - Num:2 [fit: 0.338, exp: 12.00, pred: 1164.371, error:3530.61704407038]acc: 0.75\n", + "Cond:##.#.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 50.00, pred: 1070.670, error:1738.8198267691348]acc: 0.52\n", + "Cond:######## - Act:5 - effect:.....F.. - Num:2 [fit: 0.004, exp: 1310.00, pred: 1325.216, error:2635.7752357107015]acc: 0.2603053435114504\n", + "Cond:####O#.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.058, exp: 176.00, pred: 1154.868, error:137.98140321681447]acc: 0.8295454545454546\n", + "Cond:#.#.##O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.006, exp: 173.00, pred: 1215.957, error:622.9690466007897]acc: 0.9190751445086706\n", + "Cond:...#OO.# - Act:3 - effect:....OOO. - Num:12 [fit: 0.280, exp: 144.00, pred: 1158.783, error:143.7059003841303]acc: 1.0\n", + "Cond:..#O..## - Act:4 - effect:......OO - Num:1 [fit: 0.010, exp: 16.00, pred: 1090.425, error:3411.684437812387]acc: 0.3125\n", + "Cond:####.OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 157.00, pred: 1390.346, error:258.56226486140247]acc: 1.0\n", + "Cond:..###O.O - Act:0 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1143.608, error:589.4656615862974]acc: None\n", + "Cond:...#OO.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.052, exp: 141.00, pred: 1158.783, error:143.70590038416773]acc: 1.0\n", + "Cond:.#..##.O - Act:0 - effect:...OO... - Num:1 [fit: 0.003, exp: 9.00, pred: 1181.333, error:5398.48112202584]acc: 0.0\n", + "Cond:##O####. - Act:0 - effect:......OO - Num:1 [fit: 0.006, exp: 17.00, pred: 1039.490, error:6100.839427068727]acc: 0.0\n", + "Cond:..#O.... - Act:4 - effect:...O.... - Num:1 [fit: 0.063, exp: 12.00, pred: 1060.762, error:5121.382076940348]acc: 0.25\n", + "Cond:.#O#..#. - Act:0 - effect:......OO - Num:1 [fit: 0.067, exp: 11.00, pred: 1042.201, error:5427.177460879386]acc: 0.0\n", + "Cond:####.O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 151.00, pred: 1390.346, error:258.56226486140025]acc: 1.0\n", + "Cond:####.#.. - Act:0 - effect:......OO - Num:1 [fit: 0.075, exp: 9.00, pred: 982.299, error:6120.682328270889]acc: 0.0\n", + "Cond:..#O#... - Act:4 - effect:...O.... - Num:1 [fit: 0.040, exp: 7.00, pred: 912.228, error:4554.694132765837]acc: 0.0\n", + "Cond:##.O...O - Act:7 - effect:.....F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1234.934, error:645.5130839229548]acc: None\n", + "Cond:..#..##O - Act:0 - effect:O......O - Num:1 [fit: 0.004, exp: 10.00, pred: 1138.676, error:4882.075353714672]acc: 0.3\n", + "Cond:.###.OF. - Act:3 - effect:......OO - Num:2 [fit: 0.034, exp: 27.00, pred: 1134.591, error:2180.122468976151]acc: 0.8518518518518519\n", + "Cond:..##...# - Act:4 - effect:..OO.... - Num:1 [fit: 0.206, exp: 2.00, pred: 776.427, error:1708.2039949348425]acc: 0.5\n", + "Cond:#..###F. - Act:5 - effect:.....OF. - Num:1 [fit: 0.326, exp: 13.00, pred: 957.264, error:4218.552033841053]acc: 0.7692307692307693\n", + "Cond:#####O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.008, exp: 96.00, pred: 1064.140, error:2864.244866037583]acc: 0.23958333333333334\n", + "Cond:#...O#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##O##.## - Act:0 - effect:.OOO.... - Num:1 [fit: 0.205, exp: 2.00, pred: 851.249, error:1703.5110763375992]acc: 0.5\n", + "Cond:.#O##.#. - Act:0 - effect:......OO - Num:1 [fit: 0.005, exp: 2.00, pred: 851.249, error:3103.511076337599]acc: 0.0\n", + "Cond:..OO..#. - Act:4 - effect:...O.... - Num:1 [fit: 0.006, exp: 1.00, pred: 760.019, error:1800.0]acc: 0.0\n", + "Cond:...#.#F# - Act:5 - effect:....OOO. - Num:2 [fit: 0.034, exp: 1.00, pred: 881.852, error:0.0]acc: 1.0\n", + "Cond:#...##F. - Act:5 - effect:.....OF. - Num:1 [fit: 0.034, exp: 1.00, pred: 881.852, error:2000.0]acc: 0.0\n", + "Cond:.....O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 56.00, pred: 1158.177, error:194.15130192844452]acc: 1.0\n", + "Cond:#.#..##O - Act:0 - effect:O......O - Num:1 [fit: 0.054, exp: 9.00, pred: 1064.428, error:3944.7347209853674]acc: 0.6666666666666666\n", + "Cond:#.###O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.051, exp: 80.00, pred: 1136.526, error:160.3595336423799]acc: 1.0\n", + "Cond:...#OO.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.013, exp: 12.00, pred: 1089.165, error:1221.824340269124]acc: 0.16666666666666666\n", + "Cond:..###O## - Act:5 - effect:...FOO.. - Num:1 [fit: 0.012, exp: 67.00, pred: 1064.140, error:3405.219411826027]acc: 0.3283582089552239\n", + "Cond:###.###F - Act:3 - effect:.......O - Num:1 [fit: 0.014, exp: 30.00, pred: 1140.869, error:4562.454965026948]acc: 0.7333333333333333\n", + "Cond:#..#.OO# - Act:3 - effect:.......O - Num:1 [fit: 0.014, exp: 30.00, pred: 1140.869, error:4382.860725026948]acc: 0.7333333333333333\n", + "Cond:.#.#.O.. - Act:5 - effect:....OOO. - Num:3 [fit: 0.010, exp: 0.00, pred: 1513.144, error:296.58032583921516]acc: None\n", + "Cond:###..O#F - Act:3 - effect:.......O - Num:5 [fit: 0.082, exp: 28.00, pred: 1140.917, error:4488.202726028428]acc: 0.7142857142857143\n", + "Cond:#..###O# - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#...#O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.056, exp: 70.00, pred: 1136.526, error:160.3594233204871]acc: 1.0\n", + "Cond:##O..#.# - Act:5 - effect:....FO.. - Num:2 [fit: 0.172, exp: 20.00, pred: 1109.979, error:4589.394272285976]acc: 0.8\n", + "Cond:.#..O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1379.570, error:55.307014414794004]acc: None\n", + "Cond:.###.OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 44.00, pred: 1158.156, error:194.10904277060914]acc: 1.0\n", + "Cond:..##O.## - Act:7 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.##..O#F - Act:3 - effect:.......O - Num:2 [fit: 0.018, exp: 21.00, pred: 1142.889, error:4796.697339961048]acc: 0.6190476190476191\n", + "Cond:##..#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.043, exp: 77.00, pred: 1031.818, error:1525.6365053285142]acc: 0.7272727272727273\n", + "Cond:O.#..#.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.278, exp: 4.00, pred: 1115.101, error:1369.6717013642456]acc: 0.0\n", + "Cond:..O..#.# - Act:5 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.#.#F. - Act:3 - effect:......OO - Num:2 [fit: 0.038, exp: 22.00, pred: 1139.562, error:2409.6686961250725]acc: 0.9090909090909091\n", + "Cond:####.O#F - Act:1 - effect:.......O - Num:1 [fit: 0.006, exp: 12.00, pred: 1025.754, error:4737.411505109011]acc: 0.0\n", + "Cond:##O....# - Act:5 - effect:....FO.. - Num:1 [fit: 0.489, exp: 10.00, pred: 1056.140, error:4897.292262724492]acc: 0.6\n", + "Cond:####O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.027, exp: 128.00, pred: 1154.868, error:138.68617983615766]acc: 0.765625\n", + "Cond:##.#.#F. - Act:3 - effect:......OO - Num:1 [fit: 0.065, exp: 15.00, pred: 1114.941, error:2139.185672683514]acc: 0.8666666666666667\n", + "Cond:##.O.O#. - Act:3 - effect:......OO - Num:1 [fit: 0.037, exp: 0.00, pred: 1766.237, error:44.83942368659772]acc: None\n", + "Cond:###..##F - Act:3 - effect:.......O - Num:3 [fit: 0.138, exp: 13.00, pred: 1102.999, error:4657.754705610376]acc: 0.38461538461538464\n", + "Cond:##O....O - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:####.O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1331.345, error:548.0063499299422]acc: None\n", + "Cond:.O.#.#F. - Act:3 - effect:......OO - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.#....O - Act:0 - effect:O......O - Num:1 [fit: 0.058, exp: 8.00, pred: 1084.802, error:3693.3543085701585]acc: 0.625\n", + "Cond:###..O.F - Act:3 - effect:.......O - Num:2 [fit: 0.056, exp: 0.00, pred: 1249.259, error:59.920854999761175]acc: None\n", + "Cond:.#...F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.065, exp: 79.00, pred: 1227.238, error:250.16481647228719]acc: 1.0\n", + "Cond:#..##..O - Act:6 - effect:OO...... - Num:1 [fit: 0.000, exp: 21.00, pred: 1266.454, error:3455.6313701306]acc: 0.19047619047619047\n", + "Cond:#O##...F - Act:6 - effect:.OO..... - Num:1 [fit: 0.007, exp: 0.00, pred: 1080.101, error:529.8711089049093]acc: None\n", + "Cond:..#.#O## - Act:2 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1814.018, error:249.26871940189537]acc: None\n", + "Cond:####.### - Act:1 - effect:.......O - Num:2 [fit: 0.001, exp: 17.00, pred: 970.243, error:4606.63548462986]acc: 0.0\n", + "Cond:.##.#O#F - Act:1 - effect:......OO - Num:1 [fit: 0.003, exp: 11.00, pred: 993.946, error:4630.854628158903]acc: 0.0\n", + "Cond:.#O..#.# - Act:5 - effect:.OO..... - Num:1 [fit: 0.032, exp: 0.00, pred: 1001.581, error:525.5976329328946]acc: None\n", + "Cond:...##..# - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 3.00, pred: 1160.894, error:4020.537256661734]acc: 0.0\n", + "Cond:###..#O# - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 22.00, pred: 935.450, error:3329.230674506061]acc: 0.0\n", + "Cond:#..#.#O# - Act:6 - effect:....OOO. - Num:2 [fit: 0.008, exp: 20.00, pred: 932.883, error:3261.4879460214956]acc: 0.0\n", + "Cond:.....O## - Act:1 - effect:..OO.... - Num:1 [fit: 0.200, exp: 2.00, pred: 865.156, error:21.357017593488365]acc: 1.0\n", + "Cond:..#..#OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1065.532, error:19.68942408702708]acc: None\n", + "Cond:.O#..##F - Act:3 - effect:.......O - Num:1 [fit: 0.037, exp: 0.00, pred: 1074.604, error:99.19229348512701]acc: None\n", + "Cond:#.#.O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1427.999, error:60.296974581921184]acc: None\n", + "Cond:#..#.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 76.00, pred: 1227.238, error:250.16482687195347]acc: 1.0\n", + "Cond:##.##O.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:....#O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.028, exp: 41.00, pred: 1136.593, error:160.4765231582332]acc: 1.0\n", + "Cond:###.#OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.107, exp: 2.00, pred: 791.825, error:1505.1215336859088]acc: 0.0\n", + "Cond:..OOO..# - Act:0 - effect:.....OOF - Num:1 [fit: 0.016, exp: 0.00, pred: 2338.834, error:60.688323523018894]acc: None\n", + "Cond:.##..O#. - Act:0 - effect:....OOO. - Num:2 [fit: 0.009, exp: 50.00, pred: 1070.670, error:2337.1841802376885]acc: 0.76\n", + "Cond:#.#..OO# - Act:2 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1675.808, error:283.2252482413699]acc: None\n", + "Cond:.##.#O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1711.045, error:70.46908400508757]acc: None\n", + "Cond:###..O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.025, exp: 27.00, pred: 1046.088, error:1707.343675446734]acc: 0.9259259259259259\n", + "Cond:#.###.OO - Act:6 - effect:...FOO.. - Num:1 [fit: 0.021, exp: 0.00, pred: 2154.164, error:1.069831461575518]acc: None\n", + "Cond:.#.##F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.121, exp: 71.00, pred: 1227.238, error:250.16483948976463]acc: 1.0\n", + "Cond:#######F - Act:5 - effect:....OOO. - Num:1 [fit: 0.008, exp: 45.00, pred: 1241.061, error:3007.9066573036343]acc: 0.1111111111111111\n", + "Cond:#.##.O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.029, exp: 23.00, pred: 1044.606, error:1545.0777139585575]acc: 0.9130434782608695\n", + "Cond:#.##.F#. - Act:0 - effect:.....OOF - Num:1 [fit: 0.037, exp: 0.00, pred: 1849.315, error:391.8604559068889]acc: None\n", + "Cond:#.###O.O - Act:0 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 2397.226, error:315.18653168929745]acc: None\n", + "Cond:#.###O#O - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 2397.226, error:315.18653168929745]acc: None\n", + "Cond:O.#.##.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 18.00, pred: 1227.227, error:8092.777223412167]acc: 0.0\n", + "Cond:...#O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.028, exp: 96.00, pred: 1154.868, error:138.17611365631802]acc: 0.6875\n", + "Cond:#.###O#. - Act:2 - effect:...FOO.. - Num:1 [fit: 0.018, exp: 0.00, pred: 2123.472, error:491.21471279463736]acc: None\n", + "Cond:###..#O# - Act:0 - effect:.....F.. - Num:2 [fit: 0.011, exp: 34.00, pred: 1086.056, error:3680.4385967673315]acc: 0.6176470588235294\n", + "Cond:O.##.#.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.018, exp: 4.00, pred: 1145.485, error:1142.7235704543737]acc: 0.0\n", + "Cond:O.#..O.# - Act:5 - effect:....OOO. - Num:4 [fit: 0.035, exp: 0.00, pred: 1968.667, error:27.83542346155447]acc: None\n", + "Cond:..##O#.# - Act:0 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 17.00, pred: 1151.787, error:6936.807016696508]acc: 0.058823529411764705\n", + "Cond:#.#..##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 51.00, pred: 1226.723, error:249.7934398887701]acc: 1.0\n", + "Cond:.##.#OO. - Act:0 - effect:.....F.. - Num:1 [fit: 0.017, exp: 0.00, pred: 2129.031, error:116.14479153884662]acc: None\n", + "Cond:...####. - Act:2 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1413.520, error:603.2338857229969]acc: None\n", + "Cond:#..#O#.O - Act:0 - effect:...OO... - Num:1 [fit: 0.188, exp: 0.00, pred: 1500.149, error:113.26482814715214]acc: None\n", + "Cond:..##O### - Act:0 - effect:...OO... - Num:2 [fit: 0.044, exp: 11.00, pred: 1143.908, error:5613.296636191179]acc: 0.36363636363636365\n", + "Cond:##.##OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.036, exp: 12.00, pred: 1144.769, error:175.0865740135722]acc: 1.0\n", + "Cond:#...###. - Act:6 - effect:....OOO. - Num:1 [fit: 0.449, exp: 19.00, pred: 1111.728, error:167.14725734045788]acc: 0.8947368421052632\n", + "Cond:##...OO# - Act:0 - effect:....OOO. - Num:3 [fit: 0.001, exp: 23.00, pred: 1085.946, error:6102.858205783532]acc: 0.21739130434782608\n", + "Cond:####.... - Act:2 - effect:O......O - Num:1 [fit: 0.035, exp: 3.00, pred: 925.528, error:2837.4551618976143]acc: 0.0\n", + "Cond:#.##O### - Act:0 - effect:...OO... - Num:2 [fit: 0.052, exp: 8.00, pred: 1140.109, error:4722.772366943658]acc: 0.375\n", + "Cond:##.#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1343.150, error:42.671478592426524]acc: None\n", + "Cond:#.####O# - Act:7 - effect:....OOO. - Num:3 [fit: 0.011, exp: 161.00, pred: 1215.957, error:912.5768497723955]acc: 0.9130434782608695\n", + "Cond:..##O#.# - Act:4 - effect:...OO... - Num:1 [fit: 0.005, exp: 15.00, pred: 1036.441, error:3884.6094531172043]acc: 0.06666666666666667\n", + "Cond:.#####.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.004, exp: 10.00, pred: 1062.923, error:7257.71084961187]acc: 0.0\n", + "Cond:O.#F.### - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1503.880, error:1411.5870978660141]acc: None\n", + "Cond:...#.#.# - Act:0 - effect:.O...... - Num:1 [fit: 0.005, exp: 1.00, pred: 819.021, error:0.0]acc: 1.0\n", + "Cond:###.O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1831.000, error:95.57202823651926]acc: None\n", + "Cond:...OO.## - Act:3 - effect:.....OOF - Num:1 [fit: 0.005, exp: 30.00, pred: 1235.553, error:1280.6154423676694]acc: 0.0\n", + "Cond:.#.#.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.021, exp: 17.00, pred: 1062.284, error:1671.90674758069]acc: 0.6470588235294118\n", + "Cond:....#O#. - Act:5 - effect:....FO.. - Num:2 [fit: 0.054, exp: 5.00, pred: 1029.710, error:4647.723762606136]acc: 0.4\n", + "Cond:...#.#O# - Act:0 - effect:....OOO. - Num:1 [fit: 0.010, exp: 12.00, pred: 1076.476, error:6355.524522265567]acc: 0.3333333333333333\n", + "Cond:.####OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.059, exp: 7.00, pred: 1191.824, error:190.54874674020093]acc: 1.0\n", + "Cond:.##O##.. - Act:3 - effect:...OO... - Num:1 [fit: 0.025, exp: 51.00, pred: 1233.902, error:2521.7138576099032]acc: 0.27450980392156865\n", + "Cond:O.#.#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1636.791, error:1182.8617906140882]acc: None\n", + "Cond:#.#.##O. - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O####O#. - Act:4 - effect:O......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##O#.##. - Act:3 - effect:...FOO.. - Num:3 [fit: 0.022, exp: 24.00, pred: 1087.010, error:3093.2826531717965]acc: 0.08333333333333333\n", + "Cond:#.#.##.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 25.00, pred: 1252.702, error:5901.0569143211715]acc: 0.2\n", + "Cond:#..#O##. - Act:3 - effect:....OOO. - Num:2 [fit: 0.058, exp: 79.00, pred: 1154.868, error:137.93033831472943]acc: 0.620253164556962\n", + "Cond:####.#F# - Act:3 - effect:......OO - Num:2 [fit: 1.201, exp: 6.00, pred: 1010.698, error:2438.439663305213]acc: 0.8333333333333334\n", + "Cond:...#O.## - Act:0 - effect:...FOO.. - Num:1 [fit: 0.007, exp: 1.00, pred: 947.735, error:0.0]acc: 1.0\n", + "Cond:...#O##O - Act:0 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:###.###. - Act:0 - effect:....OOO. - Num:2 [fit: 0.029, exp: 12.00, pred: 1070.073, error:2319.349278348898]acc: 0.6666666666666666\n", + "Cond:##.#.F.# - Act:3 - effect:.....OOF - Num:1 [fit: 0.143, exp: 3.00, pred: 1188.436, error:1451.3236390936133]acc: 0.6666666666666666\n", + "Cond:.#...O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.362, exp: 3.00, pred: 1030.413, error:12.168915631017057]acc: 1.0\n", + "Cond:##O#.... - Act:2 - effect:O......O - Num:1 [fit: 0.109, exp: 2.00, pred: 802.005, error:1703.5594146176832]acc: 0.0\n", + "Cond:###..OO# - Act:0 - effect:.....F.. - Num:1 [fit: 0.168, exp: 3.00, pred: 1038.158, error:1202.1089651857858]acc: 0.6666666666666666\n", + "Cond:...#O#.# - Act:0 - effect:...OO... - Num:1 [fit: 0.195, exp: 3.00, pred: 954.741, error:3685.3037345558755]acc: 0.3333333333333333\n", + "Cond:###..### - Act:0 - effect:....OOO. - Num:2 [fit: 0.014, exp: 17.00, pred: 1020.856, error:2299.9648846132513]acc: 0.47058823529411764\n", + "Cond:..OOO.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.017, exp: 0.00, pred: 1644.833, error:428.29256450878734]acc: None\n", + "Cond:#.##.#.O - Act:0 - effect:O......O - Num:1 [fit: 0.087, exp: 3.00, pred: 956.092, error:3056.809971085804]acc: 0.3333333333333333\n", + "Cond:O.#..### - Act:0 - effect:O......O - Num:1 [fit: 0.108, exp: 2.00, pred: 1024.627, error:1716.6796984364803]acc: 0.5\n", + "Cond:.#..#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.090, exp: 3.00, pred: 883.185, error:1064.320783880512]acc: 0.3333333333333333\n", + "Cond:#.##.### - Act:1 - effect:O......O - Num:1 [fit: 0.283, exp: 4.00, pred: 986.137, error:2443.893188698686]acc: 0.75\n", + "Cond:O.#F.#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1153.252, error:942.4811767413255]acc: None\n", + "Cond:.O.###.. - Act:6 - effect:.......O - Num:1 [fit: 0.001, exp: 3.00, pred: 1235.776, error:3319.7554471200647]acc: 0.0\n", + "Cond:###..##. - Act:0 - effect:....OOO. - Num:2 [fit: 0.384, exp: 7.00, pred: 1074.319, error:2121.9226560928873]acc: 0.7142857142857143\n", + "Cond:.....#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 54.00, pred: 2039.146, error:1835.146210437202]acc: 0.7962962962962963\n", + "Cond:#...#O#F - Act:5 - effect:......OO - Num:1 [fit: 0.008, exp: 32.00, pred: 1241.252, error:1167.4196431917285]acc: 0.21875\n", + "Cond:.#...#.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.110, exp: 0.00, pred: 1177.176, error:44.31796030978935]acc: None\n", + "Cond:.##..OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1039.280, error:250.83001544132995]acc: None\n", + "Cond:.#..#O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.017, exp: 0.00, pred: 1039.280, error:250.83001544132995]acc: None\n", + "Cond:O####### - Act:3 - effect:....OOO. - Num:6 [fit: 0.014, exp: 53.00, pred: 1360.526, error:4587.48320162013]acc: 0.5660377358490566\n", + "Cond:#O.##.#. - Act:6 - effect:....FO.. - Num:1 [fit: 0.001, exp: 20.00, pred: 1171.820, error:5872.941769389176]acc: 0.0\n", + "Cond:.#.#O.## - Act:1 - effect:.....OOF - Num:1 [fit: 0.006, exp: 0.00, pred: 1276.559, error:505.2786877049395]acc: None\n", + "Cond:#.##.### - Act:0 - effect:....OOO. - Num:2 [fit: 0.200, exp: 2.00, pred: 858.346, error:0.03712037778348076]acc: 1.0\n", + "Cond:.....O## - Act:2 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.#O#### - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#O##... - Act:0 - effect:......OO - Num:1 [fit: 0.014, exp: 0.00, pred: 946.725, error:491.33606715212306]acc: None\n", + "Cond:##O#.### - Act:0 - effect:.OOO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 946.725, error:491.33606715212306]acc: None\n", + "Cond:..#..##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.060, exp: 22.00, pred: 1227.722, error:250.69444446855388]acc: 1.0\n", + "Cond:###.##O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.007, exp: 160.00, pred: 1215.957, error:614.1729643625608]acc: 0.9125\n", + "Cond:....#.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.025, exp: 0.00, pred: 1512.823, error:92.4032819717715]acc: None\n", + "Cond:.#OOO... - Act:5 - effect:.....OOF - Num:1 [fit: 0.003, exp: 0.00, pred: 1328.682, error:481.86526374116465]acc: None\n", + "Cond:#.##..## - Act:2 - effect:OO.....O - Num:1 [fit: 0.017, exp: 0.00, pred: 1378.603, error:202.3769782352532]acc: None\n", + "Cond:##....#. - Act:0 - effect:......OO - Num:1 [fit: 0.001, exp: 0.00, pred: 1537.470, error:1058.4541400704106]acc: None\n", + "Cond:#..#OO#. - Act:7 - effect:...O.... - Num:9 [fit: 0.035, exp: 49.00, pred: 1135.014, error:1622.4503549176839]acc: 0.7551020408163265\n", + "Cond:.#..FO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 131.00, pred: 1810.484, error:304.51039335404965]acc: 1.0\n", + "Cond:.###.OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 138.00, pred: 1390.346, error:258.5622648613927]acc: 1.0\n", + "Cond:O..#.#.. - Act:5 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.#FO.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.071, exp: 4.00, pred: 1066.675, error:2522.6902513302803]acc: 0.5\n", + "Cond:O.#.#.O# - Act:5 - effect:OO.....O - Num:1 [fit: 0.017, exp: 0.00, pred: 1846.859, error:562.2811530143124]acc: None\n", + "Cond:.###.O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.048, exp: 0.00, pred: 1545.756, error:45.620381849615654]acc: None\n", + "Cond:..OOO.#. - Act:5 - effect:.....OOF - Num:1 [fit: 0.015, exp: 0.00, pred: 2042.543, error:104.59925413752813]acc: None\n", + "Cond:#######. - Act:1 - effect:....OOO. - Num:2 [fit: 0.009, exp: 1.00, pred: 935.299, error:1600.0]acc: 0.0\n", + "Cond:#.###O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 132.00, pred: 1552.863, error:1850.8817864492166]acc: 0.9166666666666666\n", + "Cond:#.##OO#. - Act:7 - effect:...O.... - Num:1 [fit: 0.011, exp: 42.00, pred: 1135.005, error:2158.251173787136]acc: 0.7142857142857143\n", + "Cond:.###..#O - Act:6 - effect:O......O - Num:1 [fit: 0.007, exp: 3.00, pred: 1428.866, error:2864.854539920225]acc: 0.0\n", + "Cond:#...F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 113.00, pred: 1810.484, error:304.5103933487572]acc: 1.0\n", + "Cond:####.#F# - Act:5 - effect:OO.....O - Num:1 [fit: 0.001, exp: 1.00, pred: 881.852, error:1600.0]acc: 0.0\n", + "Cond:#...#O.# - Act:4 - effect:....OOO. - Num:3 [fit: 0.117, exp: 111.00, pred: 1810.484, error:304.5103933502709]acc: 1.0\n", + "Cond:#..#.##O - Act:0 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.####O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 1.00, pred: 1065.904, error:0.0]acc: 1.0\n", + "Cond:.##..OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 128.00, pred: 1390.346, error:258.5622648612888]acc: 1.0\n", + "Cond:.#OOO.## - Act:5 - effect:.....OOF - Num:2 [fit: 0.046, exp: 0.00, pred: 2291.415, error:42.4764401284528]acc: None\n", + "Cond:####.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 2015.877, error:426.78894644334855]acc: None\n", + "Cond:..#.#O## - Act:7 - effect:....OOO. - Num:2 [fit: 0.007, exp: 236.00, pred: 1334.454, error:793.5656934214963]acc: 0.673728813559322\n", + "Cond:..#.#O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.046, exp: 3.00, pred: 878.106, error:3671.405614488382]acc: 0.3333333333333333\n", + "Cond:.#...... - Act:5 - effect:....OOO. - Num:1 [fit: 0.200, exp: 2.00, pred: 956.170, error:17.760536894678864]acc: 1.0\n", + "Cond:.##.#..# - Act:6 - effect:O......O - Num:2 [fit: 0.013, exp: 18.00, pred: 1324.437, error:5165.4488968523765]acc: 0.5\n", + "Cond:..#.#O.# - Act:7 - effect:...OO... - Num:1 [fit: 0.008, exp: 46.00, pred: 1463.133, error:1481.0437436492618]acc: 0.6739130434782609\n", + "Cond:###.OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.O.#...# - Act:3 - effect:..OO.... - Num:8 [fit: 0.083, exp: 37.00, pred: 1894.396, error:544.4345837287093]acc: 0.972972972972973\n", + "Cond:.#####F# - Act:6 - effect:....OOO. - Num:2 [fit: 0.000, exp: 1.00, pred: 1065.904, error:0.0]acc: 1.0\n", + "Cond:.##....# - Act:6 - effect:O......O - Num:1 [fit: 0.006, exp: 14.00, pred: 1319.326, error:6231.597920082826]acc: 0.42857142857142855\n", + "Cond:.#.###.# - Act:6 - effect:O......O - Num:1 [fit: 0.000, exp: 25.00, pred: 1315.664, error:2962.9481260275156]acc: 0.2\n", + "Cond:O..##### - Act:5 - effect:OO...... - Num:2 [fit: 0.006, exp: 40.00, pred: 1161.702, error:6423.168708945026]acc: 0.375\n", + "Cond:O..##### - Act:1 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..#OO## - Act:7 - effect:...O.... - Num:2 [fit: 0.020, exp: 26.00, pred: 1135.923, error:1597.9151887884639]acc: 0.7307692307692307\n", + "Cond:#.#..... - Act:6 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.#..#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.002, exp: 34.00, pred: 2349.673, error:7771.731802748431]acc: 0.35294117647058826\n", + "Cond:O#.#OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.029, exp: 0.00, pred: 1734.936, error:229.4599165861822]acc: None\n", + "Cond:..#.###F - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 118.00, pred: 1390.346, error:258.5622648581576]acc: 1.0\n", + "Cond:O..##O## - Act:5 - effect:OO...... - Num:4 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#O.#...# - Act:6 - effect:.O...... - Num:1 [fit: 0.005, exp: 22.00, pred: 1073.090, error:4070.5127274606575]acc: 0.4090909090909091\n", + "Cond:##..OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.023, exp: 0.00, pred: 1758.938, error:125.33367171550346]acc: None\n", + "Cond:##.#OO#. - Act:1 - effect:...O.... - Num:1 [fit: 0.026, exp: 1.00, pred: 935.299, error:1800.0]acc: 0.0\n", + "Cond:.##.#O.# - Act:4 - effect:....OOO. - Num:3 [fit: 0.117, exp: 86.00, pred: 1810.484, error:304.5103923092307]acc: 1.0\n", + "Cond:#..#OO.. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.109, exp: 13.00, pred: 1058.391, error:4198.221345684996]acc: 0.7692307692307693\n", + "Cond:.#.##..# - Act:4 - effect:..OO.... - Num:1 [fit: 0.127, exp: 2.00, pred: 834.046, error:1507.562118843908]acc: 0.0\n", + "Cond:.#..#OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 115.00, pred: 1390.346, error:258.56226486040384]acc: 1.0\n", + "Cond:OO.####. - Act:3 - effect:....OOO. - Num:2 [fit: 0.011, exp: 13.00, pred: 1752.775, error:764.9716966385638]acc: 0.6153846153846154\n", + "Cond:#.#####. - Act:6 - effect:.....OF. - Num:1 [fit: 0.002, exp: 36.00, pred: 1077.995, error:5475.544040570264]acc: 0.4166666666666667\n", + "Cond:#...OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 1805.431, error:433.9558797218027]acc: None\n", + "Cond:#...OO#. - Act:7 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 1805.431, error:433.9558797218027]acc: None\n", + "Cond:#..#O### - Act:7 - effect:...O.... - Num:2 [fit: 0.077, exp: 15.00, pred: 1150.668, error:1593.9931568541483]acc: 0.6\n", + "Cond:..#..O## - Act:1 - effect:....OOO. - Num:1 [fit: 0.079, exp: 0.00, pred: 1323.746, error:50.875884778680685]acc: None\n", + "Cond:.#...O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 159.00, pred: 1390.346, error:258.5628917272434]acc: 0.8742138364779874\n", + "Cond:O...O### - Act:7 - effect:...O.... - Num:1 [fit: 0.007, exp: 0.00, pred: 1862.645, error:317.1314928624257]acc: None\n", + "Cond:#.##OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 13.00, pred: 1058.391, error:3615.499809684996]acc: 0.15384615384615385\n", + "Cond:...#.##F - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 108.00, pred: 1390.346, error:258.5622647924366]acc: 1.0\n", + "Cond:#.##.#.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.001, exp: 30.00, pred: 1079.319, error:7103.2597433180035]acc: 0.13333333333333333\n", + "Cond:O..#.#OO - Act:5 - effect:...FOO.. - Num:2 [fit: 0.166, exp: 0.00, pred: 2333.000, error:50.82819257110151]acc: None\n", + "Cond:##.#O#.# - Act:7 - effect:...O.... - Num:1 [fit: 0.082, exp: 11.00, pred: 1065.789, error:1993.1105110422407]acc: 0.45454545454545453\n", + "Cond:#.#OO##. - Act:5 - effect:.....F.. - Num:2 [fit: 0.007, exp: 88.00, pred: 1779.096, error:1954.9398451520574]acc: 0.7272727272727273\n", + "Cond:##..#..# - Act:6 - effect:.....OF. - Num:3 [fit: 0.005, exp: 23.00, pred: 999.530, error:2288.2417655470713]acc: 0.0\n", + "Cond:###.###. - Act:6 - effect:.....OF. - Num:2 [fit: 0.006, exp: 18.00, pred: 1210.713, error:6697.455448515967]acc: 0.0\n", + "Cond:....#F#. - Act:4 - effect:....FO.. - Num:1 [fit: 0.008, exp: 18.00, pred: 1413.982, error:5798.971734043076]acc: 0.1111111111111111\n", + "Cond:##..#..O - Act:1 - effect:.....OF. - Num:1 [fit: 0.031, exp: 0.00, pred: 1340.449, error:92.49727091545086]acc: None\n", + "Cond:O..#O### - Act:5 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..##O.. - Act:7 - effect:...OO... - Num:1 [fit: 0.004, exp: 49.00, pred: 1463.075, error:1856.1093462579197]acc: 0.5102040816326531\n", + "Cond:.#..##.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.062, exp: 23.00, pred: 1226.448, error:249.3935074579548]acc: 1.0\n", + "Cond:.#..OO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1183.739, error:46.214348449366696]acc: None\n", + "Cond:O#.##O.# - Act:2 - effect:...O.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1141.920, error:314.1732622066137]acc: None\n", + "Cond:#..#O#.. - Act:7 - effect:...OO... - Num:1 [fit: 0.011, exp: 4.00, pred: 1021.887, error:2851.9511530089876]acc: 0.0\n", + "Cond:##...#.# - Act:6 - effect:.....OF. - Num:2 [fit: 0.002, exp: 17.00, pred: 1006.109, error:2839.0378238338785]acc: 0.0\n", + "Cond:.O.#..## - Act:3 - effect:..OO.... - Num:2 [fit: 0.025, exp: 28.00, pred: 1894.027, error:544.460109866823]acc: 0.9642857142857143\n", + "Cond:#.##O#.# - Act:4 - effect:...FOO.. - Num:1 [fit: 0.012, exp: 15.00, pred: 1036.441, error:3677.946657853204]acc: 0.5333333333333333\n", + "Cond:.####.F# - Act:3 - effect:......OO - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#.#.#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 105.00, pred: 1390.346, error:258.5622648228636]acc: 1.0\n", + "Cond:O#.##O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1252.629, error:553.0144001468202]acc: None\n", + "Cond:.##.#O#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.036, exp: 0.00, pred: 1426.636, error:140.11201491976954]acc: None\n", + "Cond:.####O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 76.00, pred: 1552.863, error:1850.8818944783216]acc: 0.8552631578947368\n", + "Cond:##.#O##. - Act:7 - effect:...O.... - Num:1 [fit: 0.208, exp: 2.00, pred: 949.867, error:40.41293808255301]acc: 1.0\n", + "Cond:##.##O#. - Act:6 - effect:.....OF. - Num:1 [fit: 0.011, exp: 0.00, pred: 1100.023, error:424.36576677350195]acc: None\n", + "Cond:.##.#OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1100.023, error:424.36576677350195]acc: None\n", + "Cond:.#.#..## - Act:6 - effect:O......O - Num:1 [fit: 0.206, exp: 2.00, pred: 964.809, error:15.29490807875817]acc: 1.0\n", + "Cond:..##.#.# - Act:7 - effect:OO.....O - Num:3 [fit: 0.002, exp: 20.00, pred: 1434.997, error:6072.516557756199]acc: 0.0\n", + "Cond:..###O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 72.00, pred: 1552.863, error:2083.739338098909]acc: 0.875\n", + "Cond:###F#O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 11.00, pred: 1040.729, error:3041.693727953356]acc: 0.18181818181818182\n", + "Cond:.##.###. - Act:7 - effect:O......O - Num:1 [fit: 0.001, exp: 74.00, pred: 1515.491, error:1814.9713071956976]acc: 0.40540540540540543\n", + "Cond:#.#.OO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1358.908, error:49.35033770746318]acc: None\n", + "Cond:##..#O#. - Act:3 - effect:..OO.... - Num:2 [fit: 0.002, exp: 19.00, pred: 1054.351, error:5838.3199152682655]acc: 0.0\n", + "Cond:.O.##..# - Act:3 - effect:..OO.... - Num:1 [fit: 0.013, exp: 25.00, pred: 1893.381, error:545.3743710888023]acc: 0.96\n", + "Cond:..##.##. - Act:6 - effect:.....OF. - Num:1 [fit: 0.004, exp: 9.00, pred: 1081.892, error:5353.1699694337185]acc: 0.0\n", + "Cond:##.##.## - Act:6 - effect:.....OF. - Num:1 [fit: 0.363, exp: 3.00, pred: 831.219, error:2013.100400622765]acc: 0.0\n", + "Cond:..##.O#. - Act:6 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1253.584, error:258.98862980984876]acc: None\n", + "Cond:..##.O#. - Act:0 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1253.584, error:258.98862980984876]acc: None\n", + "Cond:####O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.027, exp: 70.00, pred: 1154.868, error:138.80192500690075]acc: 0.5714285714285714\n", + "Cond:#..#OO#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1189.213, error:37.202323446893594]acc: None\n", + "Cond:##O.##.. - Act:6 - effect:O......O - Num:2 [fit: 0.069, exp: 7.00, pred: 1280.528, error:3673.1470655100375]acc: 0.14285714285714285\n", + "Cond:.O.##### - Act:7 - effect:..OO.... - Num:1 [fit: 0.043, exp: 0.00, pred: 1268.804, error:93.0613590294991]acc: None\n", + "Cond:.#.OO#.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1210.349, error:187.04969584537844]acc: None\n", + "Cond:.#.O##.. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 971.928, error:675.8559450876292]acc: None\n", + "Cond:..##O... - Act:4 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 971.928, error:675.8559450876292]acc: None\n", + "Cond:#O.####. - Act:3 - effect:..OO.... - Num:3 [fit: 0.005, exp: 27.00, pred: 1813.188, error:1555.3735603347022]acc: 0.7777777777777778\n", + "Cond:###O#### - Act:3 - effect:...OO... - Num:1 [fit: 0.021, exp: 43.00, pred: 1233.927, error:2339.042623393842]acc: 0.3953488372093023\n", + "Cond:.#...#.# - Act:3 - effect:..OO.... - Num:2 [fit: 0.000, exp: 26.00, pred: 1883.055, error:1025.6484550536513]acc: 0.46153846153846156\n", + "Cond:..###.O# - Act:3 - effect:......OO - Num:1 [fit: 0.046, exp: 5.00, pred: 1329.469, error:4182.647833578965]acc: 0.0\n", + "Cond:.O.#.... - Act:4 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:O.####.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1350.093, error:189.16945192227723]acc: None\n", + "Cond:O#.#.#O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.013, exp: 0.00, pred: 1396.810, error:797.5373577989442]acc: None\n", + "Cond:O##.#### - Act:3 - effect:....OOO. - Num:5 [fit: 0.012, exp: 26.00, pred: 1360.619, error:4109.447541312409]acc: 0.5384615384615384\n", + "Cond:#.###### - Act:5 - effect:.....F.. - Num:1 [fit: 0.003, exp: 282.00, pred: 1325.215, error:2072.6698508224476]acc: 0.2765957446808511\n", + "Cond:O###.#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.012, exp: 21.00, pred: 1356.948, error:4363.872694834356]acc: 0.42857142857142855\n", + "Cond:..#.#.## - Act:4 - effect:....FO.. - Num:2 [fit: 0.518, exp: 10.00, pred: 1119.882, error:3134.351408933204]acc: 0.8\n", + "Cond:...#.#.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.084, exp: 0.00, pred: 1330.378, error:68.42931461749578]acc: None\n", + "Cond:###.##.. - Act:6 - effect:.......O - Num:1 [fit: 0.315, exp: 5.00, pred: 1154.667, error:111.9561949556136]acc: 1.0\n", + "Cond:##O.#### - Act:6 - effect:O......O - Num:1 [fit: 0.004, exp: 5.00, pred: 1154.667, error:3351.9561949556137]acc: 0.0\n", + "Cond:#####.## - Act:5 - effect:.....OOF - Num:2 [fit: 0.001, exp: 295.00, pred: 1463.588, error:4491.207326531993]acc: 0.22372881355932203\n", + "Cond:.##..#.# - Act:6 - effect:.......O - Num:1 [fit: 0.280, exp: 4.00, pred: 1107.096, error:100.97316896593412]acc: 1.0\n", + "Cond:##O#.#.. - Act:3 - effect:.OOO.... - Num:2 [fit: 0.233, exp: 22.00, pred: 1089.084, error:4067.8347443197918]acc: 0.45454545454545453\n", + "Cond:#O.F###. - Act:3 - effect:..OO.... - Num:1 [fit: 0.060, exp: 0.00, pred: 1282.514, error:236.52969170793807]acc: None\n", + "Cond:#O.###.# - Act:3 - effect:..OO.... - Num:2 [fit: 0.001, exp: 30.00, pred: 1570.192, error:1864.0510593453869]acc: 0.5666666666666667\n", + "Cond:.O.#..#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.049, exp: 0.00, pred: 1470.378, error:412.7436471834021]acc: None\n", + "Cond:.....### - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 137.00, pred: 1193.613, error:1763.3840329649754]acc: 0.7883211678832117\n", + "Cond:.##..#O# - Act:2 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1413.605, error:432.0663164150459]acc: None\n", + "Cond:#.##O### - Act:7 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1367.235, error:116.72304772078746]acc: None\n", + "Cond:#O.###.. - Act:3 - effect:..OO.... - Num:1 [fit: 0.015, exp: 20.00, pred: 1816.713, error:1521.4394247613805]acc: 0.8\n", + "Cond:.O#####. - Act:3 - effect:..OO.... - Num:1 [fit: 0.004, exp: 28.00, pred: 1856.827, error:594.7288993213639]acc: 0.5714285714285714\n", + "Cond:.#.##O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.021, exp: 40.00, pred: 1158.217, error:146.39108125007044]acc: 0.775\n", + "Cond:O...#O.. - Act:7 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#...##.. - Act:7 - effect:...OO... - Num:1 [fit: 0.004, exp: 29.00, pred: 1351.845, error:3397.6330491387316]acc: 0.6206896551724138\n", + "Cond:####O.#. - Act:4 - effect:.....OOF - Num:1 [fit: 0.023, exp: 0.00, pred: 1563.873, error:72.41263172446259]acc: None\n", + "Cond:.##.OOO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##.##... - Act:5 - effect:.....F.. - Num:1 [fit: 0.001, exp: 81.00, pred: 1782.915, error:1606.7908173211304]acc: 0.5555555555555556\n", + "Cond:#.##..## - Act:7 - effect:......OO - Num:1 [fit: 0.001, exp: 39.00, pred: 1228.129, error:3029.724040014957]acc: 0.05128205128205128\n", + "Cond:#....#.. - Act:7 - effect:...OO... - Num:1 [fit: 0.031, exp: 2.00, pred: 983.942, error:3006.575163618507]acc: 0.0\n", + "Cond:#..###.. - Act:7 - effect:...OO... - Num:2 [fit: 0.006, exp: 29.00, pred: 1350.353, error:3259.005443236227]acc: 0.5172413793103449\n", + "Cond:#O.##..# - Act:3 - effect:..OO.... - Num:1 [fit: 0.001, exp: 28.00, pred: 1569.233, error:1596.2976053278912]acc: 0.5357142857142857\n", + "Cond:.O##.### - Act:7 - effect:..OO.... - Num:1 [fit: 0.001, exp: 18.00, pred: 2406.659, error:7015.775956440303]acc: 0.0\n", + "Cond:....#OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 80.00, pred: 1390.346, error:258.56226855577995]acc: 1.0\n", + "Cond:.#.#.### - Act:1 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1268.716, error:44.913641967231975]acc: None\n", + "Cond:#..##### - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 172.00, pred: 1247.309, error:1852.2806388202648]acc: 0.45930232558139533\n", + "Cond:.....### - Act:6 - effect:......OO - Num:1 [fit: 0.003, exp: 0.00, pred: 1200.959, error:521.1792755907857]acc: None\n", + "Cond:..#.##.. - Act:1 - effect:...OO... - Num:1 [fit: 0.018, exp: 0.00, pred: 1593.684, error:58.776058031620515]acc: None\n", + "Cond:#...#O## - Act:7 - effect:....OOO. - Num:2 [fit: 0.008, exp: 116.00, pred: 1334.454, error:1009.1978160741975]acc: 0.7155172413793104\n", + "Cond:.##O.##. - Act:3 - effect:O......O - Num:1 [fit: 0.130, exp: 12.00, pred: 1108.785, error:3879.9363636544645]acc: 0.0\n", + "Cond:##O##.#. - Act:3 - effect:...FOO.. - Num:1 [fit: 0.124, exp: 12.00, pred: 1108.785, error:2502.3363636544645]acc: 0.0\n", + "Cond:.#.#.##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 76.00, pred: 1390.346, error:258.56226838568904]acc: 1.0\n", + "Cond:#.##.#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.016, exp: 76.00, pred: 1390.346, error:258.56226838568904]acc: 1.0\n", + "Cond:.#..##.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.001, exp: 28.00, pred: 1859.560, error:6917.541346414081]acc: 0.03571428571428571\n", + "Cond:#.##.#.. - Act:7 - effect:...OO... - Num:3 [fit: 0.005, exp: 16.00, pred: 1430.456, error:5509.6170129469065]acc: 0.0\n", + "Cond:...###.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.001, exp: 24.00, pred: 1348.226, error:5684.677965082406]acc: 0.041666666666666664\n", + "Cond:##..#O## - Act:3 - effect:.....OOF - Num:1 [fit: 0.044, exp: 9.00, pred: 1046.474, error:3931.84357813756]acc: 0.2222222222222222\n", + "Cond:#..##O#F - Act:4 - effect:O......O - Num:2 [fit: 0.014, exp: 31.00, pred: 1311.826, error:1879.906435141995]acc: 0.8709677419354839\n", + "Cond:#O.O..#. - Act:3 - effect:..OO.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1682.747, error:317.905652058923]acc: None\n", + "Cond:..##.### - Act:6 - effect:.....OF. - Num:1 [fit: 0.016, exp: 5.00, pred: 1073.509, error:5002.252057379863]acc: 0.0\n", + "Cond:..##.##. - Act:7 - effect:.....OF. - Num:2 [fit: 0.007, exp: 32.00, pred: 1437.702, error:2035.5870539619636]acc: 0.5\n", + "Cond:#..##O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 51.00, pred: 1552.861, error:2159.3079591855617]acc: 0.9019607843137255\n", + "Cond:#.##.##. - Act:6 - effect:.....OF. - Num:1 [fit: 0.023, exp: 1.00, pred: 888.596, error:1600.0]acc: 0.0\n", + "Cond:###O..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1174.468, error:451.2296884698402]acc: None\n", + "Cond:###..#O# - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 1323.134, error:1086.4749562049308]acc: None\n", + "Cond:.##.#O## - Act:3 - effect:.....OOF - Num:1 [fit: 0.254, exp: 5.00, pred: 1065.267, error:3457.4692929015555]acc: 0.4\n", + "Cond:.##..O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.016, exp: 83.00, pred: 1390.346, error:258.562783697473]acc: 0.963855421686747\n", + "Cond:###.F### - Act:3 - effect:....OOO. - Num:1 [fit: 0.031, exp: 4.00, pred: 1102.163, error:2084.9406675518812]acc: 0.0\n", + "Cond:#O.OO..# - Act:3 - effect:..OO.... - Num:1 [fit: 0.009, exp: 0.00, pred: 1427.964, error:322.25435812191864]acc: None\n", + "Cond:.O.##.## - Act:3 - effect:..OO.... - Num:3 [fit: 0.389, exp: 12.00, pred: 1955.502, error:510.29140678788906]acc: 1.0\n", + "Cond:####O##. - Act:5 - effect:.....OOF - Num:2 [fit: 0.001, exp: 55.00, pred: 1475.245, error:4902.971106960886]acc: 0.5818181818181818\n", + "Cond:###OO.## - Act:5 - effect:.....F.. - Num:1 [fit: 0.000, exp: 43.00, pred: 1779.139, error:1636.1021798156926]acc: 0.7906976744186046\n", + "Cond:#.#.###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 73.00, pred: 1390.346, error:258.5622435481139]acc: 1.0\n", + "Cond:#.#.#OOF - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1308.763, error:56.35714034039304]acc: None\n", + "Cond:...#.#F# - Act:4 - effect:....OOO. - Num:4 [fit: 0.008, exp: 41.00, pred: 1368.571, error:3677.853789976298]acc: 0.6585365853658537\n", + "Cond:....#### - Act:5 - effect:......OO - Num:1 [fit: 0.004, exp: 56.00, pred: 1256.607, error:5610.3896337863225]acc: 0.42857142857142855\n", + "Cond:##..##.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 80.00, pred: 1456.111, error:4421.66776451531]acc: 0.7375\n", + "Cond:#..#OO.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1759.996, error:100.12360732101736]acc: None\n", + "Cond:###.#### - Act:7 - effect:O......O - Num:1 [fit: 0.005, exp: 127.00, pred: 1168.080, error:1905.1974132118016]acc: 0.6456692913385826\n", + "Cond:.####.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 2444.231, error:470.066341177044]acc: None\n", + "Cond:.##.#O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1609.889, error:49.567614653016776]acc: None\n", + "Cond:#...OO.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1609.889, error:49.567614653016776]acc: None\n", + "Cond:###.#.## - Act:1 - effect:...O.... - Num:1 [fit: 0.072, exp: 0.00, pred: 1254.506, error:382.88002663103464]acc: None\n", + "Cond:##..#### - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 109.00, pred: 1167.955, error:1580.7509986411415]acc: 0.6238532110091743\n", + "Cond:O##.###O - Act:7 - effect:....OOO. - Num:2 [fit: 0.007, exp: 11.00, pred: 1180.934, error:5963.334590821655]acc: 0.0\n", + "Cond:.##OO..# - Act:5 - effect:.....F.. - Num:1 [fit: 0.000, exp: 41.00, pred: 1779.141, error:1113.4972290471053]acc: 0.8048780487804879\n", + "Cond:###.###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 65.00, pred: 1390.346, error:258.56188103808086]acc: 1.0\n", + "Cond:#..###.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.020, exp: 50.00, pred: 1154.876, error:144.19294747233374]acc: 0.54\n", + "Cond:##...#.# - Act:4 - effect:.....OOF - Num:3 [fit: 0.000, exp: 37.00, pred: 1389.520, error:3766.691750814548]acc: 0.5405405405405406\n", + "Cond:#...##O. - Act:4 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 1661.589, error:101.7175281469512]acc: None\n", + "Cond:######.. - Act:6 - effect:...OO... - Num:1 [fit: 0.030, exp: 1.00, pred: 888.596, error:1800.0]acc: 0.0\n", + "Cond:##O#O.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.022, exp: 0.00, pred: 1964.810, error:64.20815597573967]acc: None\n", + "Cond:######## - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 129.00, pred: 1233.537, error:1916.6796755511882]acc: 0.49612403100775193\n", + "Cond:.##.#.## - Act:5 - effect:...FOO.. - Num:2 [fit: 0.004, exp: 36.00, pred: 1447.747, error:1579.7114444679378]acc: 0.3611111111111111\n", + "Cond:#####.#F - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1397.029, error:438.4676346899579]acc: None\n", + "Cond:#####O#F - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 26.00, pred: 1310.310, error:2948.1866463369442]acc: 0.11538461538461539\n", + "Cond:#..#..## - Act:4 - effect:O......O - Num:2 [fit: 0.007, exp: 17.00, pred: 1778.479, error:6675.570905367708]acc: 0.0\n", + "Cond:.#.#F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.040, exp: 27.00, pred: 1809.867, error:304.5722745712934]acc: 1.0\n", + "Cond:##..#O.. - Act:4 - effect:....OOO. - Num:3 [fit: 0.118, exp: 27.00, pred: 1809.867, error:304.5722745712934]acc: 1.0\n", + "Cond:#.#####. - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 49.00, pred: 1154.844, error:139.33309603222378]acc: 0.6122448979591837\n", + "Cond:#.#.###. - Act:7 - effect:...OO... - Num:1 [fit: 0.003, exp: 11.00, pred: 1318.594, error:3911.9659429767353]acc: 0.18181818181818182\n", + "Cond:#O..#O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1393.007, error:783.324155596424]acc: None\n", + "Cond:######O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 75.00, pred: 1215.957, error:614.1728367445355]acc: 0.84\n", + "Cond:..#.#.## - Act:7 - effect:...O.... - Num:1 [fit: 0.031, exp: 12.00, pred: 1184.286, error:1254.4273251381242]acc: 0.0\n", + "Cond:.##.#.## - Act:4 - effect:....FO.. - Num:2 [fit: 0.052, exp: 0.00, pred: 1119.882, error:383.58785223330096]acc: None\n", + "Cond:#.###### - Act:3 - effect:....OOO. - Num:4 [fit: 0.009, exp: 54.00, pred: 1268.124, error:3018.100586159962]acc: 0.4074074074074074\n", + "Cond:#.#.##.# - Act:7 - effect:.....F.. - Num:1 [fit: 0.003, exp: 19.00, pred: 1166.818, error:4003.936816714638]acc: 0.10526315789473684\n", + "Cond:####O##. - Act:7 - effect:.....OOF - Num:1 [fit: 0.024, exp: 0.00, pred: 1326.710, error:143.63460631556654]acc: None\n", + "Cond:#.##.#.. - Act:6 - effect:...OO... - Num:1 [fit: 0.003, exp: 0.00, pred: 888.596, error:0.0]acc: None\n", + "Cond:###..##. - Act:6 - effect:.....OF. - Num:1 [fit: 0.003, exp: 0.00, pred: 888.596, error:0.0]acc: None\n", + "Cond:#.##.#F# - Act:7 - effect:...OO... - Num:1 [fit: 0.087, exp: 1.00, pred: 970.667, error:1600.0]acc: 0.0\n", + "Cond:.####..# - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 28.00, pred: 1601.525, error:6732.93130241797]acc: 0.32142857142857145\n", + "Cond:#.OO#### - Act:7 - effect:...OO... - Num:1 [fit: 0.002, exp: 12.00, pred: 1585.537, error:6773.369763957195]acc: 0.0\n", + "Cond:#.#.#O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.065, exp: 25.00, pred: 1811.951, error:301.76027856344507]acc: 1.0\n", + "Cond:#.O##### - Act:3 - effect:.OOO.... - Num:1 [fit: 0.042, exp: 0.00, pred: 1165.514, error:490.1044689639359]acc: None\n", + "Cond:###.#O#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#....## - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1371.485, error:628.6298318552335]acc: None\n", + "Cond:##.#.#.# - Act:4 - effect:.....OOF - Num:1 [fit: 0.008, exp: 28.00, pred: 1391.327, error:554.7903656669554]acc: 0.42857142857142855\n", + "Cond:#####..O - Act:5 - effect:....OOO. - Num:2 [fit: 0.006, exp: 29.00, pred: 1256.842, error:3080.9726474617974]acc: 0.6551724137931034\n", + "Cond:O#.##.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.014, exp: 14.00, pred: 1381.253, error:3657.182195123049]acc: 0.2857142857142857\n", + "Cond:#.##.### - Act:3 - effect:...OO... - Num:1 [fit: 0.076, exp: 7.00, pred: 1279.431, error:3643.4745815410815]acc: 0.42857142857142855\n", + "Cond:###OO### - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 31.00, pred: 1780.238, error:5745.475093683975]acc: 0.6129032258064516\n", + "Cond:###OO.#. - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 31.00, pred: 1780.238, error:6116.019688805969]acc: 0.6129032258064516\n", + "Cond:#...#O#F - Act:4 - effect:O......O - Num:5 [fit: 0.088, exp: 23.00, pred: 1316.689, error:1711.1465400639631]acc: 0.8695652173913043\n", + "Cond:.#...#O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 71.00, pred: 1215.957, error:622.9691685911256]acc: 0.8309859154929577\n", + "Cond:.##.#.O# - Act:7 - effect:...O.... - Num:1 [fit: 0.031, exp: 12.00, pred: 1184.286, error:1854.4273251381242]acc: 0.0\n", + "Cond:..##.#FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.055, exp: 0.00, pred: 1549.018, error:71.04042238826233]acc: None\n", + "Cond:#...#O.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1467.162, error:67.36462500402988]acc: None\n", + "Cond:#O.##... - Act:3 - effect:..OO.... - Num:1 [fit: 0.021, exp: 13.00, pred: 1849.668, error:1499.8970950624512]acc: 0.8461538461538461\n", + "Cond:.O.O##.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 2401.949, error:204.05562093667643]acc: None\n", + "Cond:.####O#F - Act:1 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1613.127, error:47.72397896079944]acc: None\n", + "Cond:#.#OO.## - Act:6 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##O#O#.. - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#..#.#F# - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 34.00, pred: 1368.604, error:3522.556777922384]acc: 0.5882352941176471\n", + "Cond:.....#F# - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 34.00, pred: 1368.604, error:3776.4201111914617]acc: 0.5882352941176471\n", + "Cond:###.#.## - Act:5 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 59.00, pred: 1212.221, error:3759.9573935408916]acc: 0.3898305084745763\n", + "Cond:#O.#..## - Act:3 - effect:..OO.... - Num:3 [fit: 0.014, exp: 17.00, pred: 1588.023, error:1933.7298539032997]acc: 0.5294117647058824\n", + "Cond:..###OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 41.00, pred: 1390.338, error:258.5450954348038]acc: 1.0\n", + "Cond:.###.#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.022, exp: 41.00, pred: 1390.338, error:258.5450954348038]acc: 1.0\n", + "Cond:.####... - Act:7 - effect:....OOO. - Num:3 [fit: 0.008, exp: 21.00, pred: 1604.618, error:7143.504754689437]acc: 0.2857142857142857\n", + "Cond:#####..# - Act:7 - effect:....OOO. - Num:4 [fit: 0.003, exp: 31.00, pred: 1398.430, error:5008.345765210594]acc: 0.1935483870967742\n", + "Cond:#..#.OF# - Act:4 - effect:....OOO. - Num:3 [fit: 0.003, exp: 32.00, pred: 1368.681, error:3439.933650365328]acc: 0.5625\n", + "Cond:#####..# - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 76.00, pred: 1526.199, error:4371.394546763168]acc: 0.3684210526315789\n", + "Cond:.#..F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.050, exp: 19.00, pred: 1812.893, error:301.1436808901669]acc: 1.0\n", + "Cond:###.#.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 2739.735, error:400.99106798872964]acc: None\n", + "Cond:#...#F## - Act:4 - effect:....FO.. - Num:2 [fit: 0.002, exp: 15.00, pred: 1459.185, error:6519.562302092572]acc: 0.0\n", + "Cond:.#..#O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 34.00, pred: 1390.500, error:258.84730197773666]acc: 1.0\n", + "Cond:..#.#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.108, exp: 9.00, pred: 1345.813, error:2570.189266407418]acc: 0.0\n", + "Cond:#O.#O.## - Act:3 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 2621.117, error:334.65344707768634]acc: None\n", + "Cond:#.##...# - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 37.00, pred: 1337.677, error:2620.750852085669]acc: 0.5945945945945946\n", + "Cond:####O### - Act:5 - effect:.....OOF - Num:2 [fit: 0.002, exp: 34.00, pred: 1475.547, error:4665.499047388954]acc: 0.29411764705882354\n", + "Cond:..#.##O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 34.00, pred: 1215.763, error:613.5314130908811]acc: 0.6470588235294118\n", + "Cond:#.###.## - Act:5 - effect:....OOO. - Num:2 [fit: 0.002, exp: 81.00, pred: 1463.497, error:4960.186302139772]acc: 0.25925925925925924\n", + "Cond:O##.#.## - Act:5 - effect:....OOO. - Num:3 [fit: 0.005, exp: 24.00, pred: 1186.096, error:3403.4393581624954]acc: 0.6666666666666666\n", + "Cond:#..##.## - Act:4 - effect:O......O - Num:2 [fit: 0.007, exp: 9.00, pred: 1915.157, error:5913.617621864012]acc: 0.0\n", + "Cond:....#O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 41.00, pred: 1411.064, error:2973.2084197220747]acc: 0.6585365853658537\n", + "Cond:.###OO#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 2099.624, error:66.2273531362816]acc: None\n", + "Cond:.####O## - Act:4 - effect:......OO - Num:2 [fit: 0.003, exp: 68.00, pred: 1263.827, error:2885.0702668698495]acc: 0.39705882352941174\n", + "Cond:##.#..## - Act:3 - effect:..OO.... - Num:1 [fit: 0.004, exp: 20.00, pred: 1465.445, error:2745.007416759528]acc: 0.35\n", + "Cond:####O.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.001, exp: 18.00, pred: 1776.164, error:5851.115719421856]acc: 0.3333333333333333\n", + "Cond:#.#.###F - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1848.311, error:59.488340276707135]acc: None\n", + "Cond:#..###F# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 23.00, pred: 1368.928, error:2964.3219721304968]acc: 0.21739130434782608\n", + "Cond:#..##..# - Act:7 - effect:....OOO. - Num:2 [fit: 0.045, exp: 10.00, pred: 1201.606, error:6036.246373123637]acc: 0.0\n", + "Cond:.###OO## - Act:4 - effect:....OOO. - Num:1 [fit: 0.151, exp: 7.00, pred: 1086.205, error:3089.6303125320283]acc: 0.2857142857142857\n", + "Cond:#...O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.032, exp: 0.00, pred: 2436.813, error:105.33054747195989]acc: None\n", + "Cond:.##.#OO# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 22.00, pred: 1314.398, error:5580.62792883851]acc: 0.0\n", + "Cond:#.###OO# - Act:7 - effect:....OOO. - Num:3 [fit: 0.567, exp: 7.00, pred: 1285.887, error:141.34880202689266]acc: 1.0\n", + "Cond:##.##... - Act:1 - effect:.....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.##..## - Act:5 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 53.00, pred: 1242.618, error:2869.121629776039]acc: 0.4528301886792453\n", + "Cond:..#..#F# - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#...#### - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 2095.916, error:708.2347007775041]acc: None\n", + "Cond:O##.#.#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.208, exp: 2.00, pred: 1233.724, error:50.138229225828354]acc: 1.0\n", + "Cond:#.#O###. - Act:5 - effect:.....F.. - Num:4 [fit: 0.004, exp: 25.00, pred: 1731.005, error:1447.2362493830494]acc: 0.4\n", + "Cond:####O... - Act:5 - effect:.....OOF - Num:2 [fit: 0.010, exp: 16.00, pred: 1764.664, error:5301.352304382805]acc: 0.375\n", + "Cond:O..F##.# - Act:5 - effect:OO...... - Num:1 [fit: 0.020, exp: 0.00, pred: 1202.929, error:1027.1326266705116]acc: None\n", + "Cond:...##O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.050, exp: 22.00, pred: 1154.839, error:133.3218376950941]acc: 1.0\n", + "Cond:O#...### - Act:7 - effect:....OOO. - Num:1 [fit: 0.028, exp: 4.00, pred: 1066.305, error:3743.6869154124215]acc: 0.0\n", + "Cond:##.#.... - Act:3 - effect:..OO.... - Num:1 [fit: 0.053, exp: 7.00, pred: 1710.054, error:925.1991085441394]acc: 0.8571428571428571\n", + "Cond:#.###..# - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 13.00, pred: 1376.315, error:4616.901293223457]acc: 0.0\n", + "Cond:.####.## - Act:7 - effect:....OOO. - Num:2 [fit: 0.003, exp: 23.00, pred: 1277.165, error:2979.2593894949346]acc: 0.043478260869565216\n", + "Cond:....#F## - Act:7 - effect:....FO.. - Num:1 [fit: 0.012, exp: 1.00, pred: 970.791, error:1800.0]acc: 0.0\n", + "Cond:...#.#FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1946.194, error:1026.5169031163477]acc: None\n", + "Cond:...#.OF# - Act:2 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1946.194, error:1026.5169031163477]acc: None\n", + "Cond:#.##..## - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1678.693, error:457.9731907105183]acc: None\n", + "Cond:..#.#.O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.010, exp: 10.00, pred: 1199.121, error:1177.8602395158387]acc: 0.0\n", + "Cond:#..##### - Act:5 - effect:OO...... - Num:2 [fit: 0.002, exp: 66.00, pred: 1324.997, error:3793.3440656524726]acc: 0.0\n", + "Cond:...#O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.121, exp: 17.00, pred: 1150.505, error:123.30660180926184]acc: 1.0\n", + "Cond:#...###F - Act:4 - effect:O......O - Num:4 [fit: 0.268, exp: 14.00, pred: 1303.208, error:1792.9952282168222]acc: 0.8571428571428571\n", + "Cond:#.##.#.# - Act:3 - effect:...OO... - Num:1 [fit: 0.013, exp: 1.00, pred: 1064.626, error:1600.0]acc: 0.0\n", + "Cond:..#.#F#. - Act:4 - effect:....FO.. - Num:2 [fit: 0.006, exp: 8.00, pred: 1352.722, error:4913.837400125499]acc: 0.0\n", + "Cond:.#####O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.062, exp: 13.00, pred: 1219.101, error:515.5271872471739]acc: 0.3076923076923077\n", + "Cond:O##.O..# - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1636.632, error:409.3896777579979]acc: None\n", + "Cond:..##.#.. - Act:7 - effect:.....OF. - Num:1 [fit: 0.297, exp: 8.00, pred: 1468.094, error:1655.744428460448]acc: 0.75\n", + "Cond:#.##.F#. - Act:7 - effect:...OO... - Num:1 [fit: 0.005, exp: 1.00, pred: 970.791, error:1600.0]acc: 0.0\n", + "Cond:...#.OF# - Act:4 - effect:....OOO. - Num:3 [fit: 0.034, exp: 10.00, pred: 1378.467, error:2847.23615673073]acc: 0.4\n", + "Cond:#.###OO. - Act:7 - effect:....OOO. - Num:1 [fit: 0.042, exp: 0.00, pred: 1325.576, error:28.40927107690146]acc: None\n", + "Cond:###O##O. - Act:5 - effect:.....F.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1868.909, error:274.79683981654154]acc: None\n", + "Cond:#..##OOF - Act:4 - effect:O......O - Num:1 [fit: 0.134, exp: 10.00, pred: 1325.882, error:1640.2169244358677]acc: 0.8\n", + "Cond:#.#.##.. - Act:4 - effect:....OOO. - Num:3 [fit: 0.098, exp: 11.00, pred: 1486.978, error:3849.9511536785544]acc: 0.45454545454545453\n", + "Cond:.##....# - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 1.00, pred: 2030.309, error:0.0]acc: 1.0\n", + "Cond:##.##### - Act:5 - effect:OO...... - Num:2 [fit: 0.003, exp: 50.00, pred: 1324.998, error:3473.816098380293]acc: 0.0\n", + "Cond:...#OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.031, exp: 5.00, pred: 1019.042, error:2904.323013707909]acc: 0.4\n", + "Cond:#####O.. - Act:4 - effect:...FOO.. - Num:2 [fit: 0.088, exp: 9.00, pred: 1515.948, error:5657.018484120404]acc: 0.3333333333333333\n", + "Cond:#O.F..## - Act:3 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:...#O### - Act:4 - effect:....OOO. - Num:1 [fit: 0.229, exp: 2.00, pred: 940.365, error:1506.6338360059453]acc: 0.5\n", + "Cond:....#O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.036, exp: 0.00, pred: 1232.448, error:30.155603865213806]acc: None\n", + "Cond:##.####. - Act:5 - effect:.....OOF - Num:3 [fit: 0.014, exp: 21.00, pred: 1472.764, error:4757.199911132101]acc: 0.14285714285714285\n", + "Cond:.###O.#. - Act:5 - effect:OO...... - Num:1 [fit: 0.006, exp: 8.00, pred: 1764.676, error:5211.48467106158]acc: 0.0\n", + "Cond:##.##O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.035, exp: 19.00, pred: 1066.889, error:2975.7963851657323]acc: 0.2631578947368421\n", + "Cond:#..####F - Act:4 - effect:O......O - Num:2 [fit: 0.412, exp: 8.00, pred: 1334.862, error:2388.6655066268954]acc: 0.75\n", + "Cond:##.##### - Act:3 - effect:....OOO. - Num:3 [fit: 0.021, exp: 15.00, pred: 1389.976, error:3148.9144505851864]acc: 0.4\n", + "Cond:..#.##.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 8.00, pred: 1480.336, error:4079.012632786517]acc: 0.25\n", + "Cond:#####O## - Act:4 - effect:....OOO. - Num:2 [fit: 0.006, exp: 18.00, pred: 1265.481, error:1402.1004628768858]acc: 0.3333333333333333\n", + "Cond:...#.OFF - Act:4 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1805.529, error:360.7618121801802]acc: None\n", + "Cond:##.#OO## - Act:5 - effect:.....OOF - Num:1 [fit: 0.046, exp: 10.00, pred: 1094.825, error:1398.3196472574161]acc: 0.0\n", + "Cond:.O.##### - Act:3 - effect:..OO.... - Num:1 [fit: 0.499, exp: 4.00, pred: 1416.272, error:209.31832624994382]acc: 1.0\n", + "Cond:#####.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.072, exp: 8.00, pred: 1442.142, error:3286.4807879761806]acc: 0.0\n", + "Cond:#.##.#.# - Act:7 - effect:.....OF. - Num:2 [fit: 0.132, exp: 7.00, pred: 1355.812, error:1755.021689769152]acc: 0.5714285714285714\n", + "Cond:#.#..#.. - Act:4 - effect:.....OOF - Num:2 [fit: 0.581, exp: 5.00, pred: 1334.415, error:217.55695457350893]acc: 1.0\n", + "Cond:#...#OOF - Act:4 - effect:O......O - Num:1 [fit: 0.143, exp: 3.00, pred: 1146.214, error:1262.2290378030623]acc: 0.3333333333333333\n", + "Cond:#..####O - Act:5 - effect:...FOO.. - Num:2 [fit: 0.108, exp: 2.00, pred: 1163.474, error:2167.0099162596625]acc: 0.5\n", + "Cond:#.###.#O - Act:5 - effect:....OOO. - Num:1 [fit: 0.108, exp: 2.00, pred: 1163.474, error:1567.0099162596625]acc: 0.5\n", + "Cond:.###O### - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:#.##OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.202, exp: 2.00, pred: 1096.003, error:24.25956761971736]acc: 1.0\n", + "Cond:#######. - Act:5 - effect:.....F.. - Num:2 [fit: 0.690, exp: 4.00, pred: 1415.929, error:126.99893216917172]acc: 1.0\n", + "Cond:#....OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:####O### - Act:7 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:.#..#.O# - Act:7 - effect:...O.... - Num:1 [fit: 0.193, exp: 7.00, pred: 1158.547, error:1690.5973609965736]acc: 0.0\n", + "Cond:..#.#### - Act:7 - effect:...O.... - Num:1 [fit: 0.080, exp: 11.00, pred: 1181.142, error:2018.84065967899]acc: 0.0\n", + "Cond:.O.O...# - Act:3 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 2271.328, error:137.35069294667767]acc: None\n", + "Cond:..###O.# - Act:4 - effect:.....OOF - Num:1 [fit: 0.020, exp: 0.00, pred: 1596.984, error:50.47658439312167]acc: None\n", + "Cond:#.#..O.. - Act:4 - effect:.....OOF - Num:1 [fit: 0.020, exp: 0.00, pred: 1596.984, error:50.47658439312167]acc: None\n", + "Cond:.##.#O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:..###O## - Act:1 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", + "Cond:##O####. - Act:5 - effect:.....F.. - Num:1 [fit: 0.036, exp: 0.00, pred: 1299.649, error:13.26309697920819]acc: None\n", + "Cond:#....##. - Act:7 - effect:...OO... - Num:1 [fit: 0.009, exp: 0.00, pred: 1052.469, error:258.63673437069946]acc: None\n", + "Cond:..#.##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.009, exp: 0.00, pred: 1052.469, error:258.63673437069946]acc: None\n", + "Cond:####..## - Act:3 - effect:....OOO. - Num:1 [fit: 0.007, exp: 1.00, pred: 2027.871, error:1000.0]acc: 0.0\n", + "Cond:#.###O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.213, exp: 2.00, pred: 1348.390, error:77.85687342825929]acc: 1.0\n" ] } ], @@ -1256,29 +1457,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" - ] - } - ], - "source": [ - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1286,14 +1465,14 @@ "output_type": "stream", "text": [ "Most Numerous rules:\n", - "Cond:#.#.###F - Act:0 - effect:..O..O.. - Num:5 [fit: 0.002, exp: 21.00, pred: 810.828, error:10688.920098385375]acc: 0.0\n", - "Cond:.#..#O#F - Act:1 - effect:...OOO.. - Num:8 [fit: 0.012, exp: 22.00, pred: 317.322, error:7880.945643824181]acc: 0.0\n", - "Cond:O#..#..# - Act:2 - effect:.....O.. - Num:6 [fit: 0.004, exp: 23.00, pred: 372.033, error:7311.767484177768]acc: 0.0\n", - "Cond:.#O##... - Act:3 - effect:.OO..OO. - Num:6 [fit: 0.019, exp: 59.00, pred: 299.353, error:2988.393877525843]acc: 0.0\n", - "Cond:.####### - Act:4 - effect:.......O - Num:8 [fit: 0.001, exp: 420.00, pred: 1488.579, error:7875.997225382572]acc: 0.09047619047619047\n", - "Cond:######## - Act:5 - effect:........ - Num:68 [fit: 0.043, exp: 2334.00, pred: 1210.169, error:1714.0389427135808]acc: 0.0\n", - "Cond:######.# - Act:6 - effect:.O...... - Num:6 [fit: 0.066, exp: 656.00, pred: 271.508, error:2804.2657259436173]acc: 0.07317073170731707\n", - "Cond:.####### - Act:7 - effect:.O...... - Num:9 [fit: 0.012, exp: 818.00, pred: 1343.672, error:1833.8170765267917]acc: 0.011002444987775062\n" + "Cond:...#O### - Act:0 - effect:...OO... - Num:8 [fit: 0.280, exp: 32.00, pred: 1145.166, error:5615.340915401308]acc: 0.75\n", + "Cond:.....O#. - Act:1 - effect:.O...... - Num:3 [fit: 0.002, exp: 22.00, pred: 926.395, error:9731.598541633895]acc: 0.13636363636363635\n", + "Cond:.O#O#.#. - Act:2 - effect:.OOO.... - Num:5 [fit: 0.025, exp: 15.00, pred: 945.849, error:8328.366070324544]acc: 0.26666666666666666\n", + "Cond:...#OO.# - Act:3 - effect:....OOO. - Num:12 [fit: 0.280, exp: 144.00, pred: 1158.783, error:143.7059003841303]acc: 1.0\n", + "Cond:#.#.#O.. - Act:4 - effect:....OOO. - Num:11 [fit: 0.437, exp: 216.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:.####.O# - Act:6 - effect:......OO - Num:3 [fit: 0.013, exp: 27.00, pred: 979.212, error:3316.023485212587]acc: 0.8148148148148148\n", + "Cond:#..#OO#. - Act:7 - effect:...O.... - Num:9 [fit: 0.035, exp: 49.00, pred: 1135.014, error:1622.4503549176839]acc: 0.7551020408163265\n" ] } ], @@ -1310,86 +1489,82 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The best rules:\n", - "Cond:O......O - Act:0 - effect:O..O.... - Num:2 [fit: 0.003, exp: 45.00, pred: 571.826, error:7137.170298870321]acc: 0.0\n", - "Cond:....FO.. - Act:1 - effect:....FO.. - Num:2 [fit: 0.002, exp: 50.00, pred: 353.055, error:6337.943477712033]acc: 0.54\n", - "Cond:O......O - Act:2 - effect:.O....OO - Num:1 [fit: 0.009, exp: 13.00, pred: 520.655, error:10075.382209840245]acc: 0.0\n", - "Cond:...O.... - Act:3 - effect:...O.... - Num:1 [fit: 0.001, exp: 83.00, pred: 544.878, error:4259.89056855382]acc: 0.5301204819277109\n", - "Cond:O...F#.O - Act:4 - effect:.O.OOOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 493.170, error:1341.6924102555245]acc: 0\n", - "Cond:.#..FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 182.00, pred: 496.703, error:4613.81658032312]acc: 0.6593406593406593\n", - "Cond:.....OOF - Act:6 - effect:.OF..O.. - Num:3 [fit: 0.026, exp: 127.00, pred: 278.127, error:895.2348863473378]acc: 0.0\n", - "Cond:OO.....O - Act:7 - effect:.......O - Num:3 [fit: 0.010, exp: 16.00, pred: 446.449, error:7981.043445140795]acc: 0.0\n" + "The most numerous rules:\n", + "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:...#OO.# - Act:3 - effect:....OOO. - Num:12 [fit: 0.280, exp: 144.00, pred: 1158.783, error:143.7059003841303]acc: 1.0\n", + "Cond:#.#.#O.. - Act:4 - effect:....OOO. - Num:11 [fit: 0.437, exp: 216.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:.#...#.. - Act:5 - effect:....OOO. - Num:10 [fit: 0.692, exp: 417.00, pred: 1225.169, error:248.2805850823554]acc: 0.9256594724220624\n", + "Cond:#...#O.. - Act:4 - effect:....OOO. - Num:9 [fit: 0.342, exp: 271.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", + "Cond:#..#OO#. - Act:7 - effect:...O.... - Num:9 [fit: 0.035, exp: 49.00, pred: 1135.014, error:1622.4503549176839]acc: 0.7551020408163265\n", + "Cond:...#O### - Act:0 - effect:...OO... - Num:8 [fit: 0.280, exp: 32.00, pred: 1145.166, error:5615.340915401308]acc: 0.75\n", + "Cond:.O.#...# - Act:3 - effect:..OO.... - Num:8 [fit: 0.083, exp: 37.00, pred: 1894.396, error:544.4345837287093]acc: 0.972972972972973\n", + "Cond:...#OO#. - Act:3 - effect:....OOO. - Num:7 [fit: 0.173, exp: 396.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", + "Cond:..#..#.. - Act:5 - effect:....OOO. - Num:7 [fit: 0.365, exp: 325.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n" ] } ], "source": [ - "print(\"The best rules:\")\n", - "for action in range(agent.cfg.number_of_actions):\n", - " action_set = agent.population.generate_action_set(action)\n", - " most_numerous = action_set[0]\n", - " for cl in action_set:\n", - " if (cl.fitness) > (most_numerous.prediction):\n", - " most_numerous = cl\n", - " print(most_numerous)\n", - " " + "print(\"The most numerous rules:\")\n", + "numerous = sorted(agent.population, key=lambda cl: -1 * cl.numerosity)[0:10]\n", + "for cl in numerous:\n", + " print(cl)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Avg Error and pred\n", - "[fit: 0.006, pred: 574.682, error:6100.992211655139]\n", - "[fit: 0.018, pred: 416.524, error:4858.0924376831]\n", - "[fit: 0.009, pred: 586.372, error:5528.83380068283]\n", - "[fit: 0.017, pred: 572.786, error:4750.917418532567]\n", - "[fit: 0.014, pred: 929.342, error:4078.4389407891395]\n", - "[fit: 0.009, pred: 802.240, error:3061.474473908673]\n", - "[fit: 0.010, pred: 479.630, error:2908.2080628997805]\n", - "[fit: 0.010, pred: 809.651, error:2999.963739797117]\n" + "The fittest rules:\n", + "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", + "Cond:####.#F# - Act:3 - effect:......OO - Num:2 [fit: 1.201, exp: 6.00, pred: 1010.698, error:2438.439663305213]acc: 0.8333333333333334\n", + "Cond:#OO..#.. - Act:1 - effect:.OO..... - Num:1 [fit: 0.809, exp: 10.00, pred: 1009.578, error:2542.189272565551]acc: 0.9\n", + "Cond:.#...#.. - Act:5 - effect:....OOO. - Num:10 [fit: 0.692, exp: 417.00, pred: 1225.169, error:248.2805850823554]acc: 0.9256594724220624\n", + "Cond:#######. - Act:5 - effect:.....F.. - Num:2 [fit: 0.690, exp: 4.00, pred: 1415.929, error:126.99893216917172]acc: 1.0\n", + "Cond:#.#..#.. - Act:4 - effect:.....OOF - Num:2 [fit: 0.581, exp: 5.00, pred: 1334.415, error:217.55695457350893]acc: 1.0\n", + "Cond:#.###OO# - Act:7 - effect:....OOO. - Num:3 [fit: 0.567, exp: 7.00, pred: 1285.887, error:141.34880202689266]acc: 1.0\n", + "Cond:..#.#.## - Act:4 - effect:....FO.. - Num:2 [fit: 0.518, exp: 10.00, pred: 1119.882, error:3134.351408933204]acc: 0.8\n", + "Cond:..#OO... - Act:7 - effect:.....F.. - Num:1 [fit: 0.512, exp: 4.00, pred: 898.981, error:38.34946290007767]acc: 1.0\n", + "Cond:.O.##### - Act:3 - effect:..OO.... - Num:1 [fit: 0.499, exp: 4.00, pred: 1416.272, error:209.31832624994382]acc: 1.0\n" ] } ], "source": [ - "print(\"Avg Error and pred\")\n", - "for action in range(agent.cfg.number_of_actions):\n", - " action_set = agent.population.generate_action_set(action)\n", - " error = sum(cl.error for cl in action_set) / len(action_set)\n", - " prediction = sum(cl.prediction for cl in action_set) / len(action_set)\n", - " fitness = sum(cl.fitness for cl in action_set) / len(action_set)\n", - " print( f\"[fit: {fitness:.3f}, pred: {prediction:2.3f}, error:{error}]\")" + "print(\"The fittest rules:\")\n", + "fittest = sorted(agent.population, key=lambda cl: -1 * cl.fitness)[0:10]\n", + "for cl in fittest:\n", + " print(cl)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1408,34 +1583,27 @@ "ax = df.plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"average_specificity\")\n", - "ax.legend([\"specificity\"])\n" + "ax.legend([\"specificity\"])" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1451,17 +1619,17 @@ "ax = df.plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"fraction accuracy\")\n", - "ax.legend([\"fraction accuracy\"])\n" + "ax.legend([\"fraction accuracy\"])" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1473,34 +1641,33 @@ } ], "source": [ - "\n", "df = pd.DataFrame(zip([metric[\"population\"] for metric in explore_metrics], [metric[\"numerosity\"] for metric in explore_metrics]))\n", "ax = df.plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"population\")\n", "ax.legend([\"pop\", \"num\"])\n", "\n", - "steps_averaged = []\n" + "steps_averaged = []" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,8 +1679,6 @@ } ], "source": [ - "\n", - "\n", "xf = [metric[\"steps_in_trial\"] for metric in explore_metrics]\n", "temp_df = np.array_split(xf, 20)\n", "for i in range(len(temp_df)):\n", diff --git a/XCS_Experiments/XNCS_woods_avg.ipynb b/XCS_Experiments/XNCS_woods_avg.ipynb index 6f9f824..8e3b8b7 100644 --- a/XCS_Experiments/XNCS_woods_avg.ipynb +++ b/XCS_Experiments/XNCS_woods_avg.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -24,10 +24,10 @@ "This is how maze looks like:\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } ], @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -85,7 +85,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 27, 'reward': 1000.0, 'perf_time': 0.016212000000001225, 'numerosity': 64, 'population': 64, 'average_specificity': 8.59375, 'fraction_accuracy': 0.0}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 39, 'reward': 1000.0, 'perf_time': 0.02473699999973178, 'numerosity': 90, 'population': 86, 'average_specificity': 9.311111111111112, 'fraction_accuracy': 0.041666666666666664}\n" ] }, { @@ -99,105 +99,39 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1173.9504184900188, 'perf_time': 0.08457579999999609, 'numerosity': 1800, 'population': 1062, 'average_specificity': 14.204444444444444, 'fraction_accuracy': 0.006944444444444444}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 16, 'reward': 1004.2069356990346, 'perf_time': 0.21832999999999458, 'numerosity': 1800, 'population': 1193, 'average_specificity': 17.54111111111111, 'fraction_accuracy': 0.019230769230769232}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 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"INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 1, 'reward': 2460.657864708448, 'perf_time': 0.011019800000212854, 'numerosity': 1800, 'population': 1345, 'average_specificity': 20.835555555555555, 'fraction_accuracy': 0.3538812785388128}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1672.367205235479, 'perf_time': 0.03802749999977095, 'numerosity': 1800, 'population': 1360, 'average_specificity': 20.52, 'fraction_accuracy': 0.27781329923273657}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 1, 'reward': 1800.1126140573117, 'perf_time': 0.020959200000106648, 'numerosity': 1800, 'population': 1350, 'average_specificity': 19.385555555555555, 'fraction_accuracy': 0.2811866125760649}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 1, 'reward': 2399.910796806107, 'perf_time': 0.008633800000097835, 'numerosity': 1800, 'population': 1358, 'average_specificity': 19.255555555555556, 'fraction_accuracy': 0.30245535714285715}\n" ] } ], "source": [ "df = avg_experiment(maze=maze,\n", " cfg=cfg,\n", - " number_of_tests=3,\n", - " explore_trials=2500,\n", - " exploit_trials=2500)\n" + " number_of_tests=1,\n", + " explore_trials=1000,\n", + " exploit_trials=1000)\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -243,614 +177,254 @@ " \n", " \n", " 0\n", - " 20.333333\n", - " 1000.000000\n", - " 0.011569\n", - " 60.666667\n", - " 60.666667\n", - " 7.872087\n", - " 0.000000\n", + " 39\n", + " 1.000000e+03\n", + " 0.024737\n", + " 90\n", + " 86\n", + " 9.311111\n", + " 0.041667\n", " \n", " \n", " 100\n", - " 6.666667\n", - " 1209.451280\n", - " 0.051856\n", - " 1360.333333\n", - " 776.333333\n", - " 17.462643\n", - " 0.011789\n", + " 18\n", + " 1.003240e+03\n", + " 0.159255\n", + " 1411\n", + " 791\n", + " 17.428065\n", + " 0.016667\n", " \n", " \n", " 200\n", - " 20.333333\n", - " 785.041917\n", - " 0.304347\n", - " 1800.000000\n", - " 1055.000000\n", - 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1231.843427 0.194471 1800.000000 1426.666667 \n", - "2200 19.000000 1168.096088 0.376105 1800.000000 1424.666667 \n", - "2300 19.000000 1043.423353 0.342171 1800.000000 1452.000000 \n", - "2400 8.000000 1285.111332 0.140494 1800.000000 1458.000000 \n", - "2500 20.666667 806.374751 0.352253 1800.000000 1440.666667 \n", - "2600 18.000000 1201.885676 0.219223 1800.000000 1465.333333 \n", - "2700 23.333333 765.633664 0.300722 1800.000000 1483.666667 \n", - "2800 12.666667 1029.292824 0.216334 1800.000000 1476.666667 \n", - "2900 19.666667 929.801484 0.207916 1800.000000 1479.333333 \n", - "3000 2.333333 1584.691784 0.031993 1800.000000 1492.333333 \n", - "3100 18.666667 1082.638953 0.207062 1800.000000 1505.333333 \n", - "3200 4.000000 1288.586903 0.041993 1800.000000 1506.666667 \n", - "3300 7.000000 1171.438441 0.089677 1800.000000 1500.333333 \n", - "3400 18.666667 1051.286544 0.182575 1800.000000 1491.333333 \n", - "3500 19.000000 894.845332 0.223014 1800.000000 1503.666667 \n", - "3600 20.000000 857.303397 0.231591 1800.000000 1508.333333 \n", - "3700 27.333333 741.651443 0.302267 1800.000000 1516.666667 \n", - "3800 20.666667 666.666686 0.277704 1800.000000 1516.666667 \n", - "3900 5.333333 1479.183766 0.067978 1800.000000 1524.333333 \n", - "4000 19.000000 966.393574 0.236436 1800.000000 1519.333333 \n", - "4100 25.666667 1049.348977 0.359262 1800.000000 1524.000000 \n", - "4200 19.000000 945.753410 0.210907 1800.000000 1532.000000 \n", - "4300 17.000000 1002.806775 0.250447 1800.000000 1529.000000 \n", - "4400 20.333333 904.476119 0.274334 1800.000000 1542.666667 \n", - "4500 7.666667 1200.118281 0.121425 1800.000000 1543.000000 \n", - "4600 17.000000 1285.238940 0.199901 1800.000000 1535.666667 \n", - "4700 30.000000 809.243419 0.397577 1800.000000 1539.000000 \n", - "4800 33.000000 834.718281 0.427849 1800.000000 1537.333333 \n", - "4900 4.333333 1255.325022 0.063624 1800.000000 1531.000000 \n", + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 39 1.000000e+03 0.024737 90 86 \n", + "100 18 1.003240e+03 0.159255 1411 791 \n", + "200 6 1.128370e+03 0.097624 1800 1021 \n", + "300 24 1.000693e+03 0.345941 1800 1150 \n", + "400 6 1.128100e+03 0.153533 1800 1133 \n", + "500 2 1.684861e+03 0.030647 1800 1185 \n", + "600 11 1.023121e+03 0.170805 1800 1212 \n", + "700 21 1.000761e+03 0.325119 1800 1252 \n", + "800 12 1.028069e+03 0.174642 1800 1245 \n", + "900 9 1.069013e+03 0.115056 1800 1268 \n", + "1000 2 1.504100e+03 0.018642 1800 1320 \n", + "1100 5 1.271392e+03 0.069390 1800 1331 \n", + "1200 2 1.684524e+03 0.019589 1800 1336 \n", + "1300 50 1.675610e-12 0.641720 1800 1350 \n", + "1400 50 1.336088e-12 0.633936 1800 1330 \n", + "1500 1 1.867265e+03 0.009635 1800 1329 \n", + "1600 1 2.460658e+03 0.011020 1800 1345 \n", + "1700 3 1.672367e+03 0.038027 1800 1360 \n", + "1800 1 1.800113e+03 0.020959 1800 1350 \n", + "1900 1 2.399911e+03 0.008634 1800 1358 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 7.872087 0.000000 \n", - "100 17.462643 0.011789 \n", - "200 14.818704 0.006625 \n", - "300 15.233519 0.000000 \n", - "400 17.285370 0.004167 \n", - "500 18.876111 0.011619 \n", - "600 17.435000 0.000000 \n", - "700 19.326852 0.022637 \n", - "800 19.808148 0.012255 \n", - "900 19.396111 0.015851 \n", - "1000 18.363704 0.018472 \n", - "1100 19.157037 0.011787 \n", - "1200 18.224074 0.019393 \n", - "1300 18.803704 0.005208 \n", - "1400 18.746852 0.007576 \n", - "1500 18.212963 0.012489 \n", - "1600 19.877963 0.009455 \n", - "1700 19.122593 0.014683 \n", - "1800 18.428704 0.012649 \n", - "1900 17.960926 0.007659 \n", - "2000 18.370556 0.040618 \n", - "2100 17.818704 0.030286 \n", - "2200 17.418148 0.019097 \n", - "2300 17.861667 0.015317 \n", - "2400 18.347407 0.027657 \n", - "2500 17.878889 0.014137 \n", - "2600 17.680926 0.005952 \n", - "2700 18.171481 0.052545 \n", - "2800 20.790556 0.016004 \n", - "2900 21.283519 0.042365 \n", - "3000 20.419815 0.020032 \n", - "3100 21.386852 0.015086 \n", - "3200 20.582963 0.021245 \n", - "3300 22.397963 0.014689 \n", - "3400 22.824815 0.034271 \n", - "3500 21.672593 0.022042 \n", - "3600 22.380926 0.052997 \n", - "3700 21.171296 0.072268 \n", - "3800 21.714444 0.028810 \n", - "3900 21.698889 0.039755 \n", - "4000 21.503704 0.043138 \n", - "4100 21.858333 0.040765 \n", - "4200 21.039074 0.064732 \n", - "4300 20.644630 0.055804 \n", - "4400 19.520185 0.046726 \n", - "4500 19.176852 0.012401 \n", - "4600 19.137593 0.042193 \n", - "4700 19.197407 0.000000 \n", - "4800 19.976296 0.017544 \n", - "4900 19.556852 0.094298 " + "0 9.311111 0.041667 \n", + "100 17.428065 0.016667 \n", + "200 15.462778 0.000000 \n", + "300 17.066111 0.008333 \n", + "400 18.805000 0.000000 \n", + "500 19.858333 0.135000 \n", + "600 17.901667 0.125000 \n", + "700 20.461111 0.148292 \n", + "800 21.549444 0.125000 \n", + "900 17.720000 0.026042 \n", + "1000 18.363333 0.143182 \n", + "1100 19.087778 0.376032 \n", + "1200 19.194444 0.305444 \n", + "1300 20.960556 0.284091 \n", + "1400 19.726111 0.269737 \n", + "1500 20.177778 0.378788 \n", + "1600 20.835556 0.353881 \n", + "1700 20.520000 0.277813 \n", + "1800 19.385556 0.281187 \n", + "1900 19.255556 0.302455 " ] }, "metadata": {}, @@ -863,22 +437,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -901,22 +475,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -936,22 +510,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -971,22 +545,22 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index 65d7d03..271a69d 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -16,17 +16,12 @@ def fraction_accuracy(xncs: XNCS): accuracies = [] for action in range(xncs.cfg.number_of_actions): action_set = xncs.population.generate_action_set(action) - most_numerous = action_set[0] - for cl in action_set: - if cl.numerosity > most_numerous.numerosity: - most_numerous = cl - if most_numerous.queses > 0: - accuracies.append( - (most_numerous.queses - most_numerous.mistakes) / most_numerous.queses - ) + accuracy = sorted(action_set, key=lambda cl: -1 * cl.numerosity)[1].accuracy + if accuracy is not None: + accuracies.append(accuracy) else: accuracies.append(0) - return sum(accuracies) / xncs.cfg.number_of_actions + return sum(accuracies) / len(accuracies) def specificity(xncs, population): From f67f3930fb558b2ff92252ac5b6a6e6a9245513d Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Mon, 31 May 2021 11:55:39 +0200 Subject: [PATCH 10/19] Child fix Tests --- .../XCS_XNCS_Maze5_pre_populated.ipynb | 785 +++-- XCS_Experiments/XCS_XNCS_Woods1.ipynb | 1285 +++----- .../XNCS_Woods1_pre_populated.ipynb | 440 +-- XCS_Experiments/XNCS_woods.ipynb | 2752 ++++++++--------- XCS_Experiments/utils/nxcs_utils.py | 10 +- 5 files changed, 2483 insertions(+), 2789 deletions(-) diff --git a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb index 3753b75..119700c 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -23,15 +23,15 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '1', '1', '0', '0', '1', '1', '1')\n", + "('0', '0', '1', '0', '0', '0', '1', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -102,9 +102,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ { @@ -118,16 +118,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.2148428000000422, 'population': 1493, 'numerosity': 1600, 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'average_specificity': 7.789375}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1158.2183271550452, 'perf_time': 0.06356459999999942, 'population': 900, 'numerosity': 1600, 'average_specificity': 6.43125}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 71, 'reward': 1000.0000000275006, 'perf_time': 0.4300088000000031, 'population': 850, 'numerosity': 1600, 'average_specificity': 5.973125}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 11, 'reward': 1023.1122292121702, 'perf_time': 0.077508899999998, 'population': 791, 'numerosity': 1600, 'average_specificity': 6.88}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 61, 'reward': 1000.000000865112, 'perf_time': 0.3234722999999917, 'population': 797, 'numerosity': 1600, 'average_specificity': 6.780625}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 3, 'reward': 1390.7327132040612, 'perf_time': 0.018558599999991543, 'population': 701, 'numerosity': 1600, 'average_specificity': 5.6825}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 12, 'reward': 1017.2630887499168, 'perf_time': 0.08815830000000346, 'population': 658, 'numerosity': 1600, 'average_specificity': 6.769375}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 1, 'reward': 1729.862899894334, 'perf_time': 0.0026914999999974043, 'population': 620, 'numerosity': 1600, 'average_specificity': 6.153125}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 9, 'reward': 1073.7135270512224, 'perf_time': 0.08181700000000092, 'population': 613, 'numerosity': 1600, 'average_specificity': 5.991875}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 3, 'reward': 1375.2111181568766, 'perf_time': 0.007632499999999709, 'population': 556, 'numerosity': 1600, 'average_specificity': 6.055625}\n" ] }, { @@ -141,16 +141,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 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1084.005316\n", + " 0.149218\n", + " 800.333333\n", " 1600.0\n", - " 6.130417\n", - " 8.666667\n", - " 1022.000000\n", + " 6.381667\n", + " 39.000000\n", + " 1078.000000\n", " 1600.0\n", - " 7.707083\n", - " 0.034722\n", + " 8.207292\n", + " 0.996667\n", " \n", " \n", " 1100\n", - " 9.000000\n", - " 1098.487059\n", - " 0.088622\n", - " 763.333333\n", - " 1600.0\n", - " 6.250000\n", " 7.000000\n", - " 1022.666667\n", + " 1135.332949\n", + " 0.063757\n", + " 786.333333\n", + " 1600.0\n", + " 6.683333\n", + " 3.666667\n", + " 1075.666667\n", " 1600.0\n", - " 7.814375\n", - " 0.000000\n", + " 8.291458\n", + " 0.896667\n", " \n", " \n", " 1200\n", - " 6.666667\n", - " 1280.704944\n", - " 0.052009\n", - " 764.333333\n", - " 1600.0\n", - " 6.108750\n", " 4.666667\n", - " 1020.333333\n", + " 1237.448537\n", + " 0.027131\n", + " 760.666667\n", " 1600.0\n", - " 8.094375\n", - " 0.000000\n", + " 6.269167\n", + " 7.666667\n", + " 1095.000000\n", + " 1600.0\n", + " 8.719375\n", + " 0.990000\n", " 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+ " 11.666667\n", + " 1073.000000\n", " 1600.0\n", - " 8.174792\n", - " 0.000000\n", + " 7.812083\n", + " 0.956667\n", " \n", " \n", " 1600\n", - " 6.666667\n", - " 1208.593332\n", - " 0.037512\n", - " 723.333333\n", + " 39.000000\n", + " 713.746225\n", + " 0.171981\n", + " 711.666667\n", " 1600.0\n", - " 6.177917\n", - " 14.333333\n", - " 1044.000000\n", + " 6.134792\n", + " 11.666667\n", + " 1051.000000\n", " 1600.0\n", - " 7.778333\n", - " 0.000000\n", + " 8.012917\n", + " 0.933333\n", " \n", " \n", " 1700\n", - " 6.666667\n", - " 1062.111367\n", - " 0.049654\n", - " 713.000000\n", + " 3.333333\n", + " 1609.876784\n", + " 0.024009\n", + " 680.666667\n", " 1600.0\n", - " 6.659583\n", - " 9.333333\n", - " 1056.333333\n", + " 6.027083\n", + " 7.000000\n", + " 1057.666667\n", " 1600.0\n", - " 7.743750\n", - " 0.000000\n", + " 8.519792\n", + " 0.953333\n", " \n", " \n", " 1800\n", - " 8.000000\n", - " 1030.276534\n", - " 0.075279\n", - " 704.333333\n", + " 39.000000\n", + " 721.201194\n", + " 0.182860\n", + " 678.000000\n", " 1600.0\n", - " 6.419167\n", - " 7.666667\n", + " 6.808750\n", + " 4.000000\n", " 1047.000000\n", " 1600.0\n", - " 7.604375\n", - " 0.034722\n", + " 7.637708\n", + " 0.736667\n", " \n", " \n", " 1900\n", - " 91.333333\n", - " 333.333333\n", - " 0.396054\n", - " 698.333333\n", + " 4.333333\n", + " 1348.367282\n", + " 0.027422\n", + " 671.333333\n", " 1600.0\n", - " 6.312292\n", - " 6.666667\n", - " 1061.000000\n", + " 6.288750\n", + " 11.000000\n", + " 1063.000000\n", " 1600.0\n", - " 8.050625\n", - " 0.034722\n", + " 8.831667\n", + " 0.923333\n", " \n", " \n", " 2000\n", - " 37.000000\n", - " 980.462360\n", - " 0.144418\n", - " 669.333333\n", + " 39.666667\n", + " 691.237842\n", + " 0.176256\n", + " 659.666667\n", " 1600.0\n", - " 6.300625\n", - " 4.000000\n", - " 1047.333333\n", + " 6.066042\n", + " 6.333333\n", + " 1062.333333\n", " 1600.0\n", - " 7.732292\n", - " 0.034722\n", + " 8.038125\n", + " 0.916667\n", " \n", " \n", " 2100\n", - " 9.000000\n", - " 1135.811503\n", - " 0.060103\n", - " 669.333333\n", + " 38.000000\n", + " 757.248048\n", + " 0.161820\n", + " 640.333333\n", " 1600.0\n", - " 6.339375\n", - " 36.333333\n", - " 1033.666667\n", + " 6.223125\n", + " 38.666667\n", + " 1048.666667\n", " 1600.0\n", - " 8.096250\n", - " 0.000000\n", + " 8.042292\n", + " 0.893333\n", " \n", " \n", " 2200\n", - " 4.333333\n", - " 1268.215552\n", - " 0.020410\n", - " 657.000000\n", + " 3.333333\n", + " 1345.752468\n", + " 0.023821\n", + " 625.000000\n", " 1600.0\n", - " 6.042083\n", - " 7.666667\n", - " 1040.000000\n", + " 6.220000\n", + " 7.333333\n", + " 1024.666667\n", " 1600.0\n", - " 7.602500\n", - " 0.034722\n", + " 8.272917\n", + " 0.953333\n", " \n", " \n", " 2300\n", - " 26.000000\n", - " 1011.259016\n", - " 0.134830\n", - " 641.000000\n", + " 39.000000\n", + " 756.696230\n", + " 0.158708\n", + " 616.666667\n", " 1600.0\n", - " 5.879375\n", - " 38.333333\n", - " 1027.666667\n", + " 6.110417\n", + " 33.000000\n", + " 1018.666667\n", " 1600.0\n", - " 7.602500\n", - " 0.034722\n", + " 8.307917\n", + " 0.996667\n", " \n", " \n", " 2400\n", - " 18.666667\n", - " 1078.440430\n", - " 0.097076\n", - " 635.666667\n", + " 4.666667\n", + " 1382.093409\n", + " 0.016833\n", + " 600.666667\n", " 1600.0\n", - " 5.822917\n", - " 6.000000\n", - " 1029.000000\n", + " 6.031250\n", + " 5.000000\n", + " 1001.000000\n", " 1600.0\n", - " 7.323958\n", - " 0.034722\n", + " 8.062292\n", + " 0.986667\n", " \n", " \n", "\n", @@ -683,87 +683,87 @@ "text/plain": [ " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 100.000000 0.000000 1.339118 1491.000000 1600.0 \n", - "100 9.666667 1119.823574 0.074869 1144.666667 1600.0 \n", - "200 20.666667 1157.802732 0.143613 1076.000000 1600.0 \n", - "300 5.333333 1243.704238 0.057000 1002.000000 1600.0 \n", - "400 4.666667 1461.023992 0.051819 955.000000 1600.0 \n", - "500 5.333333 1208.483340 0.025594 937.666667 1600.0 \n", - "600 5.333333 1212.023390 0.045518 911.666667 1600.0 \n", - "700 38.666667 845.530865 0.233843 898.666667 1600.0 \n", - "800 5.666667 1304.974234 0.027716 855.000000 1600.0 \n", - "900 27.000000 1114.412811 0.160920 837.666667 1600.0 \n", - "1000 27.000000 1390.450022 0.122253 793.333333 1600.0 \n", - "1100 9.000000 1098.487059 0.088622 763.333333 1600.0 \n", - "1200 6.666667 1280.704944 0.052009 764.333333 1600.0 \n", - "1300 4.666667 1341.032887 0.044161 738.666667 1600.0 \n", - "1400 6.333333 1265.894798 0.049981 719.333333 1600.0 \n", - "1500 6.000000 1143.260412 0.038420 721.333333 1600.0 \n", - "1600 6.666667 1208.593332 0.037512 723.333333 1600.0 \n", - "1700 6.666667 1062.111367 0.049654 713.000000 1600.0 \n", - "1800 8.000000 1030.276534 0.075279 704.333333 1600.0 \n", - "1900 91.333333 333.333333 0.396054 698.333333 1600.0 \n", - "2000 37.000000 980.462360 0.144418 669.333333 1600.0 \n", - "2100 9.000000 1135.811503 0.060103 669.333333 1600.0 \n", - "2200 4.333333 1268.215552 0.020410 657.000000 1600.0 \n", - "2300 26.000000 1011.259016 0.134830 641.000000 1600.0 \n", - "2400 18.666667 1078.440430 0.097076 635.666667 1600.0 \n", + "0 97.000000 333.333333 1.182007 1500.000000 1600.0 \n", + "100 45.666667 717.675308 0.331375 1116.666667 1600.0 \n", + "200 6.000000 1145.342516 0.060114 1015.666667 1600.0 \n", + "300 7.666667 1116.691724 0.075114 966.666667 1600.0 \n", + "400 10.000000 1088.676388 0.048711 917.000000 1600.0 \n", + "500 28.666667 1043.790513 0.176887 890.666667 1600.0 \n", + "600 7.333333 1118.731469 0.075750 865.666667 1600.0 \n", + "700 9.000000 1091.082751 0.066204 846.333333 1600.0 \n", + "800 5.666667 1271.583579 0.027169 818.333333 1600.0 \n", + "900 37.666667 778.080006 0.174981 809.666667 1600.0 \n", + "1000 25.000000 1084.005316 0.149218 800.333333 1600.0 \n", + "1100 7.000000 1135.332949 0.063757 786.333333 1600.0 \n", + "1200 4.666667 1237.448537 0.027131 760.666667 1600.0 \n", + "1300 6.666667 1170.283230 0.047823 755.666667 1600.0 \n", + "1400 6.000000 1197.151720 0.049633 728.333333 1600.0 \n", + "1500 7.666667 1286.520439 0.048660 731.333333 1600.0 \n", + "1600 39.000000 713.746225 0.171981 711.666667 1600.0 \n", + "1700 3.333333 1609.876784 0.024009 680.666667 1600.0 \n", + "1800 39.000000 721.201194 0.182860 678.000000 1600.0 \n", + "1900 4.333333 1348.367282 0.027422 671.333333 1600.0 \n", + "2000 39.666667 691.237842 0.176256 659.666667 1600.0 \n", + "2100 38.000000 757.248048 0.161820 640.333333 1600.0 \n", + "2200 3.333333 1345.752468 0.023821 625.000000 1600.0 \n", + "2300 39.000000 756.696230 0.158708 616.666667 1600.0 \n", + "2400 4.666667 1382.093409 0.016833 600.666667 1600.0 \n", "\n", " average_specificity steps_in_trial_other population_other \\\n", "trial \n", - "0 8.500000 78.333333 1510.333333 \n", - "100 6.870208 8.000000 1178.666667 \n", - "200 6.828125 60.666667 1139.000000 \n", - "300 6.684583 18.000000 1074.666667 \n", - "400 6.069792 4.666667 1058.333333 \n", - "500 6.297292 7.333333 1039.666667 \n", - "600 6.353333 4.333333 1019.000000 \n", - "700 6.515833 37.666667 1026.000000 \n", - "800 6.129375 6.333333 1020.000000 \n", - "900 6.060833 8.333333 1026.000000 \n", - "1000 6.130417 8.666667 1022.000000 \n", - "1100 6.250000 7.000000 1022.666667 \n", - "1200 6.108750 4.666667 1020.333333 \n", - "1300 6.006042 8.666667 1025.666667 \n", - "1400 5.791667 3.000000 1040.000000 \n", - "1500 5.893333 11.333333 1039.333333 \n", - "1600 6.177917 14.333333 1044.000000 \n", - "1700 6.659583 9.333333 1056.333333 \n", - "1800 6.419167 7.666667 1047.000000 \n", - "1900 6.312292 6.666667 1061.000000 \n", - "2000 6.300625 4.000000 1047.333333 \n", - "2100 6.339375 36.333333 1033.666667 \n", - "2200 6.042083 7.666667 1040.000000 \n", - "2300 5.879375 38.333333 1027.666667 \n", - "2400 5.822917 6.000000 1029.000000 \n", + "0 8.365000 100.000000 1506.000000 \n", + "100 7.261250 3.333333 1186.333333 \n", + "200 7.050000 9.000000 1125.666667 \n", + "300 6.519167 5.666667 1094.666667 \n", + "400 6.826250 4.000000 1082.333333 \n", + "500 6.609167 33.666667 1056.000000 \n", + "600 6.652917 7.000000 1059.333333 \n", + "700 6.971667 9.333333 1075.000000 \n", + "800 6.389792 4.666667 1072.666667 \n", + "900 6.598333 10.333333 1084.000000 \n", + "1000 6.381667 39.000000 1078.000000 \n", + "1100 6.683333 3.666667 1075.666667 \n", + "1200 6.269167 7.666667 1095.000000 \n", + "1300 6.046042 38.666667 1088.666667 \n", + "1400 6.041875 6.333333 1079.333333 \n", + "1500 6.252083 11.666667 1073.000000 \n", + "1600 6.134792 11.666667 1051.000000 \n", + "1700 6.027083 7.000000 1057.666667 \n", + "1800 6.808750 4.000000 1047.000000 \n", + "1900 6.288750 11.000000 1063.000000 \n", + "2000 6.066042 6.333333 1062.333333 \n", + "2100 6.223125 38.666667 1048.666667 \n", + "2200 6.220000 7.333333 1024.666667 \n", + "2300 6.110417 33.000000 1018.666667 \n", + "2400 6.031250 5.000000 1001.000000 \n", "\n", " numerosity_other average_specificity_other fraction_accuracy_other \n", "trial \n", - "0 1600.0 8.261458 0.000000 \n", - "100 1600.0 6.731042 0.000000 \n", - "200 1600.0 6.609167 0.000000 \n", - "300 1600.0 6.729167 0.000000 \n", - "400 1600.0 6.934167 0.000000 \n", - "500 1600.0 7.106042 0.000000 \n", - "600 1600.0 7.282292 0.000000 \n", - "700 1600.0 7.161042 0.000000 \n", - "800 1600.0 7.221250 0.041165 \n", - "900 1600.0 7.412917 0.000000 \n", - "1000 1600.0 7.707083 0.034722 \n", - "1100 1600.0 7.814375 0.000000 \n", - "1200 1600.0 8.094375 0.000000 \n", - "1300 1600.0 8.453125 0.009524 \n", - "1400 1600.0 8.200000 0.000000 \n", - "1500 1600.0 8.174792 0.000000 \n", - "1600 1600.0 7.778333 0.000000 \n", - "1700 1600.0 7.743750 0.000000 \n", - "1800 1600.0 7.604375 0.034722 \n", - "1900 1600.0 8.050625 0.034722 \n", - "2000 1600.0 7.732292 0.034722 \n", - "2100 1600.0 8.096250 0.000000 \n", - "2200 1600.0 7.602500 0.034722 \n", - "2300 1600.0 7.602500 0.034722 \n", - "2400 1600.0 7.323958 0.034722 " + "0 1600.0 8.074167 0.976667 \n", + "100 1600.0 6.865208 0.903333 \n", + "200 1600.0 6.767917 0.956667 \n", + "300 1600.0 6.851042 0.950000 \n", + "400 1600.0 6.908958 0.800000 \n", + "500 1600.0 7.237708 0.940000 \n", + "600 1600.0 7.095417 0.983333 \n", + "700 1600.0 7.449375 0.940000 \n", + "800 1600.0 8.329167 0.953333 \n", + "900 1600.0 7.816667 0.933333 \n", + "1000 1600.0 8.207292 0.996667 \n", + "1100 1600.0 8.291458 0.896667 \n", + "1200 1600.0 8.719375 0.990000 \n", + "1300 1600.0 8.251250 0.993333 \n", + "1400 1600.0 8.237917 0.980000 \n", + "1500 1600.0 7.812083 0.956667 \n", + "1600 1600.0 8.012917 0.933333 \n", + "1700 1600.0 8.519792 0.953333 \n", + "1800 1600.0 7.637708 0.736667 \n", + "1900 1600.0 8.831667 0.923333 \n", + "2000 1600.0 8.038125 0.916667 \n", + "2100 1600.0 8.042292 0.893333 \n", + "2200 1600.0 8.272917 0.953333 \n", + "2300 1600.0 8.307917 0.996667 \n", + "2400 1600.0 8.062292 0.986667 " ] }, "metadata": {}, @@ -782,22 +782,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -821,22 +821,22 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -856,16 +856,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -891,22 +891,22 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -926,22 +926,22 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -961,15 +961,15 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "163.22222222222226\n", - "135.77777777777777\n" + "173.99999999999997\n", + "139.33333333333334\n" ] } ], @@ -977,13 +977,6 @@ "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", "print(sum(df[\"steps_in_trial_other\"])/number_of_experiments)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XCS_XNCS_Woods1.ipynb b/XCS_Experiments/XCS_XNCS_Woods1.ipynb index 919c6e0..c59500f 100644 --- a/XCS_Experiments/XCS_XNCS_Woods1.ipynb +++ b/XCS_Experiments/XCS_XNCS_Woods1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 56, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -24,7 +24,7 @@ "This is how maze looks like:\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ " ga_threshold=25,\n", " deletion_threshold=25,\n", " covering_wildcard_chance = 0.7,\n", - " chi=0.8, # crossover chi\n", + " chi=1, # crossover chi\n", " metrics_trial_frequency=100,\n", " initial_prediction =10, # p_i\n", " initial_error = 0.1, # epsilon_i\n", @@ -84,7 +84,7 @@ " ga_threshold=25,\n", " deletion_threshold=25,\n", " covering_wildcard_chance = 0.7,\n", - " chi=0.8, # crossover\n", + " chi=1, # crossover\n", " metrics_trial_frequency=100,\n", " initial_prediction =10, # p_i\n", " initial_error = 0.1, # epsilon_i\n", @@ -103,7 +103,7 @@ " ga_threshold=25,\n", " deletion_threshold=25,\n", " covering_wildcard_chance = 0.7,\n", - " chi=0.8, # crossover\n", + " chi=1, # crossover\n", " metrics_trial_frequency=100,\n", " initial_prediction =10, # p_i\n", " initial_error = 0.1, # epsilon_i\n", @@ -115,9 +115,9 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 4, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -131,92 +131,16 @@ 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1644.6629869357776, 'perf_time': 0.04286780000006729, 'numerosity': 1800, 'population': 1534, 'average_specificity': 13.870555555555555, 'fraction_accuracy': 1.0}\n" ] } ], @@ -362,9 +194,9 @@ "from utils.xcs_utils import avg_experiment as XCSExp\n", "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", - "number_of_experiments = 3\n", - "explore = 2000\n", - "exploit = 500\n", + "number_of_experiments = 1\n", + "explore = 0\n", + "exploit = 2000\n", "\n", "df = XCSExp(maze=maze,\n", " cfg=XCScfg,\n", @@ -377,22 +209,22 @@ "df20 = XNCSExp(maze=maze,\n", " cfg=XNCScfg20,\n", " number_of_tests=number_of_experiments,\n", - " explore_trials=0,\n", - " exploit_trials=exploit+explore,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", " pre_generate=True)\n", "\n", "df200 = XNCSExp(maze=maze,\n", " cfg=XNCScfg200,\n", " number_of_tests=number_of_experiments,\n", - " explore_trials=0,\n", - " exploit_trials=exploit+explore,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", " pre_generate=True)\n", "\n" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -458,623 +290,503 @@ " \n", " \n", " 0\n", - " 28.333333\n", - " 1000.000000\n", - " 0.303314\n", - " 1628.666667\n", - " 1800.0\n", - " 8.436481\n", - " 18.666667\n", - " 1629.666667\n", - " 1800.0\n", - " 8.095556\n", - " 0.000000\n", - " 19.333333\n", - " 1618.666667\n", - " 1800.0\n", - " 8.191296\n", - " 0.000000\n", + " 34\n", + " 1.000000e+03\n", + " 0.328177\n", + " 1620\n", + " 1800\n", + " 7.988889\n", + " 1\n", + " 1604\n", + " 1800\n", + " 8.038889\n", + " 1.00\n", + " 8\n", + " 1601\n", + " 1800\n", + " 7.603889\n", + " 1.00\n", " \n", " \n", " 100\n", - " 3.000000\n", - " 1474.493164\n", - " 0.035675\n", - " 1381.333333\n", - " 1800.0\n", - " 7.867222\n", - " 3.666667\n", - " 1573.000000\n", - " 1800.0\n", - " 7.994815\n", - " 0.000000\n", - " 18.333333\n", - " 1539.000000\n", - " 1800.0\n", - " 8.934259\n", - " 0.040404\n", + " 9\n", + " 1.051363e+03\n", + " 0.086761\n", + " 1542\n", + " 1800\n", + " 7.676111\n", + " 6\n", + " 1555\n", + " 1800\n", + " 8.598333\n", + " 0.90\n", + " 3\n", + " 1553\n", + " 1800\n", + " 7.540556\n", + " 1.00\n", " \n", " \n", " 200\n", - " 12.666667\n", - " 1037.445698\n", - " 0.174787\n", - " 1150.666667\n", - " 1800.0\n", - " 8.192778\n", - " 3.000000\n", - " 1544.333333\n", - " 1800.0\n", - " 8.593889\n", - " 0.000000\n", - " 3.666667\n", - " 1523.000000\n", - " 1800.0\n", - " 8.737593\n", - " 0.052362\n", + " 31\n", + " 1.000000e+03\n", + " 0.343848\n", + " 1455\n", + " 1800\n", + " 7.920000\n", + " 4\n", + " 1557\n", + " 1800\n", + " 8.656667\n", + " 0.65\n", + " 4\n", + " 1547\n", + " 1800\n", + " 7.685000\n", + " 0.74\n", " \n", " \n", " 300\n", - " 1.666667\n", - " 1678.847702\n", - " 0.015831\n", - " 943.000000\n", - " 1800.0\n", - " 9.093519\n", - " 18.333333\n", - " 1523.333333\n", - " 1800.0\n", - " 9.096667\n", - " 0.000000\n", - " 18.333333\n", - " 1495.333333\n", - " 1800.0\n", - " 9.097963\n", - " 0.022059\n", + " 5\n", + " 1.224458e+03\n", + " 0.089136\n", + " 1425\n", + " 1800\n", + " 8.066111\n", + " 3\n", + " 1527\n", + " 1800\n", + " 9.138889\n", + " 0.81\n", + " 5\n", + " 1564\n", + " 1800\n", + " 8.555556\n", + " 1.00\n", " \n", " \n", " 400\n", - " 11.666667\n", - " 1111.102128\n", - " 0.142623\n", - " 838.333333\n", - " 1800.0\n", - " 8.248148\n", - " 3.333333\n", - " 1513.000000\n", - " 1800.0\n", - " 9.736111\n", - " 0.000000\n", - " 22.666667\n", - " 1452.000000\n", - " 1800.0\n", - " 10.770741\n", - " 0.000887\n", + " 50\n", + " 1.183841e-49\n", + " 0.381245\n", + " 1358\n", + " 1800\n", + " 8.216667\n", + " 4\n", + " 1537\n", + " 1800\n", + " 9.525556\n", + " 0.78\n", + " 7\n", + " 1516\n", + " 1800\n", + " 8.445556\n", + " 0.93\n", " \n", " \n", " 500\n", - " 9.666667\n", - " 1121.731972\n", - " 0.081363\n", - " 628.666667\n", - " 1800.0\n", - " 10.496852\n", - " 8.000000\n", - " 1486.000000\n", - " 1800.0\n", - " 10.257037\n", - " 0.000000\n", - " 21.666667\n", - " 1417.666667\n", - " 1800.0\n", - " 10.626111\n", - " 0.000000\n", + " 50\n", + " 6.525100e-35\n", + " 0.481674\n", + " 1300\n", + " 1800\n", + " 10.144444\n", + " 50\n", + " 1525\n", + " 1800\n", + " 9.927222\n", + " 0.97\n", + " 6\n", + " 1518\n", + " 1800\n", + " 9.269444\n", + " 0.93\n", " \n", " \n", " 600\n", - " 1.000000\n", - " 2114.545565\n", - " 0.004402\n", - " 557.000000\n", - " 1800.0\n", - " 10.453519\n", - " 17.666667\n", - " 1453.666667\n", - " 1800.0\n", - " 10.242778\n", - " 0.003472\n", - " 13.333333\n", - " 1393.666667\n", - " 1800.0\n", - " 11.643148\n", - " 0.017157\n", + " 1\n", + " 1.882319e+03\n", + " 0.007529\n", + " 1288\n", + " 1800\n", + " 11.762222\n", + " 3\n", + " 1504\n", + " 1800\n", + " 9.568889\n", + " 0.97\n", + " 12\n", + " 1514\n", + " 1800\n", + " 11.503333\n", + " 0.75\n", " \n", " \n", " 700\n", - " 20.666667\n", - " 905.510713\n", - " 0.160681\n", - " 604.000000\n", - " 1800.0\n", - " 10.541296\n", - " 3.000000\n", - " 1429.000000\n", - " 1800.0\n", - " 10.850370\n", - " 0.003472\n", - " 19.666667\n", - " 1395.000000\n", - " 1800.0\n", - " 12.314815\n", - " 0.016204\n", + " 50\n", + " 1.336087e-12\n", + " 0.349070\n", + " 1262\n", + " 1800\n", + " 10.932778\n", + " 2\n", + " 1508\n", + " 1800\n", + " 9.852778\n", + " 0.88\n", + " 16\n", + " 1510\n", + " 1800\n", + " 11.192222\n", + " 0.69\n", " \n", " \n", " 800\n", - " 20.666667\n", - " 1003.340038\n", - " 0.153730\n", - " 646.000000\n", - " 1800.0\n", - " 10.904815\n", - " 6.666667\n", - " 1428.333333\n", - " 1800.0\n", - " 11.551111\n", - " 0.019213\n", - " 2.333333\n", - " 1380.333333\n", - " 1800.0\n", - " 12.231667\n", - " 0.014583\n", + " 4\n", + " 1.340357e+03\n", + " 0.023306\n", + " 1276\n", + " 1800\n", + " 10.543889\n", + " 8\n", + " 1523\n", + " 1800\n", + " 10.219444\n", + " 0.99\n", + " 3\n", + " 1525\n", + " 1800\n", + " 11.952222\n", + " 0.64\n", " \n", " \n", " 900\n", - " 11.666667\n", - " 1151.474786\n", - " 0.085956\n", - " 665.666667\n", - " 1800.0\n", - " 9.606481\n", - " 3.333333\n", - " 1413.333333\n", - " 1800.0\n", - " 11.067778\n", - " 0.003472\n", - " 18.333333\n", - " 1365.666667\n", - " 1800.0\n", - " 12.495556\n", - " 0.000000\n", + " 8\n", + " 1.109848e+03\n", + " 0.100791\n", + " 1220\n", + " 1800\n", + " 10.381667\n", + " 6\n", + " 1521\n", + " 1800\n", + " 10.721667\n", + " 0.91\n", + " 5\n", + " 1536\n", + " 1800\n", + " 12.186111\n", + " 0.96\n", " \n", " \n", " 1000\n", - " 11.000000\n", - " 1104.080887\n", - " 0.083276\n", - " 661.000000\n", - " 1800.0\n", - " 9.652222\n", - " 2.666667\n", - " 1408.666667\n", - " 1800.0\n", - " 11.467222\n", - " 0.008472\n", - " 21.000000\n", - " 1368.000000\n", - " 1800.0\n", - " 14.034444\n", - " 0.000000\n", + " 6\n", + " 1.143360e+03\n", + " 0.074923\n", + " 1204\n", + " 1800\n", + " 9.353889\n", + " 33\n", + " 1532\n", + " 1800\n", + " 11.747222\n", + " 0.88\n", + " 5\n", + " 1550\n", + " 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1800\n", + " 11.956111\n", + " 0.86\n", + " 2\n", + " 1550\n", + " 1800\n", + " 11.833889\n", + " 0.75\n", " \n", " \n", " 1300\n", - " 4.000000\n", - " 1345.665425\n", - " 0.025139\n", - " 661.000000\n", - " 1800.0\n", - " 10.174815\n", - " 5.000000\n", - " 1371.000000\n", - " 1800.0\n", - " 13.378889\n", - " 0.041667\n", - " 36.000000\n", - " 1371.000000\n", - " 1800.0\n", - " 14.193148\n", - " 0.013480\n", + " 7\n", + " 1.123776e+03\n", + " 0.063389\n", + " 1145\n", + " 1800\n", + " 8.892778\n", + " 9\n", + " 1543\n", + " 1800\n", + " 12.176667\n", + " 0.89\n", + " 5\n", + " 1547\n", + " 1800\n", + " 11.262778\n", + " 0.56\n", " \n", " \n", " 1400\n", - " 5.333333\n", - " 1349.824618\n", - " 0.048714\n", - " 677.000000\n", - " 1800.0\n", - " 10.949074\n", - " 21.000000\n", - " 1375.666667\n", - " 1800.0\n", - " 13.790000\n", - " 0.032051\n", - " 17.666667\n", - " 1367.000000\n", - " 1800.0\n", - " 14.166111\n", - " 0.000000\n", + " 50\n", + " 1.041450e-19\n", + " 0.399404\n", + " 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0.000000\n", + " 3\n", + " 1.590980e+03\n", + " 0.061321\n", + " 1076\n", + " 1800\n", + " 9.036111\n", + " 1\n", + " 1540\n", + " 1800\n", + " 12.092222\n", + " 0.68\n", + " 3\n", + " 1542\n", + " 1800\n", + " 12.456667\n", + " 0.99\n", " \n", " \n", " 1700\n", - " 8.000000\n", - " 1099.863803\n", - " 0.058499\n", - " 656.666667\n", - " 1800.0\n", - " 10.084074\n", - " 15.666667\n", - " 1342.333333\n", - " 1800.0\n", - " 15.375000\n", - " 0.000000\n", - " 34.000000\n", - " 1357.666667\n", - " 1800.0\n", - " 15.261852\n", - " 0.000000\n", + " 2\n", + " 2.082703e+03\n", + " 0.024753\n", + " 1091\n", + " 1800\n", + " 8.953889\n", + " 3\n", + " 1533\n", + " 1800\n", + " 13.438889\n", + " 0.79\n", + " 3\n", + " 1537\n", + " 1800\n", + " 12.912222\n", + " 0.92\n", " \n", " \n", " 1800\n", - " 11.333333\n", - " 1340.863547\n", - " 0.085692\n", - " 666.333333\n", - " 1800.0\n", - " 10.342222\n", - " 18.333333\n", - " 1338.333333\n", - " 1800.0\n", - " 15.708148\n", - " 0.000000\n", - " 18.666667\n", - " 1356.000000\n", - " 1800.0\n", - " 15.430556\n", - " 0.000000\n", + " 50\n", + " 3.655253e-05\n", + " 0.315176\n", + " 1102\n", + " 1800\n", + " 9.441667\n", + " 1\n", + " 1527\n", + " 1800\n", + " 13.238889\n", + " 0.71\n", + " 3\n", + " 1534\n", + " 1800\n", + " 13.870556\n", + " 1.00\n", " \n", " \n", " 1900\n", - " 15.666667\n", - " 1269.787683\n", - " 0.112295\n", - " 657.000000\n", - " 1800.0\n", - " 10.281667\n", - " 6.666667\n", - " 1358.000000\n", - " 1800.0\n", - " 15.467222\n", - " 0.047249\n", - " 20.666667\n", - " 1374.333333\n", - " 1800.0\n", - " 15.639259\n", - " 0.000000\n", - " \n", - " \n", - " 2000\n", - " 2.333333\n", - " 1693.664111\n", - " 0.013942\n", - " 657.333333\n", - " 1800.0\n", - " 10.857593\n", - " 3.333333\n", - " 1357.000000\n", - " 1800.0\n", - " 16.329074\n", - " 0.030797\n", - " 12.666667\n", - " 1377.666667\n", - " 1800.0\n", - " 15.042593\n", - " 0.000000\n", - " \n", - " \n", - " 2100\n", - " 8.000000\n", - " 1377.920449\n", - " 0.046348\n", - " 637.000000\n", - " 1800.0\n", - " 10.811852\n", - " 4.333333\n", - " 1362.333333\n", - " 1800.0\n", - " 16.080000\n", - " 0.011905\n", - " 5.000000\n", - " 1371.666667\n", - " 1800.0\n", - " 14.530185\n", - " 0.000000\n", - " \n", - " \n", - " 2200\n", - " 19.000000\n", - " 999.001714\n", - " 0.147441\n", - " 603.000000\n", - " 1800.0\n", - " 12.306111\n", - " 6.333333\n", - " 1372.333333\n", - " 1800.0\n", - " 16.984444\n", - " 0.000000\n", - " 4.333333\n", - " 1388.666667\n", - " 1800.0\n", - " 14.820926\n", - " 0.000000\n", - " \n", - " \n", - " 2300\n", - " 4.333333\n", - " 1384.866844\n", - " 0.018702\n", - " 661.333333\n", - " 1800.0\n", - " 18.475556\n", - " 26.333333\n", - " 1392.333333\n", - " 1800.0\n", - " 15.693704\n", - " 0.000000\n", - " 29.000000\n", - " 1388.000000\n", - " 1800.0\n", - " 15.000741\n", - " 0.000000\n", - " \n", - " \n", - " 2400\n", - " 19.000000\n", - " 979.218107\n", - " 0.103671\n", - " 664.000000\n", - " 1800.0\n", - " 16.015000\n", - " 4.666667\n", - " 1382.666667\n", - " 1800.0\n", - " 15.546111\n", - " 0.000000\n", - " 50.000000\n", - " 1379.666667\n", - " 1800.0\n", - " 15.224630\n", - " 0.000000\n", + " 50\n", + " 2.153124e-79\n", + " 0.563533\n", + " 1097\n", + " 1800\n", + " 9.522222\n", + " 4\n", + " 1531\n", + " 1800\n", + " 13.750000\n", + " 0.79\n", + " 5\n", + " 1473\n", + " 1800\n", + " 12.501667\n", + " 1.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial reward perf_time population numerosity \\\n", + " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 28.333333 1000.000000 0.303314 1628.666667 1800.0 \n", - "100 3.000000 1474.493164 0.035675 1381.333333 1800.0 \n", - "200 12.666667 1037.445698 0.174787 1150.666667 1800.0 \n", - "300 1.666667 1678.847702 0.015831 943.000000 1800.0 \n", - "400 11.666667 1111.102128 0.142623 838.333333 1800.0 \n", - "500 9.666667 1121.731972 0.081363 628.666667 1800.0 \n", - "600 1.000000 2114.545565 0.004402 557.000000 1800.0 \n", - "700 20.666667 905.510713 0.160681 604.000000 1800.0 \n", - "800 20.666667 1003.340038 0.153730 646.000000 1800.0 \n", - "900 11.666667 1151.474786 0.085956 665.666667 1800.0 \n", - "1000 11.000000 1104.080887 0.083276 661.000000 1800.0 \n", - "1100 6.666667 1206.362079 0.044596 601.333333 1800.0 \n", - "1200 6.000000 1233.794457 0.034589 619.333333 1800.0 \n", - "1300 4.000000 1345.665425 0.025139 661.000000 1800.0 \n", - "1400 5.333333 1349.824618 0.048714 677.000000 1800.0 \n", - "1500 12.666667 1110.241241 0.449240 660.666667 1800.0 \n", - "1600 9.000000 1187.933799 0.048096 655.666667 1800.0 \n", - "1700 8.000000 1099.863803 0.058499 656.666667 1800.0 \n", - "1800 11.333333 1340.863547 0.085692 666.333333 1800.0 \n", - "1900 15.666667 1269.787683 0.112295 657.000000 1800.0 \n", - "2000 2.333333 1693.664111 0.013942 657.333333 1800.0 \n", - "2100 8.000000 1377.920449 0.046348 637.000000 1800.0 \n", - "2200 19.000000 999.001714 0.147441 603.000000 1800.0 \n", - "2300 4.333333 1384.866844 0.018702 661.333333 1800.0 \n", - "2400 19.000000 979.218107 0.103671 664.000000 1800.0 \n", + "0 34 1.000000e+03 0.328177 1620 1800 \n", + "100 9 1.051363e+03 0.086761 1542 1800 \n", + "200 31 1.000000e+03 0.343848 1455 1800 \n", + "300 5 1.224458e+03 0.089136 1425 1800 \n", + "400 50 1.183841e-49 0.381245 1358 1800 \n", + "500 50 6.525100e-35 0.481674 1300 1800 \n", + "600 1 1.882319e+03 0.007529 1288 1800 \n", + "700 50 1.336087e-12 0.349070 1262 1800 \n", + "800 4 1.340357e+03 0.023306 1276 1800 \n", + "900 8 1.109848e+03 0.100791 1220 1800 \n", + "1000 6 1.143360e+03 0.074923 1204 1800 \n", + "1100 50 4.883737e-20 0.486568 1195 1800 \n", + "1200 6 1.142196e+03 0.058430 1157 1800 \n", + "1300 7 1.123776e+03 0.063389 1145 1800 \n", + "1400 50 1.041450e-19 0.399404 1095 1800 \n", + "1500 1 1.000000e+03 0.008488 1086 1800 \n", + "1600 3 1.590980e+03 0.061321 1076 1800 \n", + "1700 2 2.082703e+03 0.024753 1091 1800 \n", + "1800 50 3.655253e-05 0.315176 1102 1800 \n", + "1900 50 2.153124e-79 0.563533 1097 1800 \n", "\n", " average_specificity steps_in_trial_20 population_20 numerosity_20 \\\n", "trial \n", - "0 8.436481 18.666667 1629.666667 1800.0 \n", - "100 7.867222 3.666667 1573.000000 1800.0 \n", - "200 8.192778 3.000000 1544.333333 1800.0 \n", - "300 9.093519 18.333333 1523.333333 1800.0 \n", - "400 8.248148 3.333333 1513.000000 1800.0 \n", - "500 10.496852 8.000000 1486.000000 1800.0 \n", - "600 10.453519 17.666667 1453.666667 1800.0 \n", - "700 10.541296 3.000000 1429.000000 1800.0 \n", - "800 10.904815 6.666667 1428.333333 1800.0 \n", - "900 9.606481 3.333333 1413.333333 1800.0 \n", - "1000 9.652222 2.666667 1408.666667 1800.0 \n", - "1100 11.091667 33.666667 1393.666667 1800.0 \n", - "1200 10.219259 15.000000 1378.000000 1800.0 \n", - "1300 10.174815 5.000000 1371.000000 1800.0 \n", - "1400 10.949074 21.000000 1375.666667 1800.0 \n", - "1500 10.532407 12.333333 1348.000000 1800.0 \n", - "1600 10.050926 23.333333 1363.333333 1800.0 \n", - "1700 10.084074 15.666667 1342.333333 1800.0 \n", - "1800 10.342222 18.333333 1338.333333 1800.0 \n", - "1900 10.281667 6.666667 1358.000000 1800.0 \n", - "2000 10.857593 3.333333 1357.000000 1800.0 \n", - "2100 10.811852 4.333333 1362.333333 1800.0 \n", - "2200 12.306111 6.333333 1372.333333 1800.0 \n", - "2300 18.475556 26.333333 1392.333333 1800.0 \n", - "2400 16.015000 4.666667 1382.666667 1800.0 \n", + "0 7.988889 1 1604 1800 \n", + "100 7.676111 6 1555 1800 \n", + "200 7.920000 4 1557 1800 \n", + "300 8.066111 3 1527 1800 \n", + "400 8.216667 4 1537 1800 \n", + "500 10.144444 50 1525 1800 \n", + "600 11.762222 3 1504 1800 \n", + "700 10.932778 2 1508 1800 \n", + "800 10.543889 8 1523 1800 \n", + "900 10.381667 6 1521 1800 \n", + "1000 9.353889 33 1532 1800 \n", + "1100 9.064444 4 1527 1800 \n", + "1200 8.884444 10 1553 1800 \n", + "1300 8.892778 9 1543 1800 \n", + "1400 9.163333 4 1552 1800 \n", + "1500 9.376667 1 1509 1800 \n", + "1600 9.036111 1 1540 1800 \n", + "1700 8.953889 3 1533 1800 \n", + "1800 9.441667 1 1527 1800 \n", + "1900 9.522222 4 1531 1800 \n", "\n", " average_specificity_20 fraction_accuracy_20 steps_in_trial_200 \\\n", "trial \n", - "0 8.095556 0.000000 19.333333 \n", - "100 7.994815 0.000000 18.333333 \n", - "200 8.593889 0.000000 3.666667 \n", - "300 9.096667 0.000000 18.333333 \n", - "400 9.736111 0.000000 22.666667 \n", - "500 10.257037 0.000000 21.666667 \n", - "600 10.242778 0.003472 13.333333 \n", - "700 10.850370 0.003472 19.666667 \n", - "800 11.551111 0.019213 2.333333 \n", - "900 11.067778 0.003472 18.333333 \n", - "1000 11.467222 0.008472 21.000000 \n", - "1100 12.426667 0.003546 3.333333 \n", - "1200 12.755370 0.046769 9.666667 \n", - "1300 13.378889 0.041667 36.000000 \n", - "1400 13.790000 0.032051 17.666667 \n", - "1500 14.212593 0.000992 18.000000 \n", - "1600 15.104259 0.000000 19.333333 \n", - "1700 15.375000 0.000000 34.000000 \n", - "1800 15.708148 0.000000 18.666667 \n", - "1900 15.467222 0.047249 20.666667 \n", - "2000 16.329074 0.030797 12.666667 \n", - "2100 16.080000 0.011905 5.000000 \n", - "2200 16.984444 0.000000 4.333333 \n", - "2300 15.693704 0.000000 29.000000 \n", - "2400 15.546111 0.000000 50.000000 \n", + "0 8.038889 1.00 8 \n", + "100 8.598333 0.90 3 \n", + "200 8.656667 0.65 4 \n", + "300 9.138889 0.81 5 \n", + "400 9.525556 0.78 7 \n", + "500 9.927222 0.97 6 \n", + "600 9.568889 0.97 12 \n", + "700 9.852778 0.88 16 \n", + "800 10.219444 0.99 3 \n", + "900 10.721667 0.91 5 \n", + "1000 11.747222 0.88 5 \n", + "1100 11.754444 0.89 2 \n", + "1200 11.956111 0.86 2 \n", + "1300 12.176667 0.89 5 \n", + "1400 11.974444 0.97 5 \n", + "1500 12.362778 1.00 1 \n", + "1600 12.092222 0.68 3 \n", + "1700 13.438889 0.79 3 \n", + "1800 13.238889 0.71 3 \n", + "1900 13.750000 0.79 5 \n", "\n", " population_200 numerosity_200 average_specificity_200 \\\n", "trial \n", - "0 1618.666667 1800.0 8.191296 \n", - "100 1539.000000 1800.0 8.934259 \n", - "200 1523.000000 1800.0 8.737593 \n", - "300 1495.333333 1800.0 9.097963 \n", - "400 1452.000000 1800.0 10.770741 \n", - "500 1417.666667 1800.0 10.626111 \n", - "600 1393.666667 1800.0 11.643148 \n", - "700 1395.000000 1800.0 12.314815 \n", - "800 1380.333333 1800.0 12.231667 \n", - "900 1365.666667 1800.0 12.495556 \n", - "1000 1368.000000 1800.0 14.034444 \n", - "1100 1377.000000 1800.0 14.282222 \n", - "1200 1383.666667 1800.0 14.112593 \n", - "1300 1371.000000 1800.0 14.193148 \n", - "1400 1367.000000 1800.0 14.166111 \n", - "1500 1360.666667 1800.0 14.743889 \n", - "1600 1357.000000 1800.0 14.283889 \n", - "1700 1357.666667 1800.0 15.261852 \n", - "1800 1356.000000 1800.0 15.430556 \n", - "1900 1374.333333 1800.0 15.639259 \n", - "2000 1377.666667 1800.0 15.042593 \n", - "2100 1371.666667 1800.0 14.530185 \n", - "2200 1388.666667 1800.0 14.820926 \n", - "2300 1388.000000 1800.0 15.000741 \n", - "2400 1379.666667 1800.0 15.224630 \n", + "0 1601 1800 7.603889 \n", + "100 1553 1800 7.540556 \n", + "200 1547 1800 7.685000 \n", + "300 1564 1800 8.555556 \n", + "400 1516 1800 8.445556 \n", + "500 1518 1800 9.269444 \n", + "600 1514 1800 11.503333 \n", + "700 1510 1800 11.192222 \n", + "800 1525 1800 11.952222 \n", + "900 1536 1800 12.186111 \n", + "1000 1550 1800 12.157778 \n", + "1100 1549 1800 12.073333 \n", + "1200 1550 1800 11.833889 \n", + "1300 1547 1800 11.262778 \n", + "1400 1551 1800 13.501111 \n", + "1500 1549 1800 12.729444 \n", + "1600 1542 1800 12.456667 \n", + "1700 1537 1800 12.912222 \n", + "1800 1534 1800 13.870556 \n", + "1900 1473 1800 12.501667 \n", "\n", " fraction_accuracy_200 \n", "trial \n", - "0 0.000000 \n", - "100 0.040404 \n", - "200 0.052362 \n", - "300 0.022059 \n", - "400 0.000887 \n", - "500 0.000000 \n", - "600 0.017157 \n", - "700 0.016204 \n", - "800 0.014583 \n", - "900 0.000000 \n", - "1000 0.000000 \n", - "1100 0.000000 \n", - "1200 0.000000 \n", - "1300 0.013480 \n", - "1400 0.000000 \n", - "1500 0.000000 \n", - "1600 0.000000 \n", - "1700 0.000000 \n", - "1800 0.000000 \n", - "1900 0.000000 \n", - "2000 0.000000 \n", - "2100 0.000000 \n", - "2200 0.000000 \n", - "2300 0.000000 \n", - "2400 0.000000 " + "0 1.00 \n", + "100 1.00 \n", + "200 0.74 \n", + "300 1.00 \n", + "400 0.93 \n", + "500 0.93 \n", + "600 0.75 \n", + "700 0.69 \n", + "800 0.64 \n", + "900 0.96 \n", + "1000 0.43 \n", + "1100 0.73 \n", + "1200 0.75 \n", + "1300 0.56 \n", + "1400 0.84 \n", + "1500 0.22 \n", + "1600 0.99 \n", + "1700 0.92 \n", + "1800 1.00 \n", + "1900 1.00 " ] }, "metadata": {}, @@ -1099,22 +811,22 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 68, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1137,22 +849,22 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 69, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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YzfvPSoJ9y63EsX81FOcCYl2H7nyJlUCiBoGLm/3eo4NVTQ7r09eTWpAKnEoOA9sPZGD7gcQFxtX/UoMx1hfPgdVw8Ac48D0UHKl9fc9A8IuybtL0i7T9rfLcLwJcbA2yeYdh8WOw7TMryV/+FMSPb/01w+T18MF14OlvJYzAmMbvq+QEfPcYrH/HuofkurehXY/G7au0yEr0Gbttyx7I3g9Z+6Hw9HYQvIIhMBaCYiv/VgR04Lhve/LcPMguzuG+FffhLM58etWn+Di7W7WcgqOn/hYcqVJ2FAqOWWVVj/XoEXD1aNTb0WTRzN5fc4DHvtrOB7cN5uK4Zrgbdt9y+OpuyE+DgA7WP1aw2h8i+p6qNUQNqmxs3HhkI5O+m0SngE7MHD0Tb1fvsx+jvMxq0ziZPFLWW5etXL0hdhh0stU8QuJa9RfT4YLDJB5JPCM5+Lv7VyaHhHYJjU8OB763luO2udx9w61LfjEXW4t/lPWFn5cKuamQl2L7W+V54ZndrfEOs5LHsd1QUQZD74WLpzauVtJSHf4Z3rvaukw6cZ51ebQx+/j8DsjcAxfeDZc81ugv1joV5Vq186z9pxJI9n7IOmB9jlVqFLh4QmAMP3v5cIscYWxRKc+kp9W8X1dv8AmzFu9Q2+N2px7HjW70DzhNFs2suKyckS+soL2/B59Pvqh5GjSLcmHZNMhNsRJDhyEQ3ves/xFWpaziz8v+TEK7BP5z6X8a1l2wKNf60jt5ySorySoPvcD6kup5nV3u+rWHpJwkFh5YyKKk+STlJwPg7+pDQkgfBrZLICHyIuKCuzdRcog4lRhiLoagTg1PriXHrYSSm3JmUvEKhpGPWL9e26L0rfDeeOuH0MSvIbRr/barKIcfXoHlT1uJ9Zo3rDYKRykrgdzkMxNJYTZvuJUxvfwITwdfxFXtBp2ZDNzq+GF3DjRZOMCHPx3k0bnbmPXHgYzoVstlnhbg631f88j3j3Bph0t58VcvNn4cqewDsHepVbU/usOq4Qz9C/T9nf1+uZ2D5LxkFh5YyMKk+ezOTUIMDCgqYtSJQgYWFhFXWnr6sMzO7tav9MohWrysv1UXV28oSLfaC6omh9hh0HFo45ODOt2RHVbCAKuGUVdbTPZBmDsJDq2BHtfAr19q0e1uZRVl3LboNn7J+oXPrvqMaL8maqOpB00WDlBSVsHIF1cQ4uvOl3c1U+2ikd7f8T7Pr3+e6+Ku4/ELH68x1rKKssq7Zk/eMVvTYxdxIjg7meDtXxOcvo1gjxC8LpwCCbfWu5uvvRwuOMyiA4tYuG8+O3J2A9CnqJgrjh/nMq8OhMVfB5H9revQp40QbBsRuOrowCUnRxQuqDK68AnrPcYMPVVzCIzV5GAPx3bD7KugohRu/qrmRl1jrBsQF/zV+gzGvgi9f9MqPo+0gjSu+/o6YvximH3F7Ga7p0OThYN8vP4QD36+lZm3JHBJdzv1EW8ir258lbe3vk2P4B4IcmoIBVuCKKkoafS+PSsqCKmAYK8wgkMvINg7nBDPEII9gwn2CLb+2h439Tg+R44fYfHBxSzcN58tWTsA6FFcwpiC44x2b094j+uhx7X1v5yhWo7MfVbCKDkON39pdbw46UQWLLgPts+FDhfBNW9a92u0IosPLOb+lfdzW8/b+MuAvzTLMTVZOEhpeQWj/rUSP08Xvr774hZduzDGMH3zdH5K++m0kT5PDtBWdbC22h57unhSWl56auC4okwyCjPIPLaTzNR1ZB1PJ9PFhQw3L3JMzcnH08XTSiQewaclk5rKakssGYUZLDm4hIVJC9h4bBMG6FZSaiUIlyCi46+zEkS7Hq3iV6Y6i+wDMOsqqw3tD19Y3cCTVlg3rx4/CiMftRr7W+kw/U/8+ARf7PmCty9/m8Hhg+1+PE0WDvRpYjIPfLaFGX8YwOU9GtD3vi06uhO+fwW2fkqpkzPZva8js/f1ZLi6n5FgsgqzKstqGmwRrMRSPYEcyt3P+iMbqMDQqbSMMQUFjBF/Yi+4BnpeazX6a4JoW3KSYfaV1p3q8eNh0wdWV/Lr3j69ttEKnSg9wYQFEygoKeDzcZ83eOTphtJk4UBl5RVc+tJKPN1cWHDPxTg56RcV2QesO9B//sC65tzzOqsHVS193UsrSskuyiYzL5XM3CQyc5PJyE8ls/AomYVZZJbmkVl2gsyKEvzLSrjs+HHGGC/iul2N9LzO+rWpCaJtyztsXZLK3AsDb7fuxG4j3YZ/yfqFmxbcxNCIobx6yat2vULR7MlCRGYCVwJHjTE9bWVPAHcAx2yrPWKM+cb22sPAbUA58GdjzCJb+QBgFuAJfAPca+oRdEtKFgBzf05h6sebefP3/RnTsxGDqrVV+emw5j+QONNqKO421rpX40TmmTcdHT9mrVMTz0CrO6RPGIR2t3q8dLiw5Y99pJpWYbbV8ymir6MjaXInO6E8MvgRbux+o92O44hkMRwoAN6rliwKjDEvVls3HpgDDAIigCVAV2NMuYisA+4F1mIli1eNMd/WdfyWlizKKwyXvbwSVycnvr13mNYuqjuRBevehp/eOHXDmWdQlZuO2p1+A9LJxOATBl4hbfoOcqXAalO8a+ldrEtbx5wr59A10D4dMpp98iNjzCoRiann6uOBj4wxxcB+EdkLDBKRA4CfMWYNgIi8B1wN1JksWhpnJ+HeUXHc+9EmvtmWxpW9m3gwtNbOKwhGPAhD/2wlC+9QHfVWqSpEhKeGPsV1867jwVUPMufXc/Bwab77lxxRR79bRLaIyEwROdlSEwkkV1knxVYWaXtcvbxGInKniCSKSOKxY8dqW81hruwdQVyYD/9esofyirbZVnTOXD2tcY40USh1hmDPYJ6++Gn25uzlxcQX696gCTV3sngD6Az0BdKAf9nKa7omY85SXiNjzAxjTIIxJiE09NwnSmlqzk7CvZfGsedoAfO3HHZ0OEqpVuiiyIuYGD+Rj3d9zNJDS5vtuM2aLIwxR4wx5caYCuBtrDYKsGoMVe9njwIO28qjaihvtcb2DKdbO1/+vWQPZeUVdW+glFLV3Nv/Xi4IuoDHf3yc9OPpzXLMZk0WIlK1G9A1wDbb43nABBFxF5FYIA5YZ4xJA/JFZIhYfcVuBr5qzpibmpOTMPWyOJIyjjNvc6vOe0opB3F1duX54c9TUl7CI98/QnlFed0bnSO7JQsRmQOsAbqJSIqI3AY8LyJbRWQLMBKYCmCM2Q58AuwAFgJTjDk5ZRuTgXeAvcA+WmHjdnWXx7cnPtyPV5dq7UIp1Tgx/jE8MvgR1qevZ+a2mXY/nt6U5yCLt6dz5/sbeOH63tyQ0HwjSiql2g5jDA+uepDFBxcz+4rZ9Antc877rK3rrN6x5CCXxbejZ6Qfry7bQ6nWLpRSjSAiPHbhY7T3bs+Dqx4kvyTfbsfSZOEgIsJ9l3UlOauQLzam1L2BUkrVwNfNl2eHPUv68XSeWvsU9rpapMnCgUZ2C6NPdACvLt1LSZnWLpRSjdM3rC+T+0zmm/3f8HXS13Y5hiYLBxIRpl4aR2pOIZ9uSK57A6WUqsXtvW4noV0C09ZOs0t3Wk0WDvarrqH07xDA68v2Ulxm/+5vSqm2ydnJmWeGPcPUAVNp59X0E61psnAwq+2iG2m5RXyyXmsXSqnGa+/dngndJ9hlCHNNFi3A0C7BDIwJ5PXleykq1dqFUqrl0WTRAogIUy/rypG8YuasO+TocJRS6gyaLFqIizqHMKRTENNX7NPahVKqxdFk0YJMvbQrx/KLeWtlkqNDUUqp02iyaEEGdwrm8vh2vLxkN1P+t5Gs4yWODkkppQBNFi3O9N/154HR3Vi8PZ3LX17Fkh1HHB2SUkppsmhpXJydmDKyC19NuZgQHzdufy+RBz7dTF5RqaNDU0qdxzRZtFDxEX58dfdQpozszOcbUxjz8ip+2Jvh6LCUUucpTRYtmLuLMw+M7s7nky/Cw9WZ373zE//4ahsnSsocHZpS6jyjyaIV6NchkAV/HsatQ2N5b81Bxv57NRsOZjk6LKXUeUSTRSvh6ebMP66KZ84dQygtN9zw5hqe+Xan3pOhlGoWmixamQs7B7No6nB+OzCat1YmMe7179mWmuvosJRSbZwmi1bIx92FZ67tzX//OJCcE6Vc/Z8f+PcSnXFPKWU/mixasZHdwlg8dThX9g7n5SW7uXb6j+w5Yr9pFZVS5y9NFq1cgJcbr0zox/Tf9Sc1p5Bfv/Y9nyTqUOdKqaalyaKNGNsrnEV/GU5Cx0D+Pncbu7WGoZRqQpos2pBQX3devbEf3u7OPPDZFsor7DNxu1Lq/KPJoo0J8XHniXE92Jycw8zv9zs6HKVUG6HJog0a1yeCy+Lb8eLiXSQdK3B0OEqpNqDeyUJEnEUkQkQ6nFzsGZhqPBFh2tU9cXdx4sHPt1Chl6OUUueoXslCRO4BjgDfAQtsy3w7xqXOUZifB/+4qgfrD2Tz3poDjg5HKdXK1bdmcS/QzRjTwxjTy7b0PtsGIjJTRI6KyLYaXvuriBgRCalS9rCI7BWRXSIyukr5ABHZanvtVRGR+r658911/SMZ0S2U5xbuIjnrhKPDUUq1YvVNFslAQ8eUmAWMqV4oItHAZcChKmXxwASgh22b6SLibHv5DeBOIM62nLFPVTMR4elreuHsJDz4+RaM0ctRSqnGqW+ySAJW2H7933dyOdsGxphVQE1Do74M/A2o+s01HvjIGFNsjNkP7AUGiUg44GeMWWOsb7r3gKvrGbMCIgI8eWTsBfy4L5M56/RmPaVU49Q3WRzCaq9wA3yrLA0iIuOAVGPM5movRWLVXk5KsZVF2h5XL69t/3eKSKKIJB47dqyh4bVZNw6K5qLOwTz9zU4O5xQ6OhylVCvkUp+VjDFPAoiIr/XUNLg/poh4AY8Cl9f0ck2HPUt5bXHOAGYAJCQk6DUXGxHhuet6c/nLq3j4i63M+uNAtOlHKdUQ9e0N1VNEfga2AdtFZIOI9GjgsToDscBmETkARAEbRaQ9Vo0husq6UcBhW3lUDeWqgaKDvHhwTDdW7j7G5xtTHR2OUqqVqe9lqBnAfcaYjsaYjsD9wNsNOZAxZqsxJswYE2OMicFKBP2NMenAPGCCiLiLSCxWQ/Y6Y0wakC8iQ2y9oG4GvmrIcdUpN18Yw8CYQP7v6+0cyStydDhKqVakvsnC2xiz/OQTY8wKwPtsG4jIHGAN0E1EUkTkttrWNcZsBz4BdgALgSnGmJNTwE0G3sFq9N4HfFvPmFU1Tk7C89f3obisgkfnbtPeUUqpeqtXmwWQJCKPAe/bnv8eOOvAQ8aYG+t4Paba82nAtBrWSwR61jNOVYfYEG/+enk3pn2zk3mbDzO+b639BZRSqlJ9axa3AqHAF8Bc2+M/2isoZV+3XhxL3+gAnpi3nYyCYkeHo5RqBeqVLIwx2caYPxtj+htj+hlj7jXGZNs7OGUfzk7CC9f35nhxOY/P2+7ocJRSrcBZk4WIvGL7+7WIzKu+NEuEyi7i2vly76VxLNiSxsJtaY4ORynVwtXVZnGyjeJFeweimt+dwzvx7bY0/v7ldgbHBhPo7ebokJRSLdRZaxbGmA22h32NMSurLkBfu0en7MrV2Ynnr+tDzokS/m/+DkeHo5RqwerbwD2xhrJbmjAO5SDxEX5MGdmFuT+nsnTnEUeHo5Rqoc56GUpEbgRuAmKrtVH4Apn2DEw1nykju7BoezqPzN3K4pgg/D1dHR2SUqqFqavN4kcgDQgB/lWlPB/YYq+gVPNyc3Hihev7cPX0H3h6wU6eu/6sU5Uopc5DZ00WxpiDwEHgwuYJRzlKryh/7hzeiTdW7OPXvcMZ3jXU0SEppVqQ+g4kOERE1otIgYiUiEi5iOTZOzjVvO4dFUfnUG8e+Gyz3qynlDpNfRu4XwduBPYAnsDtwGv2Cko5hoerM6/d2J+cE6X85aNNlFfo2FFKKUt9kwXGmL2AszGm3BjzX2Ck/cJSjhIf4cf/je/B93szeG3ZHkeHo5RqIeo7kOAJEXEDNonI81iN3mcddVa1Xr9JiOan/Vn8e+keBnQMZFictl8odb6rb83iD4AzcDdwHGuiouvsFZRyLBHhqat7Ehfmw18+2kR6rs59odT5rr4DCR40xhQaY/KMMU8aY+6zXZZSbZSXmwvTf9efwtJy7pmzkdLyCkeHpJRyoLoGEtwqIltqW5orSOUYXcJ8eebaXqw/kM2Li3Y5OhyllAPV1WZxZbNEoVqs8X0jWbc/i7dWJZEQE8Rl8e0cHZJSygHqGkjw4NmW5gpSOdZjV8bTM9KP+z/ZRHLWCUeHo5RygPrelJcvInm2pUhvyju/eLg6M/2mARhgyv82UlxWXuc2Sqm2pb4N3L7GGD/b4oHVE+p1+4amWpIOwV68eEMftqTkMm3BTkeHo5RqZvW+Ka8qY8yXwCVNG4pq6Ub3aM8dw2J5b81Bvt582NHhKKWaUb1uyhORa6s8dQISAB0L4jz0tzHd2Xgoh4c+30J8hB+dQ30cHZJSqhnUt2ZxVZVlNNYQ5ePtFZRquVydnXj9pn64uThx1wcbKSzR9gulzgf1qlkYY/5o70BU6xHu78krE/pxy3/X8Y+vtvHCDX0cHZJSys7q2xuqk4h8LSLHROSoiHwlIp3sHZxquX7VNZR7Rnbh0w0pfJKY7OhwlFJ2Vt/LUP8DPgHCgQjgU2COvYJSrcO9l3blos7BPPblNnamaU9qpdqy+iYLMca8b4wpsy0foA3c5z1nJ+HfE/rh7+nKXR9uJL+o1NEhKaXspL7JYrmIPCQiMSLSUUT+BiwQkSARCappAxGZabtkta1K2T9t40ptEpHFIhJR5bWHRWSviOwSkdFVygfYxqjaKyKviog09s2qphfq685rN/bjYOZxHvpiK8bobwil2qL6JovfApOA5cAKYDJwK7ABSKxlm1nAmGplLxhjehtj+gLzgX8AiEg8MAHoYdtmuog427Z5A7gTiLMt1fepHGxwp2D+OrobC7ak8f5aHQVGqbaovr2hYhu6Y2PMKhGJqVZW9cK2N6cuZY0HPjLGFAP7RWQvMEhEDgB+xpg1ACLyHnA18G1D41H29afhnUk8kM0/5+/ggnA/BsbUWOFUSrVS9b0pzxWrNjHcVrQCeMsY0+CL1CIyDbgZyOXU1KyRwNoqq6XYykptj6uXqxbGyUl46Td9+PWr33PDm2uICfZiWFwow+JCuLBzML4ero4OUSl1Duo7reobgCsw3fb8D7ay2xt6QGPMo8CjIvIw1sx7jwM1tUOYs5TXSETuxLpkRYcOHRoamjpHAV5uzL3rIhZsTWP1ngw+35jC+2sP4uwk9IsOsJJH1xB6R/rj4tyokWaUUg4i9WmQFJHNxpg+dZXVsF0MMN8Y07OG1zoCC4wxPW2JA2PMM7bXFgFPAAeA5caY7rbyG4ERxphJdcWckJBgEhNra05RzaGkrIKNh7JZvecYq/dksDU1F2PAz8OFizqHMKxrCMPjQokO8nJ0qEopGxHZYIxJqF5e35pFuYh0Nsbss+2sE9DgcR5EJM4Ys8f2dBzwi+3xPOB/IvIS1n0cccA6Y0y5bXj0IcBPWJevXmvocZVjuLk4MaRTMEM6BfPAaMg+XsIP+zJYvTuD1XuOsXB7OgAdg70YFhfCsLhQLuwcjJ9eslKqxalvsngAq/tsku15DHDWIUBEZA4wAggRkRSsy01jRaQbUAEcBP4EYIzZLiKfADuAMmCKMeZkMpqM1bPKE6thWxu3W6lAbzeu7B3Blb0jMMaQlHGc7/dYiWPuxlQ+WHsIZydhRNdQHvn1BTpIoVItSH0vQ3kA9wOjbEXfAS8bY4rsGNs50ctQrUtJWQWbknNYseso7685SFFZObcOjeWeUXH4uNf3N41S6lzVdhmqvsniEyAP+NBWdCMQaIy5oUmjbEKaLFqvY/nFPL/wFz7dkEKYrzsPj+3O1X0j0fsxlbK/c00WjWrgdiRNFq3fz4eyeWLedjan5JLQMZAnxvWgZ6S/o8NSqk2rLVnUt//iz7ZG5pM7Gwz80FTBKVWTfh0CmXvXUJ67rhf7M44z7vXveXTuVrKPl9jleDknSvhiYwrLfzlql/0r1ZrVt2axE+gGHLIVdQB2YjVUG2NMb7tF2Ehas2hbcgtLefm73by/9iC+Hi7cf3k3bhrUAWenc7s0dSSviMXb01m4PZ21SVmUVxicnYQPbx/MkE7BTRS9Uq3HuV6G6ni2140xLW5AIE0WbdMv6Xk8MW87a5OyiA/348nxPRo8tMj+jOMs2p7Owm3pbErOAaBTqDeje7S3emLN3UpuYSlf33Mx4f6edngXSrVc55QsWiNNFm2XMYYFW9OYtmAnablFXN03gofHXkA7P49a199+OI9F29NZtD2d3UcKAOgV6c/oHu0Y07M9XcJ8K9ffezSf8a//QNf2vnx05xDcXZxr3K9SbZEmC9XmnCgpY/ryfcxYlYSrs3DPqDhuHRqLm4sT5RWGxANZLNp+hEXb00nNKcRJYGBMEGN6tufyHu2JDKi91vDt1jQmf7iR3w3uwLRrejXju1LKsTRZqDbrYOZx/jl/B0t2HqVTiDcJMYEs3XmUzOMluDk7MSwuhNE92jPqgjCCfdzrvd9nv/2FN1fu4/nre/ObhGg7vgOlWg5NFqrNW77rKP/39Q6O5RczsnsYo3u0Y0S3sEbf1FdWXsHE/65j/YFsPv/TRfSK0m67qu3TZKHOC8YYKgzn3EvqpMyCYsa9bvUS//qeiwnydmuS/SrVUp3rfRZKtQoi0mSJAiDYx503ft+fYwXF/HnOz5RXtM0fV0rVRZOFUnXoHRXAU+N78v3eDP61eJejw1HKITRZKFUPvxkYzY2DOjB9xT4Wbkt3dDhKNTtNFkrV0xPj4ukTHcBfP93M3qMFjg5HqWalyUKpenJ3cebN3/fH3cWJSe8nUlBc5uiQlGo2miyUaoBwf09eu6kfBzJP8MCnm2mrvQmVqk6ThVINdFHnEB4a051vt6Xz1qqkujdQqg3QZKFUI9w+LJZf9w7n+YW/8MPeDEeHo5TdabJQqhFEhOev602XMB/umfMzqTmFjg5JKbvSZKFUI3m7u/Dm7wdQWlbB5A82UFRa7uiQlLIbTRZKnYNOoT689Nu+bEnJ5fGvtjs6HKXsRpOFUufosvh23HNJFz5OTGbOukN1b6BUK6TJQqkm8JdLuzK8ayiPf7Wdnw9lOzocpZqcJgulmoCzk/DqhL6E+blzw5truH32ehZsSdN2DNVmNG6gf6XUGQK83Ph40oW89+MBvtyUypKdR/F1d2Fsr3Cu6R/JoJggnJpwRFylmpPOZ6GUHZRXGNbsy2Tuz6ks3JbG8ZJyIgM8ubpfBNf0i6JLmI+jQ1SqRjr5kVIOcqKkjO92HOGLjams3nOMCgO9o/y5pl8kV/WJIKQBU70qZW+aLJRqAY7mFzFv02Hm/pzK9sN5ODsJw+NCuKZ/FJfHt8PD1dnRIarzXLMnCxGZCVwJHDXG9LSVvQBcBZQA+4A/GmNybK89DNwGlAN/NsYsspUPAGYBnsA3wL2mHkFrslAt3e4j+XyxMZWvNqWSlluEj7sLV/RszxW92tMnKoBgrXEoB3BEshgOFADvVUkWlwPLjDFlIvIcgDHmQRGJB+YAg4AIYAnQ1RhTLiLrgHuBtVjJ4lVjzLd1HV+ThWotKioMa/dnMndjKt9uS68c+jwywJPeUf70ivKnT1QAPSP98fd0dXC0qq2rLVnYrTeUMWaViMRUK1tc5ela4Hrb4/HAR8aYYmC/iOwFBonIAcDPGLMGQETeA64G6kwWSrUWTk7CRZ1DuKhzCP83viebknPYmprDlpRctqTk8m2Vmfligr3oHRVgJZFIf3pG+uPtrp0alf058l/ZrcDHtseRWMnjpBRbWantcfXyGonIncCdAB06dDjj9dLSUlJSUigqKjqnwNXpPDw8iIqKwtVVf/WeK083Zy7sHMyFnYMry3JOlLA1NdeWPHJIPJDFvM2HARCBLqE+9Iryp3ekP72jA4gP99O2D9XkHJIsRORRoAz48GRRDauZs5TXyBgzA5gB1mWo6q+npKTg6+tLTEwMItrfvSkYY8jMzCQlJYXY2FhHh9MmBXi5MSwulGFxoZVlx/KL2Zaay+aUHLam5LJqdwZfbEwFINzfg3cmJtAjwt9RIas2qNmThYhMxGr4HlWloToFiK6yWhRw2FYeVUN5oxQVFWmiaGIiQnBwMMeOHXN0KOeVUF93RnYPY2T3MMBK2kfyitl4KJt/zt/Bb95cw+s39a98Xalz1azDfYjIGOBBYJwx5kSVl+YBE0TEXURigThgnTEmDcgXkSFifcPfDHx1jjGcy+aqBnpOHU9EaO/vwdhe4Xw5ZSgxId7cNns976896OjQVBtht2QhInOANUA3EUkRkduA1wFf4DsR2SQibwIYY7YDnwA7gIXAFGPMyUF1JgPvAHuxuttq47ZSZ9HOz4NPJl3IyG5hPPblNqYt2EFFRdu8n0o1H7slC2PMjcaYcGOMqzEmyhjzrjGmizEm2hjT17b8qcr604wxnY0x3ap2jTXGJBpjetpeu7s+91i0VMnJycTGxpKVlQVAdnY2sbGxHDx4kEmTJtG5c2d69OjB8OHD+emnnwCYNm0aPXr0oHfv3vTt27eyXKmz8XZ3YcbNCUy8sCNvr97P5A83UFiigxqqxtM+d80oOjqayZMn89BDDzFjxgweeugh7rzzTh588EFiY2PZs2cPTk5OJCUlsXPnTtasWcP8+fPZuHEj7u7uZGRkUFJS4ui3oVoJZyfhyfE96RjszT8X7GDC22t55+YEQn31Zj/VcOdtsnjy6+3sOJzXpPuMj/Dj8at6nHWdqVOnMmDAAF555RW+//577rvvPmbMmMGHH36Ik5NV0evUqROdOnXiiy++ICQkBHd36z93SEhIk8arzg+3XhxLVKAn9360iav/8wOz/jiQuHa+jg5LtTI6n0Uzc3V15YUXXmDq1Km88sor7Nq1i759++LsfGa/+Msvv5zk5GS6du3KXXfdxcqVKx0QsWoLLu/Rno8nDaGkvIJr3/iRH/ZmODok1cqctzWLumoA9vTtt98SHh7Otm3b6Ny5c63r+fj4sGHDBlavXs3y5cv57W9/y7PPPsstt9zSfMGqNqN3VABz77qIW2etZ+LMdTx9bS9+kxBd94ZKoTWLZrdp0ya+++471q5dy8svv0xwcDCbN2+moqKixvWdnZ0ZMWIETz75JK+//jqff/55M0es2pKoQC8+m3wRF3YO5m+fbeHFRbtoxX1GVDPSZNGMjDFMnjyZV155hQ4dOvDAAw8wffp0EhISePzxxyv/0+7Zs4evvvqKXbt2sWfPnsrtN23aRMeOHR0Vvmoj/DxcmXnLQCYMjOb15Xu596NNOv2rqpMmi2b09ttv06FDBy677DIA7rrrLn755RemTJlCeno6Xbp0oVevXtxxxx1ERERQUFDAxIkTiY+Pp3fv3uzYsYMnnnjCsW9CtQmuzk48c20v/jamG/M2H+YP7/5E1nHtaadqd15NfrRz504uuOACB0XUtum5bb3mbznMfZ9sJsLfg//+cRCxId6ODkk5UG1DlGvNQqnz3JW9I5hzx2ByC0u5ZvoPrNx9TO/4Vmc4b3tDKaVOGdAxiLl3Da3sKRXo5crQLiEMjwvl4rgQIgI8HR2icjBNFkopAGJCvJl3z8Us2XGEVXuOsXpPBvO3pAHQOdSbYXGhDO8awuDYYJ1w6Tykn7hSqpKPuwtX94vk6n6RGGPYfaSA1XuOsWpPBh+tP8SsHw/g6iz07xDI8K6hDIsLoUeEP85OOvJwW6fJQilVIxGhW3tfurX35fZhnSgqLWfDwWxW7TnG93syeGHRLl5YtIuAyktWIVwcF0qkXrJqkzRZKKXqxcPVmaFdQhjaJQSugIyCYn7Ym8HqPRms3nOMBbZLVoNig3h3YgK+HjrNbluiyUIp1SghPu6M7xvJ+L7WJau9RwtYsvMo/1q8i7s+3MjMWwbi6qwdLtsK/SSbUW3zWaxcuRIR4bXXXqtc9+6772bWrFmVz1988UW6d+9Oz5496dOnD++99x4A8+fPp1+/fvTp04f4+HjeeuutWo//0ksvVd7gN2rUKA4ePDWL2uzZs4mLiyMuLo7Zs2c38TtXbZ2IENfOl8kjOvPMtb1YvSeDhz7fqkOJtCHnb83i24cgfWvT7rN9L7ji2Vpfrm0+i44dOxIWFsa///1vJk2ahJub22nbvfnmm3z33XesW7cOPz8/cnNz+fLLLyktLeXOO+9k3bp1REVFUVxczIEDB2o9fr9+/UhMTMTLy4s33niDv/3tb3z88cdkZWXx5JNPkpiYiIgwYMAAxo0bR2BgYFOdGXUeuSEhmsM5Rby8ZDeRAR7cd3k3R4ekmoDWLJrZ1KlTWbt2beV8Fvfffz8AoaGhjBo1qsZf9U8//TTTp0/Hz88PAH9/fyZOnEh+fj5lZWUEBwcD4O7uTrdutf/HHDlyJF5eXgAMGTKElJQUABYtWsRll11GUFAQgYGBXHbZZSxcuLBJ37c6v/x5VBd+mxDNq8v2MmfdIUeHo5rA+VuzOEsNwJ5OzmcxZswYFi9efFot4qGHHuKKK67g1ltvrSzLz88nPz+/xqHMg4KCGDduHB07dmTUqFFceeWV3HjjjZWTKJ3Nu+++yxVXXAFAamoq0dGnhqqOiooiNTX1XN6mOs+JCE9d05P0vCL+/uU22vt5MLJ7mKPDUudAaxYOUHU+i6piY2MZNGgQ//vf/yrLjDGI1N6H/Z133mHp0qUMGjSIF1988bREU5sPPviAxMREHnjggcpjVHe2YypVH67OTkz/XX8uCPflrg83siUlx9EhqXOgyaKZVZ/PIi0t7bTXH3nkEZ577rnK+S38/Pzw9vYmKSmp1n326tWLqVOn8t1339U538WSJUuYNm0a8+bNq5yuNSoqiuTk5Mp1UlJSiIiIaOxbVKqSt7sLM28ZSLCPG7fOWs+hzBOODkk1kiaLZlTTfBZ//etfT1une/fuxMfHM3/+/Mqyhx9+mClTppCXZ80ZnpeXx4wZMygoKGDFihWV69U138XPP//MpEmTmDdvHmFhpy4JjB49msWLF5OdnU12djaLFy9m9OjRTfSu1fkuzNeDWX8cRGm54Zb/riNbh0JvlTRZNKPa5rOo2oUV4NFHH61sfAaYPHkyI0eOZODAgfTs2ZNf/epXeHl5YYzh+eefp1u3bvTt25fHH3/8tO621T3wwAMUFBRwww030LdvX8aNGwdYbR+PPfYYAwcOZODAgfzjH/8gKCio6U+AOm91CfPhnYkJpOQUcvt7iTrZUiuk81moJqHnVtXHN1vTmPK/jYyOb89/ftdfx5RqgXQ+C6WUw43tFc7ffx3Pwu3pPLVgh6PDUQ1w/nadbcOmTZvGp59+elrZDTfcwKOPPuqgiJQ65baLYzmcU8i73+8nMsCT24d1cnRIqh40WbRBjz76qCYG1aI9OvYC0nILeWrBTtr7e3Blb+1919LZ7TKUiMwUkaMisq1K2Q0isl1EKkQkodr6D4vIXhHZJSKjq5QPEJGtttdeFb0BQKlWz8lJeOk3fRkYE8h9H29m3f4sR4ek6mDPNotZwJhqZduAa4FVVQtFJB6YAPSwbTNdRJxtL78B3AnE2Zbq+1RKtUIers68fXMC0UGe3PFeInuP5js6JIdLzSlkzb5Mko4VUFjSsnqM2e0ylDFmlYjEVCvbCTXeHTwe+MgYUwzsF5G9wCAROQD4GWPW2LZ7D7ga+NZecSulmk+Alxuz/jiIa6b/yMSZ65l710WE+Xk4Oqxmk5pTyNp9maxNymTt/kySswpPez3Qy5Vwf08iAjwI9/ckPMCDCH9Pwv09iAjwpJ2fB24uzdNPqaW0WUQCa6s8T7GVldoeVy+vkYjciVULoUOHDk0fpVKqyUUHefHfWwby2xlr+OOs9Xw86UJ82ugc3ynZJ1iblMVP1ZJDgJcrg2ODuHVoLF3CfDiWX8zhnEIO5xaRllNISnYh6/ZnkVdUdtr+RKx5RSL8T08mf7iwIx6uzjWF0Ggt5ROpqR3CnKW8RsaYGcAMsO6zaJrQmk5ycjLDhw9nw4YNBAUFkZ2dTf/+/Zk1axYjRozg1Vdf5Z577gGs+SwSEhK45ZZbAGs+i3feeQcXFxecnZ25//77ufnmm5k/fz6PPfYYFRUVlJaWcu+99zJp0qQaj//SSy9V7iM0NJSZM2dW3vE9e/ZsnnrqKQD+/ve/M3HiRAD279/PhAkTyMrKon///rz//vtnDKGu1LnqFeXPf37Xn9tnJzJhxhouj29P7yh/ekX6E+zj7ujwGu1kclibZNUeUrLPTA5DOgXTrZ0vTvW45+R4cRlpuYUczik67W9abhF7juazas8xCkvLuWVoTJO/l5aSLFKA6CrPo4DDtvKoGsrP2XPrnuOXrF+aYleVugd158FBD9b6emucz+LBBx9k6tSpTJgwgT/96U+8++67TJ48ualOmVKVRnYL46Xf9OHfS/fw8pLdnLxfODLAkz7R/vSKDKB3lD89I/3x92yZU7Yeyy9m5e5jZySHQC9XBscGc/vFsQzpHEzXsPolh+q83V3oEuZLlzDfGl83xpBXVGaXGQpbSrKYB/xPRF4CIrAastcZY8pFJF9EhgA/ATcDr51lPy3e1KlTGTBgQOV8Fq+99hqHDx8mNDSUoUOHMnv2bO64447Ttnn66adZvnz5GfNZZGVlNXg+i5OGDBnCBx98AJw+nwVQOZ/FhAkTWLZsWeUouBMnTuSJJ57QZKHs5uQ0rflFpWxLzWNrag5bUnLZmprLN1vTK9eLDfGmV6Q/vaP86R0VQI8IP7wddOkq63gJC7elM3/LYdYmZVJhmi45NJSI2C2R2u3sisgcYAQQIiIpwONAFtaXfSiwQEQ2GWNGG2O2i8gnwA6gDJhijDnZFWAyVs8qT6yG7SZp3D5bDcCeWtN8FpmZmQQEBODi4nJauVL25uvhyoWdg7mwc3BlWc6JEram5rIlJZctKTkkHshi3mbrQoMIdAn1oVeUP32jA+jfIZDu7X1xsdMc4LmFpSzans78LWn8sDeD8gpDbIg3U0Z2YUzP9lzQ3q9ZkkNzsmdvqBtreWluLetPA6bVUJ4I9GzC0Byu6nwWJwcVhMbPZ7F161aWLFnCiy++yHfffXfWwQTh1HwWK1eurDxGdSKi81yoFiXAy41hcaEMiwutLDuWX8y21Fw2p+SwNSWXVbsz+GKj9YPGy82ZvtEBDOgYSP+OgfSPDsTfq/G/uguKy1iy4wjztxxm1e4MSsoriAr05I5hnbiydzg9Ivza9P+PlnIZ6rxRdT6Liy++mAkTJpz2+iOPPML111/P8OHDgdPns+jUqeZhEXr16kWvXr34wx/+QGxs7FmTxcn5LFauXHnafBZVhzpPSUlhxIgRhISEkJOTQ1lZGS4uLjrPhWpxQn3dGdk9rHIWPmMMqTmFbDyUw8aD2Ww4mM30Ffsor7B++MSF+VQmjwEdA+kU4n3WL/gTJWUs++Uo8zensXzXUYrLKgj39+DmCztyZZ8I+kT5t+kEUZUmi2ZU23wW06adqlBVnc9i0KBBwKn5LD7++GP8/PzIy8vjo48+4qabbiIxMZERI0YA9Z/PYuHChWfMZ/HII4+QnZ0NwOLFi3nmmWcQEUaOHMlnn33GhAkTmD17NuPHj7fDmVGqaYgIUYFeRAV6Ma6P9cPmREkZm5Nz2XjISh7fbkvno/XWZF8BXq4M6HAqefSJCkAEVuw6xvwth1m68yiFpeWE+rozYWA0V/WJoH+HwDZ3iak+NFk0o5rms5g1a1aN81n069ev8vnkyZMpKChg4MCBuLq64urqyv333185n8WkSZPw9PTE29u73vNZgHUvyrx5806bzwI4bT6L5557jgkTJvD3v/+dfv36cdtttzXlKVHK7rzcXE5r/6ioMCRlFLDBVvPYcDCbpb8cBcDFSXBzceJESTlB3m5c0z+SK3uHMzg2+LwfTl3ns1BNQs+tas2yj5fwc7KVOPIKy7gsvh0XdQ62WwN5S1bbfBZas1BKnfcCvd24pHs7LuneztGhtFiaLNognc9CKdXUzrtkUVdX1LagueezaKuXMpVSp5xXF+Q8PDzIzMzUL7cmZIwhMzMTD4/zZ6RQpc5H51XNIioqipSUFI4dO+boUNoUDw8PoqKi6l5RKdVqnVfJwtXVldjYWEeHoZRSrc55dRlKKaVU42iyUEopVSdNFkopperUZu/gFpFjwME6V6xZCJDRhOE0NY3v3Gh850bjOzctPb6OxpjQ6oVtNlmcCxFJrOl295ZC4zs3Gt+50fjOTUuPrzZ6GUoppVSdNFkopZSqkyaLms1wdAB10PjOjcZ3bjS+c9PS46uRtlkopZSqk9YslFJK1UmThVJKqTppsqhCRMaIyC4R2SsiDzkohmgRWS4iO0Vku4jcayt/QkRSRWSTbRlbZZuHbTHvEpHRzRDjARHZaosj0VYWJCLficge299AR8QnIt2qnKNNIpInIn9x5PkTkZkiclREtlUpa/D5EpEBtvO+V0RelSYca7+WGF8QkV9EZIuIzBWRAFt5jIgUVjmXb9o7xlria/Bn2szxfVwltgMisslW3uznr0kYY3Sx2m2cgX1AJ8AN2AzEOyCOcKC/7bEvsBuIB54A/lrD+vG2WN2BWNt7cLZzjAeAkGplzwMP2R4/BDznqPiqfabpQEdHnj9gONAf2HYu5wtYB1wICPAtcIWdY7wccLE9fq5KjDFV16u2H7vEWEt8Df5MmzO+aq//C/iHo85fUyxaszhlELDXGJNkjCkBPgLGN3cQxpg0Y8xG2+N8YCcQeZZNxgMfGWOKjTH7gb1Y76W5jQdm2x7PBq6uUu6o+EYB+4wxZ7uT3+7xGWNWAVk1HLfe50tEwgE/Y8waY32rvFdlG7vEaIxZbIwpsz1dC5x1HHp7xljLOaxNs5/Ds8Vnqx38Bphztn3Y+zM+V5osTokEkqs8T+HsX9J2JyIxQD/gJ1vR3bZLAjOrXLZwRNwGWCwiG0TkTltZO2NMGlgJDwhzYHwnTeD0/6At5fxBw89XpO1xc8d50q1Yv3RPihWRn0VkpYgMs5U5IsaGfKaOOofDgCPGmD1VylrK+as3TRan1HRt0GH9ikXEB/gc+IsxJg94A+gM9AXSsKq14Ji4hxpj+gNXAFNEZPhZ1nXIeRURN2AccHIy8pZ0/s6mtngcFqeIPAqUAR/aitKADsaYfsB9wP9ExM8BMTb0M3XUObyR03+0tJTz1yCaLE5JAaKrPI8CDjsiEBFxxUoUHxpjvgAwxhwxxpQbYyqAtzl1qaTZ4zbGHLb9PQrMtcVyxFaNPlmdPuqo+GyuADYaY47YYm0x58+moecrhdMvAzVLnCIyEbgS+J3t0gi2yzuZtscbsNoEujZ3jI34TJv9HIqIC3At8HGVuFvE+WsoTRanrAfiRCTW9qt0AjCvuYOwXd98F9hpjHmpSnl4ldWuAU72upgHTBARdxGJBeKwGsnsFZ+3iPiefIzVCLrNFsdE22oTga8cEV8Vp/2aaynnr4oGnS/bpap8ERli+zdyc5Vt7EJExgAPAuOMMSeqlIeKiLPtcSdbjEnNHWNDP1NHnEPgUuAXY0zl5aWWcv4azNEt7C1pAcZi9T7aBzzqoBguxqp6bgE22ZaxwPvAVlv5PCC8yjaP2mLehZ17T2D1FttsW7afPE9AMLAU2GP7G+SI+GzH8wIyAf8qZQ47f1hJKw0oxfr1eFtjzheQgPWFuA94HdsIDHaMcS/Wtf+T/w7ftK17ne2z3wxsBK6yd4y1xNfgz7Q547OVzwL+VG3dZj9/TbHocB9KKaXqpJehlFJK1UmThVJKqTppslBKKVUnTRZKKaXqpMlCKaVUnTRZKNVERCRARO46y+s/1mMfBU0blVJNQ5OFUk0nADgjWZy8AcsYc1FzB6RUU3FxdABKtSHPAp1t8xaUAgVYN2r1BeJFpMAY42Mb9+srIBBwBf5ujGk5d+oqVQO9KU+pJmIbJXi+MaaniIwAFgA9jTVMNlWShQvgZYzJE5EQrOG/44wx5uQ6DnoLStVKaxZK2c+6k4miGgGeto3WW4E1DHU7rImalGqRNFkoZT/Hayn/HRAKDDDGlIrIAcCj2aJSqhG0gVupppOPNRVuXfyBo7ZEMRJr2lelWjStWSjVRIwxmSLyg4hsAwqBI7Ws+iHwtYgkYo3m+kszhahUo2kDt1JKqTrpZSillFJ10mShlFKqTposlFJK1UmThVJKqTppslBKKVUnTRZKKaXqpMlCKaVUnf4f/0E5JybWKRgAAAAASUVORK5CYII=\n", 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" ] @@ -1172,22 +884,22 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 70, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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U9+7dOw/pm5lZS3IqKsm//idKOnw/328TcGayfA6wNjn+YcDvgasj4i9Z79tFUq9kuSvwOWA1ZmbWLuXaUxkLHAX8j6RfSTpfklraQdI8YAlwvKQaSZcBE4AZklYA1wMVyeZXAMcC1zQ6dbgbmTmYlUA1sBG4o3VNNDOztqLWzHlL6kSmt3AbsBu4C/hpRNTlJ719V15eHlVVVYVOw8ysqEhaFhHl+7p/znMqkk4CZgA3kjkL68vAduDxfX1zMzPrWHI6pVjSMuANMmdrTc463fdpSZ/KU25mZlZkcv2dyuiIWJcdkFQaEesj4qI85GVmZkUo1+Gv3+QYMzOzA1iLPRVJJwBDgEMlZfdIegDd85mYmZkVn70Nfx1P5myvw4DPZ8V3kDk92MzMrEGLRSUi5gPzJf1LRCxpo5zMzKxI7W3463sRcQPwFUnjGq+PiH/PW2ZmZlZ09jb89ULy7F8RmpnZXu1t+Ouh5HlufSz5Vf3BEbE9z7mZmVmRyfWCkvdL6iHpIOB5YI2kK/ObmpmZFZtcf6cyOOmZfBH4A9CfFu5rYmZmB6Zci0rX5NLzXwTmR8S7ZG4NbGZm1iDXonI78ApwELBY0gAyF5M0MzNrkNO1vyLiZuDmrNCrks7OT0pmZlascp2oP1TSj+vv/y5pBplei5mZWYNch7/uInNplouTx3bg7nwlZWZmxSnXS98PjIgvZb2eKqk6D/mYmVkRy7Wn8g9Jp9W/SG7M9Y/8pGRmZsUq16LydeDnkl6R9ArwM+DylnaQdJekLZJWZ8XKJC2VVJ3MzYzIWne1pJckrZF0flZ8uKRVybqbJalVLTQzszaz16IiqTPwbxFxMnAScFJEDIuIlXvZdQ4wqlHsBmBqRJQB1yavkTQYGEvm3i2jgFuT9wW4DagABiWPxsc0M7N2Yq9zKhHxvqThyXLOv02JiMWSShqHydzgC+BQYFOyfCHwq4jYBayX9BIwIukV9ai/7L6ke8j8APORXPNorW/dOZJNu7fm6/BmZnl1VKfezPw/iwr2/rlO1C+XtAB4AHirPhgRv23l+30LeFTSTWR6SZ9M4kcDS7O2q0li7ybLjeNNklRBpldD//79W5mamZntr1yLSk+gFjgnKxZAa4tKJTApIh6UdDEwGzgXaGqeJFqINykiZgGzAMrLy/fpMjKFrPBmZsUu11/U/++U3m888M1k+QHgzmS5BuiXtV1fMkNjNcly47iZmbVDuf6i/jhJf6o/k0vSSZK+vw/vtwk4M1k+B1ibLC8AxkrqJqmUzIT8MxGxGdgh6dTkrK9Lgfn78L5mZtYGcj2l+A7gajJzHCRnfo1taQdJ84AlwPGSaiRdBkwAZkhaAVxPMv8REc8B/5fMvVr+CEyMiPeTQ1WS6dG8BLxMHifpzcxs/+Q6p/JPEfFMo5+IvNfSDhHxoXvaJ4Y3s/00YFoT8SrgxBzzNDOzAsq1p/K6pIEkk+SSvgxszltWZmZWlHLtqUwkc1bVCZI2AuuBf8tbVmZmVpRyPftrHXBuco/6ThGxI79pmZlZMcqpqEg6jMyZVyVAl/q5lYj493wlZmZmxSfX4a8/kPnF+ypgd/7SMTOzYpZrUekeEd/OayZmZlb0cj3765eSJkg6UlLP+kdeMzMzs6KTa0/lHeBGYAofXHsrgGPykZSZmRWnXIvKt4FjI+L1fCZjZmbFLdfhr+eAv+czETMzK3659lTeB6olPQHsqg/6lGIzM8uWa1H5r+RhZmbWrFx/UT8334mYmVnxy/UX9etp4o6LEeGzv8zMrEGuw1/lWcvdgdFkbjFsZmbWIKezvyKiNuuxMSJmsuf96s3MzHIe/vp41stOZHouh+QlIzMzK1q5Dn/N4IM5lfeAV8gMgZmZmTXI9cePFwCzgT8BfwE2svd71N8laYuk1VmxX0uqTh6vSKpO4pdkxasl7ZZUlqx7UtKarHV9Wt9MMzNrC635ncobwLPA2znuMwf4GXBPfSAixtQvS5oBvJnE7wPuS+JDgfkRUZ11rEuSe9WbmVk7lmtR6RsRo1pz4IhYLKmkqXXK3OXrYpqe7B8HzGvNe5mZWfuQ6/DXfyc9iLScDrwWEWubWDeGDxeVu5Ohr2tUf9tJMzNrd3ItKqcBy5K5jZWSVklauR/v22RvRNIngL9HxOqs8CURMZRMITod+GpzB5VUIalKUtXWrVv3Iz0zM9sXuQ5/XZDWG0rqAlwEDG9i9VgaFZuI2Jg875B0PzCCrHmaRtvOAmYBlJeXf+gKAGZmll+5Xvvr1RTf81zgxYioyQ5K6kTmNOUzsmJdgMMi4nVJXYHPAY+lmIuZmaUo1+GvVpM0D1gCHC+pRtJlyaoP9UYSZwA1EbEuK9YNeDQZaqsmcyrzHfnK2czM9k+uw1+tFhHjmol/rZn4k8CpjWJv0fQwmZmZtUN566mYmdmBx0XFzMxS46JiZmapcVExM7PUuKiYmVlqXFTMzCw1LipmZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS42LipmZpcZFxczMUuOiYmZmqclbUZF0l6QtklZnxX4tqTp5vCKpOomXSPpH1rpfZO0zXNIqSS9JulmS8pWzmZntny55PPYc4GfAPfWBiBhTvyxpBvBm1vYvR0RZE8e5DagAlgJ/AEYBj6SfrpmZ7a+89VQiYjFQ19S6pLdxMTCvpWNIOhLoERFLIiLIFKgvppyqmZmlpFBzKqcDr0XE2qxYqaTlkp6SdHoSOxqoydqmJok1SVKFpCpJVVu3bk0/azMza1Ghiso49uylbAb6R8Qw4NvA/ZJ6AE3Nn0RzB42IWRFRHhHlvXv3TjVhMzPbu3zOqTRJUhfgImB4fSwidgG7kuVlkl4GjiPTM+mbtXtfYFPbZWtmZq1RiJ7KucCLEdEwrCWpt6TOyfIxwCBgXURsBnZIOjWZh7kUmF+AnM3MLAf5PKV4HrAEOF5SjaTLklVj+fAE/RnASkkrgN8AX4+I+kn+SuBO4CXgZXzml5lZu6XMSVUdT3l5eVRVVRU6DTOzoiJpWUSU7+v+/kW9mZmlxkXFzMxS46JiZmapcVExM7PUuKiYmVlqXFTMzCw1LipmZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS42LipmZpcZFxczMUpPPe9TfJWmLpNVZsV9Lqk4er0iqTuIjJS2TtCp5Pidrnyclrcnar0++cjYzs/3TJY/HngP8DLinPhARY+qXJc0A3kxevg58PiI2SToReBQ4OutYl0SEbzhvZtbO5a2oRMRiSSVNrZMk4GLgnGTb5VmrnwO6S+oWEbvylZ+ZmaWvUHMqpwOvRcTaJtZ9CVjeqKDcnQx9XZMUpCZJqpBUJalq69ataedsZmZ7UaiiMg6Y1zgoaQgwHbg8K3xJRAwlU4hOB77a3EEjYlZElEdEee/evVNO2czM9qbNi4qkLsBFwK8bxfsCvwMujYiX6+MRsTF53gHcD4xou2zNzKw1CtFTORd4MSJq6gOSDgN+D1wdEX/JineR1CtZ7gp8DliNmZm1S/k8pXgesAQ4XlKNpMuSVWP58NDXFcCxwDWNTh3uBjwqaSVQDWwE7shXzmZmtn8UEYXOIS/Ky8ujqspnIZuZtYakZRFRvq/7+xf1ZmaWGhcVMzNLjYuKmZmlxkXFzMxS46JiZmapcVExM7PUuKiYmVlqXFTMzCw1LipmZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS42LipmZpcZFxczMUuOiYmZmqVFEFDqHvJC0FXh1H3fvBbyeYjrF5EBuOxzY7T+Q2w4Hdvuz2z4gInrv64E6bFHZH5KqIqK80HkUwoHcdjiw238gtx0O7Pan2XYPf5mZWWpcVMzMLDUuKk2bVegECuhAbjsc2O0/kNsOB3b7U2u751TMzCw17qmYmVlqXFTMzCw1LipZJI2StEbSS5ImFzqffJH0iqRVkqolVSWxnpIWSVqbPB+etf3VyWeyRtL5hcu89STdJWmLpNVZsVa3VdLw5DN7SdLNktTWbdkXzbT/Okkbk++/WtJnstZ1mPZL6ifpCUkvSHpO0jeTeIf//ltoe/6/+4jwIzOv1Bl4GTgG+AiwAhhc6Lzy1NZXgF6NYjcAk5PlycD0ZHlw8ll0A0qTz6hzodvQiraeAXwcWL0/bQWeAf4FEPAIcEGh27Yf7b8O+G4T23ao9gNHAh9Plg8B/pq0scN//y20Pe/fvXsqHxgBvBQR6yLiHeBXwIUFzqktXQjMTZbnAl/Miv8qInZFxHrgJTKfVVGIiMVAXaNwq9oq6UigR0Qsicz/Zfdk7dOuNdP+5nSo9kfE5oh4NlneAbwAHM0B8P230PbmpNZ2F5UPHA1syHpdQ8tfQjELYKGkZZIqktg/R8RmyPwHCfRJ4h3xc2ltW49OlhvHi9kVklYmw2P1wz8dtv2SSoBhwNMcYN9/o7ZDnr97F5UPNDVO2FHPt/5URHwcuACYKOmMFrY9kD6X5tra0T6D24CBQBmwGZiRxDtk+yUdDDwIfCsitre0aROxom5/E23P+3fvovKBGqBf1uu+wKYC5ZJXEbEped4C/I7McNZrSVeX5HlLsnlH/Fxa29aaZLlxvChFxGsR8X5E7Abu4IPhzA7XfkldyfxRvS8ifpuED4jvv6m2t8V376Lygf8BBkkqlfQRYCywoMA5pU7SQZIOqV8GzgNWk2nr+GSz8cD8ZHkBMFZSN0mlwCAyE3fFrFVtTYZIdkg6NTnz5dKsfYpO/R/UxL+S+f6hg7U/yXU28EJE/DhrVYf//ptre5t894U+S6E9PYDPkDlL4mVgSqHzyVMbjyFzlscK4Ln6dgJHAH8C1ibPPbP2mZJ8Jmto52e9NNHeeWS6+e+S+VfXZfvSVqA8+R/wZeBnJFejaO+PZtr/S2AVsDL5Y3JkR2w/cBqZoZqVQHXy+MyB8P230Pa8f/e+TIuZmaXGw19mZpYaFxUzM0uNi4qZmaXGRcXMzFLjomJmZqlxUTFLiaTDJH2jhfX/ncMxdqablVnbclExS89hwIeKiqTOABHxybZOyKytdSl0AmYdyH8CAyVVk/mx4U4yPzwsAwZL2hkRByfXY5oPHA50Bb4fEe36F9pmufKPH81SklwN9uGIOFHSWcDvgRMjcylxsopKF+CfImK7pF7AUmBQRET9NgVqgtl+c0/FLH+eqS8ojQi4Prk69G4ylxL/Z+BvbZmcWT64qJjlz1vNxC8BegPDI+JdSa8A3dssK7M88kS9WXp2kLl1694cCmxJCsrZwID8pmXWdtxTMUtJRNRK+ouk1cA/gNea2fQ+4CFJVWSuHvtiG6VolneeqDczs9R4+MvMzFLjomJmZqlxUTEzs9S4qJiZWWpcVMzMLDUuKmZmlhoXFTMzS83/B4fSJ1xfKeWgAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -1199,7 +911,19 @@ } ], "source": [ - "ax = df[['numerosity', 'numerosity_20', 'numerosity_200']].plot()\n", + "ax = df[['fraction_accuracy_20', 'fraction_accuracy_200']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Fraction Accuracy\")\n", + "ax.legend([\"XNCS_20\",\"XNCS_200\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = df[['numerosity_20', 'numerosity_200']].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"numerosity\")\n", "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" @@ -1207,22 +931,22 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 71, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1242,16 +966,16 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "87.77777777777779\n", - "94.77777777777779\n", - "152.33333333333334\n" + "467.0\n", + "157.0\n", + "103.0\n" ] } ], @@ -1260,13 +984,6 @@ "print(sum(df['steps_in_trial_20'])/number_of_experiments)\n", "print(sum(df['steps_in_trial_200'])/number_of_experiments)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb index 0398c02..5d25f08 100644 --- a/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb +++ b/XCS_Experiments/XNCS_Woods1_pre_populated.ipynb @@ -23,13 +23,13 @@ "text": [ "This is how maze looks like\n", "\n", - "['.', 'O', 'O', '.', '.', '.', '.', '.']\n", + "['.', '.', '.', '.', '.', '.', '.', 'O']\n", "\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n" + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } ], @@ -96,16 +96,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 8, 'reward': 1000.0, 'perf_time': 0.08481480000000019, 'numerosity': 1800, 'population': 1637, 'average_specificity': 8.145, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 2, 'reward': 1886.4707571494996, 'perf_time': 0.02985010000000088, 'numerosity': 1800, 'population': 1585, 'average_specificity': 8.71111111111111, 'fraction_accuracy': 0.11328125}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 8, 'reward': 1082.2976794931064, 'perf_time': 0.11306730000000442, 'numerosity': 1800, 'population': 1573, 'average_specificity': 10.06611111111111, 'fraction_accuracy': 0.375}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 7, 'reward': 1093.588574743161, 'perf_time': 0.09264579999999967, 'numerosity': 1800, 'population': 1580, 'average_specificity': 11.934444444444445, 'fraction_accuracy': 0.125}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 38, 'reward': 1000.0023853594896, 'perf_time': 0.4457937999999899, 'numerosity': 1800, 'population': 1554, 'average_specificity': 13.135555555555555, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 9, 'reward': 1047.9518200343475, 'perf_time': 0.08730189999999993, 'numerosity': 1800, 'population': 1545, 'average_specificity': 14.772222222222222, 'fraction_accuracy': 0.23863636363636365}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 6, 'reward': 1141.528554666973, 'perf_time': 0.0681583999999873, 'numerosity': 1800, 'population': 1564, 'average_specificity': 14.801666666666666, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 2, 'reward': 1504.1000031547478, 'perf_time': 0.03933630000000221, 'numerosity': 1800, 'population': 1561, 'average_specificity': 14.197222222222223, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 6, 'reward': 1177.1675462768721, 'perf_time': 0.062398400000006404, 'numerosity': 1800, 'population': 1558, 'average_specificity': 14.66, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 4, 'reward': 1418.2266975072803, 'perf_time': 0.04104639999999904, 'numerosity': 1800, 'population': 1561, 'average_specificity': 15.536666666666667, 'fraction_accuracy': 0.125}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000.0, 'perf_time': 0.0101880999999997, 'numerosity': 1800, 'population': 1622, 'average_specificity': 8.035, 'fraction_accuracy': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 8, 'reward': 1078.3271337169067, 'perf_time': 0.09919509999999931, 'numerosity': 1800, 'population': 1548, 'average_specificity': 10.86, 'fraction_accuracy': 0.18455530966912712}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 7, 'reward': 1101.748979356001, 'perf_time': 0.09865720000000522, 'numerosity': 1800, 'population': 1568, 'average_specificity': 10.797222222222222, 'fraction_accuracy': 0.2016524165428857}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 6, 'reward': 1164.1117384164033, 'perf_time': 0.09157989999999927, 'numerosity': 1800, 'population': 1588, 'average_specificity': 12.010555555555555, 'fraction_accuracy': 0.20585388697704013}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 9, 'reward': 1052.5560632430102, 'perf_time': 0.1354529000000042, 'numerosity': 1800, 'population': 1601, 'average_specificity': 14.02111111111111, 'fraction_accuracy': 0.1826998071372673}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 8, 'reward': 1074.0450228370598, 'perf_time': 0.10274409999999534, 'numerosity': 1800, 'population': 1596, 'average_specificity': 15.21888888888889, 'fraction_accuracy': 0.27321950669337375}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 9, 'reward': 1000.0000016776559, 'perf_time': 0.15066450000000486, 'numerosity': 1800, 'population': 1591, 'average_specificity': 15.515, 'fraction_accuracy': 0.27757357453773884}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 2, 'reward': 1537.3784862796801, 'perf_time': 0.033960400000012214, 'numerosity': 1800, 'population': 1600, 'average_specificity': 15.283888888888889, 'fraction_accuracy': 0.2718877524777994}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 10, 'reward': 1056.7966973842194, 'perf_time': 0.12077990000000227, 'numerosity': 1800, 'population': 1605, 'average_specificity': 16.441666666666666, 'fraction_accuracy': 0.20279985359056343}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 12, 'reward': 1017.5512716605906, 'perf_time': 0.18368450000002667, 'numerosity': 1800, 'population': 1584, 'average_specificity': 15.605, 'fraction_accuracy': 0.26546746519449194}\n" ] } ], @@ -174,314 +174,314 @@ " \n", " \n", " 0\n", - " 8\n", - " 1.000000e+03\n", - " 0.084815\n", + " 1\n", + " 1000.000000\n", + " 0.010188\n", " 1800\n", - " 1637\n", - " 8.145000\n", + " 1622\n", + " 8.035000\n", " 0.000000\n", " \n", " \n", " 100\n", " 5\n", - " 1.250735e+03\n", - " 0.062643\n", + " 1199.143807\n", + " 0.059476\n", " 1800\n", - " 1597\n", - " 8.130556\n", - " 0.000000\n", + " 1559\n", + " 8.187222\n", + " 0.158505\n", " \n", " \n", " 200\n", - " 2\n", - " 1.886471e+03\n", - " 0.029850\n", + " 8\n", + " 1078.327134\n", + " 0.099195\n", " 1800\n", - " 1585\n", - " 8.711111\n", - " 0.113281\n", + " 1548\n", + " 10.860000\n", + " 0.184555\n", " \n", " \n", " 300\n", - " 8\n", - " 1.069032e+03\n", - " 0.089705\n", + " 6\n", + " 1146.002447\n", + " 0.058881\n", " 1800\n", - " 1593\n", - " 10.185556\n", - " 0.241667\n", + " 1568\n", + " 11.231667\n", + " 0.174072\n", " \n", " \n", " 400\n", - " 3\n", - " 1.707064e+03\n", - " 0.029097\n", + " 6\n", + " 1156.553744\n", + " 0.083473\n", " 1800\n", - " 1584\n", - " 10.250556\n", - " 0.241667\n", + " 1567\n", + " 10.896667\n", + " 0.185611\n", " \n", " \n", " 500\n", - " 8\n", - " 1.082298e+03\n", - " 0.113067\n", + " 7\n", + " 1101.748979\n", + " 0.098657\n", " 1800\n", - " 1573\n", - " 10.066111\n", - " 0.375000\n", + " 1568\n", + " 10.797222\n", + " 0.201652\n", " \n", " \n", " 600\n", - " 9\n", - " 1.049088e+03\n", - " 0.119529\n", + " 8\n", + " 1124.513090\n", + " 0.118927\n", " 1800\n", - " 1582\n", - " 11.092222\n", - " 0.000000\n", + " 1571\n", + " 11.238889\n", + " 0.199141\n", " \n", " \n", " 700\n", - " 7\n", - " 1.093589e+03\n", - " 0.092646\n", + " 6\n", + " 1164.111738\n", + " 0.091580\n", " 1800\n", - " 1580\n", - " 11.934444\n", - " 0.125000\n", + " 1588\n", + " 12.010556\n", + " 0.205854\n", " \n", " \n", " 800\n", - " 10\n", - " 1.071260e+03\n", - " 0.139656\n", + " 6\n", + " 1140.810820\n", + " 0.077319\n", " 1800\n", - " 1575\n", - " 12.199444\n", - " 0.375000\n", + " 1593\n", + " 12.420000\n", + " 0.203512\n", " \n", " \n", " 900\n", - " 5\n", - " 1.238699e+03\n", - " 0.064394\n", + " 2\n", + " 1665.215109\n", + " 0.019909\n", " 1800\n", - " 1576\n", - " 12.867222\n", - " 0.250000\n", + " 1604\n", + " 12.331667\n", + " 0.190575\n", " \n", " \n", " 1000\n", - " 38\n", - " 1.000002e+03\n", - " 0.445794\n", + " 9\n", + " 1052.556063\n", + " 0.135453\n", " 1800\n", - " 1554\n", - " 13.135556\n", - " 0.000000\n", + " 1601\n", + " 14.021111\n", + " 0.182700\n", " \n", " \n", " 1100\n", - " 9\n", - " 1.081911e+03\n", - " 0.143781\n", + " 19\n", + " 1001.494068\n", + " 0.216819\n", " 1800\n", - " 1545\n", - " 13.717222\n", - " 0.125000\n", + " 1598\n", + " 14.650556\n", + " 0.250071\n", " \n", " \n", " 1200\n", - " 9\n", - " 1.047952e+03\n", - " 0.087302\n", + " 8\n", + " 1074.045023\n", + " 0.102744\n", " 1800\n", - " 1545\n", - " 14.772222\n", - " 0.238636\n", + " 1596\n", + " 15.218889\n", + " 0.273220\n", " \n", " \n", " 1300\n", - " 9\n", - " 1.050219e+03\n", - " 0.121027\n", + " 27\n", + " 1000.105796\n", + " 0.367714\n", " 1800\n", - " 1549\n", - " 14.645000\n", - " 0.000000\n", + " 1582\n", + " 14.381667\n", + " 0.285966\n", " \n", " \n", " 1400\n", - " 1\n", - " 1.769820e+03\n", - " 0.009838\n", + " 2\n", + " 1000.000000\n", + " 0.032981\n", " 1800\n", - " 1558\n", - " 14.951667\n", - " 0.125000\n", + " 1578\n", + " 14.962222\n", + " 0.284248\n", " \n", " \n", " 1500\n", - " 6\n", - " 1.141529e+03\n", - " 0.068158\n", + " 9\n", + " 1000.000002\n", + " 0.150665\n", " 1800\n", - " 1564\n", - " 14.801667\n", - " 0.000000\n", + " 1591\n", + " 15.515000\n", + " 0.277574\n", " \n", " \n", " 1600\n", - " 1\n", - " 1.811296e+03\n", - " 0.009265\n", + " 6\n", + " 1137.613769\n", + " 0.071694\n", " 1800\n", - " 1558\n", - " 13.833333\n", - " 0.041667\n", + " 1589\n", + " 15.189444\n", + " 0.276329\n", " \n", " \n", " 1700\n", " 2\n", - " 1.504100e+03\n", - " 0.039336\n", + " 1537.378486\n", + " 0.033960\n", " 1800\n", - " 1561\n", - " 14.197222\n", - " 0.000000\n", + " 1600\n", + " 15.283889\n", + " 0.271888\n", " \n", " \n", " 1800\n", - " 8\n", - " 1.067196e+03\n", - " 0.102992\n", + " 7\n", + " 1141.540887\n", + " 0.096521\n", " 1800\n", - " 1566\n", - " 15.303889\n", - " 0.000000\n", + " 1590\n", + " 15.622222\n", + " 0.232106\n", " \n", " \n", " 1900\n", - " 6\n", - " 1.135569e+03\n", - " 0.083280\n", + " 4\n", + " 1345.544240\n", + " 0.040551\n", " 1800\n", - " 1561\n", - " 14.877222\n", - " 0.125000\n", + " 1591\n", + " 16.743889\n", + " 0.208933\n", " \n", " \n", " 2000\n", - " 6\n", - " 1.177168e+03\n", - " 0.062398\n", + " 10\n", + " 1056.796697\n", + " 0.120780\n", " 1800\n", - " 1558\n", - " 14.660000\n", - " 0.000000\n", + " 1605\n", + " 16.441667\n", + " 0.202800\n", " \n", " \n", " 2100\n", - " 8\n", - " 1.109383e+03\n", - " 0.111724\n", + " 2\n", + " 1000.000000\n", + " 0.043360\n", " 1800\n", - " 1568\n", - " 15.310556\n", - " 0.125000\n", + " 1600\n", + " 16.318889\n", + " 0.229790\n", " \n", " \n", " 2200\n", - " 4\n", - " 1.418227e+03\n", - " 0.041046\n", + " 12\n", + " 1017.551272\n", + " 0.183685\n", " 1800\n", - " 1561\n", - " 15.536667\n", - " 0.125000\n", + " 1584\n", + " 15.605000\n", + " 0.265467\n", " \n", " \n", " 2300\n", " 7\n", - " 1.094200e+03\n", - " 0.099101\n", + " 1095.673568\n", + " 0.097950\n", " 1800\n", - " 1547\n", - " 15.074444\n", - " 0.284914\n", + " 1601\n", + " 16.071667\n", + " 0.306780\n", " \n", " \n", " 2400\n", - " 50\n", - " 2.013565e-12\n", - " 0.638773\n", + " 5\n", + " 1197.960708\n", + " 0.057193\n", " 1800\n", - " 1542\n", - " 15.373889\n", - " 0.005435\n", + " 1592\n", + " 15.902778\n", + " 0.327172\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial reward perf_time numerosity population \\\n", - "trial \n", - "0 8 1.000000e+03 0.084815 1800 1637 \n", - "100 5 1.250735e+03 0.062643 1800 1597 \n", - "200 2 1.886471e+03 0.029850 1800 1585 \n", - "300 8 1.069032e+03 0.089705 1800 1593 \n", - "400 3 1.707064e+03 0.029097 1800 1584 \n", - "500 8 1.082298e+03 0.113067 1800 1573 \n", - "600 9 1.049088e+03 0.119529 1800 1582 \n", - "700 7 1.093589e+03 0.092646 1800 1580 \n", - "800 10 1.071260e+03 0.139656 1800 1575 \n", - "900 5 1.238699e+03 0.064394 1800 1576 \n", - "1000 38 1.000002e+03 0.445794 1800 1554 \n", - "1100 9 1.081911e+03 0.143781 1800 1545 \n", - "1200 9 1.047952e+03 0.087302 1800 1545 \n", - "1300 9 1.050219e+03 0.121027 1800 1549 \n", - "1400 1 1.769820e+03 0.009838 1800 1558 \n", - "1500 6 1.141529e+03 0.068158 1800 1564 \n", - "1600 1 1.811296e+03 0.009265 1800 1558 \n", - "1700 2 1.504100e+03 0.039336 1800 1561 \n", - "1800 8 1.067196e+03 0.102992 1800 1566 \n", - "1900 6 1.135569e+03 0.083280 1800 1561 \n", - "2000 6 1.177168e+03 0.062398 1800 1558 \n", - "2100 8 1.109383e+03 0.111724 1800 1568 \n", - "2200 4 1.418227e+03 0.041046 1800 1561 \n", - "2300 7 1.094200e+03 0.099101 1800 1547 \n", - "2400 50 2.013565e-12 0.638773 1800 1542 \n", + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 1 1000.000000 0.010188 1800 1622 \n", + "100 5 1199.143807 0.059476 1800 1559 \n", + "200 8 1078.327134 0.099195 1800 1548 \n", + "300 6 1146.002447 0.058881 1800 1568 \n", + "400 6 1156.553744 0.083473 1800 1567 \n", + "500 7 1101.748979 0.098657 1800 1568 \n", + "600 8 1124.513090 0.118927 1800 1571 \n", + "700 6 1164.111738 0.091580 1800 1588 \n", + "800 6 1140.810820 0.077319 1800 1593 \n", + "900 2 1665.215109 0.019909 1800 1604 \n", + "1000 9 1052.556063 0.135453 1800 1601 \n", + "1100 19 1001.494068 0.216819 1800 1598 \n", + "1200 8 1074.045023 0.102744 1800 1596 \n", + "1300 27 1000.105796 0.367714 1800 1582 \n", + "1400 2 1000.000000 0.032981 1800 1578 \n", + "1500 9 1000.000002 0.150665 1800 1591 \n", + "1600 6 1137.613769 0.071694 1800 1589 \n", + "1700 2 1537.378486 0.033960 1800 1600 \n", + "1800 7 1141.540887 0.096521 1800 1590 \n", + "1900 4 1345.544240 0.040551 1800 1591 \n", + "2000 10 1056.796697 0.120780 1800 1605 \n", + "2100 2 1000.000000 0.043360 1800 1600 \n", + "2200 12 1017.551272 0.183685 1800 1584 \n", + "2300 7 1095.673568 0.097950 1800 1601 \n", + "2400 5 1197.960708 0.057193 1800 1592 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 8.145000 0.000000 \n", - "100 8.130556 0.000000 \n", - "200 8.711111 0.113281 \n", - "300 10.185556 0.241667 \n", - "400 10.250556 0.241667 \n", - "500 10.066111 0.375000 \n", - "600 11.092222 0.000000 \n", - "700 11.934444 0.125000 \n", - "800 12.199444 0.375000 \n", - "900 12.867222 0.250000 \n", - "1000 13.135556 0.000000 \n", - "1100 13.717222 0.125000 \n", - "1200 14.772222 0.238636 \n", - "1300 14.645000 0.000000 \n", - "1400 14.951667 0.125000 \n", - "1500 14.801667 0.000000 \n", - "1600 13.833333 0.041667 \n", - "1700 14.197222 0.000000 \n", - "1800 15.303889 0.000000 \n", - "1900 14.877222 0.125000 \n", - "2000 14.660000 0.000000 \n", - "2100 15.310556 0.125000 \n", - "2200 15.536667 0.125000 \n", - "2300 15.074444 0.284914 \n", - "2400 15.373889 0.005435 " + "0 8.035000 0.000000 \n", + "100 8.187222 0.158505 \n", + "200 10.860000 0.184555 \n", + "300 11.231667 0.174072 \n", + "400 10.896667 0.185611 \n", + "500 10.797222 0.201652 \n", + "600 11.238889 0.199141 \n", + "700 12.010556 0.205854 \n", + "800 12.420000 0.203512 \n", + "900 12.331667 0.190575 \n", + "1000 14.021111 0.182700 \n", + "1100 14.650556 0.250071 \n", + "1200 15.218889 0.273220 \n", + "1300 14.381667 0.285966 \n", + "1400 14.962222 0.284248 \n", + "1500 15.515000 0.277574 \n", + "1600 15.189444 0.276329 \n", + "1700 15.283889 0.271888 \n", + "1800 15.622222 0.232106 \n", + "1900 16.743889 0.208933 \n", + "2000 16.441667 0.202800 \n", + "2100 16.318889 0.229790 \n", + "2200 15.605000 0.265467 \n", + "2300 16.071667 0.306780 \n", + "2400 15.902778 0.327172 " ] }, "metadata": {}, @@ -500,7 +500,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -509,7 +509,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -644,7 +644,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "229.0\n" + "184.0\n" ] } ], diff --git a/XCS_Experiments/XNCS_woods.ipynb b/XCS_Experiments/XNCS_woods.ipynb index 5c9b008..c7a23c5 100644 --- a/XCS_Experiments/XNCS_woods.ipynb +++ b/XCS_Experiments/XNCS_woods.ipynb @@ -22,9 +22,10 @@ "output_type": "stream", "text": [ "This is how maze looks like:\n", + "['O', 'O', '.', '.', '.', '.', '.', 'O']\n", "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" @@ -40,6 +41,7 @@ "maze = gym.make('Woods1-v0')\n", "print(\"This is how maze looks like:\")\n", "situation = maze.reset()\n", + "print(situation)\n", "maze.render()\n" ] }, @@ -86,26 +88,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 24, 'reward': 1000.0, 'perf_time': 0.01964560000000004, 'numerosity': 83, 'population': 83, 'average_specificity': 2.0843373493975905, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 16, 'reward': 1004.2204271968708, 'perf_time': 0.12402200000000008, 'numerosity': 1348, 'population': 812, 'average_specificity': 18.11053412462908, 'fraction_accuracy': 0.12535714285714286}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 7, 'reward': 1107.3640216671884, 'perf_time': 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error:591.6731476361124]acc: None\n", - "Cond:.#O#...O - Act:1 - effect:.OO..... - Num:1 [fit: 0.002, exp: 0.00, pred: 870.510, error:1647.7144892413003]acc: None\n", - "Cond:#.O.#..# - Act:7 - effect:....FO.. - Num:1 [fit: 0.016, exp: 0.00, pred: 961.244, error:130.18274558382836]acc: None\n", - "Cond:##..#OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.#OOO. - Act:3 - effect:....OOO. - Num:2 [fit: 0.029, exp: 0.00, pred: 1712.811, error:77.84018656925448]acc: None\n", - "Cond:#.#.#OO# - Act:7 - effect:....OOO. - Num:3 [fit: 0.032, exp: 406.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:###F#.#. - Act:3 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 1246.242, error:829.9372958520112]acc: None\n", - "Cond:#..#O#O. - Act:3 - effect:....OOO. - Num:3 [fit: 0.022, exp: 0.00, pred: 1413.811, error:318.58411037510245]acc: None\n", - "Cond:#...OO.. - Act:4 - effect:....OOO. - Num:3 [fit: 0.064, exp: 0.00, pred: 1221.388, error:321.40051658332544]acc: None\n", - "Cond:###O.O.# - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1375.992, error:558.4261778114585]acc: None\n", - "Cond:###.#.O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1225.703, error:703.5383346038936]acc: None\n", - "Cond:O...O..O - Act:2 - effect:O.O.O..O - Num:1 [fit: 0.001, exp: 0.00, pred: 1130.448, error:382.01736334774284]acc: None\n", - "Cond:O###.O.O - Act:5 - effect:O.O.O..O - Num:1 [fit: 0.001, exp: 0.00, pred: 1130.448, error:382.01736334774284]acc: None\n", - "Cond:..#.#O#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 0.00, pred: 1453.142, error:87.40187023957661]acc: None\n", - "Cond:....F.#O - Act:5 - effect:........ - Num:1 [fit: 0.000, exp: 0.00, pred: 1124.319, error:1122.58915243914]acc: None\n", - "Cond:##..OO.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.290, exp: 0.00, pred: 1239.699, error:53.818468015523706]acc: None\n", - "Cond:##.O#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1257.095, error:870.6672815606748]acc: None\n", - "Cond:#...O#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.#FF## - Act:5 - effect:F....... - Num:1 [fit: 0.001, exp: 0.00, pred: 1037.969, error:1460.975763111683]acc: None\n", - "Cond:.O...O.F - Act:3 - effect:..FFF.O. - Num:1 [fit: 0.002, exp: 0.00, pred: 1398.075, error:788.4620672090857]acc: None\n", - "Cond:.##.O.## - Act:0 - effect:.....OO. - Num:2 [fit: 0.015, exp: 0.00, pred: 915.030, error:251.8504176773527]acc: None\n", - "Cond:..#O.#O# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#O..O... - Act:4 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 1078.272, error:1932.0009957563316]acc: None\n", - "Cond:#.#..O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.065, exp: 0.00, pred: 1120.871, error:538.5933767760804]acc: None\n", - "Cond:##...#O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##..OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#...OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.086, exp: 0.00, pred: 1712.398, error:52.2968174983977]acc: None\n", - "Cond:O##F...O - Act:5 - effect:O.O.O..O - Num:1 [fit: 0.002, exp: 0.00, pred: 1109.204, error:943.3406478652846]acc: None\n", - "Cond:...#OO#. - Act:3 - effect:....OOO. - Num:7 [fit: 0.173, exp: 396.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", - "Cond:.OO..O#. - Act:7 - effect:.O.O...O - Num:1 [fit: 0.009, exp: 0.00, pred: 921.690, error:1012.2129429194255]acc: None\n", - "Cond:.#.#OO#. - Act:3 - effect:....OOO. - Num:3 [fit: 0.078, exp: 391.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", - "Cond:....OO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1292.545, error:588.7423004793769]acc: None\n", - "Cond:###.O.## - Act:6 - effect:..OO.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1307.561, error:890.4985991170431]acc: None\n", - "Cond:#.##..F# - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:..##..F# - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#..O#O## - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.O.#O.## - Act:0 - effect:OO...... - Num:2 [fit: 0.020, exp: 0.00, pred: 1100.405, error:28.686164794740478]acc: None\n", - "Cond:O##.O#.# - Act:3 - effect:.....O.. - Num:1 [fit: 0.007, exp: 0.00, pred: 1116.517, error:654.6447521276302]acc: None\n", - "Cond:.O####O. - Act:6 - effect:OO...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1245.488, error:749.8974713237016]acc: None\n", - "Cond:....OO.. - Act:1 - effect:......OO - Num:1 [fit: 0.021, exp: 0.00, pred: 1322.993, error:25.376463183913188]acc: None\n", - "Cond:..OO.F.# - Act:7 - effect:.......O - Num:1 [fit: 0.046, exp: 0.00, pred: 1197.124, error:732.8842404229987]acc: None\n", - "Cond:.#O#..F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1154.148, error:811.2300189020247]acc: None\n", - "Cond:.#..O##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1035.241, error:406.07397382479]acc: None\n", - "Cond:.####.O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1070.964, error:1099.353045352575]acc: None\n", - "Cond:#O..#.O# - Act:7 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##.#.O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1023.601, error:1212.0691337709113]acc: None\n", - "Cond:....#OOO - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:###O#O.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1093.278, error:305.6171029783959]acc: None\n", - "Cond:..#.#O.F - Act:3 - effect:.O...O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 986.143, error:1149.0428031010574]acc: None\n", - "Cond:####O#.O - Act:5 - effect:.O...... - Num:1 [fit: 0.004, exp: 0.00, pred: 1010.037, error:862.615517691796]acc: None\n", - "Cond:O...O#.O - Act:5 - effect:O.O.O..O - Num:1 [fit: 0.004, exp: 0.00, pred: 1010.037, error:862.615517691796]acc: None\n", - "Cond:..O#..F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1046.988, error:802.7140270558417]acc: None\n", - "Cond:##...O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 336.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:.####O.O - Act:5 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:###F##.O - Act:5 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 1017.279, error:992.3148403110738]acc: None\n", - "Cond:..O...#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 972.342, error:526.8414023106957]acc: None\n", - "Cond:#OOO.O.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1513.390, error:316.2505744698374]acc: None\n", - "Cond:..##.O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1341.420, error:856.740066900612]acc: None\n", - "Cond:#.#..O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 327.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:#..O.F.. - Act:3 - effect:.....OF. - Num:1 [fit: 0.006, exp: 0.00, pred: 1176.996, error:966.2011405236863]acc: None\n", - "Cond:#....... - Act:3 - effect:..OO.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1033.867, error:20.978531211787185]acc: None\n", - "Cond:O.###.#. - Act:3 - effect:.....OF. - Num:1 [fit: 0.021, exp: 0.00, pred: 1013.828, error:550.1928835185141]acc: None\n", - "Cond:#OOO#O#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:###.OO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.052, exp: 0.00, pred: 1056.944, error:22.50983175248189]acc: None\n", - "Cond:O..###O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 940.272, error:714.790654298766]acc: None\n", - "Cond:###O#O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1034.293, error:224.3069038578633]acc: None\n", - "Cond:.##O#.OO - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:###O##O# - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O#..#.O# - Act:1 - effect:OO.....O - Num:1 [fit: 0.003, exp: 0.00, pred: 862.805, error:469.37156404507925]acc: None\n", - "Cond:..###.O. - Act:6 - effect:.O...... - Num:1 [fit: 0.009, exp: 0.00, pred: 871.096, error:727.4231646460298]acc: None\n", - "Cond:#.OO.F.# - Act:7 - effect:.......O - Num:1 [fit: 0.005, exp: 0.00, pred: 1073.136, error:1253.3634244906325]acc: None\n", - "Cond:.O##FO.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.020, exp: 0.00, pred: 1381.419, error:447.1885461893281]acc: None\n", - "Cond:#...O##. - Act:2 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1340.913, error:65.86836408342623]acc: None\n", - "Cond:..O..#.# - Act:4 - effect:.....OF. - Num:1 [fit: 0.021, exp: 0.00, pred: 1142.244, error:10.777683666177097]acc: None\n", - "Cond:##.#O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##.O#.. - Act:5 - effect:.......O - Num:1 [fit: 0.012, exp: 0.00, pred: 1026.491, error:473.1368560343111]acc: None\n", - "Cond:.#.F.##. - Act:3 - effect:.....OF. - Num:1 [fit: 0.004, exp: 0.00, pred: 1028.913, error:730.1889282368737]acc: None\n", - "Cond:..O#.F.# - Act:7 - effect:.....OF. - Num:1 [fit: 0.056, exp: 0.00, pred: 1043.703, error:56.941928141147905]acc: None\n", - "Cond:..O...## - Act:7 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 937.254, error:43.25051690418307]acc: None\n", - "Cond:.##.FO#O - Act:1 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1056.651, error:922.2291868076169]acc: None\n", - "Cond:##..F..O - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1022.303, error:645.7145851666032]acc: None\n", - "Cond:#.#.O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##OO##F. - Act:7 - effect:...OO... - Num:1 [fit: 0.001, exp: 0.00, pred: 1236.669, error:279.96683656446635]acc: None\n", - "Cond:#...#F.# - Act:1 - effect:.OO..... - Num:1 [fit: 0.000, exp: 23.00, pred: 1032.311, error:6356.178042186581]acc: 0.2608695652173913\n", - "Cond:#.....#. - Act:3 - effect:OO.....O - Num:1 [fit: 0.005, exp: 0.00, pred: 1004.568, error:1088.9644399025865]acc: None\n", - "Cond:OO..O..O - Act:5 - effect:OO...... - Num:1 [fit: 0.001, exp: 0.00, pred: 1004.292, error:1151.631820108515]acc: None\n", - "Cond:#O.O.##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 931.790, error:749.3639915320946]acc: None\n", - "Cond:O###O#.# - Act:0 - effect:OO...... - Num:1 [fit: 0.011, exp: 0.00, pred: 750.301, error:482.433824467105]acc: None\n", - "Cond:.#...O.# - Act:2 - effect:.O...F.F - Num:1 [fit: 0.000, exp: 0.00, pred: 1190.696, error:1695.5100590454604]acc: None\n", - "Cond:.##.#OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1016.031, error:214.6011986904296]acc: None\n", - "Cond:..#..FF# - Act:4 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 976.558, error:787.2098183061604]acc: None\n", - "Cond:###O#.#O - Act:5 - effect:.....OOF - Num:1 [fit: 0.013, exp: 0.00, pred: 931.625, error:805.1566168038503]acc: None\n", - "Cond:..O..#.. - Act:7 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O.#..##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1045.330, error:502.0952265244017]acc: None\n", - "Cond:#OOO.#F# - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 872.772, error:249.32361657150005]acc: None\n", - "Cond:.O##..O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 955.580, error:1325.592537203126]acc: None\n", - "Cond:.##.O.## - Act:3 - effect:.O...... - Num:1 [fit: 0.020, exp: 0.00, pred: 733.325, error:28.278326875062632]acc: None\n", - "Cond:.###OOO. - Act:3 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:....OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:###.O### - Act:0 - effect:OO...... - Num:1 [fit: 0.004, exp: 0.00, pred: 962.613, error:1127.1987578896076]acc: None\n", - "Cond:O.#OO... - Act:7 - effect:...OO... - Num:1 [fit: 0.005, exp: 0.00, pred: 984.728, error:1544.7584297438507]acc: None\n", - "Cond:##..OO.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 1064.892, error:12.166061764886987]acc: None\n", - "Cond:#.#..... - Act:0 - effect:OO...... - Num:1 [fit: 0.009, exp: 0.00, pred: 805.080, error:131.90583494005463]acc: None\n", - "Cond:.#..#OO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#..#O.. - Act:4 - effect:....OOO. - Num:4 [fit: 0.156, exp: 229.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:.##....F - Act:7 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 838.451, error:164.77474804282448]acc: None\n", - "Cond:#..#.#O. - Act:0 - effect:.....OF. - Num:1 [fit: 0.003, exp: 0.00, pred: 915.151, error:764.5866828411978]acc: None\n", - "Cond:.#...OOO - Act:4 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#O..F## - Act:7 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.##O#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 929.654, error:514.1286320878355]acc: None\n", - "Cond:..O..##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 818.559, error:517.9644260116796]acc: None\n", - "Cond:#..#OOO# - Act:3 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1134.909, error:43.4023400593814]acc: None\n", - "Cond:#.O##O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 964.712, error:709.1677494475464]acc: None\n", - "Cond:..#.##O. - Act:0 - effect:.....OF. - Num:1 [fit: 0.009, exp: 0.00, pred: 971.716, error:371.8659699068131]acc: None\n", - "Cond:.##.#O.. - Act:4 - effect:....OOO. - Num:2 [fit: 0.111, exp: 227.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:##O#.O## - Act:6 - effect:....OOO. - Num:2 [fit: 0.014, exp: 0.00, pred: 863.289, error:533.4398996516292]acc: None\n", - "Cond:###O#O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.005, exp: 0.00, pred: 1092.244, error:868.6700609672657]acc: None\n", - "Cond:#O##.O## - Act:7 - effect:OO...... - Num:1 [fit: 0.005, exp: 0.00, pred: 1120.782, error:818.0517726401399]acc: None\n", - "Cond:..##.O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1161.680, error:164.01551824108128]acc: None\n", - "Cond:..O##F## - Act:4 - effect:.O...... - Num:1 [fit: 0.008, exp: 0.00, pred: 1366.903, error:470.1862232563487]acc: None\n", - "Cond:.#..O#.# - Act:0 - effect:.OO..... - Num:2 [fit: 0.072, exp: 0.00, pred: 1030.397, error:49.11370807180829]acc: None\n", - "Cond:#.#O.O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1253.428, error:664.2824684019367]acc: None\n", - "Cond:##.O.O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.O#.O## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1266.500, error:941.5112091758672]acc: None\n", - "Cond:..#.#.#. - Act:1 - effect:...O.... - Num:1 [fit: 0.021, exp: 0.00, pred: 403.308, error:75.41351828942646]acc: None\n", - "Cond:###.OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1513.666, error:95.49879982542083]acc: None\n", - "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:.#..#OO. - Act:6 - effect:....OOO. - Num:2 [fit: 0.024, exp: 0.00, pred: 1447.589, error:119.16049898953861]acc: None\n", - "Cond:.#OO##O# - Act:6 - effect:.OOO.... - Num:2 [fit: 0.021, exp: 0.00, pred: 979.937, error:37.96349960615717]acc: None\n", - "Cond:##...F.# - Act:1 - effect:.....F.. - Num:1 [fit: 0.004, exp: 20.00, pred: 1029.884, error:8515.654258146047]acc: 0.25\n", - "Cond:..#.#.#. - Act:4 - effect:OO...... - Num:1 [fit: 0.036, exp: 0.00, pred: 930.431, error:58.07335962502272]acc: None\n", - "Cond:##...#O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1136.997, error:30.99607865271706]acc: None\n", - "Cond:.#...OOO - Act:0 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 893.857, error:571.3297931467508]acc: None\n", - "Cond:.##.#OOF - Act:1 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 22.00, pred: 980.488, error:6061.912172086413]acc: 0.045454545454545456\n", - "Cond:###.OO#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1300.412, error:57.42400185589459]acc: None\n", - "Cond:#.#.#... - Act:4 - effect:..OO.... - Num:1 [fit: 0.009, exp: 0.00, pred: 928.978, error:830.4180045982635]acc: None\n", - "Cond:##.#OOO# - Act:3 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1381.904, error:58.24584308313]acc: None\n", - "Cond:.O.#O..# - Act:1 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##O#O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.039, exp: 0.00, pred: 1276.719, error:457.35769574422096]acc: None\n", - "Cond:.O###O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.039, exp: 0.00, pred: 1276.719, error:457.35769574422096]acc: None\n", - "Cond:.##.O..# - Act:0 - effect:.OO..... - Num:2 [fit: 0.014, exp: 0.00, pred: 1042.561, error:294.0139442634239]acc: None\n", - "Cond:#.###O#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.011, exp: 0.00, pred: 903.428, error:173.00392343184922]acc: None\n", - "Cond:#....... - Act:5 - effect:.....OF. - Num:1 [fit: 0.005, exp: 0.00, pred: 974.418, error:1263.1907452281937]acc: None\n", - "Cond:#O.F.##. - Act:4 - effect:..OO.... - Num:1 [fit: 0.013, exp: 0.00, pred: 1486.255, error:534.7820983971193]acc: None\n", - "Cond:##O.#..O - Act:5 - effect:....FO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 910.770, error:511.65774802786757]acc: None\n", - "Cond:.#..OO.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1223.005, error:246.92583611372223]acc: None\n", - "Cond:O..#.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1701.050, error:157.55757826416934]acc: None\n", - "Cond:..O##O## - Act:2 - effect:.......O - Num:1 [fit: 0.024, exp: 0.00, pred: 1008.903, error:448.2334731752781]acc: None\n", - "Cond:.#O#.#F# - Act:6 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1093.221, error:670.5491305483995]acc: None\n", - "Cond:###O##OF - Act:2 - effect:......OO - Num:1 [fit: 0.026, exp: 0.00, pred: 1039.096, error:62.98328176762753]acc: None\n", - "Cond:OO..#O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 898.662, error:1194.554506363608]acc: None\n", - "Cond:#.####O. - Act:3 - effect:...O.... - Num:1 [fit: 0.035, exp: 0.00, pred: 977.343, error:77.2621594002525]acc: None\n", - "Cond:.#OO#O## - Act:6 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1168.518, error:1250.5173020420696]acc: None\n", - "Cond:.##O##F. - Act:6 - effect:...O.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1353.501, error:704.3150709895405]acc: None\n", - "Cond:##O#.OO# - Act:2 - effect:......OO - Num:1 [fit: 0.007, exp: 0.00, pred: 1109.436, error:536.0384564808498]acc: None\n", - "Cond:..##.#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 351.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:###O##F# - Act:7 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1165.517, error:829.8050736029929]acc: None\n", - "Cond:#.#.#O.. - Act:4 - effect:....OOO. - Num:11 [fit: 0.437, exp: 216.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:##..OO.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O#O###.. - Act:3 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 0.00, pred: 998.944, error:763.5809422360993]acc: None\n", - "Cond:#.##O.#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.006, exp: 0.00, pred: 972.454, error:1209.2678892441409]acc: None\n", - "Cond:####OO#. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 69.00, pred: 1047.171, error:4583.402553013802]acc: 0.4492753623188406\n", - "Cond:..#.#O.. - Act:4 - effect:....OOO. - Num:2 [fit: 0.078, exp: 211.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:..###OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 342.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:.#..O### - Act:0 - effect:.OO..... - Num:1 [fit: 0.003, exp: 0.00, pred: 1009.244, error:945.8120367414008]acc: None\n", - "Cond:.#.#.O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1268.151, error:703.36509256923]acc: None\n", - "Cond:#...O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1197.999, error:825.167592832555]acc: None\n", - "Cond:#O.##O.# - Act:3 - effect:OO.....O - Num:1 [fit: 0.003, exp: 0.00, pred: 1415.370, error:228.17216904426186]acc: None\n", - "Cond:####O.#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#O.#O..# - Act:3 - effect:OO.....O - Num:2 [fit: 0.002, exp: 0.00, pred: 1181.242, error:415.0973867984543]acc: None\n", - "Cond:####F..# - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##...F#. - Act:1 - effect:.....F.. - Num:1 [fit: 0.001, exp: 15.00, pred: 1029.068, error:9719.508217033717]acc: 0.13333333333333333\n", - "Cond:..#.#OOF - Act:1 - effect:.O...O.. - Num:1 [fit: 0.000, exp: 21.00, pred: 979.546, error:5094.131113166537]acc: 0.0\n", - "Cond:.###.O.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:####.#O. - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 0.00, pred: 972.120, error:550.7469166679264]acc: None\n", - "Cond:O##F#### - Act:3 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.#.OO#. - Act:6 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 1247.081, error:893.0958731850169]acc: None\n", - "Cond:.#.#O#.O - Act:0 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##.##O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1218.710, error:36.0999348841262]acc: None\n", - "Cond:O####O.. - Act:3 - effect:...OO... - Num:1 [fit: 0.061, exp: 0.00, pred: 1506.588, error:410.06151698988685]acc: None\n", - "Cond:##.#O..O - Act:1 - effect:....FO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1181.571, error:1107.3681277358278]acc: None\n", - "Cond:#O.#O.#O - Act:4 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 1021.800, error:1269.2494760894838]acc: None\n", - "Cond:.##.OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O###OO.# - Act:3 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.FO#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 32.00, pred: 1076.073, error:1089.2917565981948]acc: 0.15625\n", - "Cond:.#..OO.# - Act:7 - effect:.O..OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1019.834, error:1183.851075920574]acc: None\n", - "Cond:###..O#F - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 20.00, pred: 980.531, error:5265.246965455828]acc: 0.0\n", - "Cond:#.##O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1186.953, error:37.860823204333464]acc: None\n", - "Cond:.###.##F - Act:7 - effect:....OOO. - Num:2 [fit: 0.018, exp: 322.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:#.#..F.O - Act:3 - effect:....OOO. - Num:1 [fit: 0.030, exp: 0.00, pred: 905.262, error:286.27934222656575]acc: None\n", - "Cond:.OOO#... - Act:2 - effect:OO...... - Num:1 [fit: 0.007, exp: 21.00, pred: 940.345, error:9551.143908792434]acc: 0.0\n", - "Cond:##O###F# - Act:7 - effect:.OOO.... - Num:1 [fit: 0.011, exp: 0.00, pred: 1057.184, error:759.1516880929711]acc: None\n", - "Cond:##..OO#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1137.403, error:34.573048125231956]acc: None\n", - "Cond:###F.### - Act:5 - effect:OO.....O - Num:1 [fit: 0.007, exp: 0.00, pred: 988.926, error:576.6385820120499]acc: None\n", - "Cond:.###OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.052, exp: 300.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", - "Cond:...O#F## - Act:6 - effect:..OO.... - Num:1 [fit: 0.018, exp: 0.00, pred: 1069.607, error:46.812761319223604]acc: None\n", - "Cond:.O##O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 775.654, error:119.90554646370725]acc: None\n", - "Cond:.##O.F## - Act:7 - effect:.....F.. - Num:1 [fit: 0.002, exp: 0.00, pred: 945.915, error:1083.1149867686468]acc: None\n", - "Cond:....##F# - Act:4 - effect:......OO - Num:1 [fit: 0.003, exp: 103.00, pred: 1368.534, error:3153.2862143935286]acc: 0.47572815533980584\n", - "Cond:.#####.F - Act:7 - effect:.......O - Num:1 [fit: 0.019, exp: 0.00, pred: 1341.929, error:234.80242710497535]acc: None\n", - "Cond:.#...OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 318.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:####.OF# - Act:6 - effect:....OOO. - Num:2 [fit: 0.022, exp: 249.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:##...O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1213.371, error:1164.1810628654935]acc: None\n", - "Cond:###..OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1290.790, error:74.41226859335936]acc: None\n", - "Cond:#O##O.## - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##...O.O - Act:4 - effect:....OOO. - Num:2 [fit: 0.001, exp: 0.00, pred: 1136.548, error:955.4343547648631]acc: None\n", - "Cond:O#.F###. - Act:7 - effect:...OO... - Num:1 [fit: 0.002, exp: 0.00, pred: 1117.402, error:1211.5034252941382]acc: None\n", - "Cond:..O####. - Act:2 - effect:.....F.. - Num:1 [fit: 0.000, exp: 25.00, pred: 971.160, error:6393.7121924022285]acc: 0.08\n", - "Cond:O##.#O.O - Act:4 - effect:O......O - Num:1 [fit: 0.002, exp: 0.00, pred: 1104.331, error:629.8255807138653]acc: None\n", - "Cond:.....O#. - Act:1 - effect:.O...... - Num:3 [fit: 0.002, exp: 22.00, pred: 926.395, error:9731.598541633895]acc: 0.13636363636363635\n", - "Cond:.O.#O### - Act:3 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:..#..#.. - Act:5 - effect:....OOO. - Num:7 [fit: 0.365, exp: 325.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:###..OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 238.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:...#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1149.751, error:39.32850747625649]acc: None\n", - "Cond:.#...OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1085.168, error:431.45322213678213]acc: None\n", - "Cond:O#.O#.#. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.061, exp: 0.00, pred: 1159.109, error:439.64233462125304]acc: None\n", - "Cond:.####OO# - Act:6 - effect:.....OOF - Num:1 [fit: 0.095, exp: 49.00, pred: 933.318, error:3236.1384018544727]acc: 0.6122448979591837\n", - "Cond:#.O..### - Act:5 - effect:....FO.. - Num:1 [fit: 0.020, exp: 0.00, pred: 1770.615, error:42.92531866578943]acc: None\n", - "Cond:#O.OO.#. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.049, exp: 0.00, pred: 1232.650, error:766.4221524342124]acc: None\n", - "Cond:.#...O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1051.819, error:1081.5533476689016]acc: None\n", - "Cond:#.#.O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1253.513, error:259.8020440745543]acc: None\n", - "Cond:..OO##.# - Act:2 - effect:.....OF. - Num:3 [fit: 0.003, exp: 22.00, pred: 969.211, error:5476.468524347716]acc: 0.09090909090909091\n", - "Cond:##.#.F## - Act:3 - effect:.....OOF - Num:1 [fit: 0.000, exp: 23.00, pred: 1252.601, error:2371.4969366342984]acc: 0.21739130434782608\n", - "Cond:#OO##O## - Act:2 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 887.361, error:760.5945495509885]acc: None\n", - "Cond:#O#O#... - Act:0 - effect:.OOO.... - Num:2 [fit: 0.000, exp: 25.00, pred: 1030.815, error:4411.786000313852]acc: 0.2\n", - "Cond:O....##. - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 936.109, error:841.7903084571736]acc: None\n", - "Cond:O###O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 899.179, error:2.037945717961648]acc: None\n", - "Cond:.#.#OO.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.052, exp: 284.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", - "Cond:##O#O#.# - Act:5 - effect:...O.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1349.388, error:860.338571438274]acc: None\n", - "Cond:O.#####. - Act:5 - effect:.O...... - Num:1 [fit: 0.005, exp: 0.00, pred: 1079.526, error:1455.0142491802617]acc: None\n", - "Cond:.O##.#O# - Act:7 - effect:....FO.. - Num:1 [fit: 0.010, exp: 0.00, pred: 1062.789, error:908.1755681207281]acc: None\n", - "Cond:###O#.#O - Act:1 - effect:O......O - Num:1 [fit: 0.021, exp: 0.00, pred: 822.781, error:26.120633460353872]acc: None\n", - "Cond:##O#..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1021.601, error:262.6330811729328]acc: None\n", - "Cond:#.O#..#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 14.00, pred: 968.009, error:7636.359034938088]acc: 0.0\n", - "Cond:###O##O# - Act:2 - effect:......OO - Num:1 [fit: 0.009, exp: 0.00, pred: 974.747, error:888.9318642454855]acc: None\n", - "Cond:.O#O#.#. - Act:2 - effect:.OOO.... - Num:5 [fit: 0.025, exp: 15.00, pred: 945.849, error:8328.366070324544]acc: 0.26666666666666666\n", - "Cond:.OOO..## - Act:4 - effect:.......O - Num:1 [fit: 0.001, exp: 25.00, pred: 1128.044, error:9034.44271686875]acc: 0.24\n", - "Cond:####.#F# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 231.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:.OOO.### - Act:4 - effect:......OO - Num:2 [fit: 0.000, exp: 24.00, pred: 1128.847, error:3570.5421240399155]acc: 0.4166666666666667\n", - "Cond:.#O##O## - Act:3 - effect:OO.....O - Num:2 [fit: 0.024, exp: 0.00, pred: 1133.023, error:84.88954122388782]acc: None\n", - "Cond:.#O#...O - Act:6 - effect:.OOO.... - Num:1 [fit: 0.009, exp: 0.00, pred: 916.294, error:289.5283432959402]acc: None\n", - "Cond:.O##.O## - Act:7 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 942.460, error:1309.5593472876953]acc: None\n", - "Cond:....#..# - Act:7 - effect:.......O - Num:1 [fit: 0.007, exp: 19.00, pred: 909.151, error:8717.296543212979]acc: 0.15789473684210525\n", - "Cond:......#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1063.407, error:1045.7580299090591]acc: None\n", - "Cond:#....#.. - Act:5 - effect:....OOO. - Num:5 [fit: 0.325, exp: 314.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:##.#.O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 229.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:###.#F#O - Act:6 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O...###. - Act:7 - effect:OO.....O - Num:1 [fit: 0.001, exp: 0.00, pred: 984.142, error:760.3215295171951]acc: None\n", - "Cond:.#.#.F#. - Act:1 - effect:...OO... - Num:1 [fit: 0.001, exp: 12.00, pred: 1049.342, error:8029.785487746452]acc: 0.08333333333333333\n", - "Cond:#..#.... - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 17.00, pred: 1036.303, error:10134.2562084032]acc: 0.0\n", - "Cond:#.#.OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1307.307, error:379.1873498518728]acc: None\n", - "Cond:###.#O#O - Act:4 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 1618.104, error:114.78010332816353]acc: None\n", - "Cond:##.#OO.# - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 24.00, pred: 1012.854, error:4167.558598157433]acc: 0.2916666666666667\n", - "Cond:.....O.# - Act:5 - effect:....OOO. - Num:3 [fit: 0.423, exp: 0.00, pred: 1422.851, error:83.66002322219938]acc: None\n", - "Cond:........ - Act:1 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 1148.456, error:0.0]acc: None\n", - "Cond:O#.##O#. - Act:0 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1073.559, error:1014.9064474411622]acc: None\n", - "Cond:###.OO.O - Act:4 - effect:..OO.... - Num:1 [fit: 0.005, exp: 0.00, pred: 1032.180, error:201.3285240770358]acc: None\n", - "Cond:.O#.#O.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.005, exp: 0.00, pred: 1071.241, error:688.314472379484]acc: None\n", - "Cond:.O#..##O - Act:0 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1176.583, error:639.3652594239725]acc: None\n", - "Cond:##..O.#. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1301.807, error:1160.6155376280176]acc: None\n", - "Cond:#...#F#. - Act:2 - effect:.....F.. - Num:1 [fit: 0.012, exp: 15.00, pred: 1026.661, error:8358.672035887259]acc: 0.3333333333333333\n", - "Cond:.#..OO.# - Act:1 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 962.685, error:1191.8858311548393]acc: None\n", - "Cond:O#.OO#.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 904.868, error:279.65432478616634]acc: None\n", - "Cond:O#O#.##. - Act:7 - effect:.......O - Num:1 [fit: 0.016, exp: 0.00, pred: 1088.227, error:352.2148094817006]acc: None\n", - "Cond:##..OO.. - Act:3 - effect:....OOO. - Num:3 [fit: 0.008, exp: 0.00, pred: 1239.852, error:67.33541676300119]acc: None\n", - "Cond:#.O..O#. - Act:1 - effect:.O...O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 912.289, error:898.5363880938601]acc: None\n", - "Cond:..#..OO. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1422.601, error:83.83675611846584]acc: None\n", - "Cond:..OO#### - Act:3 - effect:.....OOF - Num:1 [fit: 0.000, exp: 25.00, pred: 1147.681, error:2371.6761143163076]acc: 0.08\n", - "Cond:.#..#O.. - Act:1 - effect:...OO... - Num:2 [fit: 0.001, exp: 15.00, pred: 874.968, error:8489.480539792336]acc: 0.0\n", - "Cond:.O.##O.. - Act:7 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#O##.F# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#..#F## - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 21.00, pred: 1063.803, error:7576.750395072394]acc: 0.14285714285714285\n", - "Cond:####.O.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1176.870, error:267.18385858058247]acc: None\n", - "Cond:#####O.O - Act:7 - effect:OO.....O - Num:1 [fit: 0.021, exp: 0.00, pred: 775.400, error:27.630181577521896]acc: None\n", - "Cond:O###O.## - Act:5 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#O#O.##O - Act:5 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.#OO## - Act:2 - effect:.....OF. - Num:1 [fit: 0.006, exp: 21.00, pred: 1014.009, error:6506.79511269634]acc: 0.19047619047619047\n", - "Cond:O##.#.#. - Act:0 - effect:OO...... - Num:1 [fit: 0.002, exp: 24.00, pred: 857.688, error:6821.283418738479]acc: 0.375\n", - "Cond:#####O#F - Act:2 - effect:......OO - Num:3 [fit: 0.009, exp: 13.00, pred: 946.391, error:6025.758044946096]acc: 0.23076923076923078\n", - "Cond:.O#O#.## - Act:4 - effect:.......O - Num:1 [fit: 0.003, exp: 16.00, pred: 1117.235, error:8171.291984742688]acc: 0.1875\n", - "Cond:##..OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1001.980, error:152.71141754622786]acc: None\n", - "Cond:#.#.#.#. - Act:3 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#O#F#### - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##..##O. - Act:0 - effect:....OOO. - Num:1 [fit: 0.102, exp: 0.00, pred: 1186.670, error:88.59267966867945]acc: None\n", - "Cond:.OO.#.## - Act:2 - effect:.OO..... - Num:2 [fit: 0.014, exp: 21.00, pred: 953.744, error:6467.789179675442]acc: 0.2857142857142857\n", - "Cond:.....#.. - Act:2 - effect:OO...... - Num:1 [fit: 0.002, exp: 10.00, pred: 1009.894, error:8560.897664249467]acc: 0.0\n", - "Cond:#O##O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1131.787, error:929.218849854554]acc: None\n", - "Cond:###..O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 226.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:...###OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 11.00, pred: 920.050, error:8733.98821177772]acc: 0.0\n", - "Cond:..#.#OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 276.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:.O.#.... - Act:0 - effect:...OO... - Num:3 [fit: 0.000, exp: 20.00, pred: 936.856, error:9685.483228709065]acc: 0.0\n", - "Cond:.#...F## - Act:2 - effect:OO...... - Num:1 [fit: 0.003, exp: 9.00, pred: 1027.070, error:8234.678884458044]acc: 0.0\n", - "Cond:.###O.#. - Act:1 - effect:OO...... - Num:2 [fit: 0.001, exp: 19.00, pred: 1063.534, error:8723.74925557507]acc: 0.10526315789473684\n", - "Cond:#.###F.. - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 19.00, pred: 1066.536, error:6777.381512603277]acc: 0.05263157894736842\n", - "Cond:#O.#O... - Act:6 - effect:...OO... - Num:1 [fit: 0.005, exp: 0.00, pred: 1056.789, error:1357.7561687820216]acc: None\n", - "Cond:.#..OO.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1284.352, error:71.47181342268837]acc: None\n", - "Cond:..OO..#. - Act:3 - effect:.......O - Num:1 [fit: 0.003, exp: 24.00, pred: 1146.215, error:5886.829756550145]acc: 0.0\n", - "Cond:.##FO##. - Act:2 - effect:OO.....O - Num:3 [fit: 0.031, exp: 18.00, pred: 1018.944, error:9187.832221158535]acc: 0.16666666666666666\n", - "Cond:O#.#O#.# - Act:5 - effect:.O...... - Num:1 [fit: 0.003, exp: 0.00, pred: 1006.093, error:1066.3295755822382]acc: None\n", - "Cond:.O..###O - Act:4 - effect:..OO.... - Num:1 [fit: 0.016, exp: 0.00, pred: 1034.413, error:360.2779666190165]acc: None\n", - "Cond:......#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:..OO#... - Act:3 - effect:.......O - Num:1 [fit: 0.003, exp: 22.00, pred: 1146.519, error:5119.998679017744]acc: 0.0\n", - "Cond:#######F - Act:2 - effect:......OO - Num:1 [fit: 0.004, exp: 9.00, pred: 902.003, error:6056.060058693509]acc: 0.2222222222222222\n", - "Cond:OO##.#.. - Act:0 - effect:OO...... - Num:1 [fit: 0.001, exp: 19.00, pred: 850.793, error:5142.9794233053]acc: 0.42105263157894735\n", - "Cond:#O.##O#F - Act:2 - effect:......OO - Num:1 [fit: 0.004, exp: 0.00, pred: 1094.524, error:971.1088582731361]acc: None\n", - "Cond:.O#O#.#. - Act:0 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 19.00, pred: 1029.243, error:3708.798639406525]acc: 0.3157894736842105\n", - "Cond:.#.#.O#. - Act:0 - effect:....OOO. - Num:2 [fit: 0.012, exp: 67.00, pred: 1070.650, error:2160.9847580697715]acc: 0.6567164179104478\n", - "Cond:.#.#.O.# - Act:0 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#...#... - Act:7 - effect:.O...... - Num:1 [fit: 0.008, exp: 0.00, pred: 1069.668, error:1508.9817266422792]acc: None\n", - "Cond:.O#O###. - Act:2 - effect:.....OOF - Num:1 [fit: 0.084, exp: 6.00, pred: 894.728, error:5770.205625715598]acc: 0.3333333333333333\n", - "Cond:OO#.##.. - Act:0 - effect:OO...... - Num:1 [fit: 0.002, exp: 18.00, pred: 850.500, error:5722.201222666856]acc: 0.4444444444444444\n", - "Cond:OO#...#. - Act:0 - effect:.....OOF - Num:1 [fit: 0.002, exp: 18.00, pred: 850.500, error:6861.22483209687]acc: 0.0\n", - "Cond:.#.#.O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.103, exp: 46.00, pred: 933.324, error:3506.6951633507415]acc: 0.4782608695652174\n", - "Cond:#..#.O#. - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 12.00, pred: 924.291, error:8854.906396180486]acc: 0.0\n", - "Cond:O#..#O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 896.143, error:244.6402517932314]acc: None\n", - "Cond:....OO#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.O.O.#.. - Act:0 - effect:OO.....O - Num:1 [fit: 0.001, exp: 0.00, pred: 1060.214, error:1397.0872476820637]acc: None\n", - "Cond:..#FO### - Act:2 - effect:OO.....O - Num:1 [fit: 0.001, exp: 16.00, pred: 1015.376, error:9051.084453573203]acc: 0.0625\n", - "Cond:.....#.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.023, exp: 12.00, pred: 1049.342, error:9618.194319746453]acc: 0.0\n", - "Cond:..###O#O - Act:7 - effect:...OO... - Num:1 [fit: 0.015, exp: 0.00, pred: 1117.466, error:766.6614226048357]acc: None\n", - "Cond:...OO..# - Act:7 - effect:.....F.. - Num:3 [fit: 0.376, exp: 73.00, pred: 1113.593, error:269.6657456943022]acc: 0.821917808219178\n", - "Cond:###O.##O - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 982.327, error:782.5501071232829]acc: None\n", - "Cond:#..###O. - Act:4 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1165.763, error:293.6334388110495]acc: None\n", - "Cond:.#...O.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.426, exp: 0.00, pred: 1118.494, error:31.556817384914076]acc: None\n", - "Cond:.#O##O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##...O#O - Act:0 - effect:OO.....O - Num:1 [fit: 0.015, exp: 0.00, pred: 854.738, error:470.99578246073844]acc: None\n", - "Cond:.O###.#O - Act:7 - effect:.O...... - Num:1 [fit: 0.006, exp: 0.00, pred: 956.566, error:776.3966998598792]acc: None\n", - "Cond:..OO...# - Act:4 - effect:......OO - Num:3 [fit: 0.064, exp: 48.00, pred: 1151.838, error:4817.885635846153]acc: 0.5833333333333334\n", - "Cond:##.###O# - Act:2 - effect:OO...... - Num:2 [fit: 0.000, exp: 18.00, pred: 964.871, error:6325.735235005553]acc: 0.16666666666666666\n", - "Cond:#.#.##F# - Act:5 - effect:.....OF. - Num:1 [fit: 0.030, exp: 95.00, pred: 948.221, error:4802.898969221626]acc: 0.6631578947368421\n", - "Cond:.##.#O.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.025, exp: 6.00, pred: 1024.032, error:2441.176714447477]acc: 0.5\n", - "Cond:##.O.##. - Act:2 - effect:...OO... - Num:1 [fit: 0.002, exp: 13.00, pred: 1007.329, error:6254.719680946342]acc: 0.07692307692307693\n", - "Cond:.###.OO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 841.398, error:599.0736656223875]acc: None\n", - "Cond:O#..###. - Act:0 - effect:OO.....O - Num:1 [fit: 0.001, exp: 16.00, pred: 857.303, error:6853.395773564696]acc: 0.0625\n", - "Cond:O.#.#..# - Act:0 - effect:O......O - Num:1 [fit: 0.019, exp: 37.00, pred: 1028.748, error:3847.9798068190203]acc: 0.40540540540540543\n", - "Cond:##.#OO## - Act:4 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 57.00, pred: 1047.170, error:3907.877596548241]acc: 0.543859649122807\n", - "Cond:.O.#.##. - Act:0 - effect:.......O - Num:1 [fit: 0.008, exp: 16.00, pred: 937.529, error:5525.74347549681]acc: 0.3125\n", - "Cond:##...OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.033, exp: 223.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:.##O##O# - Act:6 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 791.906, error:965.327013681102]acc: None\n", - "Cond:.....##F - Act:2 - effect:.OOO.... - Num:1 [fit: 0.106, exp: 5.00, pred: 1010.040, error:5384.854890251749]acc: 0.2\n", - "Cond:.#####OF - Act:2 - effect:......OO - Num:1 [fit: 0.003, exp: 5.00, pred: 1010.040, error:3224.854890251748]acc: 0.4\n", - "Cond:#O#...#F - Act:7 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##.OO#F - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1052.891, error:16.301000973399304]acc: None\n", - "Cond:.....O.. - Act:5 - effect:....OOO. - Num:5 [fit: 0.363, exp: 0.00, pred: 1176.793, error:32.512296442912294]acc: None\n", - "Cond:O#.#O#.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.016, exp: 0.00, pred: 897.449, error:441.0635530135107]acc: None\n", - "Cond:.#.F###. - Act:2 - effect:...FOO.. - Num:2 [fit: 0.043, exp: 9.00, pred: 1009.545, error:7765.241836811662]acc: 0.1111111111111111\n", - "Cond:.#.FO### - Act:2 - effect:OO.....O - Num:1 [fit: 0.004, exp: 9.00, pred: 1009.545, error:6560.953836811662]acc: 0.1111111111111111\n", - "Cond:##...O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 1151.187, error:132.66421236771774]acc: None\n", - "Cond:#..###F# - Act:5 - effect:.....OF. - Num:2 [fit: 0.009, exp: 90.00, pred: 948.221, error:4493.86813516331]acc: 0.5888888888888889\n", - "Cond:.#.O.O#. - Act:1 - effect:..OO.... - Num:1 [fit: 0.007, exp: 0.00, pred: 940.499, error:342.8949450838872]acc: None\n", - "Cond:O..####. - Act:0 - effect:.....F.. - Num:1 [fit: 0.017, exp: 0.00, pred: 1229.109, error:209.3114901886919]acc: None\n", - "Cond:.O#O..## - Act:1 - effect:..OO.... - Num:1 [fit: 0.086, exp: 6.00, pred: 893.929, error:4770.082504109861]acc: 0.16666666666666666\n", - "Cond:O.#.#.#O - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 13.00, pred: 884.097, error:8209.072561856661]acc: 0.0\n", - "Cond:##O..... - Act:1 - effect:.OO..... - Num:1 [fit: 0.002, exp: 24.00, pred: 957.386, error:2965.3242697984847]acc: 0.5833333333333334\n", - "Cond:#..#..O# - Act:7 - effect:......OO - Num:1 [fit: 0.005, exp: 31.00, pred: 1150.489, error:3009.5830275008134]acc: 0.25806451612903225\n", - "Cond:.##O.#.. - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 24.00, pred: 900.959, error:7587.751645673359]acc: 0.041666666666666664\n", - "Cond:##.#.OF. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 221.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:##.O.... - Act:2 - effect:...O.... - Num:2 [fit: 0.081, exp: 11.00, pred: 1006.733, error:8579.125112063844]acc: 0.2727272727272727\n", - "Cond:####.O#F - Act:3 - effect:.......O - Num:3 [fit: 0.017, exp: 75.00, pred: 1140.138, error:4443.863756411878]acc: 0.6\n", - "Cond:.OO##.#. - Act:2 - effect:OO...... - Num:1 [fit: 0.010, exp: 16.00, pred: 942.001, error:6265.789508399891]acc: 0.25\n", - "Cond:#.#.###. - Act:2 - effect:.....OF. - Num:1 [fit: 0.005, exp: 36.00, pred: 1045.850, error:4067.5352741501056]acc: 0.3055555555555556\n", - "Cond:#.#.O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1207.863, error:39.195610020814875]acc: None\n", - "Cond:O.####.# - Act:3 - effect:.....OF. - Num:1 [fit: 0.001, exp: 43.00, pred: 1261.457, error:4070.0098377442073]acc: 0.4883720930232558\n", - "Cond:O#..#F.# - Act:3 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.##.O.# - Act:1 - effect:.OO..... - Num:1 [fit: 0.003, exp: 0.00, pred: 1215.669, error:1531.1879395285355]acc: None\n", - "Cond:#..#OOO. - Act:3 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1250.563, error:45.139962864878655]acc: None\n", - "Cond:#O#O..## - Act:7 - effect:.....OOF - Num:1 [fit: 0.001, exp: 27.00, pred: 2049.631, error:1678.8381281427182]acc: 0.25925925925925924\n", - "Cond:.O.#.#.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.009, exp: 67.00, pred: 1894.529, error:550.1482358605605]acc: 0.7014925373134329\n", - "Cond:.#.###.O - Act:3 - effect:OO...... - Num:1 [fit: 0.000, exp: 22.00, pred: 1212.852, error:5746.761413158551]acc: 0.045454545454545456\n", - "Cond:#.####OO - Act:6 - effect:.....OOF - Num:1 [fit: 0.005, exp: 36.00, pred: 978.032, error:2830.8684347192475]acc: 0.4166666666666667\n", - "Cond:..#.#..# - Act:0 - effect:.OOO.... - Num:1 [fit: 0.008, exp: 20.00, pred: 1245.637, error:6765.218248784539]acc: 0.1\n", - "Cond:.#...#.. - Act:5 - effect:....OOO. - Num:10 [fit: 0.692, exp: 417.00, pred: 1225.169, error:248.2805850823554]acc: 0.9256594724220624\n", - "Cond:#.#..#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 283.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:.#..OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1111.853, error:37.90975355208421]acc: None\n", - "Cond:###..O#. - Act:1 - effect:.....OF. - Num:1 [fit: 0.011, exp: 8.00, pred: 879.937, error:8136.584696373757]acc: 0.125\n", - "Cond:..#..O.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.015, exp: 0.00, pred: 986.386, error:1198.3295749210704]acc: None\n", - "Cond:.O...##. - Act:0 - effect:.......O - Num:1 [fit: 0.033, exp: 12.00, pred: 931.021, error:5575.988477887693]acc: 0.25\n", - "Cond:.#..#... - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 12.00, pred: 1156.305, error:7780.24041537731]acc: 0.0\n", - "Cond:.###O#O# - Act:3 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1088.083, error:44.77071679352069]acc: None\n", - "Cond:#..##O.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 25.00, pred: 960.622, error:3421.626114607917]acc: 0.36\n", - "Cond:#.O.###. - Act:2 - effect:.....F.. - Num:2 [fit: 0.001, exp: 0.00, pred: 1231.565, error:740.9353479898393]acc: None\n", - "Cond:.#O#.O## - Act:1 - effect:.OOO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 1032.501, error:522.6503829218943]acc: None\n", - "Cond:#..O#F#. - Act:2 - effect:.....F.. - Num:1 [fit: 0.002, exp: 0.00, pred: 1144.525, error:1222.5213098990876]acc: None\n", - "Cond:.##O.F#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:...#.F## - Act:0 - effect:.......O - Num:1 [fit: 0.002, exp: 9.00, pred: 1105.135, error:8027.867406869053]acc: 0.0\n", - "Cond:.O..#.#. - Act:0 - effect:.......O - Num:1 [fit: 0.004, exp: 10.00, pred: 908.670, error:5670.271644041398]acc: 0.2\n", - "Cond:.#O.#..# - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 7.00, pred: 858.255, error:7110.2418365759895]acc: 0.0\n", - "Cond:#.O#.O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1570.376, error:234.89592299834823]acc: None\n", - "Cond:#.O#.O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1146.145, error:454.1831616467405]acc: None\n", - "Cond:####O### - Act:4 - effect:OO.....O - Num:1 [fit: 0.005, exp: 74.00, pred: 1026.373, error:3875.197274471728]acc: 0.10810810810810811\n", - "Cond:O..#.F.# - Act:3 - effect:O......O - Num:1 [fit: 0.004, exp: 0.00, pred: 1092.201, error:306.4644654474623]acc: None\n", - "Cond:#OO..... - Act:1 - effect:.OO..... - Num:1 [fit: 0.006, exp: 20.00, pred: 957.265, error:2506.545444965662]acc: 0.65\n", - "Cond:.OO..### - Act:2 - effect:OO...... - Num:2 [fit: 0.002, exp: 12.00, pred: 933.967, error:6134.522638733617]acc: 0.16666666666666666\n", - "Cond:O...#.#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1212.195, error:549.264601459175]acc: None\n", - "Cond:..#F###. - Act:2 - effect:OO.....O - Num:1 [fit: 0.005, exp: 7.00, pred: 989.777, error:7013.11585548437]acc: 0.0\n", - "Cond:.#..##.O - Act:7 - effect:.OO..... - Num:1 [fit: 0.015, exp: 6.00, pred: 959.286, error:3714.1502632249276]acc: 0.3333333333333333\n", - "Cond:#..F#O.. - Act:5 - effect:....OOO. - Num:4 [fit: 0.052, exp: 17.00, pred: 1074.928, error:2421.304164406746]acc: 0.23529411764705882\n", - "Cond:O##.#F## - Act:7 - effect:...FOO.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1027.066, error:682.5119003527898]acc: None\n", - "Cond:.#.O.### - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 9.00, pred: 1033.085, error:8195.347572017981]acc: 0.0\n", - "Cond:.O#..##. - Act:2 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 16.00, pred: 909.757, error:7451.677856406234]acc: 0.0625\n", - "Cond:O...O... - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1065.197, error:643.162488784322]acc: None\n", - "Cond:.O#O..#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.012, exp: 1.00, pred: 841.502, error:1800.0]acc: 0.0\n", - "Cond:.O#O#... - Act:2 - effect:.....OOF - Num:1 [fit: 0.012, exp: 1.00, pred: 841.502, error:1600.0]acc: 0.0\n", - "Cond:..##OO.. - Act:4 - effect:OO...... - Num:1 [fit: 0.003, exp: 56.00, pred: 1047.171, error:2781.8296713777454]acc: 0.23214285714285715\n", - "Cond:O##.F#.# - Act:3 - effect:O......O - Num:1 [fit: 0.003, exp: 0.00, pred: 1103.218, error:648.7735131315318]acc: None\n", - "Cond:##.#.F.# - Act:1 - effect:.....F.. - Num:1 [fit: 0.009, exp: 5.00, pred: 1008.599, error:5876.987299821381]acc: 0.0\n", - "Cond:O.#.#... - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1076.793, error:754.0843658338299]acc: None\n", - "Cond:O#.####O - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 24.00, pred: 977.785, error:5479.632449687706]acc: 0.041666666666666664\n", - "Cond:.O#.#.## - Act:2 - effect:OO...... - Num:3 [fit: 0.015, exp: 14.00, pred: 908.246, error:6550.69023223451]acc: 0.14285714285714285\n", - "Cond:O#.#.O.# - Act:3 - effect:O......O - Num:1 [fit: 0.005, exp: 0.00, pred: 1159.774, error:555.6545574767681]acc: None\n", - "Cond:.....#.# - Act:7 - effect:....FO.. - Num:1 [fit: 0.002, exp: 52.00, pred: 1268.250, error:6795.152125820821]acc: 0.3269230769230769\n", - "Cond:###O.O#. - Act:2 - effect:......OO - Num:1 [fit: 0.009, exp: 0.00, pred: 1122.699, error:869.3543599420655]acc: None\n", - "Cond:#.###... - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 16.00, pred: 948.858, error:7161.262966456474]acc: 0.0625\n", - "Cond:..#O#... - Act:3 - effect:...OO... - Num:2 [fit: 0.151, exp: 58.00, pred: 1234.455, error:2250.1608840581725]acc: 0.5344827586206896\n", - "Cond:#O...##. - Act:0 - effect:.......O - Num:2 [fit: 0.000, exp: 21.00, pred: 878.038, error:6214.440586184976]acc: 0.09523809523809523\n", - "Cond:.O..###. - Act:7 - effect:.OO..... - Num:1 [fit: 0.000, exp: 19.00, pred: 1076.732, error:8187.071025752364]acc: 0.0\n", - "Cond:...##..# - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 14.00, pred: 920.966, error:9224.092882824807]acc: 0.0\n", - "Cond:#O.#O.#. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 989.542, error:1503.3375897588335]acc: None\n", - "Cond:#.#..#.# - Act:0 - effect:O......O - Num:1 [fit: 0.004, exp: 61.00, pred: 1086.034, error:4483.733345606549]acc: 0.4098360655737705\n", - "Cond:..##.F## - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 19.00, pred: 1162.723, error:4799.6932441188055]acc: 0.21052631578947367\n", - "Cond:O#..#.#O - Act:2 - effect:......OO - Num:1 [fit: 0.002, exp: 22.00, pred: 975.885, error:5884.888119189976]acc: 0.045454545454545456\n", - "Cond:..#.OO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.064, exp: 0.00, pred: 1146.172, error:478.69331607371066]acc: None\n", - "Cond:..###..O - Act:1 - effect:...O.... - Num:1 [fit: 0.006, exp: 6.00, pred: 1049.240, error:6931.501353723165]acc: 0.0\n", - "Cond:.....F.# - Act:7 - effect:....FO.. - Num:1 [fit: 0.002, exp: 45.00, pred: 1268.261, error:5839.2376780789]acc: 0.37777777777777777\n", - "Cond:##O.##O# - Act:2 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.....##. - Act:5 - effect:....OOO. - Num:2 [fit: 0.179, exp: 364.00, pred: 1226.728, error:249.8057188608285]acc: 0.7939560439560439\n", - "Cond:O.##...O - Act:1 - effect:.....OOF - Num:1 [fit: 0.032, exp: 10.00, pred: 1050.830, error:4119.098536581365]acc: 0.0\n", - "Cond:OO.###.. - Act:2 - effect:......OO - Num:2 [fit: 0.007, exp: 14.00, pred: 966.085, error:9216.000494178003]acc: 0.0\n", - "Cond:.#.F#.#. - Act:0 - effect:OO.....O - Num:2 [fit: 0.021, exp: 0.00, pred: 911.826, error:42.47580794201477]acc: None\n", - "Cond:.##O..#. - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 19.00, pred: 902.597, error:7492.128699164037]acc: 0.0\n", - "Cond:###O...# - Act:1 - effect:.....OF. - Num:1 [fit: 0.000, exp: 19.00, pred: 902.597, error:4015.566950193336]acc: 0.15789473684210525\n", - "Cond:####O#.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.041, exp: 9.00, pred: 1003.786, error:4959.16399310077]acc: 0.3333333333333333\n", - "Cond:..##OO#. - Act:2 - effect:OO.....O - Num:1 [fit: 0.009, exp: 4.00, pred: 1008.919, error:4786.2214770416695]acc: 0.0\n", - "Cond:.O#.#.O# - Act:7 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 912.433, error:1047.5437655435983]acc: None\n", - "Cond:....#..O - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 5.00, pred: 1077.739, error:5594.286819264981]acc: 0.0\n", - "Cond:O#..##.. - Act:0 - effect:OO.....O - Num:3 [fit: 0.108, exp: 12.00, pred: 871.049, error:6264.20831665201]acc: 0.08333333333333333\n", - "Cond:O#.F.O.# - Act:3 - effect:O......O - Num:1 [fit: 0.004, exp: 0.00, pred: 1009.751, error:1006.1290705479719]acc: None\n", - "Cond:.#..OO#. - Act:7 - effect:.O...... - Num:2 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:..#.#O.O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.008, exp: 0.00, pred: 988.863, error:946.8702685718273]acc: None\n", - "Cond:O.#.###. - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 988.863, error:946.8702685718273]acc: None\n", - "Cond:#..#.#.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.002, exp: 15.00, pred: 906.610, error:7805.619315124308]acc: 0.0\n", - "Cond:..###OOF - Act:2 - effect:......OO - Num:1 [fit: 0.458, exp: 4.00, pred: 1042.932, error:1943.839488949075]acc: 0.75\n", - "Cond:######.O - Act:2 - effect:.....F.. - Num:1 [fit: 0.004, exp: 20.00, pred: 978.264, error:4940.531397711445]acc: 0.2\n", - "Cond:#O.###.# - Act:4 - effect:..OO.... - Num:4 [fit: 0.004, exp: 21.00, pred: 1005.876, error:8283.633918014073]acc: 0.0\n", - "Cond:###OO### - Act:4 - effect:.....OF. - Num:2 [fit: 0.014, exp: 14.00, pred: 897.282, error:6127.372875639444]acc: 0.0\n", - "Cond:#...###F - Act:2 - effect:.OOO.... - Num:1 [fit: 0.011, exp: 3.00, pred: 1031.202, error:3920.435596727855]acc: 0.0\n", - "Cond:.#...##F - Act:0 - effect:.....F.. - Num:1 [fit: 0.005, exp: 57.00, pred: 1086.208, error:4224.416096361533]acc: 0.5614035087719298\n", - "Cond:O##..##O - Act:0 - effect:OO.....O - Num:5 [fit: 0.018, exp: 42.00, pred: 1028.099, error:3143.890364783095]acc: 0.21428571428571427\n", - "Cond:##.O###O - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1076.826, error:776.4757819035192]acc: None\n", - "Cond:O#..#... - Act:0 - effect:OO.....O - Num:2 [fit: 0.098, exp: 9.00, pred: 822.498, error:6607.248766658174]acc: 0.0\n", - "Cond:O#..#.#. - Act:7 - effect:OO...... - Num:1 [fit: 0.002, exp: 13.00, pred: 1202.255, error:8735.888047721826]acc: 0.07692307692307693\n", - "Cond:##.##O.O - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 975.410, error:784.4140408532026]acc: None\n", - "Cond:....#O.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.009, exp: 90.00, pred: 1073.741, error:5503.268271681289]acc: 0.6666666666666666\n", - "Cond:##.#OO#. - Act:7 - effect:...O.... - Num:6 [fit: 0.033, exp: 117.00, pred: 1135.011, error:1793.5468918461547]acc: 0.6495726495726496\n", - "Cond:#O.##..# - Act:4 - effect:..OO.... - Num:1 [fit: 0.004, exp: 16.00, pred: 1004.460, error:8612.367884582563]acc: 0.0\n", - "Cond:.O.##### - Act:4 - effect:..OO.... - Num:1 [fit: 0.015, exp: 3.00, pred: 852.148, error:4092.6958949227255]acc: 0.0\n", - "Cond:...O##.# - Act:1 - effect:OO...... - Num:2 [fit: 0.009, exp: 16.00, pred: 1044.056, error:8258.774526447933]acc: 0.1875\n", - "Cond:.O.F##.# - Act:4 - effect:..OO.... - Num:1 [fit: 0.010, exp: 0.00, pred: 1077.228, error:597.6797520242822]acc: None\n", - "Cond:...#.##. - Act:0 - effect:....OOO. - Num:2 [fit: 0.012, exp: 72.00, pred: 1070.646, error:1783.4359982262492]acc: 0.5555555555555556\n", - "Cond:#..OOO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 982.436, error:761.9897994496507]acc: None\n", - "Cond:#.##.#.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.#OOF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#..##O.# - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 19.00, pred: 967.871, error:4641.154852079746]acc: 0.3157894736842105\n", - "Cond:..##.O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1719.392, error:80.35469097515721]acc: None\n", - "Cond:#...OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1312.156, error:74.1541690018642]acc: None\n", - "Cond:.##..O#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 20.00, pred: 1057.940, error:3272.423683307851]acc: 0.1\n", - "Cond:.####... - Act:2 - effect:...O.... - Num:1 [fit: 0.000, exp: 22.00, pred: 900.694, error:3475.9274737327464]acc: 0.18181818181818182\n", - "Cond:.###O##. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 62.00, pred: 1026.373, error:3691.580133828454]acc: 0.3709677419354839\n", - "Cond:...O.#.# - Act:1 - effect:OO...... - Num:1 [fit: 0.003, exp: 5.00, pred: 1047.809, error:4641.506856360296]acc: 0.2\n", - "Cond:##.F.### - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1029.965, error:1231.1329294173497]acc: None\n", - "Cond:....#F## - Act:4 - effect:....FO.. - Num:5 [fit: 0.014, exp: 56.00, pred: 1414.391, error:6763.535623326024]acc: 0.4642857142857143\n", - "Cond:....FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.016, exp: 86.00, pred: 1073.741, error:4619.235431506471]acc: 0.20930232558139536\n", - "Cond:###O##.. - Act:1 - effect:.OOO.... - Num:2 [fit: 0.004, exp: 20.00, pred: 905.216, error:7565.299565666998]acc: 0.1\n", - "Cond:##..F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 177.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:.###O#.. - Act:1 - effect:.O...... - Num:1 [fit: 0.001, exp: 16.00, pred: 967.129, error:8338.701199802417]acc: 0.0\n", - "Cond:O##.##.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 11.00, pred: 1091.139, error:4774.5523888713415]acc: 0.0\n", - "Cond:##O..##. - Act:1 - effect:OO...... - Num:1 [fit: 0.003, exp: 13.00, pred: 948.155, error:905.0891072731959]acc: 0.07692307692307693\n", - "Cond:.#.F.##. - Act:0 - effect:OO.....O - Num:1 [fit: 0.018, exp: 0.00, pred: 958.195, error:227.98786814759765]acc: None\n", - "Cond:#O#O..## - Act:2 - effect:...O.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1011.534, error:940.8151651409147]acc: None\n", - "Cond:###.##O# - Act:0 - effect:.....F.. - Num:3 [fit: 0.020, exp: 104.00, pred: 1086.208, error:4255.928804071957]acc: 0.5576923076923077\n", - "Cond:.OO##.## - Act:4 - effect:...O.... - Num:1 [fit: 0.166, exp: 3.00, pred: 764.515, error:2882.0099067161586]acc: 0.3333333333333333\n", - "Cond:#.O#.#.# - Act:3 - effect:.....OF. - Num:1 [fit: 0.003, exp: 13.00, pred: 1148.873, error:5036.676194628255]acc: 0.0\n", - "Cond:..#O..#. - Act:0 - effect:...OO... - Num:1 [fit: 0.008, exp: 17.00, pred: 1166.454, error:6096.253578777854]acc: 0.0\n", - "Cond:##.#.F## - Act:1 - effect:...FOO.. - Num:1 [fit: 0.012, exp: 3.00, pred: 1151.201, error:3629.1161775863507]acc: 0.0\n", - "Cond:..#..#.O - Act:0 - effect:O......O - Num:1 [fit: 0.009, exp: 16.00, pred: 1257.893, error:5745.895320287297]acc: 0.25\n", - "Cond:##.#.F#. - Act:0 - effect:.....F.. - Num:1 [fit: 0.004, exp: 6.00, pred: 1092.346, error:4388.764028499629]acc: 0.16666666666666666\n", - "Cond:##...#.O - Act:2 - effect:OO.....O - Num:1 [fit: 0.002, exp: 14.00, pred: 977.503, error:5841.239385966655]acc: 0.0\n", - "Cond:#O####.O - Act:0 - effect:OO.....O - Num:2 [fit: 0.371, exp: 7.00, pred: 847.099, error:2117.3821333132382]acc: 0.8571428571428571\n", - "Cond:###....O - Act:0 - effect:O......O - Num:5 [fit: 0.019, exp: 50.00, pred: 1086.019, error:4630.545089317917]acc: 0.54\n", - "Cond:.O.#.... - Act:1 - effect:...O.... - Num:3 [fit: 0.078, exp: 9.00, pred: 1034.946, error:5065.751231928055]acc: 0.1111111111111111\n", - "Cond:#..##..O - Act:1 - effect:....FO.. - Num:1 [fit: 0.001, exp: 12.00, pred: 1043.901, error:4054.031571005369]acc: 0.0\n", - "Cond:####O#.. - Act:1 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 13.00, pred: 968.905, error:7538.006398373851]acc: 0.0\n", - "Cond:#..###.O - Act:7 - effect:.....F.. - Num:1 [fit: 0.000, exp: 18.00, pred: 1127.283, error:5240.581048347089]acc: 0.05555555555555555\n", - "Cond:..#FO#.. - Act:2 - effect:.....OF. - Num:1 [fit: 0.165, exp: 3.00, pred: 1062.991, error:1202.7419032485395]acc: 0.6666666666666666\n", - "Cond:#..###.. - Act:2 - effect:.....F.. - Num:1 [fit: 0.005, exp: 8.00, pred: 923.684, error:7476.630921114324]acc: 0.0\n", - "Cond:#....O.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.043, exp: 0.00, pred: 1212.607, error:56.62081362382797]acc: None\n", - "Cond:O#.F.#.. - Act:3 - effect:O......O - Num:1 [fit: 0.002, exp: 0.00, pred: 1008.738, error:980.0350435429482]acc: None\n", - "Cond:.#..#O## - Act:1 - effect:.....OOF - Num:2 [fit: 0.008, exp: 20.00, pred: 963.343, error:2707.792578440547]acc: 0.6\n", - "Cond:###O..#. - Act:0 - effect:...O.... - Num:3 [fit: 0.002, exp: 25.00, pred: 1010.035, error:5781.626516676305]acc: 0.16\n", - "Cond:#O####.. - Act:2 - effect:OO.....O - Num:1 [fit: 0.014, exp: 14.00, pred: 906.539, error:5763.436826387691]acc: 0.35714285714285715\n", - "Cond:.O#..#.# - Act:2 - effect:.OO..... - Num:1 [fit: 0.147, exp: 7.00, pred: 882.454, error:5723.604513084536]acc: 0.42857142857142855\n", - "Cond:.OO#.#.. - Act:3 - effect:......OO - Num:2 [fit: 0.007, exp: 21.00, pred: 1067.431, error:1328.6415100493077]acc: 0.14285714285714285\n", - "Cond:#.##.... - Act:2 - effect:...O.... - Num:1 [fit: 0.006, exp: 7.00, pred: 998.519, error:4586.515230589453]acc: 0.14285714285714285\n", - "Cond:.#O.#OOF - Act:7 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.###.O# - Act:3 - effect:...OO... - Num:1 [fit: 0.000, exp: 19.00, pred: 1333.707, error:5101.386601760429]acc: 0.10526315789473684\n", - "Cond:..#.#.## - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 13.00, pred: 1068.220, error:3486.0740892962704]acc: 0.0\n", - "Cond:O#.####. - Act:1 - effect:OO...... - Num:1 [fit: 0.059, exp: 11.00, pred: 1091.139, error:4380.565828871341]acc: 0.45454545454545453\n", - "Cond:.###O.## - Act:3 - effect:...OO... - Num:1 [fit: 0.139, exp: 38.00, pred: 1235.095, error:2571.214307763053]acc: 0.5789473684210527\n", - "Cond:##..##.# - Act:2 - effect:.O...... - Num:4 [fit: 0.006, exp: 21.00, pred: 941.240, error:4093.077358903058]acc: 0.09523809523809523\n", - "Cond:O....#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1035.806, error:1461.4433260220553]acc: None\n", - "Cond:O#..###O - Act:1 - effect:OO...... - Num:1 [fit: 0.000, exp: 21.00, pred: 1008.185, error:3076.1872702948795]acc: 0.09523809523809523\n", - "Cond:.#.#.O## - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 22.00, pred: 1047.888, error:3415.3476026007957]acc: 0.2727272727272727\n", - "Cond:#..O.#.. - Act:2 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 1006.733, error:1544.7812780159609]acc: None\n", - "Cond:.#...##F - Act:7 - effect:....OOO. - Num:2 [fit: 0.027, exp: 244.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:#O#.##O# - Act:2 - effect:.OO..... - Num:1 [fit: 0.009, exp: 0.00, pred: 1164.692, error:1094.0789501273748]acc: None\n", - "Cond:O#.####. - Act:0 - effect:....FO.. - Num:1 [fit: 0.007, exp: 5.00, pred: 887.521, error:5272.50087847382]acc: 0.0\n", - "Cond:..#..#.# - Act:2 - effect:...FOO.. - Num:1 [fit: 0.010, exp: 3.00, pred: 888.421, error:3504.521271782695]acc: 0.0\n", - "Cond:.....#.O - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 15.00, pred: 1270.476, error:3380.3665805145647]acc: 0.26666666666666666\n", - "Cond:#.####.O - Act:1 - effect:....FO.. - Num:1 [fit: 0.002, exp: 12.00, pred: 1043.901, error:4447.36277100537]acc: 0.0\n", - "Cond:OO..##.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.021, exp: 12.00, pred: 981.443, error:7317.262391732848]acc: 0.25\n", - "Cond:###.##.. - Act:1 - effect:.OO..... - Num:1 [fit: 0.007, exp: 35.00, pred: 971.009, error:2251.3704472450604]acc: 0.34285714285714286\n", - "Cond:OO..#### - Act:1 - effect:....FO.. - Num:1 [fit: 0.000, exp: 21.00, pred: 1005.193, error:6116.4141812417065]acc: 0.0\n", - "Cond:##.....O - Act:0 - effect:O......O - Num:1 [fit: 0.015, exp: 47.00, pred: 1086.025, error:5349.287198516267]acc: 0.5531914893617021\n", - "Cond:##..##.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.002, exp: 23.00, pred: 1019.726, error:6139.922608540119]acc: 0.13043478260869565\n", - "Cond:#...##.. - Act:1 - effect:.OOO.... - Num:1 [fit: 0.007, exp: 5.00, pred: 994.600, error:5272.605776743201]acc: 0.0\n", - "Cond:#.#.#F.O - Act:7 - effect:.....F.. - Num:1 [fit: 0.008, exp: 0.00, pred: 986.868, error:872.337430607437]acc: None\n", - "Cond:##.#O.#. - Act:3 - effect:...OO... - Num:2 [fit: 0.118, exp: 37.00, pred: 1235.090, error:2236.4349970935136]acc: 0.5405405405405406\n", - "Cond:##..###. - Act:1 - effect:.....F.. - Num:2 [fit: 0.000, exp: 23.00, pred: 999.918, error:6739.216295531036]acc: 0.08695652173913043\n", - "Cond:#..#O#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.003, exp: 9.00, pred: 996.872, error:7106.856031038244]acc: 0.0\n", - "Cond:..###.## - Act:1 - effect:...O.... - Num:1 [fit: 0.000, exp: 21.00, pred: 1059.340, error:4195.548892067742]acc: 0.047619047619047616\n", - "Cond:#O##.##. - Act:0 - effect:....FO.. - Num:1 [fit: 0.000, exp: 24.00, pred: 1113.557, error:7013.043645619518]acc: 0.0\n", - "Cond:#OO..#.. - Act:1 - effect:.OO..... - Num:1 [fit: 0.809, exp: 10.00, pred: 1009.578, error:2542.189272565551]acc: 0.9\n", - "Cond:O#.F.##. - Act:0 - effect:....FO.. - Num:1 [fit: 0.009, exp: 0.00, pred: 1106.777, error:868.6544488779643]acc: None\n", - "Cond:O###.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 26.00, pred: 1743.771, error:932.6812699260779]acc: 0.46153846153846156\n", - "Cond:##.##.#O - Act:2 - effect:OO.....O - Num:3 [fit: 0.007, exp: 15.00, pred: 971.036, error:6248.841194270313]acc: 0.0\n", - "Cond:O#...#.# - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 15.00, pred: 952.909, error:4482.9292181243145]acc: 0.06666666666666667\n", - "Cond:.O#.###. - Act:0 - effect:...O.... - Num:1 [fit: 0.003, exp: 8.00, pred: 1422.420, error:8501.700989900859]acc: 0.0\n", - "Cond:..#.#OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.022, exp: 242.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:.#.#.OF. - Act:6 - effect:....OOO. - Num:2 [fit: 0.033, exp: 195.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:.#.#..O# - Act:2 - effect:......OO - Num:3 [fit: 0.031, exp: 4.00, pred: 903.104, error:4606.124207292221]acc: 0.0\n", - "Cond:#O###.#O - Act:2 - effect:OO.....O - Num:1 [fit: 0.006, exp: 5.00, pred: 1010.935, error:6141.271308624043]acc: 0.0\n", - "Cond:.OO##.## - Act:2 - effect:...O.... - Num:1 [fit: 0.011, exp: 3.00, pred: 1016.994, error:2361.3938799130956]acc: 0.0\n", - "Cond:.##...## - Act:2 - effect:...FOO.. - Num:1 [fit: 0.020, exp: 9.00, pred: 892.973, error:6238.063231174558]acc: 0.0\n", - "Cond:O.##.### - Act:1 - effect:OO...... - Num:2 [fit: 0.015, exp: 8.00, pred: 1063.714, error:3073.570766999506]acc: 0.0\n", - "Cond:O#.##### - Act:1 - effect:OO.....O - Num:2 [fit: 0.003, exp: 21.00, pred: 998.660, error:4104.086075034141]acc: 0.19047619047619047\n", - "Cond:.O.F###. - Act:0 - effect:OO.....O - Num:1 [fit: 0.005, exp: 0.00, pred: 986.311, error:1207.1240411617118]acc: None\n", - "Cond:..##O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 201.00, pred: 1154.868, error:139.8387303607511]acc: 0.8208955223880597\n", - "Cond:OO.###.# - Act:1 - effect:OO.....O - Num:1 [fit: 0.006, exp: 11.00, pred: 1042.904, error:3072.1316455258234]acc: 0.2727272727272727\n", - "Cond:#.##..O. - Act:7 - effect:..OO.... - Num:1 [fit: 0.011, exp: 0.00, pred: 903.753, error:605.6001788126813]acc: None\n", - "Cond:O..#.### - Act:1 - effect:.....OOF - Num:1 [fit: 0.006, exp: 7.00, pred: 1037.894, error:3592.751789279163]acc: 0.0\n", - "Cond:..##.##F - Act:2 - effect:......OO - Num:1 [fit: 0.020, exp: 1.00, pred: 861.972, error:1600.0]acc: 0.0\n", - "Cond:#..##.O# - Act:3 - effect:......OO - Num:1 [fit: 0.013, exp: 18.00, pred: 1326.100, error:6438.933752313971]acc: 0.1111111111111111\n", - "Cond:.#.#.... - Act:5 - effect:....FO.. - Num:1 [fit: 0.001, exp: 341.00, pred: 2349.787, error:7533.889525113581]acc: 0.6832844574780058\n", - "Cond:..#..O.# - Act:6 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.####OOF - Act:6 - effect:.....OOF - Num:1 [fit: 0.081, exp: 38.00, pred: 933.269, error:3439.157332231989]acc: 0.7368421052631579\n", - "Cond:...O..#. - Act:0 - effect:...OO... - Num:1 [fit: 0.015, exp: 4.00, pred: 1131.811, error:5513.474272304136]acc: 0.0\n", - "Cond:.O#.#OF. - Act:3 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 954.489, error:612.3097437265495]acc: None\n", - "Cond:#####.O# - Act:3 - effect:.....F.. - Num:1 [fit: 0.012, exp: 17.00, pred: 1330.199, error:3892.148642867151]acc: 0.47058823529411764\n", - "Cond:O#.##..# - Act:1 - effect:OO.....O - Num:1 [fit: 0.002, exp: 16.00, pred: 1011.678, error:3965.2640239391685]acc: 0.1875\n", - "Cond:.OOO..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 25.00, pred: 2049.926, error:8829.359428261072]acc: 0.24\n", - "Cond:#O###O.. - Act:7 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#....O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 192.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:O#...### - Act:2 - effect:.O...... - Num:1 [fit: 0.004, exp: 9.00, pred: 934.872, error:3624.8390441050356]acc: 0.0\n", - "Cond:#..#.##O - Act:7 - effect:......OO - Num:1 [fit: 0.008, exp: 32.00, pred: 1121.057, error:3790.3966765243895]acc: 0.4375\n", - "Cond:###O..#. - Act:1 - effect:...OO... - Num:2 [fit: 0.090, exp: 6.00, pred: 934.146, error:3448.1987537499813]acc: 0.5\n", - "Cond:..#O.O.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.022, exp: 0.00, pred: 954.235, error:25.64789396802189]acc: None\n", - "Cond:..#O.#.. - Act:0 - effect:.....OF. - Num:2 [fit: 0.003, exp: 13.00, pred: 1188.087, error:3113.6198611430964]acc: 0.07692307692307693\n", - "Cond:.#...O## - Act:1 - effect:.....OOF - Num:1 [fit: 0.345, exp: 12.00, pred: 1025.754, error:2010.412465109011]acc: 0.9166666666666666\n", - "Cond:..##...F - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 854.407, error:0.0]acc: None\n", - "Cond:.#...O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.130, exp: 33.00, pred: 933.394, error:3102.7885811142933]acc: 0.6060606060606061\n", - "Cond:..OO#OOF - Act:2 - effect:......OO - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O###.##. - Act:1 - effect:OO...... - Num:1 [fit: 0.017, exp: 0.00, pred: 1040.946, error:170.9246408457614]acc: None\n", - "Cond:#O###... - Act:2 - effect:OO...... - Num:1 [fit: 0.104, exp: 5.00, pred: 985.438, error:3626.3182561509666]acc: 0.2\n", - "Cond:O.##.##. - Act:3 - effect:O......O - Num:1 [fit: 0.002, exp: 0.00, pred: 974.120, error:647.8050227623039]acc: None\n", - "Cond:##.#OO.. - Act:3 - effect:....OOO. - Num:3 [fit: 0.082, exp: 163.00, pred: 1158.783, error:143.70590038414701]acc: 1.0\n", - "Cond:.####.O# - Act:6 - effect:......OO - Num:3 [fit: 0.013, exp: 27.00, pred: 979.212, error:3316.023485212587]acc: 0.8148148148148148\n", - "Cond:.#.#.#O# - Act:2 - effect:......OO - Num:1 [fit: 0.009, exp: 1.00, pred: 949.513, error:1800.0]acc: 0.0\n", - "Cond:##.#.##O - Act:2 - effect:OO.....O - Num:1 [fit: 0.003, exp: 7.00, pred: 962.901, error:4960.096529528123]acc: 0.0\n", - "Cond:#....### - Act:2 - effect:.....F.. - Num:1 [fit: 0.001, exp: 25.00, pred: 1042.737, error:3220.7400207634764]acc: 0.12\n", - "Cond:.##O.O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1074.268, error:973.1937445573589]acc: None\n", - "Cond:#O###### - Act:4 - effect:....OOO. - Num:5 [fit: 0.354, exp: 5.00, pred: 993.941, error:2445.4314661796543]acc: 0.4\n", - "Cond:##OO..#. - Act:0 - effect:...O.... - Num:1 [fit: 0.001, exp: 17.00, pred: 1021.471, error:6219.044090702826]acc: 0.17647058823529413\n", - "Cond:#...##.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.059, exp: 3.00, pred: 936.340, error:2640.7096690387702]acc: 0.0\n", - "Cond:#.##O##. - Act:3 - effect:....OOO. - Num:3 [fit: 0.087, exp: 195.00, pred: 1154.868, error:137.91649950608277]acc: 0.8256410256410256\n", - "Cond:##.##.#. - Act:0 - effect:......OO - Num:2 [fit: 0.014, exp: 14.00, pred: 975.344, error:6625.179926055554]acc: 0.14285714285714285\n", - "Cond:OO...#.# - Act:0 - effect:OO.....O - Num:1 [fit: 0.000, exp: 1.00, pred: 780.853, error:2600.0]acc: 0.0\n", - "Cond:#.#...#. - Act:2 - effect:.......O - Num:1 [fit: 0.014, exp: 0.00, pred: 1231.601, error:640.6518201512376]acc: None\n", - "Cond:##.####O - Act:2 - effect:.....F.. - Num:1 [fit: 0.288, exp: 4.00, pred: 1031.716, error:3195.626481288689]acc: 0.25\n", - "Cond:#.#..F.. - Act:1 - effect:....FO.. - Num:1 [fit: 0.007, exp: 1.00, pred: 771.158, error:2600.0]acc: 0.0\n", - "Cond:.##FO### - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.###.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.214, exp: 2.00, pred: 812.028, error:2104.411165451589]acc: 0.5\n", - "Cond:OO#.#..# - Act:7 - effect:OO.....O - Num:2 [fit: 0.008, exp: 16.00, pred: 990.750, error:5791.336068242492]acc: 0.25\n", - "Cond:OO.###.# - Act:7 - effect:OO.....O - Num:3 [fit: 0.008, exp: 16.00, pred: 990.750, error:5585.919029599292]acc: 0.25\n", - "Cond:O#.##.## - Act:1 - effect:O......O - Num:1 [fit: 0.405, exp: 6.00, pred: 1049.159, error:1848.5416676934208]acc: 0.6666666666666666\n", - "Cond:#O##.#.. - Act:2 - effect:OO...... - Num:1 [fit: 0.081, exp: 2.00, pred: 744.253, error:1902.3279862212667]acc: 0.0\n", - "Cond:....#.O# - Act:3 - effect:......OO - Num:1 [fit: 0.003, exp: 15.00, pred: 1342.165, error:6443.7731745562105]acc: 0.0\n", - "Cond:#.##..O# - Act:3 - effect:......OO - Num:1 [fit: 0.004, exp: 15.00, pred: 1342.165, error:6692.1456647962095]acc: 0.0\n", - "Cond:#OO##.#. - Act:0 - effect:......OO - Num:1 [fit: 0.005, exp: 11.00, pred: 1162.581, error:5313.397773074591]acc: 0.18181818181818182\n", - "Cond:..#..O#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.007, exp: 17.00, pred: 1045.506, error:3567.0064414416106]acc: 0.058823529411764705\n", - "Cond:#.#O.### - Act:2 - effect:.......O - Num:1 [fit: 0.073, exp: 5.00, pred: 915.880, error:3810.9382048578827]acc: 0.0\n", - "Cond:##..###O - Act:2 - effect:.....F.. - Num:1 [fit: 0.029, exp: 0.00, pred: 1031.716, error:348.9066203221722]acc: None\n", - "Cond:##....#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.036, exp: 0.00, pred: 975.171, error:57.439108031317886]acc: None\n", - "Cond:##..##.# - Act:1 - effect:.....F.. - Num:2 [fit: 0.006, exp: 6.00, pred: 1049.159, error:2816.541667693421]acc: 0.0\n", - "Cond:#....#.. - Act:1 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 0.00, pred: 942.547, error:566.5342313653545]acc: None\n", - "Cond:.OOO##.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 19.00, pred: 2047.304, error:7360.522609997426]acc: 0.15789473684210525\n", - "Cond:.###.O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 184.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:#.####.# - Act:1 - effect:....FO.. - Num:2 [fit: 0.005, exp: 12.00, pred: 1018.717, error:4098.311730316056]acc: 0.08333333333333333\n", - "Cond:##.#O#.# - Act:2 - effect:OO.....O - Num:1 [fit: 0.013, exp: 0.00, pred: 907.907, error:432.9468948190449]acc: None\n", - "Cond:#####O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 228.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:.##.#### - Act:1 - effect:......OO - Num:1 [fit: 0.010, exp: 33.00, pred: 973.581, error:4495.954911046914]acc: 0.3333333333333333\n", - "Cond:.##.#.O# - Act:6 - effect:......OO - Num:1 [fit: 0.062, exp: 19.00, pred: 977.201, error:3151.3074548334425]acc: 0.8421052631578947\n", - "Cond:#.##.#.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.002, exp: 21.00, pred: 1272.687, error:6439.980334374316]acc: 0.0\n", - "Cond:.##..#O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1175.087, error:1241.4988319845436]acc: None\n", - "Cond:##O..### - Act:5 - effect:....FO.. - Num:1 [fit: 0.032, exp: 83.00, pred: 1107.575, error:5206.929315359281]acc: 0.4939759036144578\n", - "Cond:..##.O.. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1346.998, error:773.2570564174982]acc: None\n", - "Cond:#.O..##. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1346.998, error:773.2570564174982]acc: None\n", - "Cond:.....OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 181.00, pred: 1158.175, error:194.15050445662385]acc: 1.0\n", - "Cond:.OOO##.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.215, exp: 2.00, pred: 899.934, error:2541.2018495014827]acc: 0.5\n", - "Cond:##.F##.# - Act:5 - effect:...FOO.. - Num:2 [fit: 0.214, exp: 12.00, pred: 1089.165, error:2658.648404269124]acc: 0.8333333333333334\n", - "Cond:O..#O#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1204.064, error:839.1751626761068]acc: None\n", - "Cond:..#..O.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.040, exp: 0.00, pred: 1172.958, error:44.2814138798604]acc: None\n", - "Cond:###.#.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 33.00, pred: 2350.879, error:2794.8241027205604]acc: 0.5454545454545454\n", - "Cond:.#...#.. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 49.00, pred: 1268.090, error:5609.838198394471]acc: 0.3469387755102041\n", - "Cond:.#.#.O.# - Act:5 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#...#.# - Act:0 - effect:.....F.. - Num:2 [fit: 0.011, exp: 15.00, pred: 1268.851, error:6339.839921960003]acc: 0.06666666666666667\n", - "Cond:..#####O - Act:1 - effect:...O.... - Num:1 [fit: 0.002, exp: 11.00, pred: 1116.698, error:2980.6374227047777]acc: 0.0\n", - "Cond:..#O#.#. - Act:1 - effect:...O.... - Num:1 [fit: 0.010, exp: 1.00, pred: 842.089, error:1800.0]acc: 0.0\n", - "Cond:.##.O##. - Act:7 - effect:...O.... - Num:1 [fit: 0.006, exp: 0.00, pred: 1199.748, error:1097.0121975981342]acc: None\n", - "Cond:#.#####F - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 222.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:##OO..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 35.00, pred: 1575.636, error:7358.840028380428]acc: 0.3142857142857143\n", - "Cond:.O#O..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 15.00, pred: 2048.571, error:7115.5650135736805]acc: 0.2\n", - "Cond:.#.O##.# - Act:0 - effect:.....OF. - Num:1 [fit: 0.018, exp: 7.00, pred: 992.646, error:5717.78281537583]acc: 0.0\n", - "Cond:.#..O#.# - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#..###.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:...#.... - Act:6 - effect:O......O - Num:1 [fit: 0.000, exp: 24.00, pred: 1370.164, error:2605.652050381606]acc: 0.25\n", - "Cond:##....#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 10.00, pred: 1156.730, error:6539.6831235479085]acc: 0.0\n", - "Cond:.#..#.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 4.00, pred: 1234.022, error:4575.737153208211]acc: 0.0\n", - "Cond:##.#OO#. - Act:3 - effect:....OOO. - Num:3 [fit: 0.078, exp: 158.00, pred: 1158.783, error:143.70590038414798]acc: 1.0\n", - "Cond:###F##.# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.194, exp: 12.00, pred: 1089.165, error:2191.539540269124]acc: 0.8333333333333334\n", - "Cond:##.##O.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.026, exp: 2.00, pred: 929.043, error:2703.1278750796173]acc: 0.0\n", - "Cond:.#.#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1133.445, error:32.69617932590974]acc: None\n", - "Cond:.##O..#. - Act:0 - effect:.....OF. - Num:1 [fit: 0.005, exp: 16.00, pred: 1033.758, error:4780.7164645751745]acc: 0.0625\n", - "Cond:#.#O.#.. - Act:0 - effect:...OO... - Num:1 [fit: 0.019, exp: 9.00, pred: 1290.559, error:4013.660929514153]acc: 0.0\n", - "Cond:###.#.## - Act:7 - effect:......OO - Num:1 [fit: 0.018, exp: 46.00, pred: 1134.521, error:4662.994326761784]acc: 0.30434782608695654\n", - "Cond:.O###### - Act:2 - effect:OO...... - Num:1 [fit: 0.002, exp: 0.00, pred: 949.341, error:1363.5951536290495]acc: None\n", - "Cond:.O#.#... - Act:2 - effect:OO.....O - Num:1 [fit: 0.002, exp: 0.00, pred: 949.341, error:1363.5951536290495]acc: None\n", - "Cond:##O##.#. - Act:0 - effect:......OO - Num:4 [fit: 0.041, exp: 19.00, pred: 1044.011, error:5684.5284485501925]acc: 0.10526315789473684\n", - "Cond:.##.#.F# - Act:7 - effect:...O.... - Num:1 [fit: 0.071, exp: 0.00, pred: 987.482, error:247.31087635730563]acc: None\n", - "Cond:#.#..O## - Act:2 - effect:.....F.. - Num:1 [fit: 0.007, exp: 3.00, pred: 1053.422, error:1522.1952852355257]acc: 0.0\n", - "Cond:..#..##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 210.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:..O#.##. - Act:2 - effect:O......O - Num:1 [fit: 0.023, exp: 3.00, pred: 925.528, error:2437.4551618976143]acc: 0.0\n", - "Cond:...#O.#. - Act:3 - effect:..OO.... - Num:1 [fit: 0.019, exp: 31.00, pred: 1235.309, error:975.2334001088934]acc: 0.22580645161290322\n", - "Cond:.#.O#..# - Act:0 - effect:.....OF. - Num:1 [fit: 0.014, exp: 3.00, pred: 873.708, error:3851.6826563252234]acc: 0.0\n", - "Cond:.##O##.# - Act:0 - effect:.....OF. - Num:1 [fit: 0.001, exp: 19.00, pred: 999.129, error:5548.393956209442]acc: 0.05263157894736842\n", - "Cond:##..#OF# - Act:5 - effect:.....OF. - Num:1 [fit: 0.020, exp: 75.00, pred: 948.221, error:5043.350493758598]acc: 0.8266666666666667\n", - "Cond:O######. - Act:3 - effect:O......O - Num:3 [fit: 0.002, exp: 23.00, pred: 1745.641, error:8915.19825467468]acc: 0.0\n", - "Cond:...#O##. - Act:2 - effect:.....F.. - Num:1 [fit: 0.037, exp: 0.00, pred: 1104.508, error:70.92704177434082]acc: None\n", - "Cond:..O###O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 1232.826, error:6.014264340211241]acc: None\n", - "Cond:#####OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 209.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:#O.O.#.# - Act:5 - effect:OO.....O - Num:1 [fit: 0.011, exp: 0.00, pred: 1214.059, error:37.58310461393346]acc: None\n", - "Cond:.....#.. - Act:0 - effect:....OOO. - Num:2 [fit: 0.352, exp: 1.00, pred: 914.254, error:2200.0]acc: 0.0\n", - "Cond:#....#F. - Act:5 - effect:.....OF. - Num:1 [fit: 0.011, exp: 69.00, pred: 948.221, error:4384.319382073632]acc: 0.8405797101449275\n", - "Cond:.....#O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.008, exp: 211.00, pred: 1215.957, error:605.3768497723952]acc: 0.9241706161137441\n", - "Cond:..###OO# - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##O##.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.012, exp: 53.00, pred: 1233.898, error:734.5857520755062]acc: 0.2641509433962264\n", - "Cond:##.#.#OF - Act:1 - effect:.....OOF - Num:1 [fit: 0.345, exp: 12.00, pred: 1025.754, error:2010.412465109011]acc: 0.9166666666666666\n", - "Cond:##..###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 188.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:.####OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.014, exp: 164.00, pred: 1158.175, error:194.15050445662376]acc: 1.0\n", - "Cond:....#OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 183.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:#.#.#O.. - Act:7 - effect:...OO... - Num:2 [fit: 0.005, exp: 68.00, pred: 1463.126, error:2037.406646449693]acc: 0.6617647058823529\n", - "Cond:.#.O#..# - Act:4 - effect:.....OOF - Num:1 [fit: 0.001, exp: 21.00, pred: 1237.976, error:5425.797232369607]acc: 0.047619047619047616\n", - "Cond:#.##.##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 177.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:...#O### - Act:0 - effect:...OO... - Num:8 [fit: 0.280, exp: 32.00, pred: 1145.166, error:5615.340915401308]acc: 0.75\n", - "Cond:#O.###.# - Act:0 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 2051.626, error:610.5731082833926]acc: None\n", - "Cond:O#.###.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.003, exp: 15.00, pred: 1141.116, error:5652.195024302015]acc: 0.06666666666666667\n", - "Cond:##.OO..# - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.O##O##. - Act:7 - effect:...O.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1388.697, error:438.6370570556502]acc: None\n", - "Cond:##.#.OF# - Act:6 - effect:....OOO. - Num:3 [fit: 0.024, exp: 151.00, pred: 1158.175, error:194.1505044566229]acc: 1.0\n", - "Cond:##O..### - Act:1 - effect:O......O - Num:1 [fit: 0.003, exp: 10.00, pred: 1009.578, error:851.1172725655512]acc: 0.0\n", - "Cond:O...#O.# - Act:5 - effect:OO...... - Num:2 [fit: 0.047, exp: 0.00, pred: 2160.828, error:115.75711884237765]acc: None\n", - "Cond:.##.#.## - Act:7 - effect:...O.... - Num:5 [fit: 0.008, exp: 28.00, pred: 1247.251, error:1626.1146217216738]acc: 0.0\n", - "Cond:#.#.#O.. - Act:1 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:....#O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 229.00, pred: 1240.161, error:4283.24503580116]acc: 0.29694323144104806\n", - "Cond:.#.#...# - Act:7 - effect:....FO.. - Num:1 [fit: 0.009, exp: 17.00, pred: 1228.091, error:2801.841059860142]acc: 0.6470588235294118\n", - "Cond:O.#.#O.. - Act:7 - effect:...OO... - Num:1 [fit: 0.019, exp: 0.00, pred: 1512.252, error:329.73327805082886]acc: None\n", - "Cond:#.#.#... - Act:7 - effect:...OO... - Num:1 [fit: 0.019, exp: 0.00, pred: 1512.252, error:329.73327805082886]acc: None\n", - "Cond:O...###. - Act:5 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##.F#.O - Act:3 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:...O#... - Act:0 - effect:....OOO. - Num:1 [fit: 0.028, exp: 2.00, pred: 932.725, error:2707.5049593035847]acc: 0.0\n", - "Cond:.#..#OOF - Act:2 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1049.979, error:5.9389717513306834]acc: None\n", - "Cond:#...#O.. - Act:7 - effect:...OO... - Num:3 [fit: 0.011, exp: 49.00, pred: 1463.122, error:1485.508833455836]acc: 0.5918367346938775\n", - "Cond:.#.##..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.007, exp: 57.00, pred: 1139.432, error:4317.678212805482]acc: 0.7719298245614035\n", - "Cond:#O###O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1217.565, error:146.8456593963129]acc: None\n", - "Cond:.O###..# - Act:7 - effect:.....F.. - Num:1 [fit: 0.004, exp: 23.00, pred: 2389.527, error:6887.771289875635]acc: 0.0\n", - "Cond:##..O#.. - Act:7 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 1379.437, error:385.4198882237531]acc: None\n", - "Cond:#..##OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 118.00, pred: 1158.175, error:194.15050445844653]acc: 1.0\n", - "Cond:#.#..O.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#..#..# - Act:1 - effect:.....F.. - Num:1 [fit: 0.009, exp: 0.00, pred: 1339.420, error:588.9062030430584]acc: None\n", - "Cond:#.###OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1290.766, error:49.909352437585]acc: None\n", - "Cond:##.#FO.. - Act:5 - effect:....FO.. - Num:1 [fit: 0.012, exp: 72.00, pred: 1073.741, error:4695.940217904757]acc: 0.3194444444444444\n", - "Cond:.#.OO..# - Act:7 - effect:.....F.. - Num:1 [fit: 0.092, exp: 36.00, pred: 1113.523, error:269.37796208821567]acc: 1.0\n", - "Cond:.#.OO#.. - Act:6 - effect:.....OF. - Num:1 [fit: 0.000, exp: 25.00, pred: 1420.590, error:2928.679293101617]acc: 0.16\n", - "Cond:###..O#. - Act:5 - effect:.....OF. - Num:1 [fit: 0.009, exp: 69.00, pred: 948.221, error:5128.9291247168085]acc: 0.7681159420289855\n", - "Cond:..#OO..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.251, exp: 32.00, pred: 1113.734, error:269.5549560121449]acc: 1.0\n", - "Cond:##.OO..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.256, exp: 32.00, pred: 1113.734, error:269.5549560121449]acc: 1.0\n", - "Cond:#..#OO.. - Act:6 - effect:...O.... - Num:2 [fit: 0.013, exp: 25.00, pred: 1151.212, error:4018.0744261346667]acc: 0.72\n", - "Cond:###.O#.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1639.590, error:145.60425656953578]acc: None\n", - "Cond:.#.OO... - Act:7 - effect:.....F.. - Num:2 [fit: 0.182, exp: 29.00, pred: 1113.550, error:269.23378330949276]acc: 1.0\n", - "Cond:.#.O#..# - Act:7 - effect:.....F.. - Num:2 [fit: 0.007, exp: 41.00, pred: 1139.432, error:4092.877746000667]acc: 0.7317073170731707\n", - "Cond:##.###F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 111.00, pred: 1158.175, error:194.15050446630084]acc: 1.0\n", - "Cond:..##.#O# - Act:1 - effect:.....F.. - Num:1 [fit: 0.000, exp: 23.00, pred: 982.868, error:5723.068087766148]acc: 0.0\n", - "Cond:##.#O... - Act:6 - effect:.....OF. - Num:2 [fit: 0.018, exp: 24.00, pred: 1421.036, error:1154.3540450101648]acc: 0.7916666666666666\n", - "Cond:#..#.#O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.023, exp: 0.00, pred: 1740.435, error:59.47012174939539]acc: None\n", - "Cond:..##..O# - Act:6 - effect:.....F.. - Num:1 [fit: 0.063, exp: 2.00, pred: 1005.259, error:1511.8546060290998]acc: 0.0\n", - "Cond:.##FOO## - Act:1 - effect:.....OF. - Num:1 [fit: 0.020, exp: 2.00, pred: 929.043, error:2703.1278750796173]acc: 0.0\n", - "Cond:##.OO... - Act:6 - effect:.....OF. - Num:1 [fit: 0.014, exp: 19.00, pred: 1424.181, error:1158.773533980837]acc: 0.7368421052631579\n", - "Cond:#.##O... - Act:6 - effect:.....OF. - Num:1 [fit: 0.013, exp: 19.00, pred: 1424.181, error:1107.0191780640373]acc: 0.7368421052631579\n", - "Cond:##.#OO.. - Act:6 - effect:...O.... - Num:2 [fit: 0.024, exp: 19.00, pred: 1149.791, error:3541.9914979320965]acc: 0.631578947368421\n", - "Cond:.#.#O..# - Act:6 - effect:.....OF. - Num:2 [fit: 0.014, exp: 15.00, pred: 1407.112, error:1119.037051417967]acc: 0.6666666666666666\n", - "Cond:####OO.. - Act:6 - effect:.O...... - Num:2 [fit: 0.042, exp: 15.00, pred: 1141.473, error:4934.568880804928]acc: 0.5333333333333333\n", - "Cond:##.OOO.. - Act:6 - effect:...O.... - Num:1 [fit: 0.034, exp: 0.00, pred: 1997.792, error:465.281563782264]acc: None\n", - "Cond:...OO..# - Act:2 - effect:.....F.. - Num:1 [fit: 0.017, exp: 0.00, pred: 1748.321, error:45.76753873363314]acc: None\n", - "Cond:###.OO.# - Act:6 - effect:...OO... - Num:1 [fit: 0.030, exp: 0.00, pred: 2078.422, error:50.65527652051095]acc: None\n", - "Cond:.#.O#.## - Act:7 - effect:.....F.. - Num:4 [fit: 0.013, exp: 37.00, pred: 1139.280, error:4681.012861031628]acc: 0.7027027027027027\n", - "Cond:###.#F#. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.002, exp: 20.00, pred: 1268.526, error:5746.992324087783]acc: 0.4\n", - "Cond:###.#F.# - Act:7 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 20.00, pred: 1268.526, error:6187.763968808198]acc: 0.4\n", - "Cond:##.##O.# - Act:1 - effect:....OOO. - Num:2 [fit: 0.009, exp: 1.00, pred: 935.299, error:2000.0]acc: 0.0\n", - "Cond:#.##O#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.009, exp: 1.00, pred: 935.299, error:1600.0]acc: 0.0\n", - "Cond:O.#.F### - Act:4 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1232.683, error:627.9552248837119]acc: None\n", - "Cond:##..#O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 163.00, pred: 1810.484, error:304.51039335399423]acc: 1.0\n", - "Cond:....#..# - Act:4 - effect:....OOO. - Num:3 [fit: 0.016, exp: 39.00, pred: 1154.434, error:4630.1092007687785]acc: 0.6666666666666666\n", - "Cond:O#.#OO#. - Act:7 - effect:...O.... - Num:3 [fit: 0.018, exp: 0.00, pred: 1315.496, error:167.51006031233507]acc: None\n", - "Cond:.##..#F# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 82.00, pred: 1158.175, error:194.1505053935631]acc: 1.0\n", - "Cond:#.#..O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.198, exp: 0.00, pred: 1391.066, error:40.097674984365646]acc: None\n", - "Cond:##.#O.#. - Act:6 - effect:.....OF. - Num:2 [fit: 0.088, exp: 3.00, pred: 1099.588, error:123.22591380169827]acc: 1.0\n", - "Cond:O..#.#.. - Act:6 - effect:OO...... - Num:1 [fit: 0.037, exp: 0.00, pred: 2075.394, error:28.40629421452286]acc: None\n", - "Cond:.##.#.## - Act:2 - effect:.....OF. - Num:1 [fit: 0.017, exp: 0.00, pred: 1440.191, error:441.9473279060891]acc: None\n", - "Cond:#...##.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 37.00, pred: 1163.292, error:1509.2127535535124]acc: 0.7837837837837838\n", - "Cond:#...#O## - Act:6 - effect:....OOO. - Num:2 [fit: 0.128, exp: 125.00, pred: 1031.818, error:1525.6267389653076]acc: 0.752\n", - "Cond:....#..# - Act:0 - effect:....OOO. - Num:1 [fit: 0.009, exp: 14.00, pred: 1294.560, error:6681.447742582937]acc: 0.0\n", - "Cond:#..###O# - Act:1 - effect:.......O - Num:1 [fit: 0.001, exp: 23.00, pred: 982.868, error:4654.904962183914]acc: 0.0\n", - "Cond:...#O... - Act:6 - effect:.....OF. - Num:1 [fit: 0.101, exp: 2.00, pred: 954.757, error:7.5996570049145475]acc: 1.0\n", - "Cond:.##O..#. - Act:3 - effect:O......O - Num:2 [fit: 0.018, exp: 21.00, pred: 1084.872, error:4214.015864303169]acc: 0.0\n", - "Cond:.OO#.... - Act:3 - effect:.OOO.... - Num:1 [fit: 0.023, exp: 15.00, pred: 1079.180, error:2482.714134098973]acc: 0.4\n", - "Cond:..###.OO - Act:0 - effect:.OO..... - Num:2 [fit: 0.002, exp: 24.00, pred: 1142.450, error:4021.757995976024]acc: 0.125\n", - "Cond:#.###O.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.036, exp: 32.00, pred: 1215.612, error:231.67064945285105]acc: 0.84375\n", - "Cond:...#OO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.209, exp: 2.00, pred: 876.209, error:2518.3867250865737]acc: 0.5\n", - "Cond:##.#O... - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#...O### - Act:6 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 996.646, error:177.58081115035782]acc: None\n", - "Cond:..O#.#O# - Act:1 - effect:.......O - Num:1 [fit: 0.004, exp: 0.00, pred: 2038.914, error:0.0]acc: None\n", - "Cond:...###O# - Act:1 - effect:.....F.. - Num:1 [fit: 0.000, exp: 22.00, pred: 980.349, error:4754.116903700362]acc: 0.0\n", - "Cond:.#O.#OF. - Act:3 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.#.OF. - Act:3 - effect:......OO - Num:1 [fit: 0.006, exp: 35.00, pred: 1134.620, error:2649.1102837612234]acc: 0.6857142857142857\n", - "Cond:.#O.#### - Act:1 - effect:......OO - Num:1 [fit: 0.011, exp: 10.00, pred: 1009.578, error:1451.117272565551]acc: 0.0\n", - "Cond:##.#O#.. - Act:6 - effect:...O.... - Num:1 [fit: 0.002, exp: 1.00, pred: 839.436, error:1600.0]acc: 0.0\n", - "Cond:.####### - Act:1 - effect:......OO - Num:1 [fit: 0.002, exp: 21.00, pred: 965.364, error:5547.139915442371]acc: 0.0\n", - "Cond:O##F###. - Act:3 - effect:O......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#O.#OO#. - Act:7 - effect:...O.... - Num:1 [fit: 0.216, exp: 0.00, pred: 1109.265, error:323.24979286440873]acc: None\n", - "Cond:.O.#.OF. - Act:3 - effect:......OO - Num:1 [fit: 0.012, exp: 0.00, pred: 1018.414, error:288.9724833716014]acc: None\n", - "Cond:.O##..#. - Act:7 - effect:.....F.. - Num:1 [fit: 0.001, exp: 18.00, pred: 2406.659, error:6637.042639377646]acc: 0.0\n", - "Cond:.#OO#..# - Act:7 - effect:.....OF. - Num:1 [fit: 0.001, exp: 19.00, pred: 1585.960, error:4489.211296213905]acc: 0.15789473684210525\n", - "Cond:...#.O#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.187, exp: 2.00, pred: 927.178, error:1750.9743065948176]acc: 0.5\n", - "Cond:.###..#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.010, exp: 3.00, pred: 925.528, error:2304.121828564281]acc: 0.0\n", - "Cond:..##OO#. - Act:6 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.O.### - Act:7 - effect:.....F.. - Num:3 [fit: 0.032, exp: 12.00, pred: 1272.488, error:4996.947949204449]acc: 0.08333333333333333\n", - "Cond:..###O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.028, exp: 84.00, pred: 1136.526, error:160.3595426498383]acc: 1.0\n", - "Cond:###..#.O - Act:2 - effect:....OOO. - Num:1 [fit: 0.048, exp: 0.00, pred: 1410.939, error:73.70466975710164]acc: None\n", - "Cond:##...F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.121, exp: 120.00, pred: 1227.238, error:250.1648037622783]acc: 1.0\n", - "Cond:.#..#..# - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 18.00, pred: 1156.999, error:4263.227230469365]acc: 0.2777777777777778\n", - "Cond:....O#O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.O#.## - Act:3 - effect:.....F.. - Num:1 [fit: 0.005, exp: 30.00, pred: 1235.553, error:1485.1190419950399]acc: 0.0\n", - "Cond:###..F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 113.00, pred: 1227.238, error:250.16480376531996]acc: 1.0\n", - "Cond:#O.OO.## - Act:7 - effect:.....F.. - Num:1 [fit: 0.031, exp: 0.00, pred: 1639.656, error:58.23705505079005]acc: None\n", - "Cond:..#O...# - Act:4 - effect:......OO - Num:3 [fit: 0.007, exp: 25.00, pred: 1087.327, error:3895.230122884983]acc: 0.48\n", - "Cond:..#OO... - Act:7 - effect:.....F.. - Num:1 [fit: 0.512, exp: 4.00, pred: 898.981, error:38.34946290007767]acc: 1.0\n", - "Cond:...###F# - Act:4 - effect:......OO - Num:1 [fit: 0.001, exp: 70.00, pred: 1368.534, error:3291.8302629428094]acc: 0.4\n", - "Cond:..###O.# - Act:0 - effect:...FOO.. - Num:1 [fit: 0.005, exp: 49.00, pred: 1192.717, error:8169.438266719706]acc: 0.4897959183673469\n", - "Cond:#..O#### - Act:1 - effect:.....F.. - Num:1 [fit: 0.010, exp: 0.00, pred: 1567.255, error:489.8704905597739]acc: None\n", - "Cond:##.O...# - Act:7 - effect:.....F.. - Num:1 [fit: 0.013, exp: 7.00, pred: 1130.097, error:3803.882547348386]acc: 0.0\n", - "Cond:#..#OO.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.012, exp: 12.00, pred: 1089.165, error:1221.824340269124]acc: 0.16666666666666666\n", - "Cond:...#O..# - Act:0 - effect:...OO... - Num:2 [fit: 0.134, exp: 1.00, pred: 947.735, error:1600.0]acc: 0.0\n", - "Cond:.....F.O - Act:7 - effect:....FO.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1208.894, error:535.1452045621386]acc: None\n", - "Cond:.O#OO..# - Act:7 - effect:.....F.. - Num:1 [fit: 0.032, exp: 0.00, pred: 1187.313, error:65.84129432068677]acc: None\n", - "Cond:.#.#O..# - Act:4 - effect:.....OOF - Num:1 [fit: 0.112, exp: 2.00, pred: 834.046, error:1507.562118843908]acc: 0.0\n", - "Cond:.#.##F## - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 102.00, pred: 1227.238, error:250.16480377109886]acc: 1.0\n", - "Cond:.....#.. - Act:7 - effect:....OOO. - Num:4 [fit: 0.020, exp: 14.00, pred: 1286.805, error:6945.473498041807]acc: 0.0\n", - "Cond:..#.##OF - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 22.00, pred: 935.450, error:3006.622600088295]acc: 0.0\n", - "Cond:....OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.042, exp: 0.00, pred: 1304.085, error:61.541776202365824]acc: None\n", - "Cond:.###.... - Act:4 - effect:......OO - Num:1 [fit: 0.000, exp: 24.00, pred: 1087.259, error:4284.5253639708035]acc: 0.125\n", - "Cond:.#..###F - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 169.00, pred: 1390.346, error:258.5622648614029]acc: 1.0\n", - "Cond:###.#F.O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.002, exp: 0.00, pred: 1231.306, error:423.1760136186921]acc: None\n", - "Cond:..OO..## - Act:4 - effect:......OO - Num:2 [fit: 0.338, exp: 12.00, pred: 1164.371, error:3530.61704407038]acc: 0.75\n", - "Cond:##.#.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 50.00, pred: 1070.670, error:1738.8198267691348]acc: 0.52\n", - "Cond:######## - Act:5 - effect:.....F.. - Num:2 [fit: 0.004, exp: 1310.00, pred: 1325.216, error:2635.7752357107015]acc: 0.2603053435114504\n", - "Cond:####O#.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.058, exp: 176.00, pred: 1154.868, error:137.98140321681447]acc: 0.8295454545454546\n", - "Cond:#.#.##O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.006, exp: 173.00, pred: 1215.957, error:622.9690466007897]acc: 0.9190751445086706\n", - "Cond:...#OO.# - Act:3 - effect:....OOO. - Num:12 [fit: 0.280, exp: 144.00, pred: 1158.783, error:143.7059003841303]acc: 1.0\n", - "Cond:..#O..## - Act:4 - effect:......OO - Num:1 [fit: 0.010, exp: 16.00, pred: 1090.425, error:3411.684437812387]acc: 0.3125\n", - "Cond:####.OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 157.00, pred: 1390.346, error:258.56226486140247]acc: 1.0\n", - "Cond:..###O.O - Act:0 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1143.608, error:589.4656615862974]acc: None\n", - "Cond:...#OO.. - Act:3 - effect:....OOO. - Num:2 [fit: 0.052, exp: 141.00, pred: 1158.783, error:143.70590038416773]acc: 1.0\n", - "Cond:.#..##.O - Act:0 - effect:...OO... - Num:1 [fit: 0.003, exp: 9.00, pred: 1181.333, error:5398.48112202584]acc: 0.0\n", - "Cond:##O####. - Act:0 - effect:......OO - Num:1 [fit: 0.006, exp: 17.00, pred: 1039.490, error:6100.839427068727]acc: 0.0\n", - "Cond:..#O.... - Act:4 - effect:...O.... - Num:1 [fit: 0.063, exp: 12.00, pred: 1060.762, error:5121.382076940348]acc: 0.25\n", - "Cond:.#O#..#. - Act:0 - effect:......OO - Num:1 [fit: 0.067, exp: 11.00, pred: 1042.201, error:5427.177460879386]acc: 0.0\n", - "Cond:####.O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 151.00, pred: 1390.346, error:258.56226486140025]acc: 1.0\n", - "Cond:####.#.. - Act:0 - effect:......OO - Num:1 [fit: 0.075, exp: 9.00, pred: 982.299, error:6120.682328270889]acc: 0.0\n", - "Cond:..#O#... - Act:4 - effect:...O.... - Num:1 [fit: 0.040, exp: 7.00, pred: 912.228, error:4554.694132765837]acc: 0.0\n", - "Cond:##.O...O - Act:7 - effect:.....F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1234.934, error:645.5130839229548]acc: None\n", - "Cond:..#..##O - Act:0 - effect:O......O - Num:1 [fit: 0.004, exp: 10.00, pred: 1138.676, error:4882.075353714672]acc: 0.3\n", - "Cond:.###.OF. - Act:3 - effect:......OO - Num:2 [fit: 0.034, exp: 27.00, pred: 1134.591, error:2180.122468976151]acc: 0.8518518518518519\n", - "Cond:..##...# - Act:4 - effect:..OO.... - Num:1 [fit: 0.206, exp: 2.00, pred: 776.427, error:1708.2039949348425]acc: 0.5\n", - "Cond:#..###F. - Act:5 - effect:.....OF. - Num:1 [fit: 0.326, exp: 13.00, pred: 957.264, error:4218.552033841053]acc: 0.7692307692307693\n", - "Cond:#####O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.008, exp: 96.00, pred: 1064.140, error:2864.244866037583]acc: 0.23958333333333334\n", - "Cond:#...O#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##O##.## - Act:0 - effect:.OOO.... - Num:1 [fit: 0.205, exp: 2.00, pred: 851.249, error:1703.5110763375992]acc: 0.5\n", - "Cond:.#O##.#. - Act:0 - effect:......OO - Num:1 [fit: 0.005, exp: 2.00, pred: 851.249, error:3103.511076337599]acc: 0.0\n", - "Cond:..OO..#. - Act:4 - effect:...O.... - Num:1 [fit: 0.006, exp: 1.00, pred: 760.019, error:1800.0]acc: 0.0\n", - "Cond:...#.#F# - Act:5 - effect:....OOO. - Num:2 [fit: 0.034, exp: 1.00, pred: 881.852, error:0.0]acc: 1.0\n", - "Cond:#...##F. - Act:5 - effect:.....OF. - Num:1 [fit: 0.034, exp: 1.00, pred: 881.852, error:2000.0]acc: 0.0\n", - "Cond:.....O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 56.00, pred: 1158.177, error:194.15130192844452]acc: 1.0\n", - "Cond:#.#..##O - Act:0 - effect:O......O - Num:1 [fit: 0.054, exp: 9.00, pred: 1064.428, error:3944.7347209853674]acc: 0.6666666666666666\n", - "Cond:#.###O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.051, exp: 80.00, pred: 1136.526, error:160.3595336423799]acc: 1.0\n", - "Cond:...#OO.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.013, exp: 12.00, pred: 1089.165, error:1221.824340269124]acc: 0.16666666666666666\n", - "Cond:..###O## - Act:5 - effect:...FOO.. - Num:1 [fit: 0.012, exp: 67.00, pred: 1064.140, error:3405.219411826027]acc: 0.3283582089552239\n", - "Cond:###.###F - Act:3 - effect:.......O - Num:1 [fit: 0.014, exp: 30.00, pred: 1140.869, error:4562.454965026948]acc: 0.7333333333333333\n", - "Cond:#..#.OO# - Act:3 - effect:.......O - Num:1 [fit: 0.014, exp: 30.00, pred: 1140.869, error:4382.860725026948]acc: 0.7333333333333333\n", - "Cond:.#.#.O.. - Act:5 - effect:....OOO. - Num:3 [fit: 0.010, exp: 0.00, pred: 1513.144, error:296.58032583921516]acc: None\n", - "Cond:###..O#F - Act:3 - effect:.......O - Num:5 [fit: 0.082, exp: 28.00, pred: 1140.917, error:4488.202726028428]acc: 0.7142857142857143\n", - "Cond:#..###O# - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#...#O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.056, exp: 70.00, pred: 1136.526, error:160.3594233204871]acc: 1.0\n", - "Cond:##O..#.# - Act:5 - effect:....FO.. - Num:2 [fit: 0.172, exp: 20.00, pred: 1109.979, error:4589.394272285976]acc: 0.8\n", - "Cond:.#..O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1379.570, error:55.307014414794004]acc: None\n", - "Cond:.###.OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 44.00, pred: 1158.156, error:194.10904277060914]acc: 1.0\n", - "Cond:..##O.## - Act:7 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.##..O#F - Act:3 - effect:.......O - Num:2 [fit: 0.018, exp: 21.00, pred: 1142.889, error:4796.697339961048]acc: 0.6190476190476191\n", - "Cond:##..#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.043, exp: 77.00, pred: 1031.818, error:1525.6365053285142]acc: 0.7272727272727273\n", - "Cond:O.#..#.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.278, exp: 4.00, pred: 1115.101, error:1369.6717013642456]acc: 0.0\n", - "Cond:..O..#.# - Act:5 - effect:.OO..... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.#.#F. - Act:3 - effect:......OO - Num:2 [fit: 0.038, exp: 22.00, pred: 1139.562, error:2409.6686961250725]acc: 0.9090909090909091\n", - "Cond:####.O#F - Act:1 - effect:.......O - Num:1 [fit: 0.006, exp: 12.00, pred: 1025.754, error:4737.411505109011]acc: 0.0\n", - "Cond:##O....# - Act:5 - effect:....FO.. - Num:1 [fit: 0.489, exp: 10.00, pred: 1056.140, error:4897.292262724492]acc: 0.6\n", - "Cond:####O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.027, exp: 128.00, pred: 1154.868, error:138.68617983615766]acc: 0.765625\n", - "Cond:##.#.#F. - Act:3 - effect:......OO - Num:1 [fit: 0.065, exp: 15.00, pred: 1114.941, error:2139.185672683514]acc: 0.8666666666666667\n", - "Cond:##.O.O#. - Act:3 - effect:......OO - Num:1 [fit: 0.037, exp: 0.00, pred: 1766.237, error:44.83942368659772]acc: None\n", - "Cond:###..##F - Act:3 - effect:.......O - Num:3 [fit: 0.138, exp: 13.00, pred: 1102.999, error:4657.754705610376]acc: 0.38461538461538464\n", - "Cond:##O....O - Act:5 - effect:....FO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:####.O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1331.345, error:548.0063499299422]acc: None\n", - "Cond:.O.#.#F. - Act:3 - effect:......OO - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.#....O - Act:0 - effect:O......O - Num:1 [fit: 0.058, exp: 8.00, pred: 1084.802, error:3693.3543085701585]acc: 0.625\n", - "Cond:###..O.F - Act:3 - effect:.......O - Num:2 [fit: 0.056, exp: 0.00, pred: 1249.259, error:59.920854999761175]acc: None\n", - "Cond:.#...F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.065, exp: 79.00, pred: 1227.238, error:250.16481647228719]acc: 1.0\n", - "Cond:#..##..O - Act:6 - effect:OO...... - Num:1 [fit: 0.000, exp: 21.00, pred: 1266.454, error:3455.6313701306]acc: 0.19047619047619047\n", - "Cond:#O##...F - Act:6 - effect:.OO..... - Num:1 [fit: 0.007, exp: 0.00, pred: 1080.101, error:529.8711089049093]acc: None\n", - "Cond:..#.#O## - Act:2 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1814.018, error:249.26871940189537]acc: None\n", - "Cond:####.### - Act:1 - effect:.......O - Num:2 [fit: 0.001, exp: 17.00, pred: 970.243, error:4606.63548462986]acc: 0.0\n", - "Cond:.##.#O#F - Act:1 - effect:......OO - Num:1 [fit: 0.003, exp: 11.00, pred: 993.946, error:4630.854628158903]acc: 0.0\n", - "Cond:.#O..#.# - Act:5 - effect:.OO..... - Num:1 [fit: 0.032, exp: 0.00, pred: 1001.581, error:525.5976329328946]acc: None\n", - "Cond:...##..# - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 3.00, pred: 1160.894, error:4020.537256661734]acc: 0.0\n", - "Cond:###..#O# - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 22.00, pred: 935.450, error:3329.230674506061]acc: 0.0\n", - "Cond:#..#.#O# - Act:6 - effect:....OOO. - Num:2 [fit: 0.008, exp: 20.00, pred: 932.883, error:3261.4879460214956]acc: 0.0\n", - "Cond:.....O## - Act:1 - effect:..OO.... - Num:1 [fit: 0.200, exp: 2.00, pred: 865.156, error:21.357017593488365]acc: 1.0\n", - "Cond:..#..#OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1065.532, error:19.68942408702708]acc: None\n", - "Cond:.O#..##F - Act:3 - effect:.......O - Num:1 [fit: 0.037, exp: 0.00, pred: 1074.604, error:99.19229348512701]acc: None\n", - "Cond:#.#.O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1427.999, error:60.296974581921184]acc: None\n", - "Cond:#..#.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 76.00, pred: 1227.238, error:250.16482687195347]acc: 1.0\n", - "Cond:##.##O.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:....#O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.028, exp: 41.00, pred: 1136.593, error:160.4765231582332]acc: 1.0\n", - "Cond:###.#OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.107, exp: 2.00, pred: 791.825, error:1505.1215336859088]acc: 0.0\n", - "Cond:..OOO..# - Act:0 - effect:.....OOF - Num:1 [fit: 0.016, exp: 0.00, pred: 2338.834, error:60.688323523018894]acc: None\n", - "Cond:.##..O#. - Act:0 - effect:....OOO. - Num:2 [fit: 0.009, exp: 50.00, pred: 1070.670, error:2337.1841802376885]acc: 0.76\n", - "Cond:#.#..OO# - Act:2 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1675.808, error:283.2252482413699]acc: None\n", - "Cond:.##.#O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1711.045, error:70.46908400508757]acc: None\n", - "Cond:###..O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.025, exp: 27.00, pred: 1046.088, error:1707.343675446734]acc: 0.9259259259259259\n", - "Cond:#.###.OO - Act:6 - effect:...FOO.. - Num:1 [fit: 0.021, exp: 0.00, pred: 2154.164, error:1.069831461575518]acc: None\n", - "Cond:.#.##F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.121, exp: 71.00, pred: 1227.238, error:250.16483948976463]acc: 1.0\n", - "Cond:#######F - Act:5 - effect:....OOO. - Num:1 [fit: 0.008, exp: 45.00, pred: 1241.061, error:3007.9066573036343]acc: 0.1111111111111111\n", - "Cond:#.##.O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.029, exp: 23.00, pred: 1044.606, error:1545.0777139585575]acc: 0.9130434782608695\n", - "Cond:#.##.F#. - Act:0 - effect:.....OOF - Num:1 [fit: 0.037, exp: 0.00, pred: 1849.315, error:391.8604559068889]acc: None\n", - "Cond:#.###O.O - Act:0 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 2397.226, error:315.18653168929745]acc: None\n", - "Cond:#.###O#O - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 2397.226, error:315.18653168929745]acc: None\n", - "Cond:O.#.##.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 18.00, pred: 1227.227, error:8092.777223412167]acc: 0.0\n", - "Cond:...#O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.028, exp: 96.00, pred: 1154.868, error:138.17611365631802]acc: 0.6875\n", - "Cond:#.###O#. - Act:2 - effect:...FOO.. - Num:1 [fit: 0.018, exp: 0.00, pred: 2123.472, error:491.21471279463736]acc: None\n", - "Cond:###..#O# - Act:0 - effect:.....F.. - Num:2 [fit: 0.011, exp: 34.00, pred: 1086.056, error:3680.4385967673315]acc: 0.6176470588235294\n", - "Cond:O.##.#.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.018, exp: 4.00, pred: 1145.485, error:1142.7235704543737]acc: 0.0\n", - "Cond:O.#..O.# - Act:5 - effect:....OOO. - Num:4 [fit: 0.035, exp: 0.00, pred: 1968.667, error:27.83542346155447]acc: None\n", - "Cond:..##O#.# - Act:0 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 17.00, pred: 1151.787, error:6936.807016696508]acc: 0.058823529411764705\n", - "Cond:#.#..##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.061, exp: 51.00, pred: 1226.723, error:249.7934398887701]acc: 1.0\n", - "Cond:.##.#OO. - Act:0 - effect:.....F.. - Num:1 [fit: 0.017, exp: 0.00, pred: 2129.031, error:116.14479153884662]acc: None\n", - "Cond:...####. - Act:2 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1413.520, error:603.2338857229969]acc: None\n", - "Cond:#..#O#.O - Act:0 - effect:...OO... - Num:1 [fit: 0.188, exp: 0.00, pred: 1500.149, error:113.26482814715214]acc: None\n", - "Cond:..##O### - Act:0 - effect:...OO... - Num:2 [fit: 0.044, exp: 11.00, pred: 1143.908, error:5613.296636191179]acc: 0.36363636363636365\n", - "Cond:##.##OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.036, exp: 12.00, pred: 1144.769, error:175.0865740135722]acc: 1.0\n", - "Cond:#...###. - Act:6 - effect:....OOO. - Num:1 [fit: 0.449, exp: 19.00, pred: 1111.728, error:167.14725734045788]acc: 0.8947368421052632\n", - "Cond:##...OO# - Act:0 - effect:....OOO. - Num:3 [fit: 0.001, exp: 23.00, pred: 1085.946, error:6102.858205783532]acc: 0.21739130434782608\n", - "Cond:####.... - Act:2 - effect:O......O - Num:1 [fit: 0.035, exp: 3.00, pred: 925.528, error:2837.4551618976143]acc: 0.0\n", - "Cond:#.##O### - Act:0 - effect:...OO... - Num:2 [fit: 0.052, exp: 8.00, pred: 1140.109, error:4722.772366943658]acc: 0.375\n", - "Cond:##.#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1343.150, error:42.671478592426524]acc: None\n", - "Cond:#.####O# - Act:7 - effect:....OOO. - Num:3 [fit: 0.011, exp: 161.00, pred: 1215.957, error:912.5768497723955]acc: 0.9130434782608695\n", - "Cond:..##O#.# - Act:4 - effect:...OO... - Num:1 [fit: 0.005, exp: 15.00, pred: 1036.441, error:3884.6094531172043]acc: 0.06666666666666667\n", - "Cond:.#####.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.004, exp: 10.00, pred: 1062.923, error:7257.71084961187]acc: 0.0\n", - "Cond:O.#F.### - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1503.880, error:1411.5870978660141]acc: None\n", - "Cond:...#.#.# - Act:0 - effect:.O...... - Num:1 [fit: 0.005, exp: 1.00, pred: 819.021, error:0.0]acc: 1.0\n", - "Cond:###.O#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1831.000, error:95.57202823651926]acc: None\n", - "Cond:...OO.## - Act:3 - effect:.....OOF - Num:1 [fit: 0.005, exp: 30.00, pred: 1235.553, error:1280.6154423676694]acc: 0.0\n", - "Cond:.#.#.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.021, exp: 17.00, pred: 1062.284, error:1671.90674758069]acc: 0.6470588235294118\n", - "Cond:....#O#. - Act:5 - effect:....FO.. - Num:2 [fit: 0.054, exp: 5.00, pred: 1029.710, error:4647.723762606136]acc: 0.4\n", - "Cond:...#.#O# - Act:0 - effect:....OOO. - Num:1 [fit: 0.010, exp: 12.00, pred: 1076.476, error:6355.524522265567]acc: 0.3333333333333333\n", - "Cond:.####OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.059, exp: 7.00, pred: 1191.824, error:190.54874674020093]acc: 1.0\n", - "Cond:.##O##.. - Act:3 - effect:...OO... - Num:1 [fit: 0.025, exp: 51.00, pred: 1233.902, error:2521.7138576099032]acc: 0.27450980392156865\n", - "Cond:O.#.#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1636.791, error:1182.8617906140882]acc: None\n", - "Cond:#.#.##O. - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O####O#. - Act:4 - effect:O......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##O#.##. - Act:3 - effect:...FOO.. - Num:3 [fit: 0.022, exp: 24.00, pred: 1087.010, error:3093.2826531717965]acc: 0.08333333333333333\n", - "Cond:#.#.##.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 25.00, pred: 1252.702, error:5901.0569143211715]acc: 0.2\n", - "Cond:#..#O##. - Act:3 - effect:....OOO. - Num:2 [fit: 0.058, exp: 79.00, pred: 1154.868, error:137.93033831472943]acc: 0.620253164556962\n", - "Cond:####.#F# - Act:3 - effect:......OO - Num:2 [fit: 1.201, exp: 6.00, pred: 1010.698, error:2438.439663305213]acc: 0.8333333333333334\n", - "Cond:...#O.## - Act:0 - effect:...FOO.. - Num:1 [fit: 0.007, exp: 1.00, pred: 947.735, error:0.0]acc: 1.0\n", - "Cond:...#O##O - Act:0 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:###.###. - Act:0 - effect:....OOO. - Num:2 [fit: 0.029, exp: 12.00, pred: 1070.073, error:2319.349278348898]acc: 0.6666666666666666\n", - "Cond:##.#.F.# - Act:3 - effect:.....OOF - Num:1 [fit: 0.143, exp: 3.00, pred: 1188.436, error:1451.3236390936133]acc: 0.6666666666666666\n", - "Cond:.#...O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.362, exp: 3.00, pred: 1030.413, error:12.168915631017057]acc: 1.0\n", - "Cond:##O#.... - Act:2 - effect:O......O - Num:1 [fit: 0.109, exp: 2.00, pred: 802.005, error:1703.5594146176832]acc: 0.0\n", - "Cond:###..OO# - Act:0 - effect:.....F.. - Num:1 [fit: 0.168, exp: 3.00, pred: 1038.158, error:1202.1089651857858]acc: 0.6666666666666666\n", - "Cond:...#O#.# - Act:0 - effect:...OO... - Num:1 [fit: 0.195, exp: 3.00, pred: 954.741, error:3685.3037345558755]acc: 0.3333333333333333\n", - "Cond:###..### - Act:0 - effect:....OOO. - Num:2 [fit: 0.014, exp: 17.00, pred: 1020.856, error:2299.9648846132513]acc: 0.47058823529411764\n", - "Cond:..OOO.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.017, exp: 0.00, pred: 1644.833, error:428.29256450878734]acc: None\n", - "Cond:#.##.#.O - Act:0 - effect:O......O - Num:1 [fit: 0.087, exp: 3.00, pred: 956.092, error:3056.809971085804]acc: 0.3333333333333333\n", - "Cond:O.#..### - Act:0 - effect:O......O - Num:1 [fit: 0.108, exp: 2.00, pred: 1024.627, error:1716.6796984364803]acc: 0.5\n", - "Cond:.#..#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.090, exp: 3.00, pred: 883.185, error:1064.320783880512]acc: 0.3333333333333333\n", - "Cond:#.##.### - Act:1 - effect:O......O - Num:1 [fit: 0.283, exp: 4.00, pred: 986.137, error:2443.893188698686]acc: 0.75\n", - "Cond:O.#F.#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1153.252, error:942.4811767413255]acc: None\n", - "Cond:.O.###.. - Act:6 - effect:.......O - Num:1 [fit: 0.001, exp: 3.00, pred: 1235.776, error:3319.7554471200647]acc: 0.0\n", - "Cond:###..##. - Act:0 - effect:....OOO. - Num:2 [fit: 0.384, exp: 7.00, pred: 1074.319, error:2121.9226560928873]acc: 0.7142857142857143\n", - "Cond:.....#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 54.00, pred: 2039.146, error:1835.146210437202]acc: 0.7962962962962963\n", - "Cond:#...#O#F - Act:5 - effect:......OO - Num:1 [fit: 0.008, exp: 32.00, pred: 1241.252, error:1167.4196431917285]acc: 0.21875\n", - "Cond:.#...#.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.110, exp: 0.00, pred: 1177.176, error:44.31796030978935]acc: None\n", - "Cond:.##..OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1039.280, error:250.83001544132995]acc: None\n", - "Cond:.#..#O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.017, exp: 0.00, pred: 1039.280, error:250.83001544132995]acc: None\n", - "Cond:O####### - Act:3 - effect:....OOO. - Num:6 [fit: 0.014, exp: 53.00, pred: 1360.526, error:4587.48320162013]acc: 0.5660377358490566\n", - "Cond:#O.##.#. - Act:6 - effect:....FO.. - Num:1 [fit: 0.001, exp: 20.00, pred: 1171.820, error:5872.941769389176]acc: 0.0\n", - "Cond:.#.#O.## - Act:1 - effect:.....OOF - Num:1 [fit: 0.006, exp: 0.00, pred: 1276.559, error:505.2786877049395]acc: None\n", - "Cond:#.##.### - Act:0 - effect:....OOO. - Num:2 [fit: 0.200, exp: 2.00, pred: 858.346, error:0.03712037778348076]acc: 1.0\n", - "Cond:.....O## - Act:2 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.#O#### - Act:2 - effect:.......O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#O##... - Act:0 - effect:......OO - Num:1 [fit: 0.014, exp: 0.00, pred: 946.725, error:491.33606715212306]acc: None\n", - "Cond:##O#.### - Act:0 - effect:.OOO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 946.725, error:491.33606715212306]acc: None\n", - "Cond:..#..##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.060, exp: 22.00, pred: 1227.722, error:250.69444446855388]acc: 1.0\n", - "Cond:###.##O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.007, exp: 160.00, pred: 1215.957, error:614.1729643625608]acc: 0.9125\n", - "Cond:....#.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.025, exp: 0.00, pred: 1512.823, error:92.4032819717715]acc: None\n", - "Cond:.#OOO... - Act:5 - effect:.....OOF - Num:1 [fit: 0.003, exp: 0.00, pred: 1328.682, error:481.86526374116465]acc: None\n", - "Cond:#.##..## - Act:2 - effect:OO.....O - Num:1 [fit: 0.017, exp: 0.00, pred: 1378.603, error:202.3769782352532]acc: None\n", - "Cond:##....#. - Act:0 - effect:......OO - Num:1 [fit: 0.001, exp: 0.00, pred: 1537.470, error:1058.4541400704106]acc: None\n", - "Cond:#..#OO#. - Act:7 - effect:...O.... - Num:9 [fit: 0.035, exp: 49.00, pred: 1135.014, error:1622.4503549176839]acc: 0.7551020408163265\n", - "Cond:.#..FO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 131.00, pred: 1810.484, error:304.51039335404965]acc: 1.0\n", - "Cond:.###.OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 138.00, pred: 1390.346, error:258.5622648613927]acc: 1.0\n", - "Cond:O..#.#.. - Act:5 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.#FO.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.071, exp: 4.00, pred: 1066.675, error:2522.6902513302803]acc: 0.5\n", - "Cond:O.#.#.O# - Act:5 - effect:OO.....O - Num:1 [fit: 0.017, exp: 0.00, pred: 1846.859, error:562.2811530143124]acc: None\n", - "Cond:.###.O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.048, exp: 0.00, pred: 1545.756, error:45.620381849615654]acc: None\n", - "Cond:..OOO.#. - Act:5 - effect:.....OOF - Num:1 [fit: 0.015, exp: 0.00, pred: 2042.543, error:104.59925413752813]acc: None\n", - "Cond:#######. - Act:1 - effect:....OOO. - Num:2 [fit: 0.009, exp: 1.00, pred: 935.299, error:1600.0]acc: 0.0\n", - "Cond:#.###O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 132.00, pred: 1552.863, error:1850.8817864492166]acc: 0.9166666666666666\n", - "Cond:#.##OO#. - Act:7 - effect:...O.... - Num:1 [fit: 0.011, exp: 42.00, pred: 1135.005, error:2158.251173787136]acc: 0.7142857142857143\n", - "Cond:.###..#O - Act:6 - effect:O......O - Num:1 [fit: 0.007, exp: 3.00, pred: 1428.866, error:2864.854539920225]acc: 0.0\n", - "Cond:#...F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 113.00, pred: 1810.484, error:304.5103933487572]acc: 1.0\n", - "Cond:####.#F# - Act:5 - effect:OO.....O - Num:1 [fit: 0.001, exp: 1.00, pred: 881.852, error:1600.0]acc: 0.0\n", - "Cond:#...#O.# - Act:4 - effect:....OOO. - Num:3 [fit: 0.117, exp: 111.00, pred: 1810.484, error:304.5103933502709]acc: 1.0\n", - "Cond:#..#.##O - Act:0 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.####O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 1.00, pred: 1065.904, error:0.0]acc: 1.0\n", - "Cond:.##..OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 128.00, pred: 1390.346, error:258.5622648612888]acc: 1.0\n", - "Cond:.#OOO.## - Act:5 - effect:.....OOF - Num:2 [fit: 0.046, exp: 0.00, pred: 2291.415, error:42.4764401284528]acc: None\n", - "Cond:####.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 2015.877, error:426.78894644334855]acc: None\n", - "Cond:..#.#O## - Act:7 - effect:....OOO. - Num:2 [fit: 0.007, exp: 236.00, pred: 1334.454, error:793.5656934214963]acc: 0.673728813559322\n", - "Cond:..#.#O#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.046, exp: 3.00, pred: 878.106, error:3671.405614488382]acc: 0.3333333333333333\n", - "Cond:.#...... - Act:5 - effect:....OOO. - Num:1 [fit: 0.200, exp: 2.00, pred: 956.170, error:17.760536894678864]acc: 1.0\n", - "Cond:.##.#..# - Act:6 - effect:O......O - Num:2 [fit: 0.013, exp: 18.00, pred: 1324.437, error:5165.4488968523765]acc: 0.5\n", - "Cond:..#.#O.# - Act:7 - effect:...OO... - Num:1 [fit: 0.008, exp: 46.00, pred: 1463.133, error:1481.0437436492618]acc: 0.6739130434782609\n", - "Cond:###.OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.O.#...# - Act:3 - effect:..OO.... - Num:8 [fit: 0.083, exp: 37.00, pred: 1894.396, error:544.4345837287093]acc: 0.972972972972973\n", - "Cond:.#####F# - Act:6 - effect:....OOO. - Num:2 [fit: 0.000, exp: 1.00, pred: 1065.904, error:0.0]acc: 1.0\n", - "Cond:.##....# - Act:6 - effect:O......O - Num:1 [fit: 0.006, exp: 14.00, pred: 1319.326, error:6231.597920082826]acc: 0.42857142857142855\n", - "Cond:.#.###.# - Act:6 - effect:O......O - Num:1 [fit: 0.000, exp: 25.00, pred: 1315.664, error:2962.9481260275156]acc: 0.2\n", - "Cond:O..##### - Act:5 - effect:OO...... - Num:2 [fit: 0.006, exp: 40.00, pred: 1161.702, error:6423.168708945026]acc: 0.375\n", - "Cond:O..##### - Act:1 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#..#OO## - Act:7 - effect:...O.... - Num:2 [fit: 0.020, exp: 26.00, pred: 1135.923, error:1597.9151887884639]acc: 0.7307692307692307\n", - "Cond:#.#..... - Act:6 - effect:.....OF. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.#..#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.002, exp: 34.00, pred: 2349.673, error:7771.731802748431]acc: 0.35294117647058826\n", - "Cond:O#.#OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.029, exp: 0.00, pred: 1734.936, error:229.4599165861822]acc: None\n", - "Cond:..#.###F - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 118.00, pred: 1390.346, error:258.5622648581576]acc: 1.0\n", - "Cond:O..##O## - Act:5 - effect:OO...... - Num:4 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#O.#...# - Act:6 - effect:.O...... - Num:1 [fit: 0.005, exp: 22.00, pred: 1073.090, error:4070.5127274606575]acc: 0.4090909090909091\n", - "Cond:##..OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.023, exp: 0.00, pred: 1758.938, error:125.33367171550346]acc: None\n", - "Cond:##.#OO#. - Act:1 - effect:...O.... - Num:1 [fit: 0.026, exp: 1.00, pred: 935.299, error:1800.0]acc: 0.0\n", - "Cond:.##.#O.# - Act:4 - effect:....OOO. - Num:3 [fit: 0.117, exp: 86.00, pred: 1810.484, error:304.5103923092307]acc: 1.0\n", - "Cond:#..#OO.. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.109, exp: 13.00, pred: 1058.391, error:4198.221345684996]acc: 0.7692307692307693\n", - "Cond:.#.##..# - Act:4 - effect:..OO.... - Num:1 [fit: 0.127, exp: 2.00, pred: 834.046, error:1507.562118843908]acc: 0.0\n", - "Cond:.#..#OO# - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 115.00, pred: 1390.346, error:258.56226486040384]acc: 1.0\n", - "Cond:OO.####. - Act:3 - effect:....OOO. - Num:2 [fit: 0.011, exp: 13.00, pred: 1752.775, error:764.9716966385638]acc: 0.6153846153846154\n", - "Cond:#.#####. - Act:6 - effect:.....OF. - Num:1 [fit: 0.002, exp: 36.00, pred: 1077.995, error:5475.544040570264]acc: 0.4166666666666667\n", - "Cond:#...OO## - Act:7 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 1805.431, error:433.9558797218027]acc: None\n", - "Cond:#...OO#. - Act:7 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 1805.431, error:433.9558797218027]acc: None\n", - "Cond:#..#O### - Act:7 - effect:...O.... - Num:2 [fit: 0.077, exp: 15.00, pred: 1150.668, error:1593.9931568541483]acc: 0.6\n", - "Cond:..#..O## - Act:1 - effect:....OOO. - Num:1 [fit: 0.079, exp: 0.00, pred: 1323.746, error:50.875884778680685]acc: None\n", - "Cond:.#...O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 159.00, pred: 1390.346, error:258.5628917272434]acc: 0.8742138364779874\n", - "Cond:O...O### - Act:7 - effect:...O.... - Num:1 [fit: 0.007, exp: 0.00, pred: 1862.645, error:317.1314928624257]acc: None\n", - "Cond:#.##OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 13.00, pred: 1058.391, error:3615.499809684996]acc: 0.15384615384615385\n", - "Cond:...#.##F - Act:7 - effect:....OOO. - Num:2 [fit: 0.021, exp: 108.00, pred: 1390.346, error:258.5622647924366]acc: 1.0\n", - "Cond:#.##.#.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.001, exp: 30.00, pred: 1079.319, error:7103.2597433180035]acc: 0.13333333333333333\n", - "Cond:O..#.#OO - Act:5 - effect:...FOO.. - Num:2 [fit: 0.166, exp: 0.00, pred: 2333.000, error:50.82819257110151]acc: None\n", - "Cond:##.#O#.# - Act:7 - effect:...O.... - Num:1 [fit: 0.082, exp: 11.00, pred: 1065.789, error:1993.1105110422407]acc: 0.45454545454545453\n", - "Cond:#.#OO##. - Act:5 - effect:.....F.. - Num:2 [fit: 0.007, exp: 88.00, pred: 1779.096, error:1954.9398451520574]acc: 0.7272727272727273\n", - "Cond:##..#..# - Act:6 - effect:.....OF. - Num:3 [fit: 0.005, exp: 23.00, pred: 999.530, error:2288.2417655470713]acc: 0.0\n", - "Cond:###.###. - Act:6 - effect:.....OF. - Num:2 [fit: 0.006, exp: 18.00, pred: 1210.713, error:6697.455448515967]acc: 0.0\n", - "Cond:....#F#. - Act:4 - effect:....FO.. - Num:1 [fit: 0.008, exp: 18.00, pred: 1413.982, error:5798.971734043076]acc: 0.1111111111111111\n", - "Cond:##..#..O - Act:1 - effect:.....OF. - Num:1 [fit: 0.031, exp: 0.00, pred: 1340.449, error:92.49727091545086]acc: None\n", - "Cond:O..#O### - Act:5 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#..##O.. - Act:7 - effect:...OO... - Num:1 [fit: 0.004, exp: 49.00, pred: 1463.075, error:1856.1093462579197]acc: 0.5102040816326531\n", - "Cond:.#..##.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.062, exp: 23.00, pred: 1226.448, error:249.3935074579548]acc: 1.0\n", - "Cond:.#..OO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1183.739, error:46.214348449366696]acc: None\n", - "Cond:O#.##O.# - Act:2 - effect:...O.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1141.920, error:314.1732622066137]acc: None\n", - "Cond:#..#O#.. - Act:7 - effect:...OO... - Num:1 [fit: 0.011, exp: 4.00, pred: 1021.887, error:2851.9511530089876]acc: 0.0\n", - "Cond:##...#.# - Act:6 - effect:.....OF. - Num:2 [fit: 0.002, exp: 17.00, pred: 1006.109, error:2839.0378238338785]acc: 0.0\n", - "Cond:.O.#..## - Act:3 - effect:..OO.... - Num:2 [fit: 0.025, exp: 28.00, pred: 1894.027, error:544.460109866823]acc: 0.9642857142857143\n", - "Cond:#.##O#.# - Act:4 - effect:...FOO.. - Num:1 [fit: 0.012, exp: 15.00, pred: 1036.441, error:3677.946657853204]acc: 0.5333333333333333\n", - "Cond:.####.F# - Act:3 - effect:......OO - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#.#.#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 105.00, pred: 1390.346, error:258.5622648228636]acc: 1.0\n", - "Cond:O#.##O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1252.629, error:553.0144001468202]acc: None\n", - "Cond:.##.#O#O - Act:5 - effect:OO.....O - Num:1 [fit: 0.036, exp: 0.00, pred: 1426.636, error:140.11201491976954]acc: None\n", - "Cond:.####O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 76.00, pred: 1552.863, error:1850.8818944783216]acc: 0.8552631578947368\n", - "Cond:##.#O##. - Act:7 - effect:...O.... - Num:1 [fit: 0.208, exp: 2.00, pred: 949.867, error:40.41293808255301]acc: 1.0\n", - "Cond:##.##O#. - Act:6 - effect:.....OF. - Num:1 [fit: 0.011, exp: 0.00, pred: 1100.023, error:424.36576677350195]acc: None\n", - "Cond:.##.#OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1100.023, error:424.36576677350195]acc: None\n", - "Cond:.#.#..## - Act:6 - effect:O......O - Num:1 [fit: 0.206, exp: 2.00, pred: 964.809, error:15.29490807875817]acc: 1.0\n", - "Cond:..##.#.# - Act:7 - effect:OO.....O - Num:3 [fit: 0.002, exp: 20.00, pred: 1434.997, error:6072.516557756199]acc: 0.0\n", - "Cond:..###O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 72.00, pred: 1552.863, error:2083.739338098909]acc: 0.875\n", - "Cond:###F#O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 11.00, pred: 1040.729, error:3041.693727953356]acc: 0.18181818181818182\n", - "Cond:.##.###. - Act:7 - effect:O......O - Num:1 [fit: 0.001, exp: 74.00, pred: 1515.491, error:1814.9713071956976]acc: 0.40540540540540543\n", - "Cond:#.#.OO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1358.908, error:49.35033770746318]acc: None\n", - "Cond:##..#O#. - Act:3 - effect:..OO.... - Num:2 [fit: 0.002, exp: 19.00, pred: 1054.351, error:5838.3199152682655]acc: 0.0\n", - "Cond:.O.##..# - Act:3 - effect:..OO.... - Num:1 [fit: 0.013, exp: 25.00, pred: 1893.381, error:545.3743710888023]acc: 0.96\n", - "Cond:..##.##. - Act:6 - effect:.....OF. - Num:1 [fit: 0.004, exp: 9.00, pred: 1081.892, error:5353.1699694337185]acc: 0.0\n", - "Cond:##.##.## - Act:6 - effect:.....OF. - Num:1 [fit: 0.363, exp: 3.00, pred: 831.219, error:2013.100400622765]acc: 0.0\n", - "Cond:..##.O#. - Act:6 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1253.584, error:258.98862980984876]acc: None\n", - "Cond:..##.O#. - Act:0 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1253.584, error:258.98862980984876]acc: None\n", - "Cond:####O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.027, exp: 70.00, pred: 1154.868, error:138.80192500690075]acc: 0.5714285714285714\n", - "Cond:#..#OO#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1189.213, error:37.202323446893594]acc: None\n", - "Cond:##O.##.. - Act:6 - effect:O......O - Num:2 [fit: 0.069, exp: 7.00, pred: 1280.528, error:3673.1470655100375]acc: 0.14285714285714285\n", - "Cond:.O.##### - Act:7 - effect:..OO.... - Num:1 [fit: 0.043, exp: 0.00, pred: 1268.804, error:93.0613590294991]acc: None\n", - "Cond:.#.OO#.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1210.349, error:187.04969584537844]acc: None\n", - "Cond:.#.O##.. - Act:4 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 0.00, pred: 971.928, error:675.8559450876292]acc: None\n", - "Cond:..##O... - Act:4 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 971.928, error:675.8559450876292]acc: None\n", - "Cond:#O.####. - Act:3 - effect:..OO.... - Num:3 [fit: 0.005, exp: 27.00, pred: 1813.188, error:1555.3735603347022]acc: 0.7777777777777778\n", - "Cond:###O#### - Act:3 - effect:...OO... - Num:1 [fit: 0.021, exp: 43.00, pred: 1233.927, error:2339.042623393842]acc: 0.3953488372093023\n", - "Cond:.#...#.# - Act:3 - effect:..OO.... - Num:2 [fit: 0.000, exp: 26.00, pred: 1883.055, error:1025.6484550536513]acc: 0.46153846153846156\n", - "Cond:..###.O# - Act:3 - effect:......OO - Num:1 [fit: 0.046, exp: 5.00, pred: 1329.469, error:4182.647833578965]acc: 0.0\n", - "Cond:.O.#.... - Act:4 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:O.####.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1350.093, error:189.16945192227723]acc: None\n", - "Cond:O#.#.#O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.013, exp: 0.00, pred: 1396.810, error:797.5373577989442]acc: None\n", - "Cond:O##.#### - Act:3 - effect:....OOO. - Num:5 [fit: 0.012, exp: 26.00, pred: 1360.619, error:4109.447541312409]acc: 0.5384615384615384\n", - "Cond:#.###### - Act:5 - effect:.....F.. - Num:1 [fit: 0.003, exp: 282.00, pred: 1325.215, error:2072.6698508224476]acc: 0.2765957446808511\n", - "Cond:O###.#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.012, exp: 21.00, pred: 1356.948, error:4363.872694834356]acc: 0.42857142857142855\n", - "Cond:..#.#.## - Act:4 - effect:....FO.. - Num:2 [fit: 0.518, exp: 10.00, pred: 1119.882, error:3134.351408933204]acc: 0.8\n", - "Cond:...#.#.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.084, exp: 0.00, pred: 1330.378, error:68.42931461749578]acc: None\n", - "Cond:###.##.. - Act:6 - effect:.......O - Num:1 [fit: 0.315, exp: 5.00, pred: 1154.667, error:111.9561949556136]acc: 1.0\n", - "Cond:##O.#### - Act:6 - effect:O......O - Num:1 [fit: 0.004, exp: 5.00, pred: 1154.667, error:3351.9561949556137]acc: 0.0\n", - "Cond:#####.## - Act:5 - effect:.....OOF - Num:2 [fit: 0.001, exp: 295.00, pred: 1463.588, error:4491.207326531993]acc: 0.22372881355932203\n", - "Cond:.##..#.# - Act:6 - effect:.......O - Num:1 [fit: 0.280, exp: 4.00, pred: 1107.096, error:100.97316896593412]acc: 1.0\n", - "Cond:##O#.#.. - Act:3 - effect:.OOO.... - Num:2 [fit: 0.233, exp: 22.00, pred: 1089.084, error:4067.8347443197918]acc: 0.45454545454545453\n", - "Cond:#O.F###. - Act:3 - effect:..OO.... - Num:1 [fit: 0.060, exp: 0.00, pred: 1282.514, error:236.52969170793807]acc: None\n", - "Cond:#O.###.# - Act:3 - effect:..OO.... - Num:2 [fit: 0.001, exp: 30.00, pred: 1570.192, error:1864.0510593453869]acc: 0.5666666666666667\n", - "Cond:.O.#..#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.049, exp: 0.00, pred: 1470.378, error:412.7436471834021]acc: None\n", - "Cond:.....### - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 137.00, pred: 1193.613, error:1763.3840329649754]acc: 0.7883211678832117\n", - "Cond:.##..#O# - Act:2 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1413.605, error:432.0663164150459]acc: None\n", - "Cond:#.##O### - Act:7 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1367.235, error:116.72304772078746]acc: None\n", - "Cond:#O.###.. - Act:3 - effect:..OO.... - Num:1 [fit: 0.015, exp: 20.00, pred: 1816.713, error:1521.4394247613805]acc: 0.8\n", - "Cond:.O#####. - Act:3 - effect:..OO.... - Num:1 [fit: 0.004, exp: 28.00, pred: 1856.827, error:594.7288993213639]acc: 0.5714285714285714\n", - "Cond:.#.##O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.021, exp: 40.00, pred: 1158.217, error:146.39108125007044]acc: 0.775\n", - "Cond:O...#O.. - Act:7 - effect:...OO... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#...##.. - Act:7 - effect:...OO... - Num:1 [fit: 0.004, exp: 29.00, pred: 1351.845, error:3397.6330491387316]acc: 0.6206896551724138\n", - "Cond:####O.#. - Act:4 - effect:.....OOF - Num:1 [fit: 0.023, exp: 0.00, pred: 1563.873, error:72.41263172446259]acc: None\n", - "Cond:.##.OOO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##.##... - Act:5 - effect:.....F.. - Num:1 [fit: 0.001, exp: 81.00, pred: 1782.915, error:1606.7908173211304]acc: 0.5555555555555556\n", - "Cond:#.##..## - Act:7 - effect:......OO - Num:1 [fit: 0.001, exp: 39.00, pred: 1228.129, error:3029.724040014957]acc: 0.05128205128205128\n", - "Cond:#....#.. - Act:7 - effect:...OO... - Num:1 [fit: 0.031, exp: 2.00, pred: 983.942, error:3006.575163618507]acc: 0.0\n", - "Cond:#..###.. - Act:7 - effect:...OO... - Num:2 [fit: 0.006, exp: 29.00, pred: 1350.353, error:3259.005443236227]acc: 0.5172413793103449\n", - "Cond:#O.##..# - Act:3 - effect:..OO.... - Num:1 [fit: 0.001, exp: 28.00, pred: 1569.233, error:1596.2976053278912]acc: 0.5357142857142857\n", - "Cond:.O##.### - Act:7 - effect:..OO.... - Num:1 [fit: 0.001, exp: 18.00, pred: 2406.659, error:7015.775956440303]acc: 0.0\n", - "Cond:....#OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 80.00, pred: 1390.346, error:258.56226855577995]acc: 1.0\n", - "Cond:.#.#.### - Act:1 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1268.716, error:44.913641967231975]acc: None\n", - "Cond:#..##### - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 172.00, pred: 1247.309, error:1852.2806388202648]acc: 0.45930232558139533\n", - "Cond:.....### - Act:6 - effect:......OO - Num:1 [fit: 0.003, exp: 0.00, pred: 1200.959, error:521.1792755907857]acc: None\n", - "Cond:..#.##.. - Act:1 - effect:...OO... - Num:1 [fit: 0.018, exp: 0.00, pred: 1593.684, error:58.776058031620515]acc: None\n", - "Cond:#...#O## - Act:7 - effect:....OOO. - Num:2 [fit: 0.008, exp: 116.00, pred: 1334.454, error:1009.1978160741975]acc: 0.7155172413793104\n", - "Cond:.##O.##. - Act:3 - effect:O......O - Num:1 [fit: 0.130, exp: 12.00, pred: 1108.785, error:3879.9363636544645]acc: 0.0\n", - "Cond:##O##.#. - Act:3 - effect:...FOO.. - Num:1 [fit: 0.124, exp: 12.00, pred: 1108.785, error:2502.3363636544645]acc: 0.0\n", - "Cond:.#.#.##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 76.00, pred: 1390.346, error:258.56226838568904]acc: 1.0\n", - "Cond:#.##.#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.016, exp: 76.00, pred: 1390.346, error:258.56226838568904]acc: 1.0\n", - "Cond:.#..##.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.001, exp: 28.00, pred: 1859.560, error:6917.541346414081]acc: 0.03571428571428571\n", - "Cond:#.##.#.. - Act:7 - effect:...OO... - Num:3 [fit: 0.005, exp: 16.00, pred: 1430.456, error:5509.6170129469065]acc: 0.0\n", - "Cond:...###.# - Act:7 - effect:OO.....O - Num:1 [fit: 0.001, exp: 24.00, pred: 1348.226, error:5684.677965082406]acc: 0.041666666666666664\n", - "Cond:##..#O## - Act:3 - effect:.....OOF - Num:1 [fit: 0.044, exp: 9.00, pred: 1046.474, error:3931.84357813756]acc: 0.2222222222222222\n", - "Cond:#..##O#F - Act:4 - effect:O......O - Num:2 [fit: 0.014, exp: 31.00, pred: 1311.826, error:1879.906435141995]acc: 0.8709677419354839\n", - "Cond:#O.O..#. - Act:3 - effect:..OO.... - Num:1 [fit: 0.020, exp: 0.00, pred: 1682.747, error:317.905652058923]acc: None\n", - "Cond:..##.### - Act:6 - effect:.....OF. - Num:1 [fit: 0.016, exp: 5.00, pred: 1073.509, error:5002.252057379863]acc: 0.0\n", - "Cond:..##.##. - Act:7 - effect:.....OF. - Num:2 [fit: 0.007, exp: 32.00, pred: 1437.702, error:2035.5870539619636]acc: 0.5\n", - "Cond:#..##O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 51.00, pred: 1552.861, error:2159.3079591855617]acc: 0.9019607843137255\n", - "Cond:#.##.##. - Act:6 - effect:.....OF. - Num:1 [fit: 0.023, exp: 1.00, pred: 888.596, error:1600.0]acc: 0.0\n", - "Cond:###O..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1174.468, error:451.2296884698402]acc: None\n", - "Cond:###..#O# - Act:1 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 1323.134, error:1086.4749562049308]acc: None\n", - "Cond:.##.#O## - Act:3 - effect:.....OOF - Num:1 [fit: 0.254, exp: 5.00, pred: 1065.267, error:3457.4692929015555]acc: 0.4\n", - "Cond:.##..O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.016, exp: 83.00, pred: 1390.346, error:258.562783697473]acc: 0.963855421686747\n", - "Cond:###.F### - Act:3 - effect:....OOO. - Num:1 [fit: 0.031, exp: 4.00, pred: 1102.163, error:2084.9406675518812]acc: 0.0\n", - "Cond:#O.OO..# - Act:3 - effect:..OO.... - Num:1 [fit: 0.009, exp: 0.00, pred: 1427.964, error:322.25435812191864]acc: None\n", - "Cond:.O.##.## - Act:3 - effect:..OO.... - Num:3 [fit: 0.389, exp: 12.00, pred: 1955.502, error:510.29140678788906]acc: 1.0\n", - "Cond:####O##. - Act:5 - effect:.....OOF - Num:2 [fit: 0.001, exp: 55.00, pred: 1475.245, error:4902.971106960886]acc: 0.5818181818181818\n", - "Cond:###OO.## - Act:5 - effect:.....F.. - Num:1 [fit: 0.000, exp: 43.00, pred: 1779.139, error:1636.1021798156926]acc: 0.7906976744186046\n", - "Cond:#.#.###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 73.00, pred: 1390.346, error:258.5622435481139]acc: 1.0\n", - "Cond:#.#.#OOF - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1308.763, error:56.35714034039304]acc: None\n", - "Cond:...#.#F# - Act:4 - effect:....OOO. - Num:4 [fit: 0.008, exp: 41.00, pred: 1368.571, error:3677.853789976298]acc: 0.6585365853658537\n", - "Cond:....#### - Act:5 - effect:......OO - Num:1 [fit: 0.004, exp: 56.00, pred: 1256.607, error:5610.3896337863225]acc: 0.42857142857142855\n", - "Cond:##..##.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 80.00, pred: 1456.111, error:4421.66776451531]acc: 0.7375\n", - "Cond:#..#OO.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1759.996, error:100.12360732101736]acc: None\n", - "Cond:###.#### - Act:7 - effect:O......O - Num:1 [fit: 0.005, exp: 127.00, pred: 1168.080, error:1905.1974132118016]acc: 0.6456692913385826\n", - "Cond:.####.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 2444.231, error:470.066341177044]acc: None\n", - "Cond:.##.#O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1609.889, error:49.567614653016776]acc: None\n", - "Cond:#...OO.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1609.889, error:49.567614653016776]acc: None\n", - "Cond:###.#.## - Act:1 - effect:...O.... - Num:1 [fit: 0.072, exp: 0.00, pred: 1254.506, error:382.88002663103464]acc: None\n", - "Cond:##..#### - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 109.00, pred: 1167.955, error:1580.7509986411415]acc: 0.6238532110091743\n", - "Cond:O##.###O - Act:7 - effect:....OOO. - Num:2 [fit: 0.007, exp: 11.00, pred: 1180.934, error:5963.334590821655]acc: 0.0\n", - "Cond:.##OO..# - Act:5 - effect:.....F.. - Num:1 [fit: 0.000, exp: 41.00, pred: 1779.141, error:1113.4972290471053]acc: 0.8048780487804879\n", - "Cond:###.###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 65.00, pred: 1390.346, error:258.56188103808086]acc: 1.0\n", - "Cond:#..###.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.020, exp: 50.00, pred: 1154.876, error:144.19294747233374]acc: 0.54\n", - "Cond:##...#.# - Act:4 - effect:.....OOF - Num:3 [fit: 0.000, exp: 37.00, pred: 1389.520, error:3766.691750814548]acc: 0.5405405405405406\n", - "Cond:#...##O. - Act:4 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 1661.589, error:101.7175281469512]acc: None\n", - "Cond:######.. - Act:6 - effect:...OO... - Num:1 [fit: 0.030, exp: 1.00, pred: 888.596, error:1800.0]acc: 0.0\n", - "Cond:##O#O.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.022, exp: 0.00, pred: 1964.810, error:64.20815597573967]acc: None\n", - "Cond:######## - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 129.00, pred: 1233.537, error:1916.6796755511882]acc: 0.49612403100775193\n", - "Cond:.##.#.## - Act:5 - effect:...FOO.. - Num:2 [fit: 0.004, exp: 36.00, pred: 1447.747, error:1579.7114444679378]acc: 0.3611111111111111\n", - "Cond:#####.#F - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1397.029, error:438.4676346899579]acc: None\n", - "Cond:#####O#F - Act:4 - effect:.O...... - Num:1 [fit: 0.000, exp: 26.00, pred: 1310.310, error:2948.1866463369442]acc: 0.11538461538461539\n", - "Cond:#..#..## - Act:4 - effect:O......O - Num:2 [fit: 0.007, exp: 17.00, pred: 1778.479, error:6675.570905367708]acc: 0.0\n", - "Cond:.#.#F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.040, exp: 27.00, pred: 1809.867, error:304.5722745712934]acc: 1.0\n", - "Cond:##..#O.. - Act:4 - effect:....OOO. - Num:3 [fit: 0.118, exp: 27.00, pred: 1809.867, error:304.5722745712934]acc: 1.0\n", - "Cond:#.#####. - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 49.00, pred: 1154.844, error:139.33309603222378]acc: 0.6122448979591837\n", - "Cond:#.#.###. - Act:7 - effect:...OO... - Num:1 [fit: 0.003, exp: 11.00, pred: 1318.594, error:3911.9659429767353]acc: 0.18181818181818182\n", - "Cond:#O..#O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1393.007, error:783.324155596424]acc: None\n", - "Cond:######O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 75.00, pred: 1215.957, error:614.1728367445355]acc: 0.84\n", - "Cond:..#.#.## - Act:7 - effect:...O.... - Num:1 [fit: 0.031, exp: 12.00, pred: 1184.286, error:1254.4273251381242]acc: 0.0\n", - "Cond:.##.#.## - Act:4 - effect:....FO.. - Num:2 [fit: 0.052, exp: 0.00, pred: 1119.882, error:383.58785223330096]acc: None\n", - "Cond:#.###### - Act:3 - effect:....OOO. - Num:4 [fit: 0.009, exp: 54.00, pred: 1268.124, error:3018.100586159962]acc: 0.4074074074074074\n", - "Cond:#.#.##.# - Act:7 - effect:.....F.. - Num:1 [fit: 0.003, exp: 19.00, pred: 1166.818, error:4003.936816714638]acc: 0.10526315789473684\n", - "Cond:####O##. - Act:7 - effect:.....OOF - Num:1 [fit: 0.024, exp: 0.00, pred: 1326.710, error:143.63460631556654]acc: None\n", - "Cond:#.##.#.. - Act:6 - effect:...OO... - Num:1 [fit: 0.003, exp: 0.00, pred: 888.596, error:0.0]acc: None\n", - "Cond:###..##. - Act:6 - effect:.....OF. - Num:1 [fit: 0.003, exp: 0.00, pred: 888.596, error:0.0]acc: None\n", - "Cond:#.##.#F# - Act:7 - effect:...OO... - Num:1 [fit: 0.087, exp: 1.00, pred: 970.667, error:1600.0]acc: 0.0\n", - "Cond:.####..# - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 28.00, pred: 1601.525, error:6732.93130241797]acc: 0.32142857142857145\n", - "Cond:#.OO#### - Act:7 - effect:...OO... - Num:1 [fit: 0.002, exp: 12.00, pred: 1585.537, error:6773.369763957195]acc: 0.0\n", - "Cond:#.#.#O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.065, exp: 25.00, pred: 1811.951, error:301.76027856344507]acc: 1.0\n", - "Cond:#.O##### - Act:3 - effect:.OOO.... - Num:1 [fit: 0.042, exp: 0.00, pred: 1165.514, error:490.1044689639359]acc: None\n", - "Cond:###.#O#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#....## - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1371.485, error:628.6298318552335]acc: None\n", - "Cond:##.#.#.# - Act:4 - effect:.....OOF - Num:1 [fit: 0.008, exp: 28.00, pred: 1391.327, error:554.7903656669554]acc: 0.42857142857142855\n", - "Cond:#####..O - Act:5 - effect:....OOO. - Num:2 [fit: 0.006, exp: 29.00, pred: 1256.842, error:3080.9726474617974]acc: 0.6551724137931034\n", - "Cond:O#.##.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.014, exp: 14.00, pred: 1381.253, error:3657.182195123049]acc: 0.2857142857142857\n", - "Cond:#.##.### - Act:3 - effect:...OO... - Num:1 [fit: 0.076, exp: 7.00, pred: 1279.431, error:3643.4745815410815]acc: 0.42857142857142855\n", - "Cond:###OO### - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 31.00, pred: 1780.238, error:5745.475093683975]acc: 0.6129032258064516\n", - "Cond:###OO.#. - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 31.00, pred: 1780.238, error:6116.019688805969]acc: 0.6129032258064516\n", - "Cond:#...#O#F - Act:4 - effect:O......O - Num:5 [fit: 0.088, exp: 23.00, pred: 1316.689, error:1711.1465400639631]acc: 0.8695652173913043\n", - "Cond:.#...#O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 71.00, pred: 1215.957, error:622.9691685911256]acc: 0.8309859154929577\n", - "Cond:.##.#.O# - Act:7 - effect:...O.... - Num:1 [fit: 0.031, exp: 12.00, pred: 1184.286, error:1854.4273251381242]acc: 0.0\n", - "Cond:..##.#FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.055, exp: 0.00, pred: 1549.018, error:71.04042238826233]acc: None\n", - "Cond:#...#O.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1467.162, error:67.36462500402988]acc: None\n", - "Cond:#O.##... - Act:3 - effect:..OO.... - Num:1 [fit: 0.021, exp: 13.00, pred: 1849.668, error:1499.8970950624512]acc: 0.8461538461538461\n", - "Cond:.O.O##.# - Act:3 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 2401.949, error:204.05562093667643]acc: None\n", - "Cond:.####O#F - Act:1 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1613.127, error:47.72397896079944]acc: None\n", - "Cond:#.#OO.## - Act:6 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##O#O#.. - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#..#.#F# - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 34.00, pred: 1368.604, error:3522.556777922384]acc: 0.5882352941176471\n", - "Cond:.....#F# - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 34.00, pred: 1368.604, error:3776.4201111914617]acc: 0.5882352941176471\n", - "Cond:###.#.## - Act:5 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 59.00, pred: 1212.221, error:3759.9573935408916]acc: 0.3898305084745763\n", - "Cond:#O.#..## - Act:3 - effect:..OO.... - Num:3 [fit: 0.014, exp: 17.00, pred: 1588.023, error:1933.7298539032997]acc: 0.5294117647058824\n", - "Cond:..###OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 41.00, pred: 1390.338, error:258.5450954348038]acc: 1.0\n", - "Cond:.###.#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.022, exp: 41.00, pred: 1390.338, error:258.5450954348038]acc: 1.0\n", - "Cond:.####... - Act:7 - effect:....OOO. - Num:3 [fit: 0.008, exp: 21.00, pred: 1604.618, error:7143.504754689437]acc: 0.2857142857142857\n", - "Cond:#####..# - Act:7 - effect:....OOO. - Num:4 [fit: 0.003, exp: 31.00, pred: 1398.430, error:5008.345765210594]acc: 0.1935483870967742\n", - "Cond:#..#.OF# - Act:4 - effect:....OOO. - Num:3 [fit: 0.003, exp: 32.00, pred: 1368.681, error:3439.933650365328]acc: 0.5625\n", - "Cond:#####..# - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 76.00, pred: 1526.199, error:4371.394546763168]acc: 0.3684210526315789\n", - "Cond:.#..F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.050, exp: 19.00, pred: 1812.893, error:301.1436808901669]acc: 1.0\n", - "Cond:###.#.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 2739.735, error:400.99106798872964]acc: None\n", - "Cond:#...#F## - Act:4 - effect:....FO.. - Num:2 [fit: 0.002, exp: 15.00, pred: 1459.185, error:6519.562302092572]acc: 0.0\n", - "Cond:.#..#O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 34.00, pred: 1390.500, error:258.84730197773666]acc: 1.0\n", - "Cond:..#.#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.108, exp: 9.00, pred: 1345.813, error:2570.189266407418]acc: 0.0\n", - "Cond:#O.#O.## - Act:3 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 2621.117, error:334.65344707768634]acc: None\n", - "Cond:#.##...# - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 37.00, pred: 1337.677, error:2620.750852085669]acc: 0.5945945945945946\n", - "Cond:####O### - Act:5 - effect:.....OOF - Num:2 [fit: 0.002, exp: 34.00, pred: 1475.547, error:4665.499047388954]acc: 0.29411764705882354\n", - "Cond:..#.##O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 34.00, pred: 1215.763, error:613.5314130908811]acc: 0.6470588235294118\n", - "Cond:#.###.## - Act:5 - effect:....OOO. - Num:2 [fit: 0.002, exp: 81.00, pred: 1463.497, error:4960.186302139772]acc: 0.25925925925925924\n", - "Cond:O##.#.## - Act:5 - effect:....OOO. - Num:3 [fit: 0.005, exp: 24.00, pred: 1186.096, error:3403.4393581624954]acc: 0.6666666666666666\n", - "Cond:#..##.## - Act:4 - effect:O......O - Num:2 [fit: 0.007, exp: 9.00, pred: 1915.157, error:5913.617621864012]acc: 0.0\n", - "Cond:....#O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 41.00, pred: 1411.064, error:2973.2084197220747]acc: 0.6585365853658537\n", - "Cond:.###OO#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 2099.624, error:66.2273531362816]acc: None\n", - "Cond:.####O## - Act:4 - effect:......OO - Num:2 [fit: 0.003, exp: 68.00, pred: 1263.827, error:2885.0702668698495]acc: 0.39705882352941174\n", - "Cond:##.#..## - Act:3 - effect:..OO.... - Num:1 [fit: 0.004, exp: 20.00, pred: 1465.445, error:2745.007416759528]acc: 0.35\n", - "Cond:####O.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.001, exp: 18.00, pred: 1776.164, error:5851.115719421856]acc: 0.3333333333333333\n", - "Cond:#.#.###F - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1848.311, error:59.488340276707135]acc: None\n", - "Cond:#..###F# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 23.00, pred: 1368.928, error:2964.3219721304968]acc: 0.21739130434782608\n", - "Cond:#..##..# - Act:7 - effect:....OOO. - Num:2 [fit: 0.045, exp: 10.00, pred: 1201.606, error:6036.246373123637]acc: 0.0\n", - "Cond:.###OO## - Act:4 - effect:....OOO. - Num:1 [fit: 0.151, exp: 7.00, pred: 1086.205, error:3089.6303125320283]acc: 0.2857142857142857\n", - "Cond:#...O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.032, exp: 0.00, pred: 2436.813, error:105.33054747195989]acc: None\n", - "Cond:.##.#OO# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 22.00, pred: 1314.398, error:5580.62792883851]acc: 0.0\n", - "Cond:#.###OO# - Act:7 - effect:....OOO. - Num:3 [fit: 0.567, exp: 7.00, pred: 1285.887, error:141.34880202689266]acc: 1.0\n", - "Cond:##.##... - Act:1 - effect:.....F.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.##..## - Act:5 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 53.00, pred: 1242.618, error:2869.121629776039]acc: 0.4528301886792453\n", - "Cond:..#..#F# - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#...#### - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 2095.916, error:708.2347007775041]acc: None\n", - "Cond:O##.#.#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.208, exp: 2.00, pred: 1233.724, error:50.138229225828354]acc: 1.0\n", - "Cond:#.#O###. - Act:5 - effect:.....F.. - Num:4 [fit: 0.004, exp: 25.00, pred: 1731.005, error:1447.2362493830494]acc: 0.4\n", - "Cond:####O... - Act:5 - effect:.....OOF - Num:2 [fit: 0.010, exp: 16.00, pred: 1764.664, error:5301.352304382805]acc: 0.375\n", - "Cond:O..F##.# - Act:5 - effect:OO...... - Num:1 [fit: 0.020, exp: 0.00, pred: 1202.929, error:1027.1326266705116]acc: None\n", - "Cond:...##O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.050, exp: 22.00, pred: 1154.839, error:133.3218376950941]acc: 1.0\n", - "Cond:O#...### - Act:7 - effect:....OOO. - Num:1 [fit: 0.028, exp: 4.00, pred: 1066.305, error:3743.6869154124215]acc: 0.0\n", - "Cond:##.#.... - Act:3 - effect:..OO.... - Num:1 [fit: 0.053, exp: 7.00, pred: 1710.054, error:925.1991085441394]acc: 0.8571428571428571\n", - "Cond:#.###..# - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 13.00, pred: 1376.315, error:4616.901293223457]acc: 0.0\n", - "Cond:.####.## - Act:7 - effect:....OOO. - Num:2 [fit: 0.003, exp: 23.00, pred: 1277.165, error:2979.2593894949346]acc: 0.043478260869565216\n", - "Cond:....#F## - Act:7 - effect:....FO.. - Num:1 [fit: 0.012, exp: 1.00, pred: 970.791, error:1800.0]acc: 0.0\n", - "Cond:...#.#FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1946.194, error:1026.5169031163477]acc: None\n", - "Cond:...#.OF# - Act:2 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1946.194, error:1026.5169031163477]acc: None\n", - "Cond:#.##..## - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1678.693, error:457.9731907105183]acc: None\n", - "Cond:..#.#.O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.010, exp: 10.00, pred: 1199.121, error:1177.8602395158387]acc: 0.0\n", - "Cond:#..##### - Act:5 - effect:OO...... - Num:2 [fit: 0.002, exp: 66.00, pred: 1324.997, error:3793.3440656524726]acc: 0.0\n", - "Cond:...#O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.121, exp: 17.00, pred: 1150.505, error:123.30660180926184]acc: 1.0\n", - "Cond:#...###F - Act:4 - effect:O......O - Num:4 [fit: 0.268, exp: 14.00, pred: 1303.208, error:1792.9952282168222]acc: 0.8571428571428571\n", - "Cond:#.##.#.# - Act:3 - effect:...OO... - Num:1 [fit: 0.013, exp: 1.00, pred: 1064.626, error:1600.0]acc: 0.0\n", - "Cond:..#.#F#. - Act:4 - effect:....FO.. - Num:2 [fit: 0.006, exp: 8.00, pred: 1352.722, error:4913.837400125499]acc: 0.0\n", - "Cond:.#####O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.062, exp: 13.00, pred: 1219.101, error:515.5271872471739]acc: 0.3076923076923077\n", - "Cond:O##.O..# - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1636.632, error:409.3896777579979]acc: None\n", - "Cond:..##.#.. - Act:7 - effect:.....OF. - Num:1 [fit: 0.297, exp: 8.00, pred: 1468.094, error:1655.744428460448]acc: 0.75\n", - "Cond:#.##.F#. - Act:7 - effect:...OO... - Num:1 [fit: 0.005, exp: 1.00, pred: 970.791, error:1600.0]acc: 0.0\n", - "Cond:...#.OF# - Act:4 - effect:....OOO. - Num:3 [fit: 0.034, exp: 10.00, pred: 1378.467, error:2847.23615673073]acc: 0.4\n", - "Cond:#.###OO. - Act:7 - effect:....OOO. - Num:1 [fit: 0.042, exp: 0.00, pred: 1325.576, error:28.40927107690146]acc: None\n", - "Cond:###O##O. - Act:5 - effect:.....F.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1868.909, error:274.79683981654154]acc: None\n", - "Cond:#..##OOF - Act:4 - effect:O......O - Num:1 [fit: 0.134, exp: 10.00, pred: 1325.882, error:1640.2169244358677]acc: 0.8\n", - "Cond:#.#.##.. - Act:4 - effect:....OOO. - Num:3 [fit: 0.098, exp: 11.00, pred: 1486.978, error:3849.9511536785544]acc: 0.45454545454545453\n", - "Cond:.##....# - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 1.00, pred: 2030.309, error:0.0]acc: 1.0\n", - "Cond:##.##### - Act:5 - effect:OO...... - Num:2 [fit: 0.003, exp: 50.00, pred: 1324.998, error:3473.816098380293]acc: 0.0\n", - "Cond:...#OO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.031, exp: 5.00, pred: 1019.042, error:2904.323013707909]acc: 0.4\n", - "Cond:#####O.. - Act:4 - effect:...FOO.. - Num:2 [fit: 0.088, exp: 9.00, pred: 1515.948, error:5657.018484120404]acc: 0.3333333333333333\n", - "Cond:#O.F..## - Act:3 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:...#O### - Act:4 - effect:....OOO. - Num:1 [fit: 0.229, exp: 2.00, pred: 940.365, error:1506.6338360059453]acc: 0.5\n", - "Cond:....#O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.036, exp: 0.00, pred: 1232.448, error:30.155603865213806]acc: None\n", - "Cond:##.####. - Act:5 - effect:.....OOF - Num:3 [fit: 0.014, exp: 21.00, pred: 1472.764, error:4757.199911132101]acc: 0.14285714285714285\n", - "Cond:.###O.#. - Act:5 - effect:OO...... - Num:1 [fit: 0.006, exp: 8.00, pred: 1764.676, error:5211.48467106158]acc: 0.0\n", - "Cond:##.##O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.035, exp: 19.00, pred: 1066.889, error:2975.7963851657323]acc: 0.2631578947368421\n", - "Cond:#..####F - Act:4 - effect:O......O - Num:2 [fit: 0.412, exp: 8.00, pred: 1334.862, error:2388.6655066268954]acc: 0.75\n", - "Cond:##.##### - Act:3 - effect:....OOO. - Num:3 [fit: 0.021, exp: 15.00, pred: 1389.976, error:3148.9144505851864]acc: 0.4\n", - "Cond:..#.##.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 8.00, pred: 1480.336, error:4079.012632786517]acc: 0.25\n", - "Cond:#####O## - Act:4 - effect:....OOO. - Num:2 [fit: 0.006, exp: 18.00, pred: 1265.481, error:1402.1004628768858]acc: 0.3333333333333333\n", - "Cond:...#.OFF - Act:4 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1805.529, error:360.7618121801802]acc: None\n", - "Cond:##.#OO## - Act:5 - effect:.....OOF - Num:1 [fit: 0.046, exp: 10.00, pred: 1094.825, error:1398.3196472574161]acc: 0.0\n", - "Cond:.O.##### - Act:3 - effect:..OO.... - Num:1 [fit: 0.499, exp: 4.00, pred: 1416.272, error:209.31832624994382]acc: 1.0\n", - "Cond:#####.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.072, exp: 8.00, pred: 1442.142, error:3286.4807879761806]acc: 0.0\n", - "Cond:#.##.#.# - Act:7 - effect:.....OF. - Num:2 [fit: 0.132, exp: 7.00, pred: 1355.812, error:1755.021689769152]acc: 0.5714285714285714\n", - "Cond:#.#..#.. - Act:4 - effect:.....OOF - Num:2 [fit: 0.581, exp: 5.00, pred: 1334.415, error:217.55695457350893]acc: 1.0\n", - "Cond:#...#OOF - Act:4 - effect:O......O - Num:1 [fit: 0.143, exp: 3.00, pred: 1146.214, error:1262.2290378030623]acc: 0.3333333333333333\n", - "Cond:#..####O - Act:5 - effect:...FOO.. - Num:2 [fit: 0.108, exp: 2.00, pred: 1163.474, error:2167.0099162596625]acc: 0.5\n", - "Cond:#.###.#O - Act:5 - effect:....OOO. - Num:1 [fit: 0.108, exp: 2.00, pred: 1163.474, error:1567.0099162596625]acc: 0.5\n", - "Cond:.###O### - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:#.##OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.202, exp: 2.00, pred: 1096.003, error:24.25956761971736]acc: 1.0\n", - "Cond:#######. - Act:5 - effect:.....F.. - Num:2 [fit: 0.690, exp: 4.00, pred: 1415.929, error:126.99893216917172]acc: 1.0\n", - "Cond:#....OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:####O### - Act:7 - effect:OO...... - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:.#..#.O# - Act:7 - effect:...O.... - Num:1 [fit: 0.193, exp: 7.00, pred: 1158.547, error:1690.5973609965736]acc: 0.0\n", - "Cond:..#.#### - Act:7 - effect:...O.... - Num:1 [fit: 0.080, exp: 11.00, pred: 1181.142, error:2018.84065967899]acc: 0.0\n", - "Cond:.O.O...# - Act:3 - effect:..OO.... - Num:1 [fit: 0.014, exp: 0.00, pred: 2271.328, error:137.35069294667767]acc: None\n", - "Cond:..###O.# - Act:4 - effect:.....OOF - Num:1 [fit: 0.020, exp: 0.00, pred: 1596.984, error:50.47658439312167]acc: None\n", - "Cond:#.#..O.. - Act:4 - effect:.....OOF - Num:1 [fit: 0.020, exp: 0.00, pred: 1596.984, error:50.47658439312167]acc: None\n", - "Cond:.##.#O## - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:..###O## - Act:1 - effect:...FOO.. - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.01]acc: None\n", - "Cond:##O####. - Act:5 - effect:.....F.. - Num:1 [fit: 0.036, exp: 0.00, pred: 1299.649, error:13.26309697920819]acc: None\n", - "Cond:#....##. - Act:7 - effect:...OO... - Num:1 [fit: 0.009, exp: 0.00, pred: 1052.469, error:258.63673437069946]acc: None\n", - "Cond:..#.##.# - Act:7 - effect:...O.... - Num:1 [fit: 0.009, exp: 0.00, pred: 1052.469, error:258.63673437069946]acc: None\n", - "Cond:####..## - Act:3 - effect:....OOO. - Num:1 [fit: 0.007, exp: 1.00, pred: 2027.871, error:1000.0]acc: 0.0\n", - "Cond:#.###O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.213, exp: 2.00, pred: 1348.390, error:77.85687342825929]acc: 1.0\n" + "Cond:#..#.O#. - Act:6 - effect:....OOO. - Num:5 [fit: 0.047, exp: 484.00, pred: 1309.630, error:301.0095151762131]acc: 0.9090909090909091\n", + "Cond:..#..O.. - Act:2 - effect:OOFOO... - Num:1 [fit: 0.010, exp: 0.00, pred: 402.488, error:0.0]acc: None\n", + "Cond:.##..O.# - Act:2 - effect:.O...F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1130.801, error:0.0]acc: None\n", + "Cond:..O#.OF. - Act:3 - effect:O....... - Num:1 [fit: 0.010, exp: 0.00, pred: 1020.921, error:0.0]acc: None\n", + "Cond:#..#.#O. - Act:6 - effect:........ - Num:1 [fit: 0.051, exp: 0.00, pred: 889.235, error:235.93792823062267]acc: None\n", + "Cond:.....O#. - Act:6 - effect:....OOO. - Num:5 [fit: 0.045, exp: 464.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:##.F.O.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.024, exp: 0.00, pred: 1300.910, error:594.4919059325924]acc: None\n", + "Cond:.#.F.O.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.024, exp: 0.00, pred: 1300.910, error:594.4919059325924]acc: None\n", + "Cond:.#.#.#O. - Act:6 - effect:........ - Num:1 [fit: 0.018, exp: 0.00, pred: 1108.397, error:631.0816878301168]acc: None\n", + "Cond:.O.O.#.. - Act:4 - effect:.......O - Num:1 [fit: 0.004, exp: 0.00, pred: 1320.865, error:828.9086262063711]acc: None\n", + "Cond:.#..FOO# - Act:4 - effect:....F..F - Num:2 [fit: 0.006, exp: 0.00, pred: 1035.911, error:0.0]acc: None\n", + "Cond:.O...F.# - Act:7 - effect:.O...... - Num:1 [fit: 0.000, exp: 0.00, pred: 966.757, error:505.2542094578607]acc: None\n", + "Cond:..#..... - Act:5 - effect:.O...O.O - Num:4 [fit: 0.083, exp: 0.00, pred: 1077.450, error:5344.255947316998]acc: None\n", + "Cond:#.#..... - Act:5 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1272.329, error:515.2882970275518]acc: None\n", + "Cond:##O....O - Act:1 - effect:.OOO.... - Num:1 [fit: 0.008, exp: 0.00, pred: 1029.452, error:686.6862581970554]acc: None\n", + "Cond:.....OO. - Act:6 - effect:....OOO. - Num:3 [fit: 0.250, exp: 0.00, pred: 1117.120, error:28.63262506934837]acc: None\n", + "Cond:#...#... - Act:7 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 1129.098, error:976.9067463897857]acc: None\n", + "Cond:.#...O.# - Act:4 - effect:.O...F.. - Num:1 [fit: 0.009, exp: 0.00, pred: 953.846, error:847.7725095096298]acc: None\n", + "Cond:#....#O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.086, exp: 0.00, pred: 1021.402, error:230.08143878233392]acc: None\n", + "Cond:O.###... - Act:5 - effect:O....... - Num:1 [fit: 0.002, exp: 0.00, pred: 909.344, error:1233.9457140777274]acc: None\n", + "Cond:#..#.##F - Act:7 - effect:....OOO. - Num:5 [fit: 0.097, exp: 597.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:.#.FF### - Act:2 - effect:.......O - Num:1 [fit: 0.003, exp: 0.00, pred: 1112.408, error:320.9495558547107]acc: None\n", + "Cond:.O..#O## - Act:2 - effect:.O...F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1081.295, error:583.8091199126336]acc: None\n", + "Cond:#O..#O## - Act:2 - effect:.O...F.. - Num:2 [fit: 0.005, exp: 0.00, pred: 1081.295, error:583.8091199126336]acc: None\n", + "Cond:O.####O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1032.120, error:310.98410136218314]acc: None\n", + "Cond:#..F#... - Act:7 - effect:........ - Num:1 [fit: 0.001, exp: 0.00, pred: 901.872, error:1143.387258684124]acc: None\n", + "Cond:....O##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 837.745, error:316.9118905689682]acc: None\n", + "Cond:##.#.O.# - Act:2 - effect:.O...F.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1049.696, error:708.8547335656915]acc: None\n", + "Cond:##O#.O.F - Act:5 - effect:.O...O.O - Num:1 [fit: 0.006, exp: 0.00, pred: 903.218, error:605.855097555035]acc: None\n", + "Cond:##O#.#.F - Act:5 - effect:.O...O.O - Num:1 [fit: 0.006, exp: 0.00, pred: 903.218, error:605.855097555035]acc: None\n", + "Cond:.#...OO. - Act:6 - effect:....OOO. - Num:2 [fit: 2.487, exp: 0.00, pred: 1061.278, error:93.57621525097161]acc: None\n", + "Cond:..O#.#F. - Act:6 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 856.911, error:748.418500790591]acc: None\n", + "Cond:O.###.O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1006.350, error:856.9931338932665]acc: None\n", + "Cond:##.F.#.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 976.766, error:616.1790741521068]acc: None\n", + "Cond:##.O#.OO - Act:1 - effect:...O..OO - Num:1 [fit: 0.004, exp: 0.00, pred: 865.469, error:1113.95024023531]acc: None\n", + "Cond:.###.O.. - Act:7 - effect:.....O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 932.764, error:653.8741476251123]acc: None\n", + "Cond:#....O#. - Act:6 - effect:....OOO. - Num:11 [fit: 0.101, exp: 443.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:#.##O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.087, exp: 0.00, pred: 1048.932, error:17.98558191490018]acc: None\n", + "Cond:#.#..#.F - Act:5 - effect:.O...O.O - Num:1 [fit: 0.000, exp: 0.00, pred: 1350.463, error:902.1504061827877]acc: None\n", + "Cond:#....OO. - Act:6 - effect:....OOO. - Num:8 [fit: 0.127, exp: 0.00, pred: 1224.646, error:60.5299079420778]acc: None\n", + "Cond:.###..O. - Act:5 - effect:..O...O. - Num:1 [fit: 0.000, exp: 0.00, pred: 1462.021, error:801.951723191582]acc: None\n", + "Cond:....#.#. - Act:4 - effect:.....OF. - Num:1 [fit: 0.005, exp: 0.00, pred: 1188.504, error:492.69601459411433]acc: None\n", + "Cond:#.#..O.# - Act:1 - effect:.....OF. - Num:1 [fit: 0.008, exp: 0.00, pred: 1079.978, error:927.6931159268138]acc: None\n", + "Cond:##.#.##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 589.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#..#OO#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.065, exp: 0.00, pred: 1284.949, error:50.47015676150589]acc: None\n", + "Cond:#...#O#O - Act:0 - effect:...O..FO - Num:1 [fit: 0.003, exp: 0.00, pred: 2137.015, error:334.70497923246324]acc: None\n", + "Cond:#.##OOO. - Act:3 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 1278.064, error:393.375485804115]acc: None\n", + "Cond:#..##OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 585.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#.#..OOO - Act:4 - effect:.....OOF - Num:1 [fit: 0.013, exp: 0.00, pred: 1338.876, error:612.7531804625249]acc: None\n", + "Cond:...#FO.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 165.00, pred: 1131.127, error:205.1756601233588]acc: 1.0\n", + "Cond:###O#O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1200.983, error:255.2276701179535]acc: None\n", + "Cond:#..#OOOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1409.283, error:58.53493937923569]acc: None\n", + "Cond:...#OOOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1409.283, error:58.53493937923569]acc: None\n", + "Cond:...#F#.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.011, exp: 161.00, pred: 1131.127, error:205.17566012335968]acc: 1.0\n", + "Cond:##.#OO#. - Act:3 - effect:....OOO. - Num:9 [fit: 0.188, exp: 192.00, pred: 1105.491, error:158.3848377652093]acc: 1.0\n", + "Cond:.OO#.O.. - Act:0 - effect:...O.... - Num:1 [fit: 0.002, exp: 0.00, pred: 931.199, error:802.2597608663673]acc: None\n", + "Cond:........ - Act:7 - effect:.O.O.OO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1066.201, error:506.6114717044842]acc: None\n", + "Cond:.O#.#.OO - Act:3 - effect:......O. - Num:1 [fit: 0.011, exp: 0.00, pred: 952.820, error:0.0]acc: None\n", + "Cond:#...#.#. - Act:4 - effect:.....OF. - Num:1 [fit: 0.016, exp: 0.00, pred: 1006.129, error:318.04723330093515]acc: None\n", + "Cond:#.##.O.# - Act:4 - effect:.....OOF - Num:1 [fit: 0.004, exp: 0.00, pred: 1138.742, error:460.62816747149355]acc: None\n", + "Cond:##..OO.# - Act:0 - effect:.......O - Num:2 [fit: 0.016, exp: 0.00, pred: 1136.693, error:1918.6584432090172]acc: None\n", + "Cond:.##..#O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1125.373, error:628.3270452388567]acc: None\n", + "Cond:###O#.F# - Act:7 - effect:O......O - Num:1 [fit: 0.013, exp: 0.00, pred: 933.943, error:539.9601473950385]acc: None\n", + "Cond:###O##F. - Act:7 - effect:O......O - Num:1 [fit: 0.013, exp: 0.00, pred: 933.943, error:539.9601473950385]acc: None\n", + "Cond:##.F.#.. - Act:3 - effect:O...O... - Num:1 [fit: 0.002, exp: 0.00, pred: 853.992, error:0.0]acc: None\n", + "Cond:###O#.#F - Act:6 - effect:..O...O. - Num:1 [fit: 0.006, exp: 0.00, pred: 1311.078, error:0.0]acc: None\n", + "Cond:#O#O#.#F - Act:6 - effect:..O...O. - Num:1 [fit: 0.006, exp: 0.00, pred: 1311.078, error:0.0]acc: None\n", + "Cond:.##..O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1226.830, error:2536.638338354514]acc: None\n", + "Cond:O..#.#.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1232.957, error:119.14530456953801]acc: None\n", + "Cond:.....F#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 478.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:##.#.O#. - Act:6 - effect:....OOO. - Num:4 [fit: 0.036, exp: 412.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:#..#.O#O - Act:7 - effect:O......O - Num:1 [fit: 0.001, exp: 0.00, pred: 1240.556, error:640.580868779389]acc: None\n", + "Cond:#.#.#... - Act:7 - effect:...OO... - Num:2 [fit: 0.004, exp: 0.00, pred: 1100.646, error:1249.0696931239395]acc: None\n", + "Cond:..#..F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 473.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:####OO#. - Act:3 - effect:....OOO. - Num:5 [fit: 0.125, exp: 181.00, pred: 1105.491, error:158.3848377652093]acc: 1.0\n", + "Cond:......#. - Act:1 - effect:....O.FF - Num:1 [fit: 0.001, exp: 0.00, pred: 1040.797, error:824.5390976130279]acc: None\n", + "Cond:.O.#...O - Act:2 - effect:.....F.. - Num:1 [fit: 0.020, exp: 0.00, pred: 1207.843, error:30.489074753138993]acc: None\n", + "Cond:..OO..F# - Act:6 - effect:.O..O... - Num:1 [fit: 0.001, exp: 0.00, pred: 902.108, error:1079.354894868393]acc: None\n", + "Cond:#..O.##F - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1079.686, error:421.96731147759516]acc: None\n", + "Cond:.OO#.O.. - Act:7 - effect:...O.... - Num:1 [fit: 0.017, exp: 0.00, pred: 1122.034, error:1028.4459704816531]acc: None\n", + "Cond:.O.#OO.# - Act:7 - effect:.......O - Num:1 [fit: 0.001, exp: 0.00, pred: 1023.849, error:385.33459457805014]acc: None\n", + "Cond:##O.##.O - Act:1 - effect:OO.....O - Num:1 [fit: 0.017, exp: 0.00, pred: 894.607, error:1885.65504424539]acc: None\n", + "Cond:...#.F## - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 454.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:####..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 856.191, error:500.12926429418917]acc: None\n", + "Cond:O##...OO - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1096.646, error:907.9934129554417]acc: None\n", + "Cond:.....F## - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 452.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:.##..O.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.031, exp: 0.00, pred: 1222.459, error:0.0]acc: None\n", + "Cond:.######F - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 548.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:.#O#.F.. - Act:7 - effect:.....F.. - Num:1 [fit: 0.007, exp: 0.00, pred: 1399.745, error:2031.7109743478422]acc: None\n", + "Cond:....###F - Act:2 - effect:....OOO. - Num:2 [fit: 0.005, exp: 19.00, pred: 853.610, error:9896.398298371856]acc: 0.0\n", + "Cond:..#O.O#. - Act:6 - effect:.O..O... - Num:2 [fit: 0.002, exp: 0.00, pred: 1158.852, error:517.0909298700396]acc: None\n", + "Cond:..###.F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1156.314, error:1269.8113169437909]acc: None\n", + "Cond:O.#FO### - Act:1 - effect:...O..FO - Num:2 [fit: 0.007, exp: 0.00, pred: 803.265, error:545.3124537187368]acc: None\n", + "Cond:.#..FO.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 130.00, pred: 1131.127, error:205.17566012307773]acc: 1.0\n", + "Cond:.OO#.O#. - Act:7 - effect:...O.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1142.351, error:1122.7917930536382]acc: None\n", + "Cond:###O##F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1357.367, error:608.4248498194593]acc: None\n", + "Cond:.#####F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 393.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:...#.O.# - Act:0 - effect:..OO.... - Num:1 [fit: 0.000, exp: 0.00, pred: 1248.330, error:1417.7874363837213]acc: None\n", + "Cond:###.O.#. - Act:4 - effect:..O..... - Num:1 [fit: 0.013, exp: 0.00, pred: 875.885, error:1502.196337780852]acc: None\n", + "Cond:##..OO#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1285.002, error:1375.8762186933247]acc: None\n", + "Cond:#.#..F#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 443.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:..#..O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.018, exp: 391.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:#.#.OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 1.712, exp: 0.00, pred: 1069.779, error:59.62231876636257]acc: None\n", + "Cond:#O.O##.# - Act:5 - effect:.O..O... - Num:1 [fit: 0.000, exp: 0.00, pred: 1295.844, error:756.8560925341309]acc: None\n", + "Cond:...#O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.040, exp: 0.00, pred: 1173.381, error:37.82685641439278]acc: None\n", + "Cond:O#O...## - Act:3 - effect:.......O - Num:1 [fit: 0.005, exp: 0.00, pred: 1391.376, error:631.0579420338574]acc: None\n", + "Cond:.O#.###O - Act:1 - effect:.......O - Num:1 [fit: 0.008, exp: 0.00, pred: 1025.797, error:749.0963136173689]acc: None\n", + "Cond:.##O#F#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1100.619, error:687.9537702181892]acc: None\n", + "Cond:.#...F## - Act:5 - effect:....OOO. - Num:4 [fit: 0.070, exp: 434.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:##O####O - Act:2 - effect:..O..... - Num:1 [fit: 0.026, exp: 0.00, pred: 1044.687, error:3909.6174732321365]acc: None\n", + "Cond:#..#.OO. - Act:1 - effect:O.OO.O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1451.805, error:702.1968670089854]acc: None\n", + "Cond:#.##F.## - Act:5 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 969.796, error:324.62833442577335]acc: None\n", + "Cond:#....#.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 429.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:.#...F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 429.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:O####O.. - Act:3 - effect:.......O - Num:1 [fit: 0.015, exp: 0.00, pred: 1007.934, error:0.0]acc: None\n", + "Cond:#O..OO## - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1128.637, error:1149.9595501005044]acc: None\n", + "Cond:.###.O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1021.402, error:608.5697841496428]acc: None\n", + "Cond:..##O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.032, exp: 0.00, pred: 1249.765, error:55.681188389018565]acc: None\n", + "Cond:.#.O##.O - Act:1 - effect:.......O - Num:1 [fit: 0.208, exp: 0.00, pred: 1112.464, error:142.10072146819311]acc: None\n", + "Cond:###F#... - Act:0 - effect:.......O - Num:1 [fit: 0.001, exp: 0.00, pred: 1002.648, error:1106.6706406550547]acc: None\n", + "Cond:####OOO. - Act:3 - effect:....OOO. - Num:3 [fit: 0.218, exp: 0.00, pred: 1123.296, error:35.43226524489141]acc: None\n", + "Cond:...O###O - Act:6 - effect:.......O - Num:1 [fit: 0.168, exp: 0.00, pred: 1303.354, error:1555.327302728399]acc: None\n", + "Cond:##..O##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1025.179, error:751.0473087856219]acc: None\n", + "Cond:####OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.022, exp: 169.00, pred: 1105.491, error:158.3848377652093]acc: 1.0\n", + "Cond:O..#O### - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 979.307, error:1507.0961953160954]acc: None\n", + "Cond:##.#O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.181, exp: 0.00, pred: 1071.315, error:26.59546281179921]acc: None\n", + "Cond:##.#OOO. - Act:5 - effect:....OOO. - Num:1 [fit: 1.096, exp: 0.00, pred: 1059.072, error:103.34728876970632]acc: None\n", + "Cond:..##.O.# - Act:4 - effect:O.O...O. - Num:1 [fit: 0.010, exp: 0.00, pred: 845.435, error:1139.0302406427245]acc: None\n", + "Cond:..#...#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1004.325, error:1042.020248249573]acc: None\n", + "Cond:..#.O.#. - Act:3 - effect:OO.....O - Num:1 [fit: 0.018, exp: 0.00, pred: 927.424, error:910.3921764689766]acc: None\n", + "Cond:##..OO## - Act:2 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1009.019, error:1293.7446007821432]acc: None\n", + "Cond:#O###.#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 2078.322, error:206.04210295141172]acc: None\n", + "Cond:.#...FO# - Act:5 - effect:....OOO. - Num:1 [fit: 0.033, exp: 0.00, pred: 1393.496, error:276.46554362073937]acc: None\n", + "Cond:.#.O#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1266.881, error:776.1566146402502]acc: None\n", + "Cond:##...OO. - Act:5 - effect:O.OO.O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1064.734, error:1577.1251211566548]acc: None\n", + "Cond:#..O#O## - Act:1 - effect:O.OO.O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1064.734, error:1577.1251211566548]acc: None\n", + "Cond:#O###O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1074.737, error:1195.8873686810916]acc: None\n", + "Cond:#..#FO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 102.00, pred: 1131.127, error:205.17566003387884]acc: 1.0\n", + "Cond:O..#.O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1292.845, error:422.3488663314465]acc: None\n", + "Cond:.#OO.#O. - Act:5 - effect:.....OF. - Num:1 [fit: 0.008, exp: 0.00, pred: 1047.870, error:1267.6619579252433]acc: None\n", + "Cond:.###.F## - Act:2 - effect:.....OF. - Num:1 [fit: 0.016, exp: 53.00, pred: 1272.540, error:216.19869088382717]acc: 0.5283018867924528\n", + "Cond:...#.O#. - Act:1 - effect:....OOO. - Num:2 [fit: 0.000, exp: 24.00, pred: 1014.009, error:8875.937220950615]acc: 0.0\n", + "Cond:.#...O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.034, exp: 0.00, pred: 1046.046, error:103.7642716752265]acc: None\n", + "Cond:.###OOO# - Act:3 - effect:....OOO. - Num:1 [fit: 0.093, exp: 0.00, pred: 1039.295, error:10.955034451876042]acc: None\n", + "Cond:#O.#O.#. - Act:4 - effect:....O.FF - Num:1 [fit: 0.015, exp: 0.00, pred: 784.296, error:254.13143444597392]acc: None\n", + "Cond:OO#.#O#O - Act:4 - effect:O.O..... - Num:1 [fit: 0.020, exp: 0.00, pred: 1348.108, error:3130.4048639017324]acc: None\n", + "Cond:#...##OF - Act:2 - effect:....OOO. - Num:2 [fit: 0.012, exp: 14.00, pred: 846.479, error:8731.995288609542]acc: 0.0\n", + "Cond:###.O### - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 938.903, error:871.0115584883672]acc: None\n", + "Cond:###.O### - Act:6 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 998.870, error:467.1599039125713]acc: None\n", + "Cond:##.#OO## - Act:3 - effect:....OOO. - Num:2 [fit: 0.063, exp: 164.00, pred: 1105.491, error:158.38483776520974]acc: 1.0\n", + "Cond:...#.FO# - Act:5 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1187.035, error:60.03023287703617]acc: None\n", + "Cond:.##..OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 513.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:O##.O##O - Act:4 - effect:O.O..... - Num:1 [fit: 0.018, exp: 0.00, pred: 1402.788, error:2178.3714676998434]acc: None\n", + "Cond:#.#F#.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 990.595, error:865.9835135610208]acc: None\n", + "Cond:.#.##F## - Act:0 - effect:......OO - Num:1 [fit: 0.000, exp: 25.00, pred: 1071.779, error:6849.275133886496]acc: 0.08\n", + "Cond:..##.FO. - Act:5 - effect:....OOO. - Num:2 [fit: 0.983, exp: 0.00, pred: 1033.312, error:13.311852830669409]acc: None\n", + "Cond:#....OO. - Act:3 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 1100.649, error:30.787024084124344]acc: None\n", + "Cond:.##O.O#. - Act:6 - effect:..O..... - Num:1 [fit: 0.009, exp: 0.00, pred: 1289.849, error:1013.6015034400899]acc: None\n", + "Cond:##...O.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 887.165, error:824.6544737766515]acc: None\n", + "Cond:####OOO# - Act:3 - effect:....OOO. - Num:2 [fit: 0.700, exp: 0.00, pred: 1245.676, error:216.25650183748743]acc: None\n", + "Cond:..O...## - Act:2 - effect:..OO.... - Num:1 [fit: 0.002, exp: 0.00, pred: 927.116, error:277.7368246744554]acc: None\n", + "Cond:#O###.O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 910.026, error:831.0380782570147]acc: None\n", + "Cond:#O.O#.#. - Act:4 - effect:....O.FF - Num:1 [fit: 0.025, exp: 0.00, pred: 1157.598, error:5248.6823552240385]acc: None\n", + "Cond:#O#.##O# - Act:1 - effect:.....OF. - Num:1 [fit: 0.003, exp: 0.00, pred: 811.253, error:557.8509258442576]acc: None\n", + "Cond:.O#O.O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 913.613, error:634.2419250217433]acc: None\n", + "Cond:###.O#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1200.688, error:384.9604454872805]acc: None\n", + "Cond:.#..OO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1156.810, error:405.64434939914383]acc: None\n", + "Cond:##..OO## - Act:3 - effect:....OOO. - Num:4 [fit: 0.021, exp: 0.00, pred: 1421.387, error:60.192312346582035]acc: None\n", + "Cond:##O.#.#O - Act:6 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 1124.445, error:3556.5123974385356]acc: None\n", + "Cond:..###F.O - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 757.952, error:201.414775336809]acc: None\n", + "Cond:#.O.#### - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1045.634, error:696.6290423684479]acc: None\n", + "Cond:..#..F## - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 25.00, pred: 1071.779, error:9028.602968553936]acc: 0.0\n", + "Cond:####O##O - Act:5 - effect:.....OF. - Num:1 [fit: 0.016, exp: 0.00, pred: 1090.456, error:223.8975135697306]acc: None\n", + "Cond:.#.#.FO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.042, exp: 0.00, pred: 1102.427, error:27.60170927347098]acc: None\n", + "Cond:..O##..# - Act:0 - effect:.......O - Num:1 [fit: 0.000, exp: 25.00, pred: 1030.429, error:8728.667023550193]acc: 0.04\n", + "Cond:#.....#. - Act:4 - effect:....O.FF - Num:1 [fit: 0.001, exp: 0.00, pred: 1174.726, error:1550.575023973563]acc: None\n", + "Cond:.#...O.# - Act:5 - effect:....OOO. - Num:1 [fit: 1.040, exp: 0.00, pred: 1053.225, error:158.74627568588951]acc: None\n", + "Cond:###.F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 87.00, pred: 1131.127, error:205.17565568669235]acc: 1.0\n", + "Cond:##..OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.041, exp: 0.00, pred: 1289.220, error:62.800432362224036]acc: None\n", + "Cond:.#.#OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.040, exp: 156.00, pred: 1105.491, error:158.38483776521204]acc: 1.0\n", + "Cond:O.#.#O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.052, exp: 0.00, pred: 1196.840, error:1670.207849173594]acc: None\n", + "Cond:.###.FO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.037, exp: 0.00, pred: 1277.098, error:47.81483426395768]acc: None\n", + "Cond:.###.O.F - Act:7 - effect:.OO..... - Num:1 [fit: 0.098, exp: 0.00, pred: 1235.349, error:2620.8340139705087]acc: None\n", + "Cond:####FF#. - Act:2 - effect:..O..... - Num:1 [fit: 0.001, exp: 0.00, pred: 916.185, error:530.3928517770654]acc: None\n", + "Cond:##..O### - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 958.557, error:568.6349780501693]acc: None\n", + "Cond:.##O#O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 956.259, error:314.8084322143472]acc: None\n", + "Cond:##O###F# - Act:6 - effect:...OO... - Num:1 [fit: 0.001, exp: 0.00, pred: 1132.119, error:474.19759129401615]acc: None\n", + "Cond:###.O##O - Act:7 - effect:.....OF. - Num:1 [fit: 0.007, exp: 0.00, pred: 914.515, error:833.0146227105975]acc: None\n", + "Cond:####.O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 356.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:##.#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1165.707, error:41.37068702378868]acc: None\n", + "Cond:.##.#F.# - Act:2 - effect:.....OF. - Num:1 [fit: 0.036, exp: 48.00, pred: 1272.539, error:207.56031913250845]acc: 0.625\n", + "Cond:..##.F.# - Act:7 - effect:.OO..... - Num:1 [fit: 0.000, exp: 21.00, pred: 1091.507, error:6790.135154722228]acc: 0.0\n", + "Cond:.....#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 380.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:.###OF#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.086, exp: 0.00, pred: 1307.156, error:42.959486190499945]acc: None\n", + "Cond:O#...##. - Act:0 - effect:.O...... - Num:1 [fit: 0.000, exp: 21.00, pred: 942.395, error:5579.211621980182]acc: 0.047619047619047616\n", + "Cond:#....#.. - Act:0 - effect:......OO - Num:1 [fit: 0.000, exp: 21.00, pred: 1073.584, error:7726.367630366822]acc: 0.047619047619047616\n", + "Cond:.##O#O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.204, exp: 0.00, pred: 1091.634, error:112.09567573235904]acc: None\n", + "Cond:.#..#O#O - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1269.907, error:696.0097064350377]acc: None\n", + "Cond:OO..O..O - Act:4 - effect:O.O..... - Num:1 [fit: 0.001, exp: 0.00, pred: 1269.907, error:696.0097064350377]acc: None\n", + "Cond:#.##.OOF - Act:7 - effect:....OOO. - Num:6 [fit: 0.116, exp: 488.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:......#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.138, exp: 0.00, pred: 1471.961, error:123.56618234750192]acc: None\n", + "Cond:###.##OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 484.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:....F#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 79.00, pred: 1131.127, error:205.17564087634858]acc: 1.0\n", + "Cond:###.O##. - Act:2 - effect:OO.....O - Num:1 [fit: 0.008, exp: 0.00, pred: 948.778, error:269.33843139697007]acc: None\n", + "Cond:#.O..#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1031.224, error:585.7665089065729]acc: None\n", + "Cond:..O#.F#. - Act:7 - effect:...O..O. - Num:1 [fit: 0.005, exp: 0.00, pred: 1098.200, error:1348.6640433320222]acc: None\n", + "Cond:.O.##.#. - Act:7 - effect:.O...... - Num:1 [fit: 0.052, exp: 60.00, pred: 832.949, error:5566.107222965558]acc: 0.48333333333333334\n", + "Cond:##.#.OO. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 917.559, error:1606.4438509741753]acc: None\n", + "Cond:O.#####. - Act:3 - effect:.O...... - Num:1 [fit: 0.028, exp: 0.00, pred: 888.151, error:0.0]acc: None\n", + "Cond:..#..O.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.027, exp: 0.00, pred: 1106.117, error:262.4652760773772]acc: None\n", + "Cond:.#.###FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1264.430, error:615.7513147388979]acc: None\n", + "Cond:.#...O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 20.00, pred: 1013.825, error:9437.625208243435]acc: 0.0\n", + "Cond:OO##.##O - Act:0 - effect:OO.....O - Num:1 [fit: 0.000, exp: 22.00, pred: 956.427, error:5643.712977277961]acc: 0.4090909090909091\n", + "Cond:..#...#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1042.207, error:1444.4173639495093]acc: None\n", + "Cond:#.##FO#. - Act:4 - effect:....OOO. - Num:2 [fit: 0.008, exp: 74.00, pred: 1131.127, error:205.17570620855207]acc: 1.0\n", + "Cond:.####FO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1451.084, error:77.32682633615988]acc: None\n", + "Cond:##.#O..O - Act:1 - effect:........ - Num:1 [fit: 0.003, exp: 0.00, pred: 1023.051, error:1303.334632155421]acc: None\n", + "Cond:..#.#FO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1331.924, error:62.69626858079563]acc: None\n", + "Cond:.....O.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1020.136, error:475.541392827823]acc: None\n", + "Cond:#.##.O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1184.364, error:467.7714632465305]acc: None\n", + "Cond:..###O#O - Act:7 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1159.525, error:299.5845472631404]acc: None\n", + "Cond:##..F.#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1210.170, error:992.8444252534355]acc: None\n", + "Cond:.#..FO## - Act:4 - effect:....OOO. - Num:1 [fit: 0.039, exp: 69.00, pred: 1131.127, error:205.1754536071715]acc: 1.0\n", + "Cond:####.#OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 13.00, pred: 846.348, error:8916.619420326911]acc: 0.0\n", + "Cond:O##F##.. - Act:3 - effect:.O...... - Num:1 [fit: 0.002, exp: 0.00, pred: 1067.982, error:246.5131516957054]acc: None\n", + "Cond:O#.#OO## - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1043.422, error:1072.621609898497]acc: None\n", + "Cond:..O#.#.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 18.00, pred: 1034.005, error:9299.876963999848]acc: 0.0\n", + "Cond:###..O.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1213.020, error:388.73763526588357]acc: None\n", + "Cond:#.O##.## - Act:4 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 24.00, pred: 1005.328, error:3256.0307538946186]acc: 0.20833333333333334\n", + "Cond:#O.##O#. - Act:3 - effect:.O..O... - Num:1 [fit: 0.016, exp: 0.00, pred: 1215.661, error:305.6660722118271]acc: None\n", + "Cond:#...F##. - Act:2 - effect:....OOO. - Num:2 [fit: 0.114, exp: 170.00, pred: 2122.975, error:224.5084546682529]acc: 0.8764705882352941\n", + "Cond:...#.O#. - Act:0 - effect:....OOO. - Num:3 [fit: 0.003, exp: 24.00, pred: 1180.382, error:3867.9775723703506]acc: 0.4166666666666667\n", + "Cond:O#.FO### - Act:1 - effect:...O..FO - Num:1 [fit: 0.001, exp: 0.00, pred: 1240.878, error:258.7210145752198]acc: None\n", + "Cond:#O.##O## - Act:3 - effect:.O..O... - Num:2 [fit: 0.066, exp: 0.00, pred: 977.896, error:5407.37306217631]acc: None\n", + "Cond:..#.#... - Act:3 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 889.578, error:40.02639304110025]acc: None\n", + "Cond:#...#.OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 756.648, error:471.6200884690305]acc: None\n", + "Cond:O..#.#.O - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 25.00, pred: 1008.869, error:7201.851585005747]acc: 0.44\n", + "Cond:###.OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.054, exp: 0.00, pred: 1159.739, error:38.833878812041945]acc: None\n", + "Cond:#.#.#... - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 942.262, error:442.73654209893664]acc: None\n", + "Cond:OO##..#. - Act:0 - effect:.......O - Num:1 [fit: 0.000, exp: 18.00, pred: 936.419, error:7013.253635249569]acc: 0.0\n", + "Cond:#.#..OF. - Act:0 - effect:....FO.. - Num:2 [fit: 0.000, exp: 22.00, pred: 1182.000, error:4140.654956251837]acc: 0.18181818181818182\n", + "Cond:O..#OO.# - Act:1 - effect:...O..FO - Num:1 [fit: 0.001, exp: 0.00, pred: 1024.218, error:1186.5885320969137]acc: None\n", + "Cond:.O.#.#.. - Act:0 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 24.00, pred: 977.503, error:5330.061434861966]acc: 0.08333333333333333\n", + "Cond:.#..O### - Act:3 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1099.201, error:27.063349223215386]acc: None\n", + "Cond:#.#.#... - Act:3 - effect:....OOO. - Num:2 [fit: 0.005, exp: 0.00, pred: 1095.294, error:894.3881501217868]acc: None\n", + "Cond:####.F#. - Act:1 - effect:...O.... - Num:1 [fit: 0.000, exp: 23.00, pred: 1052.288, error:6891.685937993691]acc: 0.08695652173913043\n", + "Cond:O..##### - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 24.00, pred: 1009.456, error:6847.604024336309]acc: 0.4166666666666667\n", + "Cond:#...O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1171.721, error:47.796799570063676]acc: None\n", + "Cond:#.##.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.035, exp: 0.00, pred: 1158.214, error:40.91289526998797]acc: None\n", + "Cond:.###.OO. - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 947.526, error:606.7052023167555]acc: None\n", + "Cond:..#O#O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.001, exp: 0.00, pred: 1159.719, error:966.5799581342991]acc: None\n", + "Cond:...#.F#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.002, exp: 13.00, pred: 1068.115, error:8245.24307878279]acc: 0.0\n", + "Cond:#..#O#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1162.757, error:38.76783542189001]acc: None\n", + "Cond:.O.##O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1087.265, error:316.14210409616555]acc: None\n", + "Cond:.##.FO## - Act:5 - effect:....FO.. - Num:2 [fit: 0.016, exp: 96.00, pred: 1260.267, error:3446.641127474836]acc: 0.6041666666666666\n", + "Cond:#O##F#.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1085.928, error:1120.0334114957902]acc: None\n", + "Cond:#...#.#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1069.134, error:516.8380605974937]acc: None\n", + "Cond:.#.#FO## - Act:4 - effect:....OOO. - Num:1 [fit: 0.011, exp: 62.00, pred: 1131.127, error:205.1772480039697]acc: 1.0\n", + "Cond:..O..... - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 982.627, error:466.2177335624583]acc: None\n", + "Cond:##..#OOF - Act:7 - effect:....OOO. - Num:4 [fit: 0.078, exp: 462.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:O..#.##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.098, exp: 0.00, pred: 1000.945, error:3904.0040077609965]acc: None\n", + "Cond:.....O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 18.00, pred: 1178.321, error:3514.2800194640777]acc: 0.4444444444444444\n", + "Cond:##O..O## - Act:1 - effect:.....OF. - Num:1 [fit: 0.005, exp: 0.00, pred: 1033.451, error:1335.7425521132418]acc: None\n", + "Cond:.OO.##O# - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 991.522, error:1385.3306094192376]acc: None\n", + "Cond:##.F.##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 811.005, error:129.83086643139092]acc: None\n", + "Cond:O..###.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.053, exp: 0.00, pred: 1079.473, error:4863.172230071103]acc: None\n", + "Cond:..##.O.. - Act:3 - effect:OO...... - Num:1 [fit: 0.006, exp: 0.00, pred: 985.735, error:2477.17205485971]acc: None\n", + "Cond:....OO.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.031, exp: 0.00, pred: 1234.648, error:33.020047267780484]acc: None\n", + "Cond:#.###OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 460.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#...FO#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 53.00, pred: 1131.128, error:205.17957609628908]acc: 1.0\n", + "Cond:###..#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 458.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#O##.O## - Act:4 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1101.082, error:721.8173821090703]acc: None\n", + "Cond:#...FF#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.029, exp: 0.00, pred: 995.131, error:378.49088069425005]acc: None\n", + "Cond:##.#.F.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 22.00, pred: 1111.739, error:2219.0207029543553]acc: 0.45454545454545453\n", + "Cond:#.#O###F - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1127.205, error:1100.7478846395138]acc: None\n", + "Cond:#.#.O#.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1223.994, error:212.93037467131103]acc: None\n", + "Cond:#O.####O - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 12.00, pred: 955.079, error:8186.938864715164]acc: 0.0\n", + "Cond:####OO#. - Act:4 - effect:....OOO. - Num:2 [fit: 0.012, exp: 19.00, pred: 975.715, error:2668.9289815541624]acc: 0.3684210526315789\n", + "Cond:####F.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1244.731, error:1164.1843912208192]acc: None\n", + "Cond:..#.#... - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1044.118, error:794.272469289723]acc: None\n", + "Cond:.O###O#. - Act:7 - effect:....OOO. - Num:2 [fit: 0.209, exp: 0.00, pred: 1037.646, error:2646.63378838087]acc: None\n", + "Cond:#..F.##. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1367.212, error:913.7485338952445]acc: None\n", + "Cond:#O.#O.#O - Act:4 - effect:...O.O.. - Num:1 [fit: 0.003, exp: 0.00, pred: 1043.101, error:577.2325947156218]acc: None\n", + "Cond:###O#O.# - Act:6 - effect:.....OF. - Num:2 [fit: 0.007, exp: 0.00, pred: 1300.202, error:728.7837740974007]acc: None\n", + "Cond:.#.F#O#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 189.00, pred: 1163.269, error:1620.6957631975747]acc: 0.8465608465608465\n", + "Cond:....#.#F - Act:2 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 900.668, error:1549.4078814004029]acc: None\n", + "Cond:##..F#O# - Act:4 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1324.917, error:66.60175608419969]acc: None\n", + "Cond:.....O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 14.00, pred: 1019.402, error:8748.711262520868]acc: 0.0\n", + "Cond:#..#FOO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1076.892, error:275.566784825848]acc: None\n", + "Cond:.##O#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 993.078, error:2986.2706904637002]acc: None\n", + "Cond:.#####O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1082.843, error:642.6342478996246]acc: None\n", + "Cond:.O.O###. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1272.138, error:4466.863101724016]acc: None\n", + "Cond:..###OF# - Act:4 - effect:.OO..... - Num:1 [fit: 0.000, exp: 22.00, pred: 1229.294, error:3734.2393311600526]acc: 0.3181818181818182\n", + "Cond:##...#OF - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 443.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:.#O##..O - Act:5 - effect:.....OF. - Num:1 [fit: 0.001, exp: 0.00, pred: 1181.807, error:777.6601210037431]acc: None\n", + "Cond:#.#.#... - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1137.925, error:784.1047692556947]acc: None\n", + "Cond:O##.#O.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 902.142, error:834.8581278248705]acc: None\n", + "Cond:#..OO#.# - Act:2 - effect:....OOO. - Num:2 [fit: 0.008, exp: 20.00, pred: 963.207, error:7013.032214684326]acc: 0.3\n", + "Cond:#..OO#.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.005, exp: 20.00, pred: 963.207, error:7139.844212528603]acc: 0.3\n", + "Cond:###FOO.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.012, exp: 188.00, pred: 1163.269, error:1460.6696295117993]acc: 0.8085106382978723\n", + "Cond:.##..O.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.058, exp: 0.00, pred: 1115.305, error:2188.0648385950612]acc: None\n", + "Cond:...#F### - Act:2 - effect:....OOO. - Num:1 [fit: 0.057, exp: 165.00, pred: 2122.975, error:224.5084546746298]acc: 0.8848484848484849\n", + "Cond:.#.#OO.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 12.00, pred: 1051.643, error:7577.413047889861]acc: 0.0\n", + "Cond:####..O# - Act:1 - effect:......OO - Num:2 [fit: 0.000, exp: 25.00, pred: 942.357, error:5149.582693621289]acc: 0.24\n", + "Cond:.###...O - Act:7 - effect:.....OF. - Num:2 [fit: 0.004, exp: 25.00, pred: 995.267, error:6046.132923067544]acc: 0.0\n", + "Cond:#..#OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.033, exp: 116.00, pred: 1105.491, error:158.38483775791676]acc: 1.0\n", + "Cond:....##FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1008.606, error:4429.756784680378]acc: None\n", + "Cond:O#.#.##. - Act:7 - effect:.......O - Num:1 [fit: 0.000, exp: 21.00, pred: 1184.269, error:5396.437556116398]acc: 0.09523809523809523\n", + "Cond:O####... - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 15.00, pred: 940.989, error:5160.851757736933]acc: 0.2\n", + "Cond:...OO#.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.015, exp: 11.00, pred: 934.810, error:5905.226286782271]acc: 0.36363636363636365\n", + "Cond:##.O##.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.004, exp: 17.00, pred: 917.790, error:7036.873190554947]acc: 0.23529411764705882\n", + "Cond:##.FOO.# - Act:6 - effect:..OO.... - Num:2 [fit: 0.014, exp: 184.00, pred: 1163.269, error:1940.678340740399]acc: 0.8097826086956522\n", + "Cond:...###FF - Act:4 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 936.878, error:840.7070241664348]acc: None\n", + "Cond:..#.FO## - Act:5 - effect:....FO.. - Num:2 [fit: 0.008, exp: 91.00, pred: 1260.267, error:4403.759291506937]acc: 0.5384615384615384\n", + "Cond:.O.#.#O. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1062.435, error:1305.7379189865592]acc: None\n", + "Cond:###O.#O# - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1166.217, error:1132.814531212612]acc: None\n", + "Cond:O....##O - Act:4 - effect:....O.FF - Num:1 [fit: 0.001, exp: 16.00, pred: 1006.384, error:9465.568471325136]acc: 0.0\n", + "Cond:..O.##.O - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 876.825, error:1023.8587070732522]acc: None\n", + "Cond:#.#OO#.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.054, exp: 8.00, pred: 946.855, error:5754.639854609089]acc: 0.25\n", + "Cond:.##..O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1185.795, error:351.3689919772754]acc: None\n", + "Cond:#....O#. - Act:0 - effect:....OOO. - Num:2 [fit: 0.041, exp: 15.00, pred: 1179.208, error:4288.767584165873]acc: 0.4666666666666667\n", + "Cond:#.##.O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1028.560, error:875.4352905037606]acc: None\n", + "Cond:##O###.O - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1166.275, error:1197.751236322753]acc: None\n", + "Cond:.#..O... - Act:3 - effect:OO...... - Num:1 [fit: 0.009, exp: 0.00, pred: 865.625, error:755.0861319880246]acc: None\n", + "Cond:.###.O.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1021.769, error:904.0887852891783]acc: None\n", + "Cond:.###O### - Act:7 - effect:...O.... - Num:4 [fit: 0.001, exp: 25.00, pred: 1020.246, error:4156.0305229268515]acc: 0.36\n", + "Cond:#..F##.O - Act:5 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1052.942, error:549.6782934884673]acc: None\n", + "Cond:.O..#..# - Act:3 - effect:OO...... - Num:1 [fit: 0.003, exp: 12.00, pred: 835.930, error:4718.255183059953]acc: 0.25\n", + "Cond:##.#.#OF - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 433.00, pred: 1948.419, error:251.07661682020802]acc: 0.9953810623556582\n", + "Cond:####.F.O - Act:4 - effect:.....OF. - Num:1 [fit: 0.025, exp: 0.00, pred: 808.162, error:32.63920417700035]acc: None\n", + "Cond:O....#.# - Act:4 - effect:....OOO. - Num:2 [fit: 0.001, exp: 14.00, pred: 1004.411, error:7097.553836215022]acc: 0.35714285714285715\n", + "Cond:####FO.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.015, exp: 28.00, pred: 1131.033, error:204.80157681536758]acc: 1.0\n", + "Cond:#.#.F..# - Act:5 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 923.706, error:430.68139192476815]acc: None\n", + "Cond:.######F - Act:0 - effect:...O.... - Num:2 [fit: 0.000, exp: 22.00, pred: 997.463, error:6256.19761677974]acc: 0.2727272727272727\n", + "Cond:....#O.O - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 936.280, error:332.1196052707563]acc: None\n", + "Cond:.#..#O#O - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1058.378, error:211.97764998790768]acc: None\n", + "Cond:.#O...## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 25.00, pred: 885.490, error:5350.027376557834]acc: 0.2\n", + "Cond:#O#.#O.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.021, exp: 0.00, pred: 1109.651, error:144.34854908338855]acc: None\n", + "Cond:#.OO#.## - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 15.00, pred: 1007.693, error:7868.327377679528]acc: 0.0\n", + "Cond:##..#O#F - Act:7 - effect:....OOO. - Num:3 [fit: 0.060, exp: 431.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:##O##.O. - Act:6 - effect:.....OF. - Num:2 [fit: 0.002, exp: 0.00, pred: 918.132, error:683.4187664924968]acc: None\n", + "Cond:.#...O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.032, exp: 0.00, pred: 973.801, error:375.751527853884]acc: None\n", + "Cond:.###.F## - Act:5 - effect:....OOO. - Num:3 [fit: 0.053, exp: 319.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:###.F##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.011, exp: 26.00, pred: 1130.647, error:203.81744345551311]acc: 1.0\n", + "Cond:.O#..### - Act:3 - effect:OO...... - Num:1 [fit: 0.000, exp: 25.00, pred: 825.871, error:6539.104299206734]acc: 0.04\n", + "Cond:#OO.#### - Act:2 - effect:.....OOF - Num:2 [fit: 0.001, exp: 24.00, pred: 884.431, error:5491.6029985462565]acc: 0.08333333333333333\n", + "Cond:##.O#.#. - Act:0 - effect:.OO..... - Num:1 [fit: 0.000, exp: 21.00, pred: 787.769, error:6677.694462181593]acc: 0.047619047619047616\n", + "Cond:..##O.#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 10.00, pred: 922.765, error:7445.5902493665435]acc: 0.0\n", + "Cond:..O##.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.004, exp: 9.00, pred: 1021.005, error:8323.165820830904]acc: 0.0\n", + "Cond:##..OO#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 1060.518, error:18.55377904654062]acc: None\n", + "Cond:#..F#.## - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 912.637, error:634.3465699451797]acc: None\n", + "Cond:##.#OO## - Act:2 - effect:.....F.. - Num:5 [fit: 0.072, exp: 105.00, pred: 2306.930, error:381.90173194241623]acc: 0.7714285714285715\n", + "Cond:#...F#.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.057, exp: 164.00, pred: 2122.975, error:224.50845468891424]acc: 0.8780487804878049\n", + "Cond:.....FO# - Act:5 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1308.336, error:67.69590448735448]acc: None\n", + "Cond:.#.#O#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1113.062, error:118.47663130622537]acc: None\n", + "Cond:..OOO#.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1456.897, error:726.5811669568604]acc: None\n", + "Cond:.#..#F#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 310.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:.##..... - Act:3 - effect:OO...... - Num:4 [fit: 0.002, exp: 23.00, pred: 826.283, error:6551.705327288914]acc: 0.17391304347826086\n", + "Cond:O...#### - Act:4 - effect:....OOO. - Num:2 [fit: 0.080, exp: 10.00, pred: 1035.448, error:6718.563312298]acc: 0.4\n", + "Cond:O#.##O.# - Act:5 - effect:....O.FF - Num:1 [fit: 0.002, exp: 0.00, pred: 969.586, error:1289.8011984211457]acc: None\n", + "Cond:#.#.#OO# - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 17.00, pred: 996.992, error:6950.33004245566]acc: 0.29411764705882354\n", + "Cond:..#...## - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 21.00, pred: 854.923, error:10329.396428239139]acc: 0.0\n", + "Cond:.#O##.F# - Act:7 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 820.268, error:0.0]acc: None\n", + "Cond:...O#.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 18.00, pred: 783.612, error:8577.797410377469]acc: 0.0\n", + "Cond:##.F#O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 9.00, pred: 1079.681, error:6585.607126060172]acc: 0.0\n", + "Cond:O##..#.. - Act:4 - effect:....O.FF - Num:1 [fit: 0.001, exp: 12.00, pred: 927.406, error:8191.457296705652]acc: 0.0\n", + "Cond:.##.OO## - Act:6 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1087.604, error:1295.6822744194967]acc: None\n", + "Cond:##.F#O.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.009, exp: 179.00, pred: 1163.269, error:1460.6696295117958]acc: 0.88268156424581\n", + "Cond:#..O#.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 15.00, pred: 791.565, error:8913.31983390825]acc: 0.0\n", + "Cond:..#O#.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 24.00, pred: 794.459, error:9292.107021132622]acc: 0.0\n", + "Cond:.#.###.O - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 8.00, pred: 871.992, error:6733.987840016974]acc: 0.0\n", + "Cond:.#..#O.. - Act:0 - effect:....OOO. - Num:2 [fit: 0.001, exp: 16.00, pred: 1004.701, error:7763.414297423775]acc: 0.0\n", + "Cond:....#.## - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 19.00, pred: 858.787, error:8850.111219334238]acc: 0.0\n", + "Cond:##O...## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 21.00, pred: 885.927, error:5855.456601156446]acc: 0.14285714285714285\n", + "Cond:.#O...## - Act:7 - effect:.OOO.... - Num:2 [fit: 0.158, exp: 3.00, pred: 879.775, error:3538.8397844312008]acc: 0.0\n", + "Cond:#O#O#.#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 19.00, pred: 888.833, error:7066.98085659963]acc: 0.0\n", + "Cond:#OOO.#.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.001, exp: 23.00, pred: 975.977, error:5813.3981571762015]acc: 0.0\n", + "Cond:###.#O#F - Act:7 - effect:....OOO. - Num:4 [fit: 0.074, exp: 421.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:###O...O - Act:7 - effect:....OOO. - Num:1 [fit: 0.140, exp: 0.00, pred: 1051.743, error:3961.9952374483723]acc: None\n", + "Cond:...F#O#. - Act:6 - effect:..OO.... - Num:2 [fit: 0.021, exp: 178.00, pred: 1163.269, error:1620.6870519689855]acc: 0.8595505617977528\n", + "Cond:##.#.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 304.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:##...O#. - Act:2 - effect:.....OOF - Num:5 [fit: 1.230, exp: 52.00, pred: 2208.265, error:255.38892135914688]acc: 0.5961538461538461\n", + "Cond:##..##OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 420.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#..O#.## - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 22.00, pred: 839.935, error:4031.502101037802]acc: 0.0\n", + "Cond:OO#.#.#. - Act:6 - effect:.......O - Num:1 [fit: 0.091, exp: 36.00, pred: 1114.181, error:1718.8469268271583]acc: 0.4722222222222222\n", + "Cond:.....FO. - Act:5 - effect:....OOO. - Num:2 [fit: 0.005, exp: 0.00, pred: 1434.700, error:62.1066200614486]acc: None\n", + "Cond:##.#.O#. - Act:4 - effect:....OOO. - Num:2 [fit: 0.005, exp: 16.00, pred: 1235.882, error:7732.496695041442]acc: 0.25\n", + "Cond:.....O.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1151.771, error:1088.292194855013]acc: None\n", + "Cond:...O#O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.083, exp: 0.00, pred: 1509.945, error:1268.5871383359238]acc: None\n", + "Cond:#.#.F#.# - Act:2 - effect:....OOO. - Num:3 [fit: 0.170, exp: 161.00, pred: 2122.975, error:224.508454682177]acc: 0.8819875776397516\n", + "Cond:O###.##O - Act:0 - effect:.....F.. - Num:1 [fit: 0.000, exp: 25.00, pred: 926.870, error:3858.770800372124]acc: 0.28\n", + "Cond:OO#...## - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 20.00, pred: 916.585, error:8688.539657109766]acc: 0.0\n", + "Cond:#.#####F - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 13.00, pred: 1066.490, error:7746.085598759449]acc: 0.0\n", + "Cond:##.#O..O - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1293.110, error:619.9851913568108]acc: None\n", + "Cond:.OO...#. - Act:4 - effect:OO...... - Num:3 [fit: 0.044, exp: 12.00, pred: 887.945, error:6930.928356431701]acc: 0.0\n", + "Cond:##O....# - Act:2 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 20.00, pred: 886.808, error:4775.966501348611]acc: 0.15\n", + "Cond:#.OO#..# - Act:2 - effect:..OO.... - Num:1 [fit: 0.052, exp: 42.00, pred: 894.597, error:2349.1514127427645]acc: 0.21428571428571427\n", + "Cond:..#.F### - Act:7 - effect:...OO... - Num:2 [fit: 0.024, exp: 76.00, pred: 1034.588, error:4481.580989883558]acc: 0.6578947368421053\n", + "Cond:#..#OO## - Act:3 - effect:....OOO. - Num:2 [fit: 0.061, exp: 98.00, pred: 1105.491, error:158.38483751679058]acc: 1.0\n", + "Cond:##..F#.# - Act:2 - effect:....OOO. - Num:3 [fit: 0.160, exp: 159.00, pred: 2122.975, error:224.5084546777185]acc: 0.8867924528301887\n", + "Cond:#..#.#.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 21.00, pred: 1042.910, error:6409.685499208511]acc: 0.0\n", + "Cond:.###.O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.004, exp: 7.00, pred: 1001.028, error:7189.173497077448]acc: 0.0\n", + "Cond:#..O##.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 18.00, pred: 927.171, error:9561.76280132354]acc: 0.0\n", + "Cond:##.#OO.# - Act:7 - effect:...O.... - Num:1 [fit: 0.096, exp: 9.00, pred: 1071.765, error:2706.2570562050705]acc: 0.6666666666666666\n", + "Cond:O#.#O### - Act:4 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1157.953, error:775.3412984299828]acc: None\n", + "Cond:###.#OO# - Act:4 - effect:.....OOF - Num:3 [fit: 0.000, exp: 20.00, pred: 1034.811, error:6127.301496684324]acc: 0.05\n", + "Cond:O.....## - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 15.00, pred: 947.093, error:7111.730253178081]acc: 0.0\n", + "Cond:#.#..#.# - Act:4 - effect:.....F.. - Num:1 [fit: 0.003, exp: 42.00, pred: 1006.277, error:4893.737011127981]acc: 0.38095238095238093\n", + "Cond:.#.#O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.043, exp: 0.00, pred: 1321.569, error:50.902385214747554]acc: None\n", + "Cond:.O...#.. - Act:0 - effect:OO...... - Num:1 [fit: 0.004, exp: 13.00, pred: 997.526, error:6758.451264244362]acc: 0.07692307692307693\n", + "Cond:..O##.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.003, exp: 9.00, pred: 1021.005, error:8384.861820830904]acc: 0.0\n", + "Cond:O..#..## - Act:1 - effect:....OOO. - Num:1 [fit: 0.005, exp: 9.00, pred: 981.302, error:4243.839959297607]acc: 0.0\n", + "Cond:.##.#... - Act:3 - effect:OO...... - Num:1 [fit: 0.000, exp: 21.00, pred: 829.548, error:5893.880703674566]acc: 0.14285714285714285\n", + "Cond:####O#O. - Act:3 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1128.686, error:26.790410853213366]acc: None\n", + "Cond:##OO##.# - Act:4 - effect:....OOO. - Num:2 [fit: 0.006, exp: 18.00, pred: 885.446, error:7948.919356518577]acc: 0.0\n", + "Cond:###.#OO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1014.373, error:274.4644242964636]acc: None\n", + "Cond:.OO#..## - Act:0 - effect:....OOO. - Num:2 [fit: 0.000, exp: 25.00, pred: 931.308, error:7440.364719725592]acc: 0.0\n", + "Cond:.OO##.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 25.00, pred: 931.308, error:6860.769574667276]acc: 0.0\n", + "Cond:..#...O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.005, exp: 13.00, pred: 909.250, error:4247.205720622724]acc: 0.0\n", + "Cond:##..###F - Act:0 - effect:....OOO. - Num:1 [fit: 0.100, exp: 8.00, pred: 1002.333, error:6868.261897729179]acc: 0.375\n", + "Cond:##...F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 293.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:#....FO. - Act:5 - effect:....OOO. - Num:2 [fit: 0.011, exp: 0.00, pred: 1348.377, error:61.10575644354802]acc: None\n", + "Cond:#.....#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1100.036, error:1117.9886987161249]acc: None\n", + "Cond:#O#O.... - Act:2 - effect:....OOO. - Num:1 [fit: 0.005, exp: 20.00, pred: 981.012, error:4731.704140830461]acc: 0.0\n", + "Cond:###..OO# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 20.00, pred: 1034.811, error:7936.2366032109]acc: 0.0\n", + "Cond:##...O#. - Act:1 - effect:....OOO. - Num:2 [fit: 0.005, exp: 7.00, pred: 1001.028, error:7225.973497077448]acc: 0.0\n", + "Cond:##..#O#F - Act:1 - effect:....OOO. - Num:2 [fit: 0.007, exp: 13.00, pred: 1066.490, error:7639.983838759449]acc: 0.0\n", + "Cond:#.#.OO#. - Act:2 - effect:....OOO. - Num:2 [fit: 0.004, exp: 0.00, pred: 1163.048, error:870.1268618310969]acc: None\n", + "Cond:.#OO..F# - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 973.874, error:668.2818806024384]acc: None\n", + "Cond:......## - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 22.00, pred: 942.966, error:5133.8964203040305]acc: 0.0\n", + "Cond:#...#O#F - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 415.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:..#O#### - Act:2 - effect:..OO.... - Num:2 [fit: 0.955, exp: 48.00, pred: 894.425, error:2034.5255152647255]acc: 0.6458333333333334\n", + "Cond:..OO#.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.003, exp: 7.00, pred: 1040.430, error:7999.377026139935]acc: 0.0\n", + "Cond:.O##.... - Act:4 - effect:......OO - Num:1 [fit: 0.002, exp: 27.00, pred: 794.516, error:1372.6361672836679]acc: 0.3333333333333333\n", + "Cond:#...OO## - Act:7 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1080.109, error:18.230085362106077]acc: None\n", + "Cond:#.#.##O# - Act:1 - effect:....OOO. - Num:3 [fit: 0.003, exp: 19.00, pred: 933.608, error:9365.201202086935]acc: 0.0\n", + "Cond:###..... - Act:1 - effect:...O.... - Num:1 [fit: 0.000, exp: 24.00, pred: 871.141, error:6450.7688476236335]acc: 0.0\n", + "Cond:#.O#.O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1079.406, error:0.0]acc: None\n", + "Cond:.#O###.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 23.00, pred: 875.541, error:6421.88475580348]acc: 0.0\n", + "Cond:##O...#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.003, exp: 18.00, pred: 893.482, error:4771.666280118045]acc: 0.16666666666666666\n", + "Cond:#..#.O#F - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 410.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#..##.#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.003, exp: 10.00, pred: 919.542, error:5243.0372593535]acc: 0.2\n", + "Cond:..OO..#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.004, exp: 5.00, pred: 979.421, error:4940.556361245001]acc: 0.0\n", + "Cond:.OOO#.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.002, exp: 8.00, pred: 1004.451, error:6302.298967767769]acc: 0.0\n", + "Cond:##..#OO# - Act:4 - effect:....OOO. - Num:2 [fit: 0.003, exp: 18.00, pred: 1045.211, error:6516.255773492856]acc: 0.0\n", + "Cond:..####O# - Act:1 - effect:....OOO. - Num:1 [fit: 0.004, exp: 16.00, pred: 946.076, error:8538.619763833096]acc: 0.0\n", + "Cond:.#.#OO#. - Act:4 - effect:....OOO. - Num:2 [fit: 0.035, exp: 10.00, pred: 980.391, error:2033.4125965866044]acc: 0.4\n", + "Cond:.###OO.. - Act:2 - effect:.....F.. - Num:1 [fit: 0.038, exp: 100.00, pred: 2306.930, error:354.1819484250224]acc: 0.79\n", + "Cond:O....#.O - Act:4 - effect:....OOO. - Num:2 [fit: 0.062, exp: 9.00, pred: 1006.424, error:6204.464909678266]acc: 0.3333333333333333\n", + "Cond:.#OO#### - Act:0 - effect:...OO... - Num:2 [fit: 0.014, exp: 14.00, pred: 1017.084, error:6369.095052322562]acc: 0.14285714285714285\n", + "Cond:.O###.#. - Act:0 - effect:.OOO.... - Num:1 [fit: 0.001, exp: 23.00, pred: 878.509, error:3941.113898032443]acc: 0.2608695652173913\n", + "Cond:#...##OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 409.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#.#.##.F - Act:1 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1074.144, error:709.7881398860716]acc: None\n", + "Cond:##O#.... - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 22.00, pred: 875.837, error:5771.15099383008]acc: 0.0\n", + "Cond:.###F### - Act:4 - effect:....OOO. - Num:1 [fit: 0.017, exp: 13.00, pred: 1125.882, error:188.64978317953808]acc: 1.0\n", + "Cond:##O#O#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1261.428, error:1173.4487207634247]acc: None\n", + "Cond:#...#.O# - Act:2 - effect:OO...... - Num:1 [fit: 0.043, exp: 9.00, pred: 883.879, error:5693.040743898501]acc: 0.2222222222222222\n", + "Cond:.....#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 16.00, pred: 1117.039, error:1793.2004018354007]acc: 0.5\n", + "Cond:...##O.# - Act:5 - effect:....FO.. - Num:1 [fit: 0.004, exp: 231.00, pred: 1320.594, error:1171.1791953028549]acc: 0.5584415584415584\n", + "Cond:#.#O.O#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1123.591, error:1700.091409931207]acc: None\n", + "Cond:.#.O..## - Act:6 - effect:....OOO. - Num:2 [fit: 0.010, exp: 19.00, pred: 1069.355, error:7536.460272827986]acc: 0.15789473684210525\n", + "Cond:##.##OF# - Act:4 - effect:....OOO. - Num:1 [fit: 0.005, exp: 6.00, pred: 1088.502, error:6208.7936350304535]acc: 0.16666666666666666\n", + "Cond:###.#OO# - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 9.00, pred: 1049.984, error:6203.257866034073]acc: 0.0\n", + "Cond:.#..#.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.014, exp: 6.00, pred: 988.075, error:5834.716997303678]acc: 0.0\n", + "Cond:...##..# - Act:3 - effect:.....OF. - Num:1 [fit: 0.018, exp: 47.00, pred: 1008.264, error:1369.8845408531315]acc: 0.3829787234042553\n", + "Cond:O#..#.#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.013, exp: 6.00, pred: 876.204, error:5475.777498797797]acc: 0.0\n", + "Cond:OO#.#.## - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 19.00, pred: 955.625, error:5665.97570869106]acc: 0.0\n", + "Cond:....OF## - Act:5 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 1123.643, error:29.896631049635413]acc: None\n", + "Cond:O..#.### - Act:7 - effect:O......O - Num:1 [fit: 0.001, exp: 22.00, pred: 1118.372, error:4435.852055856041]acc: 0.5\n", + "Cond:#O.##O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1188.022, error:55.37511225523055]acc: None\n", + "Cond:.###O#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 20.00, pred: 810.796, error:2501.4158336514356]acc: 0.15\n", + "Cond:...#O#.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 20.00, pred: 810.796, error:2284.101103617628]acc: 0.15\n", + "Cond:###.OO## - Act:7 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1121.133, error:31.10525192777638]acc: None\n", + "Cond:.##.O##. - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1289.998, error:1017.8412133248366]acc: None\n", + "Cond:###..O## - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 15.00, pred: 987.047, error:7109.103306066081]acc: 0.06666666666666667\n", + "Cond:...##O#O - Act:5 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1192.881, error:197.62092705875915]acc: None\n", + "Cond:#.#....# - Act:4 - effect:....OOO. - Num:1 [fit: 0.001, exp: 23.00, pred: 1062.806, error:5444.868971052221]acc: 0.2608695652173913\n", + "Cond:..#.F### - Act:4 - effect:....OOO. - Num:1 [fit: 0.171, exp: 8.00, pred: 1088.254, error:115.54034266474295]acc: 1.0\n", + "Cond:#OO...## - Act:2 - effect:......OO - Num:1 [fit: 0.001, exp: 17.00, pred: 891.989, error:5789.253152962113]acc: 0.23529411764705882\n", + "Cond:#....OO# - Act:0 - effect:....OOO. - Num:1 [fit: 0.040, exp: 6.00, pred: 955.559, error:6255.392959681413]acc: 0.16666666666666666\n", + "Cond:...#.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 16.00, pred: 873.226, error:7812.716457785144]acc: 0.0625\n", + "Cond:O####..# - Act:1 - effect:O......O - Num:2 [fit: 0.001, exp: 25.00, pred: 934.696, error:4147.857231064772]acc: 0.24\n", + "Cond:O##F#..# - Act:3 - effect:.......O - Num:1 [fit: 0.013, exp: 0.00, pred: 985.055, error:770.9955347799336]acc: None\n", + "Cond:.#...O## - Act:4 - effect:....OOO. - Num:2 [fit: 0.009, exp: 14.00, pred: 976.229, error:7580.421292840967]acc: 0.07142857142857142\n", + "Cond:#.#F##.O - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1180.821, error:607.5723119457497]acc: None\n", + "Cond:O####O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1364.790, error:133.75372182608587]acc: None\n", + "Cond:.#.O#.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 13.00, pred: 780.828, error:7788.034951515125]acc: 0.0\n", + "Cond:.##.O### - Act:7 - effect:...O.... - Num:1 [fit: 0.008, exp: 0.00, pred: 831.823, error:924.7105510165359]acc: None\n", + "Cond:.###.F.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.012, exp: 3.00, pred: 1127.759, error:3684.222163432582]acc: 0.0\n", + "Cond:....###. - Act:0 - effect:....OOO. - Num:1 [fit: 0.002, exp: 23.00, pred: 1009.725, error:7351.056643384629]acc: 0.043478260869565216\n", + "Cond:..O#.### - Act:7 - effect:....OOO. - Num:2 [fit: 0.001, exp: 20.00, pred: 1004.851, error:5896.240223296991]acc: 0.5\n", + "Cond:#####O#. - Act:4 - effect:....OOO. - Num:3 [fit: 0.007, exp: 19.00, pred: 940.893, error:2631.4559222713624]acc: 0.5789473684210527\n", + "Cond:#.#.#OO# - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 8.00, pred: 1079.622, error:6220.824847843168]acc: 0.0\n", + "Cond:##.##O#F - Act:1 - effect:....OOO. - Num:1 [fit: 0.004, exp: 8.00, pred: 1079.622, error:5930.904847843167]acc: 0.0\n", + "Cond:###..OOF - Act:0 - effect:....OOO. - Num:1 [fit: 0.021, exp: 6.00, pred: 955.559, error:6559.392959681413]acc: 0.16666666666666666\n", + "Cond:...##O#F - Act:6 - effect:.....OOF - Num:1 [fit: 0.011, exp: 180.00, pred: 921.781, error:3931.4158313498556]acc: 0.5277777777777778\n", + "Cond:#O.#.### - Act:4 - effect:OO...... - Num:4 [fit: 0.001, exp: 19.00, pred: 825.848, error:5063.364127774808]acc: 0.15789473684210525\n", + "Cond:.#...O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.023, exp: 4.00, pred: 1026.497, error:5037.340425239013]acc: 0.0\n", + "Cond:O.....## - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 6.00, pred: 945.688, error:5316.197174365649]acc: 0.16666666666666666\n", + "Cond:#O.###.O - Act:7 - effect:.....OF. - Num:1 [fit: 0.013, exp: 9.00, pred: 909.369, error:4240.599383266738]acc: 0.0\n", + "Cond:#..O###. - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 12.00, pred: 910.876, error:9204.588380235224]acc: 0.0\n", + "Cond:####.##F - Act:2 - effect:....OOO. - Num:1 [fit: 0.013, exp: 1.00, pred: 660.550, error:2200.0]acc: 0.0\n", + "Cond:#..##O#F - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 401.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:OO##.### - Act:2 - effect:..O..... - Num:1 [fit: 0.000, exp: 21.00, pred: 1058.280, error:6765.65456948671]acc: 0.0\n", + "Cond:##..OO## - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1013.568, error:1385.4809014713112]acc: None\n", + "Cond:O.#.#### - Act:4 - effect:....OOO. - Num:1 [fit: 0.011, exp: 5.00, pred: 956.797, error:5048.356122065812]acc: 0.2\n", + "Cond:#...##.. - Act:0 - effect:....OOO. - Num:2 [fit: 0.005, exp: 11.00, pred: 986.086, error:7994.7503941852865]acc: 0.0\n", + "Cond:#.#OOO.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 938.845, error:344.1884695896672]acc: None\n", + "Cond:##..#..O - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 20.00, pred: 922.886, error:4931.738911070224]acc: 0.0\n", + "Cond:O##..##O - Act:0 - effect:....OOO. - Num:3 [fit: 0.001, exp: 13.00, pred: 918.315, error:6929.740677655802]acc: 0.0\n", + "Cond:...#O... - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 9.00, pred: 837.411, error:2904.3721531425126]acc: 0.0\n", + "Cond:O..O##.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 932.358, error:807.3249005059434]acc: None\n", + "Cond:.#OO##.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.007, exp: 16.00, pred: 897.761, error:6209.596489418058]acc: 0.0\n", + "Cond:O..####. - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1209.530, error:242.4169705767511]acc: None\n", + "Cond:##..OO.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1299.777, error:757.1374822574143]acc: None\n", + "Cond:##..#O## - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 18.00, pred: 983.492, error:5171.44948084626]acc: 0.3888888888888889\n", + "Cond:##...OOF - Act:1 - effect:....OOO. - Num:2 [fit: 0.009, exp: 7.00, pred: 1140.401, error:5483.726256211144]acc: 0.0\n", + "Cond:#..#F##. - Act:1 - effect:....FO.. - Num:1 [fit: 0.029, exp: 28.00, pred: 975.799, error:2368.050844853158]acc: 0.35714285714285715\n", + "Cond:#OO.#.#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.006, exp: 16.00, pred: 896.128, error:4860.248857020593]acc: 0.1875\n", + "Cond:####.#.O - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 18.00, pred: 921.395, error:5617.328213899707]acc: 0.0\n", + "Cond:.....F.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 274.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:..OO#..# - Act:2 - effect:....OOO. - Num:3 [fit: 0.015, exp: 33.00, pred: 894.638, error:2372.343952856445]acc: 0.0\n", + "Cond:#...##O# - Act:1 - effect:....OOO. - Num:2 [fit: 0.007, exp: 12.00, pred: 960.768, error:9132.85621377589]acc: 0.0\n", + "Cond:..#O#.## - Act:3 - effect:...OO... - Num:1 [fit: 0.007, exp: 44.00, pred: 995.946, error:2598.2678715832726]acc: 0.25\n", + "Cond:.##O.### - Act:4 - effect:...O.... - Num:1 [fit: 0.001, exp: 15.00, pred: 987.984, error:7184.175647682119]acc: 0.0\n", + "Cond:...#OO## - Act:7 - effect:...OO... - Num:1 [fit: 0.003, exp: 9.00, pred: 1071.765, error:5950.481056205071]acc: 0.0\n", + "Cond:##.#F.## - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1085.547, error:465.38443993318714]acc: None\n", + "Cond:O#..#.#O - Act:7 - effect:O......O - Num:1 [fit: 0.013, exp: 22.00, pred: 933.227, error:5329.225918824744]acc: 0.3181818181818182\n", + "Cond:###.F#.. - Act:2 - effect:....OOO. - Num:4 [fit: 0.230, exp: 155.00, pred: 2122.975, error:224.50845468114454]acc: 0.8903225806451613\n", + "Cond:..#..F#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 9.00, pred: 1026.777, error:7547.631911401478]acc: 0.0\n", + "Cond:.#...#O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1179.577, error:48.677548234721186]acc: None\n", + "Cond:..#..O.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.022, exp: 0.00, pred: 1466.902, error:34.877244006574216]acc: None\n", + "Cond:..#FO#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.152, exp: 6.00, pred: 1001.851, error:939.2201135592215]acc: 0.3333333333333333\n", + "Cond:#.##O##. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 18.00, pred: 1024.812, error:7617.665767904958]acc: 0.0\n", + "Cond:...#OO## - Act:3 - effect:....OOO. - Num:2 [fit: 0.021, exp: 85.00, pred: 1105.491, error:158.38483300476872]acc: 1.0\n", + "Cond:O###F#.# - Act:3 - effect:.......O - Num:1 [fit: 0.001, exp: 0.00, pred: 960.155, error:1168.9365901385563]acc: None\n", + "Cond:#.##FOO. - Act:4 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 1094.216, error:80.58512109215224]acc: None\n", + "Cond:#...#.O. - Act:2 - effect:OO...... - Num:1 [fit: 0.017, exp: 0.00, pred: 1172.475, error:430.65497219373026]acc: None\n", + "Cond:#.....O# - Act:2 - effect:OO...... - Num:2 [fit: 0.014, exp: 6.00, pred: 787.619, error:3089.155030683762]acc: 0.16666666666666666\n", + "Cond:#O.O##.. - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 841.365, error:752.9267705224405]acc: None\n", + "Cond:O##....O - Act:2 - effect:..OO.... - Num:1 [fit: 0.001, exp: 13.00, pred: 1007.771, error:8438.989174823282]acc: 0.0\n", + "Cond:#.#.O#.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 957.014, error:366.41658705296413]acc: None\n", + "Cond:..#O#### - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 17.00, pred: 791.705, error:9035.657879132992]acc: 0.0\n", + "Cond:.#O####. - Act:0 - effect:...OO... - Num:1 [fit: 0.001, exp: 17.00, pred: 923.582, error:6121.254225867525]acc: 0.0\n", + "Cond:###OO### - Act:3 - effect:..OO.... - Num:1 [fit: 0.003, exp: 17.00, pred: 985.410, error:1286.0914140484142]acc: 0.058823529411764705\n", + "Cond:..##O##. - Act:7 - effect:....OOO. - Num:1 [fit: 0.000, exp: 16.00, pred: 1018.736, error:7140.191610189184]acc: 0.0\n", + "Cond:.O#####. - Act:0 - effect:.....F.. - Num:1 [fit: 0.008, exp: 18.00, pred: 879.641, error:6557.2805284860915]acc: 0.1111111111111111\n", + "Cond:#.#....# - Act:2 - effect:......OO - Num:1 [fit: 0.000, exp: 25.00, pred: 939.974, error:3554.823893881584]acc: 0.12\n", + "Cond:#.###OO. - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1314.037, error:801.0197851704738]acc: None\n", + "Cond:#..#.OO# - Act:3 - effect:......OO - Num:2 [fit: 0.111, exp: 34.00, pred: 1220.611, error:2380.854261717266]acc: 0.47058823529411764\n", + "Cond:O#.#.#.O - Act:7 - effect:O......O - Num:1 [fit: 0.001, exp: 18.00, pred: 941.907, error:4437.822910745242]acc: 0.16666666666666666\n", + "Cond:#...O### - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1001.189, error:444.8713617716265]acc: None\n", + "Cond:##O#...# - Act:4 - effect:....OOO. - Num:1 [fit: 0.004, exp: 20.00, pred: 810.859, error:6963.245379383307]acc: 0.0\n", + "Cond:.#O#.... - Act:4 - effect:....OOO. - Num:3 [fit: 0.007, exp: 19.00, pred: 810.316, error:6758.424241727382]acc: 0.0\n", + "Cond:O.##...# - Act:4 - effect:....OOO. - Num:1 [fit: 0.018, exp: 2.00, pred: 1054.910, error:2706.696961108334]acc: 0.0\n", + "Cond:###..OO# - Act:0 - effect:....OOO. - Num:1 [fit: 0.022, exp: 2.00, pred: 801.721, error:2702.8868996266156]acc: 0.0\n", + "Cond:#.#..OOF - Act:4 - effect:....OOO. - Num:1 [fit: 0.007, exp: 7.00, pred: 1089.362, error:6272.037417731222]acc: 0.0\n", + "Cond:.#O#.### - Act:0 - effect:...OO... - Num:2 [fit: 0.001, exp: 16.00, pred: 915.060, error:6284.196907371799]acc: 0.0\n", + "Cond:..OO#### - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 19.00, pred: 1003.359, error:5950.927435903017]acc: 0.47368421052631576\n", + "Cond:###O##.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 20.00, pred: 891.065, error:8421.487753853147]acc: 0.0\n", + "Cond:##.#O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.025, exp: 14.00, pred: 819.527, error:2056.3069834346343]acc: 0.14285714285714285\n", + "Cond:.#.O##.# - Act:4 - effect:....OOO. - Num:2 [fit: 0.002, exp: 10.00, pred: 853.895, error:3070.6623795829564]acc: 0.0\n", + "Cond:.##.#F.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.016, exp: 2.00, pred: 1021.982, error:2611.776096305125]acc: 0.0\n", + "Cond:###..O.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 995.499, error:1063.790327621523]acc: None\n", + "Cond:#O##.#O. - Act:1 - effect:...O.... - Num:1 [fit: 0.001, exp: 0.00, pred: 1045.412, error:1183.2384225521514]acc: None\n", + "Cond:#.#.#.## - Act:1 - effect:....OOO. - Num:1 [fit: 0.011, exp: 15.00, pred: 852.428, error:6892.371857260754]acc: 0.0\n", + "Cond:.##O##.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 16.00, pred: 885.152, error:8666.061431331578]acc: 0.0\n", + "Cond:.##O#... - Act:0 - effect:.....F.. - Num:3 [fit: 0.009, exp: 17.00, pred: 789.988, error:7395.095451382409]acc: 0.23529411764705882\n", + "Cond:.##.#.#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 13.00, pred: 876.633, error:5568.184754711801]acc: 0.0\n", + "Cond:##OO#.## - Act:7 - effect:....OOO. - Num:2 [fit: 0.000, exp: 24.00, pred: 1003.448, error:6405.122146089911]acc: 0.4166666666666667\n", + "Cond:#.#O#### - Act:3 - effect:...OO... - Num:1 [fit: 0.019, exp: 37.00, pred: 995.951, error:2723.7482366843788]acc: 0.13513513513513514\n", + "Cond:O#..#..O - Act:7 - effect:O......O - Num:1 [fit: 0.003, exp: 17.00, pred: 931.602, error:4571.244980875907]acc: 0.17647058823529413\n", + "Cond:O#....#O - Act:7 - effect:O......O - Num:1 [fit: 0.005, exp: 17.00, pred: 931.602, error:4662.126414639746]acc: 0.17647058823529413\n", + "Cond:O..##### - Act:6 - effect:OO...... - Num:1 [fit: 0.003, exp: 21.00, pred: 1286.486, error:4169.04796849089]acc: 0.5714285714285714\n", + "Cond:#.##OO#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 7.00, pred: 1109.547, error:6448.58043996428]acc: 0.0\n", + "Cond:##.#O..# - Act:7 - effect:...O.... - Num:1 [fit: 0.011, exp: 7.00, pred: 944.711, error:5603.095195038535]acc: 0.14285714285714285\n", + "Cond:.#...##. - Act:1 - effect:....OOO. - Num:2 [fit: 0.001, exp: 19.00, pred: 832.497, error:5465.189505787867]acc: 0.0\n", + "Cond:#...##.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 17.00, pred: 1073.330, error:1176.1740771899172]acc: 0.6470588235294118\n", + "Cond:.##..#.# - Act:3 - effect:.....OF. - Num:2 [fit: 0.222, exp: 41.00, pred: 940.442, error:4452.376791933577]acc: 0.36585365853658536\n", + "Cond:..#O#..# - Act:2 - effect:....OOO. - Num:2 [fit: 0.009, exp: 35.00, pred: 894.495, error:1829.9956385926587]acc: 0.02857142857142857\n", + "Cond:#..O#.#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 9.00, pred: 879.399, error:2355.461593492885]acc: 0.0\n", + "Cond:#O.##..# - Act:7 - effect:.O...... - Num:1 [fit: 0.004, exp: 59.00, pred: 771.147, error:3898.408551470278]acc: 0.22033898305084745\n", + "Cond:##O#.#.. - Act:1 - effect:....OOO. - Num:2 [fit: 0.020, exp: 9.00, pred: 871.216, error:4526.67480524464]acc: 0.0\n", + "Cond:##O###.# - Act:1 - effect:....OOO. - Num:2 [fit: 0.021, exp: 9.00, pred: 871.216, error:5329.81080524464]acc: 0.0\n", + "Cond:.#O.#.## - Act:2 - effect:.OOO.... - Num:1 [fit: 0.002, exp: 9.00, pred: 959.259, error:4356.3161693107795]acc: 0.0\n", + "Cond:#O#O.##. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.010, exp: 6.00, pred: 990.975, error:2168.2184569255264]acc: 0.6666666666666666\n", + "Cond:#O.#.#.. - Act:4 - effect:OO...... - Num:2 [fit: 0.002, exp: 10.00, pred: 868.971, error:7039.559133395995]acc: 0.1\n", + "Cond:#..#.#.. - Act:6 - effect:.....OF. - Num:1 [fit: 0.047, exp: 58.00, pred: 1104.745, error:5678.155220517461]acc: 0.41379310344827586\n", + "Cond:O##..#.O - Act:2 - effect:..OO.... - Num:1 [fit: 0.014, exp: 12.00, pred: 1003.244, error:8025.312846512021]acc: 0.0\n", + "Cond:##..O##. - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 951.101, error:1059.1774366285917]acc: None\n", + "Cond:O..##..O - Act:1 - effect:O......O - Num:2 [fit: 0.061, exp: 5.00, pred: 1007.023, error:2435.302140384235]acc: 0.6\n", + "Cond:#.#.#.#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.009, exp: 0.00, pred: 786.669, error:141.41452304304414]acc: None\n", + "Cond:##OO##.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.007, exp: 6.00, pred: 971.388, error:4148.258892447254]acc: 0.0\n", + "Cond:#OO....# - Act:4 - effect:....OOO. - Num:1 [fit: 0.004, exp: 6.00, pred: 842.225, error:5452.742661913613]acc: 0.0\n", + "Cond:..OO##.# - Act:5 - effect:....OOO. - Num:3 [fit: 0.135, exp: 348.00, pred: 2237.917, error:297.4169432052039]acc: 0.8908045977011494\n", + "Cond:.O##.##. - Act:3 - effect:OO...... - Num:2 [fit: 0.003, exp: 19.00, pred: 991.291, error:4934.873559186263]acc: 0.10526315789473684\n", + "Cond:#.#...## - Act:4 - effect:.......O - Num:3 [fit: 0.006, exp: 10.00, pred: 890.227, error:6345.575759902377]acc: 0.0\n", + "Cond:.#...#.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 18.00, pred: 901.726, error:4085.036942741331]acc: 0.3888888888888889\n", + "Cond:....OO.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.026, exp: 0.00, pred: 1531.537, error:327.4199741843755]acc: None\n", + "Cond:...#O.## - Act:3 - effect:...OO... - Num:1 [fit: 0.062, exp: 15.00, pred: 987.540, error:2902.5984551705424]acc: 0.8\n", + "Cond:...O#... - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 10.00, pred: 796.139, error:7764.735012619234]acc: 0.0\n", + "Cond:.##O#.#. - Act:0 - effect:.....F.. - Num:2 [fit: 0.018, exp: 13.00, pred: 803.168, error:7250.656930501595]acc: 0.15384615384615385\n", + "Cond:.#...#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.008, exp: 14.00, pred: 959.428, error:5034.073559167924]acc: 0.2857142857142857\n", + "Cond:#.##OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.021, exp: 80.00, pred: 1105.491, error:158.38484222318021]acc: 1.0\n", + "Cond:OO##..#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.005, exp: 7.00, pred: 1153.481, error:7047.160968993182]acc: 0.0\n", + "Cond:O#####.# - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 8.00, pred: 841.735, error:6386.018099907333]acc: 0.0\n", + "Cond:OO....#O - Act:7 - effect:O......O - Num:1 [fit: 0.030, exp: 8.00, pred: 839.700, error:3922.007546818508]acc: 0.0\n", + "Cond:##.###.O - Act:7 - effect:OO...... - Num:1 [fit: 0.000, exp: 19.00, pred: 914.515, error:1442.6798260659596]acc: 0.2631578947368421\n", + "Cond:O##..... - Act:3 - effect:....OOO. - Num:1 [fit: 0.032, exp: 1.00, pred: 848.177, error:1800.0]acc: 0.0\n", + "Cond:#O####.# - Act:1 - effect:...OO... - Num:1 [fit: 0.000, exp: 25.00, pred: 833.665, error:3099.6520101945466]acc: 0.12\n", + "Cond:..#O##.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.022, exp: 6.00, pred: 893.946, error:6529.035347110384]acc: 0.0\n", + "Cond:#.##.#.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 9.00, pred: 960.996, error:7236.367250531226]acc: 0.0\n", + "Cond:##.#...O - Act:2 - effect:......OO - Num:1 [fit: 0.000, exp: 24.00, pred: 937.664, error:7081.493309465708]acc: 0.041666666666666664\n", + "Cond:.#...OO# - Act:4 - effect:....OOO. - Num:2 [fit: 0.145, exp: 6.00, pred: 1033.507, error:3958.021133601486]acc: 0.0\n", + "Cond:.##..#OO - Act:6 - effect:....OOO. - Num:2 [fit: 0.009, exp: 24.00, pred: 804.803, error:4307.003974821986]acc: 0.0\n", + "Cond:#..###.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.004, exp: 27.00, pred: 891.166, error:4105.128729364458]acc: 0.4074074074074074\n", + "Cond:#..#.O#. - Act:1 - effect:....OOO. - Num:2 [fit: 0.013, exp: 3.00, pred: 762.550, error:3916.3281043512357]acc: 0.0\n", + "Cond:#.#..F#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.005, exp: 5.00, pred: 1074.419, error:4799.723523598448]acc: 0.0\n", + "Cond:.O.#.F#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1043.649, error:1089.8700613351414]acc: None\n", + "Cond:#...F### - Act:4 - effect:....OOO. - Num:1 [fit: 0.104, exp: 2.00, pred: 1005.203, error:2.5950979897625643]acc: 1.0\n", + "Cond:#OO...#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.150, exp: 7.00, pred: 805.622, error:3763.4942591682957]acc: 0.0\n", + "Cond:#O###.#. - Act:2 - effect:.OOO.... - Num:1 [fit: 0.000, exp: 20.00, pred: 817.585, error:4388.704499593386]acc: 0.2\n", + "Cond:#O###.## - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 15.00, pred: 811.704, error:7237.989908758625]acc: 0.0\n", + "Cond:#O#####. - Act:3 - effect:OO...... - Num:1 [fit: 0.001, exp: 17.00, pred: 974.225, error:4955.279223564317]acc: 0.058823529411764705\n", + "Cond:.##O#### - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 11.00, pred: 795.373, error:8339.887743003326]acc: 0.0\n", + "Cond:OO##..## - Act:2 - effect:.OOO.... - Num:2 [fit: 0.106, exp: 6.00, pred: 1001.607, error:4028.9930783364093]acc: 0.0\n", + "Cond:..#.#### - Act:0 - effect:O......O - Num:1 [fit: 0.000, exp: 21.00, pred: 958.522, error:4792.925522444966]acc: 0.09523809523809523\n", + "Cond:..#.#OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 396.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:######## - Act:5 - effect:....OOO. - Num:3 [fit: 0.004, exp: 2431.00, pred: 2358.304, error:1509.2846166842442]acc: 0.3031674208144796\n", + "Cond:O#.####. - Act:7 - effect:O......O - Num:1 [fit: 0.002, exp: 10.00, pred: 1237.842, error:8526.446420038601]acc: 0.0\n", + "Cond:###O##.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.007, exp: 38.00, pred: 894.383, error:1805.9912794330114]acc: 0.02631578947368421\n", + "Cond:.#.###.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 22.00, pred: 811.573, error:2366.4882536783452]acc: 0.22727272727272727\n", + "Cond:..##O##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.007, exp: 9.00, pred: 822.252, error:1621.5833512147856]acc: 0.1111111111111111\n", + "Cond:#.#..##O - Act:0 - effect:.....OOF - Num:2 [fit: 0.015, exp: 14.00, pred: 924.472, error:3866.277452498752]acc: 0.35714285714285715\n", + "Cond:.#.####O - Act:1 - effect:....OOO. - Num:2 [fit: 0.009, exp: 8.00, pred: 900.348, error:6918.759002950104]acc: 0.0\n", + "Cond:O##...#O - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 5.00, pred: 882.141, error:4091.2600255039465]acc: 0.0\n", + "Cond:.O###..# - Act:3 - effect:OO...... - Num:1 [fit: 0.001, exp: 14.00, pred: 972.526, error:4559.2443379910255]acc: 0.07142857142857142\n", + "Cond:##.#.#.# - Act:1 - effect:.O...... - Num:1 [fit: 0.000, exp: 25.00, pred: 778.037, error:2565.1258726000046]acc: 0.2\n", + "Cond:#O.#.#.# - Act:1 - effect:OO...... - Num:2 [fit: 0.011, exp: 11.00, pred: 870.262, error:4338.505485389086]acc: 0.0\n", + "Cond:..#.###O - Act:1 - effect:....OOO. - Num:2 [fit: 0.020, exp: 7.00, pred: 835.235, error:7147.580473968223]acc: 0.0\n", + "Cond:.#OO..## - Act:7 - effect:....OOO. - Num:1 [fit: 0.005, exp: 19.00, pred: 994.217, error:5822.536545759045]acc: 0.47368421052631576\n", + "Cond:.OO###.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.070, exp: 4.00, pred: 806.055, error:2585.849451105307]acc: 0.0\n", + "Cond:.O.#O#.# - Act:7 - effect:...O.... - Num:1 [fit: 0.004, exp: 0.00, pred: 1220.959, error:728.1601490398256]acc: None\n", + "Cond:..#O#..# - Act:7 - effect:....FO.. - Num:1 [fit: 0.001, exp: 22.00, pred: 946.997, error:1851.0213751628953]acc: 0.5\n", + "Cond:#O##.#.. - Act:3 - effect:OO...... - Num:1 [fit: 0.014, exp: 14.00, pred: 970.663, error:4452.713644757472]acc: 0.07142857142857142\n", + "Cond:.O###... - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 11.00, pred: 816.258, error:6420.8452053315295]acc: 0.0\n", + "Cond:.#O###.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 8.00, pred: 842.209, error:5337.59941220785]acc: 0.0\n", + "Cond:##...O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.032, exp: 1.00, pred: 831.616, error:1600.0]acc: 0.0\n", + "Cond:#.###.#O - Act:0 - effect:.....OOF - Num:5 [fit: 0.040, exp: 13.00, pred: 909.376, error:3837.661342836094]acc: 0.3076923076923077\n", + "Cond:.#.##.## - Act:0 - effect:.....OOF - Num:1 [fit: 0.000, exp: 24.00, pred: 864.903, error:5747.297520037948]acc: 0.20833333333333334\n", + "Cond:#####OOF - Act:5 - effect:.....OOF - Num:1 [fit: 0.016, exp: 65.00, pred: 977.401, error:1216.6219616593341]acc: 0.4\n", + "Cond:.#.##F.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.009, exp: 1.00, pred: 1045.534, error:1600.0]acc: 0.0\n", + "Cond:#O##.#.# - Act:2 - effect:.OOO.... - Num:3 [fit: 0.100, exp: 9.00, pred: 790.509, error:3976.420283536688]acc: 0.2222222222222222\n", + "Cond:##.###F. - Act:4 - effect:....OOO. - Num:1 [fit: 0.022, exp: 1.00, pred: 831.616, error:1800.0]acc: 0.0\n", + "Cond:..#.#F## - Act:0 - effect:....OOO. - Num:1 [fit: 0.011, exp: 1.00, pred: 1045.534, error:1600.0]acc: 0.0\n", + "Cond:.##.#### - Act:0 - effect:....OOO. - Num:1 [fit: 0.003, exp: 19.00, pred: 940.761, error:7063.940615425008]acc: 0.0\n", + "Cond:##.O#.#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.007, exp: 8.00, pred: 843.503, error:2087.648938721949]acc: 0.0\n", + "Cond:#.##.#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.123, exp: 3.00, pred: 911.010, error:628.4169371724394]acc: 0.6666666666666666\n", + "Cond:..###..# - Act:2 - effect:..OO.... - Num:2 [fit: 0.070, exp: 43.00, pred: 929.516, error:4911.253022838817]acc: 0.23255813953488372\n", + "Cond:.OO#.#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.005, exp: 7.00, pred: 812.936, error:5075.4044110689265]acc: 0.0\n", + "Cond:##O..### - Act:7 - effect:....OOO. - Num:1 [fit: 0.023, exp: 2.00, pred: 914.646, error:2866.3611603963955]acc: 0.0\n", + "Cond:..##O#.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 10.00, pred: 997.078, error:6004.717833533694]acc: 0.0\n", + "Cond:.###.#OO - Act:6 - effect:....OOO. - Num:1 [fit: 0.007, exp: 20.00, pred: 805.461, error:3747.9498962533476]acc: 0.0\n", + "Cond:#.####.# - Act:4 - effect:....OOO. - Num:2 [fit: 0.016, exp: 15.00, pred: 885.604, error:3402.9366561128604]acc: 0.3333333333333333\n", + "Cond:##.O.#.# - Act:2 - effect:.OOO.... - Num:1 [fit: 0.019, exp: 0.00, pred: 952.782, error:551.514215018859]acc: None\n", + "Cond:.##O#..# - Act:3 - effect:...OO... - Num:1 [fit: 0.011, exp: 39.00, pred: 1042.313, error:3833.143804875865]acc: 0.15384615384615385\n", + "Cond:.#.#.O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.018, exp: 285.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:.O#.###. - Act:0 - effect:.....F.. - Num:1 [fit: 0.028, exp: 5.00, pred: 784.563, error:4105.518811061121]acc: 0.0\n", + "Cond:###O#.## - Act:0 - effect:.....F.. - Num:1 [fit: 0.029, exp: 9.00, pred: 787.965, error:7578.68189924553]acc: 0.1111111111111111\n", + "Cond:.######. - Act:0 - effect:....OOO. - Num:2 [fit: 0.002, exp: 18.00, pred: 813.788, error:8448.998709485288]acc: 0.0\n", + "Cond:#O##.##. - Act:3 - effect:OO...... - Num:1 [fit: 0.014, exp: 11.00, pred: 963.655, error:4914.95485129136]acc: 0.09090909090909091\n", + "Cond:..#F#..# - Act:2 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 934.130, error:697.9933032217477]acc: None\n", + "Cond:#.##O... - Act:3 - effect:....OOO. - Num:1 [fit: 0.019, exp: 14.00, pred: 976.468, error:1729.4485970893343]acc: 0.0\n", + "Cond:#.###.#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.355, exp: 5.00, pred: 714.936, error:1070.7733159004888]acc: 0.0\n", + "Cond:O#....## - Act:7 - effect:O......O - Num:1 [fit: 0.000, exp: 21.00, pred: 1187.245, error:7990.079788454115]acc: 0.14285714285714285\n", + "Cond:#..####. - Act:4 - effect:....OOO. - Num:1 [fit: 0.566, exp: 3.00, pred: 848.262, error:72.54542953196585]acc: 1.0\n", + "Cond:#.###... - Act:4 - effect:....OOO. - Num:1 [fit: 0.402, exp: 0.00, pred: 722.818, error:630.5847380716142]acc: None\n", + "Cond:.#.##.## - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 11.00, pred: 770.371, error:5475.726936280892]acc: 0.0\n", + "Cond:.#O.#### - Act:0 - effect:....OOO. - Num:1 [fit: 0.076, exp: 2.00, pred: 676.149, error:2505.6255204349945]acc: 0.0\n", + "Cond:.OO..#.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.014, exp: 4.00, pred: 760.245, error:3926.7030986524987]acc: 0.0\n", + "Cond:.#O##.#. - Act:2 - effect:..OO.... - Num:1 [fit: 0.027, exp: 32.00, pred: 890.811, error:2202.980763278454]acc: 0.1875\n", + "Cond:###O.... - Act:4 - effect:....OOO. - Num:1 [fit: 0.012, exp: 1.00, pred: 693.639, error:1600.0]acc: 0.0\n", + "Cond:..#.F### - Act:2 - effect:....OOO. - Num:1 [fit: 0.057, exp: 153.00, pred: 2122.975, error:224.50845468370034]acc: 0.8954248366013072\n", + "Cond:.#..#OO# - Act:4 - effect:....OOO. - Num:1 [fit: 0.025, exp: 1.00, pred: 829.698, error:2200.0]acc: 0.0\n", + "Cond:##O#.#.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 16.00, pred: 990.676, error:6297.868626074809]acc: 0.5\n", + "Cond:.O..O##. - Act:7 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 903.565, error:621.8732806430623]acc: None\n", + "Cond:.OO#.#.. - Act:1 - effect:.....F.. - Num:1 [fit: 0.007, exp: 1.00, pred: 659.940, error:1600.0]acc: 0.0\n", + "Cond:.O###... - Act:1 - effect:....OOO. - Num:1 [fit: 0.005, exp: 5.00, pred: 766.695, error:4217.238598926941]acc: 0.0\n", + "Cond:##..##OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 846.479, error:1782.9988221523856]acc: None\n", + "Cond:#..###OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 846.479, error:1782.9988221523856]acc: None\n", + "Cond:...##F#. - Act:5 - effect:....OOO. - Num:3 [fit: 0.057, exp: 263.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:##..##.. - Act:0 - effect:....OOO. - Num:1 [fit: 0.019, exp: 3.00, pred: 857.119, error:3918.3609130942536]acc: 0.0\n", + "Cond:..#.#### - Act:1 - effect:....FO.. - Num:1 [fit: 0.008, exp: 32.00, pred: 886.830, error:4684.721017291382]acc: 0.25\n", + "Cond:..O...O# - Act:7 - effect:.OOO.... - Num:1 [fit: 0.010, exp: 0.00, pred: 880.491, error:727.5164070750202]acc: None\n", + "Cond:#..#.##F - Act:0 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 973.977, error:971.4744072514203]acc: None\n", + "Cond:##..#OF# - Act:0 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 973.977, error:971.4744072514203]acc: None\n", + "Cond:.####... - Act:0 - effect:.....F.. - Num:1 [fit: 0.010, exp: 4.00, pred: 736.655, error:5204.253807320356]acc: 0.0\n", + "Cond:#.####OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 394.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#O.....# - Act:1 - effect:OO...... - Num:1 [fit: 0.002, exp: 10.00, pred: 826.188, error:4170.363330902508]acc: 0.0\n", + "Cond:O#####.# - Act:1 - effect:O......O - Num:1 [fit: 0.005, exp: 9.00, pred: 930.149, error:3555.827690798452]acc: 0.2222222222222222\n", + "Cond:...O###. - Act:1 - effect:....OOO. - Num:1 [fit: 0.012, exp: 3.00, pred: 885.867, error:4349.510068805084]acc: 0.0\n", + "Cond:O#.F#### - Act:4 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 911.276, error:958.7802784868622]acc: None\n", + "Cond:#O..#O.# - Act:7 - effect:...O.... - Num:1 [fit: 0.016, exp: 0.00, pred: 1024.341, error:581.2159419292111]acc: None\n", + "Cond:.#O.###. - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 670.524, error:113.20319005437429]acc: None\n", + "Cond:#####F## - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 8.00, pred: 1119.016, error:4279.934312679221]acc: 0.0\n", + "Cond:##OO##.# - Act:7 - effect:.....OF. - Num:1 [fit: 0.002, exp: 13.00, pred: 1008.490, error:6015.881596857966]acc: 0.0\n", + "Cond:.##..O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.091, exp: 1.00, pred: 810.512, error:1600.0]acc: 0.0\n", + "Cond:#.##.O#. - Act:6 - effect:....OOO. - Num:4 [fit: 0.037, exp: 283.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:..##F#O. - Act:4 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1068.116, error:25.871937615380624]acc: None\n", + "Cond:#.#.F### - Act:4 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1068.116, error:25.871937615380624]acc: None\n", + "Cond:..##...# - Act:2 - effect:..OO.... - Num:2 [fit: 0.089, exp: 28.00, pred: 929.713, error:5865.936972999651]acc: 0.5357142857142857\n", + "Cond:###.##.O - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 6.00, pred: 939.073, error:2134.9651765612143]acc: 0.0\n", + "Cond:.##.#.#O - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 4.00, pred: 904.524, error:3735.4767722895645]acc: 0.0\n", + "Cond:##..##O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 399.00, pred: 1948.419, error:251.07661682020802]acc: 0.9874686716791979\n", + "Cond:#O####.# - Act:2 - effect:.OOO.... - Num:2 [fit: 0.118, exp: 2.00, pred: 846.773, error:1522.1218146659082]acc: 0.0\n", + "Cond:O#.....# - Act:3 - effect:...O.... - Num:1 [fit: 0.069, exp: 56.00, pred: 1438.271, error:3429.6309556695355]acc: 0.5\n", + "Cond:...#.O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.457, exp: 0.00, pred: 903.767, error:53.31292829573374]acc: None\n", + "Cond:....OOO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1103.831, error:177.30353095073065]acc: None\n", + "Cond:##.#O#.# - Act:7 - effect:...O.... - Num:1 [fit: 0.011, exp: 5.00, pred: 990.508, error:1254.9917005438256]acc: 0.6\n", + "Cond:...#.O.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 786.531, error:264.54101304390446]acc: None\n", + "Cond:###..##. - Act:1 - effect:....OOO. - Num:1 [fit: 0.127, exp: 4.00, pred: 772.406, error:2390.6547292962223]acc: 0.0\n", + "Cond:.#.##.#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 963.047, error:1930.4608148577297]acc: None\n", + "Cond:###..#.# - Act:1 - effect:OO...... - Num:1 [fit: 0.005, exp: 12.00, pred: 781.001, error:3433.8546147510406]acc: 0.0\n", + "Cond:#O.#.##. - Act:1 - effect:....OOO. - Num:1 [fit: 0.009, exp: 3.00, pred: 706.344, error:2247.6240075391743]acc: 0.0\n", + "Cond:O...#.#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 876.204, error:3875.777498797797]acc: None\n", + "Cond:#O.###.# - Act:4 - effect:OO...... - Num:2 [fit: 0.021, exp: 1.00, pred: 767.582, error:0.0]acc: 1.0\n", + "Cond:##.#O... - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 950.293, error:1057.436098465086]acc: None\n", + "Cond:..###.#O - Act:0 - effect:.....OOF - Num:1 [fit: 0.111, exp: 2.00, pred: 1043.088, error:1339.187737874409]acc: 0.0\n", + "Cond:##.#.OO# - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1024.245, error:636.789445958947]acc: None\n", + "Cond:#...###F - Act:1 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1164.733, error:38.907619523399575]acc: None\n", + "Cond:O.#.#O## - Act:4 - effect:....OOO. - Num:1 [fit: 0.099, exp: 0.00, pred: 1033.931, error:3646.687066485266]acc: None\n", + "Cond:..#.#F#. - Act:6 - effect:.....OF. - Num:3 [fit: 0.218, exp: 39.00, pred: 1104.783, error:4878.666904553485]acc: 0.5128205128205128\n", + "Cond:#.#O###. - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 2.00, pred: 691.560, error:2907.5844709904486]acc: 0.0\n", + "Cond:##..#.## - Act:6 - effect:......OO - Num:1 [fit: 0.006, exp: 121.00, pred: 763.300, error:5365.048531287018]acc: 0.15702479338842976\n", + "Cond:#O###### - Act:2 - effect:.OOO.... - Num:1 [fit: 0.012, exp: 0.00, pred: 846.773, error:30.530453666477058]acc: None\n", + "Cond:####FO.# - Act:7 - effect:...OO... - Num:1 [fit: 0.010, exp: 66.00, pred: 1034.588, error:5334.550839986552]acc: 0.5454545454545454\n", + "Cond:#O.####. - Act:4 - effect:OO...... - Num:1 [fit: 0.002, exp: 0.00, pred: 767.582, error:0.0]acc: None\n", + "Cond:O#.O##.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 890.371, error:1823.9462450715146]acc: None\n", + "Cond:#.#O##.. - Act:1 - effect:...OO... - Num:1 [fit: 0.013, exp: 1.00, pred: 984.104, error:0.0]acc: 1.0\n", + "Cond:#O.##.#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 813.713, error:645.1064697171166]acc: None\n", + "Cond:O##.##.. - Act:0 - effect:.O...... - Num:1 [fit: 0.001, exp: 0.00, pred: 813.713, error:645.1064697171166]acc: None\n", + "Cond:##..#.## - Act:2 - effect:....OOO. - Num:1 [fit: 0.003, exp: 20.00, pred: 862.130, error:4237.209752585226]acc: 0.0\n", + "Cond:#..##### - Act:4 - effect:....OOO. - Num:1 [fit: 0.107, exp: 3.00, pred: 867.732, error:2882.511779253281]acc: 0.3333333333333333\n", + "Cond:##.#O### - Act:7 - effect:...O.... - Num:1 [fit: 0.209, exp: 2.00, pred: 827.086, error:27.43458807135653]acc: 1.0\n", + "Cond:#..O.### - Act:4 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1027.168, error:309.90800443767887]acc: None\n", + "Cond:#....#.. - Act:6 - effect:.....OF. - Num:1 [fit: 0.005, exp: 37.00, pred: 1104.987, error:4820.82348771049]acc: 0.3783783783783784\n", + "Cond:...#.O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 393.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:###O#.#. - Act:1 - effect:....OOO. - Num:3 [fit: 0.033, exp: 1.00, pred: 984.104, error:1600.0]acc: 0.0\n", + "Cond:#.#..#.. - Act:7 - effect:....OOO. - Num:1 [fit: 0.010, exp: 7.00, pred: 1135.641, error:3435.4007309922963]acc: 0.0\n", + "Cond:.#.#O#.. - Act:7 - effect:...FOO.. - Num:1 [fit: 0.023, exp: 0.00, pred: 1746.338, error:292.2116050190348]acc: None\n", + "Cond:...F#### - Act:5 - effect:...FOO.. - Num:1 [fit: 0.064, exp: 101.00, pred: 1320.570, error:2599.8725700284485]acc: 0.5742574257425742\n", + "Cond:#####OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1518.942, error:168.60255503323083]acc: None\n", + "Cond:...#OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.061, exp: 51.00, pred: 1105.485, error:158.35741086134072]acc: 1.0\n", + "Cond:...FO#.# - Act:5 - effect:O......O - Num:2 [fit: 0.008, exp: 91.00, pred: 1320.570, error:2658.662460530898]acc: 0.2857142857142857\n", + "Cond:.#.F#### - Act:5 - effect:...FOO.. - Num:1 [fit: 0.053, exp: 91.00, pred: 1320.570, error:2419.3406058169085]acc: 0.8351648351648352\n", + "Cond:..##OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.041, exp: 46.00, pred: 1105.483, error:158.34995429413874]acc: 1.0\n", + "Cond:..##..O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1481.471, error:4343.221114376192]acc: None\n", + "Cond:....#OO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1468.672, error:87.405671337059]acc: None\n", + "Cond:..#FOOO. - Act:3 - effect:....OOO. - Num:1 [fit: 0.033, exp: 0.00, pred: 1072.587, error:6.547142131850127]acc: None\n", + "Cond:..##OO#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.415, exp: 1.00, pred: 909.839, error:1800.0]acc: 0.0\n", + "Cond:O##.O..# - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1644.842, error:756.3818069517888]acc: None\n", + "Cond:#..#.### - Act:1 - effect:....OOO. - Num:3 [fit: 0.136, exp: 5.00, pred: 852.434, error:5459.45746786533]acc: 0.0\n", + "Cond:.###.##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.155, exp: 3.00, pred: 796.225, error:2953.8226210153884]acc: 0.0\n", + "Cond:##.#OO#. - Act:1 - effect:....OOO. - Num:4 [fit: 0.022, exp: 16.00, pred: 1125.296, error:4045.18246462395]acc: 0.0\n", + "Cond:#####OOF - Act:2 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1121.670, error:10.301395021620039]acc: None\n", + "Cond:.....OO# - Act:2 - effect:.....OOF - Num:1 [fit: 0.007, exp: 0.00, pred: 1193.341, error:508.5657116397141]acc: None\n", + "Cond:...#OO#. - Act:1 - effect:.....OF. - Num:2 [fit: 0.004, exp: 16.00, pred: 1125.296, error:3993.22991391675]acc: 0.0\n", + "Cond:#.###O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 359.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:####..O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.003, exp: 111.00, pred: 2124.705, error:1301.3548733046496]acc: 0.7207207207207207\n", + "Cond:#.#.OF.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1744.400, error:19.512710990888845]acc: None\n", + "Cond:.#.F#O#. - Act:0 - effect:..OO.... - Num:1 [fit: 0.250, exp: 1.00, pred: 909.839, error:1600.0]acc: 0.0\n", + "Cond:..#..#OF - Act:2 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1273.784, error:21.729992299637942]acc: None\n", + "Cond:.#.F#O## - Act:6 - effect:..OO.... - Num:4 [fit: 0.042, exp: 142.00, pred: 1163.269, error:1460.6783407403925]acc: 0.9788732394366197\n", + "Cond:.#.F#O.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 142.00, pred: 1163.269, error:1780.6696295117995]acc: 0.9788732394366197\n", + "Cond:##.F#O.. - Act:6 - effect:..OO.... - Num:2 [fit: 0.021, exp: 137.00, pred: 1163.269, error:1460.7044744261004]acc: 0.9781021897810219\n", + "Cond:#.#.#O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 352.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#.#.#OOF - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 351.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#.##.F#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 243.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:####.#F. - Act:6 - effect:....OOO. - Num:2 [fit: 0.018, exp: 229.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:...#.O## - Act:0 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1168.363, error:274.6026825163381]acc: None\n", + "Cond:.#O#O..# - Act:5 - effect:.....OF. - Num:1 [fit: 0.179, exp: 0.00, pred: 1760.512, error:54.60036992063097]acc: None\n", + "Cond:##...#O. - Act:6 - effect:....OOO. - Num:1 [fit: 0.075, exp: 0.00, pred: 1453.943, error:260.89758801033435]acc: None\n", + "Cond:####.#F# - Act:6 - effect:....OOO. - Num:3 [fit: 0.027, exp: 224.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:O##F.### - Act:5 - effect:...OO... - Num:1 [fit: 0.003, exp: 0.00, pred: 2125.973, error:654.0192505125349]acc: None\n", + "Cond:#...OOOF - Act:7 - effect:....OOO. - Num:2 [fit: 0.029, exp: 0.00, pred: 1064.598, error:7.071396744654988]acc: None\n", + "Cond:.##F#O.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.011, exp: 122.00, pred: 1163.269, error:1460.6783407405512]acc: 0.9754098360655737\n", + "Cond:.##.O#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1803.127, error:169.0548719410447]acc: None\n", + "Cond:..##.OO# - Act:6 - effect:.....OOF - Num:1 [fit: 0.018, exp: 88.00, pred: 921.781, error:3185.830097201937]acc: 0.5227272727272727\n", + "Cond:.#.F##.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.014, exp: 112.00, pred: 1163.269, error:2100.6696295096635]acc: 0.9732142857142857\n", + "Cond:##.#OO.. - Act:6 - effect:..OO.... - Num:2 [fit: 0.014, exp: 112.00, pred: 1163.269, error:1460.687052014696]acc: 0.9464285714285714\n", + "Cond:..#..#.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.010, exp: 59.00, pred: 933.766, error:8600.473536552912]acc: 0.4406779661016949\n", + "Cond:.#.F###. - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 109.00, pred: 1163.269, error:1620.6696294829467]acc: 0.9724770642201835\n", + "Cond:.###.... - Act:2 - effect:....OOO. - Num:1 [fit: 0.040, exp: 16.00, pred: 917.159, error:1579.33934864759]acc: 0.0\n", + "Cond:....#O#F - Act:2 - effect:....OOO. - Num:1 [fit: 0.044, exp: 0.00, pred: 1145.815, error:5.340067511560463]acc: None\n", + "Cond:...##OF# - Act:0 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 1580.416, error:425.0006350695197]acc: None\n", + "Cond:###.#O## - Act:0 - effect:....OOO. - Num:1 [fit: 0.047, exp: 2.00, pred: 942.459, error:2704.137485094424]acc: 0.0\n", + "Cond:####...O - Act:7 - effect:...FOO.. - Num:3 [fit: 0.024, exp: 15.00, pred: 931.159, error:5141.7572595231]acc: 0.2\n", + "Cond:##.F##.. - Act:6 - effect:..OO.... - Num:2 [fit: 0.009, exp: 104.00, pred: 1163.269, error:1460.6957633193504]acc: 0.9711538461538461\n", + "Cond:...#.... - Act:3 - effect:....OOO. - Num:1 [fit: 0.244, exp: 2.00, pred: 790.686, error:1509.116361759168]acc: 0.0\n", + "Cond:###.#F.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.042, exp: 0.00, pred: 1942.531, error:212.86869529563663]acc: None\n", + "Cond:...##F## - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 235.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:O###.#O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.084, exp: 0.00, pred: 2313.615, error:31.69017518363622]acc: None\n", + "Cond:###OO### - Act:6 - effect:.....OOF - Num:1 [fit: 0.190, exp: 3.00, pred: 1013.946, error:1887.784788248291]acc: 0.0\n", + "Cond:#..#.#O# - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 3.00, pred: 845.502, error:1284.4410141320363]acc: 0.0\n", + "Cond:#.#..F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 232.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:#..F#O.# - Act:1 - effect:..OO.... - Num:1 [fit: 0.005, exp: 16.00, pred: 1125.296, error:4406.52775895995]acc: 0.0\n", + "Cond:..OO#O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.067, exp: 0.00, pred: 1642.981, error:673.5088503009308]acc: None\n", + "Cond:..OO.#.# - Act:5 - effect:....OOO. - Num:5 [fit: 0.245, exp: 256.00, pred: 2237.917, error:297.4169431582121]acc: 0.94140625\n", + "Cond:##.#.#F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 199.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:####.OO# - Act:6 - effect:.....OOF - Num:1 [fit: 0.012, exp: 64.00, pred: 921.781, error:3671.462857180056]acc: 0.640625\n", + "Cond:...#OF## - Act:5 - effect:....OOO. - Num:1 [fit: 0.178, exp: 0.00, pred: 1779.293, error:146.65108673005415]acc: None\n", + "Cond:##.FO#.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 84.00, pred: 1163.269, error:1780.6783275154232]acc: 0.9642857142857143\n", + "Cond:##....F. - Act:5 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 2235.208, error:166.54559981706268]acc: None\n", + "Cond:#.#OO.## - Act:0 - effect:.....OOF - Num:1 [fit: 0.028, exp: 1.00, pred: 706.729, error:1600.0]acc: 0.0\n", + "Cond:##...O#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.020, exp: 194.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:####..O# - Act:6 - effect:...FOO.. - Num:1 [fit: 0.028, exp: 16.00, pred: 803.110, error:2840.404962358911]acc: 0.0\n", + "Cond:.##FOO.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 77.00, pred: 1163.269, error:1460.6870374731038]acc: 0.961038961038961\n", + "Cond:.#.FOO.# - Act:6 - effect:..OO.... - Num:2 [fit: 0.013, exp: 77.00, pred: 1163.269, error:1620.6696150159175]acc: 0.961038961038961\n", + "Cond:###.O##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1310.334, error:52.4543983789914]acc: None\n", + "Cond:##.#.##F - Act:5 - effect:.....OOF - Num:1 [fit: 0.039, exp: 16.00, pred: 974.919, error:1169.9717574409867]acc: 0.875\n", + "Cond:..#.F..# - Act:3 - effect:...FOO.. - Num:1 [fit: 2.195, exp: 0.00, pred: 2106.470, error:175.87720446349576]acc: None\n", + "Cond:#.#.##F. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 191.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:##..OF#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.014, exp: 0.00, pred: 1875.396, error:27.49740494221644]acc: None\n", + "Cond:##..O### - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1224.583, error:29.990572400638463]acc: None\n", + "Cond:####.OF# - Act:6 - effect:....OOO. - Num:3 [fit: 0.027, exp: 188.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:..#.#... - Act:5 - effect:...FOO.. - Num:1 [fit: 0.313, exp: 0.00, pred: 2170.061, error:42.23143561259113]acc: None\n", + "Cond:###.##O# - Act:2 - effect:.O...... - Num:1 [fit: 0.357, exp: 3.00, pred: 845.502, error:2217.77434746537]acc: 0.6666666666666666\n", + "Cond:#.#####F - Act:5 - effect:.....OOF - Num:3 [fit: 0.496, exp: 11.00, pred: 989.306, error:840.2310934577486]acc: 0.9090909090909091\n", + "Cond:.#.FOO.# - Act:4 - effect:..OO.... - Num:1 [fit: 0.012, exp: 0.00, pred: 1614.364, error:36.75558239710531]acc: None\n", + "Cond:###OO... - Act:4 - effect:.....OOF - Num:1 [fit: 0.021, exp: 0.00, pred: 1445.498, error:278.50745479699117]acc: None\n", + "Cond:...F#O## - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 64.00, pred: 1163.269, error:1460.678418421111]acc: 0.953125\n", + "Cond:#.###... - Act:3 - effect:.....OOF - Num:2 [fit: 0.016, exp: 28.00, pred: 996.707, error:1015.1245763538107]acc: 0.07142857142857142\n", + "Cond:#..#.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 222.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:#.####O# - Act:7 - effect:....OOO. - Num:3 [fit: 0.049, exp: 247.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#..F#### - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 57.00, pred: 1163.270, error:1940.7154385628387]acc: 0.9473684210526315\n", + "Cond:.#.#.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 220.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:..OO#### - Act:5 - effect:....OOO. - Num:5 [fit: 0.262, exp: 201.00, pred: 2237.917, error:297.41694316675614]acc: 0.9850746268656716\n", + "Cond:#.##O.## - Act:7 - effect:.....OOF - Num:1 [fit: 0.017, exp: 0.00, pred: 1674.694, error:42.80424548575752]acc: None\n", + "Cond:..#FF#.. - Act:6 - effect:...OO... - Num:1 [fit: 0.022, exp: 0.00, pred: 2056.453, error:32.53068463630606]acc: None\n", + "Cond:...F#O.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 53.00, pred: 1163.268, error:1460.6745116466257]acc: 0.9433962264150944\n", + "Cond:.###F#.. - Act:6 - effect:...OO... - Num:1 [fit: 0.004, exp: 31.00, pred: 1070.420, error:3171.7981885025197]acc: 0.41935483870967744\n", + "Cond:.#.#OO.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.014, exp: 48.00, pred: 1163.256, error:1620.6301512773587]acc: 0.9375\n", + "Cond:...F#O.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 48.00, pred: 1163.256, error:1460.6464848309708]acc: 0.9375\n", + "Cond:##.#O##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.017, exp: 35.00, pred: 1048.445, error:219.72108207787812]acc: 0.7142857142857143\n", + "Cond:.###OO.# - Act:6 - effect:..OO.... - Num:2 [fit: 0.024, exp: 45.00, pred: 1163.258, error:1780.5597201616079]acc: 0.9555555555555556\n", + "Cond:.##OO### - Act:6 - effect:.....OOF - Num:1 [fit: 0.021, exp: 2.00, pred: 1071.786, error:2578.144797114214]acc: 0.0\n", + "Cond:....#.## - Act:2 - effect:...FOO.. - Num:1 [fit: 0.008, exp: 14.00, pred: 897.420, error:3904.646779353376]acc: 0.0\n", + "Cond:..#.###F - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 232.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:..#..F.. - Act:5 - effect:....OOO. - Num:2 [fit: 0.036, exp: 217.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:.###.##. - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 856.471, error:1507.1793005912543]acc: None\n", + "Cond:.#.#O### - Act:3 - effect:....OOO. - Num:2 [fit: 0.031, exp: 32.00, pred: 1048.261, error:294.4328260206514]acc: 0.6875\n", + "Cond:O#..#### - Act:7 - effect:....OOO. - Num:1 [fit: 0.009, exp: 19.00, pred: 1184.442, error:8170.84067207529]acc: 0.0\n", + "Cond:O.#.#### - Act:7 - effect:O......O - Num:1 [fit: 0.273, exp: 3.00, pred: 1062.894, error:2190.3640096463632]acc: 0.6666666666666666\n", + "Cond:..##OO## - Act:0 - effect:..OO.... - Num:1 [fit: 0.033, exp: 0.00, pred: 909.839, error:0.0]acc: None\n", + "Cond:.O.F#O## - Act:0 - effect:....OOO. - Num:1 [fit: 0.033, exp: 0.00, pred: 909.839, error:0.0]acc: None\n", + "Cond:#.##.##. - Act:4 - effect:....OOO. - Num:2 [fit: 0.010, exp: 0.00, pred: 1298.347, error:345.31182006296615]acc: None\n", + "Cond:#.#.OO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1508.490, error:100.74082854883056]acc: None\n", + "Cond:#.#FOO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.097, exp: 19.00, pred: 1098.986, error:143.80581798861735]acc: 1.0\n", + "Cond:..#.#..# - Act:7 - effect:...FOO.. - Num:2 [fit: 0.099, exp: 4.00, pred: 1008.374, error:3077.2945150111773]acc: 0.0\n", + "Cond:..O#.#.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1769.091, error:87.11982030304415]acc: None\n", + "Cond:.#...#OF - Act:7 - effect:....OOO. - Num:2 [fit: 0.038, exp: 223.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:...#FO#. - Act:6 - effect:...OO... - Num:1 [fit: 0.077, exp: 21.00, pred: 1068.527, error:3029.9626758600534]acc: 0.5238095238095238\n", + "Cond:..##.F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.019, exp: 214.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:###FOO#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.007, exp: 10.00, pred: 1124.801, error:3348.5964506107175]acc: 0.0\n", + "Cond:#.OO#O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.020, exp: 0.00, pred: 1642.887, error:81.95691480009623]acc: None\n", + "Cond:#.##.O#F - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 216.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:###.#O#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 23.00, pred: 979.846, error:4956.30688563193]acc: 0.0\n", + "Cond:#O#.###. - Act:1 - effect:....OOO. - Num:1 [fit: 0.112, exp: 2.00, pred: 649.647, error:1701.1295763962432]acc: 0.0\n", + "Cond:..#.#..O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.124, exp: 0.00, pred: 1053.617, error:1724.483375766285]acc: None\n", + "Cond:.#.#O##. - Act:3 - effect:....OOO. - Num:3 [fit: 0.065, exp: 24.00, pred: 1049.212, error:220.38151512465453]acc: 0.5833333333333334\n", + "Cond:.#OOO..# - Act:5 - effect:.....OOF - Num:1 [fit: 0.041, exp: 0.00, pred: 1525.938, error:73.89102930303838]acc: None\n", + "Cond:.....F## - Act:4 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1649.414, error:133.36726572580682]acc: None\n", + "Cond:..OO.O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.138, exp: 0.00, pred: 1708.689, error:349.5100922604224]acc: None\n", + "Cond:##..OOOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1519.054, error:51.98057206693248]acc: None\n", + "Cond:##..#F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 212.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:###..O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 1470.790, error:76.5356247557689]acc: None\n", + "Cond:####.OOF - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 205.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:##.#OO.. - Act:1 - effect:....OOO. - Num:2 [fit: 0.189, exp: 6.00, pred: 1053.792, error:2870.471118876515]acc: 0.0\n", + "Cond:#..##O.# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.070, exp: 72.00, pred: 1320.594, error:2193.091203606499]acc: 0.4722222222222222\n", + "Cond:..#..O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1515.764, error:530.6265469140068]acc: None\n", + "Cond:#.#...## - Act:6 - effect:...OO... - Num:1 [fit: 0.002, exp: 28.00, pred: 798.003, error:5948.104893458627]acc: 0.0\n", + "Cond:O#.#.##O - Act:6 - effect:.O...... - Num:1 [fit: 0.220, exp: 8.00, pred: 948.891, error:2705.6014120736863]acc: 0.75\n", + "Cond:..OO###F - Act:5 - effect:....OOO. - Num:1 [fit: 0.105, exp: 0.00, pred: 1281.682, error:323.3991094123723]acc: None\n", + "Cond:#####O#F - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 204.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:#...##.. - Act:3 - effect:.OOO.... - Num:1 [fit: 0.002, exp: 72.00, pred: 1667.057, error:1773.3602475137475]acc: 0.4722222222222222\n", + "Cond:O##..### - Act:7 - effect:....OOO. - Num:2 [fit: 0.009, exp: 17.00, pred: 1183.371, error:8514.480244112257]acc: 0.0\n", + "Cond:OO.....# - Act:1 - effect:OO...... - Num:2 [fit: 0.138, exp: 4.00, pred: 883.889, error:3390.5639531339475]acc: 0.0\n", + "Cond:#O.#...O - Act:7 - effect:O......O - Num:1 [fit: 0.069, exp: 4.00, pred: 808.328, error:2858.0753773429187]acc: 0.0\n", + "Cond:###...#O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.036, exp: 7.00, pred: 896.951, error:3229.570085451004]acc: 0.14285714285714285\n", + "Cond:####.#.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.120, exp: 2.00, pred: 649.647, error:1501.1295763962432]acc: 0.0\n", + "Cond:#####O.. - Act:5 - effect:...FOO.. - Num:2 [fit: 0.132, exp: 71.00, pred: 1320.594, error:2800.1060895814676]acc: 0.3380281690140845\n", + "Cond:.###OO## - Act:6 - effect:..OO.... - Num:2 [fit: 0.021, exp: 40.00, pred: 1163.297, error:1460.610342310684]acc: 0.95\n", + "Cond:#O#...## - Act:6 - effect:.O...... - Num:4 [fit: 0.218, exp: 53.00, pred: 894.360, error:6402.234757891905]acc: 0.37735849056603776\n", + "Cond:O###..O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.011, exp: 0.00, pred: 1644.584, error:528.6147322005247]acc: None\n", + "Cond:####OO## - Act:6 - effect:..OO.... - Num:3 [fit: 0.029, exp: 39.00, pred: 1163.346, error:1620.6821151608838]acc: 0.9487179487179487\n", + "Cond:.###OO.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.007, exp: 39.00, pred: 1163.346, error:1460.6821151608838]acc: 0.9487179487179487\n", + "Cond:.##O##F# - Act:6 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 1281.973, error:195.57941016301413]acc: None\n", + "Cond:#.##.O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1309.237, error:112.27890643882908]acc: None\n", + "Cond:#O#.###. - Act:7 - effect:......OO - Num:1 [fit: 0.002, exp: 45.00, pred: 771.178, error:3069.2999300877227]acc: 0.1111111111111111\n", + "Cond:#....O## - Act:6 - effect:....OOO. - Num:8 [fit: 0.072, exp: 206.00, pred: 1309.630, error:301.01333544763446]acc: 0.7815533980582524\n", + "Cond:O#..###O - Act:6 - effect:.O...... - Num:1 [fit: 0.189, exp: 6.00, pred: 863.486, error:2693.205200847778]acc: 0.6666666666666666\n", + "Cond:####.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 25.00, pred: 1019.013, error:4869.3963828173055]acc: 0.0\n", + "Cond:###.F##. - Act:2 - effect:....OOO. - Num:6 [fit: 0.344, exp: 150.00, pred: 2122.975, error:224.5084546745183]acc: 0.9066666666666666\n", + "Cond:#O.##.#. - Act:6 - effect:....OOO. - Num:2 [fit: 0.014, exp: 15.00, pred: 843.168, error:7667.021605599387]acc: 0.0\n", + "Cond:.####.## - Act:2 - effect:....OOO. - Num:1 [fit: 0.003, exp: 24.00, pred: 862.930, error:3681.629918581282]acc: 0.0\n", + "Cond:#..##..# - Act:6 - effect:...OO... - Num:1 [fit: 0.003, exp: 12.00, pred: 936.103, error:6903.3141198729945]acc: 0.0\n", + "Cond:#.#..O#F - Act:6 - effect:....OOO. - Num:1 [fit: 0.008, exp: 48.00, pred: 921.781, error:2653.523705590048]acc: 0.1875\n", + "Cond:#.#..##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 280.00, pred: 1825.847, error:246.04780710159352]acc: 0.7785714285714286\n", + "Cond:#...#O.# - Act:1 - effect:....FO.. - Num:3 [fit: 1.096, exp: 10.00, pred: 984.570, error:2782.167280691241]acc: 0.9\n", + "Cond:###.#O.. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 10.00, pred: 984.570, error:5247.703280691241]acc: 0.0\n", + "Cond:##..#O## - Act:0 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 942.459, error:276.034371273606]acc: None\n", + "Cond:##...### - Act:0 - effect:....OOO. - Num:1 [fit: 0.079, exp: 4.00, pred: 880.369, error:1214.965448136441]acc: 0.0\n", + "Cond:###OO##. - Act:5 - effect:.....OOF - Num:1 [fit: 0.035, exp: 144.00, pred: 2439.321, error:281.155364428202]acc: 0.7013888888888888\n", + "Cond:#.OO###. - Act:5 - effect:....OOO. - Num:2 [fit: 0.105, exp: 166.00, pred: 2237.917, error:297.41694315821195]acc: 0.9879518072289156\n", + "Cond:##.##O#F - Act:7 - effect:....OOO. - Num:4 [fit: 0.076, exp: 200.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:O..##.O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.009, exp: 0.00, pred: 1523.001, error:649.0694118254892]acc: None\n", + "Cond:.....O## - Act:5 - effect:.....OF. - Num:1 [fit: 0.108, exp: 76.00, pred: 1041.317, error:4353.685556666832]acc: 0.3684210526315789\n", + "Cond:#....OO# - Act:6 - effect:....OOO. - Num:1 [fit: 0.003, exp: 39.00, pred: 921.731, error:2375.107320080895]acc: 0.10256410256410256\n", + "Cond:#...#FO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 1542.890, error:90.89386278404072]acc: None\n", + "Cond:#.OO#O#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.790, exp: 0.00, pred: 1310.895, error:102.57409173424277]acc: None\n", + "Cond:#.OO#### - Act:5 - effect:....OOO. - Num:10 [fit: 0.514, exp: 158.00, pred: 2237.917, error:297.4169431603479]acc: 0.9873417721518988\n", + "Cond:#####.OO - Act:5 - effect:...FOO.. - Num:1 [fit: 0.002, exp: 78.00, pred: 2124.705, error:1674.053616769118]acc: 0.6410256410256411\n", + "Cond:####OO.. - Act:6 - effect:..OO.... - Num:3 [fit: 0.020, exp: 34.00, pred: 1163.295, error:1460.5243787616248]acc: 0.9411764705882353\n", + "Cond:######O# - Act:7 - effect:....OOO. - Num:2 [fit: 0.039, exp: 189.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:O.#.#O.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.015, exp: 0.00, pred: 1861.172, error:414.7149987582622]acc: None\n", + "Cond:..O#.##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.109, exp: 9.00, pred: 998.489, error:2686.1222231366783]acc: 0.0\n", + "Cond:.#O#O#.# - Act:5 - effect:.....OF. - Num:1 [fit: 0.055, exp: 0.00, pred: 1313.044, error:478.0992122997639]acc: None\n", + "Cond:.....##. - Act:4 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1764.174, error:363.74359319504856]acc: None\n", + "Cond:..#O.##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 34.00, pred: 1042.810, error:2607.7595326816113]acc: 0.7647058823529411\n", + "Cond:#.O#.##. - Act:5 - effect:....OOO. - Num:5 [fit: 0.257, exp: 155.00, pred: 2237.917, error:297.41694317530016]acc: 0.9870967741935484\n", + "Cond:.##.OOO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.163, exp: 0.00, pred: 2022.326, error:301.6462779188013]acc: None\n", + "Cond:.##OO#.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.041, exp: 2.00, pred: 1071.786, error:978.1447971142139]acc: 0.0\n", + "Cond:..#.F.## - Act:7 - effect:...OO... - Num:1 [fit: 0.057, exp: 0.00, pred: 1139.036, error:88.50612314674022]acc: None\n", + "Cond:.###OO#. - Act:6 - effect:..OO.... - Num:2 [fit: 0.023, exp: 31.00, pred: 1163.550, error:1620.8877743898865]acc: 0.9354838709677419\n", + "Cond:#.O#.#O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.512, exp: 0.00, pred: 1468.311, error:104.6335346440721]acc: None\n", + "Cond:..O#.O#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.512, exp: 0.00, pred: 1468.311, error:104.6335346440721]acc: None\n", + "Cond:..#.#OF. - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1230.365, error:79.2105574417227]acc: None\n", + "Cond:#.##.### - Act:4 - effect:....OOO. - Num:1 [fit: 0.080, exp: 3.00, pred: 867.732, error:2549.178445919948]acc: 0.3333333333333333\n", + "Cond:#.#.F#.# - Act:7 - effect:...OO... - Num:2 [fit: 0.029, exp: 34.00, pred: 1034.818, error:4122.720808338575]acc: 0.6470588235294118\n", + "Cond:#.OO##.# - Act:5 - effect:....OOO. - Num:2 [fit: 0.105, exp: 143.00, pred: 2237.917, error:297.4169431710267]acc: 0.986013986013986\n", + "Cond:#.#.F..# - Act:7 - effect:...OO... - Num:1 [fit: 0.045, exp: 0.00, pred: 1880.672, error:73.15898962104794]acc: None\n", + "Cond:.##.F##O - Act:7 - effect:...OO... - Num:1 [fit: 0.045, exp: 0.00, pred: 1880.672, error:73.15898962104794]acc: None\n", + "Cond:...#.FO. - Act:5 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1674.749, error:44.100142668848605]acc: None\n", + "Cond:#.OO#OO# - Act:5 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1462.156, error:37.3641914995019]acc: None\n", + "Cond:###.###O - Act:7 - effect:...OO... - Num:1 [fit: 0.033, exp: 5.00, pred: 926.330, error:3006.522050111458]acc: 0.0\n", + "Cond:..#.F..# - Act:7 - effect:...OO... - Num:1 [fit: 0.048, exp: 0.00, pred: 1927.811, error:51.971713474646094]acc: None\n", + "Cond:.###OO.# - Act:7 - effect:..OO.... - Num:1 [fit: 0.045, exp: 0.00, pred: 1879.510, error:44.65563925331589]acc: None\n", + "Cond:.##OOO#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.045, exp: 0.00, pred: 1879.510, error:44.65563925331589]acc: None\n", + "Cond:#.O##### - Act:5 - effect:....OOO. - Num:3 [fit: 0.157, exp: 127.00, pred: 2237.917, error:297.4169431582527]acc: 0.984251968503937\n", + "Cond:....#F#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1694.996, error:39.3310394189061]acc: None\n", + "Cond:..##OO#. - Act:6 - effect:..OO.... - Num:3 [fit: 0.048, exp: 26.00, pred: 1161.923, error:1778.2065717566818]acc: 0.9230769230769231\n", + "Cond:#...F#.# - Act:7 - effect:...OO... - Num:9 [fit: 0.103, exp: 23.00, pred: 1034.493, error:4867.738624074384]acc: 0.4782608695652174\n", + "Cond:....#OO. - Act:7 - effect:....OOO. - Num:1 [fit: 0.007, exp: 0.00, pred: 1328.129, error:49.539035231956746]acc: None\n", + "Cond:..###F#. - Act:5 - effect:....OOO. - Num:3 [fit: 0.052, exp: 196.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:#.#OO### - Act:2 - effect:.....OOF - Num:1 [fit: 0.096, exp: 0.00, pred: 1340.856, error:48.06295356953851]acc: None\n", + "Cond:...#OO#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.025, exp: 23.00, pred: 1159.277, error:1454.7219602529972]acc: 0.9130434782608695\n", + "Cond:.##O#O#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.076, exp: 0.00, pred: 1445.593, error:33.946195318680964]acc: None\n", + "Cond:..#..##. - Act:7 - effect:OO.....O - Num:2 [fit: 0.001, exp: 21.00, pred: 989.121, error:4597.666689063726]acc: 0.047619047619047616\n", + "Cond:..##..## - Act:7 - effect:....OOO. - Num:3 [fit: 0.027, exp: 12.00, pred: 972.227, error:6289.692895385433]acc: 0.5\n", + "Cond:###.##.O - Act:7 - effect:...OO... - Num:1 [fit: 0.021, exp: 5.00, pred: 926.330, error:3366.522050111458]acc: 0.0\n", + "Cond:...#..## - Act:7 - effect:....OOO. - Num:2 [fit: 0.106, exp: 1.00, pred: 709.916, error:1800.0]acc: 0.0\n", + "Cond:...#..#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.106, exp: 1.00, pred: 709.916, error:1600.0]acc: 0.0\n", + "Cond:###.#..O - Act:7 - effect:...FOO.. - Num:1 [fit: 0.355, exp: 3.00, pred: 801.679, error:1667.2396281333506]acc: 0.3333333333333333\n", + "Cond:O###.##O - Act:7 - effect:....OOO. - Num:1 [fit: 0.006, exp: 3.00, pred: 801.679, error:2467.2396281333504]acc: 0.0\n", + "Cond:.#OOO#.. - Act:5 - effect:.....OOF - Num:1 [fit: 0.010, exp: 0.00, pred: 1134.959, error:875.2179165347551]acc: None\n", + "Cond:..O#.#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.052, exp: 110.00, pred: 2237.917, error:297.4169431524832]acc: 0.9818181818181818\n", + "Cond:#.#..O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 137.00, pred: 1948.419, error:251.07661682014498]acc: 1.0\n", + "Cond:#.##..## - Act:7 - effect:....OOO. - Num:2 [fit: 0.016, exp: 3.00, pred: 796.502, error:3432.320933515355]acc: 0.0\n", + "Cond:..##.F#. - Act:7 - effect:OO.....O - Num:2 [fit: 0.109, exp: 2.00, pred: 1003.396, error:1645.0926429939175]acc: 0.0\n", + "Cond:OO..#..# - Act:1 - effect:OO...... - Num:1 [fit: 0.017, exp: 0.00, pred: 934.912, error:467.432196946809]acc: None\n", + "Cond:###..#.O - Act:7 - effect:...OO... - Num:1 [fit: 0.005, exp: 0.00, pred: 970.065, error:448.29437156873615]acc: None\n", + "Cond:O####### - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 9.00, pred: 1314.563, error:7267.279212174998]acc: 0.0\n", + "Cond:###..##O - Act:0 - effect:....OOO. - Num:2 [fit: 0.498, exp: 3.00, pred: 800.005, error:1336.199910844169]acc: 0.0\n", + "Cond:.#..#### - Act:0 - effect:....OOO. - Num:1 [fit: 0.001, exp: 1.00, pred: 964.712, error:1800.0]acc: 0.0\n", + "Cond:.#.##.## - Act:2 - effect:....OOO. - Num:1 [fit: 0.101, exp: 5.00, pred: 920.193, error:3034.1117944572266]acc: 0.0\n", + "Cond:#O..#.## - Act:6 - effect:....OOO. - Num:2 [fit: 0.049, exp: 9.00, pred: 801.853, error:5445.199687415582]acc: 0.0\n", + "Cond:##..#... - Act:6 - effect:....FO.. - Num:1 [fit: 0.053, exp: 8.00, pred: 805.484, error:5731.132482618904]acc: 0.125\n", + "Cond:O..#..## - Act:6 - effect:...OO... - Num:1 [fit: 0.024, exp: 0.00, pred: 956.150, error:613.8287238127097]acc: None\n", + "Cond:#..#..#O - Act:6 - effect:...OO... - Num:1 [fit: 0.002, exp: 23.00, pred: 800.622, error:5390.4182283926475]acc: 0.0\n", + "Cond:######OO - Act:7 - effect:...FOO.. - Num:1 [fit: 0.014, exp: 0.00, pred: 975.976, error:550.6585506394133]acc: None\n", + "Cond:#...##.# - Act:4 - effect:....OOO. - Num:1 [fit: 0.045, exp: 1.00, pred: 841.449, error:0.0]acc: 1.0\n", + "Cond:##..F#.# - Act:7 - effect:...OO... - Num:1 [fit: 0.085, exp: 10.00, pred: 1040.831, error:4360.178752258289]acc: 0.0\n", + "Cond:#...#### - Act:4 - effect:....OOO. - Num:1 [fit: 0.024, exp: 1.00, pred: 720.230, error:2200.0]acc: 0.0\n", + "Cond:#..##OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1326.590, error:82.61357566130036]acc: None\n", + "Cond:.##OO#.. - Act:5 - effect:.....OOF - Num:2 [fit: 0.070, exp: 66.00, pred: 2439.321, error:281.1397660512607]acc: 0.7424242424242424\n", + "Cond:.#OOO##. - Act:5 - effect:.....OOF - Num:1 [fit: 0.026, exp: 0.00, pred: 1228.565, error:3303.331394361997]acc: None\n", + "Cond:#...#O## - Act:6 - effect:....OOO. - Num:3 [fit: 0.027, exp: 184.00, pred: 1309.630, error:301.0144678551296]acc: 0.8315217391304348\n", + "Cond:###..O## - Act:1 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1495.046, error:198.34912075199242]acc: None\n", + "Cond:#.#.F..# - Act:3 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 1120.153, error:439.10160959204177]acc: None\n", + "Cond:####OO#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.124, exp: 15.00, pred: 1165.241, error:1611.1079058568812]acc: 0.8666666666666667\n", + "Cond:.##OO.## - Act:5 - effect:.....OOF - Num:5 [fit: 0.168, exp: 64.00, pred: 2439.321, error:281.1633122752595]acc: 0.75\n", + "Cond:###OO#.. - Act:5 - effect:.....OOF - Num:2 [fit: 0.078, exp: 64.00, pred: 2439.321, error:281.14759083127757]acc: 0.75\n", + "Cond:.##.FO#. - Act:5 - effect:....FO.. - Num:1 [fit: 0.017, exp: 16.00, pred: 1261.081, error:3840.410943665717]acc: 0.75\n", + "Cond:.#...O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 130.00, pred: 1309.630, error:301.00951517614817]acc: 1.0\n", + "Cond:##OOO..# - Act:5 - effect:.....OOF - Num:1 [fit: 0.004, exp: 0.00, pred: 1272.070, error:678.2686935879257]acc: None\n", + "Cond:#.O.#### - Act:3 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1357.110, error:175.00261740786692]acc: None\n", + "Cond:#.#.#F#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.018, exp: 8.00, pred: 1127.475, error:3960.9601508370497]acc: 0.0\n", + "Cond:#....### - Act:1 - effect:....OOO. - Num:2 [fit: 0.027, exp: 2.00, pred: 743.420, error:2561.545169463935]acc: 0.0\n", + "Cond:.####..# - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 29.00, pred: 1015.335, error:4426.913077652574]acc: 0.0\n", + "Cond:.#.O.##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.076, exp: 0.00, pred: 1137.625, error:66.37866543928988]acc: None\n", + "Cond:..##.OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 119.00, pred: 1309.630, error:301.00951517736405]acc: 1.0\n", + "Cond:##...O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 140.00, pred: 1309.630, error:301.01477201403105]acc: 0.8642857142857143\n", + "Cond:#..FOO#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.016, exp: 11.00, pred: 1182.607, error:1463.870969714343]acc: 0.8181818181818182\n", + "Cond:#####O.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.000, exp: 25.00, pred: 1073.387, error:2440.088081382356]acc: 0.6\n", + "Cond:###OO#.# - Act:5 - effect:.....OOF - Num:2 [fit: 0.070, exp: 59.00, pred: 2439.322, error:281.14225109021334]acc: 0.8135593220338984\n", + "Cond:O..##.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 31.00, pred: 1320.889, error:4841.150659100348]acc: 0.5483870967741935\n", + "Cond:.#OOO### - Act:5 - effect:.....OOF - Num:1 [fit: 0.000, exp: 0.00, pred: 1491.924, error:1421.0415237696316]acc: None\n", + "Cond:.O##.##O - Act:6 - effect:.....F.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1698.703, error:257.3276775888043]acc: None\n", + "Cond:.#.#FO.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.001, exp: 13.00, pred: 1066.352, error:1956.5279604344635]acc: 0.38461538461538464\n", + "Cond:.####... - Act:3 - effect:....OOO. - Num:1 [fit: 0.008, exp: 19.00, pred: 1015.922, error:4685.192189539443]acc: 0.0\n", + "Cond:.O###O## - Act:3 - effect:....OOO. - Num:1 [fit: 0.094, exp: 0.00, pred: 1175.046, error:282.86846856914343]acc: None\n", + "Cond:#..O#O#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1203.634, error:489.8346272143816]acc: None\n", + "Cond:#.#O.##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.008, exp: 14.00, pred: 1035.642, error:3351.999185063512]acc: 0.6428571428571429\n", + "Cond:.O####.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.006, exp: 7.00, pred: 1009.162, error:3915.294491855846]acc: 0.0\n", + "Cond:##O.#O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.025, exp: 0.00, pred: 1071.371, error:574.8123054047359]acc: None\n", + "Cond:#..OOO#. - Act:6 - effect:..OO.... - Num:1 [fit: 0.027, exp: 0.00, pred: 1245.413, error:60.92965676802024]acc: None\n", + "Cond:#.O####F - Act:5 - effect:....OOO. - Num:1 [fit: 0.269, exp: 0.00, pred: 1029.722, error:192.41028258918809]acc: None\n", + "Cond:O#####.# - Act:7 - effect:....OOO. - Num:1 [fit: 0.003, exp: 8.00, pred: 1276.677, error:6807.72253665997]acc: 0.0\n", + "Cond:##O.#.O. - Act:6 - effect:.....F.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1431.971, error:952.9075770386793]acc: None\n", + "Cond:#..#..#. - Act:3 - effect:....OOO. - Num:2 [fit: 0.002, exp: 0.00, pred: 1085.923, error:471.0908749355071]acc: None\n", + "Cond:#..##..# - Act:3 - effect:....OOO. - Num:2 [fit: 0.015, exp: 39.00, pred: 1320.024, error:4526.039427949774]acc: 0.41025641025641024\n", + "Cond:...#.F.. - Act:5 - effect:....OOO. - Num:3 [fit: 0.053, exp: 184.00, pred: 1825.847, error:246.0478071015795]acc: 1.0\n", + "Cond:OO..F### - Act:3 - effect:OO.....O - Num:1 [fit: 0.000, exp: 0.00, pred: 1224.927, error:3810.4450329026176]acc: None\n", + "Cond:..#O#### - Act:6 - effect:....OOO. - Num:2 [fit: 0.023, exp: 13.00, pred: 1054.822, error:5119.511182823525]acc: 0.5384615384615384\n", + "Cond:.##.#.#. - Act:6 - effect:.....F.. - Num:1 [fit: 0.001, exp: 19.00, pred: 894.045, error:6053.53277760874]acc: 0.0\n", + "Cond:.####O#. - Act:4 - effect:....OOO. - Num:1 [fit: 0.385, exp: 0.00, pred: 1309.424, error:545.3293477518134]acc: None\n", + "Cond:##..###. - Act:5 - effect:....OOO. - Num:3 [fit: 0.044, exp: 298.00, pred: 1859.108, error:492.0484794327116]acc: 0.7046979865771812\n", + "Cond:##.#F..# - Act:5 - effect:....FO.. - Num:1 [fit: 0.016, exp: 0.00, pred: 1723.278, error:402.33619107716027]acc: None\n", + "Cond:#.O##### - Act:6 - effect:....OOO. - Num:1 [fit: 0.033, exp: 10.00, pred: 1038.225, error:3161.0313885812916]acc: 0.6\n", + "Cond:.###.#O# - Act:0 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1586.079, error:698.7170209374137]acc: None\n", + "Cond:.#.F#O.# - Act:6 - effect:..OO.... - Num:1 [fit: 0.108, exp: 5.00, pred: 1025.323, error:1196.2825746468975]acc: 0.6\n", + "Cond:..#O.##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.114, exp: 2.00, pred: 759.098, error:1707.8890414414]acc: 0.0\n", + "Cond:#.#.##.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.003, exp: 27.00, pred: 986.139, error:3611.612116042944]acc: 0.1111111111111111\n", + "Cond:.###F##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 28.00, pred: 1667.876, error:4965.475947635376]acc: 0.35714285714285715\n", + "Cond:#O#...#O - Act:6 - effect:.O...... - Num:2 [fit: 0.725, exp: 0.00, pred: 1418.354, error:509.4565074566127]acc: None\n", + "Cond:#.#O.O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.037, exp: 0.00, pred: 1247.205, error:2063.5096380068317]acc: None\n", + "Cond:.#.#.#F. - Act:0 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1140.397, error:500.4450342135306]acc: None\n", + "Cond:.###F##. - Act:1 - effect:....OOO. - Num:1 [fit: 0.249, exp: 2.00, pred: 958.517, error:2903.5097852760464]acc: 0.0\n", + "Cond:#.#.#O.# - Act:6 - effect:.....OF. - Num:2 [fit: 0.004, exp: 12.00, pred: 1072.586, error:3391.8068434625425]acc: 0.0\n", + "Cond:..##OO.. - Act:6 - effect:...O.... - Num:1 [fit: 0.257, exp: 4.00, pred: 1027.335, error:1522.538693981428]acc: 0.5\n", + "Cond:#.#O#### - Act:6 - effect:....OOO. - Num:1 [fit: 0.225, exp: 4.00, pred: 934.531, error:1679.6521728007706]acc: 0.5\n", + "Cond:#.#..#.# - Act:3 - effect:....OOO. - Num:2 [fit: 0.010, exp: 29.00, pred: 1319.669, error:3994.406898076206]acc: 0.5517241379310345\n", + "Cond:.O#...## - Act:6 - effect:.O...... - Num:4 [fit: 0.126, exp: 10.00, pred: 858.084, error:6360.4437667576685]acc: 0.3\n", + "Cond:#O....## - Act:6 - effect:.O...... - Num:1 [fit: 0.054, exp: 6.00, pred: 840.146, error:6386.595557375447]acc: 0.0\n", + "Cond:.#...#.# - Act:3 - effect:.....OF. - Num:2 [fit: 0.566, exp: 3.00, pred: 768.070, error:1958.033489823553]acc: 0.6666666666666666\n", + "Cond:.#O#.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.011, exp: 6.00, pred: 1064.772, error:3187.3492700361203]acc: 0.0\n", + "Cond:#.O#.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.025, exp: 0.00, pred: 1003.580, error:311.7343313360617]acc: None\n", + "Cond:###..OF. - Act:4 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1085.441, error:30.675580429201283]acc: None\n", + "Cond:.###.#O# - Act:6 - effect:....OOO. - Num:4 [fit: 0.247, exp: 16.00, pred: 812.622, error:3719.551458573999]acc: 0.0625\n", + "Cond:##...... - Act:7 - effect:.O...... - Num:3 [fit: 0.016, exp: 34.00, pred: 771.245, error:2769.3378710899874]acc: 0.5882352941176471\n", + "Cond:..##FO#. - Act:3 - effect:....OOO. - Num:3 [fit: 0.009, exp: 19.00, pred: 1672.917, error:4417.4298784474195]acc: 0.2631578947368421\n", + "Cond:.###.##. - Act:3 - effect:......OO - Num:2 [fit: 0.001, exp: 25.00, pred: 1215.716, error:1923.7693120971246]acc: 0.16\n", + "Cond:#..F#O.. - Act:6 - effect:..OO.... - Num:1 [fit: 0.202, exp: 2.00, pred: 875.121, error:63.96076644637196]acc: 1.0\n", + "Cond:#..#OO## - Act:6 - effect:..OO.... - Num:1 [fit: 0.002, exp: 2.00, pred: 875.121, error:1163.960766446372]acc: 0.5\n", + "Cond:#.####.# - Act:1 - effect:.....OF. - Num:2 [fit: 0.131, exp: 3.00, pred: 785.310, error:2960.428134799491]acc: 0.0\n", + "Cond:#.#..##. - Act:7 - effect:....OOO. - Num:1 [fit: 0.115, exp: 7.00, pred: 1049.687, error:2605.277662976673]acc: 0.0\n", + "Cond:.#.FFO#. - Act:6 - effect:...OO... - Num:1 [fit: 0.024, exp: 0.00, pred: 1042.036, error:137.4691224768915]acc: None\n", + "Cond:#.....## - Act:7 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 786.125, error:234.52600477115368]acc: None\n", + "Cond:#...#### - Act:1 - effect:....FO.. - Num:1 [fit: 0.056, exp: 2.00, pred: 743.420, error:2661.545169463935]acc: 0.0\n", + "Cond:.###FO#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.067, exp: 0.00, pred: 971.543, error:185.70963324591094]acc: None\n", + "Cond:##.#.O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.146, exp: 0.00, pred: 1088.683, error:110.50204573046366]acc: None\n", + "Cond:#.#..O#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 1.00, pred: 771.551, error:1800.0]acc: 0.0\n", + "Cond:...#FO## - Act:6 - effect:...OO... - Num:1 [fit: 0.010, exp: 8.00, pred: 1120.924, error:2727.560873615958]acc: 0.0\n", + "Cond:.#...... - Act:7 - effect:.O...... - Num:3 [fit: 0.304, exp: 25.00, pred: 834.495, error:4407.98944446703]acc: 0.76\n", + "Cond:##.#.### - Act:4 - effect:....OOO. - Num:1 [fit: 0.029, exp: 1.00, pred: 720.230, error:1600.0]acc: 0.0\n", + "Cond:#.#.#### - Act:4 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 793.981, error:118.64730573999347]acc: None\n", + "Cond:#....### - Act:4 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 793.981, error:118.64730573999347]acc: None\n", + "Cond:#...##.# - Act:6 - effect:.....OF. - Num:2 [fit: 0.008, exp: 15.00, pred: 991.451, error:2789.3734647722767]acc: 0.0\n", + "Cond:#...#... - Act:5 - effect:....FO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1104.325, error:599.2641296251884]acc: None\n", + "Cond:##..#... - Act:7 - effect:.O...... - Num:4 [fit: 0.043, exp: 28.00, pred: 772.074, error:4188.933140036059]acc: 0.6785714285714286\n", + "Cond:O#...### - Act:7 - effect:....OOO. - Num:1 [fit: 0.013, exp: 4.00, pred: 1093.479, error:5726.9077014063705]acc: 0.0\n", + "Cond:###.OO.# - Act:3 - effect:.....OF. - Num:1 [fit: 0.002, exp: 0.00, pred: 1271.292, error:914.2503379509014]acc: None\n", + "Cond:#..#.#O# - Act:3 - effect:......OO - Num:5 [fit: 0.105, exp: 22.00, pred: 861.645, error:3893.502667582095]acc: 0.8181818181818182\n", + "Cond:.##FO##. - Act:3 - effect:....OOO. - Num:1 [fit: 0.076, exp: 6.00, pred: 1090.165, error:115.85978472130414]acc: 1.0\n", + "Cond:#O###.## - Act:6 - effect:.O...... - Num:1 [fit: 0.140, exp: 4.00, pred: 850.326, error:4238.913234362228]acc: 0.25\n", + "Cond:#O....#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.022, exp: 2.00, pred: 765.900, error:2723.6364922489156]acc: 0.0\n", + "Cond:.###OO#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.329, exp: 4.00, pred: 1012.915, error:6.690209020212961]acc: 1.0\n", + "Cond:##.#OO## - Act:0 - effect:....OOO. - Num:1 [fit: 0.018, exp: 0.00, pred: 1238.504, error:51.783616738858846]acc: None\n", + "Cond:.#O#.### - Act:7 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 988.539, error:1048.241639835303]acc: None\n", + "Cond:..####.# - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 6.00, pred: 1001.563, error:1425.3777140862735]acc: 0.16666666666666666\n", + "Cond:#...##.. - Act:6 - effect:.....OF. - Num:1 [fit: 0.051, exp: 5.00, pred: 1060.749, error:1161.6403467806194]acc: 0.0\n", + "Cond:#.#.#O.. - Act:6 - effect:...OO... - Num:1 [fit: 0.050, exp: 5.00, pred: 1060.749, error:961.6403467806194]acc: 0.0\n", + "Cond:.##FOO## - Act:3 - effect:....OOO. - Num:1 [fit: 0.170, exp: 2.00, pred: 1013.355, error:4.878875386836739]acc: 1.0\n", + "Cond:.#.#.... - Act:7 - effect:.O...... - Num:1 [fit: 0.253, exp: 11.00, pred: 836.317, error:4549.050169819594]acc: 0.5454545454545454\n", + "Cond:#....#O# - Act:3 - effect:......OO - Num:2 [fit: 0.541, exp: 7.00, pred: 860.342, error:4213.8936755207405]acc: 0.7142857142857143\n", + "Cond:#O#.#.## - Act:6 - effect:.O...... - Num:1 [fit: 0.027, exp: 1.00, pred: 675.261, error:2000.0]acc: 0.0\n", + "Cond:.#.##..# - Act:6 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 913.607, error:701.4412126047316]acc: None\n", + "Cond:O.##..## - Act:6 - effect:...OO... - Num:1 [fit: 0.004, exp: 0.00, pred: 913.607, error:701.4412126047316]acc: None\n", + "Cond:.#.F...# - Act:5 - effect:....OOO. - Num:1 [fit: 0.058, exp: 0.00, pred: 1004.219, error:1567.5650786874749]acc: None\n", + "Cond:#.##...# - Act:2 - effect:..OO.... - Num:1 [fit: 0.214, exp: 2.00, pred: 696.538, error:1710.2853247542278]acc: 0.0\n", + "Cond:.####..# - Act:2 - effect:..OO.... - Num:1 [fit: 0.008, exp: 1.00, pred: 675.968, error:1600.0]acc: 0.0\n", + "Cond:.#.##..# - Act:2 - effect:....OOO. - Num:1 [fit: 0.016, exp: 1.00, pred: 675.968, error:2200.0]acc: 0.0\n", + "Cond:.O#...#. - Act:4 - effect:OO...... - Num:1 [fit: 0.047, exp: 2.00, pred: 709.528, error:3044.72743802484]acc: 0.0\n", + "Cond:.O#.##.. - Act:4 - effect:....OOO. - Num:1 [fit: 0.026, exp: 2.00, pred: 709.528, error:2544.72743802484]acc: 0.0\n", + "Cond:.##..#.# - Act:6 - effect:.....OF. - Num:1 [fit: 0.232, exp: 1.00, pred: 675.261, error:1600.0]acc: 0.0\n", + "Cond:O#...... - Act:7 - effect:....OOO. - Num:1 [fit: 0.009, exp: 1.00, pred: 634.980, error:1600.0]acc: 0.0\n", + "Cond:#..#...O - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 884.955, error:133.48075595713135]acc: None\n", + "Cond:.#.#.#O# - Act:6 - effect:....OOO. - Num:1 [fit: 0.131, exp: 2.00, pred: 801.752, error:1906.2949318173376]acc: 0.5\n", + "Cond:.#.##.#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 791.914, error:773.2212014785395]acc: None\n", + "Cond:#O.#.### - Act:1 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 791.914, error:773.2212014785395]acc: None\n", + "Cond:..#.##.. - Act:3 - effect:.....OF. - Num:2 [fit: 0.003, exp: 11.00, pred: 1790.186, error:5908.081412190937]acc: 0.0\n", + "Cond:.##O#.## - Act:0 - effect:.....F.. - Num:1 [fit: 0.011, exp: 0.00, pred: 747.364, error:719.7801751865054]acc: None\n", + "Cond:.#.O###. - Act:0 - effect:.......O - Num:1 [fit: 0.011, exp: 0.00, pred: 747.364, error:719.7801751865054]acc: None\n", + "Cond:######.# - Act:1 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 792.116, error:301.49206073116386]acc: None\n", + "Cond:.#.#...# - Act:7 - effect:.O...... - Num:1 [fit: 0.253, exp: 0.00, pred: 836.317, error:2949.050169819594]acc: None\n", + "Cond:.###.... - Act:7 - effect:.O...... - Num:1 [fit: 0.253, exp: 0.00, pred: 836.317, error:2949.050169819594]acc: None\n", + "Cond:###.##.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.003, exp: 11.00, pred: 1790.186, error:4100.7905321909375]acc: 0.36363636363636365\n", + "Cond:##.FFO#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 1289.830, error:637.6864650567773]acc: None\n", + "Cond:.#.#OO.. - Act:6 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1105.491, error:39.59620944129755]acc: None\n", + "Cond:#.#O##.# - Act:3 - effect:....OOO. - Num:2 [fit: 0.003, exp: 0.00, pred: 1569.282, error:121.99000583073118]acc: None\n", + "Cond:.....O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 60.00, pred: 1309.630, error:301.01137344303027]acc: 0.9833333333333333\n", + "Cond:#.OO##.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 983.175, error:445.0093691584556]acc: None\n", + "Cond:..O#.O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.012, exp: 0.00, pred: 983.175, error:445.0093691584556]acc: None\n", + "Cond:.#..FO## - Act:5 - effect:.....F.. - Num:1 [fit: 0.205, exp: 2.00, pred: 1156.059, error:1718.2894200763808]acc: 0.5\n", + "Cond:...F#### - Act:4 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 0.00, pred: 1130.032, error:968.1854372987591]acc: None\n", + "Cond:.#.#.#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 131.00, pred: 1948.419, error:251.07661682073928]acc: 1.0\n", + "Cond:...#.OO. - Act:6 - effect:....OOO. - Num:1 [fit: 0.260, exp: 0.00, pred: 873.764, error:1052.204494700778]acc: None\n", + "Cond:##..###O - Act:4 - effect:...FOO.. - Num:1 [fit: 0.001, exp: 0.00, pred: 1534.501, error:512.1110090883312]acc: None\n", + "Cond:.##..### - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 18.00, pred: 1346.783, error:5100.349864721724]acc: 0.0\n", + "Cond:###.#OOF - Act:7 - effect:....OOO. - Num:3 [fit: 0.062, exp: 123.00, pred: 1948.419, error:251.07661681708376]acc: 1.0\n", + "Cond:##.#O### - Act:2 - effect:.....F.. - Num:5 [fit: 0.075, exp: 78.00, pred: 2306.930, error:380.06542213421125]acc: 0.9102564102564102\n", + "Cond:#...##.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.048, exp: 159.00, pred: 2122.822, error:226.7030900779826]acc: 0.7484276729559748\n", + "Cond:....##.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.006, exp: 11.00, pred: 1790.186, error:4931.569412190937]acc: 0.36363636363636365\n", + "Cond:#.##.##. - Act:1 - effect:.....OF. - Num:1 [fit: 0.017, exp: 0.00, pred: 1227.125, error:350.90301200968656]acc: None\n", + "Cond:..#..F## - Act:5 - effect:....OOO. - Num:1 [fit: 0.020, exp: 161.00, pred: 1825.847, error:246.04780710157942]acc: 1.0\n", + "Cond:..OO##.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.010, exp: 0.00, pred: 1324.837, error:128.74656718286667]acc: None\n", + "Cond:##...OOF - Act:7 - effect:....OOO. - Num:2 [fit: 0.029, exp: 120.00, pred: 1948.419, error:251.07661681761726]acc: 1.0\n", + "Cond:####.#OF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 120.00, pred: 1948.419, error:251.07661681761726]acc: 1.0\n", + "Cond:##.#O#.# - Act:2 - effect:.....F.. - Num:5 [fit: 0.080, exp: 76.00, pred: 2306.930, error:378.06230958603527]acc: 0.9078947368421053\n", + "Cond:O##.##.# - Act:3 - effect:...O.... - Num:1 [fit: 0.007, exp: 30.00, pred: 1439.102, error:2927.4267324699053]acc: 0.6333333333333333\n", + "Cond:##..#.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 30.00, pred: 1439.102, error:3970.7723568991805]acc: 0.5\n", + "Cond:#.OO.#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.052, exp: 94.00, pred: 2237.917, error:297.4169428391154]acc: 1.0\n", + "Cond:.##OO### - Act:0 - effect:.....OOF - Num:1 [fit: 0.001, exp: 0.00, pred: 1663.183, error:526.4396464889498]acc: None\n", + "Cond:#.#..OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 113.00, pred: 1948.419, error:251.0766168166831]acc: 1.0\n", + "Cond:#.OO#### - Act:1 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1485.811, error:71.53752812383858]acc: None\n", + "Cond:#.OO###F - Act:5 - effect:....OOO. - Num:1 [fit: 0.024, exp: 0.00, pred: 1485.811, error:71.53752812383858]acc: None\n", + "Cond:..##FF#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.010, exp: 0.00, pred: 1586.132, error:434.60801688811864]acc: None\n", + "Cond:###.#.#. - Act:1 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1621.978, error:506.2760782099705]acc: None\n", + "Cond:...O#O#. - Act:2 - effect:...O.... - Num:1 [fit: 0.003, exp: 0.00, pred: 1517.673, error:556.4957028821489]acc: None\n", + "Cond:#.OO.### - Act:5 - effect:....OOO. - Num:4 [fit: 0.168, exp: 79.00, pred: 2237.917, error:297.4169412773152]acc: 1.0\n", + "Cond:.#OO#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1386.021, error:149.12789926784882]acc: None\n", + "Cond:...###O# - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1315.955, error:42.46764369319496]acc: None\n", + "Cond:##.#O#.# - Act:5 - effect:.....OOF - Num:1 [fit: 0.001, exp: 73.00, pred: 2397.943, error:3682.291303716083]acc: 0.4794520547945205\n", + "Cond:.##.#F.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 156.00, pred: 1825.847, error:246.04780710157942]acc: 1.0\n", + "Cond:....#F.# - Act:2 - effect:.....OF. - Num:2 [fit: 0.159, exp: 14.00, pred: 1258.311, error:182.16454230645667]acc: 1.0\n", + "Cond:.....#F# - Act:5 - effect:.....OF. - Num:1 [fit: 0.217, exp: 2.00, pred: 843.987, error:1701.6588530833346]acc: 0.5\n", + "Cond:..O#.#O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1177.618, error:37.55714238031585]acc: None\n", + "Cond:#.#...## - Act:3 - effect:....OOO. - Num:4 [fit: 0.066, exp: 25.00, pred: 1323.571, error:4128.4903439827085]acc: 0.6\n", + "Cond:..#.#.## - Act:3 - effect:....OOO. - Num:1 [fit: 0.001, exp: 0.00, pred: 1282.182, error:199.69093503988003]acc: None\n", + "Cond:####O#.# - Act:2 - effect:.....F.. - Num:1 [fit: 0.015, exp: 69.00, pred: 2306.930, error:379.91236986753677]acc: 0.8985507246376812\n", + "Cond:#...###F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 91.00, pred: 1948.419, error:251.07662000109426]acc: 1.0\n", + "Cond:..##.F## - Act:5 - effect:....OOO. - Num:4 [fit: 0.070, exp: 155.00, pred: 1825.847, error:246.04780710157948]acc: 1.0\n", + "Cond:#...#F.# - Act:2 - effect:.....OF. - Num:3 [fit: 0.122, exp: 10.00, pred: 1297.117, error:193.7066562975593]acc: 1.0\n", + "Cond:.##.#F## - Act:2 - effect:.....OF. - Num:1 [fit: 0.063, exp: 10.00, pred: 1297.117, error:193.7066562975593]acc: 1.0\n", + "Cond:#.##.#F# - Act:6 - effect:....OOO. - Num:1 [fit: 0.011, exp: 56.00, pred: 1309.634, error:301.01148413817117]acc: 1.0\n", + "Cond:#....F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 152.00, pred: 1825.847, error:246.04780710158101]acc: 1.0\n", + "Cond:..#.#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 0.00, pred: 1619.789, error:2585.7938439098034]acc: None\n", + "Cond:####..## - Act:5 - effect:....OOO. - Num:2 [fit: 0.004, exp: 347.00, pred: 2499.409, error:2494.2530535983637]acc: 0.2737752161383285\n", + "Cond:####F#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 2276.536, error:317.1361975323695]acc: None\n", + "Cond:#....O## - Act:1 - effect:....OOO. - Num:1 [fit: 0.016, exp: 0.00, pred: 1701.083, error:133.18817514488768]acc: None\n", + "Cond:#####.## - Act:5 - effect:....OOO. - Num:5 [fit: 0.010, exp: 379.00, pred: 2612.986, error:2398.164247421447]acc: 0.3087071240105541\n", + "Cond:#.##..## - Act:3 - effect:....OOO. - Num:2 [fit: 0.065, exp: 25.00, pred: 1323.571, error:3934.8129295827093]acc: 0.6\n", + "Cond:.###O#.# - Act:4 - effect:.....OOF - Num:1 [fit: 0.006, exp: 0.00, pred: 1692.425, error:366.87121072582215]acc: None\n", + "Cond:#...#F.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.002, exp: 10.00, pred: 1297.117, error:4268.7402562975585]acc: 0.0\n", + "Cond:..OO.### - Act:5 - effect:....OOO. - Num:1 [fit: 0.052, exp: 66.00, pred: 2237.918, error:297.4171213104337]acc: 1.0\n", + "Cond:..#..##. - Act:5 - effect:....OOO. - Num:3 [fit: 0.045, exp: 139.00, pred: 1825.847, error:246.04780710156098]acc: 1.0\n", + "Cond:###.FF#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.207, exp: 0.00, pred: 2027.230, error:386.51212596012226]acc: None\n", + "Cond:#.###### - Act:7 - effect:....OOO. - Num:3 [fit: 0.058, exp: 84.00, pred: 1948.419, error:251.07663112272834]acc: 1.0\n", + "Cond:##.#OO.. - Act:2 - effect:.....F.. - Num:2 [fit: 0.029, exp: 57.00, pred: 2306.929, error:381.7271595816214]acc: 0.8771929824561403\n", + "Cond:.###.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 126.00, pred: 1825.847, error:246.0478071014796]acc: 1.0\n", + "Cond:#....##F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 81.00, pred: 1948.419, error:251.07663557171]acc: 1.0\n", + "Cond:.#.OO### - Act:5 - effect:.....OOF - Num:1 [fit: 0.037, exp: 31.00, pred: 2440.069, error:281.43290047994935]acc: 1.0\n", + "Cond:.##.##.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 11.00, pred: 1790.186, error:4342.733572190938]acc: 0.36363636363636365\n", + "Cond:#...#O#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 1889.429, error:56.931640562994694]acc: None\n", + "Cond:..#.O#.. - Act:2 - effect:.....F.. - Num:1 [fit: 0.043, exp: 0.00, pred: 2193.862, error:52.86163267023902]acc: None\n", + "Cond:.##OO##. - Act:5 - effect:.....OOF - Num:2 [fit: 0.070, exp: 29.00, pred: 2440.334, error:281.9025183888516]acc: 1.0\n", + "Cond:###OO... - Act:5 - effect:.....OOF - Num:1 [fit: 0.035, exp: 29.00, pred: 2440.334, error:281.9025183888516]acc: 1.0\n", + "Cond:.####O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 38.00, pred: 1309.647, error:300.9998253114917]acc: 1.0\n", + "Cond:..#.F#.. - Act:2 - effect:....OOO. - Num:2 [fit: 0.114, exp: 80.00, pred: 2122.975, error:224.5084541756377]acc: 1.0\n", + "Cond:..OO.##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.052, exp: 56.00, pred: 2237.917, error:297.41530157487483]acc: 1.0\n", + "Cond:#.OO.#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.052, exp: 56.00, pred: 2237.917, error:297.41530157487483]acc: 1.0\n", + "Cond:###..O#F - Act:7 - effect:....OOO. - Num:2 [fit: 0.038, exp: 73.00, pred: 1948.419, error:251.0766580465728]acc: 1.0\n", + "Cond:.#O#O### - Act:5 - effect:.....OOF - Num:1 [fit: 0.013, exp: 0.00, pred: 2515.975, error:129.95034827048332]acc: None\n", + "Cond:###OO..# - Act:5 - effect:.....OOF - Num:2 [fit: 0.111, exp: 27.00, pred: 2439.507, error:280.26338373532224]acc: 1.0\n", + "Cond:...#.O#. - Act:6 - effect:....OOO. - Num:4 [fit: 0.028, exp: 35.00, pred: 1309.511, error:300.72292905281586]acc: 1.0\n", + "Cond:.#.##F#. - Act:3 - effect:....OOO. - Num:1 [fit: 0.005, exp: 0.00, pred: 1835.572, error:42.639500571894]acc: None\n", + "Cond:.##.OO#. - Act:2 - effect:.....F.. - Num:1 [fit: 0.034, exp: 0.00, pred: 2123.638, error:86.0253876386183]acc: None\n", + "Cond:..OO.O#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 2224.774, error:58.73886604733403]acc: None\n", + "Cond:...#.O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.009, exp: 31.00, pred: 1309.962, error:301.3858501986791]acc: 1.0\n", + "Cond:#.#.F#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.019, exp: 0.00, pred: 2083.615, error:67.41830543451239]acc: None\n", + "Cond:..#..#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 104.00, pred: 1825.847, error:246.04780708825155]acc: 1.0\n", + "Cond:.###O### - Act:2 - effect:.....F.. - Num:2 [fit: 0.032, exp: 44.00, pred: 2306.924, error:378.0480224982885]acc: 0.8409090909090909\n", + "Cond:#####.O# - Act:2 - effect:...FOO.. - Num:1 [fit: 0.029, exp: 0.00, pred: 2466.244, error:68.7931137029548]acc: None\n", + "Cond:#....F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 96.00, pred: 1825.847, error:246.0478070605559]acc: 1.0\n", + "Cond:##OO.O.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.032, exp: 0.00, pred: 2214.380, error:199.41740180778518]acc: None\n", + "Cond:#..#.OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 65.00, pred: 1948.419, error:251.076833627319]acc: 1.0\n", + "Cond:##.#OO.# - Act:2 - effect:.....F.. - Num:1 [fit: 0.016, exp: 38.00, pred: 2306.905, error:379.0931033071599]acc: 0.8157894736842105\n", + "Cond:..#.#F.. - Act:2 - effect:.....OF. - Num:1 [fit: 0.000, exp: 10.00, pred: 1297.117, error:3449.6682562975593]acc: 0.1\n", + "Cond:.##..F#. - Act:5 - effect:....OOO. - Num:2 [fit: 0.035, exp: 85.00, pred: 1825.847, error:246.04780690890203]acc: 1.0\n", + "Cond:####O..# - Act:5 - effect:.....OOF - Num:1 [fit: 0.071, exp: 21.00, pred: 2435.614, error:276.40116722125447]acc: 1.0\n", + "Cond:##OO##O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.023, exp: 0.00, pred: 2163.070, error:44.60160165874159]acc: None\n", + "Cond:##OO#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.023, exp: 0.00, pred: 2163.070, error:44.60160165874159]acc: None\n", + "Cond:....##F# - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1920.076, error:40.45750260778474]acc: None\n", + "Cond:####OO.# - Act:2 - effect:.....F.. - Num:2 [fit: 0.022, exp: 34.00, pred: 2306.629, error:379.7533409322018]acc: 0.7941176470588235\n", + "Cond:.#.#OO## - Act:2 - effect:.....F.. - Num:3 [fit: 0.045, exp: 34.00, pred: 2306.629, error:379.9117763277021]acc: 0.7941176470588235\n", + "Cond:.#O#O.#. - Act:5 - effect:.....OOF - Num:1 [fit: 0.027, exp: 0.00, pred: 2383.084, error:36.08573774917655]acc: None\n", + "Cond:.....##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 81.00, pred: 1825.847, error:246.0478073595747]acc: 1.0\n", + "Cond:#.##.F.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 81.00, pred: 1825.847, error:246.0478073595747]acc: 1.0\n", + "Cond:#.OO##O# - Act:5 - effect:....OOO. - Num:1 [fit: 0.028, exp: 0.00, pred: 2015.027, error:39.90055205608894]acc: None\n", + "Cond:#.##.### - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 221.00, pred: 2208.708, error:1610.938401861646]acc: 0.6199095022624435\n", + "Cond:#..FO#.# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.006, exp: 34.00, pred: 1320.778, error:2933.862387303233]acc: 0.058823529411764705\n", + "Cond:##.#OO#. - Act:2 - effect:.....F.. - Num:3 [fit: 0.049, exp: 32.00, pred: 2306.511, error:377.8896055362053]acc: 0.78125\n", + "Cond:.##.#F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 75.00, pred: 1825.847, error:246.04777941756396]acc: 1.0\n", + "Cond:####OO## - Act:2 - effect:.....F.. - Num:1 [fit: 0.014, exp: 30.00, pred: 2307.221, error:384.8856293703533]acc: 0.7666666666666667\n", + "Cond:...#O### - Act:2 - effect:.....F.. - Num:1 [fit: 0.014, exp: 30.00, pred: 2307.221, error:384.1135224504041]acc: 0.7666666666666667\n", + "Cond:..##.##. - Act:6 - effect:....OOO. - Num:1 [fit: 0.016, exp: 19.00, pred: 1311.524, error:301.1036103459686]acc: 1.0\n", + "Cond:##.##.## - Act:1 - effect:....OOO. - Num:1 [fit: 0.011, exp: 0.00, pred: 1969.920, error:49.61818496102171]acc: None\n", + "Cond:##..#### - Act:3 - effect:...O.... - Num:2 [fit: 0.003, exp: 55.00, pred: 1371.812, error:4180.427520003814]acc: 0.45454545454545453\n", + "Cond:.###...# - Act:3 - effect:....OOO. - Num:1 [fit: 0.244, exp: 5.00, pred: 1116.337, error:2833.242370883583]acc: 0.0\n", + "Cond:.#.F#O.# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.295, exp: 13.00, pred: 1324.684, error:2108.1912342951514]acc: 0.8461538461538461\n", + "Cond:.#.#.F.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 65.00, pred: 1825.847, error:246.0477256273078]acc: 1.0\n", + "Cond:##.#..OO - Act:5 - effect:...FOO.. - Num:1 [fit: 0.002, exp: 27.00, pred: 2124.941, error:1481.44009502915]acc: 0.4074074074074074\n", + "Cond:O#.#..O# - Act:5 - effect:...FOO.. - Num:1 [fit: 0.016, exp: 0.00, pred: 2333.371, error:368.40514435786565]acc: None\n", + "Cond:#.##.OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.019, exp: 18.00, pred: 1309.763, error:298.56702684491614]acc: 1.0\n", + "Cond:.##.###. - Act:5 - effect:....OOO. - Num:1 [fit: 0.015, exp: 73.00, pred: 1893.148, error:893.8423961884575]acc: 0.9041095890410958\n", + "Cond:#.##.#.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.003, exp: 163.00, pred: 2208.641, error:1572.0464840404318]acc: 0.6625766871165644\n", + "Cond:#####O.. - Act:1 - effect:...FOO.. - Num:1 [fit: 0.069, exp: 0.00, pred: 1554.495, error:2186.3156477129774]acc: None\n", + "Cond:.#.#O### - Act:2 - effect:.....F.. - Num:2 [fit: 0.034, exp: 28.00, pred: 2309.107, error:375.4924186891549]acc: 0.75\n", + "Cond:#.#.F#.. - Act:2 - effect:....OOO. - Num:5 [fit: 0.237, exp: 38.00, pred: 2123.001, error:224.47377040829275]acc: 1.0\n", + "Cond:##.##### - Act:3 - effect:....OOO. - Num:1 [fit: 0.002, exp: 44.00, pred: 1371.823, error:2991.367057558353]acc: 0.25\n", + "Cond:#..#.F#. - Act:0 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1457.363, error:44.34168667455944]acc: None\n", + "Cond:####.#O# - Act:5 - effect:...FOO.. - Num:2 [fit: 0.004, exp: 23.00, pred: 2121.149, error:1612.2924324901587]acc: 0.4782608695652174\n", + "Cond:####O.## - Act:5 - effect:.....OOF - Num:3 [fit: 0.252, exp: 14.00, pred: 2431.569, error:272.55493275072126]acc: 1.0\n", + "Cond:.#####.# - Act:3 - effect:....OOO. - Num:2 [fit: 0.056, exp: 14.00, pred: 1383.518, error:4888.602650726573]acc: 0.2857142857142857\n", + "Cond:###.#OO# - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 47.00, pred: 1948.412, error:251.05602539676852]acc: 1.0\n", + "Cond:##..O#O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.004, exp: 0.00, pred: 1679.659, error:58.87042378261822]acc: None\n", + "Cond:.##.O##. - Act:2 - effect:.....F.. - Num:1 [fit: 0.020, exp: 0.00, pred: 1966.144, error:525.5776702262747]acc: None\n", + "Cond:.#.###.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.045, exp: 7.00, pred: 1611.278, error:3701.908166915538]acc: 0.2857142857142857\n", + "Cond:O###..OO - Act:5 - effect:...FOO.. - Num:1 [fit: 0.004, exp: 0.00, pred: 2221.741, error:628.0041953100285]acc: None\n", + "Cond:.#O##..# - Act:3 - effect:....OOO. - Num:1 [fit: 0.044, exp: 3.00, pred: 1006.754, error:2814.8391922156334]acc: 0.0\n", + "Cond:#.#..O#. - Act:6 - effect:....OOO. - Num:1 [fit: 0.140, exp: 6.00, pred: 1146.212, error:93.61105190454327]acc: 1.0\n", + "Cond:.....OF# - Act:6 - effect:....OOO. - Num:1 [fit: 0.140, exp: 6.00, pred: 1146.212, error:93.61105190454327]acc: 1.0\n", + "Cond:.####.#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 101.00, pred: 2569.348, error:1681.51199290808]acc: 0.5544554455445545\n", + "Cond:###OO.## - Act:5 - effect:.....OOF - Num:2 [fit: 0.140, exp: 12.00, pred: 2430.813, error:271.147059187859]acc: 1.0\n", + "Cond:#.OO.#O. - Act:5 - effect:....OOO. - Num:1 [fit: 0.125, exp: 0.00, pred: 2032.963, error:219.40234097260935]acc: None\n", + "Cond:##OO#O.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.113, exp: 0.00, pred: 2031.173, error:213.43706406340897]acc: None\n", + "Cond:##.###.# - Act:3 - effect:....OOO. - Num:1 [fit: 0.004, exp: 15.00, pred: 1417.681, error:4973.991090388701]acc: 0.13333333333333333\n", + "Cond:.#.#OO## - Act:5 - effect:.....F.. - Num:1 [fit: 0.005, exp: 0.00, pred: 1782.964, error:828.294463570928]acc: None\n", + "Cond:#....### - Act:3 - effect:....OOO. - Num:3 [fit: 0.010, exp: 18.00, pred: 1245.873, error:3014.1524181008217]acc: 0.05555555555555555\n", + "Cond:..##.F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 47.00, pred: 1825.845, error:246.04331534133763]acc: 1.0\n", + "Cond:#....F.# - Act:2 - effect:.....OF. - Num:1 [fit: 0.347, exp: 9.00, pred: 1255.451, error:155.12700255975145]acc: 1.0\n", + "Cond:..#.##.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.048, exp: 44.00, pred: 2122.799, error:226.7589625126488]acc: 0.7954545454545454\n", + "Cond:#.##.#F. - Act:0 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1579.715, error:90.16629451997451]acc: None\n", + "Cond:####.O## - Act:6 - effect:....OOO. - Num:1 [fit: 0.246, exp: 4.00, pred: 1126.005, error:74.7958189056925]acc: 1.0\n", + "Cond:#.###### - Act:3 - effect:....OOO. - Num:2 [fit: 0.043, exp: 8.00, pred: 1214.552, error:3844.9075961237395]acc: 0.125\n", + "Cond:.##..F.# - Act:2 - effect:.....OF. - Num:1 [fit: 0.362, exp: 4.00, pred: 1272.015, error:134.05480133988493]acc: 1.0\n", + "Cond:.OOO.#.. - Act:3 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1106.722, error:1992.2357095639643]acc: None\n", + "Cond:..##.### - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 119.00, pred: 2204.840, error:1585.8512826149488]acc: 0.7142857142857143\n", + "Cond:#.#.#OF. - Act:6 - effect:....OOO. - Num:1 [fit: 0.152, exp: 2.00, pred: 1118.305, error:54.07987325235797]acc: 1.0\n", + "Cond:.####### - Act:5 - effect:....OOO. - Num:1 [fit: 0.001, exp: 140.00, pred: 2354.658, error:1778.0845253758193]acc: 0.6571428571428571\n", + "Cond:#####.## - Act:4 - effect:....OOO. - Num:1 [fit: 0.002, exp: 0.00, pred: 1282.955, error:412.88075265597956]acc: None\n", + "Cond:#..#.F.# - Act:2 - effect:.....OF. - Num:1 [fit: 0.023, exp: 0.00, pred: 1276.284, error:43.604207357163844]acc: None\n", + "Cond:###..O.. - Act:2 - effect:.....OOF - Num:1 [fit: 0.019, exp: 0.00, pred: 1449.433, error:107.37520021712714]acc: None\n", + "Cond:###..O#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.001, exp: 16.00, pred: 2231.856, error:6949.340657307325]acc: 0.0\n", + "Cond:###.##O# - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 43.00, pred: 1948.430, error:251.08915053651995]acc: 1.0\n", + "Cond:#..#OO#. - Act:2 - effect:.....F.. - Num:2 [fit: 0.249, exp: 20.00, pred: 2306.809, error:340.16169409750205]acc: 1.0\n", + "Cond:...OO### - Act:5 - effect:.....OOF - Num:1 [fit: 0.235, exp: 10.00, pred: 2419.473, error:264.29761324462925]acc: 1.0\n", + "Cond:###..F.. - Act:2 - effect:....OOO. - Num:1 [fit: 0.013, exp: 0.00, pred: 1633.906, error:456.5348075256136]acc: None\n", + "Cond:.##.F### - Act:2 - effect:....OOO. - Num:1 [fit: 0.058, exp: 31.00, pred: 2122.876, error:224.40553777193318]acc: 1.0\n", + "Cond:..O#.##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.054, exp: 30.00, pred: 2237.932, error:297.30455951193795]acc: 1.0\n", + "Cond:#.O#.### - Act:5 - effect:....OOO. - Num:1 [fit: 0.054, exp: 30.00, pred: 2237.932, error:297.30455951193795]acc: 1.0\n", + "Cond:O####.## - Act:5 - effect:....FO.. - Num:1 [fit: 0.018, exp: 21.00, pred: 2409.822, error:3954.687526047537]acc: 0.6190476190476191\n", + "Cond:###.F#.# - Act:2 - effect:....OOO. - Num:1 [fit: 0.059, exp: 28.00, pred: 2122.800, error:224.3407864043484]acc: 1.0\n", + "Cond:#.#.F##. - Act:2 - effect:....OOO. - Num:3 [fit: 0.163, exp: 28.00, pred: 2122.800, error:224.3407864043484]acc: 1.0\n", + "Cond:.#...F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.017, exp: 35.00, pred: 1825.943, error:246.1003839795294]acc: 1.0\n", + "Cond:#.##.F## - Act:1 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 1353.368, error:27.231522782389426]acc: None\n", + "Cond:..O##### - Act:5 - effect:....OOO. - Num:1 [fit: 0.054, exp: 26.00, pred: 2237.911, error:297.3289236776996]acc: 1.0\n", + "Cond:##.#.OOF - Act:7 - effect:....OOO. - Num:1 [fit: 0.020, exp: 33.00, pred: 1948.370, error:250.95942362884065]acc: 1.0\n", + "Cond:#.##OO#. - Act:2 - effect:.....F.. - Num:4 [fit: 0.356, exp: 15.00, pred: 2310.683, error:338.4881771504716]acc: 1.0\n", + "Cond:##O#O.## - Act:1 - effect:.....OOF - Num:1 [fit: 0.032, exp: 0.00, pred: 2250.791, error:26.220165385352963]acc: None\n", + "Cond:#.#.#F#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.008, exp: 0.00, pred: 1569.734, error:180.49132983358473]acc: None\n", + "Cond:###.FO#. - Act:2 - effect:....OOO. - Num:1 [fit: 0.064, exp: 24.00, pred: 2123.311, error:224.19124949204797]acc: 1.0\n", + "Cond:##...##. - Act:5 - effect:....OOO. - Num:1 [fit: 0.015, exp: 40.00, pred: 1859.105, error:491.8125004143764]acc: 0.9\n", + "Cond:#..#.#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.000, exp: 40.00, pred: 1909.877, error:1064.166881848293]acc: 0.775\n", + "Cond:..#..O## - Act:2 - effect:....OOO. - Num:1 [fit: 0.004, exp: 11.00, pred: 2219.613, error:6166.76420582545]acc: 0.0\n", + "Cond:....###F - Act:5 - effect:....OOO. - Num:1 [fit: 0.006, exp: 0.00, pred: 1788.304, error:197.97229795523342]acc: None\n", + "Cond:#..#O### - Act:2 - effect:.....F.. - Num:1 [fit: 0.068, exp: 12.00, pred: 2313.958, error:342.17575105373453]acc: 1.0\n", + "Cond:##.#OO#. - Act:6 - effect:.....F.. - Num:1 [fit: 0.036, exp: 0.00, pred: 1869.721, error:101.97639237805804]acc: None\n", + "Cond:##O##.## - Act:5 - effect:.....OOF - Num:1 [fit: 0.001, exp: 26.00, pred: 2361.448, error:4594.049453798257]acc: 0.0\n", + "Cond:####.O#F - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 25.00, pred: 1949.038, error:251.72828242816473]acc: 1.0\n", + "Cond:#.###O## - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 25.00, pred: 1949.038, error:251.72828242816473]acc: 1.0\n", + "Cond:..OO##.F - Act:5 - effect:....OOO. - Num:1 [fit: 0.056, exp: 0.00, pred: 1737.718, error:196.41813799474153]acc: None\n", + "Cond:###..### - Act:7 - effect:....OOO. - Num:1 [fit: 0.019, exp: 19.00, pred: 1950.316, error:251.3069923152742]acc: 1.0\n", + "Cond:##.#..#O - Act:5 - effect:....OOO. - Num:1 [fit: 0.030, exp: 12.00, pred: 2376.955, error:4884.229503844921]acc: 0.0\n", + "Cond:#.###.## - Act:4 - effect:....OOO. - Num:1 [fit: 0.003, exp: 0.00, pred: 2185.354, error:328.51776825812954]acc: None\n", + "Cond:###.FO#. - Act:7 - effect:....OOO. - Num:1 [fit: 0.017, exp: 0.00, pred: 1776.875, error:46.52334666165622]acc: None\n", + "Cond:##.##.#O - Act:5 - effect:....OOO. - Num:1 [fit: 0.018, exp: 10.00, pred: 2372.088, error:5826.305622097383]acc: 0.0\n", + "Cond:.#...O#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.353, exp: 5.00, pred: 2330.482, error:175.80431341343348]acc: 1.0\n", + "Cond:.....O## - Act:2 - effect:.....OOF - Num:1 [fit: 0.353, exp: 5.00, pred: 2330.482, error:175.80431341343348]acc: 1.0\n", + "Cond:#..#OO## - Act:2 - effect:.....F.. - Num:1 [fit: 0.146, exp: 7.00, pred: 2298.921, error:323.82068480048116]acc: 1.0\n", + "Cond:##..F### - Act:2 - effect:....OOO. - Num:1 [fit: 0.049, exp: 11.00, pred: 2132.731, error:227.52245755228046]acc: 1.0\n", + "Cond:.#..F##. - Act:2 - effect:....OOO. - Num:1 [fit: 0.049, exp: 11.00, pred: 2132.731, error:227.52245755228046]acc: 1.0\n", + "Cond:#.O###.# - Act:5 - effect:....OOO. - Num:1 [fit: 0.108, exp: 8.00, pred: 2254.676, error:272.27095288800245]acc: 1.0\n", + "Cond:##..#O#F - Act:2 - effect:....OOO. - Num:1 [fit: 0.058, exp: 0.00, pred: 1645.599, error:203.79339528985747]acc: None\n", + "Cond:..##OO#. - Act:2 - effect:.....F.. - Num:2 [fit: 0.356, exp: 4.00, pred: 2431.642, error:253.66148935526644]acc: 1.0\n", + "Cond:...#.#.. - Act:5 - effect:....OOO. - Num:1 [fit: 0.017, exp: 13.00, pred: 1924.192, error:972.1508243427513]acc: 0.8461538461538461\n", + "Cond:##.#.O#. - Act:2 - effect:.....OOF - Num:1 [fit: 0.071, exp: 1.00, pred: 2482.770, error:0.0]acc: 1.0\n", + "Cond:####O..# - Act:6 - effect:.....OOF - Num:1 [fit: 0.303, exp: 0.00, pred: 2156.341, error:105.24291282610426]acc: None\n", + "Cond:#..OO##. - Act:5 - effect:.....OOF - Num:1 [fit: 0.357, exp: 2.00, pred: 2891.235, error:133.33258544162527]acc: 1.0\n", + "Cond:#..##F#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.273, exp: 5.00, pred: 1836.540, error:169.24220473863255]acc: 1.0\n", + "Cond:..OO#O## - Act:5 - effect:....OOO. - Num:1 [fit: 0.021, exp: 0.00, pred: 2178.741, error:78.1487752894162]acc: None\n" ] } ], @@ -1465,14 +1457,14 @@ "output_type": "stream", "text": [ "Most Numerous rules:\n", - "Cond:...#O### - Act:0 - effect:...OO... - Num:8 [fit: 0.280, exp: 32.00, pred: 1145.166, error:5615.340915401308]acc: 0.75\n", - "Cond:.....O#. - Act:1 - effect:.O...... - Num:3 [fit: 0.002, exp: 22.00, pred: 926.395, error:9731.598541633895]acc: 0.13636363636363635\n", - "Cond:.O#O#.#. - Act:2 - effect:.OOO.... - Num:5 [fit: 0.025, exp: 15.00, pred: 945.849, error:8328.366070324544]acc: 0.26666666666666666\n", - "Cond:...#OO.# - Act:3 - effect:....OOO. - Num:12 [fit: 0.280, exp: 144.00, pred: 1158.783, error:143.7059003841303]acc: 1.0\n", - "Cond:#.#.#O.. - Act:4 - effect:....OOO. - Num:11 [fit: 0.437, exp: 216.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:.####.O# - Act:6 - effect:......OO - Num:3 [fit: 0.013, exp: 27.00, pred: 979.212, error:3316.023485212587]acc: 0.8148148148148148\n", - "Cond:#..#OO#. - Act:7 - effect:...O.... - Num:9 [fit: 0.035, exp: 49.00, pred: 1135.014, error:1622.4503549176839]acc: 0.7551020408163265\n" + "Cond:#.###.#O - Act:0 - effect:.....OOF - Num:5 [fit: 0.040, exp: 13.00, pred: 909.376, error:3837.661342836094]acc: 0.3076923076923077\n", + "Cond:##.#OO#. - Act:1 - effect:....OOO. - Num:4 [fit: 0.022, exp: 16.00, pred: 1125.296, error:4045.18246462395]acc: 0.0\n", + "Cond:###.F##. - Act:2 - effect:....OOO. - Num:6 [fit: 0.344, exp: 150.00, pred: 2122.975, error:224.5084546745183]acc: 0.9066666666666666\n", + "Cond:##.#OO#. - Act:3 - effect:....OOO. - Num:9 [fit: 0.188, exp: 192.00, pred: 1105.491, error:158.3848377652093]acc: 1.0\n", + "Cond:#O.#.### - Act:4 - effect:OO...... - Num:4 [fit: 0.001, exp: 19.00, pred: 825.848, error:5063.364127774808]acc: 0.15789473684210525\n", + "Cond:#.OO#### - Act:5 - effect:....OOO. - Num:10 [fit: 0.514, exp: 158.00, pred: 2237.917, error:297.4169431603479]acc: 0.9873417721518988\n", + "Cond:#....O#. - Act:6 - effect:....OOO. - Num:11 [fit: 0.101, exp: 443.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:#...F#.# - Act:7 - effect:...OO... - Num:9 [fit: 0.103, exp: 23.00, pred: 1034.493, error:4867.738624074384]acc: 0.4782608695652174\n" ] } ], @@ -1489,7 +1481,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1497,29 +1489,29 @@ "output_type": "stream", "text": [ "The most numerous rules:\n", - "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:...#OO.# - Act:3 - effect:....OOO. - Num:12 [fit: 0.280, exp: 144.00, pred: 1158.783, error:143.7059003841303]acc: 1.0\n", - "Cond:#.#.#O.. - Act:4 - effect:....OOO. - Num:11 [fit: 0.437, exp: 216.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:.#...#.. - Act:5 - effect:....OOO. - Num:10 [fit: 0.692, exp: 417.00, pred: 1225.169, error:248.2805850823554]acc: 0.9256594724220624\n", - "Cond:#...#O.. - Act:4 - effect:....OOO. - Num:9 [fit: 0.342, exp: 271.00, pred: 1810.484, error:304.5103933539941]acc: 1.0\n", - "Cond:#..#OO#. - Act:7 - effect:...O.... - Num:9 [fit: 0.035, exp: 49.00, pred: 1135.014, error:1622.4503549176839]acc: 0.7551020408163265\n", - "Cond:...#O### - Act:0 - effect:...OO... - Num:8 [fit: 0.280, exp: 32.00, pred: 1145.166, error:5615.340915401308]acc: 0.75\n", - "Cond:.O.#...# - Act:3 - effect:..OO.... - Num:8 [fit: 0.083, exp: 37.00, pred: 1894.396, error:544.4345837287093]acc: 0.972972972972973\n", - "Cond:...#OO#. - Act:3 - effect:....OOO. - Num:7 [fit: 0.173, exp: 396.00, pred: 1158.783, error:143.70590038414736]acc: 1.0\n", - "Cond:..#..#.. - Act:5 - effect:....OOO. - Num:7 [fit: 0.365, exp: 325.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n" + "Cond:#....O#. - Act:6 - effect:....OOO. - Num:11 [fit: 0.101, exp: 443.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n", + "Cond:#.OO#### - Act:5 - effect:....OOO. - Num:10 [fit: 0.514, exp: 158.00, pred: 2237.917, error:297.4169431603479]acc: 0.9873417721518988\n", + "Cond:##.#OO#. - Act:3 - effect:....OOO. - Num:9 [fit: 0.188, exp: 192.00, pred: 1105.491, error:158.3848377652093]acc: 1.0\n", + "Cond:#...F#.# - Act:7 - effect:...OO... - Num:9 [fit: 0.103, exp: 23.00, pred: 1034.493, error:4867.738624074384]acc: 0.4782608695652174\n", + "Cond:#....OO. - Act:6 - effect:....OOO. - Num:8 [fit: 0.127, exp: 0.00, pred: 1224.646, error:60.5299079420778]acc: None\n", + "Cond:#....O## - Act:6 - effect:....OOO. - Num:8 [fit: 0.072, exp: 206.00, pred: 1309.630, error:301.01333544763446]acc: 0.7815533980582524\n", + "Cond:#.##.OOF - Act:7 - effect:....OOO. - Num:6 [fit: 0.116, exp: 488.00, pred: 1948.419, error:251.07661682020802]acc: 1.0\n", + "Cond:###.F##. - Act:2 - effect:....OOO. - Num:6 [fit: 0.344, exp: 150.00, pred: 2122.975, error:224.5084546745183]acc: 0.9066666666666666\n", + "Cond:#..#.O#. - Act:6 - effect:....OOO. - Num:5 [fit: 0.047, exp: 484.00, pred: 1309.630, error:301.0095151762131]acc: 0.9090909090909091\n", + "Cond:.....O#. - Act:6 - effect:....OOO. - Num:5 [fit: 0.045, exp: 464.00, pred: 1309.630, error:301.0095151762131]acc: 1.0\n" ] } ], "source": [ "print(\"The most numerous rules:\")\n", - "numerous = sorted(agent.population, key=lambda cl: -1 * cl.numerosity)[0:10]\n", + "numerous = sorted(agent.population, key=lambda cl: -1*cl.numerosity)[0:10]\n", "for cl in numerous:\n", " print(cl)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1527,16 +1519,16 @@ "output_type": "stream", "text": [ "The fittest rules:\n", - "Cond:.....#.. - Act:5 - effect:....OOO. - Num:19 [fit: 1.365, exp: 354.00, pred: 1227.238, error:250.16480376297162]acc: 1.0\n", - "Cond:####.#F# - Act:3 - effect:......OO - Num:2 [fit: 1.201, exp: 6.00, pred: 1010.698, error:2438.439663305213]acc: 0.8333333333333334\n", - "Cond:#OO..#.. - Act:1 - effect:.OO..... - Num:1 [fit: 0.809, exp: 10.00, pred: 1009.578, error:2542.189272565551]acc: 0.9\n", - "Cond:.#...#.. - Act:5 - effect:....OOO. - Num:10 [fit: 0.692, exp: 417.00, pred: 1225.169, error:248.2805850823554]acc: 0.9256594724220624\n", - "Cond:#######. - Act:5 - effect:.....F.. - Num:2 [fit: 0.690, exp: 4.00, pred: 1415.929, error:126.99893216917172]acc: 1.0\n", - "Cond:#.#..#.. - Act:4 - effect:.....OOF - Num:2 [fit: 0.581, exp: 5.00, pred: 1334.415, error:217.55695457350893]acc: 1.0\n", - "Cond:#.###OO# - Act:7 - effect:....OOO. - Num:3 [fit: 0.567, exp: 7.00, pred: 1285.887, error:141.34880202689266]acc: 1.0\n", - "Cond:..#.#.## - Act:4 - effect:....FO.. - Num:2 [fit: 0.518, exp: 10.00, pred: 1119.882, error:3134.351408933204]acc: 0.8\n", - "Cond:..#OO... - Act:7 - effect:.....F.. - Num:1 [fit: 0.512, exp: 4.00, pred: 898.981, error:38.34946290007767]acc: 1.0\n", - "Cond:.O.##### - Act:3 - effect:..OO.... - Num:1 [fit: 0.499, exp: 4.00, pred: 1416.272, error:209.31832624994382]acc: 1.0\n" + "Cond:.#...OO. - Act:6 - effect:....OOO. - Num:2 [fit: 2.487, exp: 0.00, pred: 1061.278, error:93.57621525097161]acc: None\n", + "Cond:..#.F..# - Act:3 - effect:...FOO.. - Num:1 [fit: 2.195, exp: 0.00, pred: 2106.470, error:175.87720446349576]acc: None\n", + "Cond:#.#.OO#. - Act:3 - effect:....OOO. - Num:2 [fit: 1.712, exp: 0.00, pred: 1069.779, error:59.62231876636257]acc: None\n", + "Cond:##...O#. - Act:2 - effect:.....OOF - Num:5 [fit: 1.230, exp: 52.00, pred: 2208.265, error:255.38892135914688]acc: 0.5961538461538461\n", + "Cond:##.#OOO. - Act:5 - effect:....OOO. - Num:1 [fit: 1.096, exp: 0.00, pred: 1059.072, error:103.34728876970632]acc: None\n", + "Cond:#...#O.# - Act:1 - effect:....FO.. - Num:3 [fit: 1.096, exp: 10.00, pred: 984.570, error:2782.167280691241]acc: 0.9\n", + "Cond:.#...O.# - Act:5 - effect:....OOO. - Num:1 [fit: 1.040, exp: 0.00, pred: 1053.225, error:158.74627568588951]acc: None\n", + "Cond:..##.FO. - Act:5 - effect:....OOO. - Num:2 [fit: 0.983, exp: 0.00, pred: 1033.312, error:13.311852830669409]acc: None\n", + "Cond:..#O#### - Act:2 - effect:..OO.... - Num:2 [fit: 0.955, exp: 48.00, pred: 894.425, error:2034.5255152647255]acc: 0.6458333333333334\n", + "Cond:#.OO#O#. - Act:5 - effect:....OOO. - Num:1 [fit: 0.790, exp: 0.00, pred: 1310.895, error:102.57409173424277]acc: None\n" ] } ], @@ -1555,7 +1547,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -1564,7 +1556,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1629,7 +1621,7 @@ "outputs": [ { "data": { - "image/png": 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SsTwa6t4wzT8aou8mYNMQr9UAS6MYWmIZOBmvwha3xyMYVHbUn+LlWh9vHmil3U0OZfkZ3LCgmLXuyMGmaowZm8l5QPBE0ljr3Jet8DaOCep4+zk27fSxaacP3+nz5KSn8MlFM7hmbhFr5xZSMS1zUp0YZYxXLFl4rbEWCi+HzGleRzJhdPf62by3mZdrfew4dgoR+NjlRfzZzQv41OISO5nMmBiwZOElVeew2bk3eh1JwgsGlW1H23m51seWuhbO9weYU5TNn928gM+uKLeFaWNizJKFlzp8cLbVTsYbRv3Js2za6eNnOxtpPHOe3IwU7lhRzhdWVnDVrAKbYjImTixZeGlgvSKBFrfP9fkJKuR4WN+mq6ef1/c0s2mnj3frT5Mk8LF5xXxj3UI+tXjmsNctMMbEhiULLzXWQHIazPT2YK+Oc/28sf8Ev6pr4e1DbfQHgsybkcPyygKWV05jeWUB82fmkBKDE9JUlcYz59nX1Mm+xg7qmjp558hJevqDzC3O5hu3LOSzK8opyc+I+mcbYyIXcbIQkWRgZug2qno8FkFNGY07oeQKSEmP+0ef7O7lN/tOsKWuma1H2vEHlbL8DO5bPYuCzDR2N5zmjfdPsLHGKc2VlZbMsvJ8ls8qYIWbREb7Ax4MKvXtZ6lr6mRfUwf7Gjupa+q4cPZ0ksC8GbncubKSz6+s4MqKfJtmMiZBRJQsROT/xTmp7gQwUDBHgStiFNfkF/BD0y5Y8cW4fWRzx3l+VdfClroWaupPEVSnvMWD181m3dLSS36cVZXjp86xu+EMu46fYVfDGf759/U8GXC+AiV5GayYVeCOQApYVpFPVprzleoPBDnc2s2+pk7qGjvY1+RUPz3b51Q/TUt2rkWwbmkJS8ryWVKWx8KSPDuSyZgEFenI4ivAAlVtj2UwU0rbAeg/F/PigR+2n2VLXQu/qmthd8MZAObPzOGxG+exbmkJC0tyh/zrXUS4rDCbywqzuX25U+y31x/g/abOCwlkd8MZttS1AE657fkzc0lLFva3dNHnFuLLSktmcWked1ZXsrgsj6Vl+cybmZOQdZaMMeFFmiwagI5YBjLlDCxuR/lIqIFSFwMjiP3NnQAsK8/nz25ewC1LS5hbnDPm909PSWbFrGmsmDWNL1/rtLV39/Ke7wy73dFHIKh86ZoqlpTlsbQ8n6rCbKt+aswEF2myOAr8TkRex60OC6Cq349JVFNBYw1kFMD0OVF9269tfI+f7WpEBFbOmsaf/8Eibl5SEtMyF4U56dy4cCY3LpwZs88wxngr0mRx3L2luTczXo07nVFFFBdw9zV18LNdjfzH1bP46k3zmJFnRxAZY6IjomShqn8FICK5zlPtjmlUk11vN7S+Dwv/IKpv+8RbR8lJT+EbtywkPzM1qu9tjJnaIlphFJGl7iVP64B9IlLrXsnOjEXze6DBqK5XfNh+ltf3NHHfmlmWKIwxURfp4ShPAf9ZVS9T1cuArwE/jF1Yk1yjexnVKCaLp94+SkpSEg9eOztq72mMMQMiTRbZqvrmwBNV/R2QHZOIpoLGWii4DLKLovJ2rV09vFTr4/MrK2ydwhgTExEfDSUi/xV4zn3+h8Cx2IQ0BfhqYdbqqL3dP/97Pf5AkIc+Ht0jq4wxZkCkI4s/AoqBnwGvuI+/HKugJrWuFuj0RW0KqrOnn/+79UPWLSulqsgGe8aY2Ij0aKjTwJ/EOJap4cLJeNE5c/sn247T1evnkevnRuX9jDEmnGGThYj8T1X9qoj8AqcW1EVU9TMxi2yyaqwFSYbS8ZfV6ukP8KPfH+O6eUUsLc+PQnDGGBPeSCOLgTWK7472jUXkx8BtQKuqLnXbpgMvAlVAPXCXO2pBRL4FPAgEgD9R1V+77SuBp4FMYDPwFVW9JHFNGL4amLkEUsd/ZbdNO32c7O7lkeuXjz8uY4wZxrBrFqrqzpmwXFXfCr0By0d476eBWwa1fRP4rarOA37rPkdEFgP3AEvcbX7glkQH2ACsB+a5t8HvOXEEg06l2SgUD/QHgjz51lGurMhn7dzCKARnjDFDi3SB+4EwbV8abgNVfRs4Naj5duAZ9/EzwB0h7T9V1V5VPQYcBlaJSCmQp6pb3dHEsyHbTDzth6G3MyqL21vqWjh+6hyP3DDXrvlgjIm5kdYs7gX+IzBbRF4LeSkXGEu58pmq2gygqs0iMsNtLwe2hfTzuW397uPB7UPFux5nFMKsWbPGEF6MXTgZb3wjC1XlibeOMKc4m08tLolCYMYYM7yR1izeAZqBIuB7Ie1dwJ4oxhHuT2Mdpj0sVX0K52xzqqurE29do7EW0nKhaN643ubfDp1kX1Mnf/f5K0iy0t/GmDgYNlmo6ofAh8DaKH3eCREpdUcVpUCr2+4DKkP6VQBNbntFmPaJyVcD5SsgaXxXg9vwuyOU5GVw+4qyKAVmjDHDi7SQ4BoReVdEukWkT0QCItI5hs97jY/WPx4AXg1pv0dE0kVkNs5C9g53yqrL/XwB7g/ZZmLp74ETdeNer9h1/DRbj7bzn66bTXqKXYLUGBMfkZb7+N84Ryu9BFTj/GhfPtwGIvICcANQJCI+nGt4/w2wUUQexLk+xp0AqrpPRDYC7wN+4FFVDbhv9QgfHTq7xb1NPC17IOgf93rFE28dIT8zlXtWJeCajDFm0oo0WaCqh0Uk2f0R/2cReWeE/vcO8dJNQ/R/HHg8THsNsDTSOBNWFC6jeri1i1/vO8Gf3Hg5OekR/6czxphxi/QX55yIpAG7ReTvcBa9rRDRaPhqIK8c8krH/BZPvnWUjNQkHrimKnpxGWNMBCI9z+KLQDLwGHAWZzH687EKalJqrIXyq8a8edOZ8/x8dyP3XD2Lwpz0KAZmjDEji7SQ4Ifuw/PAX8UunEnqbDucPgYrvzTmt/jR748RVHjwY3ZxI2NM/I10Ut5ehj+vYfzV8KaCpp3O/RjXK06f7eOFHcf5zJVlVE7PimJgxhgTmZFGFrfFJYrJzlcDkgRlK8a0+bNbP+RcX4CHrreLGxljvBHJSXlmvBproXghpOeMetNzfX6efucYNy2cwcKSvBgEZ4wxI4v0pLwuEel0bz3jOClv6lF1F7fHNgX14rsNnD7XzyM32MWNjDHeiXSBOzf0uYjcAayKRUCTzuljcP7UmJJFfyDID98+ytVV06iumh6D4IwxJjKRHjp7EVX9OXBjdEOZpHzuyXhjuIbFa7ubaOrosVGFMcZzEY0sRORzIU+TcEp+JF5V10TUWAspmVC8aFSbBYNOGfIFM3P5xIIZI29gjDExFOkZ3J8OeezHuSTq7VGPZjJqrIWy5ZA8uvIc/3qglUOt3fzPu5fbxY2MMZ6LdM3iy7EOZFLy90Hze7Dqj0e1maryg98dpmJaJrddMfbyIMYYEy2RHg01R0R+ISJtItIqIq+KiB30P5LWfRDoHfXi9rv1p9l5/AzrPz6HlOQxLSsZY0xURfpL9DywESgFynBKlb8Qq6AmDZ97GdVRLm5v+N1hpmencefKypE7G2NMHESaLERVn1NVv3v7v9gC98gad0J2MeRH/qO/v7mTNw+28eVrqshMs4sbGWMSQ6Srrm+KyDeBn+IkibuB10VkOoCqnopRfBNbY41zsaNRLFA/+dYRstOSuX9tVeziMsaYUYo0Wdzt3j80qP2PcJKHrV8M1tMBJz+AZXdFvMmps328vreZ+1ZfRn5WagyDM8aY0Yn0aCiriz1ajW6l2YrIF7d/vquR/oBy99W2VmGMSSyRnpSXinMt7I+7Tb8DnlTV/hjFNfENXEa1LLILHqkqG2sauKIin0WlVjDQGJNYIl3g3gCsBH7g3la6bWYojbVQOA8yCyLqvrexgwMtXdxZbaMKY0ziiXTN4mpVvTLk+b+KyHtj+UARWQC8GNI0B/gLoAD4Y6DNbf8vqrrZ3eZbwINAAPgTVf31WD47blSdw2bnRl4+68V3G0hPSeIzV5bFMDBjjBmbSJNFQETmquoRcE7Sw/nhHjVVPQgsd98nGWgEXgG+DPy9qn43tL+ILAbuAZbgnOPxLyIyX1XH9Plx0eGDs60Rn19xvi/Aa7ubuHVZKfmZtrBtjEk8kSaLP8M5fPao+7wK58d9vG4Cjqjqh8PUP7od+Kmq9gLHROQwTnn0rVH4/NgYWK8oj2y94lf7munq9XOXTUEZYxJUpGsW/w48CQTd25NE58f6Hi4+E/wxEdkjIj8WkWluWznQENLH57ZdQkTWi0iNiNS0tbWF6xIfjTWQnAYzl0XU/cV3G5g1PYvVs+2aFcaYxBRpsngWmA38tXubDTw3ng8WkTTgMzilQ8BZMJ+LM0XVDHxvoGuYzcOePa6qT6lqtapWFxcXjye88WncCSVXQEraiF0/bD/LtqOnuKu6gqQkqy5rjElMkU5DLRi0wP3mWBe4Q6wDdqrqCYCBewAR+SHwS/epDwidn6kAmsb52bET8EPTLrjq/oi6v1TjI0ng8ysrYhyYMcaMXaQji10ismbgiYisxpmaGo97CZmCEpHQWtyfBercx68B94hIuojMBuYBO8b52bHTdgD6z0VUaTYQVF6u9fHx+cWU5mfGIThjjBmbSEcWq4H7ReS4+3wWsF9E9gKqqleM5kNFJAv4D1xcPuTvRGQ5zhRT/cBrqrpPRDYC7+NceOnRhD4S6sLi9sjJ4u0P2mjp7OE7n14c46CMMWZ8Ik0Wt0TzQ1X1HFA4qO2Lw/R/HHg8mjHETGMNZBTA9JHLZW2saWB6dho3LZoZ+7iMMWYcIq0N9WGsA5k0fLXOqGKESrPt3b38y/4T3L+2irQUu8CRMSax2a9UNPV0QOv7ULl6xK6vWNFAY8wEYskimnw1gELlqmG7qSovvtvA8soC5s/MjU9sxhgzDpYsoqlhO0jSiGU+djec4VBrt40qjDEThiWLaGrYDjOXQPrwo4WNNT4yU5O57YrSYfsZY0yisGQRLcGAMw01wnrFuT4/v3jPKRqYm2FFA40xE4Mli2hpfR/6ukdMFpv3ttDd67cpKGPMhGLJIlqOb3PuR0gWG2samF2UzdVV04btZ4wxicSSRbQ07ICcEiiYNWSXYyfPsuPYKe6srmCYkuzGGJNwLFlES8N255DZYZLAxpoGkpOEL1xlRQONMROLJYto6GqBMx/CrDVDdvEHgmyq9XHD/GJm5GXEMThjjBk/SxbR0LDduR9mveKtD9po7erlLlvYNsZMQJYsoqFhBySnOxc8GsKL7zZQlJPGjQtnxDEwY4yJDksW0XB8m3O97SGujNfW1cu/Hmjlc1dVkJpsu9wYM/HYL9d49Z+H5veGnYJ6ZZcPf1C5q9qmoIwxE5Mli/Fq2g3B/iGTxUDRwJWXTePyGTnxjc0YY6LEksV4XVjcDl9pdufxMxxpO8vdNqowxkxglizGq2E7FF4O2UVhX974bgNZacncakUDjTETmCWL8VB1T8YLPwV1ttfPL/c0cdsVpeSkR3oFW2OMSTyWLMbj1FE41z7kFNTre5s52xewooHGmAnPk2QhIvUisldEdotIjds2XUTeEJFD7v20kP7fEpHDInJQRG72IuawRigeuPHdBuYUZ3PVLCsaaIyZ2LwcWXxCVZer6sBl5b4J/FZV5wG/dZ8jIouBe4AlwC3AD0Qk2YuAL9GwHTLyoWjBJS8dbu2m5sPT3F1daUUDjTETXiJNQ90OPOM+fga4I6T9p6raq6rHgMPA8Be5jpeGHVCxCpIu3Y0v1TpFAz9nRQONMZOAV8lCgd+ISK2IrHfbZqpqM4B7P1AXoxxoCNnW57ZdQkTWi0iNiNS0tbXFKHTX+dPQtj/sFFR/IMim2kZuXDiD4tz02MZhjDFx4NUhOteqapOIzADeEJEDw/QNN4ej4Tqq6lPAUwDV1dVh+0SNr8a5n3VpsnjzQCsnu3vt3ApjzKThychCVZvc+1bgFZxppRMiUgrg3re63X1A6K9uBdAUv2iH0LAdJBnKrrrkpY01PmbkpnPDgmIPAjPGmOiLe7IQkWwRyR14DHwKqANeAx5wuz0AvOo+fg24R0TSRWQ2MA/YEd+ow2jYDiVLIf3iEh6tnT28ebCVz6+sIMWKBhpjJgkvpqFmAq+4RwilAM+r6q9E5F1go4g8CBwH7gRQ1X0ishF4H/ADj6pqwIO4PxLwg68WVtx3yUubdjYSCCp3rrSFbWPM5BH3ZKGqR4Erw7S3AzcNsc3jwOMxDi1yJ+qg/+wli9uqyku1DVxdNY05xVY00Bgzedg8yVg0uLNgg5LFzuOnOdp2ljttYdsYM8lYshiLhm2QWwb5F081vVTjIystmT9YZkUDjTGTiyWLsWjY4RwyG3Jm9vm+AL/c08yty0rJtqKBxphJxpLFaHU0QkfDJVNQv9rXTHev3xa2jTGTkiWL0fINrFdcXHHkpRofs6ZnsWr2dA+CMsaY2LJkMVrHt0NKJpRccaGp4dQ53jnSzhdWVljRQGPMpGTJYrQatkP5SkhOvdC0aacPEfi8TUEZYyYpSxaj0XcOWvZcNAUVDCqbdvq4dm4R5QWZHgZnjDGxY8liNJp2QtB/0eL29mOnaDh1njurbVRhjJm8LFmMRsN25z5kZPFSbQO56Sl8anGJR0EZY0zsWbIYjYYdUDQfspwjnrp7/WzZ28JtV5aRmZYYF+8zxphYsGQRqWDQGVmEjCpe39PE+f6ATUEZYyY9SxaRaj/sXB2vcs2FppdrfcwtzmZFZYF3cRljTBxYsojUhfUKZ3H72MmzvFt/mi+srLRzK4wxk54VMYpUw3bInAaFlwPwcm0DSQKfuyrs5cCNMRNUf38/Pp+Pnp4er0OJqYyMDCoqKkhNTR25M5YsItewHSpWQVISgaCyqbaR6+cXMzMvw+vIjDFR5PP5yM3NpaqqatLOGqgq7e3t+Hw+Zs+eHdE2Ng0ViXOn4OQHTqVZ4PeHT9LS2WPXrTBmEurp6aGwsHDSJgoAEaGwsHBUoydLFpHwvevcu+sVL9f6KMhK5aZFMzwMyhgTK5M5UQwY7b/RkkUkjm+DpBQou4qOc/38el8Lt19ZRnqKnVthjJkaLFlEomGHU2U2LYvX9jTR5w/aFJQxZkqJe7IQkUoReVNE9ovIPhH5itv+lyLSKCK73dutIdt8S0QOi8hBEbk5rgEH+qGx9qMpqJoGFpbksqQsL65hGGOMl7w4GsoPfE1Vd4pILlArIm+4r/29qn43tLOILAbuAZYAZcC/iMh8VQ3EJdqWPeA/D5Wr+OBEF+/5Ovivty2eEnOaxkx1f/WLfbzf1BnV91xclsd3Pr1k2D719fXccsstrF69ml27djF//nyeffZZtm7dyte//nX8fj9XX301GzZsID09naqqKu6++27efPNNAJ5//nkuv/zyqMYd95GFqjar6k73cRewHxjuZIXbgZ+qaq+qHgMOA6uG6R9dDQNXxlvNy7U+UpKEO5aXxe3jjTFT08GDB1m/fj179uwhLy+P73//+3zpS1/ixRdfZO/evfj9fjZs2HChf15eHjt27OCxxx7jq1/9atTj8fQ8CxGpAlYA24FrgcdE5H6gBmf0cRonkWwL2czHEMlFRNYD6wFmzZoVnSAbtkN+Jf05pfxs535uXDiDwpz06Ly3MSahjTQCiKXKykquvfZaAP7wD/+Qv/7rv2b27NnMnz8fgAceeIB/+qd/upAY7r333gv3f/qnfxr1eDxb4BaRHGAT8FVV7QQ2AHOB5UAz8L2BrmE213DvqapPqWq1qlYXFxdHJ9CGHVC5ircOtnGyu9cWto0xcTHaqe7Q/rGYJvckWYhIKk6i+Imq/gxAVU+oakBVg8AP+WiqyQeE/kJXAE1xCfRMA3Q2QuVqXqptoCgnjRsWRCkJGWPMMI4fP87WrVsBeOGFF/jkJz9JfX09hw8fBuC5557j+uuvv9D/xRdfvHC/du3aqMcT92kocVLej4D9qvr9kPZSVW12n34WqHMfvwY8LyLfx1ngngfsiEuwbvHAM0VX8dtXW/nytVWkJtvRxsaY2Fu0aBHPPPMMDz30EPPmzeMf/uEfWLNmDXfeeeeFBe6HH374Qv/e3l5Wr15NMBjkhRdeiHo8XqxZXAt8EdgrIrvdtv8C3Csiy3GmmOqBhwBUdZ+IbATexzmS6tG4HQnVsANSs3ilsQB/sJUvrLQpKGNMfCQlJfHEE09c1HbTTTexa9eusP0fffRRvvOd78QsnrgnC1X9PeHXITYPs83jwOMxC2ooDdugfCUbd7VwRUU+C0py4x6CMcYkAptTGUpvN7TU0VqwnP3Nndy50q6GZ4yJj6qqKurq6kbu6Kqvr6eoqCiGEVmyGFrTTtAAv+mqIi05ic9cadetMMZMXZYshnLcWdx+8uh0/sOSmeRnRXaBEGOMmYwsWQylYTtduZfTcD7dpqCMMVOeJYtwgkHw7WCnzqckL4Pr5tm5FcaYqc0uqxrOyQ+gp4Nf9lfyuevKSU6yooHGmKnNRhbhNDilqGqC8/mCTUEZY4yNLMLRhu10SB7TKxcxpzjH63CMMV7Z8k1o2Rvd9yxZBuv+Ztgu9fX1rFu3jo997GO88847lJeX8+qrr7Ju3Tq++93vUl1dzcmTJ6murqa+vp6nn36an//85wQCAerq6vja175GX18fzz33HOnp6WzevJnp06ePK2wbWYTRe3Qb7/rnWdFAY4xnDh06xKOPPsq+ffsoKChg06ZNw/avq6vj+eefZ8eOHXz7298mKyuLXbt2sXbtWp599tlxx2Mji8HOniSj8yjvyb08dEWp19EYY7w0wggglmbPns3y5csBWLlyJfX19cP2/8QnPkFubi65ubnk5+fz6U9/GoBly5axZ8+eccdjI4tB+uqd9Yr02WvJzbBzK4wx3khP/+i6OcnJyfj9flJSUggGgwD09PQM2T8pKenC86SkJPx+/7jjsWQxyIe736RPk7l67U1eh2KMMRepqqqitrYWgJdffjmun23JYpDA8W0cSprLqvlW3sMYk1i+/vWvs2HDBq655hpOnjwZ188W1bAXnZvwqqurtaamZlTbqCrbn3gY8spZc99fxCgyY0wi279/P4sWLfI6jLgI928VkVpVrR7c1xa4Q4gIax550uswjDEm4dg0lDHGmBFZsjDGmEEm6/R8qNH+Gy1ZGGNMiIyMDNrb2yd1wlBV2tvbycjIiHgbW7MwxpgQFRUV+Hw+2travA4lpjIyMqioiLz23YRJFiJyC/APQDLwf1TVu1MrjTGTVmpqKrNnz/Y6jIQzIaahRCQZ+CdgHbAYuFdEFnsblTHGTB0TIlkAq4DDqnpUVfuAnwK3exyTMcZMGRMlWZQDDSHPfW7bRURkvYjUiEjNZJ9vNMaYeJooaxbhLlV3yaEKqvoU8BSAiLSJyIdj/LwiIL7n0o+OxTc+Ft/4WHzjk+jxXRaucaIkCx8QenGJCqBpuA1UdcwXzhaRmnCnuycKi298LL7xsfjGJ9HjG8pEmYZ6F5gnIrNFJA24B3jN45iMMWbKmBAjC1X1i8hjwK9xDp39saru8zgsY4yZMiZEsgBQ1c3A5jh93FNx+pyxsvjGx+IbH4tvfBI9vrAmbYlyY4wx0TNR1iyMMcZ4yJKFMcaYEU3pZCEit4jIQRE5LCLfDPO6iMg/uq/vEZGr4hhbpYi8KSL7RWSfiHwlTJ8bRKRDRHa7t7he3k9E6kVkr/vZl1yW0OP9tyBkv+wWkU4R+eqgPnHdfyLyYxFpFZG6kLbpIvKGiBxy76cNse2w39UYxvc/ROSA+9/vFREpGGLbYb8LMYzvL0WkMeS/4a1DbOvV/nsxJLZ6Edk9xLYx33/jpqpT8oZzVNURYA6QBrwHLB7U51ZgC85JgWuA7XGMrxS4yn2cC3wQJr4bgF96uA/rgaJhXvds/4X5b90CXObl/gM+DlwF1IW0/R3wTffxN4G/HSL+Yb+rMYzvU0CK+/hvw8UXyXchhvH9JfD1CP77e7L/Br3+PeAvvNp/471N5ZFFJPWmbgeeVcc2oEBESuMRnKo2q+pO93EXsJ8wJU4SnGf7b5CbgCOqOtYz+qNCVd8GTg1qvh14xn38DHBHmE3jUhstXHyq+htV9btPt+GcEOuJIfZfJDzbfwNERIC7gBei/bnxMpWTRST1piKqSRVrIlIFrAC2h3l5rYi8JyJbRGRJfCNDgd+ISK2IrA/zekLsP5yTOIf6n9TL/QcwU1WbwfkDAZgRpk+i7Mc/whkphjPSdyGWHnOnyX48xDReIuy/64ATqnpoiNe93H8RmcrJIpJ6UxHVpIolEckBNgFfVdXOQS/vxJlauRL4X8DP4xkbcK2qXoVTOv5REfn4oNcTYf+lAZ8BXgrzstf7L1KJsB+/DfiBnwzRZaTvQqxsAOYCy4FmnKmewTzff8C9DD+q8Gr/RWwqJ4tI6k2NuiZVNIlIKk6i+Imq/mzw66raqard7uPNQKqIFMUrPlVtcu9bgVdwhvuhPN1/rnXATlU9MfgFr/ef68TA1Jx73xqmj9ffwweA24D71J1gHyyC70JMqOoJVQ2oahD44RCf6/X+SwE+B7w4VB+v9t9oTOVkEUm9qdeA+92jetYAHQNTBrHmznH+CNivqt8fok+J2w8RWYXz37M9TvFli0juwGOchdC6Qd08238hhvyLzsv9F+I14AH38QPAq2H6eFYbTZwrVH4D+IyqnhuiTyTfhVjFF7oG9tkhPtfr2nKfBA6oqi/ci17uv1HxeoXdyxvO0Tof4Bwp8W237WHgYfex4Fyh7wiwF6iOY2wfwxkq7wF2u7dbB8X3GLAP5+iObcA1cYxvjvu577kxJNT+cz8/C+fHPz+kzbP9h5O0moF+nL92HwQKgd8Ch9z76W7fMmDzcN/VOMV3GGe+f+A7+MTg+Ib6LsQpvufc79YenARQmkj7z21/euA7F9I37vtvvDcr92GMMWZEU3kayhhjTIQsWRhjjBmRJQtjjDEjsmRhjDFmRJYsjDHGjMiShTFRIiIFIvL/DPP6OxG8R3d0ozImOixZGBM9BcAlyUJEkgFU9Zp4B2RMtEyYa3AbMwH8DTDXvWZBP9CNc5LWcmCxiHSrao5b7+tVYBqQCvy5qoY7c9uYhGEn5RkTJW514F+q6lIRuQF4HViqqsfc1weSRQqQpaqdbi2qbcA8VdWBPh79E4wZko0sjImdHQOJYhAB/rtbWTSIUy57Js4FmoxJSJYsjImds0O03wcUAytVtV9E6oGMuEVlzBjYArcx0dOFcwnckeQDrW6i+ARwWWzDMmb8bGRhTJSoaruI/LuI1AHngUuuoeH6CfALEanBqeR6IE4hGjNmtsBtjDFmRDYNZYwxZkSWLIwxxozIkoUxxpgRWbIwxhgzIksWxhhjRmTJwhhjzIgsWRhjjBnR/w+/GJ9/s/yDAQAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1658,7 +1650,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1667,7 +1659,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index 271a69d..9dc6ccc 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -13,15 +13,7 @@ def cl_accuracy(cl, cfg): def fraction_accuracy(xncs: XNCS): - accuracies = [] - for action in range(xncs.cfg.number_of_actions): - action_set = xncs.population.generate_action_set(action) - accuracy = sorted(action_set, key=lambda cl: -1 * cl.numerosity)[1].accuracy - if accuracy is not None: - accuracies.append(accuracy) - else: - accuracies.append(0) - return sum(accuracies) / len(accuracies) + return xncs.fraction_accuracy def specificity(xncs, population): From f3dd4b4a227e0a11b14a2ca76112bbcc72718b75 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Tue, 1 Jun 2021 11:12:00 +0200 Subject: [PATCH 11/19] Knowledge metric --- .../XCS_XNCS_Maze5_pre_populated.ipynb | 935 ++++++++++-------- XCS_Experiments/utils/nxcs_utils.py | 33 + XCS_Experiments/utils/xcs_utils.py | 32 + 3 files changed, 609 insertions(+), 391 deletions(-) diff --git a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb index 119700c..777bb11 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb @@ -23,15 +23,15 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '0', '1', '0', '0', '0', '1', '0')\n", + "('1', '0', '1', '0', '0', '0', '0', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] @@ -62,8 +62,8 @@ "from lcs.agents.xncs import XNCS\n", "from lcs.agents.xncs import Configuration as XNCSConfig\n", "\n", - "from utils.xcs_utils import xcs_metrics\n", - "from utils.nxcs_utils import xncs_metrics\n", + "from utils.xcs_utils import *\n", + "from utils.nxcs_utils import *\n", "\n", "XCScfg = XCSConfig(number_of_actions=8,\n", " max_population=1600,\n", @@ -79,7 +79,7 @@ " initial_prediction =10, # p_i\n", " initial_error = 0, # epsilon_i\n", " initial_fitness = 10, # f_i\n", - " user_metrics_collector_fcn=xcs_metrics)\n", + " user_metrics_collector_fcn=xcs_maze_metrics)\n", "\n", "XNCScfg = XNCSConfig(number_of_actions=8,\n", " max_population=1600,\n", @@ -95,7 +95,7 @@ " initial_prediction =10, # p_i\n", " initial_error = 0.1, # epsilon_i\n", " initial_fitness = 10, # f_i\n", - " user_metrics_collector_fcn=xncs_metrics,\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", " lmc=10,\n", " lem=100)\n" ] @@ -118,16 +118,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.2710486000000003, 'population': 1494, 'numerosity': 1600, 'average_specificity': 7.789375}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1158.2183271550452, 'perf_time': 0.06356459999999942, 'population': 900, 'numerosity': 1600, 'average_specificity': 6.43125}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 71, 'reward': 1000.0000000275006, 'perf_time': 0.4300088000000031, 'population': 850, 'numerosity': 1600, 'average_specificity': 5.973125}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 11, 'reward': 1023.1122292121702, 'perf_time': 0.077508899999998, 'population': 791, 'numerosity': 1600, 'average_specificity': 6.88}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 61, 'reward': 1000.000000865112, 'perf_time': 0.3234722999999917, 'population': 797, 'numerosity': 1600, 'average_specificity': 6.780625}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 3, 'reward': 1390.7327132040612, 'perf_time': 0.018558599999991543, 'population': 701, 'numerosity': 1600, 'average_specificity': 5.6825}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 12, 'reward': 1017.2630887499168, 'perf_time': 0.08815830000000346, 'population': 658, 'numerosity': 1600, 'average_specificity': 6.769375}\n", - "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 1, 'reward': 1729.862899894334, 'perf_time': 0.0026914999999974043, 'population': 620, 'numerosity': 1600, 'average_specificity': 6.153125}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 9, 'reward': 1073.7135270512224, 'perf_time': 0.08181700000000092, 'population': 613, 'numerosity': 1600, 'average_specificity': 5.991875}\n", - "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 3, 'reward': 1375.2111181568766, 'perf_time': 0.007632499999999709, 'population': 556, 'numerosity': 1600, 'average_specificity': 6.055625}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.3876943999999996, 'numerosity': 1600, 'population': 1498, 'average_specificity': 7.911875, 'fraction_accuracy': 0.95, 'knowledge': 6.164383561643835}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1160.623320451948, 'perf_time': 0.047101699999998914, 'numerosity': 1600, 'population': 1163, 'average_specificity': 7.045625, 'fraction_accuracy': 0.91, 'knowledge': 9.58904109589041}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 7, 'reward': 1138.5646001160228, 'perf_time': 0.08202970000000676, 'numerosity': 1600, 'population': 1049, 'average_specificity': 7.64375, 'fraction_accuracy': 0.95, 'knowledge': 5.47945205479452}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 72, 'reward': 1000.0000000197018, 'perf_time': 0.6064908999999972, 'numerosity': 1600, 'population': 1117, 'average_specificity': 8.715625, 'fraction_accuracy': 0.99, 'knowledge': 5.47945205479452}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 6, 'reward': 1259.883148937201, 'perf_time': 0.06926590000000488, 'numerosity': 1600, 'population': 1074, 'average_specificity': 10.555, 'fraction_accuracy': 0.97, 'knowledge': 5.47945205479452}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 8, 'reward': 1068.8148802242863, 'perf_time': 0.0777485999999783, 'numerosity': 1600, 'population': 1083, 'average_specificity': 8.2, 'fraction_accuracy': 0.99, 'knowledge': 5.47945205479452}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 6, 'reward': 1214.0248845123629, 'perf_time': 0.06448369999998249, 'numerosity': 1600, 'population': 1050, 'average_specificity': 8.32875, 'fraction_accuracy': 0.99, 'knowledge': 5.47945205479452}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 7, 'reward': 1123.3444554648654, 'perf_time': 0.04995239999999512, 'numerosity': 1600, 'population': 1077, 'average_specificity': 8.809375, 'fraction_accuracy': 1.0, 'knowledge': 6.164383561643835}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 15, 'reward': 1007.4036301845966, 'perf_time': 0.16627369999997654, 'numerosity': 1600, 'population': 1030, 'average_specificity': 9.593125, 'fraction_accuracy': 1.0, 'knowledge': 6.8493150684931505}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 12, 'reward': 1017.5690830723419, 'perf_time': 0.16156540000002906, 'numerosity': 1600, 'population': 1038, 'average_specificity': 8.8025, 'fraction_accuracy': 0.9, 'knowledge': 6.164383561643835}\n" ] }, { @@ -141,16 +141,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.179087399999986, 'population': 1491, 'numerosity': 1600, 'average_specificity': 8.620625}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 7, 'reward': 1097.3862870497821, 'perf_time': 0.09056069999996907, 'population': 1085, 'numerosity': 1600, 'average_specificity': 7.23125}\n", - 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0.1257709999999861, 'numerosity': 1600, 'population': 1058, 'average_specificity': 8.35875, 'fraction_accuracy': 0.97, 'knowledge': 1.36986301369863}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 5, 'reward': 1182.564364357557, 'perf_time': 0.068584499999929, 'numerosity': 1600, 'population': 1018, 'average_specificity': 7.4175, 'fraction_accuracy': 0.96, 'knowledge': 2.054794520547945}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 11, 'reward': 1026.1215477863625, 'perf_time': 0.102107100000012, 'numerosity': 1600, 'population': 1028, 'average_specificity': 8.825625, 'fraction_accuracy': 0.99, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 100, 'reward': 1.5072424466158488e-12, 'perf_time': 0.7942798999999923, 'numerosity': 1600, 'population': 1054, 'average_specificity': 7.21625, 'fraction_accuracy': 1.0, 'knowledge': 1.36986301369863}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 3, 'reward': 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'numerosity': 1600, 'average_specificity': 6.1025}\n", - "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 3, 'reward': 1407.9294769496128, 'perf_time': 0.042373099999963415, 'population': 695, 'numerosity': 1600, 'average_specificity': 6.086875}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.6700117999999975, 'numerosity': 1600, 'population': 1483, 'average_specificity': 8.211875, 'fraction_accuracy': 0.97, 'knowledge': 5.47945205479452}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 9, 'reward': 1049.078543593884, 'perf_time': 0.11612999999999829, 'numerosity': 1600, 'population': 1234, 'average_specificity': 7.119375, 'fraction_accuracy': 0.94, 'knowledge': 4.794520547945205}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 3, 'reward': 1385.3327583750827, 'perf_time': 0.020420199999989563, 'numerosity': 1600, 'population': 1153, 'average_specificity': 7.55, 'fraction_accuracy': 0.97, 'knowledge': 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'fraction_accuracy': 0.92, 'knowledge': 4.10958904109589}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 2, 'reward': 1521.3661882208903, 'perf_time': 0.031411299999945186, 'numerosity': 1600, 'population': 1089, 'average_specificity': 9.226875, 'fraction_accuracy': 0.92, 'knowledge': 3.4246575342465753}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 4, 'reward': 1258.5808422520784, 'perf_time': 0.039330900000095426, 'numerosity': 1600, 'population': 1066, 'average_specificity': 7.478125, 'fraction_accuracy': 0.9, 'knowledge': 2.054794520547945}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 6, 'reward': 1128.100283921, 'perf_time': 0.07337360000019544, 'numerosity': 1600, 'population': 1071, 'average_specificity': 7.785625, 'fraction_accuracy': 0.7, 'knowledge': 2.73972602739726}\n" ] }, { @@ -187,16 +187,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 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'knowledge': 97.94520547945206}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.38428990000011254, 'population': 690, 'numerosity': 1600, 'average_specificity': 5.95, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 8, 'reward': 1073.0386613523635, 'perf_time': 0.05382550000012998, 'population': 656, 'numerosity': 1600, 'average_specificity': 6.736875, 'knowledge': 99.31506849315068}\n" ] }, { @@ -210,16 +210,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.4861783000001196, 'numerosity': 1600, 'population': 1489, 'average_specificity': 7.810625, 'fraction_accuracy': 0.98}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 4, 'reward': 1257.740048608998, 'perf_time': 0.03932069999996202, 'numerosity': 1600, 'population': 1131, 'average_specificity': 7.01, 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"INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 5, 'reward': 1242.6811185590989, 'perf_time': 0.05487130000005891, 'population': 954, 'numerosity': 1600, 'average_specificity': 7.383125, 'knowledge': 99.31506849315068}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1416.69933595584, 'perf_time': 0.018198900000015783, 'population': 888, 'numerosity': 1600, 'average_specificity': 7.556875, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 4, 'reward': 1309.5258043729204, 'perf_time': 0.04450309999992896, 'population': 886, 'numerosity': 1600, 'average_specificity': 6.963125, 'knowledge': 99.31506849315068}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 6, 'reward': 1138.191527893943, 'perf_time': 0.024286699999947814, 'population': 881, 'numerosity': 1600, 'average_specificity': 7.071875, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 100, 'reward': 1.1780436209647452e-101, 'perf_time': 0.48217780000004495, 'population': 841, 'numerosity': 1600, 'average_specificity': 7.491875, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 10, 'reward': 1035.1435445996078, 'perf_time': 0.07961400000021968, 'population': 727, 'numerosity': 1600, 'average_specificity': 6.818125, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 2, 'reward': 1504.1, 'perf_time': 0.0074400000003151945, 'population': 695, 'numerosity': 1600, 'average_specificity': 6.64625, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 2, 'reward': 1701.1361509771268, 'perf_time': 0.0099555000001601, 'population': 644, 'numerosity': 1600, 'average_specificity': 6.099375, 'knowledge': 96.57534246575342}\n" ] } ], @@ -254,14 +254,6 @@ "explore = 2000\n", "exploit = 500\n", "\n", - "df = XCSExp(maze=maze,\n", - " cfg=XCScfg,\n", - " number_of_tests=number_of_experiments,\n", - " explore_trials=0, # explore,\n", - " exploit_trials=exploit + explore,\n", - " pre_generate=True\n", - " )\n", - "\n", "df_other = XNCSExp(\n", " maze=maze,\n", " cfg=XNCScfg,\n", @@ -269,12 +261,21 @@ " explore_trials=0,\n", " exploit_trials=exploit + explore,\n", " pre_generate=True\n", - " )" + " )\n", + "\n", + "df = XCSExp(maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -304,11 +305,13 @@ " population\n", " numerosity\n", " average_specificity\n", + " knowledge\n", " steps_in_trial_other\n", " population_other\n", " numerosity_other\n", " average_specificity_other\n", " fraction_accuracy_other\n", + " knowledge_other\n", " \n", " \n", " trial\n", @@ -323,358 +326,410 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", - " 97.000000\n", - " 333.333333\n", - " 1.182007\n", - " 1500.000000\n", + " 100.000000\n", + " 0.000000\n", + " 1.237642\n", + " 1484.333333\n", " 1600.0\n", - " 8.365000\n", + " 8.555208\n", " 100.000000\n", - " 1506.000000\n", + " 100.000000\n", + " 1492.000000\n", " 1600.0\n", - " 8.074167\n", - " 0.976667\n", + " 8.235833\n", + " 0.966667\n", + " 5.936073\n", " \n", " \n", " 100\n", - " 45.666667\n", - " 717.675308\n", - " 0.331375\n", - " 1116.666667\n", + " 37.000000\n", + " 817.855486\n", + " 0.307822\n", + " 1128.333333\n", " 1600.0\n", - " 7.261250\n", - " 3.333333\n", - " 1186.333333\n", + " 6.960833\n", + " 98.630137\n", + " 7.000000\n", + " 1249.666667\n", " 1600.0\n", - " 6.865208\n", - " 0.903333\n", + " 7.300208\n", + " 0.980000\n", + " 5.936073\n", " \n", " \n", " 200\n", - " 6.000000\n", - " 1145.342516\n", - " 0.060114\n", - " 1015.666667\n", + " 4.333333\n", + " 1479.555014\n", + " 0.047548\n", + " 1049.666667\n", " 1600.0\n", - " 7.050000\n", - " 9.000000\n", - " 1125.666667\n", + " 6.975833\n", + " 99.543379\n", + " 6.333333\n", + " 1179.000000\n", " 1600.0\n", - " 6.767917\n", - " 0.956667\n", + " 6.977500\n", + " 0.943333\n", + " 5.251142\n", " \n", " \n", " 300\n", - " 7.666667\n", - " 1116.691724\n", - " 0.075114\n", - " 966.666667\n", + " 4.333333\n", + " 1151.057454\n", + " 0.062162\n", + " 997.000000\n", " 1600.0\n", - " 6.519167\n", - " 5.666667\n", - " 1094.666667\n", + " 6.640000\n", + " 99.315068\n", + " 7.000000\n", + " 1132.333333\n", " 1600.0\n", - " 6.851042\n", - " 0.950000\n", + " 7.161250\n", + " 0.826667\n", + " 4.109589\n", " \n", " \n", " 400\n", - " 10.000000\n", - " 1088.676388\n", - " 0.048711\n", - " 917.000000\n", + " 6.666667\n", + " 1153.438879\n", + " 0.058114\n", + " 956.333333\n", " 1600.0\n", - " 6.826250\n", - " 4.000000\n", - " 1082.333333\n", + " 6.878125\n", + " 100.000000\n", + " 19.000000\n", + " 1125.333333\n", " 1600.0\n", - " 6.908958\n", - " 0.800000\n", + " 7.486250\n", + " 0.950000\n", + " 3.652968\n", " \n", " \n", " 500\n", - " 28.666667\n", - " 1043.790513\n", - " 0.176887\n", - " 890.666667\n", + " 36.000000\n", + " 867.115092\n", + " 0.214088\n", + " 926.333333\n", " 1600.0\n", - " 6.609167\n", - " 33.666667\n", - " 1056.000000\n", + " 6.642708\n", + " 99.315068\n", + " 6.333333\n", + " 1090.000000\n", " 1600.0\n", - " 7.237708\n", - " 0.940000\n", + " 7.615000\n", + " 0.930000\n", + " 3.424658\n", " \n", " \n", " 600\n", - " 7.333333\n", - " 1118.731469\n", - " 0.075750\n", - " 865.666667\n", + " 5.000000\n", + " 1250.562672\n", + " 0.029232\n", + " 881.000000\n", " 1600.0\n", - " 6.652917\n", - " 7.000000\n", - " 1059.333333\n", + " 7.274167\n", + " 100.000000\n", + " 5.333333\n", + " 1093.000000\n", " 1600.0\n", - " 7.095417\n", - " 0.983333\n", + " 7.793125\n", + " 0.936667\n", + " 2.739726\n", " \n", " \n", " 700\n", - " 9.000000\n", - " 1091.082751\n", - " 0.066204\n", - " 846.333333\n", + " 5.000000\n", + " 1234.467521\n", + " 0.038874\n", + " 852.000000\n", " 1600.0\n", - " 6.971667\n", - " 9.333333\n", - " 1075.000000\n", + " 6.706875\n", + " 99.543379\n", + " 26.666667\n", + " 1111.666667\n", " 1600.0\n", - " 7.449375\n", - " 0.940000\n", + " 8.144583\n", + " 0.976667\n", + " 3.424658\n", " \n", " \n", " 800\n", - " 5.666667\n", - " 1271.583579\n", - " 0.027169\n", - " 818.333333\n", + " 3.666667\n", + " 1405.452969\n", + " 0.031245\n", + " 846.666667\n", " 1600.0\n", - " 6.389792\n", - " 4.666667\n", - " 1072.666667\n", + " 6.577083\n", + " 99.543379\n", + " 4.333333\n", + " 1093.000000\n", " 1600.0\n", - " 8.329167\n", - " 0.953333\n", + " 8.020625\n", + " 0.980000\n", + " 2.739726\n", " \n", " \n", " 900\n", - " 37.666667\n", - " 778.080006\n", - " 0.174981\n", - " 809.666667\n", + " 7.000000\n", + " 1140.513633\n", + " 0.069188\n", + " 833.333333\n", " 1600.0\n", - " 6.598333\n", - " 10.333333\n", - " 1084.000000\n", + " 6.358958\n", + " 99.771689\n", + " 69.333333\n", + " 1077.000000\n", " 1600.0\n", - " 7.816667\n", - " 0.933333\n", + " 8.296042\n", + " 0.996667\n", + " 2.511416\n", " \n", " \n", " 1000\n", - " 25.000000\n", - " 1084.005316\n", - " 0.149218\n", - " 800.333333\n", + " 10.666667\n", + " 1110.575676\n", + " 0.101012\n", + " 824.666667\n", " 1600.0\n", - " 6.381667\n", - " 39.000000\n", - " 1078.000000\n", + " 6.568333\n", + " 98.630137\n", + " 3.666667\n", + " 1072.333333\n", " 1600.0\n", - " 8.207292\n", - " 0.996667\n", + " 8.950000\n", + " 0.990000\n", + " 2.968037\n", " \n", " \n", " 1100\n", - " 7.000000\n", - " 1135.332949\n", - " 0.063757\n", - " 786.333333\n", + " 6.666667\n", + " 1145.643586\n", + " 0.071593\n", + " 810.000000\n", " 1600.0\n", - " 6.683333\n", - " 3.666667\n", - " 1075.666667\n", + " 6.761667\n", + " 98.858447\n", + " 5.333333\n", + " 1081.333333\n", " 1600.0\n", - " 8.291458\n", - " 0.896667\n", + " 8.156875\n", + " 0.966667\n", + " 2.739726\n", " \n", " \n", " 1200\n", - " 4.666667\n", - " 1237.448537\n", - " 0.027131\n", - " 760.666667\n", - " 1600.0\n", - " 6.269167\n", " 7.666667\n", - " 1095.000000\n", + " 1099.519328\n", + " 0.086582\n", + " 814.333333\n", " 1600.0\n", - " 8.719375\n", - " 0.990000\n", + " 6.631875\n", + " 99.543379\n", + " 6.000000\n", + " 1069.333333\n", + " 1600.0\n", + " 8.265833\n", + " 0.966667\n", + " 2.968037\n", " \n", " \n", " 1300\n", - " 6.666667\n", - " 1170.283230\n", - " 0.047823\n", - " 755.666667\n", + " 63.666667\n", + " 986.181956\n", + " 0.301855\n", + " 799.333333\n", " 1600.0\n", - " 6.046042\n", - " 38.666667\n", - " 1088.666667\n", + " 6.546875\n", + " 100.000000\n", + " 7.333333\n", + " 1070.333333\n", " 1600.0\n", - " 8.251250\n", - " 0.993333\n", + " 8.166667\n", + " 0.840000\n", + " 3.196347\n", " \n", " \n", " 1400\n", - " 6.000000\n", - " 1197.151720\n", - " 0.049633\n", - " 728.333333\n", + " 68.000000\n", + " 418.038937\n", + " 0.333535\n", + " 794.666667\n", " 1600.0\n", - " 6.041875\n", - " 6.333333\n", - " 1079.333333\n", + " 6.223958\n", + " 97.488584\n", + " 36.333333\n", + " 1051.000000\n", " 1600.0\n", - " 8.237917\n", - " 0.980000\n", + " 8.268542\n", + " 0.933333\n", + " 3.196347\n", " \n", " \n", " 1500\n", - " 7.666667\n", - " 1286.520439\n", - " 0.048660\n", - " 731.333333\n", + " 69.333333\n", + " 361.384052\n", + " 0.329215\n", + " 772.666667\n", " 1600.0\n", - " 6.252083\n", - " 11.666667\n", - " 1073.000000\n", + " 6.477500\n", + " 99.086758\n", + " 5.000000\n", + " 1045.000000\n", " 1600.0\n", - " 7.812083\n", + " 8.523125\n", " 0.956667\n", + " 3.881279\n", " \n", " \n", " 1600\n", - " 39.000000\n", - " 713.746225\n", - " 0.171981\n", - " 711.666667\n", + " 40.666667\n", + " 692.075323\n", + " 0.194672\n", + " 734.000000\n", " 1600.0\n", - " 6.134792\n", - " 11.666667\n", - " 1051.000000\n", + " 6.291042\n", + " 99.543379\n", + " 34.666667\n", + " 1065.666667\n", " 1600.0\n", - " 8.012917\n", - " 0.933333\n", + " 8.604583\n", + " 0.993333\n", + " 4.109589\n", " \n", " \n", " 1700\n", - " 3.333333\n", - " 1609.876784\n", - " 0.024009\n", - " 680.666667\n", + " 9.000000\n", + " 1051.560998\n", + " 0.059908\n", + " 710.000000\n", " 1600.0\n", - " 6.027083\n", - " 7.000000\n", - " 1057.666667\n", + " 6.237292\n", + " 97.716895\n", + " 6.666667\n", + " 1064.666667\n", " 1600.0\n", - " 8.519792\n", - " 0.953333\n", + " 8.953958\n", + " 0.970000\n", + " 3.196347\n", " \n", " \n", " 1800\n", - " 39.000000\n", - " 721.201194\n", - " 0.182860\n", - " 678.000000\n", + " 5.333333\n", + " 1272.851696\n", + " 0.037196\n", + " 702.666667\n", " 1600.0\n", - " 6.808750\n", - " 4.000000\n", - " 1047.000000\n", + " 6.112917\n", + " 100.000000\n", + " 24.666667\n", + " 1045.000000\n", " 1600.0\n", - " 7.637708\n", - " 0.736667\n", + " 8.628125\n", + " 0.960000\n", + " 2.739726\n", " \n", " \n", " 1900\n", - " 4.333333\n", - " 1348.367282\n", - " 0.027422\n", - " 671.333333\n", + " 23.000000\n", + " 1106.977237\n", + " 0.136973\n", + " 688.333333\n", " 1600.0\n", - " 6.288750\n", - " 11.000000\n", - " 1063.000000\n", + " 6.136875\n", + " 98.630137\n", + " 16.666667\n", + " 1045.333333\n", " 1600.0\n", - " 8.831667\n", - " 0.923333\n", + " 8.392083\n", + " 0.990000\n", + " 3.196347\n", " \n", " \n", " 2000\n", - " 39.666667\n", - " 691.237842\n", - " 0.176256\n", - " 659.666667\n", + " 43.333333\n", + " 834.700000\n", + " 0.170842\n", + " 689.000000\n", " 1600.0\n", - " 6.066042\n", - " 6.333333\n", - " 1062.333333\n", + " 6.124792\n", + " 98.401826\n", + " 39.666667\n", + " 1050.000000\n", " 1600.0\n", - " 8.038125\n", - " 0.916667\n", + " 8.095833\n", + " 0.966667\n", + " 3.424658\n", " \n", " \n", " 2100\n", - " 38.000000\n", - " 757.248048\n", - " 0.161820\n", - " 640.333333\n", + " 4.333333\n", + " 1163.360331\n", + " 0.026712\n", + " 670.333333\n", " 1600.0\n", - " 6.223125\n", - " 38.666667\n", - " 1048.666667\n", + " 6.119167\n", + " 99.543379\n", + " 36.333333\n", + " 1059.666667\n", " 1600.0\n", - " 8.042292\n", - " 0.893333\n", + " 8.376250\n", + " 0.996667\n", + " 3.424658\n", " \n", " \n", " 2200\n", - " 3.333333\n", - " 1345.752468\n", - " 0.023821\n", - " 625.000000\n", + " 9.000000\n", + " 1259.295963\n", + " 0.047323\n", + " 652.666667\n", " 1600.0\n", - " 6.220000\n", - " 7.333333\n", - " 1024.666667\n", + " 6.328333\n", + " 98.401826\n", + " 7.000000\n", + " 1041.000000\n", " 1600.0\n", - " 8.272917\n", - " 0.953333\n", + " 7.813125\n", + " 0.863333\n", + " 3.196347\n", " \n", " \n", " 2300\n", - " 39.000000\n", - " 756.696230\n", - " 0.158708\n", - " 616.666667\n", + " 10.333333\n", + " 1142.920166\n", + " 0.068638\n", + " 654.333333\n", " 1600.0\n", - " 6.110417\n", - " 33.000000\n", - " 1018.666667\n", + " 6.350000\n", + " 97.716895\n", + " 7.666667\n", + " 1034.333333\n", " 1600.0\n", - " 8.307917\n", + " 7.932500\n", " 0.996667\n", + " 2.739726\n", " \n", " \n", " 2400\n", - " 4.666667\n", - " 1382.093409\n", - " 0.016833\n", - " 600.666667\n", + " 7.000000\n", + " 1127.863666\n", + " 0.032243\n", + " 636.666667\n", " 1600.0\n", - " 6.031250\n", - " 5.000000\n", - " 1001.000000\n", + " 7.146667\n", + " 99.771689\n", + " 4.000000\n", + " 1034.000000\n", " 1600.0\n", - " 8.062292\n", - " 0.986667\n", + " 8.160417\n", + " 0.990000\n", + " 2.739726\n", " \n", " \n", "\n", @@ -683,87 +738,115 @@ "text/plain": [ " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 97.000000 333.333333 1.182007 1500.000000 1600.0 \n", - "100 45.666667 717.675308 0.331375 1116.666667 1600.0 \n", - "200 6.000000 1145.342516 0.060114 1015.666667 1600.0 \n", - "300 7.666667 1116.691724 0.075114 966.666667 1600.0 \n", - "400 10.000000 1088.676388 0.048711 917.000000 1600.0 \n", - "500 28.666667 1043.790513 0.176887 890.666667 1600.0 \n", - "600 7.333333 1118.731469 0.075750 865.666667 1600.0 \n", - "700 9.000000 1091.082751 0.066204 846.333333 1600.0 \n", - "800 5.666667 1271.583579 0.027169 818.333333 1600.0 \n", - "900 37.666667 778.080006 0.174981 809.666667 1600.0 \n", - "1000 25.000000 1084.005316 0.149218 800.333333 1600.0 \n", - "1100 7.000000 1135.332949 0.063757 786.333333 1600.0 \n", - "1200 4.666667 1237.448537 0.027131 760.666667 1600.0 \n", - "1300 6.666667 1170.283230 0.047823 755.666667 1600.0 \n", - "1400 6.000000 1197.151720 0.049633 728.333333 1600.0 \n", - "1500 7.666667 1286.520439 0.048660 731.333333 1600.0 \n", - "1600 39.000000 713.746225 0.171981 711.666667 1600.0 \n", - "1700 3.333333 1609.876784 0.024009 680.666667 1600.0 \n", - "1800 39.000000 721.201194 0.182860 678.000000 1600.0 \n", - "1900 4.333333 1348.367282 0.027422 671.333333 1600.0 \n", - "2000 39.666667 691.237842 0.176256 659.666667 1600.0 \n", - "2100 38.000000 757.248048 0.161820 640.333333 1600.0 \n", - "2200 3.333333 1345.752468 0.023821 625.000000 1600.0 \n", - "2300 39.000000 756.696230 0.158708 616.666667 1600.0 \n", - "2400 4.666667 1382.093409 0.016833 600.666667 1600.0 \n", + "0 100.000000 0.000000 1.237642 1484.333333 1600.0 \n", + "100 37.000000 817.855486 0.307822 1128.333333 1600.0 \n", + "200 4.333333 1479.555014 0.047548 1049.666667 1600.0 \n", + "300 4.333333 1151.057454 0.062162 997.000000 1600.0 \n", + "400 6.666667 1153.438879 0.058114 956.333333 1600.0 \n", + "500 36.000000 867.115092 0.214088 926.333333 1600.0 \n", + "600 5.000000 1250.562672 0.029232 881.000000 1600.0 \n", + "700 5.000000 1234.467521 0.038874 852.000000 1600.0 \n", + "800 3.666667 1405.452969 0.031245 846.666667 1600.0 \n", + "900 7.000000 1140.513633 0.069188 833.333333 1600.0 \n", + "1000 10.666667 1110.575676 0.101012 824.666667 1600.0 \n", + "1100 6.666667 1145.643586 0.071593 810.000000 1600.0 \n", + "1200 7.666667 1099.519328 0.086582 814.333333 1600.0 \n", + "1300 63.666667 986.181956 0.301855 799.333333 1600.0 \n", + "1400 68.000000 418.038937 0.333535 794.666667 1600.0 \n", + "1500 69.333333 361.384052 0.329215 772.666667 1600.0 \n", + "1600 40.666667 692.075323 0.194672 734.000000 1600.0 \n", + "1700 9.000000 1051.560998 0.059908 710.000000 1600.0 \n", + "1800 5.333333 1272.851696 0.037196 702.666667 1600.0 \n", + "1900 23.000000 1106.977237 0.136973 688.333333 1600.0 \n", + "2000 43.333333 834.700000 0.170842 689.000000 1600.0 \n", + "2100 4.333333 1163.360331 0.026712 670.333333 1600.0 \n", + "2200 9.000000 1259.295963 0.047323 652.666667 1600.0 \n", + "2300 10.333333 1142.920166 0.068638 654.333333 1600.0 \n", + "2400 7.000000 1127.863666 0.032243 636.666667 1600.0 \n", "\n", - " average_specificity steps_in_trial_other population_other \\\n", - "trial \n", - "0 8.365000 100.000000 1506.000000 \n", - "100 7.261250 3.333333 1186.333333 \n", - "200 7.050000 9.000000 1125.666667 \n", - "300 6.519167 5.666667 1094.666667 \n", - "400 6.826250 4.000000 1082.333333 \n", - "500 6.609167 33.666667 1056.000000 \n", - "600 6.652917 7.000000 1059.333333 \n", - "700 6.971667 9.333333 1075.000000 \n", - "800 6.389792 4.666667 1072.666667 \n", - "900 6.598333 10.333333 1084.000000 \n", - "1000 6.381667 39.000000 1078.000000 \n", - "1100 6.683333 3.666667 1075.666667 \n", - "1200 6.269167 7.666667 1095.000000 \n", - "1300 6.046042 38.666667 1088.666667 \n", - "1400 6.041875 6.333333 1079.333333 \n", - "1500 6.252083 11.666667 1073.000000 \n", - "1600 6.134792 11.666667 1051.000000 \n", - "1700 6.027083 7.000000 1057.666667 \n", - "1800 6.808750 4.000000 1047.000000 \n", - "1900 6.288750 11.000000 1063.000000 \n", - "2000 6.066042 6.333333 1062.333333 \n", - "2100 6.223125 38.666667 1048.666667 \n", - "2200 6.220000 7.333333 1024.666667 \n", - "2300 6.110417 33.000000 1018.666667 \n", - "2400 6.031250 5.000000 1001.000000 \n", + " average_specificity knowledge steps_in_trial_other \\\n", + "trial \n", + "0 8.555208 100.000000 100.000000 \n", + "100 6.960833 98.630137 7.000000 \n", + "200 6.975833 99.543379 6.333333 \n", + "300 6.640000 99.315068 7.000000 \n", + "400 6.878125 100.000000 19.000000 \n", + "500 6.642708 99.315068 6.333333 \n", + "600 7.274167 100.000000 5.333333 \n", + "700 6.706875 99.543379 26.666667 \n", + "800 6.577083 99.543379 4.333333 \n", + "900 6.358958 99.771689 69.333333 \n", + "1000 6.568333 98.630137 3.666667 \n", + "1100 6.761667 98.858447 5.333333 \n", + "1200 6.631875 99.543379 6.000000 \n", + "1300 6.546875 100.000000 7.333333 \n", + "1400 6.223958 97.488584 36.333333 \n", + "1500 6.477500 99.086758 5.000000 \n", + "1600 6.291042 99.543379 34.666667 \n", + "1700 6.237292 97.716895 6.666667 \n", + "1800 6.112917 100.000000 24.666667 \n", + "1900 6.136875 98.630137 16.666667 \n", + "2000 6.124792 98.401826 39.666667 \n", + "2100 6.119167 99.543379 36.333333 \n", + "2200 6.328333 98.401826 7.000000 \n", + "2300 6.350000 97.716895 7.666667 \n", + "2400 7.146667 99.771689 4.000000 \n", "\n", - " numerosity_other average_specificity_other fraction_accuracy_other \n", - "trial \n", - "0 1600.0 8.074167 0.976667 \n", - "100 1600.0 6.865208 0.903333 \n", - "200 1600.0 6.767917 0.956667 \n", - "300 1600.0 6.851042 0.950000 \n", - "400 1600.0 6.908958 0.800000 \n", - "500 1600.0 7.237708 0.940000 \n", - "600 1600.0 7.095417 0.983333 \n", - "700 1600.0 7.449375 0.940000 \n", - "800 1600.0 8.329167 0.953333 \n", - "900 1600.0 7.816667 0.933333 \n", - "1000 1600.0 8.207292 0.996667 \n", - "1100 1600.0 8.291458 0.896667 \n", - "1200 1600.0 8.719375 0.990000 \n", - "1300 1600.0 8.251250 0.993333 \n", - "1400 1600.0 8.237917 0.980000 \n", - "1500 1600.0 7.812083 0.956667 \n", - "1600 1600.0 8.012917 0.933333 \n", - "1700 1600.0 8.519792 0.953333 \n", - "1800 1600.0 7.637708 0.736667 \n", - "1900 1600.0 8.831667 0.923333 \n", - "2000 1600.0 8.038125 0.916667 \n", - "2100 1600.0 8.042292 0.893333 \n", - "2200 1600.0 8.272917 0.953333 \n", - "2300 1600.0 8.307917 0.996667 \n", - "2400 1600.0 8.062292 0.986667 " + " population_other numerosity_other average_specificity_other \\\n", + "trial \n", + "0 1492.000000 1600.0 8.235833 \n", + "100 1249.666667 1600.0 7.300208 \n", + "200 1179.000000 1600.0 6.977500 \n", + "300 1132.333333 1600.0 7.161250 \n", + "400 1125.333333 1600.0 7.486250 \n", + "500 1090.000000 1600.0 7.615000 \n", + "600 1093.000000 1600.0 7.793125 \n", + "700 1111.666667 1600.0 8.144583 \n", + "800 1093.000000 1600.0 8.020625 \n", + "900 1077.000000 1600.0 8.296042 \n", + "1000 1072.333333 1600.0 8.950000 \n", + "1100 1081.333333 1600.0 8.156875 \n", + "1200 1069.333333 1600.0 8.265833 \n", + "1300 1070.333333 1600.0 8.166667 \n", + "1400 1051.000000 1600.0 8.268542 \n", + "1500 1045.000000 1600.0 8.523125 \n", + "1600 1065.666667 1600.0 8.604583 \n", + "1700 1064.666667 1600.0 8.953958 \n", + "1800 1045.000000 1600.0 8.628125 \n", + "1900 1045.333333 1600.0 8.392083 \n", + "2000 1050.000000 1600.0 8.095833 \n", + "2100 1059.666667 1600.0 8.376250 \n", + "2200 1041.000000 1600.0 7.813125 \n", + "2300 1034.333333 1600.0 7.932500 \n", + "2400 1034.000000 1600.0 8.160417 \n", + "\n", + " fraction_accuracy_other knowledge_other \n", + "trial \n", + "0 0.966667 5.936073 \n", + "100 0.980000 5.936073 \n", + "200 0.943333 5.251142 \n", + "300 0.826667 4.109589 \n", + "400 0.950000 3.652968 \n", + "500 0.930000 3.424658 \n", + "600 0.936667 2.739726 \n", + "700 0.976667 3.424658 \n", + "800 0.980000 2.739726 \n", + "900 0.996667 2.511416 \n", + "1000 0.990000 2.968037 \n", + "1100 0.966667 2.739726 \n", + "1200 0.966667 2.968037 \n", + "1300 0.840000 3.196347 \n", + "1400 0.933333 3.196347 \n", + "1500 0.956667 3.881279 \n", + "1600 0.993333 4.109589 \n", + "1700 0.970000 3.196347 \n", + "1800 0.960000 2.739726 \n", + "1900 0.990000 3.196347 \n", + "2000 0.966667 3.424658 \n", + "2100 0.996667 3.424658 \n", + "2200 0.863333 3.196347 \n", + "2300 0.996667 2.739726 \n", + "2400 0.990000 2.739726 " ] }, "metadata": {}, @@ -776,6 +859,7 @@ "df['numerosity_other']=df_other['numerosity']\n", "df['average_specificity_other']=df_other['average_specificity']\n", "df['fraction_accuracy_other']=df_other['fraction_accuracy']\n", + "df['knowledge_other']=df_other['knowledge']\n", "\n", "display(df)" ] @@ -788,7 +872,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -797,7 +881,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -815,8 +899,7 @@ "ax = df[['average_specificity', \"average_specificity_other\"]].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"average_specificity\")\n", - "ax.legend([\"XCS\",\"XNCS\"])\n", - "\n" + "ax.legend([\"XCS\",\"XNCS\"])" ] }, { @@ -827,7 +910,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -836,7 +919,7 @@ }, { "data": { - "image/png": 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RRvNlboW7tFXd2ElWahKzQjioyE7/+nfLWDg9S1OCo+D3VQ1TpkHjWDSQ+PEtfejyluf4z2CzS66umE3jkT5ec9BfvK9728afHWB/xCcSmVu7m7pYPCPbcemdnpTg1bT3DGpK8CQyxrBhi6dBo1OWOyNNA4mfVaW5JCVI3AeSULNLzls2nfyMZDZscc7y1us1reSkJbHCe6xuIOXF2WFlbhljqG7sdOwbxvKZuXztosWaEjyJ3q07wp7DXVw1RWcjoIHkOGnJiSyflWvLPonbbRgcjo0meqEe/5malMhlJ89i464mRywPGmN4rcbFGQsKLZ+ZUj7d0yol1H2Sps4+OvuGWOrQQALwubM0JXgyPVZZS1pyAh85aWo0aByLBpJRKsry2VbXEfHOqd9/aQ8rv/USX318G28fanf0ssKGMLJLrj5lNoPDht+/E/1GjofajlLf0Rsw7defL3Mr1DfY6kbvYVYO2mgfLSFBuOfKk8hISeIfH9GUYDv1DgzzbFUDl6yYOg0ax6KBZJS1Zfn0D7nZ1Ri53lFut+eNtSAjhT9sb+Tyn73OxT9+lYffeN9x52yPHP8ZYnbJkhk5rCrNZUNl9Bs5bq5pBcZuGz+e4pxUstNCz9za7T3MyqlLWz7Tc9L4/hWr2NXYyT2aEmybF3Z4GjROlXNHxqOBZJSRwsT32yL2mtvrj9DU2cdXLlzMW/9yHv/xsZUkJQrfeHonp/3HH/mKg2YpkTj+86qK2VQ3dbG9/kjIr7Gj/gi/fuP9sH4mm2tczMhJY/60zMAP9hIRyqdnhdxOvrqxi1l56THx1+d5y4r51OlzeGDTAV7bZ0+CRNORPn7x6gFH/L8dDRsqaykrzOD0+VOnQeNYbA0kInKRiOwRkRoRuXOM+/NF5CkReVdE3hKRFX73vS8i20WkSkQq7Rynv+KcNGblpfN2BBs4btzVRGKC8KEl08lKTeITp83huVs/wLO3nM3HTi7lBb9ZykOvR2+W4n/8Zzh/UX/kpJlhNXLcUFnL5T9/nf/39E7u/XNNSK/hdhte3+/izIWFQR8stag4O+SlrT1NXY6oaLfqXy45lhIc6X2tnv4hrv/VFv7tD7vjsvWQq7ufNw+08bGTZ03a4WbRYlsgEZFE4D7gYmAZcK2ILBv1sK8DVcaYVcBngB+Puv+DxpjVxpgKu8Y5loq5+Ww9GLkZwsadhzltXgF5GSnH3b6yNJfvXb6Sv3lnKcmJCXzzmWOzlEiOwQrf8Z/hZpfkpidzycoSnq5qCKqR48CQm288vYOv/e5dTpmbz9+tKuGel/fywvbGoMewq7GT9qODltN+/YWaudU/NMz+lu6onEESqvSURH5yzcl0HB3ka7+LXEqw22348mNV7PYuEbf1OGsJdzL4ZnkfXDw9wCNjn50zklOBGmPMAWPMAPAocOmoxywD/gRgjKkG5opI1PsHrC3L53BnP/UdvWG/1oGWbvY1d3PBsvEvyzdLefbWs4+bpVzx89d5cPP7YY/Bqg0RzC65sqI0qEaOLV39fPIXb/LwGwdZv24+D11/KvdceRInz8njyxuq2BHkMpmvfiSY/RGfRcWhZW7tb+5hyG0m/VTEcC2bmcPXLlrMH3cf5rd/OxSR1/zBxj1s3HWYj3vrkNqPRj+Lb7Jt2ttCfkay5dTzWGZnIJkF+K9t1Hlv87cNuBxARE4FygBfBZwBNorIVhFZP943EZH1IlIpIpUtLS0RGfiaOZErTNy46zDgObXOCv9ZSllhBm+91xr2GKwYOf4zQtklp88rZE5BBo9ZqCmpqu3gIz99je31R/jxNav5+iVLSUpMIC05kQc+XUFBRgqff6iS5s4+y99/c00rC6dnUZxz4rG6gfjOEAl2eau6KbqHWYXDlxL8b3/YRU2YvcaefLuOn/1lP9eeOocvn78IIO4q6d1uw6Z9Ls4uL7Kceh7L7AwkY/30Rs+b7wLyRaQKuBV4B/AdnH6WMWYNnqWxm0Vk3VjfxBjzgDGmwhhTUVRUFJGBL5mRTUZKYmQCyc4mVs7KZWaQ7TKyUpMoK8yk8Yj1N89w+I7/jFR2SUKCcFVFKW8cmLiR44YttVx1/xskJQpP3nQWl64+/m+NouxUfnHdKXT2DfKFhystLZUNDLl56702zlow9iFWgYSaubWnqYuUxATmBbG57xT+KcG3PlIVckrw1oPt3PnEdk6fX8B3Ll1OgXc5ty3OZiS7mzpxdfezrjz4GXEssjOQ1AH+70qlQIP/A4wxncaY640xq/HskRQB73nva/D+txl4Cs9S2aRISkxg9ey8sANJc2cf79R2TLisNZGSnLRJCySPbYl8dskVa0tJEE+7ldEGhtz8v9/v4GtPvMup8wp49pazWTZz7CWhZTNz+K+rV7Ot7ghftbCO/86hdnoHhzkzhGUt8GRuLSrODjpza3dTF+XFWSQlxmYy5PScNO7++Cp2N3Zy94t7gn5+XftRbvh1JSV5afz8k2tJTkwgPSWRtOQEOo7G1x7Jpr2epdV1iyLzx63T2fl//BagXETmiUgKcA3wjP8DRCTPex/A54FNxphOEckUkWzvYzKBC4AdNo71BBVl+exu7KSnfyjwg8fxx93NGGN9WWu0krw0XN39ES+OHM13/OeVa0sjml1SkpvOukVF/G5UI8fmrj4++Ys3+fWbB7lh3Xx+df0p5GemTPBKcOHyGXztosU8u62BnwbI5Noc4FhdK8qnZ7HvcFdQm8/VjZ0xtz8y2oeXFvPp08v4xWvvsWmv9aXinv4hPv9QJf1Dbv73uuN/n/kZKY7odDCZNu1tYcmM7JCWVmORbYHEGDME3AK8BOwGNhhjdorIjSJyo/dhS4GdIlKNZwnrNu/txcBrIrINeAv4gzHmRbvGOpY1Zfm4jWf9PlQbdzVRVpgxsnkbrJLcNIyBw0HsDYTCzuM/r/I2cnx1n+dN6Z1D7Xz0p5vZXn+En1x7Mv/s3Q+x4qZzFnD5ybP44ct7eX6CTK7N+1tZWZo34bG6gZQXZ9N+dJBWi2+AbT0DNHf1x+T+yGj/8ndLKZ+exT89vo1WC5lrbrfhS49VsfdwF/d9Yg0Lpx///3t+Rkpc7ZH09A9RebCNc+JkNgI215EYY543xiwyxiwwxvy797b7jTH3ez9/wxhTboxZYoy53BjT7r39gDHmJO/Hct9zJ9PJYW64d/UN8npNKxcsKw75r3xfw0Q7l7fsPv7zvKXFFGSm8HhlHRu21HL1f79JcpJnP+SjQWaHiQj/cflK1szJ4/YNVWyvOzGTq6tvkKraDs5eGPpsBILP3BrZaI+h1N/xpCUn8pNrT+bIUWtdgu/euIeXdx3mG3+/bMylnILMlLjaI3ljfyuDwyZulrVAK9vHlZuezKLirJADyV/3tjAw7A55WQs8MxKAxiPhpyGPx3f859U2tXBISUrgstWzeH5HI1974l1Om1/AMzePvx8SSFpyIv/96QoKM1P5wsOVJ8zW3nqvjWG34SwL549MxJe5ZfVskpEeWzG+tOWztCSHOy5ewh93N/ObCVKCn9hax8//sp9PnDaH686cO+Zj8jKS42qPZNO+FtKTE6mYO/GJnFOJBpIJrC0r4O1D7bhDOKhp487DFGamjKQSh6LEm+nVZOOM5C/VzWSmJPJhG4///MRpc8hKSeKGdfP55WcD74cE4snkqqCzb5D1ozK5fMfqrglwrG4gvswtq8fuVjd1Mi0rhaLs1LC+r5Ncf+Zc1i0q4t+e28W+MWZmWw+28c9PbufMBYV8+6PLx515F2TG1x7Jpr0tnD6/gNSkiU/knEo0kExgbVk+XX1D1ATZ3mFgyM0r1c2ct7Q4rBzyrNQkslOTbF3aqu/oZXZBhq3Hfy6cnsW2b14Q1H5IIEtLcvjxNSfzbv0RvvL4tpHll801Lk6ZWxDwWN1Ags3cqvYeZjWVJCQIP7hyFVmpSfzjo8enBNe1H2X9w1uZmZfGzz65huQJfq/5GSkc6R1kKEaOUQjHodajvN96NK6WtUADyYSONXAMbnnrzQOtdPUPccHy8P/Kn5GbZuvSVl1776QcCWvHaYHnLyvmaxcu4bl3G/nJn2po7upjz+Euzgxzf8RnUbG1zK1ht2Hv4a4ps6zlb3p2Gt/3pgR/35sS3O3N0BoYdvOL6045ofXPaAXeGajTOl3b4a/epJJ42mgHSIr2AJxsbmEGhZkpbD3YzidOm2P5eRt3NZGRkhhSe47RSvLSbZ2RNHT0cuq82O1MeuM589nX3MV//XEve73LUKH01xrLwunZtB+txdU9MOGS1cHWHvoG3VMiY2ssH15azGfOKON/X3uPs8un8ds3D7GvuZtffvaUEzK0xpKX4cmeaz86QGHW1Fn6G8tf97RQmp8ek0Wp4dBAMgERYU1ZflCdgN1uw8u7DnPOoqKwl1fAU5S4O4Jno/jr6huks28o6Kp7JxERvnf5Sg61HuUP7zaSk5bE8pmR6W3ky9za19w1YSCpbvIEsKUOPswqXF+/ZClvHmjlCw9VMuQ2fPujyy0v3/hmJFO9cePAkJs39ru4NA66/Y6mS1sBrC3L5z1Xj6V8eoB3649wuLM/IstaYG9RYkOHZ6YzGUtbdkpNSuT+T69ldkE6H1oyPWK9jXynJQbK3Kpu7CRBsPTXeaxKS07kx9ecTEpSAp85o4zPnFFm+bn53qWvqd648e1D7fQMDLOuPL6WtUBnJAH59km2Hmy3lMq7caf37JHFEQokfkWJswsyIvKaPvUdnh5YsTwj8ZmWlcrGL50T0QZ507Ot9dyqbupi3rTMiMxAnWxpSQ6V/3oeGSnBvW34svSmelHipr0tJCZIxPboYonOSAJYOSuX5ERhq8XlrY27DnP6/AJyMyJzQp6dRYn13hlJaX7sBxLwnK0RyewzX+bWvgBdgKubuhx9RnskBRtEgLhp3LhpXwtr5+THxOmYkaaBJIC05ESWz8zlbQuFiftbuqlp7uaCZaEXIY5mZ1FifXsvyYlC0RTfAA1HoMyt7v4hDrUdZekU3WiPBF/jxqk8I3F197OjvpN1i+Kj2+9oGkgsqCjLZ1vdkYD7FBt3es4eOT/Ebr9j8RUl2jEjaejopSQ33ZbU3KmifLqn55are+w3wT1NU6ui3S4FGSm0T+Hqdl8vuXirH/HRQGLB2rJ8Bobc7GyY+JS+jbtCO3tkIr6iRDuq2+s7epmZFx/dSUNV7pe5NRZfIJlqxYiRljfFGzdu2uuiIDOFFRHKGIw1GkgsWFMWuIFjc2cf7xzq4MIIZWv5K8lLoyECx/6O1tDRy6y8yG7gTzWBMreqmzrJSk2aMvtMdpnKjRvdbsOr+1o4e+G0uJ3dayCxoDgnjdL89AkDycu7gztSNxgzctNpinAr+cFhN4c7+5ilM5IJTc9OJWeCzK3qxi6WzMiOu7qBYOVnptjWuNEYw3ee3cU9G/dQ1z7+aZx22dXYiat7IO6q2f1p+q9FFWX5bN7fijFmzDeNjTsPM7cwg3IbagnsKEpsOtKH28As/Ut6QiJCeXH2mDMSYwy7mzq5dHVw7fDjUX5Gsm2NG7v7h3hw83sA3PtKDecsKuLaU+fw4SXTJ+W0yk3e/ZEPxOlGO1iYkYjI34tI3M9c1pbl09LVT137iUtMXX2DvL7fxQXLZ9jyl6kdRYn13qWyqVBDYrdFxVnsbT4xc6vxSB9dfUMs1o32gOxs3OhLhPjqhYu59UPlVDd2ccOvt3LmXX/mno17qG2zd5ayaW8LS0tymJ4dv7N7KwHiGmCfiHxfRJbaPSCnmmif5C97WhgcNiGfzR6IHScl+vZcYr2qfTKUT8+mY4zMLd9hVpr6G5ivTUqHDY0bXd6uEytm5XL7+Yt47Y4P8ovPVLBiVi73vVLDurtf4boH3+KlnU0MRjiQdfcPUfl+e9ym/foEXNoyxnxKRHKAa4FfiogBfgk8YoyxdljDFLC4OJvMlES2HmznspNnHXffxl2HmZaVMnKqYqT5FyVGqrq9vl1nJFYd23A/vufWbu9hVos0kATka9zYcXSAaRGuW3J1eQLJtCxPsEpKTOC8ZcWct6yY+o5eHttSy4Yttdzw661Mz07l6lNmc1XF7Ij8W3pjfytDbsM5cdgWxZ+lJStjTCfwBPAoUAJ8DHhbRG61cWyOkpSYwOo5eSfMSPqHhiNy9shE7ChKbDjSy7SslCnf1iMSjqUAH79PUt3URWl+elxWMgfLzsaNLu/ey1iFtbPy0sedpfzDr7ZwJMwEgE17W8hISWRtHJ2GOBYreyQfEZGngD8DycCpxpiLgZOAr9g8PkdZW1ZAdVMn3f1DI7e9eaCN7gidPTIeO4oS69p7dTZi0XiZW3uaOqds6/hI8zVutGPD3TcjmejkTd8s5cHPnsKrd3yIWz9Uzl/3tvD1328PeN7MRDbta+GM+YVxdRriWKzMSK4E/ssYs8oYc7cxphnAGHMU+Jyto3OYtWX5uA1sq+0YuW3jTs/ZI2eGeUb4ROwoSvTUkGggsWKk55Zf5lb/0DD7W3q0ot2ikT0SG2pJXN395GckT3hKoz/fLOXL5y/iD+828rutdSF934OtPRyMw9MQx2LlJ/9N4C3fFyKSLiJzAYwxf7JpXI60enYeIsdOTPSdPXLu4sicPTKRSBYlGmO8Ve0aSKwqH5W5VdPczbDbsKREZyRW5NvYuNHV3R/SvsuN5yzgtHkFfPOZnbzv6gn6+Zv2xndbFH9WAsnjgH+qw7D3triTm57MounZI52At9V10NzVH9EmjeOJZFFi+9FB+gbdOiMJwujMrepG7bEVDDsbN7q6Q9vAT0wQ/uvq1SQnJnDbo+8EndH1170tzC5IZ26hdoewEkiSjDEjv33v5xMf0jyFrZ2bzzsH23G7DRt3HSYpQfjg4um2f9+ZuWkjB1GFSzO2guefuQWw53AXKUkJ+iYSBLsaN7q6+5k2wQmWE5mZl873Ll/Jtroj/OiPey0/z3MaYivryou0qwHWAkmLiHzU94WIXAq47BuSs62dk09X/xD7mrvZuLOJ0+cXRuzskYnMyI1cUaKvGFH7Q1nnO3bXt+G+u7GTRcVZk1I5PVXY1bjR1dU/kvobiktWlnBVRSk/+8t+3jzQauk5Ww96TkOM57Yo/qz8K7gR+LqIHBKRWuAO4AZ7h+VcvhMTH9tSy/6WHluztfzN9NaSRKIoUavag1fkzdzypQBXN3XpslaQ7Gjc2DswTM/AcNi1Kd/8yHLmFmby5ceqLKUEb9rXQlKCcMaC+DsNcSwBA4kxZr8x5nRgGbDMGHOmMabG/qE5U1lhBoWZKfzmzYMAnLd0cgLJjJFakvADSUNHL+nJieRPwkxqqvDP3Grt7qelq19Tf4OUnxn5GYmvqj3cw9kyU5P40dWraenq5+tPBU4J3rS3hTVl+WRrDRFgsSBRRP4O+CLwZRH5hoh8w95hOZeIsKYsn4FhN6tKI3v2yEQiWZRY3+45h0TXdoNTXpzN3uYuqr1nkCyNk+N1I6UgIznieyS+QDItO/xt25Nm53H7BYv4w/ZGHp8gJbilq5+dDZ26rOXHSkHi/cDVwK2A4KkrKbN5XI5W4V3esqu31lgiWZTYcKSXWfm6SRys8ulZdBwd5LUazxahHmYVnDwbGjf6sugKMyPTduWGdQs4fX4B33pmJ++NkxI8chpinLdF8WdlRnKmMeYzQLsx5tvAGcBsKy8uIheJyB4RqRGRO8e4P19EnhKRd0XkLRFZYfW50fThpcXML8rkoyfNCvzgCIlkUWJ9e6+eQxICX+bWs9samJaVGvGeUVOdHY0bj81IIvO78E8J/tI4KcGb9rZQmJnC8pk6I/WxEkh871xHRWQmMAjMC/QkEUkE7gMuxrO/cq2ILBv1sK8DVcaYVcBngB8H8dyoWTg9iz//07nMmeTUz0gUJfYNDtPaM6A1JCHwZW7VtfeyVAsRg5ZvQ3W7rz1K4QTtUYJVkpvOXd6U4P96+fiUYM9piC4+UB6/pyGOxUogeVZE8oC7gbeB94FHLDzvVKDGGHPAW3vyKHDpqMcsA/4EYIypBuaKSLHF58adSBQlasZW6IqyU8lN92yu6kZ78HzJHZFs3Ojq7ic7LSninSUuXlnC1RWz+flf9/PG/mMpwbsaO2ntGdBq9lEmDCTeA63+ZIzpMMY8gWdvZIkxxspm+yyg1u/rOu9t/rYBl3u/16ne1y+1+FzfGNeLSKWIVLa0tFgYVuyKRFGinkMSOhEZOQFTD7MKnh2NG13dA2FnbI3nGx9ZxtzCTG7fUDUyi/qrty3KB3R/5DgTBhJjjBu4x+/rfmPMEYuvPda8b3RO3V1AvohU4dnMfwcYsvhc35geMMZUGGMqioqm9i83EkWJWtUennLvPonOSILn2yNpj+DSVkuIfbasyExN4sfXHJ8SvGlvC8tKco47l0ZZW9raKCJXSPC5onUcvylfCjT4P8AY02mMud4YsxrPHkkR8J6V58ajSBQlNnT0kiDH6lJUcNaVT2PetMyRM0qUdb4ZSSQDSWt3f0RSf8ezqjSPf7pgMc9vb+KXm99n68F2zlk8tf9gDUXAExKB24FMYEhE+vDMFowxJtDcfgtQLiLzgHo8R/Z+wv8B3r2Xo959kM8Dm4wxnSIS8LnxyL8oMdTT3eo6einOSbPcclsd7+KVJVy8siTaw4hJdjRudHUPcJbN2XM3rJvPpr0tfOe5XYCm/Y7FSmV7tjEmwRiTYozJ8X4dcIHYGDME3AK8BOwGNhhjdorIjSJyo/dhS4GdIlKNJ0PrtomeG8oFTiUz88IvStRzSFQ0FWSkRGyzfWDIzZHewYjVkIwnIUH44dUnkZueTGZK4kibJHVMwBmJiKwb63ZjzKZAzzXGPA88P+q2+/0+fwMot/rceDcjN/yixPqOXk6erf8QVHTkZ6ZELP23tSdyVe2BlOSm8+BnT6G5s4+UJJ3Nj2Zlaeurfp+n4UnN3Qp8yJYRqXFlpSaRnZZEY4i1JMNuQ9ORPmat0hmJio78jMg1bnR1eV5nsgpDdSYyvoCBxBjzEf+vRWQ28H3bRqQmVJKbFvKMpKWrn8FhoxlbKmryM1Ooaz8akdcaqWrXDgNRF8ocrQ5YEfBRyhYzctNDDiQj55BoIFFRUpCRHLE6kpYIdf5V4bOyR/JTjtVwJACr8RQSqiiYmZvGrobOkJ6rVe0q2vIyUujsG2Jo2B32oWCR7PyrwmNlj6TS7/Mh4BFjzGabxqMC8BUl9g8Nk5oUXFuIhpFAojUkKjr8GzeGuyTV2j1ARkoiGSlW3saUnaz8Bn4H9BljhsHTUFFEMowxkVnoVEHxFSU2d/YHXUtS395LTlqSHsajosbXuLG9ZyDsQOKysapdBcfK3PJPgP9aSDrwR3uGowIJ56TEhg49h0RFV8FIdXv4tSSu7n4KwzirXUWOlUCSZozp9n3h/VzfjaIknKLE+g49h0RFV95IB+DwN9xdXeHPalRkWAkkPSKyxveFiKwFwj/vVYUknKLEeq1qV1EWycaNurTlHFb2SL4EPC4ivqaJJXiO3lVREGpRYmffIF19Q5qxpaIqUo0bh4bdtB0doEiXthzBSkHiFhFZAizG07Cx2hgTuZNpVNBCKUocOYckXwOJip5INW5sOzqAMZE7YleFJ+DSlojcDGQaY3YYY7YDWSLyRfuHpsZTEkJRop5DopwiEo0bJ7s9ipqYlT2SLxhjOnxfGGPagS/YNiIVUDgzEq1qV9GWn5kS9tLWSMNGDSSOYCWQJPgfaiUiiYAuTEZRSW76SFGiVXUdvaQkJug/PBV1BREIJMf6bOlbkRNYCSQvARtE5MMi8iHgEeBFe4elJlLirSVp7uy3/JyGjj5K8tJISAj2oEulIisvIyXsPRLf0lah/mHkCFaytu4AbgBuwrPZvhH4hZ2DUhPzFSU2dPRarm6vbz86UhWvVDRFonGjq7uflMQEctK0PYoTWMnacgM/934oB/AVJTYFcXZ7Q0cfZ5dPs2tISlmWnxl+48aW7n6mZaXgt+quoshK1la5iPxORHaJyAHfx2QMTo3NV5TY0GEtkAwMuTnc1acZW8oRfLUkHb2hZ265ugc09ddBrPw58Es8s5Eh4IPAw8Cv7RyUmpivKLHJYpuUw519GKMZW8oZ/Bs3hsrVpVXtTmIlkKQbY/4EiDHmoDHmW+gxu1EXTApwndaQKAfxNW4MZ5/E5V3aUs5gZaeqT0QSgH0icgtQD0y3d1gqkGCKErWqXTmJr3FjqB2A3W5DawTa0KvIsTIj+RKebr//CKwFPgVcZ+OYlAXBzEh8JyP60oaViqZwGzce6R1k2G00kDiIpV5b3k+7gevtHY6yyr8oMdBJiQ0dvUzLSiUtObgTFZWyQ36YS1vHjtjVQOIU4R2arKImmKJEPYdEOUl6SiLpyYl0hDgjafEFkkzdI3EKDSQxqiTvWFFiIPUdvbo/ohwlPyM55MaNrm5vw0adkTiGBpIY5ZuRBCpKNMbQ0NGrVe3KUcJp3Ojq0oaNThNwj0REivB0+53r/3hjzOfsG5YKxGpRYlvPAH2Dbp2RKEcJp3Gjq7ufxAQhLz05wqNSobKS/vs08CrwR8B6u1llK6tFib6MLa0hUU6Sl5FCbdvRkJ7r6u6nMDNFG5A6iJVAkmGMucP2kaigleSm0RAgBXikhkQDiXKQcBo3urq1hsRprOyRPCcil4Ty4iJykYjsEZEaEblzjPtzReRZEdkmIjtF5Hq/+94Xke0iUiUilaF8/6muJDedpgCBxFfVroFEOYl/48ZgtXb360a7w1gJJLfhCSZ9ItLl/egM9CTvAVj3ARcDy4BrRWTZqIfdDOwyxpwEnAvcIyL+OX0fNMasNsZUWLmYeOMpSpx4aauho4+MlMSRamKlnMBXlBhK40bPjERTf50kYCAxxmQbYxKMMWnez7ONMTkWXvtUoMYYc8AYMwA8Clw6+uWBbO8JjFlAG57mkMoCT1HiwIQnJdZ3HGVmXrq221aOkpcRWuNGY4y3hbzOSJzEUvqviHxURH7g/fh7i689C6j1+7rOe5u/e4GlQAOwHbjNe/4JeILMRhHZKiLrJxjbehGpFJHKlpYWi0ObGnwpwIePjF+U2NDRp8taynFCbdzY1T/EwJBbZyQOY+U8krvwLG/t8n7c5r0t4FPHuM2M+vpCoAqYCawG7hUR32znLGPMGjxLYzeLyLqxvokx5gFjTIUxpqKoqMjCsKYOX1HiRMtb9R29mrGlHCc/M7TGjVpD4kxWZiSXAOcbYx40xjwIXOS9LZA6YLbf16V4Zh7+rgeeNB41wHvAEgBjTIP3v83AU3iWypSfQEWJvQPDtPUMUKo1JMphfP22gq0lGalq10DiKFYr2/P8Ps+1+JwtQLmIzPNuoF8DPDPqMYeADwOISDGwGDggIpkiku29PRO4ANhh8fvGjUBFicdqSLTPlnKWUBs3jjRs1EDiKFbqSL4HvCMir+BZrloH/HOgJxljhrznl7wEJAIPGmN2isiN3vvvB74L/EpEtntf+w5jjEtE5gNPeTeIk4D/M8a8GPzlTW2BihKP1ZBkTOawlArI17gx2M32Y51/dY/ESay0kX9ERP4CnMKxN/smKy9ujHkeeH7Ubff7fd6AZ7Yx+nkHgJOsfI94NzM3fdyiRJ2RKCfLz0gOfo+kewCRY5v1yhnGXdoSkSXe/64BSvDsedQCM723KQeYkZs2blFiQ0cvCQIzcjSQKOcJpXGjq7ufgowUkhK136yTTDQjuR1YD9wzxn0GPbfdEWbmpbGz4ciY99W39zIjJ03/0SlHKshMCX6PpKufQk39dZxxA4kxxle7cbEx5rg/eUVE/8R1iBk5x4oSR5+UqOeQKCfLD6Fxo0uLER3Jyp+qr1u8TUXBREWJWkOinCw/hMaN2rDRmcadkYjIDDyV6OkicjLHCgxzAE0Dcgj/osQ5hcd+LcNuQ9MRrWpXzuXfuNHq8qvOSJxpoj2SC4HP4ikkvIdjgaQT+Lq9w1JW+WYkjaM23Ju7+hhyG52RKMfyb9xoJTgcHRji6MCwpv460ER7JA8BD4nIFcaYJyZxTCoIvqLE0YFkpIZE90iUQ/k3brQSSFxdWtXuVFbmk2tFJM/3hYjki8i/2TckFQxfUeLoflt6DolyumAbN7p6PPuARRpIHMdKILnYGNPh+8IY0461XltqkszMTR9jRuL5Wpe2lFMda9xoMZBow0bHshJIEkVk5DcnIumA/iYdZMYYB1zVdxwlNz2ZrFQrXXCUmny+PRKr1e2+ho1aR+I8Vt5lfgP8SUR+iacQ8XPAQ7aOSgVlrKJEPYdEOV2wjRt9fbY0kDiPlV5b3/c2Vfwwnsyt7xpjXrJ9ZMqysYoS69t7mV2gWdrKudKSg2vc6OruJyct6YTCWxV9ltY9jDEvAC/YPBYVIl8tyeEj/SO1JA0dvZyxoDCaw1IqoILMlCCWtvqZlq2r6k5k5YTE00Vki4h0i8iAiAyLSOdkDE5Zc6yWxLNPcqR3kK7+Ie36qxwvLyM5iM12rWp3Kiub7fcC1wL7gHTg88BP7RyUCk7JqFoSPYdExYpgGje6uvs19dehLPUl8B6Dm2iMGTbG/BL4oL3DUsGYMaq6vb5dzyFRsSE/w3oreU97FN1odyIreyRHvUflVonI94FGINPeYalgjC5KbDiiVe0qNhRkpljabO8fGqazb0iXthzKyozk097H3QL0ALOBK+wclAqef1FifXsvKYkJTMvUf3TK2fIykunsG2Jw2D3h41q9NSS62e5ME85IRCQR+HdjzKeAPuDbkzIqFTT/okRP+/g0EhIkwLOUiq6Rxo1HBymaIEiM1JBk6tKWE004IzHGDANF3qUt5WAz844duavnkKhY4StK7AiwT+ILJDojcSYreyTvA5tF5Bk8S1sAGGN+aNegVPD8ixIbOnpZV14U7SEpFZDV6nZf51/N2nImK4GkwfuRAGTbOxwVKl9RYm1bL81d/TojUTHBauPGlm5t2OhkE52Q+GtjzKeBDmPMjydxTCoEvqLEtw+1Y4xmbKnYYLVxo6u7n8yURNJTtD2KE020R7JWRMqAz3nPICnw/5isASprfEWJW99vB/QcEhUbrC5ttXYP6P6Ig020tHU/8CIwH9jKsaN2wdMFeL6N41JB8s1Ith7SQKJih9XGjXpWu7ONOyMxxvzEGLMUeNAYM98YM8/vQ4OIw2SmJpGTlkRNczdwrNpdKacryEyhzULWlla1O1fAgkRjzE2TMRAVPt/yVlF2KmnJupasYkN+ZjIdAfdIBijUGYljWeq1pWKDL3NLM7ZULMnPmLhx49Cwm/aj2vnXyTSQTCG+fZJSDSQqhgRq3NjWM4AxUKRLW45layARkYtEZI+I1IjInWPcnysiz4rINhHZKSLXW32uOtGMHE8A0a6/KpYEatyoNSTOZ1sg8fbpug+4GFgGXCsiy0Y97GZglzHmJOBc4B4RSbH4XDWKb2lLM7ZULAnUuNGlDRsdz84ZyalAjTHmgDFmAHgUuHTUYwyQLSICZAFtwJDF56pRfAFkVr4eaKVih3/jxrG06ozE8ewMJLOAWr+v67y3+bsXWIqnBct24DZjjNvicwEQkfUiUikilS0tLZEae0w6fX4h37t8Jecu1j5bKnb4ihLH2ycZadioeySOZWcgGauHuRn19YVAFTATWA3cKyI5Fp/rudGYB4wxFcaYiqKi+H4DTUwQrj11DsmJmkOhYsdIm5Rx9klc3QOkJiWQlWqlNaCKBjvfcerwHILlU4pn5uHveuBJ41EDvAcssfhcpdQUkJcxceNGV5enqt2zAq6cyM5AsgUoF5F53vNMrgGeGfWYQ8CHAUSkGFgMHLD4XKXUFOCbkbT1jL1H0qJV7Y5n21zRGDMkIrcALwGJeFqt7BSRG7333w98F/iViGzHs5x1hzHGBTDWc+0aq1IqegLvkQwwU1v+OJqti47GmOeB50fddr/f5w3ABVafq5SaegI1bnR197NqVu4kj0oFQ3dllVJRN17jRrfb0NYzwLRsXdpyMg0kSqmoy89MHnNG0tE7yLDbaA2Jw2kgUUpFnaff1omb7S4tRowJGkiUUlE3XuNGV5cGkliggUQpFXUFmWO3km/RqvaYoIFEKRV1+RkpdI3RuHGkYaPOSBxNA4lSKuryMz3V7aMbN7q6+0lKEHLTk6MxLGWRBhKlVNSNV5To6uqnMCuFhARtj+JkGkiUUlE3XuNGV3e/LmvFAA0kSqmoG69xY2uPntUeCzSQKKWibrzGjb7Ov8rZNJAopaJurD0SYwyubm2PEgs0kCiloi4tOZGMlOMbN3b2DTEw7KZIZySOp4FEKeUI+RnHN270tUcp1GJEx9NAopRyhNGNG7U9SuzQQKKUcoTRjRu1qj12aCBRSjnC6MaN2vk3dmggUUo5wujGja3d/STIsdRg5VwaSJRSjjC6cWNL9wAFmSkkansUx9NAopRyhNGNG7U9SuzQQKKUcoTRRYkaSGKHBhKllCMca5NyLJBoDUls0ECilHIE34ykwzcj6dKGjbFCA4lSyhF8eyRtPYP09A/ROzisgSRGaCBRSjmC/x6JS89qjykaSJRSjuDfuHGkqj1bZySxQAOJUsoxfI0bfTMS7fwbGzSQKKUcw9e4UdujxBYNJEopx/DMSAZxdXmWtrQ9SmywNZCIyEUiskdEakTkzjHu/6qIVHk/dojIsIgUeO97X0S2e++rtHOcSilnKMhMocO7tJWbnkxKkv6tGwuS7HphEUkE7gPOB+qALSLyjDFml+8xxpi7gbu9j/8I8GVjTJvfy3zQGOOya4xKKWfJz/A0bvRUtetsJFbYGe5PBWqMMQeMMQPAo8ClEzz+WuARG8ejlHI4X+PGps4+3R+JIXYGkllArd/Xdd7bTiAiGcBFwBN+Nxtgo4hsFZH1430TEVkvIpUiUtnS0hKBYSuloqXAW5RY09ytqb8xxM5AMlbvZzPOYz8CbB61rHWWMWYNcDFws4isG+uJxpgHjDEVxpiKoqKi8EaslIqqPG9RYlffkKb+xhA7A0kdMNvv61KgYZzHXsOoZS1jTIP3v83AU3iWypRSU5h/lpbukcQOOwPJFqBcROaJSAqeYPHM6AeJSC5wDvC0322ZIpLt+xy4ANhh41iVUg7ga5MCWkMSS2zL2jLGDInILcBLQCLwoDFmp4jc6L3/fu9DPwZsNMb0+D29GHhKRHxj/D9jzIt2jVUp5QzHz0g0kMQK2wIJgDHmeeD5UbfdP+rrXwG/GnXbAeAkO8emlHKevIzkkc/1LJLYodU+SinH8DVuBJ2RxBINJEopR/HtkxRp+m/M0ECilHKU/MxkslKTSEtOjPZQlEW27pEopVSw8jNS6M4aivYwVBA0kCilHOXzH5g/cm67ig0aSJRSjnLOIu1QEWt0j0QppVRYNJAopZQKiwYSpZRSYdFAopRSKiwaSJRSSoVFA4lSSqmwaCBRSikVFg0kSimlwiLGjHf6bewRkRbgYIhPnwa4IjicWBLP1w7xff167fHLd/1lxpiwqkCnVCAJh4hUGmMqoj2OaIjna4f4vn699vi8dojs9evSllJKqbBoIFFKKRUWDSTHPBDtAURRPF87xPf167XHr4hdv+6RKKWUCovOSJRSSoVFA4lSSqmwxH0gEZGLRGSPiNSIyJ3RHo8dROR9EdkuIlUiUum9rUBEXhaRfd7/5vs9/p+9P489InJh9EYeGhF5UESaRWSH321BX6+IrPX+3GpE5CciIpN9LcEa59q/JSL13t9/lYhc4nffVLr22SLyiojsFpGdInKb9/Z4+d2Pd/32//6NMXH7ASQC+4H5QAqwDVgW7XHZcJ3vA9NG3fZ94E7v53cC/+n9fJn355AKzPP+fBKjfQ1BXu86YA2wI5zrBd4CzgAEeAG4ONrXFuK1fwv4yhiPnWrXXgKs8X6eDez1XmO8/O7Hu37bf//xPiM5FagxxhwwxgwAjwKXRnlMk+VS4CHv5w8Bl/nd/qgxpt8Y8x5Qg+fnFDOMMZuAtlE3B3W9IlIC5Bhj3jCef1kP+z3Hsca59vFMtWtvNMa87f28C9gNzCJ+fvfjXf94Inb98R5IZgG1fl/XMfEPPlYZYKOIbBWR9d7bio0xjeD5HxCY7r19qv5Mgr3eWd7PR98eq24RkXe9S1++pZ0pe+0iMhc4Gfgbcfi7H3X9YPPvP94DyVjrflMxH/osY8wa4GLgZhFZN8Fj4+Vn4jPe9U6ln8PPgQXAaqARuMd7+5S8dhHJAp4AvmSM6ZzooWPcNhWv3/bff7wHkjpgtt/XpUBDlMZiG2NMg/e/zcBTeJaqDnunsHj/2+x9+FT9mQR7vXXez0ffHnOMMYeNMcPGGDfwPxxbqpxy1y4iyXjeRH9rjHnSe3Pc/O7Huv7J+P3HeyDZApSLyDwRSQGuAZ6J8pgiSkQyRSTb9zlwAbADz3Ve533YdcDT3s+fAa4RkVQRmQeU49l4i3VBXa93CaRLRE73Zqx8xu85McX3Jur1MTy/f5hi1+4d6/8Cu40xP/S7Ky5+9+Nd/6T8/qOdaRDtD+ASPNkN+4F/ifZ4bLi++XgyM7YBO33XCBQCfwL2ef9b4Pecf/H+PPYQA9kqY1zzI3im8IN4/rr6h1CuF6jw/qPbD9yLtxOEkz/GufZfA9uBd71vHiVT9NrPxrME8y5Q5f24JI5+9+Ndv+2/f22RopRSKizxvrSllFIqTBpIlFJKhUUDiVJKqbBoIFFKKRUWDSRKKaXCooFEqTCISJ6IfHGC+1+38BrdkR2VUpNLA4lS4ckDTggkIpIIYIw5c7IHpNRkS4r2AJSKcXcBC0SkCk8RYDeegsDVwDIR6TbGZHn7Hz0N5APJwL8aYxxfLa2UFVqQqFQYvF1WnzPGrBCRc4E/ACuMpy03foEkCcgwxnSKyDTgTaDcGGN8j4nSJSgVNp2RKBVZb/mCyCgC/Ie387IbT1vuYqBpMgenlB00kCgVWT3j3P5JoAhYa4wZFJH3gbRJG5VSNtLNdqXC04XnWNNAcoFmbxD5IFBm77CUmjw6I1EqDMaYVhHZLCI7gF7g8DgP/S3wrIhU4unKWj1JQ1TKdrrZrpRSKiy6tKWUUiosGkiUUkqFRQOJUkqpsGggUUopFRYNJEoppcKigUQppVRYNJAopZQKy/8HrmdT0jyw60MAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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" ] @@ -924,6 +1007,76 @@ "ax.legend([\"XCS\",\"XNCS\"])" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['knowledge'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"XCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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mJyYmehhy9zQqNZZD9U1s21fj71CMMaZTPE0YtwELRGSriGwDFgCzRCQG+J8TPOYc4HL3+a8C54vIi0efoKqVqlrtvv4OECEi/TpfjMBxZKlz24HPGBNgPB0ltUJVRwNjgbGqeoaq5qlqjaq+doLH/FRV01R1CC19Hh+q6jHNVyKSIiLivp7jjqejDvSANzypN5GuMBspZYwJOB32YYhIux3V7u94VPUPnX1BEbnD/di5wFXAnSLSCBwGrvF0uG6gigwP47SUXtbxbYwJOCfr9G7trB4BTADedN/+FvCxpy+iqkuBpe7rc486/ijwqKfPEywy+8fx3rpSVPVI8jXGmO6uwyYpVf2Vqv4K6AeMU9V7VfVeYDwtK9aaLsgcEMuBQw3sqqj1dyjGGOMxTzu9BwH1R92uB4Z4PZoQkZkaB0ChdXwbYwKIp/MwXgDyROR1WmZ8Xwk871hUQW5k/96ItOyNcXFmir/DMcYYj3iUMFT1NyLyLnCe+9DNqrrKubCCW8/IcDL6xdjeGMaYgNKZPb17ApWq+kegRETSHYopJGQNiLORUsaYgOLpFq2/BP4d+Kn7UATw4okfYU4mMzWW3RW17K+pP/nJxhjTDXhaw7gSuByoAVDVXXS8PpQ5iSMd39YsZYwJEJ4mjHr3hDoFcC8JYk7BN0uEWLOUMSYweJowXhORJ4E+IjILeB+Y71xYwa9Pz0gG9OlhNQxjTMDwdJTUQ+69vCtpmfX9C1Vd7GhkISAzNdbWlDLGBAxP52HgThCWJLwoMzWOxUVlVNc10ivK44/CGGP8osMmKRGpEpHKo/6tPPq2r4IMVpmpsahC0W57K40x3V+HP2tV1UZCOShrwDdLhEwYEu/naIwxpmOezsP4tYhcaKOjvCs5NoqEmEibwGeMCQiejpLaBlwH5ItInoj8r4hc4VxYoUFEGJUaS4ElDGNMAPB0x72nVfUWYBotM7y/i8309orM1Dg2lVVR19jk71CMOaK5Oaj3MTNd5GmT1AIR+Rx4gpZ+j6uAvk4GFiqyBsTS2Kys3n7Q36EYg6rywDtFTPrtB1QcbvB3OKab8bRJKgFwAQeB/cBeVW10KqhQMm1EEn16RjD/ky3+DsUYHnpvA/M+3kJZZR352/b7OxzTzXjaJHWlqk4EHgT6AEtEpMTJwEJFTFQ4N5+dzvtF5TaJz/jVox9u4rElm7lqfBoRLiHPEoZpw9MmqctE5HfA08AdwIfAL5wMLJTcdPYQekWF89jSYn+HYkLUgk+28NB7G/n2mQN48DtnMCatD3lbLWGYY3naJDUD+BL4jqqerqo3q+rTnjxQRFwiskpE3m7nPhGRR0SkWES+EpFxnYg9aMT1jGDmpMG8s3Y3m/dU+zscE2JeWPY1//3PIi4ZncKDV51BWJgwIT2etSUVHKq3lmfzDU+bpOYAS4Fx7tpGUide4x6g6AT3zQCGuy+zaelUD0m3nptOVHgYTyzd7O9QTAj5S/4O7n+jgAtHJvHw984k3NXylZCTHm+DMcxxPG2S+i6QR8tw2quB5SJylQePSwMuBRac4JQrgOe1xTJaVsPt71HkQaZfryiumTCIN1btpOTAIX+HY0LAm2t28e9/+4rzhvfj0evGERn+zdfB+MF9CRNYbs1S5iieNkn9BzBBVW9U1RuAHOB+Dx73MPD/gOYT3D8A2HHU7RL3sWOIyGwRyReR/D179ngYcuC5fUoGIvDkRzZiyjhrUWEpP/zzarKHxDNvZjbREa5j7o+NjmBk/1hWWMe3OYqnCSNMVcuPur3vZI8VkcuAclVd2dFp7Rw7bsaQqs5T1WxVzU5MTPQo4EDUP64H3xmXxp/zd1BeWevvcEyQWrqhnB+8vIrRA+J4+qYJ9Ih0tXteTno8X24/QH3jiX7vmVDjacJYKCKLROQmEbkJ+Cfwzkkecw5wuYhsA14FzheRtrPDS4CBR91OA3Z5GFNQumPKUBqbmm1ehnHE55v3cvsLKxme3IvnbsnpcFn9nCHx1DY0s3anbfJlWnja6f0TYB5wBjAGmKeq/36Sx/xUVdNUdQhwDfChqn6/zWlvAje4R0udBVSo6u7OFiKYDOkXw+VjUnlp+XYO1NT7OxwTRFZ+vZ/bnstncEJPXrh1InE9Ijo8f0J6ywrK1ixlWnlaw0BV/6aqP1LVH6rq6119QRG5Q0TucN98B9gCFNOy5etdXX3eYHLXtGEcqm/imc+2+jsUEyTWllRw09MrSI6N5sXbJhIfE3nSx/TrFcXQxBibj2GO8HSU1LdFZJOIVHRlAyVVXaqql7mvz1XVue7rqqpzVHWoqo5W1fyuFSO4nJbcm+mZyTz7+Taqam09H3NqinZXMvPp5cT1jOCl2yaS1Dva48fmpMezYtt+mmwxQoPnNYwHgctVNU5VY1W1t6rGOhlYqLt72nAqaxt5YdnX/g7FBLDK2gZmPpVHdLiLV2adRWqfHp16fE56PFW1jWworXIoQhNIPE0YZap6osl3xgGj0+KYfFoiT32ylcP1tvS56ZoPi8rZW13HH68Zy8D4np1+fOtOkNaPYcDzhJEvIn8WkWvdzVPfFpFvOxqZ4e5pw9hXU88redv9HYoJUAsLSkmOjeryFsBpfXsyoE8P68cwgOcJIxY4BFwMfMt9ucypoEyLnPR4ctLjmffxFttgyXTa4fomlm4sZ3pmCmFh7U158kxOejzLt+5H1foxQt3JJt+lAbgXGzzmAnR5pJTx3N3ThlFaWcvfv9zp71BMgPlo4x5qG5rJzUw5peeZMCSevdV1bNtnS9aEupPVMD4QkSFtD4rIzbQs+2Ecdt7wfpyRFscTSzfT2GQzbo3nFhWW0qdnBDnpXWuOatX6+Lyt+7wRlglgJ0sYPwQWi8jw1gMi8lPgR8AUJwMzLUSEOdOGsX3/Id7+KqTnNJpOqG9s5v2iMi4amXxkBdquGpoYQ0JMJHlbD3gpOhOoOvxLUtV3aNkw6V0RyRKRh2npu5isqrbjno9cNDKZ05J78diSYpptPLzxwOeb91JV20hu1qk1R0HLj5YJQ+LJ22Y1jFB30p8eqvoBcBMt+2FkABeoqv3U8KGwsJZaxqbyat5bV+rvcEwAWFRYSkyki3OG9fPK8+Wkx7Nj/2F2Vxz2yvOZwHSyTu/WGd3v0jJS6gKgvLMzvc2pu3R0f4Yk9OTRJcU2WsV0qKlZea+wjGmnJx23bHlXfdOPYcNrQ9nJmqR6HzWzO1JVY2ymt3+Eu8K4c+pQCnZW8tHG4N0TxJy6/G372VdTz4ws7+1FNrJ/LL2iwi1hhLhT6w0zPnXlmWmkxkXz2JJif4diurGFhaVEhocxdYT39o5xhQnjB/e1Gd8hzhJGAIkMD2P25AxWbDvA8i3WAWmOp6osKihl8vBEYjrY66IrctLj2VhWzX5bdj9kWcIIMNfkDKJfr0j+sHijzcswx1m7s4JdFbVeGR3V1kTbHyPkWcIIMNERLn588QiWb93PT/76lQ2zNcdYWFCKK0y4cGSS1597dFockeFhrLB+jJDl3Tqr8Ylrcgaxt7qOh97bSHREGA9cORqRrq8VZIKDqrKwoJRJGQn06XnyDZI6KyrcxZkD+5BnNYyQZTWMAHX3+cO5e9owXsnbwa/eWmdDbQ3F5dVs2VvDdAeao1rlpMdTuKuS6rpGx17DdF+WMALYvRefxq3npvPs59v43cINljRC3LsFpYjA9FHJjr1GTno8Tc3Kl1/b3N1QZE1SAUxE+I9LR1Lb0MTcjzbTM9LFv14w/OQPNEFpYUEp4wb1JSnW8y1YO2vcoL64woS8rfuZfJr3hu2awGAJI8CJCP91RRa1Dc38YXFLn8bsyUP9HZbxse37DrFudyU/v2Sko68TExVOVmqs9WOEKMeapEQkWkTyRGSNiBSKyK/aOWeqiFSIyGr35RdOxRPMwsKEB686g8vO6M8D76zn+S+2+Tsk42OLClvWGHNiOG1bOenxrN5x0Db1CkFO9mHUAeer6hhgLJArIme1c94nqjrWffm1g/EENVeY8H/fG8tFo5L5xT8K+fMK29Y1lCwsLCUzNbZL+3Z31oQh8dQ3NvNVSYXjr2W6F8cShraodt+McF+sV9ZBEa4wHr3uTKaclsh9f1/LP1bbLn2hoLyylpVfHzjlnfU81bo/uK0rFXocHSUlIi4RWQ2UA4tVdXk7p01yN1u9KyKZJ3ie2SKSLyL5e/bYwnsdiQp38eTM8ZyVnsCPXlvDwgLbdCnYLVpXBvimOQqgb0wkI5J7W8IIQY4mDFVtUtWxQBqQIyJZbU75Ehjsbrb6E/DGCZ5nnqpmq2p2YqKNzDiZ6AgXC27MZkxaHD94ZRVL1pf7OyTjoEUFpWQkxjAsqZfPXnNCel9Wfn3AlqcJMT6Zh6GqB2nZgCm3zfHK1mYr9+5+ESLinR1fQlxMVDjP3pLD6Smx3P7iSj4r3uvvkIwDDh6q54st+8jNTPHpbP+c9ASq6xop2l3ls9c0/ufYsFoRSQQaVPWgiPQALgR+1+acFKBMVVVEcmhJYLYMq5fERkfw/C05XDt/Gbc9l89LsyYyblBfx16vuLyKdburuHxMqmOvYY71flE5Tc3qs+aoVjmt/Rjb9jM6Lc6nr90Zn2/e2+kadliYcHX2QIYm+q7GFiicnIfRH3hORFy0JILXVPVtEbkDQFXnAlcBd4pII3AYuEZturJX9Y2J5IVbJ3LZnz7hkQ828ezNOY691m/fXc8H68uZlJFAYu8ox17HfGNhwW5S46IZPcC3X9opcdEMiu9J3tZ93Hpuuk9f21MfFJVx+wsrCRMh3OV57au+sZm/rSzh1dmTfNrMFwgcSxiq+hVwZjvH5x51/VHgUadiMC0Se0dxxdgBPPPZViprG4iNjvD6a1TXNfLxpr2owvtFZVybM8jrr2GO1fqeXz9xkF8Wn8xJj+fD9eWoardb/PKTTXu488UvGZUay4u3TezU3/zmPdV878kvuH7BMl67fRKDE2IcjDSw2FpSIWJ6ZgoNTepYB/jSDeXUNzYTFR7GwoJSR17DHKv1PffVcNq2cobEs7+mns17qk9+sg8t37KPWc/nk5EYw/O35HT6B9LQxF68eNtE6hqbuW7+cnYdPOxQpIHHEkaIOHNgH5J6Rzn2Zb6woJR+vSL5/lmD+XzzXioONzjyOuYbre95trs/wddy3BsqLe9Gw2tXbT/ALc+uYECfHrx428QuL/N+ekosL946kcraBq6bv4zyylovRxqYLGGEiLAwYXpmCks37OFwvXeXdKhtaGLJ+nIuGpXCpWf0d7QmY1oc/Z67wvzTHDQ4oSeJvaO6zYZKBTsruPHpPPr1juLlWWfRr9ep9aNlDYjj2ZtzKK+q4/oFy9lXXeelSAOXJYwQkpuVwuGGJj7e5N3Jj59u2ktNfRO5WSmMTetDcqxzNRnT4rPib95zfxERctLju8UEvo1lVcx8ajm9oyN46baJJHtpxd7xg/vy1I0T2L7/EDOfyqPiUGjXnC1hhJCc9Hj69IxgkZe/zBcWltI7OpxJGQnf1GQ2lnOo3jbZccrCgm/ec3+amB7PropaSg4c8lsMW/ZUc9385US4wnjptomk9fXuelqThiYw74ZsisurueGZPKpqQzdpWMIIIRGuMC4cmcziojLqG70zQ7ehqZn3i8q4cGQykeEtf065mSnUNjTz8UZbxsUJjU3NLG7znvuLv9eV2rH/ENcvWI6q8vKsiQzp58yIpimnJfLY9eMo3FnBrc/mh+yPIUsYISY3M4Wq2ka+2OKd+ZF5W/dz8FAD048aqdNak7FmKWe09577y4jk3sRGh/slYeyuOMx1C5ZxqL6JF26dyLCk3o6+3kWjkvm/740l/+v9zH5+JbUNobe8uyWMEHPu8H7ERLq89mW+sKCU6Igwphy1+1q4K4yLRibzQVG512oy5hsLC49/z/0lLEyYMCTe5xsq7amq4/r5yzlQ08Dzt+QwKjXWJ6/7rTGpPHjVGD4t3stdL30Zcn/fljBCTHSEi2mnJ7F4XSlNzac2qb65WVlUWMrU05LoEek65r7crBSq6hr5fLOtYeVNHb3n/pKTHs+WPTXsqfLNKKIDNfV8f8FydlfU8szNExgzsI9PXrfVVePT+M2VWXy4vpx7Xl0VUgswWsIIQblZKeytrmfl1wdO6XlW7ThIeVVduyN1zhnWUpNp3QnOeMeqHQcpq2z/PfeXCe75GPk+qGVUHG5g5tPL2bqvhqduzD7Sh+Jr108czP2XjeLdglJ+/Jc1p/zjK1DYnt4haOqIJCLdM7JbJ191xaLCUiJcwvkjk467LzrCxfkjk3mvsIz//hf121yB7qzicANLN5TT2OT5l83idWVEuIRppx//nvtLVmocPSJcLN+6nxmj+zv2OjV1jdz8TB4bSquYNzObs4f5d2HrW89Np7ahid8v2kB0hIv/+fbobrdEirdZwghBvaLCmTy8H4sKS7n/spFd+iNXVd4t2M05w/qdcOmF3MwU3lqzi/xt+5no5+Gf3U3FoQaunb+MdbsrO/3Yi0YlE9fD++uBdVVkeBgT0uN5r7CUn186kgiXMw0Xjy0pZtWOgzxx/fhukzDnTBtGTV0jjy/dzPmnJ3FxNxiI4CRLGCFqemYK7xeVU7CzskvLU6/bXcmO/YeZM3XYCc+ZOiKxpSZTWGoJ4yhVtQ3c8EwexeXVzP3+OEb179z7nxLnnUlp3nTz2UO4+dkVvL5qJ1dnD/T681ccbuCFL75mRlZKt2qOA/jRRafx1le7eGxJMReNSg7qWob1YYSoC0cm4woTFhZ2bQvXRQWlhAlcOCr5hOfERIUzeXgiiwpKsVXrWxyqb+SWZ1dQuLOCx64fR25WfwYl9OzUxd9zL9ozdUQimamxPLF0syPt+c9/vo2qukbu6uAHir+Eu8K4c8ow1pRU8GmQb1TW/f7yjE/0jYnkrIz4Lg+vXVhYyoQh8Sddryc3K4VdFbV8VVLRpdcJJrUNTcx6Pp+VXx/g4WvGclEHyTbQiAhzpg1j694a3lnr3X3ka+oaefqzrZx/ehJZPt73w1PfGT+AlNhoHv2w2N+hOMoSRgjLzUxh854aNpV1bpvNzXuq2VhW7VHTwIUjk9w1mdAeLVXf2MxdL33JZ8X7+P1VY7jsjODblTA3M4WhiTE8tqSYZi/WMl7J286BQw3Mmdb9ahetosJdzJqcwfKt+1nh4zkpvmQJI4RNz0xBhE7XMlqHynoy07hPz0gmZSSwMISbpRqbmrnn1VV8uL6c31yZxXfGp/k7JEeEhbXUMtaXVvGBl1Yrrm1o4smPtzApI4Hxg53bXtgbrs0ZSHxMZFDXMixhhLCk2GjGDerb6V//iwpKGZMWR2qfHh6dPz0rha17a9hU3r022vGFpmbl3r+s4d2CUu6/bBTXTxzs75AcdfmYVAbG9+DRJcVe+YHwl5Ul7Kmq4+7zu2/tolXPyHBuPTedjzbuYW2QNsFawghxuZkpFO6qZMd+z1Yb3XnwMGtKKsjN8ny8/fRRyV2qyQS65mblZ39fyz9W7+In00d0272vvSncFcYdU4ayZsdBPis+tfXKGpqambt0M2MH9uHsoYExym7mpMH0jg7nsSXBWcuwhBHiWpuVPJ2R3bo0+vRMzztsk2KjGT+ob0glDFXlV28V8uf8Hfzr+cO6dfu7t101Po3k2CgeXbLplJ7nH6t3sfPgYX5w/rCAGaoaGx3BTWcPYWFhaaf7BgOBYwlDRKJFJE9E1ohIoYj8qp1zREQeEZFiEflKRMY5FY9p36CEnozqH+vxl/nCwlJGJPcmI7FXp14nNyuFdbsr2b7Pf/sm+Iqq8tt31/PcF18z67x0fnjRaf4Oyaeiwl3MOi+DZVv2d3m5kKZm5fGlxYzsH8v53WSSnqduPiedHhEuHl+62d+heJ2TNYw64HxVHQOMBXJF5Kw258wAhrsvs4EnHIzHnEBuVgortx846b7Fe6rqWLFtP9O7MHGqtSbT1XkfgeTh9zfx5MdbmHnWYH52Sddm0ge66yYOaukA7mLTzMKCUrbsqWHOtKEB9/7Fx0Ry/cRBvLlmF1/vq/F3OF7lWMLQFq29nBHuS9tesCuA593nLgP6iIhzi9GYduVmpaAK760r6/C894vKUG3p9+isgfE9yUz1vCYTqJ5Yupk/frCJ745P41eXZwbcl523tHYAL92wh4KdnesAVlUeXVJMRmIMMzrRV9adzJqcgStMmPtRcNUyHO3DEBGXiKwGyoHFqrq8zSkDgB1H3S5xH2v7PLNFJF9E8vfssV3cvG14Ui8y+sWctB9jYUEpg+J7MrJ/1zaqyc1M4cvtByk7SU3G25qb1Sd7MT/z2VZ+t3A9l49J5bffOYOwEF9wsasdwB+uL6dodyV3ThkasItWJsdGc3V2Gn9dWcLuisP+DsdrHE0YqtqkqmOBNCBHRLLanNLeX8NxY/FUdZ6qZqtqdmKi/zeNCTYiwvSsFL7YvI+Dh+rbPaficAOfb95LblZKl381t070e8/Hk/j+590ich54n88dXLbhlbzt/OqtdUzPTOZ/rx4TsF903hQbHcGNk1o6gIvLPesAbq1dDOjTg38587jfjgHl9slDaVaY9/EWf4fiNT4ZJaWqB4GlQG6bu0qAo1cqSwN2+SImc6wZWSk0NivvF7U/4WrJ+nIamvSUtgUdltSLjMQYn8763lNVx/NffE1DUzO3PpfvyJ4Nr68q4Wevr2XqiEQeufZMx1ZrDUS3nJtOdLiLx5d41jTzxeZ9rNp+kDumDg3493FgfE/+ZewAXsnbzt5q32wu5TQnR0klikgf9/UewIXA+janvQnc4B4tdRZQoarB3yvaDY0eEEdqXPQJ+xgWFpSSHBvFmaewu5mIMCMrhWVb9nOgpv2ajLct+HQLDU3NvDp7Ev3jornpmRWs2XHQa8//z692c+9ra5iUkcDc748nKrx77ILXXbR2AP9jzS6PRsg9uqSYpN5RfDdIZsPfNW0odY3NPP3pVn+H4hVOpvD+wBIR+QpYQUsfxtsicoeI3OE+5x1gC1AMzAfucjAe04HWZqmPN+2hpq7xmPsO1TeydGM50zNTTrldPjezP03NyvtFHXewe8PBQ/W8+MXXXDK6Pznp8bw0ayJ9YyK44ek8irqwD0VbHxSVcc+rqxg3qC/zb8gmOsKSRXtmTc7AJcITJ+kA/nL7AT7fvI9Z52UEzXs5NLEXl4zuzwtffE3FYef70Zzm5Cipr1T1TFU9Q1WzVPXX7uNzVXWu+7qq6hxVHaqqo1U136l4zMnlZqZQ39jM0g3HDiz4eOMeahuauzQ6qq2sAbEM6NPDJ1u3Pvv5Nmrqm45Mmusf14OXbzuLHhEuvr9gucft6u35ZNMe7nzxS0alxvL0zROIibKtZU4kOTaa72an8beVJZRWnHjAw2MfFtOnZwTXTRzkw+icN2fqMKrqGnn+823+DuWUBXYjofGq7CHxJMREHtfHsLCglD49I05pO9dWIsL0zBQ+3rSX6jY1GW+qrmvkmc+2ceHIZEb2jz1yfGB8T16eNRER4br5y9m2t/Pj5Jdv2ces5/PJSIzh+VtyTrjjoPnGHVOG0qR6wg7gwl0VfLC+nFvOSQ+65DsqNZYLTk/iqc+2Hld7DzSWMMwRrjDh4sxkPiwqo7ahCWhZlvuDonIuGplMuJc6IXOzWmoyS7y0oml7XlzW0gTQ3qJ1GYm9eOm2iTQ0NXP9guXsPOj5sMdV2w9wy7MrGNCnBy/eNpE+PSO9GXbQGhjfkyvGpvJy3tfsa6cD+PElm+kdFc6NZw/xfXA+MOf8YRw81MDLy7f7O5RTYgnDHGN6Zgo19U18vrllCOrnm/dSVdfo1W0xxw/uS79ex9dkvKW2oYkFn2zl3GH9GHuCTvoRKb154daJVNY2cN38ZR7NDSnYWcGNT+fRr3cUL88666SbR5lj3TV1GHWNzTzVpgO4uLyadwp2M3PS4G61V7k3jRvUl7OHJjDvky1HfowFIksY5hhnD+1H76jwI6OlFhWWEhPp4pxh/bz2Gq4w4aJRKSxZX+7If54/r9jB3uq6ky74lzUgjuduyWFvVR3XzV/W4dDHjWVVzHxqOb2jI3jptokkx3a/fbW7u2FJvbgk6/gO4CeWbiYqPCzoV/O9+/xh7Kmq4y8rS/wdSpdZwjDHiAwP44KRSSxeV0ZdYxPvFZYx7fQkr49ayc1K4VB9E59u8u5kuvrGZp78aDPjB/flrIyT97mMG9SXp26awM6Dh5n5VF67Exe37KnmuvnLiXCF8dJtE0nr29OrMYeSu6YNPaYDeMf+Q7yxeifX5gwiIchrbJMyEhg3qA9zl26moanZ3+F0iSUMc5zcrP4cONTAE0s3s6+m3pH1fCZlJBAbHe71Zqk3Vu1kV0Utd3diSeyzMhKYNzObzeXV3Ph0HpW13/z63bH/ENcvWI6q8vKsiQzpF+PVeENNZmoc55+exNPuDuAnP95MmMDsyRn+Ds1xIsLd5w9j58HD/GN1YM5PtoRhjjPltESiI8J4bEkxkeFhTB3h/eVYIsPDuHBkMu8XlXnt11ZTs/LER5vJGhDL1NM6F/Pk0xJ5/PpxFO6q5JZnVnCovpHdFYe5bsEyDtU38cKtExmW1LU1tMyx5kwbxoFDDTz8/kZeyy/hqvFp9I/zbPfGQDdtRBKj+sfy+JJimry477mvWMIwx+kR6WLqaUk0NCmThyc6NsxxelYKBw81kLfVO8t1/HPtbrburWHO1K5tuHPhqGT+eM2ZfLn9ALc+m8/185dzsKaB52/JYVRq7MmfwHhk/OCWDuD5n2ylqVm5c0robC7VWsvYsreGf67dTVOzdurS7OckE1wDno3X5GalsLCwtFM763XW5OGJ9IhwMf+TLUxMjz+lYbvNzcpjHxYzLKnXKa13dekZ/alvGsOPXltDdLiLF27NYcwpLIdi2nf3tGF8vnkfl49JZVBCaPUJ5WamMDQxhn99ZRX/+sqqTj32jilDuW/G6Q5FdnKWMEy7Lj2jP/WNzVwx1rkVQ3tEuvj33BH851vr+PFf1vC/V4/t8iqv7xeVsaGsij9cPeaUly+58sw0EmKiiI+JJGtA3Ck9l2nfpKEJPPy9sV4dfRcowsKER649kw9OsNBnR8YP7utARJ6zhGHaFeEK4+oJA09+4im66Zx0auqb+P2iDURHuHjgytGd/sJXVR5bUszA+B5cPibVK3FN7mQfiOkcEQn45ctPRWZqHJmpgfdjxBKG8bs504ZR29DEnz4sJjrCxS+/NapTfRCfFu9lTUkFD1w52muz0Y0xx7OEYbqFH110Gofrm1jw6VaiIsK4L/d0j5PGox8WkxIbzXfGh+4vVmN8wRKG6RZEhJ9fOpLaxiae/GgLPSJc/NuFp530cSu27Wf51v3cf9ko24vCGIdZwjDdhojw68uzqG1o5uH3NxEd4eKOKUM7fMyjHxYTHxPJtTnO97cYE+osYZhuJSxM+N13zqCusZnfvrue6PAwbjqn/TWG1pZU8NHGPfxk+gh6RtqfsjFOs/9lpttxhQl/uHoMdQ1N/Odb64iOcHFNzvGb6jy2pJje0eHMnDTYD1EaE3psSInpliJcYfzpujOZcloiP319La+vOnaFz01lVSwsLOWms4fYBkbG+IglDNNtRYW7eHLmeM5KT+De19bwztrdR+57fOlmekS4uPkEzVXGGO9zLGGIyEARWSIiRSJSKCL3tHPOVBGpEJHV7ssvnIrHBKboCBcLbszmzEF9+ddXVvHh+jK27zvEm2t2cf3EQcTH2I53xviKk30YjcC9qvqliPQGVorIYlVd1+a8T1T1MgfjMAEuJiqcZ26ewPXzl3PHi18yNq0PLhFmhcCS2MZ0J47VMFR1t6p+6b5eBRQBNrPKdElsdATP35JDRr8Y8rbt5+oJabbrnTE+5pM+DBEZApwJLG/n7kkiskZE3hWRTF/EYwJT35hIXrxtIrMnZ3DPBSef1GeM8S7Hh9WKSC/gb8C/qWplm7u/BAararWIXAK8AQxv5zlmA7MBBg06fnilCR39ekXxs0tG+jsMY0KSozUMEYmgJVm8pKp/b3u/qlaqarX7+jtAhIgct96xqs5T1WxVzU5MtFVEjTHGH5wcJSXAU0CRqv7hBOekuM9DRHLc8exzKiZjjDFd52ST1DnATGCtiKx2H/sZMAhAVecCVwF3ikgjcBi4RlUDb6NbY4wJAY4lDFX9FOhwfWpVfRR41KkYjDHGeI/N9DbGGOMRSxjGGGM8YgnDGGOMRyxhGGOM8YgE2qAkEdkDfN3Fh/cD9noxnEATyuUP5bJDaJffyt5isKqe0kS2gEsYp0JE8lU1299x+Esolz+Uyw6hXX4ru/fKbk1SxhhjPGIJwxhjjEdCLWHM83cAfhbK5Q/lskNol9/K7iUh1YdhjDGm60KthmGMMaaLLGEYY4zxSMgkDBHJFZENIlIsIvf5Ox4niMg2EVkrIqtFJN99LF5EFovIJve/fY86/6fu92ODiEz3X+RdIyJPi0i5iBQcdazT5RWR8e73rVhEHmldcr87O0HZ/1NEdro//9XuTcla7wumsg8UkSUiUiQihSJyj/t40H/2HZTdN5+9qgb9BXABm4EMIBJYA4zyd1wOlHMb0K/NsQeB+9zX7wN+574+yv0+RAHp7vfH5e8ydLK8k4FxQMGplBfIAybRsrryu8AMf5eti2X/T+DH7ZwbbGXvD4xzX+8NbHSXMeg/+w7K7pPPPlRqGDlAsapuUdV64FXgCj/H5CtXAM+5rz8H/MtRx19V1TpV3QoU0/I+BQxV/RjY3+Zwp8orIv2BWFX9Qlv+Fz1/1GO6rROU/USCrey7VfVL9/UqoAgYQAh89h2U/US8WvZQSRgDgB1H3S6h4zc5UCnwnoisdO+DDpCsqruh5Y8NSHIfD9b3pLPlHeC+3vZ4oLpbRL5yN1m1NskEbdlFZAhwJrCcEPvs25QdfPDZh0rCaK9tLhjHE5+jquOAGcAcEZncwbmh8p60OlF5g+l9eAIYCowFdgP/6z4elGUXkV7A34B/U9XKjk5t51hAl7+dsvvksw+VhFECDDzqdhqwy0+xOEZVd7n/LQdep6WJqcxd/cT9b7n79GB9Tzpb3hL39bbHA46qlqlqk6o2A/P5pokx6MouIhG0fGG+pKp/dx8Oic++vbL76rMPlYSxAhguIukiEglcA7zp55i8SkRiRKR363XgYqCAlnLe6D7tRuAf7utvAteISJSIpAPDaekEC3SdKq+76aJKRM5yjxK54ajHBJTWL0u3K2n5/CHIyu6O9SmgSFX/cNRdQf/Zn6jsPvvs/d3r76sLcAktIwo2Az/3dzwOlC+DltEQa4DC1jICCcAHwCb3v/FHPebn7vdjA918dMgJyvwKLdXvBlp+Md3alfIC2e7/YJtp2WNe/F22Lpb9BWAt8JX7i6J/kJb9XFqaT74CVrsvl4TCZ99B2X3y2dvSIMYYYzwSKk1SxhhjTpElDGOMMR6xhGGMMcYjljCMMcZ4xBKGMcYYj1jCMMYDItJHRO7q4P7PPXiOau9GZYxvWcIwxjN9gOMShoi4AFT1bF8HZIyvhfs7AGMCxG+BoSKympbJctW0TJwbC4wSkWpV7eVe4+cfQF8gAvgPVe3Ws4eN8ZRN3DPGA+6VQd9W1SwRmQr8E8jSliWjOSphhAM9VbVSRPoBy4Dhqqqt5/ipCMacMqthGNM1ea3Jog0BHnCvFNxMy5LRyUCpL4MzxgmWMIzpmpoTHL8eSATGq2qDiGwDon0WlTEOsk5vYzxTRcuWmCcTB5S7k8U0YLCzYRnjO1bDMMYDqrpPRD4TkQLgMFB2glNfAt4SkXxaVhJd76MQjXGcdXobY4zxiDVJGWOM8YglDGOMMR6xhGGMMcYjljCMMcZ4xBKGMcYYj1jCMMYY4xFLGMYYYzzy/wFiOAHR03M8vAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['knowledge_other'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"XNCS\"])" + ] + }, { "cell_type": "code", "execution_count": 10, @@ -932,7 +1085,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -941,7 +1094,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -968,8 +1121,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "173.99999999999997\n", - "139.33333333333334\n" + "195.6666666666667\n", + "164.11111111111111\n" ] } ], diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index 9dc6ccc..dbecb3a 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -5,6 +5,29 @@ from lcs.agents.xcs import Condition +def maze_knowledge(population, environment): + transitions = environment.env.get_all_possible_transitions() + + # Count how many transitions are anticipated correctly + nr_correct = 0 + + # For all possible destinations from each path cell + for start, action, end in transitions: + p0 = environment.env.maze.perception(*start) + p1 = environment.env.maze.perception(*end) + if any([True for cl in population + if predicts_successfully(cl, p0, action, p1)]): + nr_correct += 1 + return nr_correct / len(transitions) * 100.0 + + +def predicts_successfully(cl: Classifier, p0, action, p1): + if cl.does_match(p0): + if cl.action == action: + if cl.effect == Effect(p1): + return True + return False + def cl_accuracy(cl, cfg): if cl.error < cfg.epsilon_0: return 1 @@ -23,6 +46,16 @@ def specificity(xncs, population): return total_specificity / xncs.population.numerosity +def xncs_maze_metrics(xncs: XNCS, environment): + return { + 'numerosity': xncs.population.numerosity, + 'population': len(xncs.population), + 'average_specificity': specificity(xncs, xncs.population), + 'fraction_accuracy': fraction_accuracy(xncs), + 'knowledge': maze_knowledge(xncs.population, environment), + } + + def xncs_metrics(xncs: XNCS, environment): return { 'numerosity': xncs.population.numerosity, diff --git a/XCS_Experiments/utils/xcs_utils.py b/XCS_Experiments/utils/xcs_utils.py index 74634ca..19702e9 100644 --- a/XCS_Experiments/utils/xcs_utils.py +++ b/XCS_Experiments/utils/xcs_utils.py @@ -14,6 +14,28 @@ from lcs.agents.xcs import Condition +def maze_knowledge(population, environment): + transitions = environment.env.get_all_possible_transitions() + + # Count how many transitions are anticipated correctly + nr_correct = 0 + + # For all possible destinations from each path cell + for start, action, end in transitions: + p0 = environment.env.maze.perception(*start) + if any([True for cl in population + if predicts_successfully(cl, p0, action)]): + nr_correct += 1 + return nr_correct / len(transitions) * 100.0 + + +def predicts_successfully(cl: Classifier, p0, action): + if cl.does_match(p0): + if cl.action == action: + return True + return False + + def cl_accuracy(cl, cfg): if cl.error < cfg.epsilon_0: return 1 @@ -35,6 +57,16 @@ def xcs_metrics(xcs: XCS, environment): 'average_specificity': specificity(xcs, xcs.population), } + +def xcs_maze_metrics(xcs: XCS, environment): + return { + 'population': len(xcs.population), + 'numerosity': sum(cl.numerosity for cl in xcs.population), + 'average_specificity': specificity(xcs, xcs.population), + 'knowledge': maze_knowledge(xcs.population, environment), + } + + def XCS_classifier(situation, cfg): generalized = [] for i in range(len(situation)): From 6c509d5b86fdfa7d24d3a6a38f52a907dd581bcc Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Tue, 1 Jun 2021 11:12:54 +0200 Subject: [PATCH 12/19] XNCS with most knowledge --- .../XNCS_maze5_avg_pre_populated.ipynb | 32 +++++++++---------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb index 30f8a42..6bccdeb 100644 --- a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb +++ b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb @@ -2,19 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# logging \n", "import logging\n", "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n" + "logger = logging.getLogger(__name__)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -23,16 +23,16 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '1', '1', '1', '1', '1', '1', '0')\n", + "('0', '1', '1', '1', '1', '0', '0', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] } @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -95,9 +95,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.3141028000000006, 'numerosity': 1800, 'population': 1193, 'average_specificity': 2.216111111111111, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 19, 'reward': 1002.076792233784, 'perf_time': 0.22438270000020566, 'numerosity': 1800, 'population': 1478, 'average_specificity': 14.624444444444444, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 1, 'reward': 1890.4276392918227, 'perf_time': 0.009888000000046304, 'numerosity': 1800, 'population': 1496, 'average_specificity': 16.871111111111112, 'fraction_accuracy': 0.0}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.1227418999999998, 'numerosity': 1800, 'population': 1192, 'average_specificity': 2.235, 'fraction_accuracy': 0.97}\n" ] }, { @@ -107,26 +105,26 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m pre_generate=True)\n\u001b[0m", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in \u001b[0;36mavg_experiment\u001b[1;34m(maze, cfg, number_of_tests, explore_trials, exploit_trials, pre_generate)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m population))\n\u001b[0m\u001b[0;32m 60\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'trial'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in 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75\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexploit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexplore_metrics\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_exploit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexplore_exploit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\Agent.py\u001b[0m in \u001b[0;36m_evaluate\u001b[1;34m(self, env, n_trials, func, decay)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mcurrent_trial\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[0mstart_ts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m \u001b[0msteps_in_trial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcurrent_trial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 126\u001b[0m \u001b[0mend_ts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\XCS.py\u001b[0m in \u001b[0;36m_run_trial_exploit\u001b[1;34m(self, env, trials, current_trial)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mmetrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_explore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'duplicates found'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete_from_population\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;31m# We are in t+1 here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\Classifier.py\u001b[0m in 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\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\ImmutableSequence.py\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_items\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "\n", "df = avg_experiment(maze=maze,\n", " cfg=cfg,\n", " number_of_tests=1,\n", " explore_trials=0,\n", - " exploit_trials=10000,\n", - " pre_generate=True)\n" + " exploit_trials=2500,\n", + " pre_generate=True) " ] }, { From 12e11bbe2e89b309ab542bacc73bc47935f9a15f Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Tue, 1 Jun 2021 11:17:32 +0200 Subject: [PATCH 13/19] High update % --- .../XNCS_maze5_avg_pre_populate_high_%.ipynb | 1057 +++++++++++++++++ .../XNCS_maze5_avg_pre_populated-Copy1.ipynb | 254 ---- .../XNCS_maze5_avg_pre_populated.ipynb | 228 ---- 3 files changed, 1057 insertions(+), 482 deletions(-) create mode 100644 XCS_Experiments/XNCS_maze5_avg_pre_populate_high_%.ipynb delete mode 100644 XCS_Experiments/XNCS_maze5_avg_pre_populated-Copy1.ipynb delete mode 100644 XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populate_high_%.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populate_high_%.ipynb new file mode 100644 index 0000000..b953839 --- /dev/null +++ b/XCS_Experiments/XNCS_maze5_avg_pre_populate_high_%.ipynb @@ -0,0 +1,1057 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('1', '0', '0', '0', '1', '0', '1', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m 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\u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xncs import XNCS, Configuration\n", + "from utils.nxcs_utils import *\n", + "\n", + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " mutation_chance=0.08,\n", + " chi=0.8,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " delta=0.1,\n", + " initial_error=0.01,\n", + " metrics_trial_frequency=50,\n", + " covering_wildcard_chance=0.9,\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=200,\n", + " update_percentage=0.8,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.2515588000001117, 'numerosity': 1800, 'population': 1185, 'average_specificity': 2.1816666666666666, 'fraction_accuracy': 0.87, 'knowledge': 13.698630136986301}\n", + "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 6, 'reward': 1145.621951676395, 'perf_time': 0.08970569999996769, 'numerosity': 1800, 'population': 1381, 'average_specificity': 7.49, 'fraction_accuracy': 0.14, 'knowledge': 29.45205479452055}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 23, 'reward': 1000.5172347287778, 'perf_time': 0.36151959999983774, 'numerosity': 1800, 'population': 1400, 'average_specificity': 9.233333333333333, 'fraction_accuracy': 0.5, 'knowledge': 31.506849315068493}\n", + "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 17, 'reward': 1002.9761016296761, 'perf_time': 0.23235790000012457, 'numerosity': 1800, 'population': 1446, 'average_specificity': 11.629444444444445, 'fraction_accuracy': 0.34, 'knowledge': 31.506849315068493}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 5, 'reward': 1192.0760374577244, 'perf_time': 0.06481979999989562, 'numerosity': 1800, 'population': 1488, 'average_specificity': 16.053333333333335, 'fraction_accuracy': 0.54, 'knowledge': 29.45205479452055}\n", + "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 4, 'reward': 1269.2756724311316, 'perf_time': 0.05363770000008117, 'numerosity': 1800, 'population': 1501, 'average_specificity': 14.864444444444445, 'fraction_accuracy': 0.4, 'knowledge': 28.767123287671232}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 5, 'reward': 1180.4317007596853, 'perf_time': 0.07184489999985999, 'numerosity': 1800, 'population': 1486, 'average_specificity': 17.59888888888889, 'fraction_accuracy': 0.33, 'knowledge': 34.93150684931507}\n", + "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 12, 'reward': 1018.5616492159927, 'perf_time': 0.17546459999994113, 'numerosity': 1800, 'population': 1542, 'average_specificity': 17.554444444444446, 'fraction_accuracy': 0.46, 'knowledge': 30.82191780821918}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 5, 'reward': 1180.4667847058154, 'perf_time': 0.09298179999996137, 'numerosity': 1800, 'population': 1533, 'average_specificity': 15.79, 'fraction_accuracy': 0.37, 'knowledge': 31.506849315068493}\n", + "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 11, 'reward': 1023.1185561389009, 'perf_time': 0.1835204999997586, 'numerosity': 1800, 'population': 1488, 'average_specificity': 16.520555555555557, 'fraction_accuracy': 0.3, 'knowledge': 36.3013698630137}\n" + ] + } + ], + "source": [ + "\n", + "df = avg_experiment(maze=maze,\n", + " cfg=cfg,\n", + " number_of_tests=1,\n", + " explore_trials=0,\n", + " exploit_trials=2500,\n", + " pre_generate=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 100 0.000000e+00 1.251559 1800 1185 \n", + "50 100 5.339645e-72 1.019422 1800 1262 \n", + "100 5 1.284255e+03 0.069634 1800 1363 \n", + "150 7 1.095186e+03 0.104572 1800 1366 \n", + "200 1 1.814115e+03 0.010820 1800 1368 \n", + "250 6 1.145622e+03 0.089706 1800 1381 \n", + "300 25 1.000416e+03 0.428393 1800 1382 \n", + "350 4 1.477468e+03 0.061940 1800 1413 \n", + "400 4 1.293183e+03 0.075180 1800 1397 \n", + "450 7 1.116260e+03 0.104086 1800 1408 \n", + "500 23 1.000517e+03 0.361520 1800 1400 \n", + "550 11 1.024250e+03 0.145943 1800 1412 \n", + "600 4 1.335123e+03 0.039791 1800 1409 \n", + "650 6 1.219192e+03 0.080499 1800 1428 \n", + "700 3 1.403475e+03 0.043333 1800 1441 \n", + "750 17 1.002976e+03 0.232358 1800 1446 \n", + "800 9 1.047573e+03 0.120996 1800 1445 \n", + "850 15 1.005954e+03 0.221163 1800 1453 \n", + "900 7 1.162533e+03 0.108995 1800 1469 \n", + "950 9 1.047366e+03 0.159421 1800 1477 \n", + "1000 5 1.192076e+03 0.064820 1800 1488 \n", + "1050 10 1.037100e+03 0.158424 1800 1499 \n", + "1100 6 1.130399e+03 0.129476 1800 1505 \n", + "1150 9 1.058768e+03 0.156787 1800 1509 \n", + "1200 6 1.138054e+03 0.103674 1800 1514 \n", + "1250 4 1.269276e+03 0.053638 1800 1501 \n", + "1300 2 1.574743e+03 0.038537 1800 1527 \n", + "1350 10 1.040548e+03 0.152355 1800 1526 \n", + "1400 11 1.025399e+03 0.214739 1800 1540 \n", + "1450 35 1.000006e+03 0.664496 1800 1522 \n", + "1500 5 1.180432e+03 0.071845 1800 1486 \n", + "1550 6 1.134122e+03 0.075249 1800 1497 \n", + "1600 22 1.000535e+03 0.317362 1800 1505 \n", + "1650 20 1.001129e+03 0.290836 1800 1515 \n", + "1700 19 1.001561e+03 0.305963 1800 1498 \n", + "1750 12 1.018562e+03 0.175465 1800 1542 \n", + "1800 7 1.145081e+03 0.145008 1800 1540 \n", + "1850 5 1.230172e+03 0.067462 1800 1536 \n", + "1900 7 1.125626e+03 0.119539 1800 1527 \n", + "1950 27 1.000123e+03 0.437177 1800 1534 \n", + "2000 5 1.180467e+03 0.092982 1800 1533 \n", + "2050 6 1.192798e+03 0.091586 1800 1531 \n", + "2100 24 1.000269e+03 0.296934 1800 1503 \n", + "2150 29 1.000053e+03 0.444357 1800 1489 \n", + "2200 5 1.229385e+03 0.093888 1800 1479 \n", + "2250 11 1.023119e+03 0.183520 1800 1488 \n", + "2300 12 1.017539e+03 0.209545 1800 1502 \n", + "2350 19 1.001497e+03 0.241358 1800 1494 \n", + "2400 17 1.002969e+03 0.292965 1800 1485 \n", + "2450 11 1.026940e+03 0.216658 1800 1485 \n", + "\n", + " average_specificity fraction_accuracy knowledge \n", + "trial \n", + "0 2.181667 0.87 13.698630 \n", + "50 3.728333 0.43 29.452055 \n", + "100 6.529444 0.25 32.191781 \n", + "150 6.950000 0.28 30.136986 \n", + "200 6.811111 0.26 31.506849 \n", + "250 7.490000 0.14 29.452055 \n", + "300 8.522222 0.31 28.767123 \n", + "350 9.390556 0.39 28.082192 \n", + "400 10.873333 0.36 29.452055 \n", + "450 10.446667 0.24 30.821918 \n", + "500 9.233333 0.50 31.506849 \n", + "550 10.643333 0.45 30.136986 \n", + "600 11.479444 0.50 29.452055 \n", + "650 11.066111 0.39 32.191781 \n", + "700 12.487778 0.41 30.136986 \n", + "750 11.629444 0.34 31.506849 \n", + "800 11.870556 0.28 30.136986 \n", + "850 12.281111 0.37 28.767123 \n", + "900 13.020556 0.39 29.452055 \n", + "950 13.777778 0.28 28.767123 \n", + "1000 16.053333 0.54 29.452055 \n", + "1050 14.384444 0.36 29.452055 \n", + "1100 14.791667 0.17 29.452055 \n", + "1150 15.020556 0.38 30.136986 \n", + "1200 15.121111 0.36 30.821918 \n", + "1250 14.864444 0.40 28.767123 \n", + "1300 15.589444 0.30 30.136986 \n", + "1350 17.103889 0.39 30.136986 \n", + "1400 17.460000 0.35 29.452055 \n", + "1450 16.653333 0.67 28.082192 \n", + "1500 17.598889 0.33 34.931507 \n", + "1550 18.808889 0.38 32.876712 \n", + "1600 17.133889 0.60 36.301370 \n", + "1650 16.871667 0.30 36.301370 \n", + "1700 16.979444 0.32 32.191781 \n", + "1750 17.554444 0.46 30.821918 \n", + "1800 16.223889 0.38 28.767123 \n", + "1850 16.252778 0.48 28.082192 \n", + "1900 15.550000 0.40 30.821918 \n", + "1950 16.426667 0.41 32.191781 \n", + "2000 15.790000 0.37 31.506849 \n", + "2050 16.115556 0.59 32.876712 \n", + "2100 16.255000 0.27 40.410959 \n", + "2150 15.888333 0.44 36.301370 \n", + "2200 16.127222 0.25 33.561644 \n", + "2250 16.520556 0.30 36.301370 \n", + "2300 16.661111 0.45 31.506849 \n", + "2350 15.676111 0.27 31.506849 \n", + "2400 17.033333 0.40 30.136986 \n", + "2450 16.206111 0.22 29.452055 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['knowledge'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"Knowledge%\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "243.33333333333334\n" + ] + } + ], + "source": [ + "print(sum(df['steps_in_trial'])/3)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populated-Copy1.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populated-Copy1.ipynb deleted file mode 100644 index 1737c2b..0000000 --- a/XCS_Experiments/XNCS_maze5_avg_pre_populated-Copy1.ipynb +++ /dev/null @@ -1,254 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "('0', '0', '1', '0', '0', '1', '1', '1')\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_maze\n", - "\n", - "maze = gym.make('Maze5-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xncs import XNCS, Configuration\n", - "from utils.nxcs_utils import *\n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1800,\n", - " learning_rate=0.2,\n", - " mutation_chance=0.08,\n", - " chi=0.8,\n", - " ga_threshold=25,\n", - " deletion_threshold=25,\n", - " delta=0.1,\n", - " initial_error=0.01,\n", - " metrics_trial_frequency=50,\n", - " covering_wildcard_chance=0.9,\n", - " user_metrics_collector_fcn=xncs_metrics,\n", - " lmc=10,\n", - " lem=200\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.2289368000000005, 'numerosity': 1800, 'population': 1181, 'average_specificity': 2.167777777777778, 'fraction_accuracy': 0.0035211267605633804}\n", - "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 47, 'reward': 1000.0001386869448, 'perf_time': 0.5891079999999818, 'numerosity': 1800, 'population': 1398, 'average_specificity': 10.25388888888889, 'fraction_accuracy': 0.019970266709397142}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 28, 'reward': 1000.0929716237177, 'perf_time': 0.33813449999999534, 'numerosity': 1800, 'population': 1440, 'average_specificity': 14.006666666666666, 'fraction_accuracy': 0.028447078738155947}\n", - "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 6, 'reward': 1180.4579753392954, 'perf_time': 0.08292820000002621, 'numerosity': 1800, 'population': 1454, 'average_specificity': 14.765555555555556, 'fraction_accuracy': 0.015276193121876851}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 5, 'reward': 1184.61030663137, 'perf_time': 0.05487260000001015, 'numerosity': 1800, 'population': 1519, 'average_specificity': 15.879444444444445, 'fraction_accuracy': 0.019782387034453147}\n", - "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 22, 'reward': 1000.610834890912, 'perf_time': 0.33302090000000817, 'numerosity': 1800, 'population': 1504, 'average_specificity': 17.66722222222222, 'fraction_accuracy': 0.01843609015051703}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 2, 'reward': 1626.6065922745815, 'perf_time': 0.022644099999979517, 'numerosity': 1800, 'population': 1546, 'average_specificity': 15.996666666666666, 'fraction_accuracy': 0.010403897532610404}\n", - "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 19, 'reward': 1001.6837660118813, 'perf_time': 0.35801870000000235, 'numerosity': 1800, 'population': 1504, 'average_specificity': 15.512777777777778, 'fraction_accuracy': 0.011062158068146092}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 9, 'reward': 1052.0190263148272, 'perf_time': 0.14770529999998416, 'numerosity': 1800, 'population': 1522, 'average_specificity': 15.075555555555555, 'fraction_accuracy': 0.008008278271436166}\n", - "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 3, 'reward': 1497.2441413864253, 'perf_time': 0.03906779999999799, 'numerosity': 1800, 'population': 1498, 'average_specificity': 14.955555555555556, 'fraction_accuracy': 0.00947121692916369}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.150392399999987, 'numerosity': 1800, 'population': 1194, 'average_specificity': 2.1733333333333333, 'fraction_accuracy': 0.0018050541516245488}\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2500\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m pre_generate=True)\n\u001b[0m", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in \u001b[0;36mavg_experiment\u001b[1;34m(maze, cfg, number_of_tests, explore_trials, exploit_trials, pre_generate)\u001b[0m\n\u001b[0;32m 49\u001b[0m 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\XCS.py\u001b[0m in \u001b[0;36m_run_trial_exploit\u001b[1;34m(self, env, trials, current_trial)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mmetrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_explore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m 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max(prediction_array))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m self._distribute_and_update(action_set,\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\XNCS.py\u001b[0m in \u001b[0;36m_distribute_and_update\u001b[1;34m(self, action_set, current_situation, next_situation, p)\u001b[0m\n\u001b[0;32m 50\u001b[0m self.back_propagation.update_cycle(\n\u001b[0;32m 51\u001b[0m \u001b[0maction_set\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m \u001b[0mEffect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnext_situation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 53\u001b[0m )\n\u001b[0;32m 54\u001b[0m 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"\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\Backpropagation.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meffect\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mnext_vector\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmistakes\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcl\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0minside\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minside\u001b[0m \u001b[1;32min\u001b[0m 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\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcondition\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcondition\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "\n", - "df = avg_experiment(maze=maze,\n", - " cfg=cfg,\n", - " number_of_tests=3,\n", - " explore_trials=0,\n", - " exploit_trials=2500,\n", - " pre_generate=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "ax = df['average_specificity'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"average_specificity\")\n", - "ax.legend([\"specificity\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = df['fraction_accuracy'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"fraction accuracy\")\n", - "ax.legend([\"fraction accuracy\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(sum(df['steps_in_trial'])/3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb b/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb deleted file mode 100644 index 6bccdeb..0000000 --- a/XCS_Experiments/XNCS_maze5_avg_pre_populated.ipynb +++ /dev/null @@ -1,228 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "('0', '1', '1', '1', '1', '0', '0', '0')\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m 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"print(type(situation))\n", - "print(situation)\n", - "maze.render()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from lcs.agents.xncs import XNCS, Configuration\n", - "from utils.nxcs_utils import *\n", - "\n", - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1800,\n", - " learning_rate=0.2,\n", - " mutation_chance=0.08,\n", - " chi=0.8,\n", - " ga_threshold=25,\n", - " deletion_threshold=25,\n", - " delta=0.1,\n", - " initial_error=0.01,\n", - " metrics_trial_frequency=50,\n", - " covering_wildcard_chance=0.9,\n", - " user_metrics_collector_fcn=xncs_metrics,\n", - " lmc=10,\n", - " lem=200\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.1227418999999998, 'numerosity': 1800, 'population': 1192, 'average_specificity': 2.235, 'fraction_accuracy': 0.97}\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m pre_generate=True) \n\u001b[0m", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in \u001b[0;36mavg_experiment\u001b[1;34m(maze, cfg, number_of_tests, explore_trials, exploit_trials, pre_generate)\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m population))\n\u001b[0m\u001b[0;32m 70\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'trial'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\nxcs_utils.py\u001b[0m in \u001b[0;36mstart_single_experiment\u001b[1;34m(maze, cfg, explore_trials, exploit_trials, population)\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[0magent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mXNCS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexploit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexplore_metrics\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mcurrent_trial\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xcs\\XCS.py\u001b[0m in \u001b[0;36m_run_trial_explore\u001b[1;34m(self, env, trials, current_trial)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'duplicates found'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete_from_population\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;31m# We are in t+1 here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\xncs\\Classifier.py\u001b[0m in \u001b[0;36m__hash__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mhash\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcondition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meffect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\ImmutableSequence.py\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_items\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "df = avg_experiment(maze=maze,\n", - " cfg=cfg,\n", - " number_of_tests=1,\n", - " explore_trials=0,\n", - " exploit_trials=2500,\n", - " pre_generate=True) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "ax = df['average_specificity'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"average_specificity\")\n", - "ax.legend([\"specificity\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = df['fraction_accuracy'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"fraction accuracy\")\n", - "ax.legend([\"fraction accuracy\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(sum(df['steps_in_trial'])/3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From 2fbe434868d7e7e745711aa98274bfd36f25877f Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sat, 12 Jun 2021 15:59:08 +0200 Subject: [PATCH 14/19] Experiments with no comments --- .../Experiment_XCS_XNCS_Maze5.ipynb | 2609 ++++++++++++++ ...eriment_XCS_XNCS_Maze5_pre_populated.ipynb | 3025 +++++++++++++++++ .../Experiment_XCS_XNCS_Woods1.ipynb | 2464 ++++++++++++++ ...riment_XCS_XNCS_Woods1_pre_populated.ipynb | 2868 ++++++++++++++++ XCS_Experiments/XCS_maze5.ipynb | 1081 ------ XCS_Experiments/XCS_maze5_avg.ipynb | 959 ++++++ XCS_Experiments/XCS_woods1.ipynb | 784 +---- ...oods1_pre_populated_default_settings.ipynb | 387 +-- XCS_Experiments/XNCS_maze5_avg.ipynb | 892 ++++- XCS_Experiments/utils/nxcs_utils.py | 10 + 10 files changed, 12872 insertions(+), 2207 deletions(-) create mode 100644 XCS_Experiments/Experiment_XCS_XNCS_Maze5.ipynb create mode 100644 XCS_Experiments/Experiment_XCS_XNCS_Maze5_pre_populated.ipynb create mode 100644 XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb create mode 100644 XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb delete mode 100644 XCS_Experiments/XCS_maze5.ipynb create mode 100644 XCS_Experiments/XCS_maze5_avg.ipynb diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Maze5.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Maze5.ipynb new file mode 100644 index 0000000..888659e --- /dev/null +++ b/XCS_Experiments/Experiment_XCS_XNCS_Maze5.ipynb @@ -0,0 +1,2609 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import copy\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '1', '0', '1', '1', '1', '1', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import *\n", + "from utils.nxcs_utils import *\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_maze_metrics)\n", + "\n", + "XNCScfg_no_mods = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = False,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_update = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = True,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_cover = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = False,\n", + " cover_env_input = True,)\n", + "\n", + "XNCScfg_both = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = True,\n", + " cover_env_input = True,)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting XCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during cover\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during update\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with all enviromental inputs\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n" + ] + } + ], + "source": [ + "from utils.xcs_utils import avg_experiment as XCSExp\n", + "from utils.nxcs_utils import avg_experiment as XNCSExp\n", + "\n", + "number_of_experiments = 10\n", + "explore = 1000\n", + "exploit = 1000\n", + "print(\"Starting XCS\")\n", + "df = XCSExp(\n", + " maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS\")\n", + "df_no_mods = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_no_mods,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS with enviromental input during cover\")\n", + "df_cover = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_cover,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS with enviromental input during update\")\n", + "df_update = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_update,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS with all enviromental inputs\")\n", + "df_both = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_both,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_all = pd.DataFrame(df)\n", + "\n", + "df_all['steps_in_trial_no_mods']=df_no_mods['steps_in_trial']\n", + "df_all['steps_in_trial_update'] =df_update['steps_in_trial']\n", + "df_all['steps_in_trial_cover'] =df_cover['steps_in_trial']\n", + "df_all['steps_in_trial_both'] =df_both['steps_in_trial']\n", + "\n", + "df_all['population_no_mods']=df_no_mods['population']\n", + "df_all['population_update'] =df_update['population']\n", + "df_all['population_cover'] =df_cover['population']\n", + "df_all['population_both'] =df_both['population']\n", + "\n", + "df_all['average_specificity_no_mods']=df_no_mods['average_specificity']\n", + "df_all['average_specificity_update']=df_update['average_specificity']\n", + "df_all['average_specificity_cover']=df_cover['average_specificity']\n", + "df_all['average_specificity_both']=df_both['average_specificity']\n", + "\n", + "df_all['fraction_accuracy_no_mods']=df_no_mods['fraction_accuracy']\n", + "df_all['fraction_accuracy_update']=df_update['fraction_accuracy']\n", + "df_all['fraction_accuracy_cover']=df_cover['fraction_accuracy']\n", + "df_all['fraction_accuracy_both']=df_both['fraction_accuracy']\n", + "\n", + "df_all['knowledge_no_mods']=df_no_mods['knowledge']\n", + "df_all['knowledge_update']=df_update['knowledge']\n", + "df_all['knowledge_cover']=df_cover['knowledge']\n", + "df_all['knowledge_both']=df_both['knowledge']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['average_specificity',\n", + " \"average_specificity_no_mods\",\n", + " \"average_specificity_update\",\n", + " \"average_specificity_cover\",\n", + " \"average_specificity_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[[\"fraction_accuracy_no_mods\",\n", + " \"fraction_accuracy_update\",\n", + " \"fraction_accuracy_cover\",\n", + " \"fraction_accuracy_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rxpiE+/BdYKR7/zLgaWNM1BizBdgIzBSRUiBsjHnHOLXUfwNc3lcx94fK5e+x6IFVGMmgcNzrXP7DJ/F4tA6TUmpgS+ev1HXAy+79EcCOVs+Vu20j3Pvt2zskIgtEZLmILK+qqurlcI9cxasv89eH1hLz51M0ZTmf+X8/TndISinVI2lJFiLybSABPNXS1EE300V7h4wxjxpjZhhjZhQVFXXWLS12PP04/3h0M42Zoxg+/WM+e+vd6Q5JKaV6rN+PhhKR+cAlwHnmwGX6yoFRrbqNBHa57SM7aB80jDFs/fH3eP/NAPuKT2bUzC1cet1t6Q5LKaUOSb+OLETkIuAO4FJjTFOrp14E5olIQETKcCaylxljKoB6ETnNPQrqGmBRf8Z8JEw8zoav3cyqJUmqik9m9Iw9XHrd9ekOSymlDlmfjSxE5A/AHKBQRMqBu3GOfgoAi90jYN81xtxojFkrIs8CH+HsnrrZGJN0V3UTzpFVGThzHC8zCCTr69lw43+ysXYcu8bMYtRJ+/n0F65Od1hKKXVY5MCeoKFlxowZZvny5Wl57/jOnWy47hq22cezefxnGTGxkcu+conWd1JKDXgissIYM6N9ux6z2cua13zIhiuvYHtyFJvHf5bSY6Jc+mVNFEqpwU3LffSyjd/6Ktszx7LlmP+gZFScy269EI9HE4VSanDTkUUvq9ofYOv46ykogcu+dh6WTzexUmrw01+yXmRiMcpHfQZL6rj8G2fjD+rATSk1NGiy6EWNFTuIBAsIBraRkeVPdzhKKdVrNFn0ospPVhL3ZRHIGppHmCmljl6aLHpR9eZNAGQVBNMciVJK9S5NFr1o/84aAPKGD6y6VEopdaQ0WfSixtooAMOOOSbNkSilVO/SZNGL4o3O5hxWNiHNkSilVO/SZNGLEtEgGJtQXjjdoSilVK/SZNGLbDsLT7Jez9hWSg05mix6kU0Yi/3pDkMppXqdJoteYpJJkt4cPFZjukNRSqlep8milzRXVhDzh/H6I+kORSmlep0mi15StWGVc/Z2ZrL7zkopNchosugllRvXg3jIzA+kOxSllOp1mix6yf7yagByh+enORKllOp9mix6SWNNMwAl48alORKllOp9mix6SazBObdi2Ljj0hyJUkr1Pk0WvSQRDYCxyS7S3VBKqaFHk0UvsROZeOwGPJZuUqXU0KO/bL3EljAeU5fuMJRSqk/0WbIQkcdEpFJEPmzVli8ii0Vkg7vMa/XcXSKyUUTWi8iFrdqni8ga97mHRWTAFV4yxmB7crA8DekORSml+kRfjiyeAC5q13Yn8KoxZgLwqvsYEZkIzAMmua95REQs9zULgQXABPfWfp1pF62pdM/ebk53KEop1Sf6LFkYY94Aats1XwY86d5/Eri8VfvTxpioMWYLsBGYKSKlQNgY844xxgC/afWaAaN60xpi/jD+kJ69rZQamvp7zqLEGFMB4C6L3fYRwI5W/crdthHu/fbtHRKRBSKyXESWV1VV9WrgXan8ZJ1z9naer9/eUyml+tNAmeDuaB7CdNHeIWPMo8aYGcaYGUVF/Xcd7L079gCQMyy3395TKaX6U38niz3uriXcZaXbXg6MatVvJLDLbR/ZQfuA0lDtlCUvLhub3kCUUqqP9HeyeBGY796fDyxq1T5PRAIiUoYzkb3M3VVVLyKnuUdBXdPqNQNGrN5Zlo7Xs7eVUkOTt69WLCJ/AOYAhSJSDtwN3Ac8KyLXA9uBqwCMMWtF5FngIyAB3GyMaZktvgnnyKoM4GX3NqAkIn7IhHBJSbpDUUqpPtFnycIYc3UnT53XSf97gXs7aF8OTO7F0HqdncjEk2zA8lvdd1ZKqUFooExwD2o22XhsPXtbKTV0abI4QgfO3q5PdyhKKdVnNFkcoVjdPuK+HCyfnr2tlBq6NFkcoZrNHxLzhwmE4ukORSml+owmiyO055O1GI9FRk6fHSuglFJpp8niCO3d5pwjmFMSTnMkSinVdzRZHKGGKufs7aKy0WmORCml+o4miyMUrbMBKD3mhDRHopRSfUeTxRFKNDuVZnOHD09zJEop1Xc0WRwhOxHCk2zCG9AJbqXU0KXJ4gjZJozH3p/uMJRSqk/1+M9h9zKnJa1fY4zZ3hdBDSa2hLFEr72tlBraepQsROQrOFVj9wC222yAqX0U16AQb2wg4Qtj+cq776yUUoNYT0cWtwLHGWNq+jKYwaZm2zqigRxyMrakOxSllOpTPZ2z2AHojvl2Kj9eg/H4COXo1I9Samjr6chiM7BERP4XiLY0GmN+1CdRDRI128qBsWQXZac7FKWU6lM9TRbb3ZvfvSmgYY8z2Coco+dYKHU02tcUY2tNE9tqGtlW08TWmkYi8SQnj87j1LICTijNxmsNjT0PPUoWxpjvAohItvPQ6OE/QGS/M9c/fIKeva3UUGSMoaoh6iSCaichbKt1ksPW6kbqIok2/Utzglge4a9rdgOQFfAyfUwep47L59SyfKaMyMXvHZzJo6dHQ00Gfgvku4+rgWuMMWv7MLYBL9HkhQzIHTkm3aEoBUBDNEFDJMGwnGC6QxlUYgmbDZX1rKuoZ8Oe+tQoYXttE02xZKqf5RFG5GYwpiDEZdNGMKYgxJiCTMYWhBiVHyLocy6tvHt/hPe21LBsSy3LttRy/9/WAxD0eThpVB4zy/I5dVw+J43KI6OXL8dsjEFEenWd0PPdUI8CXzXGvAYgInOAXwFn9HpEg0gyHsLjb8Yf0j1zKr3K9zbx+Ftbeeb9HTREE0wsDXPR5GFcNHkYE4qz+uTHY7Da2xhjXUUdH7m3dRX1bKysJ540APgtD6MLQozJD3HG+ELGFoYYnR9ibEEmI/Iy8PVgt9KwnCCXTRvBZdNGAFDTEOX9rbW85yaPh/+xAfMq+Cxh6shcJ3mU5TN9TB7ZQaeEkG0b6qMJ6prj7GuKs6855i7j7G86cH9fU5z9rZ9rjrP2uxf2KM5D0dNkkdmSKACMMUtEJLNXIxmEjNFrb6v0WrVjH79eupmXP3R2e/zrlFJOKA2z+KPd/GjxJ/xo8SeMK8zkwsnDuGjSMKaOzDlqEodtG7bXNjlJYVddKkFU7I+k+hRnBzihNMyc44qYWBrmhNIwZYWZWJ7e3UYFWQEumlzKRZNLAdjfHGfFtgPJ41dvbGbhkk14BEbkZdAQSbC/OY5tOl9nyG+Rm+EjJ+QnN8PHMcVZ5IZ85GT4SdoGX+8OWHp+NJSIfAdnVxTA54Cj/uQCW8J4RK+9rfpX0jb8fd0efr10M+9v3Ut2wMv1s8q49oyxDM/NAOCmOePZUxfhlbW7+dva3Tzq/hgNzwlywSRnxHHK2Pxe/1FMp6r6KG9trGb5tlrWVdTzcUUdje4uJMsjjC/K5NSyfE4oDTNxuJMYCrMCaYk1J8PHuceXcO7xJQA0RhOs3L6PZVtq2FLTRE6Gl9wMv/vj7yM35Nx3koPTFvD2cjbohhjTRepq6SSSB3wXmAUI8AZwjzFm72G9qcjtwBdwzgJfA/wnEAKeAcYCW4F/a1m/iNwFXA8kgVuMMf/X3XvMmDHDLF++/HDC65FkNMKTCxYhGRX85y9u67P3UapFUyzBcyvKeezNLWytaWJEbgbXzSrj32aMTO266Mzexhh/X7eH/1u7hzc2VBFL2BRk+jl/YgkXTh7GGeML+v3H50hF4kmWbanlzY3VvPFJFR/vdv5wywp4mZhKCNlMLM1hQklWaj5BdU1EVhhjZhzU3pNk0cuBjADeBCYaY5pF5Fngr8BEoNYYc5+I3AnkGWPuEJGJwB+AmcBw4O/AscaYZCdvAfR9sqjcuIbnf1BOduY/+dxDd/TZ+6i+0RxLUlUfpaohQlV9jKqGqPO4PorPktSPzbEl2Wn/kamsi/DE21t56r3t7G+Oc+KoXG44q4yLJg07rMMyG6IJXl9fxd/W7uYf6/bQGEuSHfBy7gnFXDRpGNPH5GGAeNImaRviSUPCtkkkDQnbkLRt4knjPnegT9J2+gW8HkblOxO/Wb1Yjdm2DR9V1LF0QzVvbqzi/a17iSVs/JaH6WPymDWhkLMmFDJpeM6QGjH1t86SRZffpIg8ZIy5TUT+gjMKaMMYc+lhxuMFMkQkjjOi2AXcBcxxn38SWALcAVwGPG2MiQJbRGQjTuJ45zDfu1dUrl+NbQ0nI3tw/6M0xrC/Oc6euiiV9REq66LEkjZBn4eg1yLotwh6LTL8Vqotw20L+DwEvJ5+3Qdu284PVqLVD1YiaTtt7o9aQzSR+uGvqo9S3RBtkwyqG2I0RBMHrVsECjL9NMeS/KbV7otjirKYODzMpOHhVBLJ7YeDGtZV1PHrpVt4cfVOErbhwonD+MJZZUwfk3dE2zwr4OVfp5byr1NLicSTvL2pmr99uJvFH+1h0apdvfgJID/Tz+j8EGMKnEni0W4SGZ0fojg7gKebH/Wd+5p5c0MVSzdU8/amGmobYwAcV5LN508bw6wJhZxalk/Ir5cI6GvdbeGWOYoHe+sNjTE7ReRBnJP8moFXjDGviEiJMabC7VMhIsXuS0YA77ZaRbnbdhARWQAsABg9um8vc1q9eTswnHBhz+f5m2NJahqj7G2MO8umGDUNMfY2xahtdG57G+MYDNlBH+Gg11lmOMvsoJdwyzKj1fNBH0Ff2x9t2zbsbYpRWR9lT12ESveHck+dkxAq6yPsqXN+RGMJu4uouyZCqwTiIeiz8FqCMQf+ujDGOPfdBtO6Ddy+hpZBrm0b4nbbv1xbEkFXE36dCQe9FGUHKMoOMGVkLoVZfudxltNWmBWgODtAfqYfr+XBtg079jaxdpczMfpRRR1vb6rmhZU7U+sckZuR2vfdkkRG5mUc0Y+4MU4ifGtjNb9euoU3N1aT4bP495mjuW5WGWMKev+YkqDPSu07TyRtlm2pZWNVA5ZH8Hk8WB7Bawk+y7nvswTL48HnEfc5j9t2oE9zLMn22ia21zaxraaJ7bWNfLB9L39ZvavN95cageSHGF0QSiWVRNLZBks3VrO55bLF2QHmHFvErAmFzDqmkOKwHhrc37pMFsaYFe7dacaYn7R+TkRuBV4/1Dd05z8uA8qAfcAfReRzXb2ko9A6ifdRnMN8mTFjRp/uX2vY40zXFIwe1qb9jU+qWLK+itrGKLVNcWpbJYdIvOMfZcsj5IX85Gf6yAv5scRDZX2EjZUJ6iNx6iIJkt38Sno9QjjDSSTxhE1lfZREB68JB70Uh4MUZweYWZZPsfsjWhwOUuIuA14PkXiSSNwmkkgSiSWdZdymudV9p49za44faEskDSJOIpGWr0+cL7Llx9S5f+DLFXF7CnhEUj9AXo/nwA9Uux8lb8vN8qSWPkvI8FkUh4MUZQcoyPQf8m4kj0cYU5DJmIJMPjWlNNVe3RBlXUVdmyTyj4/3pH4Aw0EvE4eHKc3JIJa0iSdsZ5m0iSXcW9IQSySJJ4372OkXdfu1JMyScIBvXnQc/zFzDDmhrucjeovX8nDGMYWccUzhEa9r8oicg9riSZude5vZ5iaS7e5Zz9trm3hnc02b8xmCPg+njSvg32eO5qwJRRxboof/pltPx27zgZ+0a7u2g7ae+BdgizGmCkBE/oRzvsYeESl1RxWlQKXbvxwY1er1I3F2W6VVZG8cgOETjm/T/v/+/CG76yKUhAPkh/wUZQU4tiSbgkw/eZl+ZxnyU5DlLjMDZAe9XQ7HjTE0x5PUNR9IHnWROPUR5xjs+khLe5y65gReSyhxE0JxdpCSsLMsDgfSvv99MCvMCnDWhCLOmlCUamuOJfl4d13q8My1u+p4f2stfq8Hv+Vpswz5vW0e+yxxl87jgOXcH10Q4uLJpYP2TN/O+CwPYwszGdvBaNwYQ3VDjO21jSRtOHFUzqCbcB/qupuzuBr4d6BMRF5s9VQ2cLjlyrcDp4lICGc31HnAcqARJynd5y4Xuf1fBH4vIj/CmeCeACw7zPfuNfEmHwQgb0xZqi0ST7JjbxO3nDuB288/ttfeS0QI+b2E/F49M3eAyfBbnDQ6j5NG56U7lEFNRFK7CtXA1N3I4m2gAigEftiqvR745+G8oTHmPRF5DvgASAArcXYdZQHPisj1OAnlKrf/WveIqY/c/jd3dyRUf0jGMhBfDH9WRqpta00jxsD44qw0RqaUUr2vuzmLbcA24PTefFNjzN04V95rLYozyuio/73Avb0Zw5EydhZWcn+b/agtk3Hji476k9uVUkNMj3aKishpIvK+iDSISExEkiJyVNe5sD1hLNqevb2p0inGW3YIR0gppdRg0NMZtJ8BVwMbgAycs69/2ldBDXR2IkHSysGyGtu0b6pqYERuhh7zrZQacnp8uIUxZiNgGWOSxpjHgXP6LqyBbe+uzcT8YXzBaJv2zdWNjNNdUEqpIainyaJJRPzAKhG5363tdNT+Ku5ev4qkN4Ng1oHzGIwxbKpsYHyRTm4rpYaeniaLzwMW8GWcQ1xHAZ/tq6AGuppNTsHd7IIDh7FW1kdpjCV1clspNST19LKq29y7zTjVZ49qdbtrASgYWZJqa5ncHqcjC6XUENTdSXlr6KS0BoAxZmqvRzQIRGqds7dLJxyXattU5SQL3Q2llBqKuhtZXNIvUQwysQYP+KFg3PhU26aqRjL9FiVhPQNVKTX09OSkPNWOHctAvHECOdmptk1VDYwr0mJnSqmhqacn5dWLSJ17ixztJ+U5Z2/XHXT2tk5uK6WGqp5OcGe3fiwil+NcgOioZJONxYFc2RxLsnNfM3OLRnXxKqWUGrwOqwayMebPwLm9G8rgYNs2SW/bs7c3V+vktlJqaOvRyEJEPtPqoQeYQRdHSQ1l+yvLiftyCPkPXDUtVUCwWHdDKaWGpp4WMfp0q/sJYCvO1e6OOns+WUXCl9Xm7O1NVQ2IwNg+uOylUkoNBD2ds/jPvg5ksKjeuBGYRlb+gUNkN1U1MjIvQ69Cp5Qasnp6NNQ4EfmLiFSJSKWILBKRcX0d3EBUV14NQMGIA9cp3lylNaGUUkNbTye4fw88C5TiXNr0j8Af+iqogaypNgLAsGOcE/Js27C5qpFxhZoslFJDV0+ThRhjfmuMSbi333GUTnDHG5xNVjTeucb27roIzfGkTm4rpYa0nk5wvyYidwJP4ySJucD/ikg+gDGmto/iG3CS0QBYSYJ5ecCBmlA6slBKDWU9TRZz3eUX27Vfh5M8jpr5CzuZhTdZh1jOCKOl2qyOLJRSQ1lPj4Yq6+tABgtDGI85cPb25upGsoNeirK0gKBSaujq6Ul5PuAmYLbbtAT4pTEm3kdxDUi2bWNbYSzP3lSbFhBUSh0NejrBvRCYDjzi3qa7bUeV+n17iPtz8PkjqbZNlVpAUCk19PV0zuIUY8yJrR7/Q0RWH+6bikgu8GtgMs6cx3XAeuAZYCzOGeL/ZozZ6/a/C7geSAK3GGP+73Df+0js2bSGuC+L3EwbgIZogt11ET3HQik15PV0ZJEUkdSVftwT8pJH8L4/Af5mjDkeOBFYB9wJvGqMmQC86j5GRCYC84BJwEXAIyKSllOlq9evByAr1wfAlpaaUDqyUEoNcT1NFt/AOXx2iYgsAf4BfO1w3lBEwjhzH/8DYIyJGWP24dSaetLt9iRwuXv/MuBpY0zUGLMF2EiayqPv27EHgLxS57BZrTarlDpa9DRZvAX8ErDd2y+Bdw7zPccBVcDjIrJSRH4tIplAiTGmAsBdFrv9RwA7Wr2+3G07iIgsEJHlIrK8qqrqMMPrXFN1MwDDxjtHCm+qbMAjMLog1OvvpZRSA0lPk8VvgDLg/3NvZcBvD/M9vcDJwEJjzElAI+4up050dJhRh2ePG2MeNcbMMMbMKCoqOszwOhevd0IpPOY4wCkgODo/RMCrBQSVUkNbTye4j2s3wf3aEUxwlwPlxpj33MfP4SSLPSJSaoypEJFSoLJV/9aXoBsJ7DrM9z4iyagfgjaZJSWAc9is7oJSSh0NejqyWCkip7U8EJFTcXZNHTJjzG5gh4gc5zadB3wEvAjMd9vmA4vc+y8C80QkICJlwARg2eG895GyE5lYyTo8loekbdhS3cg4ndxWSh0FejqyOBW4RkS2u49HA+tEZA1gjDFTD/F9vwI8JSJ+YDPwnziJ61kRuR7YDlyFs/K1IvIsTkJJADcbY47kSKzDZghj2fUA7NrXTDRh68hCdSgej1NeXk4kEum+s1JpEAwGGTlyJD6fr0f9e5osLjr8kA5mjFmFc2nW9s7rpP+9wL29GcPhsD1hLI+TLFIFBDVZqA6Ul5eTnZ3N2LFj9ex+NeAYY6ipqaG8vJyysp5Vc+ppbahtRxTZEFBfX0PCl0PQ50ylbNJzLFQXIpGIJgo1YIkIBQUFHMpRoz2dszjq7dm0hpgvi0AoAThXx8sN+cjP9Kc5MjVQaaJQA9mh/vvUZNFDVevXgXjIzHUGY5uqGhhXmKk/CEqpo4Imix7av2M3ALkluYCzG0ont9VAtmPHDsrKyqitda5NtnfvXsrKyti2bRtf/OIXGT9+PJMmTWL27Nm8955zJPu9997LpEmTmDp1KtOmTUu1K9XTCe6jXmOlM0dRXDaGukicqvoo44s1WaiBa9SoUdx0003ceeedPProo9x5550sWLCAO+64g7KyMjZs2IDH42Hz5s2sW7eOd955h5deeokPPviAQCBAdXU1sVgs3R9DDRCaLHoo5l7vqOTY49nsTm6PK9TJbdW97/5lLR/tquu+4yGYODzM3Z+e1G2/22+/nenTp/PQQw/x5ptv8tWvfpVHH32Up556Co/H2bEwbtw4xo0bx5/+9CcKCwsJBJwLeRUWFvZqzGpw091QPZSI+MDYZA4fzuaqlkup6shCDWw+n48HHniA22+/nYceeoj169czbdo0LOvgEjUXXHABO3bs4Nhjj+VLX/oSr7/+ehoiVgOVjix6yMQzsbwNWD4vm6oa8HqE0flaQFB1rycjgL708ssvU1payocffsj48eM77ZeVlcWKFStYunQpr732GnPnzuW+++7j2muv7b9g1YClI4seMmRj2c6uhE2VjYwuCOGzdPOpgW3VqlUsXryYd999lx//+McUFBSwevVqbNvusL9lWcyZM4fvfve7/OxnP+P555/v54jVQKW/dj1kSxhLnLO3N1drAUE18BljuOmmm3jooYcYPXo03/jGN3jkkUeYMWMGd999N8Y4xZs3bNjAokWLWL9+PRs2bEi9ftWqVYwZMyZd4asBRpNFDzQ115Hw5eD1NZNI2mytbtICgmrA+9WvfsXo0aM5//zzAfjSl77Exx9/zM0338zu3bs55phjmDJlCjfccAPDhw+noaGB+fPnM3HiRKZOncpHH33EPffck94PoQYMnbPogcqt64j7swlnxCjf20wsqQUE1cC3YMECFixYkHpsWRYrVqwA4Oyzz+7wNW+//Xa/xKYGHx1Z9MCejz/EiEVmjlcvpaqUOippsuiBvdt2ApBTnM2mSi0gqJQ6+miy6IHGSmc0UTx2JJurGyjI9JMb0gKCSqmjhyaLHojud661NGzCCWyq1KvjKaWOPposeiDZ5IwiskaN0utuK6WOSno0VA/Y8Qwsq4F6W6hpjGmyUEoddXRk0RPGOXu75ep4uhtKDQadlSh//fXXERF++tOfpvp++ctf5oknnkg9fvDBBzn++OOZPHkyJ554Ir/5zW8AeOmllzjppJM48cQTmThxIr/85S/79TOp9NFk0QO25GBJ/YECgjqyUINA6xLlQKpE+ZgxYyguLuYnP/lJhyXIf/GLX7B48WKWLVvGhx9+yBtvvIExhng8zoIFC/jLX/7C6tWrWblyJXPmzOnnT6XSRXdDdaM52kjSG8ZvbWNTVSM+SxiZl5HusNRg8vKdsHtN765z2BS4+L5uu7UvUf7Tn/6UXbt2UVRUxJlnnsmTTz7JDTfc0OY13/ve93jttdcIh8MA5OTkMH/+fGpra0kkEhQUFAAQCAQ47rjjevdzqQFLRxbdqCz/hJg/jD8jyqaqBsYWZOLVAoJqkGhfotzvP3DI95133skPf/hDkslkqq2+vp76+voOq9Pm5+dz6aWXMmbMGK6++mqeeuqpTgsSqqFHRxbdqFq/FuMZTmbYw+aqBiYUZ6c7JDXY9GAE0JdalyhvqRMFUFZWxsyZM/n973+fajPGdHld+V//+tesWbOGv//97zz44IMsXry4zVyHGrrS9ieyiFgislJEXnIf54vIYhHZ4C7zWvW9S0Q2ish6EbmwP+Os2bIdgKyCTLbVaAFBNbi0L1FeUVHR5vlvfetb/OAHP0iNEMLhMJmZmWzevLnTdU6ZMoXbb7+dxYsXawnzo0g696fcCqxr9fhO4FVjzATgVfcxIjIRmAdMAi4CHhGRgy/z1Ucad+8HwF9USMI2OrmtBo2OSpR//etfb9Pn+OOPZ+LEibz00kuptrvuuoubb76Zujrn+i11dXU8+uijNDQ0sGTJklQ/LWF+dElLshCRkcC/Ar9u1XwZ8KR7/0ng8lbtTxtjosaYLcBGYGY/hUp0XwKApvxRgF5KVQ0enZUo37ZtW5t+3/72tykvL089vummmzjnnHM45ZRTmDx5MmeffTahUAhjDPfffz/HHXcc06ZN4+6779ZdUEeRdM1ZPAR8E2g9AVBijKkAMMZUiEix2z4CeLdVv3K37SAisgBYADB69OheCTTR5AUfbLHygP26G0oNGl2VKP/www9T7SeeeGKbiWoR4Zvf/Cbf/OY3D1rnX//61z6MWA1k/T6yEJFLgEpjzIqevqSDNtNRR2PMo8aYGcaYGUVFRYcdY2t2LIQn2cTGugRF2QHCQV+vrFcppQaTdIwszgQuFZFPAUEgLCK/A/aISKk7qigFKt3+5cCoVq8fCezqr2CNnYWVdM7eHleoowql1NGp30cWxpi7jDEjjTFjcSau/2GM+RzwIjDf7TYfWOTefxGYJyIBESkDJgDL+i1eCeOlzikgqPMVSqmj1EA6z+I+4FkRuR7YDlwFYIxZKyLPAh8BCeBmY0yy89X0nmgiStKbg8/awb6muB4JpZQ6aqU1WRhjlgBL3Ps1wHmd9LsXuLffAnNV7tpI3J9D0LcB0AKCSqmjl9at6ELlJx9ie3xIwBnIHKMjC6XUUUqTRRf2bnaOR48Fvfi9HobnagFBNTh0Vp5827ZtbN26Na0lypcvX84tt9wCwJIlS3j77bdTz1177bU899xzh/OR0+6JJ57gy1/+cpd92n/ewUSTRRfqdu0FYK8vxLjCTCxP5zVzlBpIuipPDqS1RPmMGTN4+OGHgcH943k4BvPnHUgT3ANOdG8cgPWSr5Pb6rD9YNkP+Lj2415d5/H5x3PHzDu67NNRefIWfVmifMqUKSxdupScnBwKCwv58Y9/zDXXXMPnP/955s+fj9fr5cEHH+RnP/sZv/jFL7Asi9/97nep+N544w1+9KMfsXv3bu6//36uvPLKg97jd7/7HQ8//DCxWIxTTz2VRx55BMuyyMrK4tZbb+Wll14iIyODRYsWEQwGOfHEE9m8eTMej4empiaOO+44Nm/ejM934Lypa6+9lksuuST1fllZWakSJ//1X/9FQUEB69evZ/bs2TzyyCN4PB4ef/xxvv/971NaWsqxxx5LIBAA4C9/+Qv//d//TSwWo6CggKeeeorm5uaDPu/xxx/PjTfeyPbtTg26hx56iDPPPLPrLz9NdGTRhXijs3nej2bp5LYadLoqTw59V6L8zDPP5K233mLt2rWMGzeOpUuXAvDuu+9y2mmnpfqNHTuWG2+8kdtvv51Vq1Zx1llnAVBRUcGbb77JSy+9lBoZtbZu3TqeeeYZ3nrrLVatWoVlWTz11FMANDY2ctppp7F69Wpmz57Nr371K3JycjjxxBN5/fXXAeeH/MILL2yTKLqzbNkyfvjDH7JmzRo2bdrEn/70JyoqKrj77rt56623WLx4MR999FGq/6xZs3j33XdZuXIl8+bN4/777+/w8956663cfvvtvP/++zz//PN84Qtf6HFM/U1HFl0wsQw8EqHWBHRkoQ5bdyOAvtRZeXLouxLlZ511Fm+88QZjxozhpptu4tFHH2Xnzp3k5+eTldX9/6PLL78cj8fDxIkT2bNnz0HPv/rqq6xYsYJTTjkFgObmZoqLnepAfr+fSy65BIDp06ezePFiAObOncszzzzDOeecw9NPP82XvvSlbuNobebMmYwbNw6Aq6++mjfffBOv18ucOXNoqRYxd+5cPvnkEwDKy8uZO3cuFRUVxGIxysrKOlzv3//+9zZJpq6ujvr6erKzB96lEHRk0QVjZ2MlnaqzmizUYNNdeXLomxLls2fPZunSpSxdujT1Y/rcc8+lRg7dadmVA07yas8Yw/z581m1ahWrVq1i/fr13HPPPYAzmmpJdpZlkUg4hUAvvfRSXn75ZWpra1mxYgXnnnvuQev1er2p7WCMaTOf0z6BtjzuLLF+5Stf4ctf/jJr1qzhl7/8JZFIpMN+tm3zzjvvpD7Lzp07B2SiAE0WXTKE8Zh6AMp0N5QaRHpSnhz6pkT5qFGjqK6uZsOGDYwbN45Zs2bx4IMPdpgssrOzqa+vP6TPdt555/Hcc89RWelUBKqtrT2okm57WVlZzJw5k1tvvZVLLrkEyzr4Kgdjx45NFVpctGgR8Xg89dyyZcvYsmULtm3zzDPPMGvWLE499VSWLFlCTU0N8XicP/7xj6n++/fvZ8QIp97pk08+mWpv/3kvuOACfvazn6Uer1q16hC2RP/SZNGJWDKGbYXxSD3DwkGyArrHTg0enZUnb9lv31pflCg/9dRTOfbYYwFnt9TOnTuZNWvWQf0+/elP88ILLzBt2rTU3EZ3Jk6cyH//939zwQUXMHXqVM4///wOR03tzZ07l9/97nfMnTu3w+dvuOEGXn/9dWbOnMl7771HZuaBPxBPP/107rzzTiZPnkxZWRlXXHEFpaWl3HPPPZx++un8y7/8CyeffHKq/z333MNVV13FWWedRWFhYaef9+GHH2b58uVMnTqViRMn8otf/KJH2yAdpKNh3lAwY8YMs3z58sN+/a7dG1n0nQ34zHL+cer5/P6G07p/kVKudevWccIJJ6Q7DNULlixZwoMPPthm9DVUdPTvVERWGGNmtO+rI4tO7PnkQ2wrQNQT1fkKpdRRT5NFJ2o2bgKgzgPjdb5CqaPWnDlzhuSo4lBpsuhE/S6nTEKtL8g4HVkopY5ymiw6EamNArDdm6/XsVBKHfU0WXQi0eAcWrczo5jScDDN0SilVHppsuhEMhpA7BjhoiI8WkBQKXWU02TRmWQW3sR+xg8LpzsSpQ6ZlihXvU2TRSecs7frGFeoR0KpwUdLlA9cxpgOCzAOdHpacgcSdgLbCoPZrZPb6ojt/t73iK7r3RLlgROOZ9i3vtVlHy1Rfmglyvfs2cONN96Yqou1cOFCzjjjDH70ox/x2GOPAfCFL3yB2267jTvuuIMxY8akChLec889ZGdn87WvfY0HHniAZ599lmg0yhVXXMF3v/tdtm7dysUXX8w555zDO++8w5///OcOS6UMZDqy6EDNvgrivhxsT6OeY6EGLS1Rfmglym+55RbOPvtsVq9ezQcffMCkSZNYsWIFjz/+OO+99x7vvvsuv/rVr1Jlx5955pnUa5999lmuuuoqXnnlFTZs2MCyZctYtWoVK1as4I033gBg/fr1XHPNNaxcuXLQJQrQkUWH9mxcS9KbQTIRpUx3Q6kj1N0IoC9pifKelyj/xz/+kZqbsSyLnJwc3nzzTa644opUnajPfOYzLF26lFtuuYXKykp27dpFVVUVeXl5jB49mocffphXXnmFk046CYCGhgY2bNjA6NGjGTNmTJtkOdhosuhA9ScbgEnEA0LIr5tIDU6tS5TPmjWLefPmUVpa2qbPt771La688kpmz54NtC1R3nL9hvamTJnClClT+PznP09ZWdlByWL27Nn8/Oc/Z/v27dx777288MILfVKi/Pvf//5Bz3VVovyuu+7qskR5R7qqnXfllVfy3HPPsXv3bubNm5fqf9ddd/HFL36xTd+tW7e2KUw4GPX7bigRGSUir4nIOhFZKyK3uu35IrJYRDa4y7xWr7lLRDaKyHoRubCvY6wrr3LuhHW+Qg1OWqK8rZ6UKD/vvPNYuHAhAMlkkrq6OmbPns2f//xnmpqaaGxs5IUXXkh9jnnz5vH000/z3HPPpeZVLrzwQh577DEaGhoA2LlzZyrOwS4dcxYJ4GvGmBOA04CbRWQicCfwqjFmAvCq+xj3uXnAJOAi4BEROfib7kXN1c0ABIeP6su3UarPaInyg3VXovwnP/kJr732GlOmTGH69OmsXbuWk08+mWuvvZaZM2dy6qmn8oUvfCG1i2nSpEnU19czYsSI1Ijtggsu4N///d85/fTTmTJlCldeeeUhJ8OBKu0lykVkEfAz9zbHGFMhIqXAEmPMcSJyF4Ax5vtu//8D7jHGvNPVeo+kRPmTX/wmDXIRhZdlMffimYe1DnV00xLlajAYNCXKRWQscBLwHlBijKkAcJfFbrcRwI5WLyt32zpa3wIRWS4iy6uqqg47LjsSQOw4I8YPviMWlFKqL6QtWYhIFvA8cJsxpq6rrh20dTgcMsY8aoyZYYyZ0XIR9cOSzMSbqOOYkpzDX4dSSg0haUkWIuLDSRRPGWP+5DbvcXc/4S5bZoXKgdaTByOBXX0bYRiPXUdJONB9V6WUOgqk42goAf4HWGeM+VGrp14E5rv35wOLWrXPE5GAiJQBE4BlfRVf0k5ie3IQqe/yeHOllDqapOMkgjOBzwNrRGSV2/Yt4D7gWRG5HtgOXAVgjFkrIs8CH+EcSXWzMSZ50Fp7SW1DFQlfGL9s7au3UEqpQaffk4Ux5k06nocAOK+T19wL3NtnQbWy45O1JHyZZFjx/ng7pZQaFLQ2VDtbVq8BICOsZ26rwWsolyjfunUrkydPPqT3fOihh2hqako97knZkb7yqU99in379vXqOvft28cjjzzSq+tsT5NFO/u2O7VoCkrzuump1MClJcrbap8s0umvf/0rubm5vbrO/kgW+udzO7G9EQDKju24Lo5Sh2rps59QvaOhV9dZOCqLs/7t2C77DOUS5YlEgvnz57Ny5UqOPfZYfvOb3xAKhXj11Vf5+te/TiKR4JRTTmHhwoX88pe/ZNeuXZxzzjkUFhby2muvAc6Z661LmZeUlLR5j8bGRr7yla+wZs0aEokE99xzD5dddhlPPPEEL774Ik1NTWzatIkrrriC+++/n4ULF7Jlyxbuv/9+AJ544glWrFjRZruDU213+fLlNDQ0cPHFFzNr1izefvttRowYwaJFi8jIyGDOnDlMmzaNZcuWUVdXx2OPPcbMmTO55557yMrKSpVumTx5cqo676ZNm5g2bRrnn38+X/3qV5k7dy51dXUkEgkWLlzY49pcndGRRTvS6EynFE/Us2/V4DZUS5SDU+57wYIF/POf/yQcDvPII48QiUS49tpreeaZZ1I/8AsXLuSWW25h+PDhvPbaa6lE0VEp8/buvfdezj33XN5//31ee+01vvGNb9DY2Ag4dbFa3ueZZ55hx44dXHnllfzpT39Kvf6ZZ57ptLRIiw0bNnDzzTezdu1acnNzef7551PPNTY28vbbb/PII49w3XXXdbme++67j/Hjx7Nq1SoeeOABfv/733PhhReyatUqVq9ezbRp07p8fU/oyKIdKx7EtpKESjs8SVypQ9bdCKAvDcUS5eDsZjvzzDMB+NznPsfDDz/M+eefT1lZWaom1fz58/n5z3/ObbfddtDrOytl3torr7zCiy++yIMPPghAJBJh+/btgFN0MCfHOWl34sSJbNu2jVmzZjFu3DjeffddJkyYwPr161MxdqasrCz1Qz59+nS2bt2aeu7qq68GnCq+dXV1hzTPccopp3DdddcRj8e5/PLLeyVZ6MiiHctkYyXrkHYXRlFqsGldovzHP/5xh8X2vvWtb/GDH/wgNUJoXaK8M1OmTOH2229n8eLFbf4SbjF79myWLl3K0qVLmTNnDkVFRb1aohw4KKGJSJflxNvrrJR5a8YYnn/+eVatWsWqVavYvn17qo5S6xhbv37u3Lk8++yzPP/881xxxRXdnqvV2Xo6+4xer7fNaC4SiXS43tmzZ/PGG28wYsQIPv/5z6cOUDgSmizasUwYn91V9RGlBr6hXKIcYPv27bzzjlNL9A9/+AOzZs3i+OOPZ+vWrWzcuBGA3/72t5x99tmH/T4XXnghP/3pT1NJaOXKld2+5jOf+Qx//vOf+cMf/tDtLqjutFyJ78033yQnJ4ecnBzGjh3LBx98AMAHH3zAli1bgIM/37Zt2yguLuaGG27g+uuvT73mSGiyaMeTkUN+oONsrdRgMZRLlAOccMIJPPnkk0ydOpXa2lpuuukmgsEgjz/+OFdddRVTpkzB4/Fw4403ArBgwYLUNbB76jvf+Q7xeJypU6cyefJkvvOd73T7mry8vNRuqZkzj6xidV5eHmeccQY33ngj//M//wPAZz/7WWpra5k2bRoLFy5MbeOCggLOPPNMJk+ezDe+8Q2WLFnCtGnTOOmkk3j++ee59dZbjygWGAAlyvvK4ZYoX/fLnxLIyWbcvGt7Pyh11NAS5epIzJkzhwcffJAZMw6qFN6rDqVEuU5wt3PCF7+S7hCUUmrA0WShlFIDTOu5oYFC5yyU6iNDdRevGhoO9d+nJgul+kAwGKSmpkYThhqQjDHU1NQQDAZ7/BrdDaVUHxg5ciTl5eUcyeV9lepLwWCQkSNH9ri/Jgul+oDP56OsrCzdYSjVa3Q3lFJKqW5pslBKKdUtTRZKKaW6NWTP4BaRKmDbYb68EKjuxXB6m8Z3ZDS+I6PxHZmBHt8YY0xR+8YhmyyOhIgs7+h094FC4zsyGt+R0fiOzECPrzO6G0oppVS3NFkopZTqliaLjj2a7gC6ofEdGY3vyGh8R2agx9chnbNQSinVLR1ZKKWU6pYmC6WUUt3SZNGKiFwkIutFZKOI3JmmGEaJyGsisk5E1orIrW77PSKyU0RWubdPtXrNXW7M60Xkwn6IcauIrHHjWO625YvIYhHZ4C7z0hGfiBzXahutEpE6EbktndtPRB4TkUoR+bBV2yFvLxGZ7m73jSLysIhIH8f4gIh8LCL/FJEXRCTXbR8rIs2ttuUv+jrGTuI75O+0n+N7plVsW0Vkldve79uvVxhj9ObM21jAJmAc4AdWAxPTEEcpcLJ7Pxv4BJgI3AN8vYP+E91YA0CZ+xmsPo5xK1DYru1+4E73/p3AD9IVX7vvdDcwJp3bD5gNnAx8eCTbC1gGnA4I8DJwcR/HeAHgde//oFWMY1v3a7eePomxk/gO+Tvtz/jaPf9D4L/Stf1646YjiwNmAhuNMZuNMTHgaeCy/g7CGFNhjPnAvV8PrANGdPGSy4CnjTFRY8wWYCPOZ+lvlwFPuvefBC5v1Z6u+M4DNhljujqTv8/jM8a8AdR28L493l4iUgqEjTHvGOdX5TetXtMnMRpjXjHGJNyH7wJd1rPuyxg72Yad6fdt2FV87ujg34A/dLWOvv6Oj5QmiwNGADtaPS6n6x/pPiciY4GTgPfcpi+7uwQea7XbIh1xG+AVEVkhIgvcthJjTAU4CQ8oTmN8LebR9j/oQNl+cOjba4R7v7/jbHEdzl+6LcpEZKWIvC4iZ7lt6YjxUL7TdG3Ds4A9xpgNrdoGyvbrMU0WB3S0bzBtxxWLSBbwPHCbMaYOWAiMB6YBFTjDWkhP3GcaY04GLgZuFpHZXfRNy3YVET9wKfBHt2kgbb+udBZP2uIUkW8DCeApt6kCGG2MOQn4KvB7EQmnIcZD/U7TtQ2vpu0fLQNl+x0STRYHlAOjWj0eCexKRyAi4sNJFE8ZY/4EYIzZY4xJGmNs4Fcc2FXS73EbY3a5y0rgBTeWPe4wumU4XZmu+FwXAx8YY/a4sQ6Y7ec61O1VTtvdQP0Sp4jMBy4B/sPdNYK7e6fGvb8CZ07g2P6O8TC+037fhiLiBT4DPNMq7gGx/Q6VJosD3gcmiEiZ+1fpPODF/g7C3b/5P8A6Y8yPWrWXtup2BdBy1MWLwDwRCYhIGTABZ5Ksr+LLFJHslvs4k6AfunHMd7vNBxalI75W2vw1N1C2XyuHtL3cXVX1InKa+2/kmlav6RMichFwB3CpMaapVXuRiFju/XFujJv7O8ZD/U7TsQ2BfwE+Nsakdi8NlO13yNI9wz6QbsCncI4+2gR8O00xzMIZev4TWOXePgX8Fljjtr8IlLZ6zbfdmNfTx0dP4Bwtttq9rW3ZTkAB8CqwwV3mpyM+9/1CQA2Q06otbdsPJ2lVAHGcvx6vP5ztBczA+UHcBPwMtwJDH8a4EWfff8u/w1+4fT/rfvergQ+AT/d1jJ3Ed8jfaX/G57Y/AdzYrm+/b7/euGm5D6WUUt3S3VBKKaW6pclCKaVUtzRZKKWU6pYmC6WUUt3SZKGUUqpbmiyU6iUikisiX+ri+bd7sI6G3o1Kqd6hyUKp3pMLHJQsWk7AMsac0d8BKdVbvOkOQKkh5D5gvHvdgjjQgHOi1jRgoog0GGOy3Lpfi4A8wAf8P2PMwDlTV6kO6El5SvUSt0rwS8aYySIyB/hfYLJxymTTKll4gZAxpk5ECnHKf08wxpiWPmn6CEp1SkcWSvWdZS2Joh0BvudW67VxylCX4FyoSakBSZOFUn2nsZP2/wCKgOnGmLiIbAWC/RaVUodBJ7iV6j31OJfC7U4OUOkminNwLvuq1ICmIwuleokxpkZE3hKRD4FmYE8nXZ8C/iIiy3GquX7cTyEqddh0glsppVS3dDeUUkqpbmmyUEop1S1NFkoppbqlyUIppVS3NFkopZTqliYLpZRS3dJkoZRSqlv/P9PPr/B6GMEKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population',\n", + " \"population_no_mods\",\n", + " \"population_update\",\n", + " \"population_cover\",\n", + " \"population_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['knowledge'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"XCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[[\"knowledge_no_mods\",\n", + " \"knowledge_update\",\n", + " \"knowledge_cover\",\n", + " \"knowledge_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial',\n", + " \"steps_in_trial_no_mods\",\n", + " \"steps_in_trial_update\",\n", + " \"steps_in_trial_cover\",\n", + " \"steps_in_trial_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "98.33000000000001\n", + "82.91999999999999\n", + "81.52000000000001\n", + "82.37000000000002\n", + "79.90999999999998\n" + ] + } + ], + "source": [ + "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_no_mods\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_update\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_cover\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_both\"])/number_of_experiments)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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{}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)\n", + "display(df_no_mods)\n", + "display(df_update)\n", + "display(df_cover)\n", + "display(df_both)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Maze5_pre_populated.ipynb new file mode 100644 index 0000000..fc11562 --- /dev/null +++ b/XCS_Experiments/Experiment_XCS_XNCS_Maze5_pre_populated.ipynb @@ -0,0 +1,3025 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import copy\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('1', '1', '0', '1', '1', '0', '0', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import *\n", + "from utils.nxcs_utils import *\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_maze_metrics)\n", + "\n", + "XNCScfg_no_mods = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = False,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_update = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = True,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_cover = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = False,\n", + " cover_env_input = True,)\n", + "\n", + "XNCScfg_both = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", + " lmc=10,\n", + " lem=100,\n", + " update_env_input = True,\n", + " cover_env_input = True,)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting XCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during cover\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during update\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with all enviromental inputs\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n" + ] + } + ], + "source": [ + "from utils.xcs_utils import avg_experiment as XCSExp\n", + "from utils.nxcs_utils import avg_experiment as XNCSExp\n", + "\n", + "number_of_experiments = 10\n", + "explore = 0\n", + "exploit = 2500\n", + "print(\"Starting XCS\")\n", + "df = XCSExp(maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS\")\n", + "df_no_mods = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_no_mods,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS with enviromental input during cover\")\n", + "df_cover = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_cover,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS with enviromental input during update\")\n", + "df_update = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_update,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS with all enviromental inputs\")\n", + "df_both = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_both,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_all = pd.DataFrame(df)\n", + "\n", + "df_all['steps_in_trial_no_mods']=df_no_mods['steps_in_trial']\n", + "df_all['steps_in_trial_update'] =df_update['steps_in_trial']\n", + "df_all['steps_in_trial_cover'] =df_cover['steps_in_trial']\n", + "df_all['steps_in_trial_both'] =df_both['steps_in_trial']\n", + "\n", + "df_all['population_no_mods']=df_no_mods['population']\n", + "df_all['population_update'] =df_update['population']\n", + "df_all['population_cover'] =df_cover['population']\n", + "df_all['population_both'] =df_both['population']\n", + "\n", + "df_all['average_specificity_no_mods']=df_no_mods['average_specificity']\n", + "df_all['average_specificity_update']=df_update['average_specificity']\n", + "df_all['average_specificity_cover']=df_cover['average_specificity']\n", + "df_all['average_specificity_both']=df_both['average_specificity']\n", + "\n", + "df['fraction_accuracy_no_mods']=df_no_mods['fraction_accuracy']\n", + "df['fraction_accuracy_update']=df_update['fraction_accuracy']\n", + "df['fraction_accuracy_cover']=df_cover['fraction_accuracy']\n", + "df['fraction_accuracy_both']=df_both['fraction_accuracy']\n", + "\n", + "df['knowledge_no_mods']=df_no_mods['knowledge']\n", + "df['knowledge_update']=df_update['knowledge']\n", + "df['knowledge_cover']=df_cover['knowledge']\n", + "df['knowledge_both']=df_both['knowledge']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['average_specificity',\n", + " \"average_specificity_no_mods\",\n", + " \"average_specificity_update\",\n", + " \"average_specificity_cover\",\n", + " \"average_specificity_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[[\"fraction_accuracy_no_mods\",\n", + " \"fraction_accuracy_update\",\n", + " \"fraction_accuracy_cover\",\n", + " \"fraction_accuracy_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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roRp7YzUV+GlsGoSjLoP42pEMrskgtjEwQc4rHkpd+6lw7aExIg8ichlEDSOaPIwu95FWEUnEkQiayq00FjfhdwfHFZx2wkYMJXz8WJwTJiFRiZimRvwNjZjGBvwNjfgbGzANjfgbTywzxo89ORnb4BTsgwdjGzwIe0rweVISYrcDYPwGr8ePp8mHxSo4I2y0l5bN+P346+oCv7Qrq/BXV2FNSMCRloYlsv0vNmMM5Y3lHKg5QEF1AflV+eyt2su+qn0cqDmAz+8nrmEQg2rSSW8Yz+DadMJqYgI7iyEqyk1MfDiupJjAEuckMtZJVHwYrjgnjvD269xQ66Z4XzUl+dUU59dQkl9FY50XAJvFS5JzP8myhQRbASB4ceCzxeB1JOK1x+G1x+KzRuG1uPB4HXgaBU+jH0+Dh/L6MGx4meBbRmb53xBPTeBNI5MhdlhgiUykdZCxuFzY4uMCwSMu/ujz+HgsjvbPdnxVVdSvX099Tg71OTk0bt0WCE5WK2Hjx+OcNoXGiSMoH52MO8JOfFg8CeEJxIfFY7OcWheex+8h90huSwDZXLaZguqCQP3Fwui40aS6UlldtJo6Tx2DIwdzWeZlfCXzK2TGZp7Se/VnZQ1lbC/fzvby7eRX5zMlaQoL0hcQGxZ77IZed3DyS2lwCT6vL4fzH4LTTJOoQaUdXRlUAC785adkJEbyzA3H/q09xcXs/vIV1JsI6sOTaQhPpi5qMPXRKdQ7E/HKsV+a7rA66iIr8Vk8xFQNxu4Odkk4fYQlNxGf4GZQVCMpjnqi6t1IdQ2+ykp8VVW4D5bQuDsff10wFb/TSdjYsYRlZRGWlUV41gQcmZltD0L3Mx6fhwO1B9hXuY991fvYVxVYCsuKcB1JYlBNOnENg4j3DSLKHY+t0RnoT2vF5rQSFQw0rvgwIqMdVJc1UJxfTXVZ4CxHBOKHRJKcHs2g9GiS06NJGBKJxWoJ5H0rzw0Eg+gUcJz8l78xhgP7S1j953xK8+pwpdgYPqccu+dTGg7m0HBkLw0iNIRFU584koa44TS6krDZw4m0RxJhiwg82iOItEUSaY9sed16nSBUu6spri+muK6YkvoSSupLKK8oxLItj9idB0nNrSLjoBe7D/xAcRxURUJ1hFAdITRFh+GPdSFxsdjjE3AmJeNKGkJ0choJrmTiw+I5VHuIzaWb2VS6ie3l22kMnlkmhCUwOWkyk5ImMSlpEhMSJhBhD1xI3OBt4JP9n/De3vdYdWgVPuNjfMJ4Lh9xORenX0xCeELX/mNph6+2Dm9JCd6SEnyVlThHj8KRkdHuD43WPG4ftRWNxCRHtEze6ZDfF+x6LQuMZfq9GJ+P4sZyttXuZ0ddITvqDrGjvohSby0Q+EkRb42g3FePDWGOJYrLPDbm1dUSVlcGjZVtv5fVAT/cG+gJOQ0aVNrR1UHllpdz2F9Rx4f3zTthnXG7cR88iGf/ftwF+3EfOIB7fwGe/QeoLyqlwRZHfXgS9RHJNEQOpiEmFZ89nJimImKq9hJTsp2wioL2Z0dZLFhjYrCnphI2MYvwCRMIy8rCOWJEy5mDCjDGUNpQyt6qvews38nG0o1sKt1EeX0FEe5oEnyDGOPIIt06iiR/CpFNMbirDbUVjdRVu3HFOhmUEd0SRJKGReEIO7NB98N1h1ldtDqwHFpNeWM5GMisyObs/CtxuePYlbSW1cP+RoOj5ph9HX5DmDF4LRbqT+GHp81iw+v3HlMmCPFh8QyKHERyRDKDIgYx2JbAsMJGEneV4Cg4jLeiAn9FJdaqGuzVjcExrhPVhAUC0MFEYfdQG54JmSRNnsWklClMSppESmRKp76cyxrK+Pvev/P+3vfZUbEDq1g5J/UcvpL5FeYPnU+Y7dTHgYzbjaekFG9JcUvQ8JaU4CkuOeZ184+z1qzx8URMm0r41GlETJ9G2NixGJsVv/FjjKHsYA07VhaR90UZngYfNoeFuKHhwSWM2DQnzjgLprYY3+HN+Iu34iveir90Fz5PHYU2GzucDnY4HOxwOjgS/AFo8/mZWu5lSomXURV+Uo4YoqsEf72Fplg/W9Md/DXDzpZkiBQrFzlTuCx2HNMSsrC4kgNT8iOTAme4zujTPksBDSrt6uqg8sgH23llVQE7/vvizv0yCTI+H97Dh3Hv3497f3Ow2Y+34gjW6GissbFYY2KwxsYEHmNisMTEYI2JbSmzREbq1NUzYIzhUN0hNpVsYlNpYNlVsQuvCXzpDosaFvhVnTiZkXEjSItKIzki+bT7+2vcNXxx+IuWQLKvah8A8WHxzE6ZzfiE8YTbwgm3hRPmj6BqlY2SNT4sNmHsggTGnTuYSGcEYViwFX4BeR9B8Tb85Xk0VBVSZxHqRQKPriTqolOocyVTHxlPfXgMdY5ImhzhJIQnMShyEIMiAkEkKTwJu7XzP0JaJgdUVOAtL8dXUUFD6WFqig/SUHYYT2kpYXsPIUWBiQ8SFkb4pEmET5tKxNSpgXGbqM7/Ws47ksd7e9/j/b3vU1Jfgsvu4qL0i5iTOgeHxYFFLFjFisViweo1WA+XYTtYiqWwGMvBYqTwMBQWQXFZyzTzZn67lca4SBpinNREO6iKtlDugrJIH8URHiqsjaQVuRm938fo/T4GVQa+FxvtsCvNQX7aNEzY2UR7MvGKh70JmzgUnUtC/RCSaoeRWJeGzQT+tg22WkpcBZS69lPi2k+Jq4AmWy1hboivgdQjQlZ9NCNqwhlUCVGlDUhpVcuYmQFMeBSWocOQhGS8+/ZByUEsfg8mOpKDmdF8lljBplQv9RmDuGTUZVyWeRmj4kZ1+m/dEQ0q7ejqoPKnNQU8+M5WPr//S8dMK1Z9U4O3gW1l21qCzKbSTVQ0Hk2qbbfYSXWlkhqVSporjaFRQ0lzpZEWlUaqKxWXw9WyrcfnYVPpJlYXrWZV0Sq2lW3DZ3yE28KZNmgas1NmMztlNqPjRrf76/3I4TpWvJnLge0VxA+JZN71oxkyKu7EDT0NULEv0O1WngdleYHH8tzAVOlmtnCYdhOcc0+ga64reRph/Suw6jfg8+IJy6ShKo76w4aGvWU07i0MfKmL4Bw9OvDLf8pUIqZNxT5kyAmHMz4fpqkJ43bjb3Lja2pg86F1LN/zEesOrMFe18TgI5BSYQKPRwxJVWBp9dVVGwaH4+BwnFAUB2UxQkUUVLiEIy6oDQcRC1GOqGMXe3BCjd2FzWLD6vchfh/h5TVEbWnCFKRS6xuHzxpORP1hUoo+I9qylcZhFhoTwFZVga2mBovbisefRqNtFPX2TOrsw2mwJ0Hwh0lYQxnRNQXYPXVHJ784IzHhUfgcEfhsYfjEjtdY8HppM8OhiMGGF6u7HmtTLVZfExZ/E/WOBqrCm/DE20nMyGRM6jjOWTAOq/30fhRpUGlHVweVlmnFt8zi7JGJJ99B9SnNZzMFVQUU1hYGlpqjS43n2G6pWGcsaa40Iu2RbC7bTIO3AYtYyErMagki2UnZp3ZmYAz7Npax4s+7qa1oYtSMQZxzzUgiY9uYmt2W+orANTfleZC/Aja/GbiodNpNMOfewIWkZ8LTAOtehs+egJoiGDob4tID1/iU7gZPoDvJ5xEaaxOor02mocRCw4Ea/I0eIDDbT6wWTFMT/qYmjMcDPj8GcDuiqY8YRF3EYOojBrU891kduOqKiKwvJNJfSLh1P+Fh+zHRHnzxVtzxdnyRdnw2B36rA5/NTpixEGUMUX5/YPH5iPB7sfgD4xm0PAYXnxc89XiNnbzGs9lWfxGHPeOw4GFE2OeMs39EbFUeDaUO6sscNJY7MP6jPxDEYccSFYU1KjpwgW2UC39kLDVhKVTakjjij+VIYyRerNidVuwRDhwRDmyO4Ot2Fqvdgs/jx93oxd3ow9Pow9PopbGqnsayStyVdTTVu/H6LHhtYYEJQ2Lhmv8cxuC0kaf1MWtQaUdXB5WOphWr/q+qqYrC2kIO1hw8JuBUNlUyOWkys4fMZsbgGUQ7zjyLgsftY/0/C1j/YQFWq4UZX84gIzsRm92C1WbBag8sFot0PG5RsRdWPA4blwSCy9QbYM59ENPu3SPaqVADrHsJVj4BtYfxDT0Xz+zv4xk8C2NALIIFg9QdRo7sQSpysZTnIuW7kfJdUF+Bu8pCQ5mdhspw6mwJ1DlSqHEMpsY+hBpbCjW2FDytJrXYxEOMs4aYSA+2MBtVTZFUVDnweJp/fRtiIhtIiK4m0VVBQkQZCeHFRNvKEG9DYBOLrdViDTxa7WCxYcSKHwdeHHiNg3p3JDsLh7KrIIkmt43YWC8TsryMmeggPDYyMAmj1eL3W/GWlQdS/ERGIh3MwOsJvupq6tevp+izjynfvpXZr77dqTGttmhQaUdXB5WOphUr1R0qS+pZ+edcCraUt72BgK05yLT1aBUsNsHib8JSuQ9LzX4s4sMaPwzL4HFYwl1YrILVasFiFQzgaQr8EvY0+fA0uPFUFOOprsDtteOxROEx4fjbvlzmtEREWYgbFEHckGjiUiKJGxRJ7OAIXHHOE74Ujd9QU9FIWWEt5QeblzoqS+pbuovsTisJqZE4wmx4PX68Hj8+jw+vu/m5H6/Hh9fjP6GLyWITRkxJZsK5QxgyKva0v5T7ujMNKppLopPay1asVHeJTY7gsjsncyi3kpryhsCXotfg8/jxeX34vCZY5g+WNX9p+vF7/fh8Br/Pj9dnxx8+Cp9lGP7acvyH6/Af3ovP5sJvjcDvF/y+wDesI8yK3WHB7qvC3liIw19DpMuFY8gY7Akp2MOs2J22lu4ZsQSu5/X7DcZvMMZg/IGuvJayVq8FiE4KbwkezvDOfwWJRYhODCc6MZzM7KP3S/I0+agoqqP8YC1lhbVUHKylsd6LzW4hLNKGze7EardgCy5WhzXw2FwW7IIaNiGecFdozzj6Aw0qpyA9MfKE+6oo1d2GjIqFUbFdd8DK/bDyV7D+j4HXU74B534PIhLgi+fg898ELqIbNw/m3w/Dz+669+4GdqeVQcEp4Cr0NKicgvSECJbvPjZbsVJ9TuwwuOxXgUCy8leBmVwbXg1cLNdwJHALh/n3w7DZoa6p6oM0qJyC9MS2sxUr1SfFpMGXH4c534XPnwzcCuGsOwPJM5U6TRpUTkF6QmB2Sn5ZnQYV1X/EpMIl/xfqWqh+Qi/TPgXpiYGgsk/HVZRSqk0aVE5BSnQYDpuFgvL6UFdFKaV6JQ0qp0CnFSulVMc0qJwinVaslFLt67agIiIviEiJiGxtVfY/IrJZRDaKyIciMqTVugdEJE9EdonIglbl00RkS3DdkxLiy1zTEyIoKK/H7++fmQiUUupMdOeZykvAxceV/cIYM8kYkw28D/wXgIiMB64DJgT3eUpEmu809TSwCBgVXI4/Zo9qnlZcVN148o2VUmqA6bYpxcaY5SKSflxZdauXkRzNvnMF8LoxpgnYJyJ5wEwRyQeijTGrAETkFeBK4B/dVe+TyQhOKy4oqyNVpxWrdng8HgoLC2ls1B8fqncKCwsjLS0Nexff6K/Hr1MRkUeAG4Aq4LxgcSqwutVmhcEyT/D58eXtHXsRgbMahg3rnkzCw1tNK9YU+Ko9hYWFREVFkZ6ePmATE6reyxhDeXk5hYWFZGRkdOmxe3yg3hjzoDFmKPAn4K5gcVv/60wH5e0d+xljzHRjzPSkpKT2NjsjOq1YdUZjYyMJCQkaUFSvJCIkJCR0y5l0KGd/vQZcE3xeCAxttS4NOBQsT2ujPGR0WrHqLA0oqjfrrn+fPRpURKT1TZQvB3YGn/8NuE5EnCKSQWBAfq0xpgioEZHZwVlfNwDv9mSd25KeGEm+BhWllDpBd04pXgKsAsaISKGIfBt4VES2ishm4CLgHgBjzDbgTWA78E/gTmNM862AFgPPAXnAHkI4SN8sIzGSggqdVqx6twMHDpCRkUFFRQUAR44cISMjg4KCAm677TZGjBjBhAkTmDt3LmvWrAHgkUceYcKECUyaNIns7OyWcqU6qztnf13fRvHzHWz/CPBIG+U5QFYXVu2MDU+IwB2cVqwzwFRvNXToUBYvXsz999/PM888w/3338+iRYv40Y9+REZGBrm5uVgsFvbu3cuOHTtYtWoV77//PuvXr8fpdFJWVobb7Q51M1Qfo1fUn4bW04qV6s3uu+8+Vq9ezRNPPMHKlSu5+uqrWbNmDT/96U+xWAL//TMzM/nyl79MUVERiYmJOJ1OABITExkyZEhHh1fqBJr6/jSk67RidQr+33vb2H6o+uQbnoLxQ6J56CsTTrqd3W7nF7/4BRdffDEffvghu3btIjs7G6vVesK2F110Ef/93//N6NGjueCCC1i4cCHz5s3r0nqr/k/PVE7D4OgwnDaLDtarPuEf//gHKSkpbN26tcPtXC4X69at45lnniEpKYmFCxfy0ksv9UwlVb+hZyqnwWIRhidEkK/XqqhO6MwZRXfZuHEjH330EatXr2bOnDksWbKETZs24ff7W7q/WrNarcyfP5/58+czceJEXn75ZW666aaer7jqs/RM5TQNT9Bpxap3M8awePFinnjiCYYNG8YPfvADnnrqKaZPn85DDz2EMYHZi7m5ubz77rvs2rWL3Nzclv03btzI8OHDQ1V91UdpUDlNOq1Y9XbPPvssw4YN48ILLwTgjjvuYOfOndx5550cPnyYkSNHMnHiRG699VaGDBlCbW0tN954I+PHj2fSpEls376dhx9+OLSNUH2Odn+dpvSESJ1WrHq1RYsWsWjRopbXVquVdevWAbQ7AP/555/3SN1U/6VnKqcpPSECQLvAlFKqlU4HFRGxisgQERnWvHRnxXq75mnF+XoXSKWUatGp7i8RuRt4CCgG/MFiA0zqpnr1ejqtWCmlTtTZMZV7gDHGmPLurExf0jyteF+ZTitWSqlmne3+OkDgplqqlfSESAq0+0sppVp09kxlL7BMRD4AmpoLjTG/7JZa9RHpiZEs212K32+wWPTeGUop1dkzlf3AR4ADiGq1DGitpxUr1du0l/r+008/RUT4zW9+07LtXXfddUxKlscee4yxY8eSlZXF5MmTeeWVVwB4//33mTJlCpMnT2b8+PH84Q9/6NE2qd6vU2cqxpj/ByAiUYGXprZba9VHtJ5WrNeqqN6mvdT3w4cPJzk5mV//+tfcdtttOByOY/b7/e9/z0cffcTatWuJjo6mqqqKv/71r3g8HhYtWsTatWtJS0ujqamJ/Pz80DRO9VqdOlMRkSwR2QBsBbaJyDoRCV1Co16iJVuxzgBTvdTxqe+/973vAZCUlMT555/Pyy+/fMI+P/vZz3jqqaeIjo4GICYmhhtvvJGamhq8Xi8JCQkAOJ1OxowZ03ONUX1CZ8dUngG+a4z5BEBE5gPPAmd3T7X6huZpxTpYrzr0j/vh8JauPebgiXDJoyfd7PjU963PSu6//34uueQSbr755paympoaampqGDFixAnHio+P5/LLL2f48OGcf/75XHbZZVx//fVtJqZUA1dn/zVENgcUAGPMMiCyW2rUh+i0YtUXtJf6PiMjg5kzZ/Laa6+1lBljEGl/0slzzz3H0qVLmTlzJo899tgxAUkpOIXZXyLyE+CPwdffBPZ1T5X6lvSESO3+Uh3rxBlFdzk+9f111113zPof//jHXHvttcydOxeA6OhoIiMj2bt3L5mZmW0ec+LEiUycOJFvfetbZGRk6D1X1DE6e6ZyM5AE/AV4J/j8P7qrUn1JumYrVr1UW6nvv//97x+zzdixYxk/fjzvv/9+S9kDDzzAnXfeSXV14G6V1dXVPPPMM9TW1rJs2bKW7TQ1vmpLZ2d/HQG+cyoHFpEXgMuAEmNMVrDsF8BXADewB/gPY0xlcN0DwLcBH/AdY8y/guXTgJeAcODvwD2m+UYQvUDztOJDVQ2kxUWEujpKtWgr9f1LL71EQUHBMds9+OCDTJkypeX14sWLqa2tZcaMGdjtdux2O9/73vcwxvDzn/+c2267jfDwcCIjI/UsRZ1AOvp+FpEnjDH3ish7BHJ9HcMYc3kH+84FaoFXWgWVi4CPjTFeEfm/4DF+JCLjgSXATGAI8G9gtDHGJyJrCaSJWU0gqDxpjPnHyRo2ffp0k5OTc7LNztjne8r4+rNrePXbs5gzSu9XrwJ27NjBuHHjQl0NpTrU1r9TEVlnjJl+usc82ZlK8xjKY6d6YGPMchFJP67sw1YvVwPXBp9fAbxujGkC9olIHjBTRPKBaGPMKgAReQW4EjhpUOkpYwdHY7UIK/PKNKgopQa8DsdUjDHrgk+zjTGftl6A7DN875s5GhxSCeQXa1YYLEsNPj++vE0iskhEckQkp7S09Ayr1znxkQ7OG5PMW+sK8fj8J99BKaX6sc4O1N/YRtlNp/umIvIg4AX+1FzUxmamg/I2GWOeMcZMN8ZMT0pKOt3qnbKFM4ZSVtvEJztLeuw9lVKqN+qw+0tErge+DmSIyN9arYoCTisNvojcSGAA//xWA+6FwNBWm6UBh4LlaW2U9yrnjUkiKcrJmzkHuGjC4FBXRymlQuZkYyqfA0VAIvB4q/IaYPOpvpmIXAz8CJhnjGl9xeDfgNdE5JcEBupHAWuDA/U1IjIbWAPcAPzm+OOGms1q4ZqpaTy7Yi8l1Y0kR4eFukpKKRUSJxtTKTDGLDPGnHXcmMp6Y4y3o31FZAmwChgjIoUi8m3gtwTOcj4SkY0i8vvg+2wD3gS2A/8E7jTG+IKHWgw8B+QRmIbcawbpW/va9DR8fsNb6wtPvrFSSvVTnU0oOVtEvhCRWhFxi4hPRKo72scYc70xJsUYYzfGpBljnjfGjDTGDDXGZAeX21tt/4gxZoQxZkzrKcPGmBxjTFZw3V296RqV1jKTXMxMj+fPOYX00iqqAaS9tPcFBQXk5+eHNPV9Tk4O3/lO4LK3ZcuW8fnnn7esu+mmm3jrrbdOp8kh99JLL3HXXXd1uM3x7e2POjtQ/1vgeiCXwEWIt9ALu6FC7WszhrKvrI61+ypCXRU1wLVOew8ck/YeaEl973a7T9i3der7rVu3snz5cowxLanv33vvPTZt2sSGDRuYP3/+Kddt+vTpPPnkk8DA+JJtbSC0t9PpRY0xeYDVGOMzxrwInNd91eqbLp04GJfTxhs5B06+sVLdrL2099C9qe8nTpxIZWUlxhgSEhJaznK+9a1v8e9//5tly5Zx2WWXkZ+fz+9//3t+9atfkZ2dzYoVKwBYvnw5Z599NpmZme2etbz66qvMnDmT7OxsbrvtNny+QG+5y+XiwQcfZPLkycyePZvi4mKqqqpIT0/H7w9M+a+vr2fo0KF4PJ5jjnn8WZLL5QICgWDu3LlcddVVjB8/nttvv73lWC+++CKjR49m3rx5fPbZZy37vvfee8yaNYspU6ZwwQUXUFxc3GZ7S0tLueaaa5gxYwYzZsw45hh9VWcTStaLiAPYKCI/JzB4P+CzFB8vwmHj8uwh/GV9IQ9fPoHoMHuoq6R6gf9b+3/srNjZpcccGz+WH838UYfbdJT2Hrov9f0555zDZ599xvDhw8nMzGTFihXccMMNrF69mqeffprmTBfp6encfvvtuFyulpxkzz//PEVFRaxcuZKdO3dy+eWXc+211x5z/B07dvDGG2/w2WefYbfbueOOO/jTn/7EDTfcQF1dHbNnz+aRRx7hhz/8Ic8++yz/+Z//yeTJk/n0008577zzeO+991iwYAF2e+f/f65du5bt27czfPhwLr74Yv7yl79wzjnn8NBDD7Fu3TpiYmI477zzWtLdzJkzh9WrVyMiPPfcc/z85z/n8ccfP6G9X//617nvvvuYM2cO+/fvZ8GCBezYsaPT9eqNOhtUvgVYgbuA+whM/72muyrVly2cPpTX1uznbxsP8c3ZmmxPhVbrtPfNOcCanW7q+y1btvDvf/+bxx57jI8++uiE/F/nnnsuy5cvZ/jw4SxevJhnnnmGgwcPEh8f3/LrvyNXXnklFouF8ePHU1xcfML6pUuXsm7dOmbMmAFAQ0MDycnJADgcDi677DIApk2bxkcffQTAwoULeeONNzjvvPN4/fXXueOOO05aj9ZmzpzZkrX5+uuvZ+XKldhsNubPn0/zNXELFy5k9+7dABQWFrJw4UKKiopwu91kZGS0edx///vfbN++veV1dXU1NTU1REX13bu1dzahZHMGugbg/3Vfdfq+SWkxjB0cxZs5BzSoKICTnlF0l7bS3qekpByzTXekvp87dy6/+93v2L9/P4888gjvvPMOb731Fueee26n6u10OluetzXpxRjDjTfeyP/+7/+esM5ut7cERavVitcbmKR6+eWX88ADD1BRUcG6dev40pe+dMK+NputpVvLGHPMeNPxgbb5dXsB+O677+a73/0ul19+OcuWLePhhx9uczu/38+qVasID+8/tyPvcExFRLaIyOb2lp6qZF8iInxt+lA2F1axo6jDCXJKdZvOpL2H7kl9P3ToUMrKysjNzSUzM5M5c+bw2GOPtRlUoqKiqKmpOaW2nX/++bz11luUlAQyWFRUVJyQefl4LpeLmTNncs8993DZZZdhtVpP2CY9PZ116wKZqd59991jxlzWrl3Lvn378Pv9vPHGG8yZM4dZs2axbNkyysvL8Xg8/PnPf27ZvqqqitTUQEap1uNWx7f3oosu4re//W3L640bN57CX6J3OtlA/WUEUtW3t6g2XDUlFYfVwhtf6IC9Co220t7v3LmTTz/99IRtH3zwQQoLj15ftXjxYs477zxmzJhBVlYW8+bNIyIioiX1/ZgxY8jOzuahhx5qN/X9rFmzGD16NBDoDjt48CBz5sw5YbuvfOUrvPPOO8cM1J/M+PHj+elPf8pFF13EpEmTuPDCCykqKjrpfgsXLuTVV19l4cKFba6/9dZb+fTTT5k5cyZr1qwhMvLosPFZZ53F/fffT1ZWFhkZGVx11VWkpKTw8MMPc9ZZZ3HBBRcwderUlu0ffvhhvvrVr3LuueeSmHg00ezx7X3yySfJyclh0qRJjB8/nt///ved+hv0Zh2mvu/Leir1fXvufG09n+WVsebH5+O0nfirSPVvmvq+/1i2bBmPPfbYMWdz/UV3pL7v7MWPNSJSHVwaO3Px40C3cPpQKus9fLjtxIFGpZTqrzoVVIwxUcaY6OASRmDm129Ptt9ANmdkIqmx4byp16wo1afNnz+/X56ldJdOX/zYmjHmr8CJ0ydUC4tFuHZaGityyzhQUX/yHZRSqh/obPfX1a2Wa0XkUTq4r4kK+Or0NETgz+s0yaRSamDo7JlK6xlfCwikvr+iuyrVX6TFRTBnZCJv5RzA59cYrJTq/zp78eN/dHdF+quFM4Zy12sbWJlXxrzRPXc3SqWUCoXOdn9lish7IlIqIiUi8q6ItH25rTrGheMHERdh5029ZkX1IE19r0Kls91frxG4iVYKgTsz/hlY0l2V6k+cNitXTknlw+2Hqag7Mc24Ut1BU9/3XsaYlnQw/VFng4oYY/5ojPEGl1fRgfpOWzhjKB6f4Z0NB0NdFTWAaOr7U0t9X1xczFVXXcXkyZOZPHlyS7D75S9/SVZWFllZWTzxxBMA/OhHP+Kpp55q2ffhhx/m8ccDd1z/xS9+wYwZM5g0aRIPPfQQAPn5+YwbN4477riDqVOncuBA/+256GyW4k9E5H7gdQLBZCHwgYjEAxhj9K5UHRg7OJrJaTG8+cUBbj4nvcMssKr/Ofyzn9G0o2tT3zvHjWXwj3/c4Taa+v7UUt9/5zvfYd68ebzzzjv4fD5qa2tZt24dL774ImvWrMEYw6xZs5g3bx7XXXcd9957b0u24zfffJN//vOffPjhh+Tm5rJ27VqMMVx++eUsX76cYcOGsWvXLl588cVjglF/1NkzlYXAbcAnwDIC942/GVgHhC4XSh/ytRlD2VVcw6bCqlBXRQ0grVPfH+90U98vXbqUmTNn8thjjx0TkJo1p75fvnw5ixcvZsuWLd2W+j47O5ulS5eyd+9e4MTU9/n5+cDR1PcAr7/+epv5vz7++GMWL14MBDIcx8TEsHLlSq666ioiIyNxuVxcffXVrFixgilTplBSUsKhQ4fYtGkTcXFxDBs2jA8//JAPP/yQKVOmMHXqVHbu3Elubi4Aw4cPZ/bs2Sdtf1/X2dlfbd8MQHXaVyYP4X/e384bX+wne2hsqKujetDJzii6i6a+P7XU923pKDfitddey1tvvcXhw4e57rrrWrZ/4IEHuO22247ZNj8//5gElf1ZZ2d/2UXkOyLyVnC5S0Q6vG2aiLwQnCm2tVXZV0Vkm4j4RWT6cds/ICJ5IrJLRBa0Kp8WTMGfJyJPSh/tO4oOs/PliUN4b1MR9W5vqKuj+jlNfX+szqS+P//883n66acB8Pl8VFdXM3fuXP76179SX19PXV0d77zzTks7rrvuOl5//XXeeuutli66BQsW8MILL1BbWwvAwYMHW+o5UHS2++tpYBrwVHCZFizryEvAxceVbQWuBpa3LhSR8cB1wITgPk+JSPOn/jSwCBgVXI4/Zp+xcMZQapu8fLD55Gm6lToTmvr+RCdLff/rX/+aTz75hIkTJzJt2jS2bdvG1KlTuemmm5g5cyazZs3illtuabll8IQJE6ipqSE1NbXlDPCiiy7i61//OmeddRYTJ07k2muvPeWg2dd1KvW9iGwyxkw+WVkb+6UD7xtjso4rXwZ83xiTE3z9AIAx5n+Dr/8FPAzkA58YY8YGy68H5htjjj23bEOoU9+3xRjD+Y9/SoLLwZ9vPzvU1VHdSFPfq74gZKnvAZ+ItEwHCV746DvdN21DKtB6jl1hsCw1+Pz48jaJyCIRyRGRnNLS0i6sXtcQEb46fShf5B9hT2ltqKujlFJdrrNB5QcEphUvC55lfAx8r+NdTklb4ySmg/I2GWOeMcZMN8ZMT0rqnSlRrpmWitUimhJfKdUvdTaofAb8AfAHlz8Aq7qwHoXA0Fav04BDwfK0Nsr7rOSoMM4bk8zb6wqpa9IBe6VU/9LZoPIKkAH8T3DJAP7YhfX4G3CdiDhFJIPAgPxaY0wRUCMis4Ozvm4A3u3C9w2JxfMzKa9z89/vbQ91VZRSqkt19or6MccNyn8iIps62kFElgDzgUQRKQQeAiqA3wBJBK7I32iMWWCM2SYibwLbAS9wpzGmecxmMYGZZOHAP4JLnzZteDx3zB/B7z7Zw/wxSVwyMeXkOymlVB/Q2aCyQURmG2NWA4jILAJdYu0yxlzfzqp32tn+EeCRNspzgKwT9+jb7r1gNCtyy7j/L1vIHhZLSkx4qKuklFJnrLPdX7OAz0UkX0TyCYynzAtelLi522rXj9mtFp5YmI3b6+d7b27CrzfxUl2oP6e+z8/PJyvr1H5nPvHEE9TXH72td2fSxXSXSy+9lMrKyi49ZmVlZa/JKdbZoHIxgXGUecElA7gUuIzA3SDVachMcvHQV8bz+Z5ynlu5N9TVUf2Ipr4/1vFBJZT+/ve/Exsb26XH7HNBxRhT0NHS3ZXszxbOGMqCCYP4xb92sfWgJptUXac/p773er3ceOONTJo0iWuvvbYlYCxdupQpU6YwceJEbr75ZpqamnjyySc5dOgQ5513Huedd17LMY5PkX+8uro6br75ZmbMmMGUKVN4993AHKGXXnqJq6++mosvvphRo0bxwx/+EICnn3665XnzdnffffcJx01PT6esrKwlHf6tt97KhAkTuOiii2hoaABg/vz53HvvvZx99tlkZWWxdu1aIJBi/7HHHms5VlZWFvn5+dx///3s2bOH7OxsfvCDH1BUVMTcuXPJzs4mKyur09kKukJnx1RUNxERHr16Ehf/ejn3vrGR9+6aQ7jjxLxEqu9a8eZuyg507cWuiUNdnPu10R1u019T3wPs2rWL559/nnPOOYebb76Zp556irvuuoubbrqJpUuXMnr0aG644Qaefvpp7r33Xn75y1/yySefkJiYCNBuivzWHnnkEb70pS/xwgsvUFlZycyZM7nggguAQN6zDRs2tATWu+++m2uvvZazzjqLn//85wC88cYbPPjggx1+Rrm5uSxZsoRnn32Wr33ta7z99tt885vfbKnj559/zvLly7n55pvbzDTd7NFHH2Xr1q1s3LgRgMcff5wFCxbw4IMP4vP5evQsrbPdX6obxUU6ePyr2eSV1PKzv+8IdXVUP9IfU99DoHvvnHPOAeCb3/wmK1euZNeuXWRkZLTkHLvxxhtZvnx5m/u3lyK/tQ8//JBHH32U7Oxs5s+fT2NjI/v37wcCySdjYmIICwtj/PjxFBQUkJSURGZmJqtXr6a8vJxdu3a11LE9GRkZZGdnt1mP668PzHWaO3cu1dXVpzQOM2PGDF588UUefvhhtmzZQlRUVKf3PVN6ptJLzBmVyK3nZvDsin3MH5PE+eMGhbpKqouc7Iyiu/TX1PfACYFPRDpMU3+89lLkt2aM4e233z6hi2/NmjXH1LH1/gsXLuTNN99k7NixXHXVVSe9Id/xx2nu/mpuU2sigs1mO+ZWxI2NjW0ed+7cuSxfvpwPPviAb33rW/zgBz/ghhtu6LAuXUXPVHqR7y8Yw7iUaH741mZKatr+x6JUZ/Tn1PcA+/fvZ9WqQFKPJUuWMGfOHMaOHUt+fj55eXkA/PGPf2TevHmn/T4LFizgN7/5TUuw2rBhw0n3ufrqq/nrX//KkiVL2s2G3FnNNxVbuXIlMTExxMTEkJ6ezvr16wFYv349+/btA05sX0FBAcnJydx66618+9vfbtmnJ2hQ6UWcNiu/vi6b2iYvP/jz5lP65aVUa/059T3AuHHjePnll5k0aRIVFRUsXryYsLAwXnzxRb761a8yceJELBYLt99+OwCLFi3ikksuOWag/mR+8pOf4PF4mDRpEllZWfzkJz856T5xcXEt3WEzZ87s9Hu1d6yzzz6b22+/neeffx6Aa665hoqKCrKzs3n66adb/sYJCQmcc845ZGVl8YMf/IBly5aRnZ3NlClTePvtt7nnnnvOqC6nolOp7/ui3pj6vrNeWZXPf727jYe/Mp6bztGbbvZFmvpenYn58+fz2GOPMX36aWeg75RQpr5XPehbs4dz3pgkfvaPnew6PLBu8KOU6ts0qPRCIsLPr51MdJiNe17fQKOnK29do5Tq7ZYtW9btZyndRYNKL5UU5eQX105m5+EafvGvXaGujjoN/bVrWfUP3fXvU4NKL3be2GRuOGs4z6/cx/Ldve9Olqp9YWFhlJeXa2BRvZIxhvLycsLCwrr82HqdSi/340vH8fmecr7350386965xEc6Tr6TCrm0tDQKCwvpjbe1VgoCP3zS0tJOvuEp0tlffcC2Q1Vc9bvPmZUZz/M3zsBh0xNMpVT30NlfA8CEITH89MosVuSWcd8bG/FpmnylVC+l3V99xNdmDKW60cNPP9hBpNPKo1dPwmLpOAWEUkr1NA0qfcgt52ZS3ejlyaW5RDpt/Ndl40+aW0gppXqSBpU+5r4LRlHb6OWFz/YRFWbnuxeGJlmhUkq1RYNKHyMi/OSycdQ2eXhyaS7RYTZuObftbLJKKdXTum2gXkReEJESEdnaqixeRD4SkdzgY1yrdQ+ISJ6I7BKRBa3Kp4nIluC6J0X7exAR/vfqSXx5Ygo//WAHr6/dH+oqKaUU0L2zv14icG/71u4HlhpjRgFLg68RkfHAdcCE4D5PiUjz7Q+fBhYBo4LL8ccckKwW4VcLs5k/JokH3tnCe5sOhbpKSinVfUHFGLMcqDiu+Aqg+abYLwNXtip/3RjTZIzZB+QBM0UkBYg2xqwygQtqXmm1z4DnsFl4+hvTmDE8nvve2MjHO9u+S55SSvWUnr5OZZAxpggg+JgcLE8FDrTarjBYlhp8fnx5m0RkkYjkiEjOQLmSOdxh5fmbpjN+SDSLX13Pqj3loa6SUmoA6y0XP7Y1TmI6KG+TMeYZY8x0Y8z0pKSkLqtcbxcVZufl/5jJsPgIbnn5CzYeqAx1lZRSA1RPB5XiYJcWwceSYHkhMLTVdmnAoWB5Whvl6jhxkQ5evWUWCS4nN76wVu/DopQKiZ4OKn8Dbgw+vxF4t1X5dSLiFJEMAgPya4NdZDUiMjs46+uGVvuo4wyKDuNPt8wizG7hm8+vIb+sLtRVUkoNMN05pXgJsAoYIyKFIvJt4FHgQhHJBS4MvsYYsw14E9gO/BO40xjTfGeqxcBzBAbv9wD/6K469wdD4yN49duz8Pr8fOO5NRRVNYS6SkqpAUSzFPdTWw9Wcf0zq0mJDePPt51NTIQ91FVSSvUBmqVYtSkrNYY/3DCN/LJ6bnnlC70lsVKqR2hQ6cfOHpHIrxZmk1NwhO8s2YDX5w91lZRS/ZwGlX7uy5NSeOiy8Xy4vZifvLtNb2+rlOpWmlByALjpnAxKapp4atkekqOc3KeZjZVS3USDygDxgwVjKK1p4tdLc0mOdvKNWcNDXSWlVD+kQWWACGQ2nkh5nZuf/HUriS4nCyYMDnW1lFL9jI6pDCA2q4XffX0qk4fGcveSDazdd3y+T6WUOjMaVAaYcIeV52+cQVpcOLe8/IWmc1FKdSkNKgNQfKSDV26eSbjDyo0vrOVgpV51r5TqGhpUBqi0uAhevnkmdW4vNzy/hiN17lBXSSnVD2hQGcDGDo7m2Rumc+BIA99++Qsa3HrVvVLqzGhQGeBmZybw64XZbDhQyV2vrder7pVSZ0SDiuKSiSn89xVZLN1ZwgN/2YJHA4tS6jTpdSoKgG/NHk5Z8OLI3cU1PP61bEYmu0JdLaVUH6NnKqrFfReO5qlvTGV/RT1ffnIFL6zch9+vucKUUp2nQUUd49KJKfzrvrnMGZnIf7+/nW88t4bCI/WhrpZSqo/QoKJOkBwVxnM3Tufn10xic2ElFz+xgjdzDmiGY6XUSWlQUW0SEb42Yyj/vHcuE4ZE88O3NnPrK+sorWkKddWUUr2YBhXVoaHxESy5dTb/+eVxLM8tZcETy/nHlqJQV0sp1UuFJKiIyD0islVEtonIvcGyeBH5SERyg49xrbZ/QETyRGSXiCwIRZ0HMotFuOXcTD64ew6pseEs/tN67ntjI1UNnlBXTSnVy/R4UBGRLOBWYCYwGbhMREYB9wNLjTGjgKXB14jIeOA6YAJwMfCUiFh7ut4KRg2K4i93nM29F4zib5sOseBXy1m+uzTU1VJK9SKhOFMZB6w2xtQbY7zAp8BVwBXAy8FtXgauDD6/AnjdGNNkjNkH5BEISCoE7FYL914wmnfuOJtIp5UbXljL3Us2sCK3FJ9OP1ZqwAvFxY9bgUdEJAFoAC4FcoBBxpgiAGNMkYgkB7dPBVa32r8wWKZCaFJaLB9851x+9e/dvLZmP+9tOkRylJPLJw/hqqmpjE+JRkRCXU2lVA/r8aBijNkhIv8HfATUApsAbwe7tPXN1OZPYhFZBCwCGDZs2BnWVJ1MmN3KA5eM474LRvPxzhLe2XCQl1fl89zKfYwe5OLKKalckZ1Kamx4qKuqlOohEuprD0TkZwTOPu4B5gfPUlKAZcaYMSLyAIAx5n+D2/8LeNgYs6qj406fPt3k5OR0c+3V8Y7UuflgSxF/3XCQnIIjAMzOjOeqKalcnJVCTLg9xDVUSnVERNYZY6af9v6hCCoikmyMKRGRYcCHwFnAj4FyY8yjInI/EG+M+aGITABeIzCOMoTAIP4oY0yHedo1qITe/vJ63t14kHc2HGRvWR0Om4ULxiVzZXYq88ck47DpjHalepu+GlRWAAmAB/iuMWZpcIzlTWAYsB/4qjGmIrj9g8DNBLrJ7jXG/ONk76FBpfcwxrC5sIp3NhzkvU2HKK9zExdh54rsVK6dlsaEITr+olRv0SeDSk/QoNI7eXx+VuaW8db6Qj7aXozb62fs4CiumZrGFVOGkBwVFuoqKjWgaVBphwaV3q+q3sN7mw/x1rpCNh6oxGoR5o9O4pppaZw/Lhmnrf9cjmSMoazWTV5JLeV1TfhNoMxvDMaA34DfGAg+Nr82wX2tFsFusWC3CXarBbvVgiP4aLPK0dfB9eF2KykxYXoGqE6ZBpV2aFDpW/JKanl7fSF/WV9IcXUTMeF2Lp88hGunpTEpLabdL8dGj4/SmiZKa5sCj81LbRMCpMaFkxobTlpcBGlx4SS5nFgs3fdFa4zhUFUjucU15JXUsqe0ltziWvJKa6ms79kMBDHhdialxQSXWCanxTI4Rs8Ez1R5bRMbD1TS4PFx4fhB/erHD2hQaZcGlb7J5zd8llfGW+sK+de2wzR5/YxMdnHh+EE0uH0twaMsGDxqmk6cjS4C8REO/MZw5LgvcofVwpDYMFLjwkmLjQg8BgNPalw4DpsFr8/g8xu8foPP78frN8eVGbx+Pz6/oa7Jx96yWvKCgSOvpJZ699E5JHERdkYlRzEi2cXIZBejkl0MjgnDIoIIWESwBB8hkBKn+bUQSOwpAn6/we3z4/UZPD4/bp8fT/C5p/m519+yrqbRy7ZD1Ww6UMmu4pqWC1OTo5zBABPDpKGBx9gIR/d9oH2c2+tn5+FqNuyvZMP+I2w4UElB+dFbQaTEhHH7vBEsnDGUMHv/CC4aVNqhQaXvq2708MHmIt5eV0hOwRGinDaSopwkRjlJinKS5Ao+tnqdHOUkPtKBzRqYWVbX5OVQZQOFRxoorGyg8Eg9B48EXh+sbOiyrMuDo8MYNcjFiKSjwWNksosEl7NLjn8mGj0+th2qZnNhJZsLq9hUWMne0rqW9cPiI5g8NJapw2KZMzKRkcmuAdttVlTVcDSA7K9ky8EqmryB22snRzmZOiyOKcNimTIsjjq3l999nEdOwRGSopwsOjeTb8weRoSjb99QV4NKOzSo9C8enx+7teunIDd6fC1B51BlA16/wWYRrBbBZhWsFsvR1y2PgXEMm0Vw2qykJ0YQFda3rr+pbvSwtbCKTYVVLcHmYGUDEAiQc0Ylcu6oRM4ZmUhiLwiMXcHnN5TXHe0iLWnVXXqosoHNhVUcrm4EwGGzMDE1hilDAwFkyrDYNseojDGs2lvObz/O4/M95cRHOvj2nAxuOGt4n/s30UyDSjs0qCh1agqP1LMyt4wVeWV8llfWMgY0PiWac0clcu6oJKanx/Xqbh6vz8+/thWzvajqmMBRUtNEeW1ggsTxosJsJEc5yWoVRMalRJ/ydVTrCip4cmken+4uJSbczn+ck85/nJ1BTETfCi4aVNqhQUWp0+fzG7YdqmJFbhkrcktZV3AEj8/gtFmYmRHPuaMSmTMyiXEpUb2iq6zJ6+OtdYX8/tM9HKhowGoREl0OkqPCSIoKdIs2d5MmtzyGkehyEu7o2iC5ubCS33ycx0fbi3E5bdxw1nC+PSejV3SFdoYGlXZoUFGq69Q1eVm7r6IlyOSW1AIwfXgcj14ziZHJrpDVa8na/Ty7Yi/F1U1MHhrLXeeN5Etjk7F24yy/zthRVM1vP87j71uLCLNZ+ebsYVw6MSUwLuhy9tozPg0q7dCgolT3OVzVyIfbD/P4h7tpcPu454JRLJqb2S3jXm2pqvfw8qp8XvxsH0fqPZyVmcBdXxrJ2SMSesWZU2t5JTX87pM9vLvx4DHdby6njUSXg0SXk4TgY6IrOBHF5SDBFZh0YhE5ZhZiy6zEE2YkHp2teGlWymlPndeg0g4NKkp1v9KaJh7+2zY+2FLE+JRofn7tJLJSY7r1/Z5fuY9XVxdQ2+TlgnHJLJ4/kmnD406+c4gVHqlnZ1EN5XVNlNW6Ka1porzOTVlNE2W1geX4KfCna+f/XHzaZ0IaVNqhQUWpnvPPrYf5ybtbqahzs2huJvecP6pLu3cOVjbwzKd7eP2LA3h8fr48aQh3zB/BuJToLnuP3sDr81NR56a0tonyWjcVdW6AY2cfWoMzEDuYpTgiyaVnKl1Ng4pSPauq3sMjf9/OmzmFZCZG8ug1k5iZEX/ax/P6/GwqrOL1tft5Z8NBRODqKWncPn8EGYmRXVhz1ZoGlXZoUFEqNFbmlnH/XzZTeKSBb80ezg8vHtPpazYOVzWyfHcpn+4uZUVuKdWNXsLsFq6bMYxFczMZojd863YaVNqhQUWp0Kl3e3nsX7t58fN9pESH8cjVEzlvTPIJ2zV5fXyx7wjLc0v5dFcpu4prABgU7WTe6CTmjk7i3JFJfe5aj75Mg0o7NKgoFXrrCo7wo7c3k1dSy9VTUvnJZeOpbPDw6a4SlueWsWpPOQ0eHw6rhRkZcS2BZMyg3nH9y0CkQaUdGlSU6h2avD5+93EeTy3bgwh4fIHvnPSECOaNTmLemCRmZyb0+ZxZ/cWZBhX9FJVS3cpps/Ldi8ZwycQU/rSmgDGDopg7OonhCTrY3h9pUFFK9YhxKdH89MqJoa6G6mY9c/mrUkqpAUGDilJKqS4TkqAiIveJyDYR2SoiS0QkTETiReQjEckNPsa12v4BEckTkV0isiAUdVZKKXVyPR5URCQV+A4w3RiTBViB64D7gaXGmFHA0uBrRGR8cP0E4GLgKRHpnek9lVJqgAtV95cNCBcRGxABHAKuAF4Orn8ZuDL4/ArgdWNMkzFmH5AHzOzZ6iqllOqMHg8qxpiDwGPAfqAIqDLGfAgMMsYUBbcpApovv00FDrQ6RGGwTCmlVC8Tiu6vOAJnHxnAECBSRL7Z0S5tlLV5xaaILBKRHBHJKS0tPfPKKqWUOiWh6P66ANhnjCk1xniAvwBnA8UikgIQfCwJbl8IDG21fxqB7rITGGOeMcZMN8ZMT0pK6rYGKKWUaluPp2kRkVnAC8AMoAF4CcgBhgHlxphHReR+IN4Y80MRmQC8RmAcZQiBQfxRxhjfSd6nFCg4zWomAmWnuW9fN5DbDgO7/QO57TCw29+67cONMaf9q7zHr6g3xqwRkbeA9YAX2AA8A7iAN0Xk2wTGW74a3H6biLwJbA9uf+fJAkpwv9P+o4hIzpnkvunLBnLbYWC3fyC3HQZ2+7uy7SFJ02KMeQh46LjiJuD8drZ/BHiku+ullFLqzOgV9UoppbqMBpW2PRPqCoTQQG47DOz2D+S2w8Buf5e1vd/eT0UppVTP0zMVpZRSXUaDilJKqS6jQaUVEbk4mAk5L3itTL8kIvkiskVENopITrCsX2aJFpEXRKRERLa2KjvltorItODfLE9EnpQ+cgP1dtr/sIgcDH7+G0Xk0lbr+k37RWSoiHwiIjuCWdHvCZb3+8+/g7Z3/2dvjNElMK5kBfYAmYAD2ASMD3W9uqmt+UDicWU/B+4PPr8f+L/g8/HBv4WTQGqdPYA11G04hbbOBaYCW8+krcBa4CwCaYP+AVwS6radQfsfBr7fxrb9qv1ACjA1+DwK2B1sY7///Dtoe7d/9nqmctRMIM8Ys9cY4wZeJ5CjbKDol1mijTHLgYrjik+prcG0QdHGmFUm8L/slVb79GrttL89/ar9xpgiY8z64PMaYAeBZLT9/vPvoO3t6bK2a1A5aiBlQzbAhyKyTkQWBcsGUpboU21ravD58eV92V0isjnYPdbc/dNv2y8i6cAUYA0D7PM/ru3QzZ+9BpWjOp0NuR84xxgzFbgEuFNE5naw7UD6u7TX1v72N3gaGAFkE7j9xOPB8n7ZfhFxAW8D9xpjqjvatI2yPt3+Ntre7Z+9BpWjOp0Nua8zxhwKPpYA7xDozjrjLNF9yKm2tTD4/PjyPskYU2yM8Rlj/MCzHO3O7HftFxE7gS/VPxlj/hIsHhCff1tt74nPXoPKUV8Ao0QkQ0QcBG5h/LcQ16nLiUikiEQ1PwcuArYSaOuNwc1uBN4NPv8bcJ2IOEUkAxhFYOCuLzultga7SGpEZHZw5ssNrfbpc5q/UIOuIvD5Qz9rf7CuzwM7jDG/bLWq33/+7bW9Rz77UM9S6E0LcCmBWRJ7gAdDXZ9uamMmgVkem4Btze0EEgjcViA3+Bjfap8Hg3+TXfTyWS9ttHcJgdN8D4FfXd8+nbYC04P/AfcAvyWYjaK3L+20/4/AFmBz8MskpT+2H5hDoKtmM7AxuFw6ED7/Dtre7Z+9pmlRSinVZbT7SymlVJfRoKKUUqrLaFBRSinVZTSoKKWU6jIaVJRSSnUZDSpKdRERiRWROzpY/3knjlHbtbVSqmdpUFGq68QCJwQVEbECGGPO7ukKKdXTbKGugFL9yKPACBHZSOBiw1oCFx5mA+NFpNYY4wrmY3oXiAPswH8aY3r1FdpKdZZe/KhUFwlmg33fGJMlIvOBD4AsE0glTqugYgMijDHVIpIIrAZGGWNM8zYhaoJSZ0zPVJTqPmubA8pxBPhZMDu0n0Aq8UHA4Z6snFLdQYOKUt2nrp3ybwBJwDRjjEdE8oGwHquVUt1IB+qV6jo1BG7dejIxQEkwoJwHDO/eainVc/RMRakuYowpF5HPRGQr0AAUt7Ppn4D3RCSHQPbYnT1URaW6nQ7UK6WU6jLa/aWUUqrLaFBRSinVZTSoKKWU6jIaVJRSSnUZDSpKKaW6jAYVpZRSXUaDilJKqS7z/wH8x8BCPhgAVwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population',\n", + " \"population_no_mods\",\n", + " \"population_update\",\n", + " \"population_cover\",\n", + " \"population_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['knowledge'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"XCS\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[[\"knowledge_no_mods\",\n", + " \"knowledge_update\",\n", + " \"knowledge_cover\",\n", + " \"knowledge_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial',\n", + " \"steps_in_trial_no_mods\",\n", + " \"steps_in_trial_update\",\n", + " \"steps_in_trial_cover\",\n", + " \"steps_in_trial_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "127.03999999999999\n", + "70.00000000000001\n", + "64.45\n", + "72.22\n", + "73.51\n" + ] + } + ], + "source": [ + "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_no_mods\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_update\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_cover\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_both\"])/number_of_experiments)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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trial
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90028.9974.1404130.4217491600.01370.713.4946250.9961.712329
100023.41038.0833190.3628521600.01377.614.0117500.9991.849315
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150016.51073.6458620.2474951600.01331.413.8082500.9931.575342
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170058.6646.6826761.0094491600.01346.113.8793130.9911.506849
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190024.21014.6547110.4027611600.01348.913.3561250.9931.301370
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210028.4977.5170220.3955051600.01351.614.1415000.9991.095890
220036.7916.0382040.5294461600.01361.014.8900000.9971.643836
230032.6943.9950590.4420641600.01361.614.4911881.0000.890411
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942.902117 0.391535 1600.0 1342.8 \n", + "1800 44.9 669.967474 0.523945 1600.0 1349.7 \n", + "1900 32.4 1026.477604 0.343825 1600.0 1341.8 \n", + "2000 30.7 886.346511 0.318347 1600.0 1343.3 \n", + "2100 24.1 944.467929 0.267384 1600.0 1360.9 \n", + "2200 30.1 1004.293669 0.347660 1600.0 1351.7 \n", + "2300 26.4 966.582602 0.306483 1600.0 1353.7 \n", + "2400 26.9 983.294166 0.297097 1600.0 1351.4 \n", + "\n", + " average_specificity fraction_accuracy knowledge \n", + "trial \n", + "0 8.261312 0.949 14.041096 \n", + "100 8.553250 0.701 18.493151 \n", + "200 9.701312 0.622 17.739726 \n", + "300 11.211813 0.602 17.876712 \n", + "400 11.653937 0.627 17.602740 \n", + "500 12.230000 0.645 17.671233 \n", + "600 12.673312 0.610 18.219178 \n", + "700 12.996437 0.681 17.260274 \n", + "800 13.453875 0.705 15.753425 \n", + "900 13.233750 0.616 17.191781 \n", + "1000 13.579437 0.680 16.232877 \n", + "1100 13.556188 0.662 16.917808 \n", + "1200 13.746125 0.702 15.410959 \n", + "1300 12.531312 0.731 17.945205 \n", + "1400 12.793688 0.739 16.917808 \n", + "1500 13.473937 0.708 16.643836 \n", + "1600 13.554313 0.728 19.726027 \n", + "1700 13.864625 0.698 18.835616 \n", + "1800 14.251250 0.734 17.876712 \n", + "1900 13.223875 0.686 19.041096 \n", + "2000 13.130187 0.770 19.520548 \n", + "2100 13.853563 0.615 17.534247 \n", + "2200 13.707187 0.561 18.287671 \n", + "2300 13.795500 0.693 16.164384 \n", + "2400 13.814312 0.614 16.780822 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)\n", + "display(df_no_mods)\n", + "display(df_update)\n", + "display(df_cover)\n", + "display(df_both)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb new file mode 100644 index 0000000..09be353 --- /dev/null +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb @@ -0,0 +1,2464 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import copy\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['.', 'O', 'O', '.', '.', '.', '.', '.']\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import *\n", + "from utils.nxcs_utils import *\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "XNCScfg_no_mods = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = False,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_update = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = True,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_cover = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = False,\n", + " cover_env_input = True,)\n", + "\n", + "XNCScfg_both = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = True,\n", + " cover_env_input = True,)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting XCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during cover\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during update\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with all enviromental inputs\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n" + ] + } + ], + "source": [ + "from utils.xcs_utils import avg_experiment as XCSExp\n", + "from utils.nxcs_utils import avg_experiment as XNCSExp\n", + "\n", + "number_of_experiments = 10\n", + "explore = 1000\n", + "exploit = 1000\n", + "print(\"Starting XCS\")\n", + "df = XCSExp(\n", + " maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS\")\n", + "df_no_mods = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_no_mods,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS with enviromental input during cover\")\n", + "df_cover = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_cover,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS with enviromental input during update\")\n", + "df_update = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_update,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + " )\n", + "print(\"Starting XNCS with all enviromental inputs\")\n", + "df_both = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_both,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=False\n", + "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_all = pd.DataFrame(df)\n", + "\n", + "df_all['steps_in_trial_no_mods']=df_no_mods['steps_in_trial']\n", + "df_all['steps_in_trial_update'] =df_update['steps_in_trial']\n", + "df_all['steps_in_trial_cover'] =df_cover['steps_in_trial']\n", + "df_all['steps_in_trial_both'] =df_both['steps_in_trial']\n", + "\n", + "df_all['population_no_mods']=df_no_mods['population']\n", + "df_all['population_update'] =df_update['population']\n", + "df_all['population_cover'] =df_cover['population']\n", + "df_all['population_both'] =df_both['population']\n", + "\n", + "df_all['average_specificity_no_mods']=df_no_mods['average_specificity']\n", + "df_all['average_specificity_update']=df_update['average_specificity']\n", + "df_all['average_specificity_cover']=df_cover['average_specificity']\n", + "df_all['average_specificity_both']=df_both['average_specificity']\n", + "\n", + "df_all['fraction_accuracy_no_mods']=df_no_mods['fraction_accuracy']\n", + "df_all['fraction_accuracy_update']=df_update['fraction_accuracy']\n", + "df_all['fraction_accuracy_cover']=df_cover['fraction_accuracy']\n", + "df_all['fraction_accuracy_both']=df_both['fraction_accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['average_specificity',\n", + " \"average_specificity_no_mods\",\n", + " \"average_specificity_update\",\n", + " \"average_specificity_cover\",\n", + " \"average_specificity_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[[\"fraction_accuracy_no_mods\",\n", + " \"fraction_accuracy_update\",\n", + " \"fraction_accuracy_cover\",\n", + " \"fraction_accuracy_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population',\n", + " \"population_no_mods\",\n", + " \"population_update\",\n", + " \"population_cover\",\n", + " \"population_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial',\n", + " \"steps_in_trial_no_mods\",\n", + " \"steps_in_trial_update\",\n", + " \"steps_in_trial_cover\",\n", + " \"steps_in_trial_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.090000000000003\n", + "21.96\n", + "20.27\n", + "20.19\n", + "16.4\n" + ] + } + ], + "source": [ + "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_no_mods\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_update\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_cover\"])/number_of_experiments)\n", + "print(sum(df[\"steps_in_trial_both\"])/number_of_experiments)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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steps_in_trialrewardperf_timenumerositypopulationaverage_specificityfraction_accuracy
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steps_in_trialrewardperf_timenumerositypopulationaverage_specificityfraction_accuracy
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 16.4 1000.000000 0.009913 49.3 48.4 \n", + "100 7.9 1361.271487 0.060853 1211.2 677.9 \n", + "200 12.9 1212.254701 0.161913 1600.0 916.5 \n", + "300 7.0 1391.110547 0.101733 1600.0 1018.4 \n", + "400 11.6 1189.616061 0.196893 1600.0 1078.3 \n", + "500 12.8 1080.610024 0.213625 1600.0 1136.0 \n", + "600 7.8 1296.385204 0.137334 1600.0 1177.4 \n", + "700 17.0 1107.833345 0.321735 1600.0 1210.9 \n", + "800 6.5 1355.103457 0.094710 1600.0 1215.5 \n", + "900 5.1 1379.515701 0.092159 1600.0 1236.8 \n", + "1000 14.9 1115.686736 0.213837 1600.0 1255.5 \n", + "1100 3.6 1535.159016 0.044629 1600.0 1250.6 \n", + "1200 4.0 1545.666275 0.058915 1600.0 1258.9 \n", + "1300 3.6 1604.892079 0.057837 1600.0 1255.3 \n", + "1400 5.5 1406.009543 0.080619 1600.0 1258.5 \n", + "1500 8.5 1440.822189 0.119539 1600.0 1262.7 \n", + "1600 4.2 1589.038677 0.067650 1600.0 1269.2 \n", + "1700 6.3 1350.584447 0.081440 1600.0 1278.1 \n", + "1800 3.3 1525.365672 0.048491 1600.0 1279.8 \n", + "1900 5.1 1564.625838 0.073603 1600.0 1289.3 \n", + "\n", + " average_specificity fraction_accuracy \n", + "trial \n", + "0 10.134139 0.07085 \n", + "100 12.852362 0.77100 \n", + "200 10.530125 0.81900 \n", + "300 11.579125 0.82900 \n", + "400 12.902125 0.84300 \n", + "500 14.264000 0.83100 \n", + "600 14.810937 0.83300 \n", + "700 16.049812 0.84900 \n", + "800 16.925250 0.83400 \n", + "900 16.842250 0.87800 \n", + "1000 17.152937 0.85000 \n", + "1100 16.990438 0.79300 \n", + "1200 16.922125 0.77700 \n", + "1300 17.115687 0.79200 \n", + "1400 17.046500 0.70700 \n", + "1500 17.092625 0.72200 \n", + "1600 17.169125 0.77000 \n", + "1700 17.085000 0.73800 \n", + "1800 16.890812 0.77500 \n", + "1900 17.039687 0.66600 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)\n", + "display(df_no_mods)\n", + "display(df_update)\n", + "display(df_cover)\n", + "display(df_both)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb new file mode 100644 index 0000000..c62b1c4 --- /dev/null +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb @@ -0,0 +1,2868 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import copy\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['.', '.', '.', '.', '.', 'O', 'O', 'F']\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import *\n", + "from utils.nxcs_utils import *\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "XNCScfg_no_mods = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = False,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_update = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = True,\n", + " cover_env_input = False,)\n", + "\n", + "XNCScfg_cover = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = False,\n", + " cover_env_input = True,)\n", + "\n", + "XNCScfg_both = XNCSConfig(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=0.8, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20,\n", + " update_env_input = True,\n", + " cover_env_input = True,)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting XCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during cover\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with enviromental input during update\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n", + "Starting XNCS with all enviromental inputs\n", + "Executing 0 experiment\n", + "Executing 1 experiment\n", + "Executing 2 experiment\n", + "Executing 3 experiment\n", + "Executing 4 experiment\n", + "Executing 5 experiment\n", + "Executing 6 experiment\n", + "Executing 7 experiment\n", + "Executing 8 experiment\n", + "Executing 9 experiment\n" + ] + } + ], + "source": [ + "from utils.xcs_utils import avg_experiment as XCSExp\n", + "from utils.nxcs_utils import avg_experiment as XNCSExp\n", + "\n", + "number_of_experiments = 10\n", + "explore = 0\n", + "exploit = 2500\n", + "print(\"Starting XCS\")\n", + "df = XCSExp(maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS\")\n", + "df_no_mods = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_no_mods,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS with enviromental input during cover\")\n", + "df_cover = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_cover,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS with enviromental input during update\")\n", + "df_update = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_update,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + " )\n", + "print(\"Starting XNCS with all enviromental inputs\")\n", + "df_both = XNCSExp(\n", + " maze=maze,\n", + " cfg=XNCScfg_both,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=0,\n", + " exploit_trials=exploit + explore,\n", + " pre_generate=True\n", + "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_all = pd.DataFrame(df)\n", + "\n", + "df_all['steps_in_trial_no_mods']=df_no_mods['steps_in_trial']\n", + "df_all['steps_in_trial_update'] =df_update['steps_in_trial']\n", + "df_all['steps_in_trial_cover'] =df_cover['steps_in_trial']\n", + "df_all['steps_in_trial_both'] =df_both['steps_in_trial']\n", + "\n", + "df_all['population_no_mods']=df_no_mods['population']\n", + "df_all['population_update'] =df_update['population']\n", + "df_all['population_cover'] =df_cover['population']\n", + "df_all['population_both'] =df_both['population']\n", + "\n", + "df_all['average_specificity_no_mods']=df_no_mods['average_specificity']\n", + "df_all['average_specificity_update']=df_update['average_specificity']\n", + "df_all['average_specificity_cover']=df_cover['average_specificity']\n", + "df_all['average_specificity_both']=df_both['average_specificity']\n", + "\n", + "df['fraction_accuracy_no_mods']=df_no_mods['fraction_accuracy']\n", + "df['fraction_accuracy_update']=df_update['fraction_accuracy']\n", + "df['fraction_accuracy_cover']=df_cover['fraction_accuracy']\n", + "df['fraction_accuracy_both']=df_both['fraction_accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['average_specificity',\n", + " \"average_specificity_no_mods\",\n", + " \"average_specificity_update\",\n", + " \"average_specificity_cover\",\n", + " \"average_specificity_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[[\"fraction_accuracy_no_mods\",\n", + " \"fraction_accuracy_update\",\n", + " \"fraction_accuracy_cover\",\n", + " \"fraction_accuracy_both\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population',\n", + " \"population_no_mods\",\n", + " \"population_update\",\n", + " \"population_cover\",\n", + " \"population_both\",\n", + " ]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS\" , \"XNCS with env update\", \"XNCS with env cover\", \"XNCS with both env inputs\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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steps_in_trialrewardperf_timenumerositypopulationaverage_specificityfraction_accuracy
trial
022.9900.0000000.4297071600.01457.08.2110000.982192
1005.81413.2305910.0980901600.01405.08.3465630.674000
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7008.21414.9668540.1316561600.01379.310.7545000.602000
8009.21188.4136720.1351591600.01380.910.5633750.685000
9007.61514.7256590.1091071600.01381.110.9217500.678000
10006.21381.3275340.1144951600.01378.511.1600000.637000
11007.51445.5706690.1112001600.01374.111.8643750.596000
120010.91280.6849150.1830981600.01379.611.9753130.619000
130017.31077.2683160.2625311600.01372.611.7810630.631000
14009.61311.6462570.1539941600.01373.112.3241250.619000
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20007.01167.9519070.1312391600.01368.012.8683120.623000
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220022.9781.2204730.4514361600.01366.013.4358750.598000
230014.21210.0089920.2190511600.01365.213.2608120.580000
24009.51109.3736770.1638101600.01365.113.2625000.614000
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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### XCS in Maze 5 without #\n", - "This experiment is meant to replicate An Analysis of Generalization in the XCS Classifier System Maze5 experiment. \n", - "In this experiment I am repeating experiment that involves XCS without wildcards in classifiers similarly to experiment in An Analysis of Generalization." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# logging \n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# environment setup\n", - "import gym\n", - "# noinspection PyUnresolvedReferences\n", - "import gym_maze\n", - "\n", - "from lcs.agents.xcs import XCS, Configuration\n", - "from utils.xcs_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is how maze looks like\n", - "\n", - "('0', '1', '1', '1', '1', '0', '0', '0')\n", - "\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" - ] - } - ], - "source": [ - "maze = gym.make('Maze5-v0')\n", - "print(\"This is how maze looks like\")\n", - "situation = maze.reset()\n", - "print(type(situation))\n", - "print(situation)\n", - "maze.render()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.08488149999999983, 'population': 129, 'numerosity': 158}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 1000.0000365528504, 'perf_time': 0.18531830000000582, 'population': 408, 'numerosity': 1601}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 13, 'reward': 1011.6540120677265, 'perf_time': 0.06103619999998955, 'population': 413, 'numerosity': 1608}\n", - 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}, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 26, 'reward': 1000.1357462068407, 'perf_time': 0.12296279999998205, 'population': 403, 'numerosity': 1603}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 11, 'reward': 1023.8645885666168, 'perf_time': 0.05471420000003491, 'population': 388, 'numerosity': 1629}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.8194111999998768, 'population': 424, 'numerosity': 1607}\n", - "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 94, 'reward': 1000.0000000000123, 'perf_time': 0.5796378999998524, 'population': 466, 'numerosity': 1617}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 32, 'reward': 1000.0, 'perf_time': 0.27035829999999805, 'population': 411, 'numerosity': 1600}\n", - 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"INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 12, 'reward': 1018.6632250194959, 'perf_time': 0.02555130000018835, 'population': 414, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 10, 'reward': 1033.410276623872, 'perf_time': 0.02101330000004964, 'population': 414, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 7, 'reward': 1108.492531798027, 'perf_time': 0.014777099999264465, 'population': 414, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 7, 'reward': 1100.5038122357378, 'perf_time': 0.014674799999738752, 'population': 414, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 11, 'reward': 1026.3022079997672, 'perf_time': 0.023061899999447633, 'population': 414, 'numerosity': 1600}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 4, 'reward': 1475.9610047441915, 'perf_time': 0.008260599999630358, 'population': 414, 'numerosity': 1600}\n" - ] - } - ], - "source": [ - "cfg = Configuration(number_of_actions=8,\n", - " max_population=1600,\n", - " learning_rate=0.2,\n", - " alpha=0.1,\n", - " gamma=0.71,\n", - " mutation_chance=0.01,\n", - " delta=0.1,\n", - " ga_threshold=25,\n", - " covering_wildcard_chance = 0.7,\n", - " chi=1, # crossover\n", - " metrics_trial_frequency=100,\n", - " initial_prediction =10, # p_i\n", - " initial_error = 0, # epsilon_i\n", - " initial_fitness = 10, # f_i\n", - " user_metrics_collector_fcn=xcs_metrics)\n", - "\n", - "df = avg_experiment(maze,\n", - " cfg,\n", - " number_of_tests=10,\n", - " explore_trials=4000,\n", - " exploit_metrics=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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340045.3798.1109920.198865412.11608.0
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55.1 902.503179 0.240463 411.5 1601.8\n", - "1900 29.9 1073.104994 0.125898 415.1 1605.4\n", - "2000 52.7 702.841748 0.232894 412.7 1608.8\n", - "2100 46.9 816.429593 0.195120 412.6 1605.4\n", - "2200 29.5 1010.784989 0.128012 415.2 1614.2\n", - "2300 49.8 768.640571 0.205957 419.6 1604.6\n", - "2400 42.7 980.939986 0.205186 407.1 1607.5\n", - "2500 55.0 715.335693 0.256959 419.5 1603.6\n", - "2600 48.1 941.183872 0.208127 415.5 1606.9\n", - "2700 44.5 812.407615 0.180920 424.0 1613.6\n", - "2800 23.0 1064.272052 0.097381 420.0 1609.9\n", - "2900 47.9 889.646661 0.204406 419.2 1601.9\n", - "3000 46.9 815.581951 0.214013 417.6 1607.3\n", - "3100 19.7 1176.331103 0.082842 421.7 1611.2\n", - "3200 35.2 902.400717 0.155223 413.6 1606.1\n", - "3300 56.4 706.520153 0.237292 415.1 1609.1\n", - "3400 45.3 798.110992 0.198865 412.1 1608.0\n", - "3500 50.8 962.454246 0.200385 412.9 1606.7\n", - "3600 37.0 963.947120 0.159397 414.2 1603.1\n", - "3700 18.8 1136.462400 0.079272 420.7 1611.3\n", - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df[['numerosity', 'population']].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"numerosity\", \"number of rules\"])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['steps_in_trial'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"steps in trial\")\n", - "ax.legend([\"steps\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ax = df['population'].plot()\n", - "ax.set_xlabel(\"trial\")\n", - "ax.set_ylabel(\"population\")\n", - "ax.legend([\"number of rules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/XCS_Experiments/XCS_maze5_avg.ipynb b/XCS_Experiments/XCS_maze5_avg.ipynb new file mode 100644 index 0000000..a886cca --- /dev/null +++ b/XCS_Experiments/XCS_maze5_avg.ipynb @@ -0,0 +1,959 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_maze\n", + "\n", + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "('0', '1', '0', '1', '1', '0', '0', '1')\n", + "\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" + ] + } + ], + "source": [ + "maze = gym.make('Maze5-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.055027700000000124, 'population': 108, 'numerosity': 167, 'average_specificity': 7.245508982035928, 'knowledge': 59.589041095890416}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 70, 'reward': 1000.0, 'perf_time': 0.27830520000000547, 'population': 410, 'numerosity': 1600, 'average_specificity': 5.7225, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 13, 'reward': 1012.782498522416, 'perf_time': 0.030381000000005542, 'population': 426, 'numerosity': 1600, 'average_specificity': 6.3775, 'knowledge': 96.57534246575342}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 37, 'reward': 1000.0031738801285, 'perf_time': 0.18006750000000693, 'population': 423, 'numerosity': 1600, 'average_specificity': 5.80125, 'knowledge': 97.94520547945206}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 44, 'reward': 1000.0002984256316, 'perf_time': 0.1673784999999839, 'population': 404, 'numerosity': 1600, 'average_specificity': 6.54625, 'knowledge': 97.26027397260275}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 5, 'reward': 1000.0000000000002, 'perf_time': 0.020100200000001678, 'population': 422, 'numerosity': 1600, 'average_specificity': 6.125, 'knowledge': 99.31506849315068}\n", + "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 46, 'reward': 1000.0001697955181, 'perf_time': 0.2517175000000407, 'population': 434, 'numerosity': 1600, 'average_specificity': 6.938125, 'knowledge': 97.26027397260275}\n", + "INFO:lcs.agents.Agent:{'trial': 2800, 'steps_in_trial': 22, 'reward': 1000.5341751391559, 'perf_time': 0.0999247999999966, 'population': 418, 'numerosity': 1600, 'average_specificity': 6.25375, 'knowledge': 95.2054794520548}\n", + "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 19, 'reward': 1001.497199283639, 'perf_time': 0.07930079999994177, 'population': 435, 'numerosity': 1600, 'average_specificity': 6.07, 'knowledge': 97.26027397260275}\n", + "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 64, 'reward': 1000.000000302367, 'perf_time': 0.2234621000000061, 'population': 400, 'numerosity': 1600, 'average_specificity': 6.294375, 'knowledge': 97.94520547945206}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1527.2184528198086, 'perf_time': 0.0037139000000934175, 'population': 415, 'numerosity': 1600, 'average_specificity': 7.343125, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 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510, 'numerosity': 1600, 'average_specificity': 7.86, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 6, 'reward': 1170.036841884431, 'perf_time': 0.01597679999997581, 'population': 497, 'numerosity': 1600, 'average_specificity': 7.361875, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 14, 'reward': 1000.0, 'perf_time': 0.07232060000001184, 'population': 508, 'numerosity': 1600, 'average_specificity': 7.9625, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 7, 'reward': 1155.526554708486, 'perf_time': 0.02238050000005387, 'population': 489, 'numerosity': 1600, 'average_specificity': 7.10125, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 12, 'reward': 1028.8523603862018, 'perf_time': 0.05500140000003739, 'population': 481, 'numerosity': 1600, 'average_specificity': 6.349375, 'knowledge': 99.31506849315068}\n" + ] + } + ], + "source": [ + "cfg = Configuration(number_of_actions=8,\n", + " max_population=1600,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.01,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_maze_metrics)\n", + "\n", + "df = avg_experiment(maze,\n", + " cfg,\n", + " number_of_tests=1,\n", + " explore_trials=4000,\n", + " exploit_trials=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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460061.170037e+030.01597749716007.361875100.000000
4700141.000000e+030.07232150816007.962500100.000000
480071.155527e+030.02238148916007.10125098.630137
4900121.028852e+030.05500148116006.34937599.315068
\n", + "
" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 100 0.000000e+00 0.055028 108 167 \n", + "100 100 1.336087e-12 0.356117 425 1600 \n", + "200 29 1.000000e+03 0.110790 384 1600 \n", + "300 33 1.000012e+03 0.153194 397 1600 \n", + "400 70 1.000000e+03 0.278305 410 1600 \n", + "500 16 1.005230e+03 0.057963 410 1600 \n", + "600 48 1.000000e+03 0.156724 414 1600 \n", + "700 100 1.336087e-12 0.347222 405 1600 \n", + "800 13 1.012782e+03 0.030381 426 1600 \n", + "900 32 1.000017e+03 0.128177 416 1600 \n", + "1000 27 1.000000e+03 0.136447 425 1600 \n", + "1100 23 1.000379e+03 0.080909 430 1600 \n", + "1200 37 1.000003e+03 0.180068 423 1600 \n", + "1300 33 1.000012e+03 0.118465 399 1600 \n", + "1400 2 1.504100e+03 0.004387 391 1600 \n", + "1500 51 1.000000e+03 0.173603 421 1600 \n", + "1600 44 1.000000e+03 0.167378 404 1600 \n", + "1700 46 1.000000e+03 0.206975 421 1600 \n", + "1800 29 1.000049e+03 0.128477 417 1600 \n", + "1900 2 1.632200e+03 0.003618 408 1600 \n", + "2000 5 1.000000e+03 0.020100 422 1600 \n", + "2100 76 1.000000e+03 0.268280 420 1600 \n", + "2200 70 1.000000e+03 0.282579 430 1600 \n", + "2300 89 1.000000e+03 0.387760 435 1600 \n", + "2400 46 1.000000e+03 0.251718 434 1600 \n", + "2500 13 1.011787e+03 0.061125 410 1600 \n", + "2600 100 1.336090e-12 0.362560 424 1600 \n", + "2700 100 5.688650e-87 0.446025 416 1600 \n", + "2800 22 1.000534e+03 0.099925 418 1600 \n", + "2900 100 1.336087e-12 0.320588 408 1600 \n", + "3000 93 1.000000e+03 0.410648 415 1600 \n", + "3100 63 1.000000e+03 0.266077 412 1600 \n", + "3200 19 1.001497e+03 0.079301 435 1600 \n", + "3300 28 1.000068e+03 0.097678 407 1600 \n", + "3400 22 1.000534e+03 0.087050 442 1600 \n", + "3500 1 1.711287e+03 0.002187 425 1600 \n", + "3600 64 1.000000e+03 0.223462 400 1600 \n", + "3700 2 1.504106e+03 0.003406 402 1600 \n", + "3800 48 1.000000e+03 0.229014 452 1600 \n", + "3900 28 1.000076e+03 0.125957 430 1600 \n", + "4000 2 1.527218e+03 0.003714 415 1600 \n", + "4100 73 1.000000e+03 0.329450 483 1600 \n", + "4200 13 1.011787e+03 0.043513 500 1600 \n", + "4300 10 1.000000e+03 0.061012 457 1600 \n", + "4400 5 1.180423e+03 0.019264 454 1600 \n", + "4500 11 1.023112e+03 0.028424 510 1600 \n", + "4600 6 1.170037e+03 0.015977 497 1600 \n", + "4700 14 1.000000e+03 0.072321 508 1600 \n", + "4800 7 1.155527e+03 0.022381 489 1600 \n", + "4900 12 1.028852e+03 0.055001 481 1600 \n", + "\n", + " average_specificity knowledge \n", + "trial \n", + "0 7.245509 59.589041 \n", + "100 6.675000 99.315068 \n", + "200 6.528750 97.945205 \n", + "300 7.879375 95.890411 \n", + "400 5.722500 98.630137 \n", + "500 6.540000 99.315068 \n", + "600 5.796250 97.945205 \n", + "700 6.329375 93.835616 \n", + "800 6.377500 96.575342 \n", + "900 5.876250 97.945205 \n", + "1000 6.293750 97.945205 \n", + "1100 5.943125 95.205479 \n", + "1200 5.801250 97.945205 \n", + "1300 6.774375 98.630137 \n", + "1400 6.448125 93.150685 \n", + "1500 5.656250 97.260274 \n", + "1600 6.546250 97.260274 \n", + "1700 7.603125 97.260274 \n", + "1800 6.098125 97.260274 \n", + "1900 7.208750 96.575342 \n", + "2000 6.125000 99.315068 \n", + "2100 6.541250 98.630137 \n", + "2200 6.363750 99.315068 \n", + "2300 6.456250 97.260274 \n", + "2400 6.938125 97.260274 \n", + "2500 6.042500 98.630137 \n", + "2600 7.073125 100.000000 \n", + "2700 5.903750 95.890411 \n", + "2800 6.253750 95.205479 \n", + "2900 6.211250 97.260274 \n", + "3000 5.560625 97.945205 \n", + "3100 6.769375 95.890411 \n", + "3200 6.070000 97.260274 \n", + "3300 5.535625 99.315068 \n", + "3400 6.360000 99.315068 \n", + "3500 6.555000 98.630137 \n", + "3600 6.294375 97.945205 \n", + "3700 7.173750 97.260274 \n", + "3800 6.813125 97.945205 \n", + "3900 6.946875 97.945205 \n", + "4000 7.343125 100.000000 \n", + "4100 13.568750 100.000000 \n", + "4200 10.360000 99.315068 \n", + "4300 8.755000 97.945205 \n", + "4400 8.353750 93.835616 \n", + "4500 7.860000 100.000000 \n", + "4600 7.361875 100.000000 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity', 'population']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"numerosity\", \"number of rules\"])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['population'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"number of rules\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['knowledge'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"knowledge\")\n", + "ax.legend([\"knowledge%\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/XCS_Experiments/XCS_woods1.ipynb b/XCS_Experiments/XCS_woods1.ipynb index 1f03c0f..63eb326 100644 --- a/XCS_Experiments/XCS_woods1.ipynb +++ b/XCS_Experiments/XCS_woods1.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -42,12 +42,12 @@ "text": [ "This is how maze looks like\n", "\n", - "['.', '.', '.', 'O', '.', '.', '.', '.']\n", + "['.', 'O', 'O', 'O', '.', '.', '.', '.']\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } @@ -63,18 +63,11 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 36, 'reward': 1000.0, 'perf_time': 0.0187924000001658, 'population': 84, 'numerosity': 89, 'average_specificity': 9.561797752808989, 'fraction_accuracy': 1.0}\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -83,325 +76,20 @@ ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 1, 'reward': 2006.1456306207742, 'perf_time': 0.002873100000215345, 'population': 603, 'numerosity': 1800, 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"output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mnumber_of_tests\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m exploit_trials=500)\n\u001b[0m", + "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\xcs_utils.py\u001b[0m in \u001b[0;36mavg_experiment\u001b[1;34m(maze, cfg, number_of_tests, explore_trials, exploit_trials, pre_generate)\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[0mpopulation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m \u001b[0mtest_metrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart_single_experiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexplore_trials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexploit_trials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 105\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'trial'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Documents\\GitHub\\pyalcs-experiments\\XCS_Experiments\\utils\\xcs_utils.py\u001b[0m in \u001b[0;36mstart_single_experiment\u001b[1;34m(maze, cfg, explore_trials, exploit_metrics, population)\u001b[0m\n\u001b[0;32m 108\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstart_single_experiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaze\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_trial_explore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecay\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexploit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\pyalcs\\lcs\\agents\\Agent.py\u001b[0m in \u001b[0;36m_evaluate\u001b[1;34m(self, env, n_trials, func, decay)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[0muser_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_cfg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muser_metrics_collector_fcn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0muser_metrics\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_metrics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 139\u001b[0m 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"execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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steps_in_trialrewardperf_timepopulationnumerosityaverage_specificityfraction_accuracy
trial
020.2900.0000000.01035955.456.98.5581131.000000e+00
1009.01403.3789390.060909572.81776.812.1776556.953332e-16
2008.11427.6783910.056508632.21800.010.4612227.718738e-16
30015.21200.3474870.129537643.71800.010.4509441.611548e-15
4009.81276.1993330.082102617.31800.010.3293892.836941e-15
50016.4970.8903410.122038608.31800.010.3155006.008248e-15
60015.71041.6719330.140665624.31800.010.2540001.802908e-15
70010.21312.4788530.101546641.61800.010.3892222.112054e-15
8009.41234.0592600.073682646.61800.09.8983891.312832e-15
9007.91256.7957700.060683640.21800.010.1761679.353784e-16
10006.01443.3203110.055585668.91800.010.5637228.861977e-16
11004.51470.9183040.038603670.01800.010.7318337.022988e-16
120010.81162.6468160.121073662.41800.010.4580004.883559e-15
13007.11197.0804360.056639670.11800.010.1558337.444679e-15
140016.21164.4554810.144555674.21800.010.6106118.436648e-14
150017.21107.3417410.148655669.11800.010.3720563.682139e-15
160013.81161.7188830.113599651.81800.010.4440564.204611e-16
170012.61205.0067810.097266666.31800.010.9050561.027987e-14
180016.51195.2252470.155213660.91800.010.4897222.001767e-15
190015.01139.9033370.132232676.61800.010.0529441.210971e-14
20009.31000.0000000.037497672.81800.09.7370001.277823e-14
21003.61503.6314050.012683672.91800.09.7370001.275005e-14
22002.81790.8393860.009350672.91800.09.7370001.274822e-14
23004.41601.8595640.014384672.91800.09.7370001.275061e-14
24005.51431.8738680.017843672.91800.09.7370001.274875e-14
\n", - "
" - ], - "text/plain": [ - " steps_in_trial reward perf_time population numerosity \\\n", - "trial \n", - "0 20.2 900.000000 0.010359 55.4 56.9 \n", - "100 9.0 1403.378939 0.060909 572.8 1776.8 \n", - "200 8.1 1427.678391 0.056508 632.2 1800.0 \n", - "300 15.2 1200.347487 0.129537 643.7 1800.0 \n", - "400 9.8 1276.199333 0.082102 617.3 1800.0 \n", - "500 16.4 970.890341 0.122038 608.3 1800.0 \n", - "600 15.7 1041.671933 0.140665 624.3 1800.0 \n", - "700 10.2 1312.478853 0.101546 641.6 1800.0 \n", - "800 9.4 1234.059260 0.073682 646.6 1800.0 \n", - "900 7.9 1256.795770 0.060683 640.2 1800.0 \n", - "1000 6.0 1443.320311 0.055585 668.9 1800.0 \n", - "1100 4.5 1470.918304 0.038603 670.0 1800.0 \n", - "1200 10.8 1162.646816 0.121073 662.4 1800.0 \n", - "1300 7.1 1197.080436 0.056639 670.1 1800.0 \n", - "1400 16.2 1164.455481 0.144555 674.2 1800.0 \n", - "1500 17.2 1107.341741 0.148655 669.1 1800.0 \n", - "1600 13.8 1161.718883 0.113599 651.8 1800.0 \n", - "1700 12.6 1205.006781 0.097266 666.3 1800.0 \n", - "1800 16.5 1195.225247 0.155213 660.9 1800.0 \n", - "1900 15.0 1139.903337 0.132232 676.6 1800.0 \n", - "2000 9.3 1000.000000 0.037497 672.8 1800.0 \n", - "2100 3.6 1503.631405 0.012683 672.9 1800.0 \n", - "2200 2.8 1790.839386 0.009350 672.9 1800.0 \n", - "2300 4.4 1601.859564 0.014384 672.9 1800.0 \n", - "2400 5.5 1431.873868 0.017843 672.9 1800.0 \n", - "\n", - " average_specificity fraction_accuracy \n", - "trial \n", - "0 8.558113 1.000000e+00 \n", - "100 12.177655 6.953332e-16 \n", - "200 10.461222 7.718738e-16 \n", - "300 10.450944 1.611548e-15 \n", - "400 10.329389 2.836941e-15 \n", - "500 10.315500 6.008248e-15 \n", - "600 10.254000 1.802908e-15 \n", - "700 10.389222 2.112054e-15 \n", - "800 9.898389 1.312832e-15 \n", - "900 10.176167 9.353784e-16 \n", - "1000 10.563722 8.861977e-16 \n", - "1100 10.731833 7.022988e-16 \n", - "1200 10.458000 4.883559e-15 \n", - "1300 10.155833 7.444679e-15 \n", - "1400 10.610611 8.436648e-14 \n", - "1500 10.372056 3.682139e-15 \n", - "1600 10.444056 4.204611e-16 \n", - "1700 10.905056 1.027987e-14 \n", - "1800 10.489722 2.001767e-15 \n", - "1900 10.052944 1.210971e-14 \n", - "2000 9.737000 1.277823e-14 \n", - "2100 9.737000 1.275005e-14 \n", - "2200 9.737000 1.274822e-14 \n", - "2300 9.737000 1.275061e-14 \n", - "2400 9.737000 1.274875e-14 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(df)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df[['numerosity', 'population']].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -870,32 +165,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df['steps_in_trial'].plot()\n", "ax.set_xlabel(\"trial\")\n", diff --git a/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb b/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb index 976101f..62ee0d1 100644 --- a/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb +++ b/XCS_Experiments/XNCS_Woods1_pre_populated_default_settings.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,22 +14,28 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "This is how maze looks like\n", - "\n", - "['.', '.', '.', '.', '.', '.', '.', 'O']\n", - "\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + "This is how maze looks like\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'int' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mmaze\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgym\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Woods1-v0'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"This is how maze looks like\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0msituation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaze\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msituation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msituation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\miniconda3\\envs\\pyalcs-experiments\\lib\\site-packages\\gym\\wrappers\\time_limit.py\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_episode_started_at\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_elapsed_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\openai-envs\\gym_woods\\envs\\woods_env.py\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[0mlogging\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Resetting the environment'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_insert_animat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 53\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_observe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\metron\\documents\\github\\openai-envs\\gym_woods\\envs\\woods_env.py\u001b[0m in \u001b[0;36m_insert_animat\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[0mstarting_position\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpossible_coords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 162\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpos_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstarting_position\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 163\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpos_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstarting_position\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: 'int' object is not subscriptable" ] } ], @@ -44,12 +50,12 @@ "situation = maze.reset()\n", "print(type(situation))\n", "print(situation)\n", - "maze.render()\n" + "maze.render()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -66,58 +72,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 0 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 15, 'reward': 1000.0, 'perf_time': 0.1265051999998832, 'numerosity': 1000, 'population': 806, 'average_specificity': 60.11, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 4, 'reward': 1000.0000092886122, 'perf_time': 0.025052799999912168, 'numerosity': 1000, 'population': 692, 'average_specificity': 70.576, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 50, 'reward': 1.8142877965529527e-12, 'perf_time': 0.28711920000000646, 'numerosity': 1000, 'population': 682, 'average_specificity': 56.262, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 50, 'reward': 3.655253076036953e-05, 'perf_time': 0.29238710000004176, 'numerosity': 1000, 'population': 633, 'average_specificity': 55.454, 'fraction_accuracy': 0.009540117416829745}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 50, 'reward': 1.5562945950803363e-79, 'perf_time': 0.23974199999997836, 'numerosity': 1000, 'population': 567, 'average_specificity': 50.586, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 10, 'reward': 1035.6984443065674, 'perf_time': 0.04180100000007769, 'numerosity': 1000, 'population': 535, 'average_specificity': 42.112, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 20, 'reward': 1001.0597655484517, 'perf_time': 0.09176580000007561, 'numerosity': 1000, 'population': 562, 'average_specificity': 38.994, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 3, 'reward': 1358.102259434339, 'perf_time': 0.011067800000091665, 'numerosity': 1000, 'population': 560, 'average_specificity': 38.526, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 10, 'reward': 1041.2217947216698, 'perf_time': 0.05651409999995849, 'numerosity': 1000, 'population': 575, 'average_specificity': 35.321, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 5, 'reward': 1196.1816232805677, 'perf_time': 0.025229599999875063, 'numerosity': 1000, 'population': 581, 'average_specificity': 35.385, 'fraction_accuracy': 0.06944444444444445}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Executing 1 experiment\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 12, 'reward': 1000.0, 'perf_time': 0.1048000000000684, 'numerosity': 1000, 'population': 808, 'average_specificity': 63.404, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 4, 'reward': 1271.4399322294107, 'perf_time': 0.018099200000051496, 'numerosity': 1000, 'population': 701, 'average_specificity': 52.824, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 4, 'reward': 1278.2960306875025, 'perf_time': 0.017446099999915532, 'numerosity': 1000, 'population': 675, 'average_specificity': 47.536, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 750, 'steps_in_trial': 7, 'reward': 1091.6214180314962, 'perf_time': 0.03658799999993789, 'numerosity': 1000, 'population': 636, 'average_specificity': 45.459, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 5, 'reward': 1219.35614631337, 'perf_time': 0.021666899999900124, 'numerosity': 1000, 'population': 652, 'average_specificity': 43.837, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1250, 'steps_in_trial': 6, 'reward': 1175.643591521514, 'perf_time': 0.02585999999996602, 'numerosity': 1000, 'population': 647, 'average_specificity': 40.477, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 9, 'reward': 1074.844471182247, 'perf_time': 0.058271200000035606, 'numerosity': 1000, 'population': 674, 'average_specificity': 39.439, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1750, 'steps_in_trial': 9, 'reward': 1049.0586011431242, 'perf_time': 0.04900720000000547, 'numerosity': 1000, 'population': 639, 'average_specificity': 36.87, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 50, 'reward': 5.497866015975701e-05, 'perf_time': 0.23830449999991288, 'numerosity': 1000, 'population': 654, 'average_specificity': 37.573, 'fraction_accuracy': 0.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2250, 'steps_in_trial': 4, 'reward': 1292.0071188549996, 'perf_time': 0.021888200000148572, 'numerosity': 1000, 'population': 648, 'average_specificity': 34.501, 'fraction_accuracy': 0.0}\n" - ] - } - ], + "outputs": [], 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df['fraction_accuracy'].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -413,32 +134,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df[['numerosity', 'population']].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -455,32 +153,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df['steps_in_trial'].plot()\n", "ax.set_xlabel(\"trial\")\n", diff --git a/XCS_Experiments/XNCS_maze5_avg.ipynb b/XCS_Experiments/XNCS_maze5_avg.ipynb index 3c8bb7b..375ad01 100644 --- a/XCS_Experiments/XNCS_maze5_avg.ipynb +++ b/XCS_Experiments/XNCS_maze5_avg.ipynb @@ -75,7 +75,7 @@ " initial_prediction =10, # p_i\n", " initial_error = 0.1, # epsilon_i\n", " initial_fitness = 10, # f_i\n", - " user_metrics_collector_fcn=xncs_metrics,\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", " lmc=10,\n", " lem=20\n", " )\n" @@ -83,16 +83,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.0656737999999999, 'numerosity': 154, 'population': 140, 'average_specificity': 8.37012987012987, 'fraction_accuracy': 0.0}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.0675823000000002, 'numerosity': 142, 'population': 120, 'average_specificity': 11.330985915492958, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n" ] }, { @@ -106,7 +106,58 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 39, 'reward': 1000.0015815234814, 'perf_time': 0.6533796999999879, 'numerosity': 1800, 'population': 1385, 'average_specificity': 22.82777777777778, 'fraction_accuracy': 0.0}\n" + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 10, 'reward': 1032.552731331664, 'perf_time': 0.2017388000000011, 'numerosity': 1800, 'population': 1403, 'average_specificity': 20.795, 'fraction_accuracy': 0.84, 'knowledge': 53.42465753424658}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 24, 'reward': 1000.2715051852821, 'perf_time': 0.4192424000000301, 'numerosity': 1800, 'population': 1491, 'average_specificity': 28.43888888888889, 'fraction_accuracy': 0.81, 'knowledge': 54.794520547945204}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 31, 'reward': 1000.0247764723307, 'perf_time': 0.5578600000000051, 'numerosity': 1800, 'population': 1452, 'average_specificity': 30.996666666666666, 'fraction_accuracy': 0.91, 'knowledge': 59.589041095890416}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 9, 'reward': 1045.9873809239048, 'perf_time': 0.1266768000000411, 'numerosity': 1800, 'population': 1470, 'average_specificity': 32.58833333333333, 'fraction_accuracy': 0.92, 'knowledge': 58.9041095890411}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 1.6572696000000633, 'numerosity': 1800, 'population': 1466, 'average_specificity': 28.324444444444445, 'fraction_accuracy': 0.92, 'knowledge': 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" + ], + "text/plain": [ + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 100.0 0.000000 0.069996 145.0 121.0 \n", + "100 70.0 500.000561 0.984925 1800.0 1333.5 \n", + "200 55.0 516.276366 0.799799 1800.0 1407.5 \n", + "300 34.5 1000.786646 0.595199 1800.0 1461.0 \n", + "400 2.5 1483.634372 0.029921 1800.0 1466.5 \n", + "500 28.0 1000.144448 0.521904 1800.0 1492.0 \n", + "600 23.5 1355.746595 0.413378 1800.0 1460.0 \n", + "700 24.0 1001.529286 0.426210 1800.0 1469.5 \n", + "800 62.5 500.095594 1.135274 1800.0 1472.5 \n", + "900 48.5 1252.052209 0.870956 1800.0 1481.0 \n", + "1000 5.0 1022.993690 0.074215 1800.0 1463.0 \n", + "1100 35.5 1000.049602 0.631769 1800.0 1469.5 \n", + "1200 51.0 500.000000 0.850797 1800.0 1469.5 \n", + "1300 62.0 500.134639 1.215521 1800.0 1493.5 \n", + "1400 70.5 1000.000000 1.244140 1800.0 1472.5 \n", + "1500 61.5 1000.137751 0.983772 1800.0 1487.0 \n", + "1600 54.0 540.628726 0.912167 1800.0 1490.0 \n", + "1700 25.5 1000.000791 0.435786 1800.0 1474.0 \n", + "1800 42.0 1355.267088 0.761390 1800.0 1491.0 \n", + "1900 60.5 1000.000013 0.993086 1800.0 1488.5 \n", + "2000 52.0 1000.000791 0.885380 1800.0 1478.5 \n", + "2100 55.0 1000.000143 0.780990 1800.0 1469.0 \n", + "2200 96.0 500.000000 1.700811 1800.0 1470.5 \n", + "2300 7.5 1189.245344 0.123608 1800.0 1492.5 \n", + "2400 37.0 1000.048312 0.639642 1800.0 1493.5 \n", + "2500 41.0 1000.001333 0.586432 1800.0 1481.5 \n", + "2600 4.0 1415.277862 0.043132 1800.0 1487.0 \n", + "2700 18.5 1078.214391 0.226590 1800.0 1472.0 \n", + "2800 14.5 1102.584099 0.171283 1800.0 1451.0 \n", + "2900 12.0 1343.045389 0.170188 1800.0 1453.5 \n", + "3000 10.5 1129.783910 0.140102 1800.0 1437.0 \n", + "3100 5.0 1194.276053 0.073919 1800.0 1440.5 \n", + "3200 12.0 1165.370745 0.135427 1800.0 1462.0 \n", + "3300 5.0 1180.929020 0.070255 1800.0 1469.0 \n", + "3400 10.0 1082.543634 0.115155 1800.0 1470.0 \n", + "3500 4.5 1235.588179 0.056824 1800.0 1478.5 \n", + "3600 18.0 1400.568498 0.243222 1800.0 1482.0 \n", + "3700 4.0 1293.555682 0.051959 1800.0 1484.5 \n", + "3800 19.0 1003.127863 0.222973 1800.0 1457.5 \n", + "3900 10.5 1057.224226 0.146836 1800.0 1468.0 \n", + "4000 5.0 1233.582511 0.065150 1800.0 1468.0 \n", + "4100 7.0 1108.549140 0.110883 1800.0 1454.0 \n", + "4200 4.0 1364.282429 0.034481 1800.0 1437.5 \n", + "4300 27.5 1001.075253 0.364979 1800.0 1456.0 \n", + "4400 25.5 1000.294849 0.322313 1800.0 1473.5 \n", + "4500 16.5 1005.806084 0.218426 1800.0 1451.0 \n", + "4600 14.0 1179.085354 0.145653 1800.0 1438.5 \n", + "4700 5.0 1306.952751 0.069333 1800.0 1448.5 \n", + "4800 10.0 1097.150819 0.092332 1800.0 1436.5 \n", + "4900 4.0 1531.874253 0.046110 1800.0 1448.5 \n", + "\n", + " average_specificity fraction_accuracy knowledge \n", + "trial \n", + "0 10.385088 1.000 0.684932 \n", + "100 16.170278 0.905 40.068493 \n", + "200 19.308056 0.885 50.342466 \n", + "300 25.392778 0.860 55.136986 \n", + "400 29.612500 0.920 60.616438 \n", + "500 27.996944 0.800 54.794521 \n", + "600 30.663056 0.925 56.849315 \n", + "700 30.141944 0.880 58.219178 \n", + "800 29.418056 0.890 54.794521 \n", + "900 29.673611 0.825 57.534247 \n", + "1000 30.230278 0.920 57.191781 \n", + "1100 31.049167 0.900 53.424658 \n", + "1200 29.374444 0.940 51.027397 \n", + "1300 29.268889 0.820 53.767123 \n", + "1400 31.123889 0.955 51.369863 \n", + "1500 31.105278 0.905 59.246575 \n", + "1600 27.199444 0.875 54.109589 \n", + "1700 29.273611 0.940 49.315068 \n", + "1800 29.321667 0.940 53.424658 \n", + "1900 32.563611 0.910 54.794521 \n", + "2000 32.758889 0.930 53.767123 \n", + "2100 32.542500 0.910 52.397260 \n", + "2200 31.011667 0.895 52.739726 \n", + "2300 28.280000 0.900 53.424658 \n", + "2400 30.337500 0.905 54.794521 \n", + "2500 28.788056 0.915 50.684932 \n", + "2600 33.973611 0.950 39.041096 \n", + "2700 34.555556 0.930 42.123288 \n", + "2800 33.791111 0.925 33.561644 \n", + "2900 34.580833 0.970 32.191781 \n", + "3000 37.584444 0.975 32.534247 \n", + "3100 36.292778 0.815 31.849315 \n", + "3200 31.287778 0.695 30.136986 \n", + "3300 28.892500 0.705 31.164384 \n", + "3400 26.179444 0.515 29.109589 \n", + "3500 28.174722 0.730 27.739726 \n", + "3600 24.110278 0.595 26.369863 \n", + "3700 24.312222 0.600 26.027397 \n", + "3800 24.320556 0.710 25.342466 \n", + "3900 24.251944 0.500 19.520548 \n", + "4000 23.493056 0.570 20.890411 \n", + "4100 23.231667 0.565 21.232877 \n", + "4200 23.806389 0.635 21.917808 \n", + "4300 25.600000 0.550 24.657534 \n", + "4400 25.453611 0.915 24.315068 \n", + "4500 26.867500 0.675 19.178082 \n", + "4600 29.072222 0.820 23.630137 \n", + "4700 28.917778 0.760 21.232877 \n", + "4800 29.515278 0.620 24.657534 \n", + "4900 28.675833 0.575 30.821918 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(df)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -141,14 +924,37 @@ "ax = df['average_specificity'].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"average_specificity\")\n", - "ax.legend([\"specificity\"])\n" + "ax.legend([\"specificity\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df['fraction_accuracy'].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -158,9 +964,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df[['numerosity', 'population']].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -173,6 +1002,41 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "ax = df['knowledge'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Knowledge\")\n", + "ax.legend([\"Knowledge %\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df['steps_in_trial'].plot()\n", "ax.set_xlabel(\"trial\")\n", diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index dbecb3a..cfb7e4a 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -24,6 +24,8 @@ def maze_knowledge(population, environment): def predicts_successfully(cl: Classifier, p0, action, p1): if cl.does_match(p0): if cl.action == action: + if cl.effect is None: + return False if cl.effect == Effect(p1): return True return False @@ -55,6 +57,14 @@ def xncs_maze_metrics(xncs: XNCS, environment): 'knowledge': maze_knowledge(xncs.population, environment), } +def xncs_woods_metrics(xncs: XNCS, environment): + return { + 'numerosity': xncs.population.numerosity, + 'population': len(xncs.population), + 'average_specificity': specificity(xncs, xncs.population), + 'fraction_accuracy': fraction_accuracy(xncs), + } + def xncs_metrics(xncs: XNCS, environment): return { From 4cd1780ae8e55e38b64385e837399ecc78ab9b59 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 20 Jun 2021 10:54:16 +0200 Subject: [PATCH 15/19] Simple Maze --- XCS_Experiments/XCS_XNCS_Maze5.ipynb | 1516 +++++------------ .../XCS_XNCS_Maze5_pre_populated.ipynb | 1036 +++++------ XCS_Experiments/XCS_maze5_avg.ipynb | 728 ++------ XCS_Experiments/XCS_simple_maze.ipynb | 552 ++++++ XCS_Experiments/XNCS_simple_maze_.ipynb | 401 +++++ XCS_Experiments/utils/nxcs_utils.py | 2 + 6 files changed, 1990 insertions(+), 2245 deletions(-) create mode 100644 XCS_Experiments/XCS_simple_maze.ipynb create mode 100644 XCS_Experiments/XNCS_simple_maze_.ipynb diff --git a/XCS_Experiments/XCS_XNCS_Maze5.ipynb b/XCS_Experiments/XCS_XNCS_Maze5.ipynb index a2907db..28fff3f 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -23,13 +23,13 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '1', '0', '1', '1', '0', '0', '1')\n", + "('0', '0', '0', '1', '0', '0', '1', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -63,10 +63,10 @@ "from lcs.agents.xncs import Configuration as XNCSConfig\n", "\n", "from utils.xcs_utils import xcs_metrics\n", - "from utils.nxcs_utils import xcs_maze_metrics\n", + "from utils.nxcs_utils import xncs_maze_metrics\n", "\n", "XCScfg = XCSConfig(number_of_actions=8,\n", - " max_population=1600,\n", + " max_population=7000,\n", " learning_rate=0.2,\n", " alpha=0.1,\n", " gamma=0.71,\n", @@ -82,7 +82,7 @@ " user_metrics_collector_fcn=xcs_metrics)\n", "\n", "XNCScfg = XNCSConfig(number_of_actions=8,\n", - " max_population=1600,\n", + " max_population=7000,\n", " learning_rate=0.2,\n", " alpha=0.1,\n", " gamma=0.71,\n", @@ -95,14 +95,14 @@ " initial_prediction =10, # p_i\n", " initial_error = 0, # epsilon_i\n", " initial_fitness = 10, # f_i\n", - " user_metrics_collector_fcn=xcs_maze_metrics,\n", + " user_metrics_collector_fcn=xncs_maze_metrics,\n", " lmc=10,\n", " lem=200)\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -111,7 +111,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.06253820000000587, 'population': 126, 'numerosity': 165, 'average_specificity': 7.096969696969697, 'fraction_accuracy': 1.0}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.07538530000000021, 'population': 143, 'numerosity': 194, 'average_specificity': 6.365979381443299}\n" ] }, { @@ -125,323 +125,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 78, 'reward': 1000.000000002505, 'perf_time': 0.27959270000002334, 'population': 396, 'numerosity': 1608, 'average_specificity': 1.4987562189054726, 'fraction_accuracy': 4.2643295759091064e-14}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 91, 'reward': 1000.0000000000439, 'perf_time': 0.4170358999999735, 'population': 384, 'numerosity': 1600, 'average_specificity': 1.58625, 'fraction_accuracy': 2.1415101542369858e-16}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 100, 'reward': 1.3360874108439811e-12, 'perf_time': 0.33724660000007134, 'population': 425, 'numerosity': 1600, 'average_specificity': 1.925, 'fraction_accuracy': 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"INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 3.704880199999934, 'numerosity': 7000, 'population': 3114, 'average_specificity': 14.599428571428572, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 3.704880199999934, 'numerosity': 7000, 'population': 3114, 'average_specificity': 14.599428571428572, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 17, 'reward': 1002.9610874844004, 'perf_time': 0.4661066999997274, 'numerosity': 7000, 'population': 3039, 'average_specificity': 14.158857142857142, 'fraction_accuracy': 0.98, 'knowledge': 1.36986301369863}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 17, 'reward': 1002.9610874844004, 'perf_time': 0.4661066999997274, 'numerosity': 7000, 'population': 3039, 'average_specificity': 14.158857142857142, 'fraction_accuracy': 0.98, 'knowledge': 1.36986301369863}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 16, 'reward': 1009.2501340317529, 'perf_time': 0.44733010000027207, 'numerosity': 7000, 'population': 2948, 'average_specificity': 13.59, 'fraction_accuracy': 1.0, 'knowledge': 1.36986301369863}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 16, 'reward': 1009.2501340317529, 'perf_time': 0.44733010000027207, 'numerosity': 7000, 'population': 2948, 'average_specificity': 13.59, 'fraction_accuracy': 1.0, 'knowledge': 1.36986301369863}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 9, 'reward': 1068.9611183227762, 'perf_time': 0.41897579999977097, 'numerosity': 7000, 'population': 2893, 'average_specificity': 13.632714285714286, 'fraction_accuracy': 1.0, 'knowledge': 0.684931506849315}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 9, 'reward': 1068.9611183227762, 'perf_time': 0.41897579999977097, 'numerosity': 7000, 'population': 2893, 'average_specificity': 13.632714285714286, 'fraction_accuracy': 1.0, 'knowledge': 0.684931506849315}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 22, 'reward': 1000.6072519232745, 'perf_time': 0.648130300000048, 'numerosity': 7000, 'population': 2889, 'average_specificity': 13.62042857142857, 'fraction_accuracy': 1.0, 'knowledge': 0.684931506849315}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 22, 'reward': 1000.6072519232745, 'perf_time': 0.648130300000048, 'numerosity': 7000, 'population': 2889, 'average_specificity': 13.62042857142857, 'fraction_accuracy': 1.0, 'knowledge': 0.684931506849315}\n" ] } ], @@ -778,7 +184,7 @@ "from utils.xcs_utils import avg_experiment as XCSExp\n", "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", - "number_of_experiments = 10\n", + "number_of_experiments = 1\n", "explore = 2000\n", "exploit = 500\n", "\n", @@ -797,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -827,7 +233,6 @@ " population\n", " numerosity\n", " average_specificity\n", - " fraction_accuracy\n", " steps_in_trial_other\n", " population_other\n", " numerosity_other\n", @@ -847,501 +252,447 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " 0\n", - " 91.8\n", - " 100.000000\n", - " 0.052199\n", - " 108.1\n", - " 152.7\n", - " 6.084159\n", - " 1.000000e+00\n", - " 89.0\n", - " 97.5\n", - " 142.1\n", - " 5.778131\n", - " 8.875000e-01\n", + " 100\n", + " 0.000000e+00\n", + " 0.075385\n", + " 143\n", + " 194\n", + " 6.365979\n", + " 100\n", + " 124\n", + " 173\n", + " 6.693642\n", + " 1.00\n", " \n", " \n", " 100\n", - " 52.0\n", - " 800.411296\n", - " 0.204609\n", - " 399.4\n", - " 1605.4\n", - " 1.582136\n", - " 3.003213e-13\n", - " 42.5\n", - " 479.1\n", - " 1600.0\n", - " 2.437562\n", - " 1.142850e-15\n", + " 8\n", + " 1.101298e+03\n", + " 0.052353\n", + " 757\n", + " 7000\n", + " 9.154143\n", + " 1\n", + " 1017\n", + " 5944\n", + " 11.546265\n", + " 1.00\n", " \n", " \n", " 200\n", - " 39.8\n", - " 914.138554\n", - " 0.145111\n", - " 395.9\n", - " 1605.5\n", - " 1.520576\n", - " 3.750000e-02\n", - " 27.9\n", - " 547.6\n", - " 1600.0\n", - " 2.819063\n", - " 3.380321e-15\n", + " 11\n", + " 1.023112e+03\n", + " 0.160615\n", + " 750\n", + " 7000\n", + " 6.187286\n", + " 47\n", + " 1396\n", + " 7000\n", + " 10.090143\n", + " 1.00\n", " \n", " \n", " 300\n", - " 44.6\n", - " 916.551896\n", - " 0.166921\n", - " 410.7\n", - " 1613.2\n", - " 1.682143\n", - " 3.031430e-14\n", - " 48.0\n", - " 595.0\n", - " 1600.0\n", - " 3.256438\n", - " 2.500000e-02\n", + " 9\n", + " 1.046040e+03\n", + " 0.070715\n", + " 687\n", + " 7000\n", + " 6.249857\n", + " 24\n", + " 1629\n", + " 7000\n", + " 9.047286\n", + " 1.00\n", " \n", " \n", " 400\n", - " 33.8\n", - " 1077.355982\n", - " 0.139215\n", - " 404.6\n", - " 1606.6\n", - " 1.617811\n", - " 3.700835e-14\n", - " 28.1\n", - " 621.1\n", - " 1600.0\n", - " 3.648625\n", - " 2.511887e-15\n", + " 58\n", + " 1.000000e+03\n", + " 0.593510\n", + " 659\n", + " 7000\n", + " 5.825714\n", + " 100\n", + " 2040\n", + " 7000\n", + " 8.935857\n", + " 1.00\n", " \n", " \n", " 500\n", - " 69.0\n", - " 614.028490\n", - " 0.266113\n", - " 420.3\n", - " 1604.0\n", - " 1.619119\n", - " 2.041384e-14\n", - " 55.5\n", - " 641.8\n", - " 1600.0\n", - " 3.975375\n", - " 1.250000e-02\n", + " 24\n", + " 1.000269e+03\n", + " 0.115538\n", + " 669\n", + " 7000\n", + " 5.720000\n", + " 3\n", + " 2557\n", + " 7000\n", + " 11.392857\n", + " 1.00\n", " \n", " \n", " 600\n", - " 54.4\n", - " 736.694854\n", - " 0.195789\n", - " 422.9\n", - " 1602.9\n", - " 1.728276\n", - " 1.250000e-02\n", - " 35.6\n", - " 642.4\n", - " 1600.0\n", - " 4.006437\n", - " 1.250000e-02\n", + " 100\n", + " 2.013829e-27\n", + " 0.833867\n", + " 688\n", + " 7000\n", + " 5.380286\n", + " 100\n", + " 2711\n", + " 7000\n", + " 13.068429\n", + " 1.00\n", " \n", " \n", " 700\n", - " 54.6\n", - " 807.164862\n", - " 0.210975\n", - " 408.0\n", - " 1607.9\n", - " 1.599390\n", - " 2.500000e-02\n", - " 39.8\n", - " 663.4\n", - " 1600.0\n", - " 4.307125\n", - " 1.250000e-02\n", + " 19\n", + " 1.001492e+03\n", + " 0.101426\n", + " 726\n", + " 7000\n", + " 7.827571\n", + " 4\n", + " 2874\n", + " 7000\n", + " 15.086857\n", + " 1.00\n", " \n", " \n", " 800\n", - " 60.9\n", - " 519.334100\n", - " 0.234345\n", - " 413.6\n", - " 1605.1\n", - " 1.618144\n", - " 1.250000e-02\n", - " 34.6\n", - " 669.6\n", - " 1600.0\n", - " 4.293938\n", - " 1.196585e-15\n", + " 5\n", + " 1.180491e+03\n", + " 0.100662\n", + " 785\n", + " 7000\n", + " 7.065571\n", + " 54\n", + " 2892\n", + " 7000\n", + " 14.202429\n", + " 0.96\n", " \n", " \n", " 900\n", - " 40.2\n", - " 1062.417179\n", - " 0.155673\n", - " 417.9\n", - " 1605.7\n", - " 1.618205\n", - " 1.250000e-02\n", - " 45.0\n", - " 671.0\n", - " 1600.0\n", - " 4.395313\n", - " 1.250000e-02\n", + " 2\n", + " 1.508270e+03\n", + " 0.005748\n", + " 754\n", + " 7000\n", + " 6.694000\n", + " 13\n", + " 2915\n", + " 7000\n", + " 14.928143\n", + " 1.00\n", " \n", " \n", " 1000\n", - " 30.2\n", - " 1071.180009\n", - " 0.113604\n", - " 410.8\n", - " 1608.4\n", - " 1.719336\n", - " 1.284551e-14\n", - " 46.7\n", - " 687.1\n", - " 1600.0\n", - " 4.616313\n", - " 3.750000e-02\n", + " 48\n", + " 1.000000e+03\n", + " 0.421508\n", + " 687\n", + " 7000\n", + " 7.576571\n", + " 34\n", + " 3035\n", + " 7000\n", + " 14.731571\n", + " 1.00\n", " \n", " \n", " 1100\n", - " 38.0\n", - " 1006.875921\n", - " 0.144240\n", - " 413.7\n", - " 1603.2\n", - " 1.571106\n", - " 2.037540e-14\n", - " 36.9\n", - " 665.8\n", - " 1600.0\n", - " 4.224000\n", - " 1.250000e-02\n", + " 95\n", + " 1.000000e+03\n", + " 0.888588\n", + " 721\n", + " 7000\n", + " 6.867429\n", + " 12\n", + " 3067\n", + " 7000\n", + " 14.954143\n", + " 1.00\n", " \n", " \n", " 1200\n", - " 30.2\n", - " 957.599273\n", - " 0.117028\n", - " 416.4\n", - " 1607.1\n", - " 1.627203\n", - " 7.909460e-14\n", - " 54.7\n", - " 666.2\n", - " 1600.0\n", - " 4.066562\n", - " 3.750000e-02\n", + " 21\n", + " 1.000753e+03\n", + " 0.128918\n", + " 724\n", + " 7000\n", + " 5.954286\n", + " 53\n", + " 2949\n", + " 7000\n", + " 14.322857\n", + " 1.00\n", " \n", " \n", " 1300\n", - " 38.4\n", - " 941.186158\n", - " 0.147198\n", - " 422.9\n", - " 1609.2\n", - " 1.635573\n", - " 2.500000e-02\n", - " 40.5\n", - " 674.2\n", - " 1600.0\n", - " 4.218938\n", - " 1.250000e-02\n", + " 10\n", + " 1.032552e+03\n", + " 0.094600\n", + " 753\n", + " 7000\n", + " 5.646143\n", + " 30\n", + " 2959\n", + " 7000\n", + " 13.953286\n", + " 0.99\n", " \n", " \n", " 1400\n", - " 52.8\n", - " 604.697668\n", - " 0.186518\n", - " 410.5\n", - " 1611.3\n", - " 1.540516\n", - " 2.500000e-02\n", - " 40.4\n", - " 681.1\n", - " 1600.0\n", - " 4.200563\n", - " 2.397498e-15\n", + " 24\n", + " 1.000304e+03\n", + " 0.233504\n", + " 759\n", + " 7000\n", + " 6.648286\n", + " 33\n", + " 3009\n", + " 7000\n", + " 14.837429\n", + " 0.98\n", " \n", " \n", " 1500\n", - " 31.4\n", - " 1051.288333\n", - " 0.117136\n", - " 419.8\n", - " 1605.5\n", - " 1.676694\n", - " 5.779108e-15\n", - " 51.1\n", - " 685.2\n", - " 1600.0\n", - " 4.344250\n", - " 1.250000e-02\n", + " 74\n", + " 1.000000e+03\n", + " 0.682727\n", + " 764\n", + " 7000\n", + " 5.865714\n", + " 3\n", + " 2934\n", + " 7000\n", + " 15.395571\n", + " 1.00\n", " \n", " \n", " 1600\n", - " 64.8\n", - " 607.705827\n", - " 0.242201\n", - " 424.8\n", - " 1605.5\n", - " 1.660007\n", - " 2.500000e-02\n", - " 27.3\n", - " 690.6\n", - " 1600.0\n", - " 4.481812\n", - " 5.683935e-15\n", + " 16\n", + " 1.004179e+03\n", + " 0.240538\n", + " 787\n", + " 7000\n", + " 6.648286\n", + " 91\n", + " 3163\n", + " 7000\n", + " 14.119429\n", + " 1.00\n", " \n", " \n", " 1700\n", - " 37.3\n", - " 972.079749\n", - " 0.149581\n", - " 413.6\n", - " 1604.6\n", - " 1.655249\n", - " 1.250000e-02\n", - " 51.9\n", - " 698.0\n", - " 1600.0\n", - " 4.638937\n", - " 1.250000e-02\n", + " 100\n", + " 1.336087e-12\n", + " 0.680336\n", + " 807\n", + " 7000\n", + " 5.991143\n", + " 25\n", + " 3074\n", + " 7000\n", + " 14.365286\n", + " 1.00\n", " \n", " \n", " 1800\n", - " 51.9\n", - " 717.962502\n", - " 0.186570\n", - " 412.5\n", - " 1604.5\n", - " 1.707604\n", - " 2.366642e-14\n", - " 47.9\n", - " 703.2\n", - " 1600.0\n", - " 4.404437\n", - " 1.192077e-14\n", + " 3\n", + " 1.000000e+03\n", + " 0.025747\n", + " 776\n", + " 7000\n", + " 5.943857\n", + " 28\n", + " 3090\n", + " 7000\n", + " 14.499429\n", + " 1.00\n", " \n", " \n", " 1900\n", - " 24.4\n", - " 1102.641579\n", - " 0.091645\n", - " 408.9\n", - " 1608.9\n", - " 1.645934\n", - " 3.750000e-02\n", - " 60.8\n", - " 690.3\n", - " 1600.0\n", - " 4.226063\n", - " 1.250000e-02\n", + " 100\n", + " 1.336087e-12\n", + " 0.974427\n", + " 805\n", + " 7000\n", + " 5.646571\n", + " 100\n", + " 3165\n", + " 7000\n", + " 14.962857\n", + " 1.00\n", " \n", " \n", " 2000\n", - " 22.0\n", - " 900.000000\n", - " 0.049691\n", - " 421.1\n", - " 1605.6\n", - " 1.704471\n", - " 1.250000e-02\n", - " 57.8\n", - " 699.2\n", - " 1600.0\n", - " 4.390062\n", - " 2.500000e-02\n", + " 42\n", + " 1.000001e+03\n", + " 0.229624\n", + " 775\n", + " 7000\n", + " 6.068000\n", + " 100\n", + " 3114\n", + " 7000\n", + " 14.599429\n", + " 1.00\n", " \n", " \n", " 2100\n", - " 12.8\n", - " 1119.135316\n", - " 0.025083\n", - " 425.3\n", - " 1600.0\n", - " 1.713625\n", - " 1.250000e-02\n", - " 10.1\n", - " 751.5\n", - " 1600.0\n", - " 5.276750\n", - " 1.625000e-01\n", + " 100\n", + " 1.336087e-12\n", + " 0.562800\n", + " 738\n", + " 7000\n", + " 19.489571\n", + " 17\n", + " 3039\n", + " 7000\n", + " 14.158857\n", + " 0.98\n", " \n", " \n", " 2200\n", - " 9.2\n", - " 1176.769788\n", - " 0.018771\n", - " 425.3\n", - " 1600.0\n", - " 1.713625\n", - " 1.250000e-02\n", - " 61.3\n", - " 793.5\n", - " 1600.0\n", - " 5.965875\n", - " 4.375000e-01\n", + " 7\n", + " 1.111330e+03\n", + " 0.131905\n", + " 791\n", + " 7000\n", + " 16.667714\n", + " 16\n", + " 2948\n", + " 7000\n", + " 13.590000\n", + " 1.00\n", " \n", " \n", " 2300\n", - " 9.2\n", - " 1222.998903\n", - " 0.020475\n", - " 425.3\n", - " 1600.0\n", - " 1.713625\n", - " 1.250000e-02\n", - " 28.2\n", - " 786.5\n", - " 1600.0\n", - " 5.689813\n", - " 4.500000e-01\n", + " 10\n", + " 1.000000e+03\n", + " 0.138609\n", + " 772\n", + " 7000\n", + " 16.608143\n", + " 9\n", + " 2893\n", + " 7000\n", + " 13.632714\n", + " 1.00\n", " \n", " \n", " 2400\n", - " 5.4\n", - " 1283.962274\n", - " 0.010418\n", - " 425.3\n", - " 1600.0\n", - " 1.712125\n", - " 1.250000e-02\n", - " 8.5\n", - " 786.2\n", - " 1600.0\n", - " 5.603688\n", - " 5.000000e-01\n", + " 9\n", + " 1.048945e+03\n", + " 0.070076\n", + " 826\n", + " 7000\n", + " 18.742571\n", + " 22\n", + " 2889\n", + " 7000\n", + " 13.620429\n", + " 1.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial reward perf_time population numerosity \\\n", - "trial \n", - "0 91.8 100.000000 0.052199 108.1 152.7 \n", - "100 52.0 800.411296 0.204609 399.4 1605.4 \n", - "200 39.8 914.138554 0.145111 395.9 1605.5 \n", - "300 44.6 916.551896 0.166921 410.7 1613.2 \n", - "400 33.8 1077.355982 0.139215 404.6 1606.6 \n", - "500 69.0 614.028490 0.266113 420.3 1604.0 \n", - "600 54.4 736.694854 0.195789 422.9 1602.9 \n", - "700 54.6 807.164862 0.210975 408.0 1607.9 \n", - "800 60.9 519.334100 0.234345 413.6 1605.1 \n", - "900 40.2 1062.417179 0.155673 417.9 1605.7 \n", - "1000 30.2 1071.180009 0.113604 410.8 1608.4 \n", - "1100 38.0 1006.875921 0.144240 413.7 1603.2 \n", - "1200 30.2 957.599273 0.117028 416.4 1607.1 \n", - "1300 38.4 941.186158 0.147198 422.9 1609.2 \n", - "1400 52.8 604.697668 0.186518 410.5 1611.3 \n", - "1500 31.4 1051.288333 0.117136 419.8 1605.5 \n", - "1600 64.8 607.705827 0.242201 424.8 1605.5 \n", - "1700 37.3 972.079749 0.149581 413.6 1604.6 \n", - "1800 51.9 717.962502 0.186570 412.5 1604.5 \n", - "1900 24.4 1102.641579 0.091645 408.9 1608.9 \n", - "2000 22.0 900.000000 0.049691 421.1 1605.6 \n", - "2100 12.8 1119.135316 0.025083 425.3 1600.0 \n", - "2200 9.2 1176.769788 0.018771 425.3 1600.0 \n", - "2300 9.2 1222.998903 0.020475 425.3 1600.0 \n", - "2400 5.4 1283.962274 0.010418 425.3 1600.0 \n", + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 100 0.000000e+00 0.075385 143 194 \n", + "100 8 1.101298e+03 0.052353 757 7000 \n", + "200 11 1.023112e+03 0.160615 750 7000 \n", + "300 9 1.046040e+03 0.070715 687 7000 \n", + "400 58 1.000000e+03 0.593510 659 7000 \n", + "500 24 1.000269e+03 0.115538 669 7000 \n", + "600 100 2.013829e-27 0.833867 688 7000 \n", + "700 19 1.001492e+03 0.101426 726 7000 \n", + "800 5 1.180491e+03 0.100662 785 7000 \n", + "900 2 1.508270e+03 0.005748 754 7000 \n", + "1000 48 1.000000e+03 0.421508 687 7000 \n", + "1100 95 1.000000e+03 0.888588 721 7000 \n", + "1200 21 1.000753e+03 0.128918 724 7000 \n", + "1300 10 1.032552e+03 0.094600 753 7000 \n", + "1400 24 1.000304e+03 0.233504 759 7000 \n", + "1500 74 1.000000e+03 0.682727 764 7000 \n", + "1600 16 1.004179e+03 0.240538 787 7000 \n", + "1700 100 1.336087e-12 0.680336 807 7000 \n", + "1800 3 1.000000e+03 0.025747 776 7000 \n", + "1900 100 1.336087e-12 0.974427 805 7000 \n", + "2000 42 1.000001e+03 0.229624 775 7000 \n", + "2100 100 1.336087e-12 0.562800 738 7000 \n", + "2200 7 1.111330e+03 0.131905 791 7000 \n", + "2300 10 1.000000e+03 0.138609 772 7000 \n", + "2400 9 1.048945e+03 0.070076 826 7000 \n", "\n", - " average_specificity fraction_accuracy steps_in_trial_other \\\n", - "trial \n", - "0 6.084159 1.000000e+00 89.0 \n", - "100 1.582136 3.003213e-13 42.5 \n", - "200 1.520576 3.750000e-02 27.9 \n", - "300 1.682143 3.031430e-14 48.0 \n", - "400 1.617811 3.700835e-14 28.1 \n", - "500 1.619119 2.041384e-14 55.5 \n", - "600 1.728276 1.250000e-02 35.6 \n", - "700 1.599390 2.500000e-02 39.8 \n", - "800 1.618144 1.250000e-02 34.6 \n", - "900 1.618205 1.250000e-02 45.0 \n", - "1000 1.719336 1.284551e-14 46.7 \n", - "1100 1.571106 2.037540e-14 36.9 \n", - "1200 1.627203 7.909460e-14 54.7 \n", - "1300 1.635573 2.500000e-02 40.5 \n", - "1400 1.540516 2.500000e-02 40.4 \n", - "1500 1.676694 5.779108e-15 51.1 \n", - "1600 1.660007 2.500000e-02 27.3 \n", - "1700 1.655249 1.250000e-02 51.9 \n", - "1800 1.707604 2.366642e-14 47.9 \n", - "1900 1.645934 3.750000e-02 60.8 \n", - "2000 1.704471 1.250000e-02 57.8 \n", - "2100 1.713625 1.250000e-02 10.1 \n", - "2200 1.713625 1.250000e-02 61.3 \n", - "2300 1.713625 1.250000e-02 28.2 \n", - "2400 1.712125 1.250000e-02 8.5 \n", + " average_specificity steps_in_trial_other population_other \\\n", + "trial \n", + "0 6.365979 100 124 \n", + "100 9.154143 1 1017 \n", + "200 6.187286 47 1396 \n", + "300 6.249857 24 1629 \n", + "400 5.825714 100 2040 \n", + "500 5.720000 3 2557 \n", + "600 5.380286 100 2711 \n", + "700 7.827571 4 2874 \n", + "800 7.065571 54 2892 \n", + "900 6.694000 13 2915 \n", + "1000 7.576571 34 3035 \n", + "1100 6.867429 12 3067 \n", + "1200 5.954286 53 2949 \n", + "1300 5.646143 30 2959 \n", + "1400 6.648286 33 3009 \n", + "1500 5.865714 3 2934 \n", + "1600 6.648286 91 3163 \n", + "1700 5.991143 25 3074 \n", + "1800 5.943857 28 3090 \n", + "1900 5.646571 100 3165 \n", + "2000 6.068000 100 3114 \n", + "2100 19.489571 17 3039 \n", + "2200 16.667714 16 2948 \n", + "2300 16.608143 9 2893 \n", + "2400 18.742571 22 2889 \n", "\n", - " population_other numerosity_other average_specificity_other \\\n", - "trial \n", - "0 97.5 142.1 5.778131 \n", - "100 479.1 1600.0 2.437562 \n", - "200 547.6 1600.0 2.819063 \n", - "300 595.0 1600.0 3.256438 \n", - "400 621.1 1600.0 3.648625 \n", - "500 641.8 1600.0 3.975375 \n", - "600 642.4 1600.0 4.006437 \n", - "700 663.4 1600.0 4.307125 \n", - "800 669.6 1600.0 4.293938 \n", - "900 671.0 1600.0 4.395313 \n", - "1000 687.1 1600.0 4.616313 \n", - "1100 665.8 1600.0 4.224000 \n", - "1200 666.2 1600.0 4.066562 \n", - "1300 674.2 1600.0 4.218938 \n", - "1400 681.1 1600.0 4.200563 \n", - "1500 685.2 1600.0 4.344250 \n", - "1600 690.6 1600.0 4.481812 \n", - "1700 698.0 1600.0 4.638937 \n", - "1800 703.2 1600.0 4.404437 \n", - "1900 690.3 1600.0 4.226063 \n", - "2000 699.2 1600.0 4.390062 \n", - "2100 751.5 1600.0 5.276750 \n", - "2200 793.5 1600.0 5.965875 \n", - "2300 786.5 1600.0 5.689813 \n", - "2400 786.2 1600.0 5.603688 \n", - "\n", - " fraction_accuracy_other \n", - "trial \n", - "0 8.875000e-01 \n", - "100 1.142850e-15 \n", - "200 3.380321e-15 \n", - "300 2.500000e-02 \n", - "400 2.511887e-15 \n", - "500 1.250000e-02 \n", - "600 1.250000e-02 \n", - "700 1.250000e-02 \n", - "800 1.196585e-15 \n", - "900 1.250000e-02 \n", - "1000 3.750000e-02 \n", - "1100 1.250000e-02 \n", - "1200 3.750000e-02 \n", - "1300 1.250000e-02 \n", - "1400 2.397498e-15 \n", - "1500 1.250000e-02 \n", - "1600 5.683935e-15 \n", - "1700 1.250000e-02 \n", - "1800 1.192077e-14 \n", - "1900 1.250000e-02 \n", - "2000 2.500000e-02 \n", - "2100 1.625000e-01 \n", - "2200 4.375000e-01 \n", - "2300 4.500000e-01 \n", - "2400 5.000000e-01 " + " numerosity_other average_specificity_other fraction_accuracy_other \n", + "trial \n", + "0 173 6.693642 1.00 \n", + "100 5944 11.546265 1.00 \n", + "200 7000 10.090143 1.00 \n", + "300 7000 9.047286 1.00 \n", + "400 7000 8.935857 1.00 \n", + "500 7000 11.392857 1.00 \n", + "600 7000 13.068429 1.00 \n", + "700 7000 15.086857 1.00 \n", + "800 7000 14.202429 0.96 \n", + "900 7000 14.928143 1.00 \n", + "1000 7000 14.731571 1.00 \n", + "1100 7000 14.954143 1.00 \n", + "1200 7000 14.322857 1.00 \n", + "1300 7000 13.953286 0.99 \n", + "1400 7000 14.837429 0.98 \n", + "1500 7000 15.395571 1.00 \n", + "1600 7000 14.119429 1.00 \n", + "1700 7000 14.365286 1.00 \n", + "1800 7000 14.499429 1.00 \n", + "1900 7000 14.962857 1.00 \n", + "2000 7000 14.599429 1.00 \n", + "2100 7000 14.158857 0.98 \n", + "2200 7000 13.590000 1.00 \n", + "2300 7000 13.632714 1.00 \n", + "2400 7000 13.620429 1.00 " ] }, "metadata": {}, @@ -1360,22 +711,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1399,22 +750,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1426,7 +777,7 @@ } ], "source": [ - "ax = df[['fraction_accuracy', \"fraction_accuracy_other\"]].plot()\n", + "ax = df[\"fraction_accuracy_other\"].plot()\n", "ax.set_xlabel(\"trial\")\n", "ax.set_ylabel(\"fraction accuracy\")\n", "ax.legend([\"XCS\",\"XNCS\"])" @@ -1434,22 +785,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1469,22 +820,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1504,22 +855,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1536,6 +887,13 @@ "ax.set_ylabel(\"steps in trial\")\n", "ax.legend([\"XCS\",\"XNCS\"])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb index 777bb11..a33aa3d 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb @@ -23,15 +23,15 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '0', '1', '0', '0', '0', '0', '0')\n", + "('1', '0', '0', '0', '1', '0', '1', '1')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] @@ -66,7 +66,7 @@ "from utils.nxcs_utils import *\n", "\n", "XCScfg = XCSConfig(number_of_actions=8,\n", - " max_population=1600,\n", + " max_population=7000,\n", " learning_rate=0.2,\n", " alpha=0.1,\n", " gamma=0.71,\n", @@ -82,7 +82,7 @@ " user_metrics_collector_fcn=xcs_maze_metrics)\n", "\n", "XNCScfg = XNCSConfig(number_of_actions=8,\n", - " max_population=1600,\n", + " max_population=7000,\n", " learning_rate=0.2,\n", " alpha=0.1,\n", " gamma=0.71,\n", @@ -118,62 +118,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 1.3876943999999996, 'numerosity': 1600, 'population': 1498, 'average_specificity': 7.911875, 'fraction_accuracy': 0.95, 'knowledge': 6.164383561643835}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1160.623320451948, 'perf_time': 0.047101699999998914, 'numerosity': 1600, 'population': 1163, 'average_specificity': 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-250,7 +158,7 @@ "from utils.xcs_utils import avg_experiment as XCSExp\n", "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", - "number_of_experiments = 3\n", + "number_of_experiments = 1\n", "explore = 2000\n", "exploit = 500\n", "\n", @@ -275,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -333,520 +241,520 @@ " \n", " \n", " 0\n", + " 100\n", + " 0.000000e+00\n", + " 4.241750\n", + " 5853\n", + " 7000\n", + " 8.011143\n", " 100.000000\n", - " 0.000000\n", - " 1.237642\n", - " 1484.333333\n", - " 1600.0\n", - " 8.555208\n", - " 100.000000\n", - " 100.000000\n", - " 1492.000000\n", - " 1600.0\n", - " 8.235833\n", - " 0.966667\n", - " 5.936073\n", + " 100\n", + " 5892\n", + " 7000\n", + " 8.564286\n", + " 1.00\n", + " 10.958904\n", " \n", " \n", " 100\n", - " 37.000000\n", - " 817.855486\n", - " 0.307822\n", - " 1128.333333\n", - " 1600.0\n", - " 6.960833\n", - " 98.630137\n", - " 7.000000\n", - " 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7.908571\n", + " 100.000000\n", + " 4\n", + " 5175\n", + " 7000\n", + " 9.630429\n", + " 1.00\n", + " 6.164384\n", " \n", " \n", " 2200\n", - " 9.000000\n", - " 1259.295963\n", - " 0.047323\n", - " 652.666667\n", - " 1600.0\n", - " 6.328333\n", - " 98.401826\n", - " 7.000000\n", - " 1041.000000\n", - " 1600.0\n", - " 7.813125\n", - " 0.863333\n", - " 3.196347\n", + " 3\n", + " 1.000000e+03\n", + " 0.202673\n", + " 4107\n", + " 7000\n", + " 7.486286\n", + " 100.000000\n", + " 8\n", + " 5228\n", + " 7000\n", + " 9.921714\n", + " 1.00\n", + " 6.164384\n", " \n", " \n", " 2300\n", - " 10.333333\n", - " 1142.920166\n", - " 0.068638\n", - " 654.333333\n", - " 1600.0\n", - " 6.350000\n", - " 97.716895\n", - " 7.666667\n", - " 1034.333333\n", - " 1600.0\n", - " 7.932500\n", - " 0.996667\n", - " 2.739726\n", + " 100\n", + " 1.336087e-12\n", + " 2.744855\n", + " 4051\n", + " 7000\n", + " 7.606571\n", + " 99.315068\n", + " 8\n", + " 5215\n", + " 7000\n", + " 10.057286\n", + " 1.00\n", + " 6.849315\n", " \n", " \n", " 2400\n", - " 7.000000\n", - " 1127.863666\n", - " 0.032243\n", - " 636.666667\n", - " 1600.0\n", - " 7.146667\n", - " 99.771689\n", - " 4.000000\n", - " 1034.000000\n", - " 1600.0\n", - " 8.160417\n", - " 0.990000\n", - " 2.739726\n", + " 6\n", + " 1.151329e+03\n", + " 0.234328\n", + " 4035\n", + " 7000\n", + " 7.732571\n", + " 100.000000\n", + " 9\n", + " 5220\n", + " 7000\n", + " 10.158571\n", + " 1.00\n", + " 6.849315\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial reward perf_time population numerosity \\\n", + " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 100.000000 0.000000 1.237642 1484.333333 1600.0 \n", - "100 37.000000 817.855486 0.307822 1128.333333 1600.0 \n", - "200 4.333333 1479.555014 0.047548 1049.666667 1600.0 \n", - "300 4.333333 1151.057454 0.062162 997.000000 1600.0 \n", - "400 6.666667 1153.438879 0.058114 956.333333 1600.0 \n", - "500 36.000000 867.115092 0.214088 926.333333 1600.0 \n", - "600 5.000000 1250.562672 0.029232 881.000000 1600.0 \n", - "700 5.000000 1234.467521 0.038874 852.000000 1600.0 \n", - "800 3.666667 1405.452969 0.031245 846.666667 1600.0 \n", - "900 7.000000 1140.513633 0.069188 833.333333 1600.0 \n", - "1000 10.666667 1110.575676 0.101012 824.666667 1600.0 \n", - "1100 6.666667 1145.643586 0.071593 810.000000 1600.0 \n", - "1200 7.666667 1099.519328 0.086582 814.333333 1600.0 \n", - "1300 63.666667 986.181956 0.301855 799.333333 1600.0 \n", - "1400 68.000000 418.038937 0.333535 794.666667 1600.0 \n", - "1500 69.333333 361.384052 0.329215 772.666667 1600.0 \n", - "1600 40.666667 692.075323 0.194672 734.000000 1600.0 \n", - "1700 9.000000 1051.560998 0.059908 710.000000 1600.0 \n", - "1800 5.333333 1272.851696 0.037196 702.666667 1600.0 \n", - "1900 23.000000 1106.977237 0.136973 688.333333 1600.0 \n", - "2000 43.333333 834.700000 0.170842 689.000000 1600.0 \n", - "2100 4.333333 1163.360331 0.026712 670.333333 1600.0 \n", - "2200 9.000000 1259.295963 0.047323 652.666667 1600.0 \n", - "2300 10.333333 1142.920166 0.068638 654.333333 1600.0 \n", - "2400 7.000000 1127.863666 0.032243 636.666667 1600.0 \n", + "0 100 0.000000e+00 4.241750 5853 7000 \n", + "100 5 1.286283e+03 0.178884 5521 7000 \n", + "200 6 1.132409e+03 0.308753 5287 7000 \n", + "300 7 1.091801e+03 0.221936 5220 7000 \n", + "400 3 1.427822e+03 0.371512 5107 7000 \n", + "500 4 1.301275e+03 0.395128 5057 7000 \n", + "600 6 1.128100e+03 0.532877 5016 7000 \n", + "700 5 1.180423e+03 0.269629 4927 7000 \n", + "800 100 1.336087e-12 2.555656 4835 7000 \n", + "900 7 1.105494e+03 0.206130 4755 7000 \n", + "1000 6 1.135064e+03 0.181245 4735 7000 \n", + "1100 5 1.206901e+03 0.108420 4673 7000 \n", + "1200 100 1.713517e-12 2.567825 4653 7000 \n", + "1300 6 1.000000e+03 0.368133 4558 7000 \n", + "1400 6 1.225702e+03 0.191057 4394 7000 \n", + "1500 5 1.189481e+03 0.194219 4416 7000 \n", + "1600 6 1.165147e+03 0.197963 4360 7000 \n", + "1700 6 1.175747e+03 0.440291 4355 7000 \n", + "1800 5 1.194507e+03 0.512993 4326 7000 \n", + "1900 11 1.023654e+03 0.221673 4265 7000 \n", + "2000 6 1.128100e+03 0.314414 4186 7000 \n", + "2100 9 1.061988e+03 0.325730 4150 7000 \n", + "2200 3 1.000000e+03 0.202673 4107 7000 \n", + "2300 100 1.336087e-12 2.744855 4051 7000 \n", + "2400 6 1.151329e+03 0.234328 4035 7000 \n", "\n", " average_specificity knowledge steps_in_trial_other \\\n", "trial \n", - "0 8.555208 100.000000 100.000000 \n", - "100 6.960833 98.630137 7.000000 \n", - "200 6.975833 99.543379 6.333333 \n", - "300 6.640000 99.315068 7.000000 \n", - "400 6.878125 100.000000 19.000000 \n", - "500 6.642708 99.315068 6.333333 \n", - "600 7.274167 100.000000 5.333333 \n", - "700 6.706875 99.543379 26.666667 \n", - "800 6.577083 99.543379 4.333333 \n", - "900 6.358958 99.771689 69.333333 \n", - "1000 6.568333 98.630137 3.666667 \n", - "1100 6.761667 98.858447 5.333333 \n", - "1200 6.631875 99.543379 6.000000 \n", - "1300 6.546875 100.000000 7.333333 \n", - "1400 6.223958 97.488584 36.333333 \n", - "1500 6.477500 99.086758 5.000000 \n", - "1600 6.291042 99.543379 34.666667 \n", - "1700 6.237292 97.716895 6.666667 \n", - "1800 6.112917 100.000000 24.666667 \n", - "1900 6.136875 98.630137 16.666667 \n", - "2000 6.124792 98.401826 39.666667 \n", - "2100 6.119167 99.543379 36.333333 \n", - "2200 6.328333 98.401826 7.000000 \n", - "2300 6.350000 97.716895 7.666667 \n", - "2400 7.146667 99.771689 4.000000 \n", + "0 8.011143 100.000000 100 \n", + "100 7.266571 100.000000 2 \n", + "200 7.460714 99.315068 6 \n", + "300 7.202857 100.000000 19 \n", + "400 7.427857 100.000000 18 \n", + "500 7.381714 100.000000 11 \n", + "600 7.463000 100.000000 6 \n", + "700 7.288857 100.000000 9 \n", + "800 7.322857 99.315068 5 \n", + "900 7.140857 100.000000 5 \n", + "1000 7.303143 100.000000 8 \n", + "1100 7.170714 100.000000 7 \n", + "1200 7.367571 99.315068 5 \n", + "1300 7.286429 100.000000 7 \n", + "1400 7.248429 100.000000 7 \n", + "1500 7.136857 100.000000 7 \n", + "1600 7.028714 100.000000 8 \n", + "1700 7.443143 99.315068 8 \n", + "1800 8.047000 98.630137 11 \n", + "1900 7.686000 100.000000 6 \n", + "2000 8.084571 100.000000 4 \n", + "2100 7.908571 100.000000 4 \n", + "2200 7.486286 100.000000 8 \n", + "2300 7.606571 99.315068 8 \n", + "2400 7.732571 100.000000 9 \n", "\n", " population_other numerosity_other average_specificity_other \\\n", "trial \n", - "0 1492.000000 1600.0 8.235833 \n", - "100 1249.666667 1600.0 7.300208 \n", - "200 1179.000000 1600.0 6.977500 \n", - "300 1132.333333 1600.0 7.161250 \n", - "400 1125.333333 1600.0 7.486250 \n", - "500 1090.000000 1600.0 7.615000 \n", - "600 1093.000000 1600.0 7.793125 \n", - "700 1111.666667 1600.0 8.144583 \n", - "800 1093.000000 1600.0 8.020625 \n", - "900 1077.000000 1600.0 8.296042 \n", - "1000 1072.333333 1600.0 8.950000 \n", - "1100 1081.333333 1600.0 8.156875 \n", - "1200 1069.333333 1600.0 8.265833 \n", - "1300 1070.333333 1600.0 8.166667 \n", - "1400 1051.000000 1600.0 8.268542 \n", - "1500 1045.000000 1600.0 8.523125 \n", - "1600 1065.666667 1600.0 8.604583 \n", - "1700 1064.666667 1600.0 8.953958 \n", - "1800 1045.000000 1600.0 8.628125 \n", - "1900 1045.333333 1600.0 8.392083 \n", - "2000 1050.000000 1600.0 8.095833 \n", - "2100 1059.666667 1600.0 8.376250 \n", - "2200 1041.000000 1600.0 7.813125 \n", - "2300 1034.333333 1600.0 7.932500 \n", - "2400 1034.000000 1600.0 8.160417 \n", + "0 5892 7000 8.564286 \n", + "100 5548 7000 7.969000 \n", + "200 5450 7000 7.980571 \n", + "300 5341 7000 7.974000 \n", + "400 5317 7000 8.079571 \n", + "500 5286 7000 8.095286 \n", + "600 5264 7000 8.080286 \n", + "700 5268 7000 8.511000 \n", + "800 5268 7000 8.618000 \n", + "900 5243 7000 8.767286 \n", + "1000 5194 7000 8.718571 \n", + "1100 5187 7000 8.862143 \n", + "1200 5185 7000 9.072000 \n", + "1300 5194 7000 9.215857 \n", + "1400 5192 7000 9.093857 \n", + "1500 5190 7000 9.154429 \n", + "1600 5218 7000 9.329429 \n", + "1700 5172 7000 9.311857 \n", + "1800 5166 7000 8.916143 \n", + "1900 5169 7000 9.170429 \n", + "2000 5157 7000 9.414286 \n", + "2100 5175 7000 9.630429 \n", + "2200 5228 7000 9.921714 \n", + "2300 5215 7000 10.057286 \n", + "2400 5220 7000 10.158571 \n", "\n", " fraction_accuracy_other knowledge_other \n", "trial \n", - "0 0.966667 5.936073 \n", - "100 0.980000 5.936073 \n", - "200 0.943333 5.251142 \n", - "300 0.826667 4.109589 \n", - "400 0.950000 3.652968 \n", - "500 0.930000 3.424658 \n", - "600 0.936667 2.739726 \n", - "700 0.976667 3.424658 \n", - "800 0.980000 2.739726 \n", - "900 0.996667 2.511416 \n", - "1000 0.990000 2.968037 \n", - "1100 0.966667 2.739726 \n", - "1200 0.966667 2.968037 \n", - "1300 0.840000 3.196347 \n", - "1400 0.933333 3.196347 \n", - "1500 0.956667 3.881279 \n", - "1600 0.993333 4.109589 \n", - "1700 0.970000 3.196347 \n", - "1800 0.960000 2.739726 \n", - "1900 0.990000 3.196347 \n", - "2000 0.966667 3.424658 \n", - "2100 0.996667 3.424658 \n", - "2200 0.863333 3.196347 \n", - "2300 0.996667 2.739726 \n", - "2400 0.990000 2.739726 " + "0 1.00 10.958904 \n", + "100 1.00 7.534247 \n", + "200 1.00 7.534247 \n", + "300 1.00 6.849315 \n", + "400 1.00 6.849315 \n", + "500 1.00 6.849315 \n", + "600 1.00 6.849315 \n", + "700 1.00 6.849315 \n", + "800 1.00 6.849315 \n", + "900 1.00 6.849315 \n", + "1000 1.00 6.849315 \n", + "1100 1.00 6.849315 \n", + "1200 1.00 6.849315 \n", + "1300 1.00 6.849315 \n", + "1400 1.00 6.849315 \n", + "1500 1.00 6.849315 \n", + "1600 1.00 6.849315 \n", + "1700 0.97 6.164384 \n", + "1800 0.94 6.164384 \n", + "1900 1.00 6.164384 \n", + "2000 1.00 6.164384 \n", + "2100 1.00 6.164384 \n", + "2200 1.00 6.164384 \n", + "2300 1.00 6.849315 \n", + "2400 1.00 6.849315 " ] }, "metadata": {}, @@ -872,7 +780,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -881,7 +789,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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PtA1ZKbEcbbqxZFvCo62FkQAvfwPqTwQ1NqWUckrIk4qIzAWuAr5ud2mB1QLJaHJaOnDQLk9vodxxjTPA/OoCA0jKhK88BUVb4J17dWGkUqpHCmlSEZHZwP3A1caYpos83gJuFJFIEcnEGpBfa4w5BFSIyFR71tctwJuhjLk12SntTCtuyfDZMOOHsOlvsGFhkCJTSinnhAXrjUXkRWAmkCIihcCPsWZ7RQIf2DODPzHG/KcxZquIvAxsw+oWu9sY0zgH9y6smWTRWGMwS+gCBvaNJiLM1fa04pbMfMBaEPnOveAKhwlfD06ASinlgKAlFWPMTS0U/7mN8x8BzrggiTFmHTA6gKEFhNslZCbHdqylAuByw/V/gZdvgTfnQ/kuuOi/rQWTSinVzekv2Vk443r1/oruAze/ak03Xv4reOWbOnivlOoR/E4qIuIWkTQRGdR4BDOw7iDbE8f+8mrqGlrYWLI97nD48hNw6cOw7S147kqoOBz4IJVSKoT8Sioi8m3gMNaK+HfsY3EQ4+oWsjyxeH2G/eWdaK2AtYbl/G/DnL9B8XZ45mI4vDWwQSqlVAj521L5LjDcGDPKGDPGPsYGM7DuoPF69Z3qAmtq5FVw67vgrYc/XwY7/xmA6JRSKvT8TSoFwLFgBtIdndpYsoOD9S1JmwDf+hf0HQKLboC1T5/9eyqlVIj5O/trN/CxiLwD1DYWGmP+X1Ci6ibio8JJjY9k99m2VBolDoTb3rN2NX73XijLh8t+Zs0YU0qpbsDflsp+rPGUCCC+ydHrWTPAAtBSaRQZBzcugqnzYc0f4cWboLYicO+vlFJB5FdLxRjzEwARibcemgD+inZv2Z44Fm8+hDGGgF3qxeWG2T+H5Gx49z54djbc9BL0yWj/tUop5SB/Z3+NFpGNQB6wVUTWi8io4IbWPWR54jh2ws+NJTtq0h3w9Zfh6H5rZtjml+HgRqgu173DlFJdkr9jKk8B/2WM+QhARGYCTwPnByes7iO7yVUgk+MiA/8BQy+B29+HRV+1ts5vFBEPfQdDn8HQZ9CZ9yO1d1IpFXr+JpXYxoQCYIz5WERigxRTt5I7IIFwt7Bw1V4mDekbuC6wplJHwt2fQukXcHQfHNlntV6O7oMje2D3x1DfbLJAdJI1k2zKnTB2jrUmRimlgszv2V8i8j/AX+3HNwN6tSkgNSGK710yjEeX7uCSTal8ZUJ6+y/qjPAoGDDWOpozBqrLzkw4hZ/C63fCln/AVb+2WjFKKRVEYvzomxeRvsBPgGlYF85aBjxkjDkS3PA6b+LEiWbdunUh+SyvzzDnT6vZUVTBku9NJ71vTEg+t10+H3z6DHz4EyvxXPy/MPlbOkVZKdUqEVlvjJnY6df7k1S6o1AmFYCC8mpmP76M0QMTefFbU3G5ulB309ECWPx9yP8A0ifB1b+1utRCzeeFfatg5/uQMszaSSC6b+jjUEq1KqhJRUQeN8Z8T0TepoXL+Bpjru7sBwdbqJMKwMvrCrjvlc386IoRzJuRHdLPbpcxVjfYkvutdS8z7oVp/wVhEcH9XJ8PCtdC3muw7Q2oPAziAuOzricz9GIYdR0MvxyiEoIbi1KqXWebVNobU2kcQ3mssx/Qm9xwbjr/3HaYx5Z+wfQcDyMHdKEfSREY+1XI/hK89wB8/HPY+obVasmYFNjPMgYObICtr8HW1+H4AXBHQs4sGH0d5FwGJTtOPf/Fe6c/P2w2ROg8EKW6I3/HVL5rjHmivbKuxImWCkBZZS2XPb6clLgI3rj7AqLCu+j4xRfvW11ixw9YM8S+9D/Wav7OMgaKNlstkq2vWxMF/GmJtNSSCY+BYZdZr8uZBeHRnY9LKdUhIRlTEZENxphzmpVtNMZM6OwHB5tTSQXgX58f5rbn1jFvRhY/usKBsQt/1VbAhz+1Nq9MzIAv/9paF9Man8+aulxbceqoOQb7P7FaHWX5IG7Immm1OEZc2bExk8Yxl62vwbY3rRltEfFWQhp9HWReCBFdZBKEUj1UsMdUbgK+hjXra3mTp+IBrzGmjV8gZzmZVAAefH0Li9buZ9EdUzkvO9mxOPyyfw28dY+1DmboLOuHu2niaHqcObRmjZEMmWa1LEZeDbEBqK+3AfYug7xXYfvbVvIKi4Ih061WTM6l1iJPpVRABTupDAYygZ8DC5o8VQFsNsY0dPaDg83ppFJd18CVv1lBbb2X974/g4SocMdi8UtDLSx7DDa/BGHR1or8xiMqASITTi+LjD9VlpQFcalBjK0O9i63Zo19sdRa8AmQMhyGXWqN0Qyaal1NUyl1VnRKcSucTioAmwqO8h9/WMXV49L49ZzxjsbSo5TmWwlm51LYuxJ89VaCy77ISjBDL4H4fsH5bJ/PGi8q2QEln0NFEfQbZU3VThkGLr+v0K1UlxTs2V+NHzIV+C0wEmv7ezdQZYzpQtObup7xGX349peG8vg/d3LxyFSuGpvmdEg9Q8pQ6zhvvtUlt/vfVoLZ+YE1FgPWRc/SJ0OcB2LtIyYFYlOs+5HxbW9d422AI3utxFHy+akkUroTGk6cOi8sChpqrPuRCTDwXCvBpE+C9IkQkxS0P4NSXZG/A/XrgBuBfwATgVuAocaYB4MbXud1hZYKQL3Xx/V/XM3e0iqWfm8G/ROjnA6p5zIGiracSjDF26H2eMvnuiPtZJN8KulEJ0HFISuBlO0Eb5OdpxMzwDMcPCNO3aYMsxJJ+S5rS5zG4/BWax0OQFL2qQSTPslq1fjTTWeMNXHBV2/dRsTq/m0qJEI1+2udMWaiiGxuvDa9iKwyxnTZXYq7SlIB2FNaxRVPLGfikL4svHVy11pt39M11EJVKVSVnLqtbva4qtQ6qkut5OIZAakjTiWQlGEd2/W5thIObbKTzDooWAtVxdZzYdHWdXF8DVZryNdgJ47Gx/Z9X7PhyqRsGHODdaQMDdifR6nmQpVUlgGXAM8ARcAh4JvGmHGd/eBg60pJBeCFNft48PU8HvpyLt+8INPpcFQoGQPHCqwkU/ApVBy01vC4wsAd1uR+s1tXuPW88Vk7Ue9ZDhira2/MDdZsu4QBoa9PyQ7YtMjaoaGqtEm8jbGHW/vLnVEeZq1BypkFo/8D4vuHPnbVrlAllcFAMRAOfB9IBJ40xuR39oODraslFWMMty9cx8r8UhZ/exo5/fR6J6qDjh+0Folu+YfVEkIgc7qVYEZeDdF9gvfZJ45a64c2vgAH1lnrkXJmWa05XwN460+1upq2wLz1p7rxvPVWEireak1Dz5ppXZZhxJXd4/o/xlg7gBdvg8N5UF8DI66AtHN6VNekzv5qRVdLKgDFFTXMfnw5aX2ieO2uC4gI05lCqpNKd8KWV2DLy1C+G9wR1tqdMTdY63gCsQuBz2u1kDa9ANsXg7cWPCNhwtetZNDZaeQlX1hxb/679SMdFm0llrFzrBl8XWFqeHW5nTy2WUnw8DZrjK6u4tQ54gbjta5bNOorVsux/5hun2CCvU5lCy2udrM0jq90RV0xqQAs3VrEnX9dz90XZfPDy0Y4HY7q7oyBgxusBJP3qrXNTWSCtX+aZxgkDISENOs2foB/W/GU5luJ5LOXrK66qD5WsprwdRgwPnA/msZAwRrrMtlbX4MTRyAm2eoaGzvHmkkXrB/ohjprx4aqEuuoPGwljcZEUnHw1LlRfawJFqm50C8X+o22WmjGayXbra9ZMxCNF5JzrN0fRl1njct1Q6FY/NgqY8y+zn5wsHXVpAJw3yuf8fK6Qh6+djQ3T9VV4SpAfF5rkeiWf1iLRKtKzjwnKhHi0+xEk3Z60jl+wEomBWus7qmhl8D4r8HwKyAsCJfKbqqhDvL/abVediyxWkVJWTDG3gTV5baSkPEB9m1bj2uOtzExo8TaoaE5d4S1oLZfrp1ARlv34we0n9yqSmH7W1b35N4VVgypuVZyGX0dJHexXcvboN1frejKSaWm3sv8Fzbwr8+L+e8rR3LH9CynQ1I9UX2N9S/u443HgTPvVxZzWmdEyjAYb3dvOTEJAKwf/O1vWwmmcXJCp4nV+on12GuUUpqsW2oynTwu1boyaiC63ioOW+ultr4G+1dbZf3HWsllyHRrenpcapftJgvVQH3TTZ8isAbsu/Tix66cVADqGnx87+8beXdLEfdeOox7vpTjdEiqN2qog8oiK8GERcGAcV3rx+7YAWtQHLFaT4J1e/KxnPkYsQb+Yz3W4lMnr3R6rNC6xMTW1+DA+lPl7khITLemlydmWAktMePU44SB1sw/BzjSUhGRa4HJxpgfdfaDg62rJxWABq+P+17ZzGsbDzB/ZjY/vGw40pX+h1ZKBc7R/dbC2KMFcGy/fVtg3TauY2okLqubMjHd6npsKXmKq0mZnH7OdU93+gJ8IdmmpTljzBsisqD9M1VbwtwuHrthHJHhbp78eBcn6r3871W5mliU6on6DLKOltSfsFplx/Zbyacx4Rw/aC3gbWns6GSZOXN86ay6DM+Ov3t/XdfkoQtrq5aeORgTYi6X8LOvjCYq3MVfVu6lpt7HI9eO1lX3SvUm4dGn9rTr5vxtqXy5yf0GYC9wTcCj6aVEhP+9KpeYCDe//2gXNfVeHr1+LGFuXceilOpe/Eoqxphbgx1Ibyci/PCyEUSHu3ns/S+oqffyxI0TdIGkUqpb8esXS0SyRORtESkRkWIReVNEdB5sENzzpRz++8qRLMkr4j//tp6aeq/TISmllN/8/WfwIuBlYACQhrUF/ottvUBEnrUTUF6TsiQR+UBEdtq3fZs894CI5IvIDhG5rEn5uSKyxX7uN9ILRrHvmJ7Fw9eO5l+fF3P7wk+pruuyF9hUSqnT+JtUxBjzV2NMg338jfYH6p8DZjcrWwB8aIzJAT60HyMiuVjXaxllv+ZJEWmcXP4HYB6QYx/N37NHunnqYH51wzhW7ypj7rNrqaipdzokpZRql79J5SMRWSAiQ0RksIjcB7xjtzxavLSdMWYZUN6s+BpgoX1/IXBtk/KXjDG1xpg9QD4wWUQGAAnGmNXGWlDzfJPX9Hj/cW46v73pHDbuP8rXn1nD0eq69l+klFIO8nf21xz79s5m5bdhtVj8HV/pZ4w5BGCMOSQijducDgQ+aXJeoV1Wb99vXt4iEZmH1aph0KBW5oN3M1eOHUBkmIv5L2zgK0+u4ndfm8CotESnw1JKqRb51VIxxmS2cQRiwL6lcRLTRnlrcT5ljJlojJno8XgCEFbXcEluP/52xxSq6xr4ypOr+OvqvfTUPduUUt2bv7O/wkXkOyLyin3cIyKd2XntsN2lhX3buDdBIZDR5Lx04KBdnt5Cea8zOTOJd78znfOzk/mfN7cy/4UNHDuh4yxKqa7F3zGVPwDnAk/ax7l2WUe9Bcy1788F3mxSfqOIRIpIJtaA/Fq7q6xCRKbas75uafKaXic5LpJn507iR1eM4INth7nyN8vZuP+I02EppdRJ/iaVScaYucaYf9nHrcCktl4gIi8Cq4HhIlIoIrcDvwBmichOYJb9GGPMVqwpy9uA94C7jTGNCzTuAp7BGrzfBSzpUA17GJdLmDcjm5f/8zyMgRv+uJqnl+3G59PuMKWU8/zd+n4DcIMxZpf9OAt4xRhzTpDj67TusEvx2TpWXc/9r27mva1FXDTcw6++Op6k2M7tTKqUUnD2uxT721L5Ida04o9F5GPgX8APOvuhKjASY8L5w83n8NNrRrEyv4wrnljOmt1lToellOrF/E0qK4E/AT77+BNW15ZymIhwy3lDeG3++URHuLnp6U/47Yc78Wp3mFLKAf4mleeBTOD/2Ecm8NdgBaU6bvTARN7+9jSuHpfGrz74glueXUNxRY3TYSmlehl/k8pwY8wdxpiP7GMeMCyYgamOi4sM49dzxvPL68eyft8RrnhiOW9uOqCD+EqpkPE3qWwUkamND0RkClaXmOpiRISvTszgrXum0S8hiu++tIlrfr+SVfmlToemlOoF/J39tR0YDuy3iwYB27HGV4wxZmzQIuyk3jD7qz0+n+GNTQd4bOkODh6rYeZwDwsuH8GI/glOh6aU6qLOdvaXv0llcFvPG2P2dTaAYNGkckpNvZeFq/byu4/yqaxt4Ppz0vmvS4cxIDHa6dCUUl1MSJJKd6RJ5UxHqur4/Uf5PL96Hy4X3D4tkzsvzCYhqjM77iileiJNKq3QpNK6gvJqfvX+Dt7YdJCk2Ai+86WhfG3KYL10sVIqZIsfVQ+SkRTD4zdO4O17pjGifzwPvb2NWb/+N+9sPqS7HyulzoomlV5sTHoiL9wxhedunUR0uJu7F23guj+sovBItdOhKaW6KU0qvZyIMHN4Ku98Zzq/vH4s+cWVXPO7lazb2/yinUop1T5NKgoAt8ta3/LG3ReQEB3OTU9/wsufFjgdllKqm9Gkok6T7YnjjfkXMCUzmfte3czDi7fpPmJKKb9pUlFnSIwJ57lbJ/HN84fwzIo93Pbcpxyv0atMKqXap0lFtSjM7eKhq0fx8+vGsDK/lK/8fiV7SqucDksp1cVpUlFtumnyIF64YwpHquu59vcrWbFT9xBTSrVOk4pq15SsZN68+wL6J0Qx9y9rWbhqr65nUUq1SJOK8ktGUgyvzj+fi4Z7+PFbW3nwjTzqvT6nw1JKdTGaVJTf4iLDeOobE5k/M5tFa/Zz8zNrKK+qczospVQXoklFdYjLJdw3ewSPzxnPxoKjXPP7FWw/dNzpsJRSXYQmFdUp104YyMt3nkdtvY8rf7Oc+1/ZzMGjJ5wOSynlME0qqtPGZ/RhyXen883zM3l94wFmPvYx/2fxNsoqa50OTSnlEN36XgXEgaMneOKfX/DK+kKiw93cMT2LO6ZnEq/XalGqW9HrqbRCk4oz8osr+NX7X7Akr4ik2Ajmz8zm5qmDiQp3Ox2aUsoPmlRaoUnFWZ8VHOXRpTtYkV9KWmIU370kh/84J50wt/a4KtWV6UW6VJc0LqMPf7tjCovumIInIYr7X93CpY8v490teiEwpXqyMKcDUD3b+UNTeCM7maVbD/PY+zuY/8IGxgxMZFpOCqnxkaTGR+GJj7TuJ0QSE6H/SSrVnen/wSroRITZo/szK7cfr288wB//vYunl+2moYUt9eMiw0iNj7QSTUKUnXgiGdAnmnMH92Vgn2gHaqCU8pcmFRUybpdw/bnpXH9uOj6f4Uh1HcUVtRRX1FJSUUtxRQ3Fx0/d31x4lOLjtZyo9558j/S+0UzOTGJKZhKTM5MZkhyDiDhYK6VUU5pUlCNcLiE5LpLkuEhGDmj9PGMMVXVe9pZW8enectbuKeffO0p4bcMBAFLjI09LMjmpcbhcmmSUcorO/lLdjjGGXSVVrNlTxto95azZXU7R8RoA+saEM2lIEpMzk5gwqA9DPfEkxuhaGaX8dbazv7SlorodEWFoahxDU+P4+pTBGGMoPHKCT3ZbSWbt3nLe33b45PkpcRFke+LITo0j22O9LtsTS1pitLZqlAowTSqq2xMRMpJiyEiK4YaJGQAUHath68Fj7CqpJL+4kl0lVbyz+RDHTpy6LHJ0uJssT2yTRBPHOYP7MCBRJwMo1VmaVFSP1D8xiv6JUVw8st/JMmMMZVV17CquJL+kkl3FVewqqWT9viO89dnBk+dle2KZNjSFaTkepmYl6VYzSnWAJhXVa4gIKXGRpMRFMiUr+bTnTtR5yS+u5JPdZazIL+Xv6wpYuHofbpcwPqMPFwxNYXpOCuMz+hCuuwIo1SodqFeqBbUNXjbsO8rK/FKW55eypfAoPgOxEW6mZiWfTDJDU+MAqKhtoLyyjvLqOo5U1VHeeJx8XM+RaqssJsLNtKEpTM/xMHFIX90XTXUp3XLvLxH5PnAHYIAtwK1ADPB3YAiwF/iqMeaIff4DwO2AF/iOMWZpe5+hSUUF0rHqelbvLmVFfikrdpayt6wagPjIME7Ue1tcyAkQ4XaRFBtB39gIkmLDSYqNpKSihvX7jlDvNUSGuZiSlcyMHCvJDOsXp+tulKO6XVIRkYHACiDXGHNCRF4G3gVygXJjzC9EZAHQ1xhzv4jkAi8Ck4E04J/AMGOMt5WPADSpqOAqKK9m1a5S8g4cJz4qjKTYiFPJIybi5OOYCHeLSaK6roE1u8tZtrOE5TtLyS+uBKx1N9NzPMwYlsIFQ1NIiYsMddVUL9ddk8onwDjgOPAG8Bvgt8BMY8whERkAfGyMGW63UjDG/Nx+/VLgIWPM6rY+R5OK6k4OHj3Bip2lLNtZwor8Uo5WW7PURqUlMC0nhZzUeJLjIkiJjSQ5zkpY2m2mgqHbrVMxxhwQkceA/cAJ4H1jzPsi0s8Yc8g+55CIpNovaUxCjQrtsjOIyDxgHsCgQYOCVQWlAi6tTzRfnZTBVydl4PUZth48xvKdpSz7ooRnV+yh3tvyPmnJcREkx0ZYuxPERtiPI+mfGMWFwzzERupcHBVaIf8vTkT6AtcAmcBR4B8icnNbL2mhrMXmlTHmKeApsFoqZxepUs5wu4Sx6X0Ym96Huy8aSk29l8PHayirqqOsso6yylrKquoorayl3C4rKK9mU8FRyqvq8NrjO3GRYVwzPo2bJg9i9MBEh2ulegsn/hlzCbDHGFMCICKvAecDh0VkQJPur2L7/EIgo8nr04GDKNVLRIW7GZwcy+Dk2HbP9fkMx2vq+eJwJX//tIBX1hfywpr9jEtP5KbJg/jyuDRtvaigcmJMZQrwLDAJq/vrOWAdMAgoazJQn2SMuU9ERgGLODVQ/yGQowP1SrXvWHU9r20sZNGa/ewsrjzZevnalEGMStPWizpTtxuoBxCRnwBzgAZgI9b04jjgZazksh+4wRhTbp//IHCbff73jDFL2vsMTSpKnWKMYf2+Iyxau593Nh+itsHHuIw+fG1yBl8el6YXR1MndcukEgqaVJRq2dHqOl7feOBk6yU+MoxrJwzklvMGk9Mv3unwlMM0qbRCk4pSbTvZelmzn8VbDuHzGb53SQ53zRyKW3dv7rU0qbRCk4pS/iuvquPHb23l7c8OMnFwX349ZzwZSTFOh6UccLZJRXfGU0qRFBvBb24cz+NzxrOjqILLn1jOq+sL6an/6FTBo0lFKQVYuzhfO2Eg7353OrkDEvjBPz7jnhc3crS6zunQVDeiSUUpdZqMpBhenDeV+2YPZ2leEbMfX87K/FKnw1LdhCYVpdQZ3C5h/syhvD7/AmIi3Xz9mTU88s42ahvaXB6mlCYVpVTrxqQn8s63p3Pz1EE8vXwP1/xuJTuKKpwOS3VhmlSUUm2KjnDz8LVj+PPciZRU1PLl363g2RV78LVyDRnVu2lSUUr55eKR/XjvezOYNjSFny7exty/rKWgvNrpsFQXo0lFKeU3T3wkf547kYevHc2ne8u58NGPmP/CetbtLdfpxwpwZpdipVQ3JiLcPHUwXxqRyvOr9/Hi2v28u6WIsemJ3HZBJleMGUBEmP57tbfSFfVKqbNSXdfAqxsO8JeVe9hdUkVqfCS3nDeYr00ZTFJshNPhqQ7SbVpaoUlFqdDy+Qz/3mldqXL5zlIiw1x8ZcJAbr0gk+H9daPK7qLbXU5YKdUzuVzCRcNTuWh4Kl8cruAvK/fy2oZCXvq0gGlDU7ht2hBmDkvFpZtV9mjaUlFKBc2RqjoWrd3P86v3cvh4LYOSYhiTnsjgpBgG2UdGUgxpfaJ1Z+QuQru/WqFJRamuo97r490th3h94wH2llZReOQEDU3WuYS5hPS+0WQkxTA4+fSEk5kSqxcRCyHt/lJKdXnhbhfXjB/INeMHAtDg9XHoWA0F5dXst4995dUUlFezePMhjlbXn3yt2yWMS0/kgqEpnJ+dwjmD+xAZ5naqKqod2lJRSnU5x07Un0w4Ww8eY2V+GZsLj+IzEBXuYtKQJM7PTuGCocmMSkvUrrMA0u6vVmhSUapnOV5Tz5rd5azML2XVrlK+OFwJQEJUGFOzkrlgqJVksj1xiGiS6Szt/lJK9QoJUeHMyu3HrNx+ABRX1LB6Vxmr8stYuauU97cdBqBfQiT9E6Px+Qxen8FnrFuvz+C17/tO3gevz4fbJZyXncJVYwdw4TAPUeHavdZZ2lJRSvUI+8uqWbmrlNW7yjh6oh63WOMxLhHcriaHCK6mty6orvXy0Y5ijlTXEx8ZxqxR/fjy2DQuGJrS63YH0O6vVmhSUUp1RL3Xx6pdZSz+7CBLtxZxvKaBxOhwZo/qz5VjB3B+djJh7p6fYDSptEKTilKqs+oafCzfWcLizYf4YNthKmsbSIqNYPbo/lw1dgBTMpN77OQATSqt0KSilAqEmnovH+8oYfHmg3y4vZgT9V488ZFcMbo/s0cPYHJmUo9KMJpUWqFJRSkVaNV1DXy4vZjFmw/y8Y4Saht8JMdGMCu3H5eN7s8F2d1/DEaTSis0qSilgqmqtoF/f1HCkrwi/rX9MFV1XuKjwrh4RCqzR1uzyKIjut8sMk0qrdCkopQKlZp6LyvzS3kvr4gPth/maHU90eFuZg73MHt0f740IpX4qHCnw/SLrlNRSimHRYW7uXhkPy4e2Y96r4+1e8pZkneIpVsPsySviAi36+TCzNoGH7UNXuu23ked135c7zv5XF2Ddd/rM0waksRlo/tz0XBPt0hM2lJRSqkg8fkMG/Yf4b28IpZuK6K8so7IcDcRbheR4S4iw1xEhrmJDHMREdbksf1cg9ewPL+UkopaItwupuekcNno/swa2Y++QboAmnZ/tUKTilKqJ/D6DBv3H2FJXhHv5RVx4OgJ3C5halYSs0cP4LLcfqQmRAXs8zSptEKTilKqpzHGkHfgOO9tPcSSvCJ2l1QhAucM6svlo/tz2aj+ZCTFnNVnaFJphSYVpVRPt/NwBe/lFbEkr4hth44DMCotgYW3TSYlLrJT76kD9Uop1Uvl9Isnp1883744h/1l1SzdWsS6feUkB2m8xR+aVJRSqgcYlBzDt2Zk8S2yHI2jey/9VEop1aVoUlFKKRUwmlSUUkoFjCNJRUT6iMgrIvK5iGwXkfNEJElEPhCRnfZt3ybnPyAi+SKyQ0QucyJmpZRS7XOqpfIE8J4xZgQwDtgOLAA+NMbkAB/ajxGRXOBGYBQwG3hSRLrfLm1KKdULhDypiEgCMAP4M4Axps4YcxS4Blhon7YQuNa+fw3wkjGm1hizB8gHJocyZqWUUv5xoqWSBZQAfxGRjSLyjIjEAv2MMYcA7NtU+/yBQEGT1xfaZWcQkXkisk5E1pWUlASvBkoppVrkRFIJA84B/mCMmQBUYXd1taKlS6q1uA2AMeYpY8xEY8xEj8dz9pEqpZTqECcWPxYChcaYNfbjV7CSymERGWCMOSQiA4DiJudnNHl9OnCwvQ9Zv359qYjs62SMKUBpJ1/b3fXmukPvrn9vrjv07vo3rfvgs3kjR/b+EpHlwB3GmB0i8hAQaz9VZoz5hYgsAJKMMfeJyChgEdY4ShrWIH6OMcYbxPjWnc3eN91Zb6479O769+a6Q++ufyDr7tQ2Ld8GXhCRCGA3cCtWV9zLInI7sB+4AcAYs1VEXga2AQ3A3cFMKEoppTrPkaRijNkEtJQVL27l/EeAR4IZk1JKqbOnK+pb9pTTATioN9cdenf9e3PdoXfXP2B177HXU1FKKRV62lJRSikVMJpUlFJKBYwmlSZEZLa9aWW+Pa25RxKRvSKyRUQ2icg6u6xHbugpIs+KSLGI5DUp63BdReRc+2+WLyK/EZGWFuV2Oa3U/yEROWB//5tE5Iomz/WY+otIhoh8ZG9au1VEvmuX9/jvv426B/+7N8boYY0ruYFdWNvIRACfAblOxxWkuu4FUpqV/RJYYN9fAPxf+36u/beIBDLtv5Hb6Tp0oK4zsHZwyDubugJrgfOwdnhYAlzudN3Oov4PAfe2cG6Pqj8wADjHvh8PfGHXscd//23UPejfvbZUTpkM5Btjdhtj6oCXsDaz7C165IaexphlQHmz4g7V1d7hIcEYs9pY/5c93+Q1XVor9W9Nj6q/MeaQMWaDfb8Cazf0gfSC77+NurcmYHXXpHKK3xtX9gAGeF9E1ovIPLvsrDf07EY6WteB9v3m5d3ZPSKy2e4ea+z+6bH1F5EhwARgDb3s+29Wdwjyd69J5RS/N67sAS4wxpwDXA7cLSIz2ji3N/1dWqtrT/sb/AHIBsYDh4Bf2eU9sv4iEge8CnzPGHO8rVNbKOvW9W+h7kH/7jWpnNKpjSu7I2PMQfu2GHgdqzvrsN3UJRAbenZxHa1roX2/eXm3ZIw5bIzxGmN8wNOc6s7scfUXkXCsH9UXjDGv2cW94vtvqe6h+O41qZzyKZAjIpn2nmQ3Am85HFPAiUisiMQ33gcuBfKw6jrXPm0u8KZ9/y3gRhGJFJFMIAdr4K4761Bd7S6SChGZas98uaXJa7qdxh9U21ewvn/oYfW3Y/0zsN0Y8/+aPNXjv//W6h6S797pWQpd6QCuwJolsQt40Ol4glTHLKxZHp8BWxvrCSRj7QC9075NavKaB+2/yQ66+KyXFur7IlYzvx7rX123d6auWHvV5dnP/Q57N4qufrRS/78CW4DN9o/JgJ5Yf2AaVlfNZmCTfVzRG77/Nuoe9O9et2lRSikVMNr9pZRSKmA0qSillAoYTSpKKaUCRpOKUkqpgNGkopRSKmA0qSgVICLSR0Tmt/H8Kj/eozKwUSkVWppUlAqcPsAZSUVE3ADGmPNDHZBSoRbmdABK9SC/ALJFZBPWYsNKrIWH44FcEak0xsTZ+zG9CfQFwoH/NsZ06RXaSvlLFz8qFSD2brCLjTGjRWQm8A4w2lhbidMkqYQBMcaY4yKSAnwC5BhjTOM5DlVBqbOmLRWlgmdtY0JpRoCf2btD+7C2Eu8HFIUyOKWCQZOKUsFT1Ur51wEPcK4xpl5E9gJRIYtKqSDSgXqlAqcC69Kt7UkEiu2EchEwOLhhKRU62lJRKkCMMWUislJE8oATwOFWTn0BeFtE1mHtHvt5iEJUKuh0oF4ppVTAaPeXUkqpgNGkopRSKmA0qSillAoYTSpKKaUCRpOKUkqpgNGkopRSKmA0qSillAqY/w9oJDJT2YiYFgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1009,22 +917,22 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1044,22 +952,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1079,22 +987,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1114,15 +1022,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "195.6666666666667\n", - "164.11111111111111\n" + "523.0\n", + "288.0\n" ] } ], diff --git a/XCS_Experiments/XCS_maze5_avg.ipynb b/XCS_Experiments/XCS_maze5_avg.ipynb index a886cca..e59a376 100644 --- a/XCS_Experiments/XCS_maze5_avg.ipynb +++ b/XCS_Experiments/XCS_maze5_avg.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -35,12 +35,12 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '1', '0', '1', '1', '0', '0', '1')\n", + "('0', '0', '0', '1', '0', '1', '1', '1')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -69,7 +69,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.055027700000000124, 'population': 108, 'numerosity': 167, 'average_specificity': 7.245508982035928, 'knowledge': 59.589041095890416}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.031224900000097477, 'population': 52, 'numerosity': 102, 'average_specificity': 103.54901960784314, 'knowledge': 97.94520547945206}\n" ] }, { @@ -83,55 +83,44 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 70, 'reward': 1000.0, 'perf_time': 0.27830520000000547, 'population': 410, 'numerosity': 1600, 'average_specificity': 5.7225, 'knowledge': 98.63013698630137}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 13, 'reward': 1012.782498522416, 'perf_time': 0.030381000000005542, 'population': 426, 'numerosity': 1600, 'average_specificity': 6.3775, 'knowledge': 96.57534246575342}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 37, 'reward': 1000.0031738801285, 'perf_time': 0.18006750000000693, 'population': 423, 'numerosity': 1600, 'average_specificity': 5.80125, 'knowledge': 97.94520547945206}\n", - "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 44, 'reward': 1000.0002984256316, 'perf_time': 0.1673784999999839, 'population': 404, 'numerosity': 1600, 'average_specificity': 6.54625, 'knowledge': 97.26027397260275}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 5, 'reward': 1000.0000000000002, 'perf_time': 0.020100200000001678, 'population': 422, 'numerosity': 1600, 'average_specificity': 6.125, 'knowledge': 99.31506849315068}\n", - "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 46, 'reward': 1000.0001697955181, 'perf_time': 0.2517175000000407, 'population': 434, 'numerosity': 1600, 'average_specificity': 6.938125, 'knowledge': 97.26027397260275}\n", - "INFO:lcs.agents.Agent:{'trial': 2800, 'steps_in_trial': 22, 'reward': 1000.5341751391559, 'perf_time': 0.0999247999999966, 'population': 418, 'numerosity': 1600, 'average_specificity': 6.25375, 'knowledge': 95.2054794520548}\n", - "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 19, 'reward': 1001.497199283639, 'perf_time': 0.07930079999994177, 'population': 435, 'numerosity': 1600, 'average_specificity': 6.07, 'knowledge': 97.26027397260275}\n", - "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 64, 'reward': 1000.000000302367, 'perf_time': 0.2234621000000061, 'population': 400, 'numerosity': 1600, 'average_specificity': 6.294375, 'knowledge': 97.94520547945206}\n", - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1527.2184528198086, 'perf_time': 0.0037139000000934175, 'population': 415, 'numerosity': 1600, 'average_specificity': 7.343125, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 73, 'reward': 1000.0, 'perf_time': 0.32945010000003094, 'population': 483, 'numerosity': 1600, 'average_specificity': 13.56875, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 13, 'reward': 1011.7866176433523, 'perf_time': 0.04351310000004105, 'population': 500, 'numerosity': 1600, 'average_specificity': 10.36, 'knowledge': 99.31506849315068}\n", - 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"INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 7, 'reward': 1155.526554708486, 'perf_time': 0.02238050000005387, 'population': 489, 'numerosity': 1600, 'average_specificity': 7.10125, 'knowledge': 98.63013698630137}\n", - "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 12, 'reward': 1028.8523603862018, 'perf_time': 0.05500140000003739, 'population': 481, 'numerosity': 1600, 'average_specificity': 6.349375, 'knowledge': 99.31506849315068}\n" + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 17, 'reward': 1002.9606864486374, 'perf_time': 0.05687790000001769, 'population': 350, 'numerosity': 1600, 'average_specificity': 28.04875, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 23, 'reward': 1000.3792643488007, 'perf_time': 0.08490669999991951, 'population': 334, 'numerosity': 1600, 'average_specificity': 34.48, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.23183819999985644, 'population': 229, 'numerosity': 1600, 'average_specificity': 54.68875, 'knowledge': 97.94520547945206}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 100, 'reward': 9.878432084326981e-206, 'perf_time': 0.26911330000007183, 'population': 246, 'numerosity': 1600, 'average_specificity': 60.8075, 'knowledge': 99.31506849315068}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 1, 'reward': 1710.0, 'perf_time': 0.0013762999999471504, 'population': 321, 'numerosity': 1600, 'average_specificity': 25.565, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 57, 'reward': 1000.0, 'perf_time': 0.1790046999999504, 'population': 295, 'numerosity': 1600, 'average_specificity': 33.2475, 'knowledge': 98.63013698630137}\n", + "INFO:lcs.agents.Agent:{'trial': 2800, 'steps_in_trial': 2, 'reward': 1507.0696698159047, 'perf_time': 0.002492800000027273, 'population': 285, 'numerosity': 1600, 'average_specificity': 35.60375, 'knowledge': 97.94520547945206}\n", + "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 100, 'reward': 1.7860831411821753e-27, 'perf_time': 0.3196778000001359, 'population': 296, 'numerosity': 1600, 'average_specificity': 68.7775, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 100, 'reward': 1.3360874108272584e-12, 'perf_time': 0.3191027000000304, 'population': 329, 'numerosity': 1600, 'average_specificity': 31.73375, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.16431009999996604, 'population': 279, 'numerosity': 1600, 'average_specificity': 63.51, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 1, 'reward': 1710.0000011898737, 'perf_time': 0.0031443000002582266, 'population': 286, 'numerosity': 1600, 'average_specificity': 85.86125, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 100, 'reward': 1.675609681536095e-12, 'perf_time': 0.18465650000007372, 'population': 307, 'numerosity': 1600, 'average_specificity': 74.25375, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 100, 'reward': 5.688650226172007e-87, 'perf_time': 0.2229588000000149, 'population': 303, 'numerosity': 1600, 'average_specificity': 99.14125, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.1667013000001134, 'population': 281, 'numerosity': 1600, 'average_specificity': 86.4475, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 100, 'reward': 1.3360874108178934e-12, 'perf_time': 0.23304109999980938, 'population': 252, 'numerosity': 1600, 'average_specificity': 88.5925, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2400, 'steps_in_trial': 5, 'reward': 1205.2566061211637, 'perf_time': 0.006280199999764591, 'population': 289, 'numerosity': 1600, 'average_specificity': 64.9425, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2800, 'steps_in_trial': 100, 'reward': 5.688650226172007e-87, 'perf_time': 0.19819280000001527, 'population': 337, 'numerosity': 1600, 'average_specificity': 97.8425, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3200, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.14753710000013598, 'population': 283, 'numerosity': 1600, 'average_specificity': 86.1125, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3600, 'steps_in_trial': 14, 'reward': 1000.0, 'perf_time': 0.053561700000045676, 'population': 288, 'numerosity': 1600, 'average_specificity': 87.34875, 'knowledge': 100.0}\n" ] } ], "source": [ "cfg = Configuration(number_of_actions=8,\n", " max_population=1600,\n", - " learning_rate=0.2,\n", - " alpha=0.1,\n", - " gamma=0.71,\n", - " mutation_chance=0.01,\n", - " delta=0.1,\n", - " ga_threshold=25,\n", - " covering_wildcard_chance = 0.7,\n", - " chi=1, # crossover\n", " metrics_trial_frequency=100,\n", - " initial_prediction =10, # p_i\n", - " initial_error = 0, # epsilon_i\n", - " initial_fitness = 10, # f_i\n", " user_metrics_collector_fcn=xcs_maze_metrics)\n", "\n", "df = avg_experiment(maze,\n", " cfg,\n", " number_of_tests=1,\n", " explore_trials=4000,\n", - " exploit_trials=1000)" + " exploit_trials=4000)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -179,612 +168,147 @@ " 0\n", " 100\n", " 0.000000e+00\n", - " 0.055028\n", - " 108\n", - " 167\n", - " 7.245509\n", - " 59.589041\n", + " 0.031225\n", + " 52\n", + " 102\n", + " 103.54902\n", + " 97.945205\n", " \n", " \n", " 100\n", " 100\n", - " 1.336087e-12\n", - " 0.356117\n", - " 425\n", + " 5.688650e-87\n", + " 0.242209\n", + " 222\n", " 1600\n", - " 6.675000\n", + " 40.70500\n", " 99.315068\n", " \n", " \n", " 200\n", - " 29\n", - " 1.000000e+03\n", - " 0.110790\n", - " 384\n", - " 1600\n", - " 6.528750\n", - " 97.945205\n", - " \n", - " \n", - " 300\n", - " 33\n", - " 1.000012e+03\n", - " 0.153194\n", - " 397\n", - " 1600\n", - " 7.879375\n", - " 95.890411\n", - " \n", - " \n", - " 400\n", - " 70\n", - " 1.000000e+03\n", - " 0.278305\n", - " 410\n", - " 1600\n", - " 5.722500\n", - " 98.630137\n", - " \n", - " \n", - " 500\n", - " 16\n", - " 1.005230e+03\n", - " 0.057963\n", - " 410\n", - " 1600\n", - " 6.540000\n", - " 99.315068\n", - " \n", - " \n", - " 600\n", - " 48\n", - " 1.000000e+03\n", - " 0.156724\n", - " 414\n", - " 1600\n", - " 5.796250\n", - " 97.945205\n", - " \n", - " \n", - " 700\n", " 100\n", - " 1.336087e-12\n", - " 0.347222\n", - " 405\n", - " 1600\n", - " 6.329375\n", - " 93.835616\n", - " \n", - " \n", - " 800\n", - " 13\n", - " 1.012782e+03\n", - " 0.030381\n", - " 426\n", - " 1600\n", - " 6.377500\n", - " 96.575342\n", - " \n", - " \n", - " 900\n", - " 32\n", - " 1.000017e+03\n", - " 0.128177\n", - " 416\n", - " 1600\n", - " 5.876250\n", - " 97.945205\n", - " \n", - " \n", - " 1000\n", - " 27\n", - " 1.000000e+03\n", - " 0.136447\n", - " 425\n", - " 1600\n", - " 6.293750\n", - " 97.945205\n", - " \n", - " \n", - " 1100\n", - " 23\n", - " 1.000379e+03\n", - " 0.080909\n", - " 430\n", - " 1600\n", - " 5.943125\n", - " 95.205479\n", - " \n", - " \n", - " 1200\n", - " 37\n", - " 1.000003e+03\n", - " 0.180068\n", - " 423\n", + " 1.790415e-27\n", + " 0.314603\n", + " 258\n", " 1600\n", - " 5.801250\n", + " 52.13750\n", " 97.945205\n", " \n", " \n", - " 1300\n", - " 33\n", - " 1.000012e+03\n", - " 0.118465\n", - " 399\n", - " 1600\n", - " 6.774375\n", - " 98.630137\n", - " \n", - " \n", - " 1400\n", - " 2\n", - " 1.504100e+03\n", - " 0.004387\n", - " 391\n", - " 1600\n", - " 6.448125\n", - " 93.150685\n", - " \n", - " \n", - " 1500\n", - " 51\n", - " 1.000000e+03\n", - " 0.173603\n", - " 421\n", - " 1600\n", - 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80 rows × 7 columns

\n", "" ], "text/plain": [ " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 100 0.000000e+00 0.055028 108 167 \n", - "100 100 1.336087e-12 0.356117 425 1600 \n", - "200 29 1.000000e+03 0.110790 384 1600 \n", - "300 33 1.000012e+03 0.153194 397 1600 \n", - "400 70 1.000000e+03 0.278305 410 1600 \n", - "500 16 1.005230e+03 0.057963 410 1600 \n", - "600 48 1.000000e+03 0.156724 414 1600 \n", - "700 100 1.336087e-12 0.347222 405 1600 \n", - "800 13 1.012782e+03 0.030381 426 1600 \n", - "900 32 1.000017e+03 0.128177 416 1600 \n", - "1000 27 1.000000e+03 0.136447 425 1600 \n", - "1100 23 1.000379e+03 0.080909 430 1600 \n", - "1200 37 1.000003e+03 0.180068 423 1600 \n", - "1300 33 1.000012e+03 0.118465 399 1600 \n", - "1400 2 1.504100e+03 0.004387 391 1600 \n", - "1500 51 1.000000e+03 0.173603 421 1600 \n", - "1600 44 1.000000e+03 0.167378 404 1600 \n", - "1700 46 1.000000e+03 0.206975 421 1600 \n", - "1800 29 1.000049e+03 0.128477 417 1600 \n", - "1900 2 1.632200e+03 0.003618 408 1600 \n", - "2000 5 1.000000e+03 0.020100 422 1600 \n", - "2100 76 1.000000e+03 0.268280 420 1600 \n", - "2200 70 1.000000e+03 0.282579 430 1600 \n", - "2300 89 1.000000e+03 0.387760 435 1600 \n", - "2400 46 1.000000e+03 0.251718 434 1600 \n", - "2500 13 1.011787e+03 0.061125 410 1600 \n", - "2600 100 1.336090e-12 0.362560 424 1600 \n", - "2700 100 5.688650e-87 0.446025 416 1600 \n", - "2800 22 1.000534e+03 0.099925 418 1600 \n", - "2900 100 1.336087e-12 0.320588 408 1600 \n", - "3000 93 1.000000e+03 0.410648 415 1600 \n", - "3100 63 1.000000e+03 0.266077 412 1600 \n", - "3200 19 1.001497e+03 0.079301 435 1600 \n", - "3300 28 1.000068e+03 0.097678 407 1600 \n", - "3400 22 1.000534e+03 0.087050 442 1600 \n", - "3500 1 1.711287e+03 0.002187 425 1600 \n", - "3600 64 1.000000e+03 0.223462 400 1600 \n", - "3700 2 1.504106e+03 0.003406 402 1600 \n", - "3800 48 1.000000e+03 0.229014 452 1600 \n", - "3900 28 1.000076e+03 0.125957 430 1600 \n", - "4000 2 1.527218e+03 0.003714 415 1600 \n", - "4100 73 1.000000e+03 0.329450 483 1600 \n", - "4200 13 1.011787e+03 0.043513 500 1600 \n", - "4300 10 1.000000e+03 0.061012 457 1600 \n", - "4400 5 1.180423e+03 0.019264 454 1600 \n", - "4500 11 1.023112e+03 0.028424 510 1600 \n", - "4600 6 1.170037e+03 0.015977 497 1600 \n", - "4700 14 1.000000e+03 0.072321 508 1600 \n", - "4800 7 1.155527e+03 0.022381 489 1600 \n", - "4900 12 1.028852e+03 0.055001 481 1600 \n", + "0 100 0.000000e+00 0.031225 52 102 \n", + "100 100 5.688650e-87 0.242209 222 1600 \n", + "200 100 1.790415e-27 0.314603 258 1600 \n", + "300 63 1.000000e+03 0.202063 326 1600 \n", + "400 17 1.002961e+03 0.056878 350 1600 \n", + "... ... ... ... ... ... \n", + "7500 17 1.000000e+03 0.028059 282 1600 \n", + "7600 14 1.000000e+03 0.053562 288 1600 \n", + "7700 45 1.000000e+03 0.123023 279 1600 \n", + "7800 18 1.002102e+03 0.077041 249 1600 \n", + "7900 4 1.255062e+03 0.007113 272 1600 \n", "\n", " average_specificity knowledge \n", "trial \n", - "0 7.245509 59.589041 \n", - "100 6.675000 99.315068 \n", - "200 6.528750 97.945205 \n", - "300 7.879375 95.890411 \n", - "400 5.722500 98.630137 \n", - "500 6.540000 99.315068 \n", - "600 5.796250 97.945205 \n", - "700 6.329375 93.835616 \n", - "800 6.377500 96.575342 \n", - "900 5.876250 97.945205 \n", - "1000 6.293750 97.945205 \n", - "1100 5.943125 95.205479 \n", - "1200 5.801250 97.945205 \n", - "1300 6.774375 98.630137 \n", - "1400 6.448125 93.150685 \n", - "1500 5.656250 97.260274 \n", - "1600 6.546250 97.260274 \n", - "1700 7.603125 97.260274 \n", - "1800 6.098125 97.260274 \n", - "1900 7.208750 96.575342 \n", - "2000 6.125000 99.315068 \n", - "2100 6.541250 98.630137 \n", - "2200 6.363750 99.315068 \n", - "2300 6.456250 97.260274 \n", - "2400 6.938125 97.260274 \n", - "2500 6.042500 98.630137 \n", - "2600 7.073125 100.000000 \n", - "2700 5.903750 95.890411 \n", - "2800 6.253750 95.205479 \n", - "2900 6.211250 97.260274 \n", - "3000 5.560625 97.945205 \n", - "3100 6.769375 95.890411 \n", - "3200 6.070000 97.260274 \n", - "3300 5.535625 99.315068 \n", - "3400 6.360000 99.315068 \n", - "3500 6.555000 98.630137 \n", - "3600 6.294375 97.945205 \n", - "3700 7.173750 97.260274 \n", - "3800 6.813125 97.945205 \n", - "3900 6.946875 97.945205 \n", - "4000 7.343125 100.000000 \n", - "4100 13.568750 100.000000 \n", - "4200 10.360000 99.315068 \n", - "4300 8.755000 97.945205 \n", - "4400 8.353750 93.835616 \n", - "4500 7.860000 100.000000 \n", - "4600 7.361875 100.000000 \n", - "4700 7.962500 100.000000 \n", - "4800 7.101250 98.630137 \n", - "4900 6.349375 99.315068 " + "0 103.54902 97.945205 \n", + "100 40.70500 99.315068 \n", + "200 52.13750 97.945205 \n", + "300 31.38250 99.315068 \n", + "400 28.04875 100.000000 \n", + "... ... ... \n", + "7500 79.16250 100.000000 \n", + "7600 87.34875 100.000000 \n", + "7700 91.55625 100.000000 \n", + "7800 86.19625 100.000000 \n", + "7900 69.48125 100.000000 \n", + "\n", + "[80 rows x 7 columns]" ] }, "metadata": {}, @@ -797,12 +321,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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AMMIpo7eBa93945p+obu/a2aPA+8Rspj5hFNNewEzzewyQoA5J1p+iZnNBJZGy4939+Kafm9NpTIPXSQoIlJRtqetHgb+DJwdvR8DPAIcU5svdfebgJvKFccJWUim5ScCE2vzXbWVGqqriRFFRCrK9rDa3P0Bd09EjwepZYf57iJWpMxDRKQy2WYe/zCzCcAMQtD4MfCsmXUBcPevclS/BqOLBEVEKpdt8Phx9Pzv5covJQSTA+qsRo1EKvPQ9CQiIhVlO9qqX64r0tgo8xARqVy2o61aAlcCJ0RFrwL/6+5FOapXg4sr8xARqVS2p63uAloCd0bvL4zKLs9FpRqDmDIPEZFKZRs8vuXuR6S9f8XM3s9FhRoLTU8iIlK5bA+ri82sf+pNdNFgzi/Ua0jxRDEtmxvNm2WaWktEZM+WbebxC8Jw3dQV5X0JN3BqsmJFSWUdIiKVyDbz+Cfwv0AyevwvYYqSJiue0P3LRUQqk23mcT+wBfht9P5c4AGi+aeaolhRUlOTiIhUItvgcXC5DvN/NPkOc2UeIiKVyrZ1nG9mw1JvzOwYwqmsJiueSNJamYeISEbZZh7HABeZ2WfR+z7AMjNbBLi7D85J7RpQrEiZh4hIZbINHqfltBaNUDyRVPAQEalEtnNbfZrrijQ28USSTm1aNnQ1REQaJR1aVyKu01YiIpVS61gJdZiLiFSuQYKHmXUys8fN7AMzW2Zmx5pZFzN7ycyWR8+d05a/3sxWmNmHZjaiPuoYKyomT5mHiEhGDdU6/hF4wd0PAY4AlgETgDnuPgCYE73HzAYS7pk+iNBxf6eZ5TwlCJmHgoeISCb13jqaWQfCfUHuAXD3ne6+CRgJ3Bctdh8wKno9Epjh7nF3/wRYAQzNdT3jRcW6l4eISCUa4tD6AGA9cK+ZzTezv5pZO2Afd18LED3vHS3fE1idtn5hVFaBmY0zswIzK1i/fv0uVTKmzENEpFIN0Tq2AI4G7nL3o4DtRKeoKpFpTnTPtKC7T3X3fHfP7969e60rmChOUpx0zaorIlKJhggehUChu78bvX+cEEy+NLMeANHzurTle6et3wtYk8sKpu5fnqfMQ0Qko3pvHd39C2C1mR0cFZ0CLAVmA2OjsrHA09Hr2cAYM2ttZv2AAcDcXNYxFt2/XJmHiEhm2U5PUteuBh4ys1bAx4QbSzUDZprZZcBnRNO9u/sSM5tJCDAJYLy75/QuhqnMQxcJiohk1iDBw90XAPkZPjqlkuUnAhNzWad0qcxD9/MQEclMh9YZKPMQEamaWscMSoKHOsxFRDJS65hByWkrdZiLiGSk4JGBMg8RkaqpdcwgrqG6IpILO3c0dA3qTEMN1W3UYrpIUETq2r/+Cs/+HLofAv1PgQNPhv2Ph5Ztyi634ytYPRc+exsKC6BTHxhyMfQeCpZpwo2GoeCRgTIPEWHnDvjwOVjyJBx8Ohx1Qe23tfIVeO4/ofcwaJkXAsk7f4YWebD/cdD327BpNXz2DqxfFtZp1hL2PRyW/Q3efxj2HgT5l8Dg0ZDXsW72cRcoeGQQU5+HyJ4pWQyr3oCFM2HpbNi5FVq2gw+ega1r4TvX1fzof/1HMPPikHFc8Di0bh8C06f/hBVzYOUcmPP/oHWHkF0c/m/Q51joeXTISuLbYPHj8K974Lnr4KUbwzKH/Qj2OQzadcvJT1EdBY8MlHmI7GGKi+C1W2D+Q7B1TWjIB42CI8ZAr2/B7KvhlZth+0YY8d/QLMsDyx1fwcOjoUUrOG9GCBwArdrCgFPDI7VcXkdolqHNab1XOG119FhY8x4U3AuLHof37g+ft+sOex8K3Q8Nz3sPDIGnectd/VWqpOCRgS4SFNnDvPY7eP1WGDACRkwMp6nS+yJG3Q1tu8I7d8I3X8HIP1ffOCd2wsyLYMvnMPaZ0HdRmbZdqq+jGfQcEh4jJsLn82DdstLHgodg57aw7PWFCh4NoTTzUPAQafIKC+CN38MR58HZd2VeplmzkHG07Qqv/DZkCqPvg1btMi/vHk4xrXoDzp4KfY6p2zrndYT+J4dHSjIJm1fDVytLM5wcUuuYQTyRpHWLZlgjGtkgIjmwcwc8+e/QYT84fVLVy5rBCdfBmbeFfor7R8G2dSFQlPfOXfDeffCdn8MRP85FzStq1gw67182oOSQMo8M4omkJkUU2R18/h4kE6GjuTZevgk2roCxf8t+BFP+JeE006zLYfIAaNYirJvXEfI6haP+VW/AIWfCSf9Vu3rtBhQ8MogVFeuUlUhdSCaz71yuqY9fhYfOgeKdYQjst6+Fg0ZkPxpq5SswdyoM+wn0O6Fm3z1wZOjD+Pg1iG2OHpvC8zeb4NCzYNSdudv3RkDBI4O47l8uNVUUg21fhKGezVuGMfrNW5a+btmmUV3gVSuxLdBqr+waxK8+Dkfm6z+EQ38Qrk3od2Lm0US1UVgAj5wHXQ+Eoy4MHdmP/DiMNPr2tTDoh9C8iubtm6/hqfHQ7WA45cba1WG/o8JjD6XgkUGsqHjPnRQxWRzO425ZE4YsblkDRd9AlwOg2wDo3C9c5LQn2vFVGOGyZkHomNyyJoz937ImjMCpStcBMHxCaNR2t6PRLxaHkUhLnw6N5fcnhxE/lVnyVBjaagaHfB8+eA7efwTa9wjXJwweA/seVvv6fLkEHvwR7LU3XPgktN8Xhv4fWDwL3rwNnvg/oVN76L+H4bYde1XcxnO/gO3r4NyHK17hLVkxz9TZ0wTk5+d7QUFBrda9dPq/WLc1xjNXf6e0cPEsWP5y6FRrBFd3VmpzIbx7Nyx8LPyn6pUPPfPDf/auB5Y2XMUJ2PARfLEQ1i4MzxtXwrYvocobNRp06h22tfdAyL8UuvbftTpvWwfb14ftZXN0vnMHrH43jHRp1w3adgvnmWt6ZJ+Iw6o3w363bBPG3rdsV/qcLII180PAKCyArz8pXbdd99AYdugJHXpA+/3C7928VViveGf4jYt3QiIW/n7WLQ1j8U+6Hg75QcUg4h6O1D96AdYugM59w2/S/RDodlD9B+01C0LQ+OAZaNU+ZA8fPBP+vY6+CE65Cdp1LV2+KAYv3hCunu6ZD/82LXTgFsXgo+fh/UdhxUuhj2Lfw2H49XDwGTX7d9u4Eu49Haw5XPpC2H66ZBKW/x3e/EP4GwHY7+iQ/Rx6FnQ7EBY/AY9fAifdACf+5y7/TE2Nmc1z90w36yu7XEMFDzNrDhQAn7v7mWbWBXgU6AusAka7+9fRstcDlwHFwE/d/e/VbX9Xgsf5f32HWFGSWVceV1r4wA/DCIu9B8L5j2U+mqkr7uHc6fYNoVHdvh7wcDVp536Zj1zXzIe3/xymUnCHg06D+JZQnhr73boj7HckxLeGhiwRC+Ut8mCfQSGF77BfaAw79Iwax/2gRetwGmLjStiwPHQwblwRxpYnE3DEueE/Yfn/yNVJFoeG5uXfQNH20EAOHg2Df1xxTHzqyt/3H4Vls0v3KaV569Cgt+sWttNjMOw7ODy36Vy63LZ18NHfQwO98h/he6vToWe46CoVhPc7suZDIZNJWPIEvDoJNi4PjedJN4SRMZ++Ferz0Qvw9aqwfMfeIatJJsJ7axZlfweHxnbndijaEQJp0faQHXboGerXK6pnlwPKNszuYZupf8PtGyCvQ2lHb15HaNMp/O3984+hPnkd4ZgrYdgV4XeMbQkX071zV1j35F+FC9i+XgWPXRwOQo69KgSWFq0q/g7bN4bf4Z27wpDS3sfAd38dpuiozubPYdpp4d/+0heg+8FVL79heZjaY9nfwsV1EIL31rXhgOfSF6s+tbWH2h2Cx88It6LtEAWP3wFfufskM5sAdHb3X5rZQOARYCiwH/AycFB19zHfleDxo7veIq9lMx66fFhp4W2Dw/nezavDEe95M0PDlK3EznAktHIOfPJ6+E9YgYdGYfuGcPSaSav2oeFJNY4t24RpCz59M3w2ZCwc8++ljW+yOGQYhQXRKZf5oeHrcURp49p1QO3+E239MhzhFUwL2cpRF4ahjNkE1i+XhlMbnxfAgd8NF2UtfiJM2QBhwrjBPw77uvSpkEmlrvwdODI8sNLgun19+N22fQHrPgjLpnTsE/Zz69rwG0BoaA8aAQedHn6LRCw0wEXbowY5mv1038EhmNaV4gQsegxemxQa3OatQnbSIi/0CRw0IgT+jj3DVc8bV4ZAv/6D8LxheQgkLduWzZRatAnbWzO/NCDmdQpBJK9DFPBXlu5Xddp0hmPHw9BxmTPtdcvCqZ9Vb4SDmq8/DX9Do+4K/5bV/g5FMP/BEIi2rg0X551yY+Wns7ZvCBnHlrVw8d9q3tewaTV88GzInDYsh4ufCadhpYJGHTzMrBdwH+G+5D+LgseHwHB3X2tmPYBX3f3gKOvA3f8nWvfvwK/d/e2qvmNXgseZd7zB3u3zmHbxt0JBIg4T94UTfhEarYfOCUdno+8LDV9lvvo4zF2zYk74T7ZzWxjW1+tb4ag+k5ZtYa/u0VF093BRUrvu4Qj0i0XRaab3w3nfVEPQsTccc0U4lZDXoVb7vEs2fx4usnrv/nCkO+QSOOSMkKW161726LcoFpZ9c0polE67JZwHTy3z9aewaGbIMDYuD2XNWoTfefCPK175W5lt68NvVXJablH4voNOg4NPCw1eQ3ZgFxeFfoAvl8ABw0PgaNW2DrabCIHm83khMBfOC38nXQ8MjWXX/uF11wNhr31CFpoaJZQaKZRMhCBWXXblHrKIv/9XOFj50V/DKc2a2LkjjHh6c0o4oBo4Mpz+K59ZbfwYdmyAC56AvsfX9teRLDT24PE48D9Ae+C6KHhscvdOact87e6dzexPwDvu/mBUfg/wvLs/nmG744BxAH369Bny6aef1qp+p055jQH77MWd50edgus/hD8PDVeKHvHj0EH60OhwJPiD20KjDeE/4ievl052ljoF0Wl/OPCUMA1zvxPqpoFPFoejyW1fQp/jGkf6/fWn4Rz5godL+03adi2dc6dLvzAvz8blodN0xH+XPWeezj2calj/IQz4XoNN/iZZSCZDIN6VYPzN1+FU2b+mAV4xs2q1Fxx3NfQ/qc6qLZllGzzqvcUxszOBde4+z8yGZ7NKhrKMEc/dpwJTIWQeta1jLFFcdlLEjSvCc9cDw3OH/eCS58I53tlXh07XzYXhtFQyEf7g+30Hho0PQaP8uee60Kx5OOdb3Xnf+tR5fxj5p3AO+8vF0Zw7S8NppPdnhBlKO/aBC2ZVnbFB2Xl8pHGri9FjbTqHv5vv/nrXtyX1oiEOV48HzjKzM4A8oIOZPQh8aWY90k5brYuWLwTSc+FewBpyKF6ULHuRYEnwOKC0LK8DnPcoPPszeO+BcK722KtCsOh9TOhk3lO16xZOxRwwvLQs1Vnbtuue/duINBH1Hjzc/XrgeoAo87jO3S8ws1uBscCk6PnpaJXZwMNmNoXQYT4AmJvLOsaKistOT7JxRRgOmj5qB8IFYGfdAWf8PvPIEillFjI2EWkSGsGJ8hKTgJlmdhnwGXAOgLsvMbOZwFIgAYyvbqTVrkpNjFhi48rSU1aZKHCIyB6mQYOHu78KvBq93gicUslyEwkjs+qjThmCxwo48NT6+HoRkd3CbjZPQu6V3AgqddoqvjWMaNrVq6hFRJoQBY9yKtxFcOPK8FzVaSsRkT2Mgkc58UToTinpMC8ZaaXMQ0QkRcGjnHhRJZlHlwMqWUNEZM+j4FFOKvNonZ55dOytaZtFRNI0pqG6jUIsyjzySjKPFTplJRIpKiqisLCQWCzW0FWRXZSXl0evXr1o2bJlrdZX8CinTObhHk5bDT6ngWsl0jgUFhbSvn17+vbti+3ud0bcg7k7GzdupLCwkH79+tVqGzptVU6ZPo8dGyG+WSOtRCKxWIyuXbsqcOzmzIyuXbvuUgap4FFOLH20VfkJEUVEgaOJ2NV/RwWPcspkHqngoZFWIiJlKHiUk7pIsCTzaNYi3I9DRKSGZs+ezaRJkwB46qmnWLp0aQPXqO4oeJQTK4o6zFOZR+d+jeNGSyLS4BKJRI2WP+uss5gwYQLQ9IKHWsVyykxPUt1suiJ7sN/8bQlL12yp020O3K8DN/1gUJXLrFq1itNPP51vf/vbvPXWW/Ts2ZOnn36a008/ncmTJ5Ofn8+GDRvIz89n1apVTJ8+naeeeori4mIWL17Mz3/+c3bu3MkDDzxA69atee655+jSpQsrV65k/PjxrF+/nrZt2/KXv/yFQw45hIsvvpguXbowf/58jj76aC688EKuuOIKduzYQf/+/Zk2bRqdO3fm9ttv5+6776ZFixYMHDiQGTNmMH36dAoKCjjvvPOYPXs2r732GjfffDOzZs3inHPO4b333gNg+fLljBkzhnnz5tXp75lLyjzKKZmepIWFe5DrGg+RRmf58uWMHz+eJUuW0KlTJ2bNmlXl8osXL+bhhx9m7ty53HDDDbRt25b58+dz7LHHcv/99wMwbtw47rjjDubNm8fkyZP5yU9+UrL+Rx99xMsvv8zvf/97LrroIm655RYWLlzI4Ycfzm9+8xsAJk2axPz581m4cCF33313me8/7rjjOOuss7j11ltZsGAB/fv3p2PHjixYsACAe++9l4svvrjufqB6oMyjnNRFgq13rIVETJmHSCWqyxByqV+/fhx55JEADBkyhFWrVlW5/EknnUT79u1p3749HTt25Ac/+AEAhx9+OAsXLmTbtm289dZbnHNO6TVd8Xi85PU555xD8+bN2bx5M5s2beLEE08EYOzYsSXrDB48mPPPP59Ro0YxatSoavfh8ssv595772XKlCk8+uijzJ2b03vc1TllHuXEE8U0b2a0+PrjUKDgIdLotG5deivj5s2bk0gkaNGiBclkOPgrf/1C+vLNmjUred+sWTMSiQTJZJJOnTqxYMGCkseyZctK1mnXrl21dXr22WcZP3488+bNY8iQIdX2j/zoRz/i+eef55lnnmHIkCF07dq1+h1vRBQ8yokVJcPUJLrGQ2S30rdv35I+g8cff7xG63bo0IF+/frx2GOPAeEK7Pfff7/Cch07dqRz58688cYbADzwwAOceOKJJJNJVq9ezUknncTvfvc7Nm3axLZt28qs2759e7Zu3VryPi8vjxEjRnDllVdyySWX1Ki+jYGCRznxRHGYmmTjSmjZDtrv29BVEpEsXHfdddx1110cd9xxbNiwocbrP/TQQ9xzzz0cccQRDBo0iKeffjrjcvfddx+/+MUvGDx4MAsWLODGG2+kuLiYCy64gMMPP5yjjjqKa6+9lk6dOpVZb8yYMdx6660cddRRrFwZZus+//zzMTO+973v1bi+Dc3cvX6/0Kw3cD+wL5AEprr7H82sC/Ao0BdYBYx296+jda4HLgOKgZ+6+9+r+578/HwvKCiocf1+8dj7/HPFBt7qfRds+wKueLPG2xBpqpYtW8ahhx7a0NVoMiZPnszmzZv57W9/2yDfn+nf08zmuXt+des2RId5Avi5u79nZu2BeWb2EnAxMMfdJ5nZBGAC8EszGwiMAQYB+wEvm9lB7l6ci8rFEsko81gB+x2Zi68QEeHss89m5cqVvPLKKw1dlVqp9+Dh7muBtdHrrWa2DOgJjASGR4vdB7wK/DIqn+HuceATM1sBDAXezkX94kXFtGteDJs+hcP/LRdfISLCk08+2dBV2CUN2udhZn2Bo4B3gX2iwJIKMHtHi/UEVqetVhiVZdreODMrMLOC9evX16pO8USSPs02gCfVWS4iUokGCx5mthcwC7jG3au6TDXT1I8ZO2rcfaq757t7fvfu3WtVr1hRMfuzJrxR8BARyahBgoeZtSQEjofc/Ymo+Esz6xF93gNYF5UXAr3TVu8Fqda97sUTSXono81rNl0RkYzqPXhYmET+HmCZu09J+2g2MDZ6PRZ4Oq18jJm1NrN+wAAgZ5dixoqK6Zn8HNp0gbZdcvU1IiK7tYbIPI4HLgRONrMF0eMMYBJwqpktB06N3uPuS4CZwFLgBWB8rkZaAezbMY8+vlanrET2MMOHD6c2w/tr6vbbb+fQQw/l/PPPr/U26quuVWmI0VZvkrkfA+CUStaZCEzMWaXSTL9kKPx+LXQ9qT6+TkSagNT0KNm48847ef7556u8d3hNttdQGnftGkJ8G2xdq9l0Rarz/AT4YlHdbnPfw+H0SZV+XNl07G3atGH48OG7NCU7wIMPPshPf/pTtmzZwrRp0xg6dCjbt2/n6quvZtGiRSQSCX79618zcuRIpk+fzrPPPkssFmP79u0VrteYMmUK06ZNA8IkiNdccw1XXHEFH3/8MWeddRaXXnop1157bcny5bd34403MnnyZJ555hkArrrqKvLz8yvMvvviiy9y0003EY/H6d+/P/feey977bUXEyZMYPbs2bRo0YLvfe97TJ48uS7+hUooeJT3VZg2QKetRBqn5cuX88gjj/CXv/yF0aNHM2vWLC644IIq11m8eDHz588nFotx4IEHcssttzB//nyuvfZa7r//fq655hoAtm/fzltvvcXrr7/OpZdeyuLFi5k4cSInn3wy06ZNY9OmTQwdOpTvfve7ALz99tssXLiwJPikzJs3j3vvvZd3330Xd+eYY47hxBNP5O677+aFF17gH//4B926datQz/Ttvfrqq9X+Fhs2bODmm2/m5Zdfpl27dtxyyy1MmTKFq666iieffJIPPvgAM2PTpk1Z/bY1oeBR3kYFD5GsVJEh5FJNp2OH6qdkTzn33HMBOOGEE9iyZQubNm3ixRdfZPbs2SVH7rFYjM8++wyAU089tULgAHjzzTc5++yzS2bj/eEPf8gbb7zBUUcdVWU9K9teZd555x2WLl3K8ccfD8DOnTs59thj6dChA3l5eVx++eV8//vf58wzz8x6m9lS8CgvFTw0TFekUSo/Hfs333wDsEtTsqeEwaCUee/uzJo1i4MPPrjMZ++++26lU7XXds7A9O2l7w9U3KfU95x66qk88sgjFT6bO3cuc+bMYcaMGfzpT3+q82lQNKtueRtXQIee0KptQ9dERGpgV6ZkT3n00UeBkDl07NiRjh07MmLECO64446SgDB//vxqt3PCCSfw1FNPsWPHDrZv386TTz7Jd77znRrVZf/992fp0qXE43E2b97MnDlzKiwzbNgw/vnPf7JiRbiFxI4dO/joo4/Ytm0bmzdv5owzzuC2224ruWNhXVLmUd7GFeosF9kNXXfddYwePZoHHniAk08+uVbb6Ny5M8cdd1xJhznAr371K6655hoGDx6Mu9O3b9+STuzKHH300Vx88cUMHToUCB3m1Z2yKq93796MHj2awYMHM2DAgIzrd+/enenTp3PuueeW3Pnw5ptvpn379owcOZJYLIa784c//KFG352Nep+Svb7Udkp2Xvi/0GE/OO6quq+UyG5OU7I3LbvblOyN22n/3dA1EBFp9NTnISIiNabgISI10lRPde9pdvXfUcFDRLKWl5fHxo0bFUB2c+7Oxo0bycvLq/U21OchIlnr1asXhYWF1PZma9J45OXl0atXr1qvr+AhIllr2bJllRP6yZ5Dp61ERKTGFDxERKTGFDxERKTGmuwV5ma2Hvi0lqt3AzbUYXV2F9rvPYv2e8+S7X7v7+7dq1uoyQaPXWFmBdlcnt/UaL/3LNrvPUtd77dOW4mISI0peIiISI0peGQ2taEr0EC033sW7feepU73W30eIiJSY8o8RESkxhQ8RESkxhQ80pjZaWb2oZmtMLMJDV2fXWVm08xsnZktTivrYmYvmdny6Llz2mfXR/v+oZmNSCsfYmaLos9uNzOr732pCTPrbWb/MLNlZrbEzP4jKm/S+25meWY218zej/b7N1F5k97vFDNrbmbzzeyZ6P2est+rojovMLOCqCz3++7ueoR+n+bASuAAoBXwPjCwoeu1i/t0AnA0sDit7HfAhOj1BOCW6PXAaJ9bA/2i36J59Nlc4FjAgOeB0xt636rZ7x7A0dHr9sBH0f416X2P6rhX9Lol8C4wrKnvd9r+/wx4GHgmer+n7PcqoFu5spzvuzKPUkOBFe7+sbvvBGYAIxu4TrvE3V8HvipXPBK4L3p9HzAqrXyGu8fd/RNgBTDUzHoAHdz9bQ9/YfenrdMouftad38ver0VWAb0pInvuwfborcto4fTxPcbwMx6Ad8H/ppW3OT3uwo533cFj1I9gdVp7wujsqZmH3dfC6GRBfaOyivb/57R6/LluwUz6wscRTgKb/L7Hp26WQCsA15y9z1iv4HbgP8Ekmlle8J+QzhAeNHM5pnZuKgs5/uu+3mUynR+b08ax1zZ/u+2v4uZ7QXMAq5x9y1VnMJtMvvu7sXAkWbWCXjSzA6rYvEmsd9mdiawzt3nmdnwbFbJULbb7Xea4919jZntDbxkZh9UsWyd7bsyj1KFQO+0972ANQ1Ul1z6MkpRiZ7XReWV7X9h9Lp8eaNmZi0JgeMhd38iKt4j9h3A3TcBrwKn0fT3+3jgLDNbRTjdfLKZPUjT328A3H1N9LwOeJJwCj7n+67gUepfwAAz62dmrYAxwOwGrlMuzAbGRq/HAk+nlY8xs9Zm1g8YAMyNUt6tZjYsGn1xUdo6jVJUz3uAZe4+Je2jJr3vZtY9yjgwszbAd4EPaOL77e7Xu3svd+9L+H/7irtfQBPfbwAza2dm7VOvge8Bi6mPfW/okQKN6QGcQRiZsxK4oaHrUwf78wiwFigiHFlcBnQF5gDLo+cuacvfEO37h6SNtADyoz/IlcCfiGYmaKwP4NuElHshsCB6nNHU9x0YDMyP9nsxcGNU3qT3u9xvMJzS0VZNfr8Jo0Pfjx5LUu1Wfey7picREZEa02krERGpMQUPERGpMQUPERGpMQUPERGpMQUPERGpMQUPkTpiZp3M7CdVfP5WFtvYVt0yIo2BgodI3ekEVAgeZtYcwN2Pq+8KieSK5rYSqTuTgP7RxIRFwDbCRZpHAgPNbJu77xXNufU00Jkw8+1/uXujvpJZpDxdJChSR6IZfJ9x98OiCfqeBQ7zMPU1acGjBdDWw2SN3YB3gAHu7qllGmgXRLKmzEMkd+amAkc5Bvy3mZ1AmEK8J7AP8EV9Vk5kVyh4iOTO9krKzwe6A0PcvSiaDTav3molUgfUYS5Sd7YSbntbnY6E+08UmdlJwP65rZZI3VPmIVJH3H2jmf3TzBYD3wBfVrLoQ8DfzKyAMONvVTfvEWmU1GEuIiI1ptNWIiJSYwoeIiJSYwoeIiJSYwoeIiJSYwoeIiJSYwoeIiJSYwoeIiJSY/8fhKOOT7oobP0AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -824,22 +348,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -859,22 +383,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -894,22 +418,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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N0ZFlm2q6t4jjzqHtmL58FxP6tyr+Qpd0Is/OzFV7ubpXc1o1iq30+UcnN+WTX/axMvMoI7yo1irJ6TQ8/vkG5mfk8PTVyR4HBN07ogOfrslixk+uIn953lq8k1aN6nClh55XRRPTpe0+Sks3QaS0MzYHp/LLVhlURZ2ocI8Z9cb9J1i6/TB/GNvFZ5O4hYcJz1zbk3GvL+O5b7fwoJsMY8n2XJ76ahOjk5vy/ITKVX8Mat+Y+jERLMg4WKUgsfvwGV6cv5W567NpFBvJk1clc4vVU2v/sbO8tXgn4/q0rLDayeYo5M9fbKRN41juv6Tyvfq8UXIg2ye/7OPV77dz3Rs/cVn3ZlzWoxn/WZLJ5uyT9GjZgOcm9Ax6h47RyU15Yd5Wsk+c9dhLK+PASZyGctsgL1QaJEo4V5Jw357/8OjOfLMhmyfmbOTr+4eWGZH9wcrd5BUUcu+Iqg2QG9oxnjqR4SzIOFipIGGM4dlvNvPp6iweurQTdwz1XIrpmFiP0d2aMmPFHu4d0YG60e6/Amm7j7J6zzH+ek13jyPPuzarT92ocNJ2H3Nb0ijpVL6di577wWdBIjxMuGlAax4c1YnEBjHnvTd1SSb1ol2zavpSz1Zx3D4kifd+2s3MVXvd7jO4feMqjdaPDA/j4i6JLNx8iEKnqVQVWWbuaa58bRkAv7ukI1NS21O/REP8n67sxvebc3hizgY+mTLYY+nGGMPf52aQmXuG9+4c4LdZUotEhodx2+C2XNe3JdOW7eI/i3cyb9NB2jSO5bWb+3JVz+Y1YuLDMVaQ+D4jh9uGJLndZ11Ro3Ulp+O4EGiQKKF0w3Vp9aIjeOrq7vz6w9W899Nu7hl+rl+7a9DWbkZ0Tqhy/WhMZDipneP5PuMQfx9nvK6qeGPRTt5Ztos7Lkpye4db2r0jOjA/I4dZv+zjrmHuA8pbi10jPstrSI0ID6Nvm0akedEusXhbLqfyHTwwqhPN42Iq3L8imw6c4JNV+/h8zX4mD2/HZCtj3Hc0j7nrD3DP8PYeSxrV8ccrutK3TUO3nQKiI8IY271ZlTPX0clN+WrdAdbuPeb1iGVjDH/5ciMRYcI3Dw6ndeOyJdj4etE8fnk3/jRnA5+v2e+xlPnKgm18uHIv96a2Z6SH7rr+UDc6ggdGdWLioDZs2H+CoR3iifJwoxYMHRLq0S6+LvPLCRLrs47TPC6GxPrV/27XNBokSnDXcF3a2O5NGdU1kZcXbOOKns1pYdXFf7o6iyNnXIO2qmNMcjO+25TDhv0nvCq6frByD//z3VbG923Jk1clexVY+rdtxMCkxkxbtovbhrQlstRd77acU3y/+RAPXdqpwvrelKRGvLpwOyfz7R67kQIsyMihcd0oHhzVyWeD7+4Z1p4X52/ltR928OHPe/ndJR3ZlnOa8DDhrnJKU9URHRFeYampqkZ2SSAy3DXhn7dB4sv0AyzfcYS/j+vuNkAUuWlAaz5dvY9nvtnMqG6JNIw9v1PFtGW7eO2HHdyY0jpoc4rF14v2OJYkmIo6Fry7fJfH7/n6rBP0rKHjhaqr5oTrGuBckPCcMYoIT1/THacx/PXrTYA1aGtpJr1bNzxv0FZVXNI10euZQRduzuHJLzcyqmsiL/yqV6WK5veOaM/+42fdjv8o6sI7ycNdU0kpbRtjDKzde9zjPvZCJz9uOcQo69p8JSm+Lv+e2I+v7h9K12b1+evXGcxctZdr+7SkmQ9KK4FWPyaSwe2beD0r7Ik8O//4bwa9Wzdk4qDyq9bCwoRnxvfkxFk7z8/bct57n612Nchf1r2Zz3oz1Tajk5tiLzQs3ppb5r0TZ+3sOnyG3gGcKiOQNEiUYLN7brguqXXjWB4c1ZnvNuXwfUYO327MZs+RPH6d2r7a/2CN6kaR0rZRhRmFMYYX5m2lQ0I9Xr+lX5nSQEUu7pJI56b1+M/izPOmJc4+4erCe+OA1l514e3TpiFhAqt3H/W4z6pdRzmZ76hWr53y9GrVkI/uGcT7dw3k6t4tKuyqWJONSW5K5uEz7Dh0usJ9X/huC0fPFPDMtT28Cr7dmjfg7mHtmLlqH6v3uP5eCzJyePSz9Qzt2IRXb+7j85mPa4t+bRrRpG6U2//Ljft9O4iuptFvRAneVDcVuWd4Ozo3rcdTX23ijR99M2iryOjkpmw5eIq9RzzPF//j1kNszTnFb0d2qFIdeFiYcG9qB7YcPMWiEndH05buwmlc0214o150BMktGvDLbs/tEvM3HSQmMsyvvVREhNTOCfzr5r7lVrvUdJdagXR+xsFy91uz9xgfr9rLHRdVblqUB0d1okVcDH/6fCNLt+dy38dr6NEyjqm31bwV0WqS8DBhVLdEftx6CHupdUmKpgf3xXQcNZEGiRJs9kJEIMqLu6nI8DCeGd+T/cfPkpF9kimp7X1WlTIm2RVsysso3lqcSYu4GK6uYGBXea7u3YLmcTG8tXgncH4X3spktCltG5O+73iZfx5wlXgWZOQwrGOCX/uz1xbN4+rQq1Uc32086HHtBkehkyfmbKRp/RgeGdO5Up9fNzqCp6/pztacU9w+fRVtG8fy3h0DPPZyU+eMTm7GqXwHP2eeX2pev+8EbZvE+mVeqppAg0QJNoeT6Igwr6uMBiQ15o6LkmgfX5fxfX3XmNmmSSxdmtb3WOW0Zu8xVu06yt3D21e6mqmkqIgw7h7Wjp93HWXt3mN8+PMezlShC2//to04ay9kc/bJMu9tOnCSAyfya+UgI38Z16cl67JOcMWrS/lhS06ZVcre+2k3m7NP8vQ1ydSrQuY+pnszrurVnLaNY/ng7kGVnpcpVA3rGE9MZBgLSt28rc86XivHRxTRIFGCK0hU7m736Wu6V3rKA2+MTm7KL7uPul3q9K1FrgWLfDGh2U0D29AgJoLXFm4vXnehsl14U5Jcg+rcVTktyMhBBC7pVvN6rdRUdw1N4vWJ/bA5CrnrvTRunLqyePLHA8fP8vKCbVzSNZGx1ajefO2mvnz/yIgLsoE/WOpEhTO8UwILMs4F7txTNg6cyKd3LW2PgCAFCRF5UEQ2isgmEXnI2va0iOwXkXTr54pAp8s1HUblfyX+aOwb0901M+gPpWYG3XHoNAs253D7kLY+qSKoFx3B7UOS+HFrbvG6C5XVPK4OLRvWKW4MLWlBRg792zSq9hTOoUREuLJXcxY8MoK/X9uDzNwzXPfGT9z7QRqPfbbe1bPumu7V6iQRFibaSF0Fo5ObcuBEPpsOuErNG/YfB2rnSOsiAf+WiEgPYDIwEOgNXCUiRd1RXjHG9LF+vgl02vLtzgp7NgVKz5ZxNGsQU6bK6e0lmUSFh/l05tU7hiYRFRFWZt2FykhJakTa7mPnVY1kHcsjI/uk33o11XZFI5IX/2Ekj4zuzLLth1m6/TAPjup8QTfOX8hGdU0kTGC+9X+5bt8JwsQ1k29tFYzWqm7ASmNMHoCILAbGByEdZVR1Yj1/EBEuTU7k8zX7ybcXEhMZTs7JfOasdXVP9eWdeXy9aN6/ayDNGsRU+e40JakxX6YfYN/Rs7Rp4srAvrf+kTRIVE/RiORbBrVh2Y7DHufSUv7XpF40/a0u6o+M7sz6rON0TKxXqxv+g3HbvBFIFZEmIhILXAEUVa7fLyLrRWS6iJSdQQ8QkSkikiYiabm5ZQe2VIfN7iSmhpQkwNWbIq+gkJ92HgZg+vJdOJxOJpeYDsRXBrdvct7yqpWVUjTZX4kqpwWbc+iQUJf2CZVby0C516ReNOP6tNRqoiAbndyUzdkn2Xc0z+uZES5kAf+2GWM2A88DC4B5wDrAAbwJdAD6ANnASx6On2qMSTHGpCQk+LbffVUarv1pcPvG1IuOYP6mHE7m2/l45V6u6Nm8+E69JunctD71oyOK53E6cdbOz5lHGZ3sm7EjStUURd/pD1bu4fDpglrdaA1BmrvJGDMNmAYgIs8CWcaY4sp3EXkbmBvodFW14dpfoiPCGdElge83H6JNk1hO2RzVnhvKX8LDhH5tG5FmjbxetPUQDqfRqiZV6xSt9Dfjp90A9NSShO+JSKL12Aa4DpgpIiUrWsfjqpYKqHy7s0YFCXBN03D4tI1Xv9/O8E7xlRpdG2gpbRuxLec0J/LszM/IIb5eNH1r6Xw2KrSNSW6KzeEkMlzo1ty3CzPVNMHKET8TkQzga+A+Y8wx4AUR2SAi64GLgYcDnaia1HBdZGSXRCLCBJvDyb2pNbMUUaS/NV5iReYRFm05xOjkxBqxHoBSvlZUQu7arEGNyzN8LVjVTcPdbLstGGkpyeaoOV1gi8TViWRkl0SOnrExtGOTYCenXH1aNyQ8THj9xx2cKSjUqiZVa/Vu1ZCkJrFcVMP/J32h9vbbqgKb3UlMDbwreP2WvhhDjZ/COTYqgh4tGrAu6wSxUeFc1CE+2ElSyi/CwoRvH0wlMrxm/0/6Qs26bQ4ym6OwxpUkwNWA7e+lJH2lf1vXYLzUTgkXTJqVqoo6UeEh0R259l9hJRRN8KeqboDVLqFVTUrVDpojWowx5NtrXsP1hebS5KY8P6FntaYwV0rVHNomYXE4DU7j3YJDyrPI8DBuHNAm2MlQSvmI5oiWolXptB5dKaXO0SBh8XZ9a6WUCiWaI1oqs761UkqFCs0RLflFJQltuFZKqWIaJCxaklBKqbI0R7QUBwltk1BKqWKaI1qKGq5r4rQcSikVLBokLFqSUEqpsjRHtJxrk9CShFJKFdEgYTnXu0l/JUopVSRYK9M9KCIbRWSTiDxkbWssIgtEZLv12CiQadKShFJKlRXwICEiPYDJwECgN3CViHQCHgcWGmM6AQut1wFjc+iIa6WUKi0YOWI3YKUxJs8Y4wAW41rTehwww9pnBnBtIBNls1tzN2lJQimligUjSGwEUkWkiYjEAlcArYGmxphsAOsx0d3BIjJFRNJEJC03N9dnidLeTUopVVbAc0RjzGbgeWABMA9YBzgqcfxUY0yKMSYlISHBZ+kqariOCoGVppRSyltByRGNMdOMMf2MManAUWA7kCMizQGsx0OBTJPN4SQqPIywsNq/Zq1SSnkrWL2bEq3HNsB1wEzgK2CStcsk4MtApsnmKNTur0opVUqwVqb7TESaAHbgPmPMMRF5DpgtIncDe4HrA5kgm8NJtC44pJRS5wlKkDDGDHez7QgwKgjJAVy9m7QkoZRS59Nc0WJzFGrPJqWUKkVzRUu+3amjrZVSqhQNEhZtuFZKqbI0V7TYHNomoZRSpWmuaLE5nMRo7yallDqPBgmLza7VTUopVZrmihYdJ6GUUmVpkLBoSUIppcrSXNGiDddKKVWW5ooWbbhWSqmyNEhYdJyEUkqVpbkiUOg02AuNjrhWSqlSNEig61srpZQnmitybn1rrW5SSqnzaa5IifWttbpJKaXOE6yV6R4WkU0islFEZopIjIg8LSL7RSTd+rkiUOkpqm6K0eompZQ6T8AXHRKRlsADQLIx5qyIzAZust5+xRjzYqDTpCUJpZRyL1i3zhFAHRGJAGKBA0FKBwD5dqvhWtsklFLqPAHPFY0x+4EXca1jnQ2cMMbMt96+X0TWi8h0EWkUqDQVlyS0ukkppc4T8FzRyvzHAe2AFkBdEbkVeBPoAPTBFTxe8nD8FBFJE5G03Nxcn6TpXO8mrW5SSqmSvAoS4nKriDxpvW4jIgOreM5LgV3GmFxjjB34HLjIGJNjjCk0xjiBtwG3n2+MmWqMSTHGpCQkJFQxCefThmullHLP21zxDWAIcLP1+hTwehXPuRcYLCKxIiLAKGCziDQvsc94YGMVP7/StOFaKaXc87Z30yBjTD8RWQtgjDkmIlFVOaEx5mcR+RRYAziAtcBU4B0R6QMYYDdwb1U+vyqKR1xrw7VSSp3H2yBhF5FwXBk4IpIAOKt6UmPMU8BTpTbfVtXPq658uzZcK6WUO97miq8Bc4BEEXkGWAY867dUBZituAusVjcppVRJXpUkjDEfichqXO0HAlxrjNns15QF0Lk2CS1JKKVUSV4FCRFpDBwCZpbYFmn1TrrgaZBQSin3vM0V1wC5wDZgu/V8l4isEZH+/kpcoNgchUSECRHhGiSUUqokb3PFecAVxph4Y0wT4HJgNvBbXN1jL2j5dl3fWiml3PE2Z0wxxnxX9MKaRiPVGLMSiPZLygLI5igkWte3VkqpMrztAntURB4DPrFe3wgcs7rFVrkrbE1h05KEUkq55W3OOBFoBXwBfAm0sbaFAzf4JWUBZHM4idGShFJKleFtF9jDwO88vL3Dd8kJDpujUEsSSinlRrlBQkS+xhpl7Y4x5hqfpygIbA6tblJKKXcqKkkUrRJ3HdAM+NB6fTOu+ZVqhXx7oY62VkopN8oNEsaYxQAi8ndjTGqJt74WkSV+TVkA2RxO6kUHfCVXpZSq8bytY0kQkfZFL0SkHeCbxRxqAO3dpJRS7nl7+/wwsEhEMq3XSQRwKm9/03ESSinlnre9m+aJSCegq7VpizHG5r9kBZY2XCullHveLl8aC/wBuN8Ysw5oIyJX+TVlAeSalkNLEkopVZq3t8/vAgW4ljAFyAL+UdWTisjDIrJJRDaKyEwRiRGRxiKyQES2W4+Nqvr5laXjJJRSyj1vc8YOxpgXADuAMeYsrnUlKk1EWgIP4JoPqgeuUds3AY8DC40xnYCF1uuAsDmcuiqdUkq54W3OWCAidTi3fGkHoDptEhFAHRGJAGKBA8A4YIb1/gzg2mp8vteMMRQ4nMRodZNSSpXhbZB4Ctd04a1F5CNcd/qPVuWExpj9uAbp7QWygRPWrLJNjTHZ1j7ZQKK740VkioikiUhabm5uVZJwnuIFh7QkoZRSZXiVMxpjFuAadX0HrtXpUowxi6pyQqutYRzQDmgB1BWRW7093hgz1RiTYoxJSUio/lCNc6vSaUlCKaVKq2jupn6lNmVbj21EpI0xZk0VznkpsMsYk2ud43PgIiBHRJobY7JFpDmu5VL9zmYvBHTpUqWUcqeicRIvlfOeAS6pwjn3AoOtbrVngVFAGnAGmAQ8Zz1+WYXPrjRd31oppTyraO6mi319QmPMzyLyKa51sx3AWmAqUA+YLSJ34wok1/v63O7YHFZJQkdcK6VUGV6NuBaRpcASYCmw3BhzqjonNcY8hasxvCQbrlJFQOXbXSWJGC1JKKVUGd7mjJOArcAE4Cerd9Er/ktW4Jzr3aQlCaWUKs3buZsyReQsrlHXBcDFQDd/JixQtOFaKaU883bupp241rduCkwDehhjLvNjugJGG66VUsozb3PG13A1Jt+Ma0qNSdao6wteccO1jpNQSqkyvB1M96ox5npcYxxWA08D2/yYroApKknE6IhrpZQqw9veTS8Bw3B1U10JPImrp9MFz2bXhmullPLE25XpVgIvGGNy/JmYYDhX3aQlCaWUKs3b3k3/JyLXiEiqtWmxMeZrP6YrYIrGSWiQUEqpsrzt3fRP4EEgw/p5wNp2wdOGa6WU8szb6qYrgT7GGCeAiMzANZ3GH/2VsECxOZyIQGR4ldZQUkqpWq0ydSwNSzyP83E6gsZmLTgkokFCKaVK87Yk8U9grYj8iGvZ0lRqQSkCXCOudcEhpZRyz9uG65kisggYgCtIPGaMOejPhAVKvt2pjdZKKeVBZXLHMOAwcAzoXKKn0wXN5ijURmullPLA28F0zwM3ApsAp7XZ4Jo+/IJmc2hJQimlPPG2TeJaoIsxxlbdE4pIF2BWiU3tcY3gbghMBnKt7X8yxnxT3fNVxOZwEqOjrZVSyi1vg0QmEIlrYaBqMcZsBfoAiEg4sB+YA9wJvGKMebG656gMV3WTliSUUsodb4NEHpAuIgspESiMMQ9U8/yjgJ3GmD3B6oJqszu1d5NSSnngbZBYAXxValsDH5z/JmBmidf3i8jtQBrw/4wxx3xwjnLlOwppUCfS36dRSqkLkre30BOBNcaYGcaYGbhWp7u1OicWkSjgGuD/rE1vAh1wVUVlAy95OG6KtXxqWm5urrtdKsWmXWCVUsojb3PHXwEzRKSbiEwGfguMqea5L8cVeHIAjDE5xphCa+qPt4GB7g4yxkw1xqQYY1ISEhKqmQTt3aSUUuWpzBrXN+FawnQfMMYYc7aa576ZElVNItLcGJNtvRwPbKzm53vF5ijU3k1KKeVBuUFCRDbgGg9RpDEQDvwsIhhjelXlpCISC4wG7i2x+QUR6WOdb3ep9/xGSxJKKeVZRSWJq/xxUmNMHtCk1Lbb/HGuiuTbC3VVOqWU8qDcIGGM2ROohASDMUZLEkopVY6Qzh3thQZjdFU6pZTyJKRzx6JV6bThWiml3AvxIKHrWyulVHlCOnc8FyS0JKGUUu6EdJDIt7uqm3TuJqWUci+kc0ebXaublFKqPCGdOxY1XGt1k1JKuRfiQcIqSWh1k1JKuRXSuaM2XCulVPlCOkgUN1xrm4RSSrkV0rljUUkiRqublFLKrZDOHW12bbhWSqnyhHaQ0IZrpZQqV0jnjtpwrZRS5QvxIKEN10opVZ6A544i0kVE0kv8nBSRh0SksYgsEJHt1mMjf6clX0dcK6VUuQKeOxpjthpj+hhj+gD9gTxgDvA4sNAY0wlYaL32K5ujkKiIMETE36dSSqkLUrBvoUcBO60V8MYBM6ztM4Br/X1ym11XpVNKqfIEO4e8CZhpPW9qjMkGsB4T3R0gIlNEJE1E0nJzc6t1cpvDqQsOKaVUOYIWJEQkCrgG+L/KHGeMmWqMSTHGpCQkJFQrDTZHoZYklFKqHMHMIS8H1hhjcqzXOSLSHMB6POTvBGh1k1JKlS+YOeTNnKtqAvgKmGQ9nwR86e8EuEoSWt2klFKeBCVIiEgsMBr4vMTm54DRIrLdeu85f6fD5nDqaGullCpHRDBOaozJA5qU2nYEV2+ngLHZncRoSUIppTwK6dtom6NQSxJKKVWOkM4hbQ5tuFZKqfKEdA6Zb9eGa6WUKk9IBwktSSilVPlCOofU3k1KKVW+kM4hbfZC7d2klFLlCO0goSUJpZQqV8jmkI5CJw6n0YZrpZQqR8gGiXNLl4bsr0AppSoUsjmkBgmllKpYyOaQRetb63oSSinlWegGiaL1rbXhWimlPArZHPJcdZOWJJRSypOQDRL5dld1k7ZJKKWUZyGbQ2pJQimlKhasRYcaisinIrJFRDaLyBAReVpE9otIuvVzhT/TUNRwrW0SSinlWVAWHQJeBeYZY34lIlFALDAWeMUY82IgElDUcK3TciillGcBDxIi0gBIBe4AMMYUAAUiEtB0FFc3aUlCKaU8CkYO2R7IBd4VkbUi8o6I1LXeu19E1ovIdBFp5O5gEZkiImkikpabm1vlRGjDtVJKVSwYOWQE0A940xjTFzgDPA68CXQA+gDZwEvuDjbGTDXGpBhjUhISEqqcCG24VkqpigUjSGQBWcaYn63XnwL9jDE5xphCY4wTeBsY6M9EFDdca0lCKaU8CngOaYw5COwTkS7WplFAhog0L7HbeGCjP9OhbRJKKVWxYPVu+h3wkdWzKRO4E3hNRPoABtgN3OvPBBRPy6HVTUop5VFQgoQxJh1IKbX5tkCmweYoJDJcCA8LbK8qpZS6kIRsXUu+3amlCKWUqkDIBgmbo1AbrZVSqgIhm0vaHE4NEkopVYGQzSVtDqcuOKSUUhUI3SBhLyRKSxJKKVWukM0l8x1OorUkoZRS5QrZIGGza8O1UkpVJGRzSW24VkqpigVrxHXQ2RxO4nWchFJ+YbfbycrKIj8/P9hJCWkxMTG0atWKyMjIKn9GCAeJQmJ03ial/CIrK4v69euTlJREoNeKUS7GGI4cOUJWVhbt2rWr8ueEbC5p0xHXSvlNfn4+TZo00QARRCJCkyZNql2aC90g4SjUGWCV8iMNEMHni79ByOaSrpJEyF6+Ukp5JWRzSVfvJq1uUqo22r17Nz169PD7eUaOHElaWprX+y9atIirrrqqSuey2Wxcdtll9OjRgzfeeKN4+5QpU1i7dm2VPtMbIRkknE5DQaFTG66VUheM7777jv79+7N+/XqmTp0KwLp163A6nfTt29dv5w3J3k0FhbrgkFKB8tevN5Fx4KRPPzO5RQOeurq7V/tmZmYyYcIEJk6cyIoVK8jLy2Pnzp2MHz+eF154AYCZM2fy7LPPYozhyiuv5Pnnn2f27NmsXLmSl19+mVdffZVXX32VzMxMdu7cyaRJk1i2bNl555k/fz5PPfUUNpuNDh068O6771KvXj3mzZvHQw89RHx8PP369SvePzc3l4kTJ3LkyBEGDBjAvHnzWL16NfHx8Xz44Ye89tprFBQUMGjQIN544w0iIyM5e/YsDoej+DP+8pe/8NZbb/ngN+pZUG6lRaShiHwqIltEZLOIDBGRxiKyQES2W4+N/HX+fLuub61UKNi6dSsTJkzg3XffJSEhgfT0dGbNmsWGDRuYNWsW+/bt48CBAzz22GP88MMPpKen88svv/DFF1+QmprK0qVLAVi6dClNmjRh//79LFu2jOHDh593nsOHD/OPf/yD77//njVr1pCSksLLL79Mfn4+kydP5uuvv2bp0qUcPHiw+Ji//vWvXHLJJaxZs4bx48ezd+9eADZv3sysWbNYvnw56enphIeH89FHHzF69GgOHjzIoEGDePTRR/nqq6/o378/LVq08OvvMFgliVeBecaYX1lLmMYCfwIWGmOeE5HHgceBx/xxcl3fWqnA8faO39dyc3MZN24cn332Gd27dyc9PZ1Ro0YRFxcHQHJyMnv27OHIkSOMHDmShIQEAG655RaWLFnCtddey+nTpzl16hT79u1j4sSJLFmyhKVLl3Ldddedd66VK1eSkZHB0KFDASgoKGDIkCFs2bKFdu3a0alTJwBuvfXW4qqiZcuWMWfOHAAuu+wyGjVy3RcvXLiQ1atXM2DAAADOnj1LYmIiERERfPzxx4BrsOLYsWP56quveOSRR9i7dy+3334711xzjc9/jwEPEiLSAEgF7gAwxhQABSIyDhhp7TYDWIS/goSub61UrRcXF0fr1q1Zvnw53bu7AlV0dHTx++Hh4TgcDowxHj9jyJAhvPvuu3Tp0oXhw4czffp0VqxYwUsvvXTefsYYRo8ezcyZM8/bnp6e7rEbqqfzGmOYNGkS//znPz2m64033mDSpEmsWLGCqKgoZs2axZAhQ/wSJIJxK90eyAXeFZG1IvKOiNQFmhpjsgGsx0R3B4vIFBFJE5G03NzcKiXA5tDqJqVqu6ioKL744gvef//94jtwdwYNGsTixYs5fPgwhYWFzJw5kxEjRgCQmprKiy++SGpqKn379uXHH38kOjq6uDRSZPDgwSxfvpwdO3YAkJeXx7Zt2+jatSu7du1i586dAOcFkWHDhjF79mzA1Z5x7NgxAEaNGsWnn37KoUOHADh69Ch79uwpPu7YsWPMnTuX22+/nby8PMLCwhARv02BEoxcMgLoB7xpjOkLnMFVteQVY8xUY0yKMSalqHhYWUXVTbrokFK1W926dZk7dy6vvPIKJ06ccLtP8+bN+ec//8nFF19M79696devH+PGjQNg+PDh7Nu3j9TUVMLDw2ndujXDhg0r8xkJCQm899573HzzzfTq1YvBgwezZcsWYmJimDp1KldeeSXDhg2jbdu2xcc89dRTzJ8/n379+vHtt9/SvHlz6tevT3JyMv/4xz8YM2YMvXr1YvTo0WRnZxcf97e//Y0///nPiAhjx44lLS2Nnj17MnnyZB//9lykvKKWX04o0gxYaYxJsl4PxxUkOgIjjTHZItIcWGSM6VLeZ6WkpJjK9FEusuvwGV78biu/GdmBHi3jKj5AKVUpmzdvplu3bsFORo1ms9kIDw8nIiKCFStW8Jvf/Ib09HSfn8fd30JEVhtjUrw5PuBtEsaYgyKyT0S6GGO2AqOADOtnEvCc9filv9LQLr4ur9/Sr+IdlVLKT/bu3csNN9yA0+kkKiqKt99+O9hJcitYvZt+B3xk9WzKBO7EVfU1W0TuBvYC1wcpbUop5XedOnXy60hpXwlKkDDGpAPuijqjApwUpZSfGGN0kr8g80VzgnbvUUr5XExMDEeOHPFJJqWqpmg9iZiYmGp9TkhOy6GU8q9WrVqRlZVFVbupK98oWpmuOjRIKKV8LjIyslqroamaQ6ublFJKeaRBQimllEcaJJRSSnkU8BHXviQiucCeCnf0LB447KPkXEj0ukOLXndo8ea62xpjvJrX6IIOEtUlImneDk2vTfS6Q4ted2jx9XVrdZNSSimPNEgopZTyKNSDxNRgJyBI9LpDi153aPHpdYd0m4RSSqnyhXpJQimlVDk0SCillPIoJIOEiFwmIltFZIeIeL10ak0lItNF5JCIbCyxrbGILBCR7dZjoxLv/dG69q0iMrbE9v4issF67zWp4fM8i0hrEflRRDaLyCYRedDaXquvXURiRGSViKyzrvuv1vZafd1FRCRcRNaKyFzrda2/bhHZbaU3XUTSrG2BuW5jTEj9AOHATqA9EAWsA5KDna5qXlMqrnXDN5bY9gLwuPX8ceB563mydc3RQDvrdxFuvbcKGAII8C1webCvrYLrbg70s57XB7ZZ11err91KYz3reSTwMzC4tl93iet/BPgYmGu9rvXXDewG4kttC8h1h2JJYiCwwxiTaYwpAD4BxgU5TdVijFkCHC21eRwww3o+A7i2xPZPjDE2Y8wuYAcw0FpXvIExZoVxfZveL3FMjWSMyTbGrLGenwI2Ay2p5dduXE5bLyOtH0Mtv24AEWkFXAm8U2Jzrb9uDwJy3aEYJFoC+0q8zrK21TZNjTHZ4MpMgURru6frb2k9L739giAiSUBfXHfVtf7arSqXdOAQsMAYExLXDfwv8CjgLLEtFK7bAPNFZLWITLG2BeS6Q3E9CXd1cKHUD9jT9V+wvxcRqQd8BjxkjDlZTjVrrbl2Y0wh0EdEGgJzRKRHObvXiusWkauAQ8aY1SIy0ptD3Gy74K7bMtQYc0BEEoEFIrKlnH19et2hWJLIAlqXeN0KOBCktPhTjlW8xHo8ZG33dP1Z1vPS22s0EYnEFSA+MsZ8bm0OiWsHMMYcBxYBl1H7r3socI2I7MZVTXyJiHxI7b9ujDEHrMdDwBxc1eYBue5QDBK/AJ1EpJ2IRAE3AV8FOU3+8BUwyXo+CfiyxPabRCRaRNoBnYBVVnH1lIgMtno83F7imBrJSuc0YLMx5uUSb9XqaxeRBKsEgYjUAS4FtlDLr9sY80djTCtjTBKu/9sfjDG3UsuvW0Tqikj9oufAGGAjgbruYLfaB+MHuAJXT5idwBPBTo8PrmcmkA3Ycd0t3A00ARYC263HxiX2f8K69q2U6N0ApFhfvp3Av7FG5NfUH2AYruLyeiDd+rmitl870AtYa133RuBJa3utvu5Sv4ORnOvdVKuvG1dPzHXWz6aiPCtQ163TciillPIoFKublFJKeUmDhFJKKY80SCillPJIg4RSSimPNEgopZTySIOEUpUkIg1F5LflvP+TF59xuqJ9lKoJNEgoVXkNgTJBQkTCAYwxFwU6QUr5SyjO3aRUdT0HdLAm2LMDp3ENZuwDJIvIaWNMPWtOqS+BRrhmav2zMabGjuxVyh0dTKdUJVkzzs41xvSwJpr7L9DDuKZlpkSQiABijWvSwXhgJdDJGGOK9gnSJSjlNS1JKFV9q4oCRCkCPCsiqbimtm4JNAUOBjJxSlWHBgmlqu+Mh+23AAlAf2OM3Zq9NCZgqVLKB7ThWqnKO4VrudSKxOFa/8AuIhcDbf2bLKV8T0sSSlWSMeaIiCwXkY3AWSDHw64fAV9bC9en45rOW6kLijZcK6WU8kirm5RSSnmkQUIppZRHGiSUUkp5pEFCKaWURxoklFJKeaRBQimllEcaJJRSSnn0/wHN6pDpztK9XQAAAABJRU5ErkJggg==\n", + "image/png": 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"text/plain": [ "
" ] diff --git a/XCS_Experiments/XCS_simple_maze.ipynb b/XCS_Experiments/XCS_simple_maze.ipynb new file mode 100644 index 0000000..e5627ad --- /dev/null +++ b/XCS_Experiments/XCS_simple_maze.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['0', '1', '1', '1']\n" + ] + }, + { + "data": { + "text/plain": [ + "'State: 3'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_yacs_simple_maze\n", + "\n", + "maze = gym.make('SimpleMaze-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS, Configuration\n", + "from utils.xcs_utils import *\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = Configuration(number_of_actions=4,\n", + " user_metrics_collector_fcn=xcs_metrics,\n", + " covering_wildcard_chance = 1,\n", + " mutation_chance = 0,\n", + " epsilon = 1,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 57, 'reward': 1000.0, 'perf_time': 0.009571499999992739, 'population': 32, 'numerosity': 36, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 103, 'reward': 1000.0000000000005, 'perf_time': 0.03537640000001829, 'population': 31, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 82, 'reward': 1000.0000000006356, 'perf_time': 0.02387029999999868, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 335, 'reward': 1000.0, 'perf_time': 0.094538899999975, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 262, 'reward': 1000.0, 'perf_time': 0.07639949999997953, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2500, 'steps_in_trial': 26, 'reward': 1000.1357428823436, 'perf_time': 0.010510000000010677, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3000, 'steps_in_trial': 188, 'reward': 1000.0, 'perf_time': 0.05975800000004483, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3500, 'steps_in_trial': 273, 'reward': 1000.0, 'perf_time': 0.08238000000000056, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 4000, 'steps_in_trial': 73, 'reward': 1000.0000000138631, 'perf_time': 0.024332100000037826, 'population': 32, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 4500, 'steps_in_trial': 278, 'reward': 1000.0, 'perf_time': 0.1191023999999743, 'population': 31, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 77, 'reward': 1000.0000000035228, 'perf_time': 0.018505000000004657, 'population': 31, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 50, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0013782999999989443, 'population': 31, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0012757000000078733, 'population': 31, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 150, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.001599300000009407, 'population': 31, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0011202999999682106, 'population': 30, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0014271000000007916, 'population': 30, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0013026999999965483, 'population': 30, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 350, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0013284999999996217, 'population': 30, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0011438999999882071, 'population': 30, 'numerosity': 200, 'average_specificity': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 450, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0013928000000191787, 'population': 30, 'numerosity': 200, 'average_specificity': 1.0}\n" + ] + } + ], + "source": [ + "explore_trials = 5000\n", + "exploit_trials = 500\n", + "\n", + "agent = XCS(cfg)\n", + "_, explore_metrics =\\\n", + " agent.explore(maze, explore_trials, False)\n", + "population, exploit_metrics =\\\n", + " agent.exploit(maze, exploit_trials)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(explore_metrics)\n", + "df['trial'] = df.index * cfg.metrics_trial_frequency\n", + "df.set_index('trial', inplace=True)\n", + "df_exploit = pd.DataFrame(exploit_metrics)\n", + "df_exploit['trial'] = df_exploit.index * cfg.metrics_trial_frequency + explore_trials\n", + "df_exploit.set_index('trial', inplace=True)\n", + "df = df.append(df_exploit)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 57 1000.000000 0.009571 32 36 \n", + "5 11 1023.112229 0.005407 32 200 \n", + "10 17 1002.960683 0.006189 32 200 \n", + "15 31 1000.024491 0.011041 32 200 \n", + "20 176 1000.000000 0.058673 32 200 \n", + "... ... ... ... ... ... \n", + "5475 7 1100.050956 0.002458 30 200 \n", + "5480 7 1100.050956 0.001158 30 200 \n", + "5485 7 1100.050956 0.001148 30 200 \n", + "5490 7 1100.050956 0.001332 29 200 \n", + "5495 7 1100.050956 0.002386 29 200 \n", + "\n", + " average_specificity \n", + "trial \n", + "0 1.0 \n", + "5 1.0 \n", + "10 1.0 \n", + "15 1.0 \n", + "20 1.0 \n", + "... ... \n", + "5475 1.0 \n", + "5480 1.0 \n", + "5485 1.0 \n", + "5490 1.0 \n", + "5495 1.0 \n", + "\n", + "[1100 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population', 'numerosity']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"pop\", \"num\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps for solution\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best action: 0\n", + "1 [189.4468880751367, 1280.3139057920484, 1e-06, 1e-06]\n", + "\n", + "The best action: 1\n", + "1 [1e-06, 1022.2228432857141, 1e-06, 1e-06]\n", + "\n", + "The best action: 1\n", + "2 [1e-06, 1e-06, 885.2147964418139, 1e-06]\n", + "\n", + "The best action: 2\n", + "2 [1e-06, 1e-06, 853.0610656271485, 1e-06]\n", + "\n", + "The best action: 2\n", + "3 [1e-06, 504.44956740303866, 1e-06, 921.9530187832484]\n", + "\n", + "The best action: 3\n", + "3 [1e-06, 1e-06, 1e-06, 1100.0509562769134]\n", + "\n" + ] + } + ], + "source": [ + "moves = [\n", + " {'action': 0, 'exp_state': '1001'}, # 3 -> 0\n", + " {'action': 1, 'exp_state': '1010'}, # 0 -> 1\n", + " {'action': 1, 'exp_state': '1100'}, # 1 -> 2\n", + " {'action': 2, 'exp_state': '0101'}, # 2 -> 5\n", + " {'action': 2, 'exp_state': '0110'}, # 5 -> 8\n", + " {'action': 3, 'exp_state': '0010'}, # 8 -> 7\n", + "]\n", + "\n", + "for step in moves:\n", + " match_set = population.generate_match_set(step['exp_state'], 0)\n", + " print(f\"The best action: {step['action']}\")\n", + " prediction = match_set.prediction_array\n", + " print(f\"{prediction.index(max(prediction))} {prediction}\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best action: 0\n", + "Cond:1001 - Act:0 - Num:1 [fit: 1.000, exp: 5747.00, pred: 189.447, Error:62.900812807082936]\n", + "Cond:1001 - Act:1 - Num:24 [fit: 1.000, exp: 30598.00, pred: 1280.314, Error:1.145587242960733e-12]\n", + "Cond:1001 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:1001 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "The best action: 1\n", + "Cond:1010 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:1010 - Act:1 - Num:24 [fit: 1.000, exp: 6967.00, pred: 1022.223, Error:4.560794602049363e-13]\n", + "Cond:1010 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:1010 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "The best action: 1\n", + "Cond:1100 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:1100 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:1100 - Act:2 - Num:25 [fit: 1.000, exp: 12982.00, pred: 885.215, Error:5.685935895621327e-13]\n", + "Cond:1100 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "The best action: 2\n", + "Cond:0101 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0101 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0101 - Act:2 - Num:25 [fit: 1.000, exp: 6005.00, pred: 853.061, Error:4.547645146517495e-13]\n", + "Cond:0101 - Act:3 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "The best action: 2\n", + "Cond:0110 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0110 - Act:1 - Num:1 [fit: 1.000, exp: 90.00, pred: 504.450, Error:0.21057623942027062]\n", + "Cond:0110 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0110 - Act:3 - Num:25 [fit: 1.000, exp: 2037.00, pred: 921.953, Error:4.547488415483494e-13]\n", + "The best action: 3\n", + "Cond:0010 - Act:0 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0010 - Act:1 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0010 - Act:2 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, Error:1e-06]\n", + "Cond:0010 - Act:3 - Num:25 [fit: 1.000, exp: 670.00, pred: 1100.051, Error:1.1368684684463848e-12]\n" + ] + } + ], + "source": [ + "for step in moves:\n", + " match_set = population.generate_match_set(step['exp_state'], 0)\n", + " print(f\"The best action: {step['action']}\")\n", + " for cl in sorted(match_set, key=lambda cl: cl.action)[0:10]:\n", + " print(cl)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/XNCS_simple_maze_.ipynb b/XCS_Experiments/XNCS_simple_maze_.ipynb new file mode 100644 index 0000000..0934003 --- /dev/null +++ b/XCS_Experiments/XNCS_simple_maze_.ipynb @@ -0,0 +1,401 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like\n", + "\n", + "['0', '1', '1', '1']\n" + ] + }, + { + "data": { + "text/plain": [ + "'State: 3'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_yacs_simple_maze\n", + "\n", + "maze = gym.make('SimpleMaze-v0')\n", + "print(\"This is how maze looks like\")\n", + "situation = maze.reset()\n", + "print(type(situation))\n", + "print(situation)\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xncs import XNCS, Configuration\n", + "from utils.nxcs_utils import *\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = Configuration(number_of_actions=4,\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " covering_wildcard_chance = 1,\n", + " mutation_chance = 0,\n", + " epsilon = 1,\n", + " lmc=10,\n", + " lem=20\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 153, 'reward': 1000.0, 'perf_time': 0.04619869999999082, 'numerosity': 128, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.88}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 46, 'reward': 1000.0001438416078, 'perf_time': 0.0181773999999848, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.98}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 175, 'reward': 1000.0, 'perf_time': 0.06847080000000005, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.97}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 183, 'reward': 1000.0, 'perf_time': 0.071507199999985, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.98}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 1000.0000365525294, 'perf_time': 0.021861300000011852, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.95}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 36, 'reward': 1000.0044187614235, 'perf_time': 0.01650449999999637, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.95}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 153, 'reward': 1000.0, 'perf_time': 0.06557420000001457, 'numerosity': 200, 'population': 30, 'average_specificity': 1.0, 'fraction_accuracy': 0.95}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 109, 'reward': 1000.0000000000001, 'perf_time': 0.04441679999999337, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 38, 'reward': 1000.0022274976336, 'perf_time': 0.018530300000008992, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 900, 'steps_in_trial': 105, 'reward': 1000.0000000000002, 'perf_time': 0.04501369999999838, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 74, 'reward': 1000.0000000098428, 'perf_time': 0.025106300000004467, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 50, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0017582000000118114, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0020932999999843105, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 150, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0019136999999886939, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0020466999999939617, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 250, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0020284000000003743, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.002980600000000777, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 350, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0026459000000045307, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.00211020000000417, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 450, 'steps_in_trial': 7, 'reward': 1100.0509562769146, 'perf_time': 0.0017841999999745894, 'numerosity': 200, 'population': 32, 'average_specificity': 1.0, 'fraction_accuracy': 1.0}\n" + ] + } + ], + "source": [ + "explore_trials = 1000\n", + "exploit_trials = 500\n", + "\n", + "agent = XNCS(cfg)\n", + "_, explore_metrics =\\\n", + " agent.explore(maze, explore_trials, False)\n", + "population, exploit_metrics =\\\n", + " agent.exploit(maze, exploit_trials)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(explore_metrics)\n", + "df['trial'] = df.index * cfg.metrics_trial_frequency\n", + "df.set_index('trial', inplace=True)\n", + "df_exploit = pd.DataFrame(exploit_metrics)\n", + "df_exploit['trial'] = df_exploit.index * cfg.metrics_trial_frequency + explore_trials\n", + "df_exploit.set_index('trial', inplace=True)\n", + "df = df.append(df_exploit)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df['average_specificity'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"specificity\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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L7hn1YnOTNjWZzPRErX0PM23asnQW+23PMJOLYtK/G01MWUyfwWSimJRSbYel7hnz4rSMgZpH3UtrDEkF2qn3QbTjKxnzg9g8ZGoNLTWIQOG6kqpR5DeYmJJgAi2wlvZ2ZTQIvzGKyZWUQ7qliWmmNYhIIFWsgNirsN/2DJOdJNuhVUe5LGYs/2TyILxAxSGkRVfnKXPNSL2hH0SRCrQTGWc4xjZMTIYGYWoNAy3CXP0goORIKtS1MYopKcJX9wNEYHGv9kGEx47U/ViYjeZEMQGGianzAkJrLBPthW2Zm1gBMcNMxgeR1QTC1X0TE5Mxmes5M2sKKYJ5/aI5BunJtk62o9x0ZFL7fvtC0AuaaBAtCvF5gcJ1JFXNNPuxmM9b9xVlx6G/Wk5llZsmrTwfBGBEMaV9ECKhOWom0QLJmpj2LmypjRlmMlFMWUesF6hshY2YuumDMKKYlFJxYbYijBrRUUU1CO2gXtRTZveI4YOYxkxq/ZnUC45RRSGo2eqsi3rKLU1MfqAoOZLqqJZ1jOv3gVJ4fkDJDTWOWj2IE93yIqXCfIPxTUyTKbMxUfQ9J9OoyDL3mNa/NBE5RUTuEZFtIvKhnP2LReSHInKbiNwoIocZ+94rIneIyO0icomIVLPnz0Wyk2Q7ZDWIVkKm7jWamMLX7d0zpUEUNN9oE83qxd3sHBqLtzdmUk99FFPRMWaF1kCtjiOwb3913DwIN9IINNkwVx1q6wfh8WXXiX0WT0WfR162tplJDTSNYupEJFFsYrIaxF7FtH3bIuICXwVOBdYDbxSR9ZnDPgJsVkodAbwF+FJ07irg3cAGpdRhgAu8YbrGOpPEztSJaBAZX8JovbmQMVe15kq93XDVsYkIiGg1vnpRD7uGk5Wyl6lD1UkfhNaGzDyI/u4yC7vL4+ZBhBqE4aTOmphiDS9MMCy7iUlKC8w8LcXNmpiaRDF1oh5SYmKyGsTexHSamI4Dtiml7gUQke8CpwN3GsesBz4NoJS6W0TWisgKY2zdIlIHeoAd0zjWKeWOHbv5wc0PoVAcut9CXnvsah7cOcwtDzw1bhTT0KjH9296kNccu5rzf/UnSo7wrr98Bl0ltyEjuqUGYZhaghxz07V3PsrqJd08srvGsgVdHNakTLZ5jzwT072P7+HuRwZ5qVHhU09+qxd3p47NZlJPxAfxvU0P8IJDVrDMKBP+480PxdE1RU1MdaNeEoRCTXdQ2/7UCLc/tJvLbwm/Q4BDVvbzumNX48c+iEYn9e6ROldsfigRPtrE5DixSerJodHofnkahJPSFrRgyEYOdaIekr5nJ8xbls4xnQJiFfCg8X47cHzmmC3Aq4Ffi8hxwAHAaqXUzSLyeeABYAT4mVLqZ3k3EZGzgbMB1qxZM7VPMEG+8Zv7ueyW7ZSdsBvYa49dzWlf+TVPDdc58elLgeaT+6/ueZxP/uROhsd8/u1XfwLg+Qct59gDljTUG9Kre7Op/dOX9/Lw7lpqte+nNIjw9f/58e2c+IxlXHbzdgDu/8zLcsdjmsLyNIhv//4BLr3pwbSAiCa//RalBUR23O3mQTw1NMYHf7CVT54WxL0ulFK87/tb4s+1qJajTXBaodoz6tPbVaK34jI85vHt3/+Z7256kL5qidF6qAW8+uhVQGgKOmL/xYiEVda1ienqrQ/zsR/fwSEr+4HIxOQrSq7QWwn/1bRGlatBuMLC7jLPXLGAh3fVWB9dx3GEkw5azmDN4+Y/P9WRekj6nrZY397FdP6l5f0lZWeEzwCLRWQzcC5wK+CJyGJCbWMdsB/QKyJvyruJUuoCpdQGpdSG5cuXT9ngJ8PukToHrejjLc8+IJ6cnxpOGtJDc/PQSLR913Biu49t9iqtQejJ3mxW86N3PYfnHrgsHcVk2Mh9I8wyW54jj7QPonFCr/tBHKapGazV6a24TYvaTbSaaz12RKeFnx8ohsfac3zHBQSNWkzVsoPjSBx9tGpRN1s/8RLe/hfrqPsqPsd1hec/czmX/vWzU8+xO64GG/4OAsWYH1Bxnfg70v0c9Fdiho3qWkw/e+/z2frJl3DSQfvE+75x5nG88qj9wuOsD8IyQ0ynBrEd2N94v5qMmUgpNQCcCSBhaM190c9LgPuUUo9H+y4HTgS+PY3jnTIGaqG5wo0mGxM9KTfTIPSK3TRBZMNEsy08Tbt12XUouU4qJDXI0SDMctOtMMNx8/wXXqAa7P4DtTp91XIq0id974lFMenbeznPoz/XwhqEUf5DhwtXXAdXhCBQBErFoaRlV6gHQfx560ld748joSLBoHs26ES5kpsU4dP7NIt6KjwRtV4dL3RVm586kaym72k1iL2L6fxL2wQcKCLrRKRC6GS+wjxARBZF+wDOAq6PhMYDwAki0hMJjhcAd03jWKeUgRGP/u4SjiMNES7jCghdUbTW6NzNRjHp7ZWMgCg70lyDaFdApExMjRO6HwQEKq0N6Oc3I32y99bntkNWgwrHlC5WWNRsZR4XKOIObq4jcae5REA4KGWWxHCi3xkBEQl1rSXoct8lIwFuT4OASD6j8ZLQtE+ikxqEbRi0dzFt37ZSygPOAa4hnNy/r5S6Q0Q2isjG6LBDgDtE5G7CaKfzonNvAC4DbgG2RuO8YLrGOtXEGoRIwypZl5RuFuaqJ09TQOiwyWxGdFaD0AlUZddp4YMI22D6gWoZBZUdD+SvzvVYzH36+c1IH31v85rtahBxMpxvCgitQTSan1qR/XxG6z5dJQc3Eup+oJIeCNHkqM1/eiJ3onwSP5NsV88myhklNLJC1qwMO74GEQmITvggbKLcXsm0Jsoppa4CrspsO994/TvgwCbnfhz4+HSOb7rQIZOuIyhFqshdrEE0yaTWgsOMk2+uQWgBEU4+ZaOgmjkRmQLCD5Lsa223b4UpyPJW53mlxAdqdfbpqzZ0XtPmnLEJ+iASDcKMrMpoEAWTAVNRXtGYtIDQfo3YMRv91vdwMyamvJLh+vnqfkDZdXLLZYiQ0rLGm/jj77kTUUy23PdeiV0OTDFBoBis1emrlhpMEFDEBxFpEEa5h8QHQep6sQaRMT2UXUn5C7JRTPoereL94/EYgiyv1EZe/sFgzaO/mm9iSoXNtisgcoSRzhg3He5Frpv9fEa9gK6SiyORgFAKx/BBhPdIqq6av81QWZPA8EHkCYi+rlLKXFRUg+hsHoSdMvYm7Lc9xQyNeQSK2EkNaR9ALVPBM0vig0hWo4lDNV+TyE4cJcfJ2NizGoTfcI9mmNVR6zlj1qtn87iBkTr93WV6Km5q0vMyAqJdDSLI8UHEGkSbCX0pE1NUsbarrE1M4XPpuVBPirrvt5s1MWWc1PF1g7Dcd6hBNPZ06O8up7SG2e2DsE7qvRErIKYYPen2d5fiCaQ21jgpNvdB6CimZLJJykeH7xt9EGnTQ7kkmXLfyfU9XyVCqEVRung89dYr/mwfBqUUAzWP/moZkcaEsvFMVq3IK1uebUYUbhv/utlEwtF6xsSkFK5kNYi0D6LUxEkdj1drEI5QdoWs1auvWo61FBHi181IvudOaBDWSb03Yr/tKUZPuqEGEW57yshp0DTt45DjwI2LvzX1QaSdl2XHyS33rc/VwqOIKWa8RLls8t7wmI8fqNj/oB3VpSg6yBQ4bUcx5ZmYcoRBkXIbpnDyAsWonzExBYmJSX+uo81MTE00CF3uu+w6iDSamfqrJfSCvEgZ7Ur8PXdAg3A6d29L57ACYooZjDWIcqxBtCMgzO09lXTkS2MeRJRJHUXIlEs6mUlSoafpyTBoq9T4eKU24iimIB19pQWDFhTdZXfSPoh8E1PzMbWinvFBaCd1yRH8KIop1iBKaSe1niy1AAlUKAiyTn/PEBDQWHSvvzvRIIqU7+6oD6KU/iwsewf2255itAbRVy3Fqy2zYJ2mWYipuWJfELW1jM0o2UxqP+ODcNITSPY8fW47lWTHC3P1M9nNsYCMHNT6d1fZjXwQvnFuewIiW4EV8h3nRQohmv6UkWhi7zIyqU0NQkfujGSjmGIfBOzJ8ecEQZh9rX0GlQYNohxfwy1Qgr2jJqZMRJdl78B+21NMvII2nNQT1SC0gNAmkyAzQWad1GYUk3lcNpO6nV4UpiCr50zoXiY3ITaxRVnUWkBUyw5+EExOg4ifu7XjvFgUU3KMXvl3lVxj0leJr0E7qSPNK86DiP57fKVyayslPoj8vg793aUGc1Urst/zTJKU2rAmpr0JKyCmmGSCTMwHT+VpEONEMQH0xhpEWjAolaxOIVlZJnbi8Lc3RRqEnpjybPvZPgymgIREUFTLbspBbp5blDwfRK7jvM0oppF6uPqvlJzYbzTmB/GkXco4qV03o0H4QW51Vj8Ir1OJzDP6c9SahLmIKBI+GkcxdaJhkI1i2iuxAmKK0VFMfdVSPIHsytUgxjcxVaOwS7NDmUaXg4Bk4iiX0v/EWrA0aBBt+iCypi6TJJM6HcmjfRBaUCQ+iImbmFqV2jAZKyQg8jQIJxbqY14Q+5B0HaJsJrWeqH2VX53VLPcdXj8U5PrzNHNlimgQehxa4MwkNopp78R+2y2o+wGPD4621ct5YKROT8Wl7DqGBtEoIMx2lyamZtFVcsNCcX7jxOgbGkRcSM1J/xPnhYBmzTxAQ/hlejx+gyZjkvhDshqEngRNE5PiscHRhnOLklesLz+KqYCJyRAiQ6OJgNBCPaVBRL9HMz4IvZAPAsUDO4cb7uEHSaIcJIK8tysUFGYgQ5HooJIbOtE7We7bJsrtXdhvuwVv+toNPOufruXvr7xz/IMj9ox68Qox0SDSq8t4wsnRItICwqHsOPzpsT0c9HdX88fH9sT7Dvk/P+Wntz8cHpdJoNL/xJ6veN7nfsl3bnwgPs/zG01MrRyPY14QCwhz4v3qL7fxyq/+xohiSpebWBAJiKULKoiEq+Z7nxjiA5fdBoQCwwsC/vDoIAf93dU88GTjBJsl1iDMqKxcrSbZdsbXfs8/X3N3wzGmP0WbmLpKSWJf3U80iCRRTvsg0sX6fnrHI3z48q1A2hGtE+UqcRRT+HtJb9jsaNmCSlsaBIRah/4+ZpIFXSVEQk3QsvdgBUQL/vT4ntTvIoTF2dITdrbvgq7gma3sCWmhUSk5lFzhvieGGPWChuica+96DGiMbtHmgDE/4IGdw6nwy2yoqTnOZs/TVXJwJD3x3v3IIH9+cih2GOuJWl9bT4qvPmYVF591PEuNDnCfPO1QnrZsAX6g2PbYHka9gO27xhcQWSc95DvOTa3i/ieGuf+JxmubgmXYiGKKBYSXZFJXYid1fib1g5H28JlXH57qoqeL9cUaRPQ9HbpfPxee+Sye/8x9GrSU8fjGmcdx1nPXFTp2KnnFkftx8VnHN/T4sMxvrIBogV4N5zWYb4ZSKjY96AkkOyEv7gn/yfIcm6Z/oKsU9nbIEyQmeaU2oFEwQb4PotXkpKN5Sq6Tsu0P1uqpJjranFWPGuToYnk9lRInPn1Z6h5vOG7/qF6UiutBFYmsSqK3Gov1mZh+iTE/yNXUUk5qwwdhahCJWUVSx2X7QejP+eVH7pfKddB+omwUU8V1OPmgUDjov5GiGsSR+y9KtVudKborLic+fdmM39fSWayAaEKt7ueW3h6PQCmE9D99duW/OFqF5V0364OotBAQeqLqymTYZktDmORFMbVyPHpBaIvP9pgYGKlT9wMjiinxReRpJK4xVm3K8QMVC8kijvP8TOocE1PGBJUnfEwtY8QIc9UT9pgXNBbri/tBpKOYEtNTOltaf+/l2Afhxsdpsg5vi2U2Yf8qm2BqDXkr/WYoiPsIOIbD00T3AMirhWRO3l3l0MTUrCy3tnfHmdRuWpOo5Uy6OmvYpJWA8KNEr5KbLt8xUPOibnLp7O66r3I1Ej2pJqU3woKCA7EGMX7obV4mdbM2qJpsaK25XTNcb9QgRv3AKIORyaR2tZM6rK+UCIJ0WW9d3K+c8UGYjt52MqktlpnGCogm6Ilr5cJqmxoEDWaDMS9IOS+XxBpEWvDo1pcaXfrBxIw4yk485Uwyk6lB6PPyfBCtJicvUDgSNiEay2gQut8BJBO1WVoi7x46uinRIIqbmHLzIHJNTOlM6/FMTMORhlYtJ1FMdd/QIBpKbSSflz7ekfCZugwnrhZMpTg8VZuYGs+3CWiW2YgVEE3QE9fqxd2MeUGuuSaPQKl4Mi4ZAsJcWSY+iLTgCZvdJO/DMNf0V2T2I9aNiCqZlak+Z8QYsz4vL5M62xbVRPsgyq5kNIhw7Nle0J7hpDcpZTWIqGeFFpJFBERuLabcCrNmeZD8zHGzRIeZSa2FglLJ5K3Dh/WzOoaUjgv6ZYR1+Exag8iYAo3PR7+0GoRlNmIFRBO0iWn14p7U+/Ewu5nFSVd+kAoP1BpE9prZiayr5DQKiJQJI4iPAzOZqVGD0Of5OSvqVgLC8xWu40QTenhcKDDTfRi0yaYeNPNBJNnD4ftQg4id1AUEcJ4GkZcUpzUIFRXdyxMQzUxMKe2gaakNY4LPCJGUiSlT3C/OeHdzBIwVEJZZyLgCQkReLiJ7nSDRK2QdtljUzKRU4oNwDYdn1RAQPZUSFddpuKaeJPUCVfsgTMwomTEvQCTRDrKlNmoZcxUkUUymqapV4nGsQThJn+vBVL/sdLG+8TQIXd1Vl//W/p0i2c9+bhRT81IbWlDkCR8vJ4qpYmRSQyIgsgLXdZsLEfP7Gc06qY0opvj8NqOYLJaZpMjE/wbgjyLyORE5ZLoHNFvQE9eqRZGAKNBcB8LVuBNrEOE27YOITU+u0FctNVxTTyg60a6r5DYksWULvpUcMSaydFjmaI6JSa+oFxjJVqqVBhEEuG7og0jqLTVqU6YPopWTui+jQcRO6naimMZJlIuFVZDOzTAx/SnDY0ainOQJCC1wG30Q+mXsDyobJqZ6kLsvV0uxUUyWWci4f5VKqTcBRwN/Ai4Ukd+JyNki0jfto+sgeuJaFWsQxUxMgSI2MZkahF6FQzhB9HeXG66pJ7K4RHapUYPIlox2jdILeqWaTewyz9Mlt81+0X4BH0TJTcJc84SluWpvpUEs6EoKC3rtOqnzopiM19WyLjESCStPNb12Mw3CHHq2DEa2JzU0tuLM80FkE+XSPgirQVhmL4WWLUqpAeAHwHeBlcCrgFtE5NxpHFtHGRipU3KEFf3V+H0RlFLof/VUyGTk6NXb+3M1iHBC0Y7crnKOD8LNahCOYebIt5mD6YMINQh9D72tGV6gQiHkOvFknGduq8f+gSC34qf2G/RU0lFMg7GTukCYa5AjIIzJX5vx4pDbWIPITxjUDI35lF1JJa7pMZq/86KYnEwUkmliyvossr4i89rWB2GZjRTxQbxCRH4I/AIoA8cppU4FjgTeP83j6xgDtTr93WUWRhNpcSd1Yloyw1x1NjKEE0aoQWR9EOGEou30ulifSa4Gkam0WcpMaOZ5Oi+gr2qamJo/j9YgKq7Ek3FeXkiiQQS5Bd20Gae3K0kWG/MCBkeLRzGNV+5bBwIkSXtJEl+24OJYRoPQE7ubY/4RESqu09ByNHwd/i5nhED4TFozSWsXptBvN5PaYplJilT9eh3wr0qp682NSqlhEXn79Ayr8wzWPPqrpdgUU9RJbfogspNNWoMo8/DuWurcrImp4joNtumsgMjzQeSFuZac0Aeio5gW9VTiSrHjaxBOZBJqdFJr6ikTU+NkNxSZcboNDcIsg17IBxENs1m572osIIKGfWN+QNUxymD4oaNeqfBz0pO3m6MdAKmExdwopkyug0iOBlFubmKyeRCW2UgRE9PHgRv1GxHpFpG1AEqp61qdKCKniMg9IrJNRD6Us3+xiPxQRG4TkRtF5DBj3yIRuUxE7haRu0Tk2YWfagoYGAk1iGrZoexKG07qxAeRmmAcJ7XCz3dSaxNTpEGUnXF7AIc+iLSZIom6SSbIUnSczoPQJS+guA8i7vmQKyCSUht5PgidjNZbSUI9h4wM8XZMTF6TKKayK2FRQcNhHl8/I4A8X1GNnn94zMsVEHklMbLHOJkJXl+nWnIbfBDZsuzmtVzrpLbMQor8VV4KmP9dfrStJSLiAl8FTgXWA28UkfWZwz4CbFZKHQG8BfiSse9LwE+VUgcTmrPuKjDWKWOg5tFXLSEi9FUbzUHNCHJ8EPq1nihcV1qamBIntZuaTKCxrpOpQcQmrEzUDYQTmbb7j3oBXWU3Xu22jGKK/SeJBpFrYtJVXYP8UhtaGJg+iNSzt1WsL1+DCPM1nNj3YJqfsgKoHqg4qqhWD+LVvZuTBAdps1CrXAl9na6yg759QxRTTpir9UFYZiNFTEwlpVRsC1BKjYlIkZq/xwHblFL3AojId4HTAbO5wnrg09F17xaRtSKyAhgBnge8Td8TaOy6M40MjNTZp28BQORQTibF6//wOIFSnHTQPgBsun8nO4fGeMmh+wJGHkQq2iUTxVQtUauH5p49NY/v37Sd/Zd0x/eD/CimbEa36yZRTBU3rUGYYa6OhFrML+95jEcHahx7wOJ4tZtnYvqfPz5OoNKZ1DoqKFeD8JIkujwfhI4U6u1Kt0fV7B6u84kr7mBo1KO74vI3LzqIvmqJf/vVNs44/gC+d9ODPDYYmuT0xP/N396fKsUe+kqceCwpDSISQNfd9SjdZZd6Jrtdv07lQUijgBAhP1cikyhnXrtZSRTzfOuDsMxGigiIx0XkNKXUFQAicjrwRIHzVgEPGu+3A8dnjtkCvBr4tYgcBxwArCbUUh4nDKs9ErgZOE8pNZS9iYicDZwNsGbNmgLDKsZArW70VS6n7O5fvu6PeEEiIL72P/dy7+NDvOTQfdN5ENJEg3AktsXXxgKuvetRPvvTu/nblxwEwLFrl3Dc2iWsXdbbMNmedNBy6n7An3cOo1Q40S7prfC8Zy7nqP0XA8nka/og/EDx/Gcu59YHnmJxT4Xj1y2hr1rip7c/0uALgbAhkBeV83ZdoVp24+vlmdvMVXs20grgb178TB7ePcLRaxbHn4Gmr6vEpj/v5Mb7d7Kwu8zukTrPPXA5a5b08Pmf/YGHdo1wyY3Jn5IfKIZGPT5+xR2pe2g/T7Y+FCQaxOd/9geW9JapeT69lRIQdrjLMzGZj5E1E8XHZHwQR++/iGc/bSl1P+DRgfDaWjs4aEUfz1q7mIP37Y/Pt5nUltlMEQGxEbhYRL4CCOGk/5YC5+X9xWeXqp8BviQim4GtwK2ARxgtdQxwrlLqBhH5EvAh4GMNF1TqAuACgA0bNrTXw7IFgzUv9gX0V9M5CwO1esp8UasHsR0/CBrj5/Vrs1+DXkT6SsUTmY70edqyXr6/MXS5ZCekJb1d/OpvT+ZlX/4f7tgxgOsIlZLDt95+XHxMng+i7iu+esYxDc+5sLvMF6/9I0GgUivjuh/2U9YaRH81EZJ5EV2e4YPIc7gevWYx1/3NSanPQ7Osr4vBJ8JrfvK0Q3nP9zZT94NYU9n+1EjqWmaCnUlYbtuNzXBmroP+LHT48vCYF5ZdfyJcc2h/TF6dJUgEwEIjPBgancwHrujjkrNP4G0Xxm672Hm+dEEXl248MX2+jWKyzGLGFRBKqT8BJ4jIAkCUUoMFr70d2N94vxrYkbn2AHAmgISe3fuinx5gu1LqhujQywgFxIxQ9wOGx3xDgyjx6ECyyh4Y8dL1gLwgDhUNlIpFY6MPQq9Sk31ekPRU0FEyzezd+lzzmLyVp0jokDbNUaZjN3W9aILylcIxZLrnB4xpDcJx6KuWGKx5KBVOzj0VN1WGPF3ue3zXljn5Ll/QxX3RRL10QSW6ThBrKo8NjKbONUt0pJ7FEbrKTqwtpDWIpLeHjkhat6w33l/J0yBSJqbwtRkeDIlAyTrmzXOz2e/pMYe/rQZhmY0Uam4rIi8DDgWqOkJHKfX345y2CThQRNYBDxGW7PhfmesuAoYjH8NZwPWR0BgQkQdF5CCl1D3AC0j7LqYVvULWk0F/xkmd1SBGPT8ueJfqB5HSIJzYTu06Thy1EgSJTX0kFhCm7yKb95C2YzdbeZbcjIDIqVlkjjFbsG/M6F1dcoSeShkvUIzUfQZGPJYuqDC8M1nZjxl2/7ww14bxGePWQgGSQoZ1o1fEo4NpE5hZ5C91TTds2KOFgSkURz2fIFDsGfUoOcJI3Wdhd3LfbNMlyEY0hfv7m2kQme/B/O7N8htZ9Pdpo5gss5FxBYSInE+4oj8Z+BrwWoyw12YopTwROQe4BnCBryul7hCRjdH+84FDgG+JiE8oAP7KuMS5hKatCnAvkaYxE+iVq54M+gwntRdpFxA6jKtll1EvSASEUjj6n76JDyKMPAq3+0rFxedG4oY0poMzo0FEb0tOcw0iPM9JmZjyymJDsgLOKhieH6QSw+J8kBGPgVqdffqrPGgICDOKqVUDovg5onH3dZXormjzDiyKSqGHn3N4zV3DaWHgB4rdhh+kWg6f1XXC0N3RTAFBCDWIPWMeSsHukTqBSho3QTKJ52VSQ/I9mCVKgIaeEfG5KQ3CpRlJue+mh1gsHaOIBnGiUuoIEblNKfVJEfkCcHmRiyulrgKuymw733j9O+DAJuduBjYUuc9Uo1eusYmpWmak7lP3A/YY9vfBmpcIiGguMhsGmYtC0wdhlnQIgqSv83COBtFgusjYvJtpEKGAMDWIJiYmQ1CZeIFKlZbQ/piBWp2BkTqHrOxPH28W62tDg+jvLscT6IKuUuxzqRt1mvJ4yhAa3WWXWj2IW37mmpjqiclKy0rd+hVomUkNidDOahBx7kmDKbCYiSlJqrQSwjL7KPJXqfX7YRHZD6gD66ZvSJ1Hm5j0ZNBvlNvImpogbWIyGwZlNQjTb6AnED9Q+NFEpifklA8iM9nqFWu2xHeWrA8irzUnGBpERkDU/aRJkqlBPDU0xtCYH5uCNGNG9nIxDSI8pq9aiifQ/u5yLBzrXtCyQOJTQ0nUsy6x4Ujkg6ine1RA+B1l/RaLTA0i9kEk+50cH0R/xgeRdIRLP7NTUEDYWkyW2UwRDeInka/gn4FbCM3s/zmdg+o0iYmplPo9MFLP9KpOSlXruUQZmdSpkgxGxrNrCgjVqEGYk0W23He2OFyzhWfZdRrCXPMwNRmTuh/E57uOxEJyx+7QrLQ0IyC0huI16UmdJdYgquXYvNNfLccTrRcELTWInUaZjmolqe/UVXITc2CQNjFlI5+W9DT6IJqbmPI1CP35Z02B+vnCMu/NPw8bxWSZzbQUEFGjoOuUUruAH4jIlUBVKbV7JgbXKfRE0meYmPT2lIAw2mXqCUQpZTipk2umNQgnrUEYAqLkSGpCyWoQ+tjSOBpE2RXMOb9pFJMxDhPPV/H5ujQIwPbI75DVIMJ2qaptH0R/d4muePItxROr6aTOw6zjpEtmuK7QJYmJycw6H/WChvDcRaaAKOeYmIzvQfszslFM2TpYGi1oWmkPYPMgLLObln+9SqkA+ILxfnS+CwdISkloc0Kf6aA1VrVJLwM/Lldh+iCydX3iyCNXkvBSwwdRq/sNAiE78eiJvGL4M/LImjyaO6mJx22SLWGhhaTOSTAFhC76p+9RKIrJNTWIcHLuq5bj5/X8/FBWzU7DxKTNY6UoJySJYkqHImc1knwTU34exEimDEp8TI62GF5HX7e5g1qPGdJd6iyW2UIRH8TPROQ10kpPnmcM1Oo4QpRpS8pBO5hxUiulUlFMZi2mtLnCMVb9Ek8+fqDic0fG/AaBoCdbfSk96WUL9GXJbm83zNWcXFMaxK5hIB2aWi251P0gFip5pTayJBpEOfFBVMuRAz8UUHmhrJqnhpJ9+r6udlLHPoh0mGtWIxlPQJifYW0s3asj+xwN0WaZ0hvNsBqEZTZTxAfxPqAX8ESkRpgGppRS/a1Pm7sMjNTpq5bjf169ahxsMDHVqfsKpZIVuOmDaKjFZEQeuYZz2DMyqbMCQq9Meysl9ox68aRXGkeDyF6n3iyKqYWTOj7GCUttdJUcHoo1iK54f1fZxfOD2BHeng/CdFKX4merB62d1KYPwhSaXSXXcJino5h0jSaNFkh+oJIopiaZ1MP1tFaZfY6JmphcG8VkmcUUyaSe161F8zDLbECyahwY8Ris1REJ/7EHRuqxvVs7eQPDB9GQB2HkLqR9EEkexOJy2iShTTG9XW4oIDJmnGYhpdkV7Xh5EKYPQhnlP8x79HeXeWhXo5O6Wnbi0hzQ2LMiD9dIPNOTsxbEZUciE1OdSslpqGALYRST3qcFbJgH4cRFCrN5EHU/iM9xHaGnEvag9kkquzYr1jfSRIPIBg0kz5c4qVt/DlaDsMxeiiTKPS9ve7aB0HzCLNQHYQ8DRxITk155DtTq8eSVJMqZeRASN6VpyIMwTDt68s7r56x9Db1dYWE5Pekl12oS5trEd5ElHkfQ/FjXWO0/PhiWvejvLsf9JbrLLrtH6oYGMb6A0BNiX7WURDFFk2+55MS1mPZf3MO9TzTUaGTXSJ0DloT7YtNWXGoj7YPQuRFDox5Leys8NTxGd9lFRMJAAt/w6aSEenK/WEBUm5mYmmgQ5dY+CNtRzjKbKWJi+lvjdZWwjPfNwF9Oy4hmAQMjXipaRSTq3xCFufZ3lyINwosnIz2nmnkQEE44nlKpjnIlx4knSC/T0a0hXDKaeBZ0leLjw+2tV55FHMWQTIKmiSmrbSSTeTI5LugqUXJDAVEtuzw5NGb4IMa/dyJ0yvHnpT/zkuMwWPOo+4pVi7tzBYQfJPv0eN3IxOQFKjJ5BfFYR70g/l61UNP3giBXgzB9SMN1LSAytZiaaAD6/bgmJqtBWGYxRUxMrzDfi8j+wOembUQdZs+ox12PDPDspy1Nbe+rlhiohSamvq5EgxjNahDkVAQ1urJBOpPazIOAxpV/bGKKHOaxiclpzwfRDHMcY16YzzGW8Ve4mTpEvRU3FHiOQ42Aatlh59AYT0V+gbxy31nMTGqthcUmJlfiKKVVi7qbXkPvq9V9RLQPIrz3WCQgHAmrqY7Wg1gz9AOVKu8BSbRRs1pMWn42OKmbFetrU0BYDcIyG5mIZ2w7cNi4R81RXv1vv2Gw5rF0QVdqe19XWO5ad5rT1U1jH0ROJjWknZD91bCFqemDCIK0SSe7ktST5tFrFgFwwNIeoHU113B7+qs9fNXC3OPMRLn//e2b+egPtzZEPOl7LIv8Dsv7ws9GCy9dAvu0r/wmtb0VC6KV+PK+rnjS1dctu04sIMyKq1m0gDh6zSL6q2UWGA7v0Xromyi7oV+iFkUx9XeX2aevyvLo+81O5M1KbRyxemHquOxnk9XYnPi6rU1MuoFSNr/CYpkNFPFB/F+SPg4OcBRho595yY5dNZb3dfHeF6VLRGnb9pgX0N9dxpXQma1DKs0oprxs3JIjvPH4NTz3wOVR+89wf1aDyK5Ej1mziEs3PpsNByzmLw/eh2MPWBxeb9xaTJEfROCq857LyoX5K/FYUCm474mhsFJtgwYRHvPBUw/mec9czkH79qXG+tfPfzoP765xx46B6FnHX3c85+nLuGzjs3nmij6UUly68dkcEwnBkitxiOu6Zb1c/s4T+ccr7+SWB3alrrG4t8JV734u+y/p5qFdI6xc2M1VWx8GtFM6FBB91RJ7ah4DIx7PWF7ik6cd2rByz+sHYX62F/3V8Tyyu9aQFZ30pM4v1teqkivAyoXdXP7OEzmiiQC3WDpJkWXLTcZrD7hEKfWbaRpPx6n7AWccvYZ9+qqp7WXHiTqsBZQjJ3PdDxpMTA0ahDERLegqsX6/MDrYXLn7hoc4uxIVEZ61dgkAG6Lf5nFNNQgj09rsYJZFn66b8JRdh3oTH8SK/iqvPHqVMYbwHkt7K7zxuDX83Y9uz32G3Ps6Ej+P+YwQftY7a6EG0VV2OWbN4tgkZNLfXY4/z4P31X28Iw3C8/GCsHCg7v89GGkQ+y/pMZ4/PZE3y6Re2F1uaBZkHtOs3HdXAXPbMVGXPYtltlFEQFwG1JRSPoCIuCLSo5Qant6hzTw66S3PblyKWll6vqLkhiYiL0h6JigVnm+2HIXG6qvZ7X6gUiadIklmkKzSm0UxjdcvQmMW6xsYCfMw6l6+BtEwBsPpbppIivo/mlFyhaHRMO8g6dPQeM2swzg8PhQkOqy1FJn2Hto1wkAUgWaSNTGZj+qM89mZx2TDWZv5JiyWuUSRv97rANM+0Q1cOz3D6SzaOZsXmlhyHcZ8xZgfUHIdSk4Yijlq9FwIlE6US85rFsaYdJRrHcXUDN1/oGkexDj9IrLjGx7zGYu6uGXrNjXPtYiElCsp520RH0Qryq4TFy7UE3feRNtXbVzRx07qyMRUccNS5Y/uruEHqmlHOC1YRCTJYykgIJKOcPmlNib7WVgsnaSIgKgqpfboN9HrnhbHz1l0NE2eBlFxBS/SICquQ9kNTU6jnikgVI4PIvzd2Dq0MQ8iPK7YirPstNYQYh/FOBOUPl9HIA2N+alGQ+ExTXItDP+KuTKf7KrZFJJ64s6WHAFY2N2oQVRME1MkzPuqZYaaJLrpz8n8zvVn4kgBAdE0Ua65YLNY5gpF/nqHROQY/UZEjgVGWhw/ZxltISBK2gfhB3HhvdAHkZTUDiITkzlVNCulYBbr81s4qZtRGieKabwoJ402kew2GvCYhfCK3MN1JDVZTzam3xSS2jegn9cMoc2ai4BUFFM9CM2BpimqwcSU40xuJzch2wJW044WYrHMVor4IN4DXCoiO6L3K4HXT9uIOkgiIPJMTKFAGPMVJdfBdYh8EMlqO6zJpBrzIGjuxAw1iOZO6maM52Mo7oMIf+8aSYRCVkCM74OQlLln0hqEIaBjE5PhK9CfeVYbgMQ8OOoF1L2Aiuukjuvvzk90M7/zdno06EdtqLprVJi1WOYqRRLlNonIwcBBhIX67lZKNS+zOYfRNXzyQhMrUQE5LwiouFEUkxfE50CoDSjV2GoUcnwQTTSIok7qcfMgxukXkR2H2cLzyQloEFNqYjLuV8n4IColF/CoRPkNWdJRTFqDMAREEw3CdDI7bZiYkgVAeizZjHeLZS4y7n+yiLwL6FVK3a6U2gosEJF3Tv/QZp6WJiZXIhNTqEGUI4FhZh2HJiZSsfJOMwFhRjFNwEmdCJ4mUUwFM3T1+HalTEyjuWNtuIehpVQNoTrZSbGU44PI+gr6u0u5ndoSAWFEMeUUXtQ4Tvq6+nnM361IopXSx8ZVd22VVsscpshf7zuijnIAKKWeAt4xbSPqIK1NTGHF0rofxtaXnFBgNEYxNfNBNBcQKR9EUSd1vHpvvb9oFJPZoe3JPQVNTEa5D3OyLvoMzTC1qGwUk17p50UwgWFiijOp0xpEY0e40MyWKrERf2fjjzUJY85oEG00T7JYZitF/pMds1mQiLhApcXxc5bYxJSjQZSjxLi6H1COmv94gaJmOKlb5kE0ExAqmwdR1AfROg9ivH4RyTjC37tamJjGq/eUXSWXS5ObFCu5AkJS+/JyIMzjRz0//K6iKCZNg4CQsMBfntZXyMTUTIPItIa1WOYiRZzU1wDfF5HzCUtubAR+Oq2j6hCxBpHjgyi7YR+BQIWv9f/90GjGB0Em2aqJBpHOpJ5IFFPrSJuiTmo9Me4aaRXF1DoZL3uLyZpV9DNVSk48Pj3RauGT56CGjIkpUPS4iYmpWnYatEPXkYbvOwnfHf85mjUM8tponmSxzFaKCIgPAn8N/G9CJ/XPgK9N56A6hRYQFTffxKS1BZ1JDWH1V02gwglfcjWIbCKVYWJSE0iUG0cAxJPceHkQholpcU+ZXSP1NqKYwsKDWV/AZM0qWhiYmlwSxZRuLpTFzKT2/LAsij427xzdptQk6ecx/libRanpyDQrICxzmXH/BZRSgVLq35VSr1VKvUYp9R+67MZ4iMgpInKPiGwTkQ/l7F8sIj8UkdtE5EYROSyz3xWRW0XkyuKPNHF0TkO+BiFxyedy5IMA4pIQEJqYspnUzRyecRSTYkJRTKVxMqV1qOh4rSz1uHYN11nUU6Gvq8STe9JO6qb3cCRXeEzWrFJ208LAvGZsYspJktPnimgBEUYx9UTlyfO0DicyMZm05aRuokHEzZOsickyhykSxXSgiFwmIneKyL36p8B5LvBV4FRgPfBGEVmfOewjwGal1BHAW4AvZfafB9xV5EGmAu1wbpYoZ77WE0KDBlGwFpO+XBBk8iAKrjjL4/gYipba0EMdqfv0V0tRYbt0L+hm2dhl18m9/mQ1iHKeBpFxUjfTIEQk7iCnfRAiYbJcnt8iT4OIBUQbmdSNJibd+c9qEJa5S5HlzYXAvxNWcj0Z+BZwUYHzjgO2KaXuVUqNAd8FTs8cs56w1hNKqbuBtSKyAkBEVgMvYwbNWa2imEzHa7mULyB81eiDaBbFpAWOFyj8CRTrG68n9XjlwOPxGfv7u8u50UHNcy3yNYjJRzFFGkTZFBCJX0KPtRldJTfKpA7i76m/u5x7Tp4PQj9SO8X6st9D7KS2Ya6WOUwRH0S3Uuo6ERGl1J+BT4jI/wAfH+e8VcCDxvvtwPGZY7YArwZ+LSLHAQcAq4FHgS8CHwD6Wt1ERM4GzgZYs2ZNgcdpTmxiyo1iStvDnRwTUxA0RjHp0xozqaNzxukH0Yz9l/TwovUrmpaKHq8Uh8ZcJfdU3Kar7DxOPmiflDC98Mxn8YObtxeaWFuhP+uUiSm65qLuMqcduR9/8YxlTc/vqbgMjXqMjAVxfsZrjlnNvv3VhmNPOWxf9mQ1pjY0iOPXLeHlR6xkcU86sO89LzyQh54a4UWHrhj3GhbLbKWIgKiJiAP8UUTOAR4C9ilwXt5/l8q8/wzwJRHZDGwFbgU8EXk58JhS6mYROanVTZRSFwAXAGzYsCF7/bYYaxHFZK4QS02imMJSG+Q6qYtmUhc1SVTLLv/5lg1N91fajGKCcELuz+kr1GwV/ML1K3jh+mQCPPmgfTj5oCJ/Gq3JmpPArF7r8M+vO7Ll+brbn24xCvDuFxyYe+wZxx/QsE37bYoIusNWLeQr/+uYhu0HLO3l+xufPe75Fstspmgtph7g3cA/EJqZ3lrgvO3A/sb71cAO8wCl1ABwJkCUa3Ff9PMG4DQReSlQBfpF5NtKqTcVuO+ESaKY8gRE2tyhtYS0D0KFiXI5Ya6topjS1VynxmY9nhM7Ow4INac8DWamA3HyKqwW9alA6J94Ys9o3P2vXZpV4LVY9jYK1WKKXu4hmswLsgk4UETWEWodbwD+l3mAiCwChiMfxVnA9ZHQ+HD0Q6RBvH+6hQOEJqaSI7l+gIqxsi+7TjzxD2V9ECo9oTarxSQSRtsEamK1mMYj8UG0vp45rK5yo4DIC2Odbso5AqKoTwVCf8NdDw+EryfQ67mdYn0Wy3xm2jqlK6W8yCR1DeACX1dK3SEiG6P95wOHAN8SER+4E/ir6RpPEUbr+d3kIBvFlDhnzdV/O5nUEE5EfiaKKU97mQiVgj4IJ2Ni6u0K/yREwlX7DMsGIPms88Jci2Q391dLPDJQC19PQINoJ5PaYpnPTJuAAFBKXQVcldl2vvH6d0C+cTg55lfAr6ZheA2MekFuNzlI+yDKrpOyT4e9IVQU5pop1tdiNeo6klPNdYpMTEa3t1ZkTUx6xR2WE5FcR9J0o/0Npi8o8amMf35/dznOWWkWDtsKq0FYLCE2Bs9g1PObahCm6aXkSipfoRoJFT3Rm9NKszwIva/BBzHFJqZ2NQi94i67EpUUmflJ0uz9oCm14ThOlfduklDXikSDaPtUi2VeMe5/j4gsJ6zeutY8Xin19ukbVmcY9ZqbmMopJ3V64uwuuwzWvFhA5DUMytUgRPCiHhLxtadoVtJO3fHLfSevu8pOPLmGJc2FQM38LJmU2jBNTMVDT82CfM2qvrbCjfpSz7TvxWKZbRRZXv0Y+B/gWqBQiY25SuiDKGJikpQQ6K6E53ixgDDOa1H4zXEk1ZEuvPbUaBA6sa+dPIiuUlLYLiwn4qTqRM0UeU7qoj4VSPsdJmJiKkXPbrHs7RQRED1KqQ9O+0hmAaOen4q9NylnSm1kNQgAP3I2m2aQVvZs15E490L7MabMB1FUg8iamLQG4TiUS4J0YEkQl9pINSGaQROTSKFCfRbLfKfIv8GVUT7CvKe1iSntpDZX+rEG4TeutptV+4RwItId6bQfY8o0iKI9qRuc1NrEJJSdzvgg9GfVlfH7QDETkxYKJUdi4d0OriOF7mOxzHeKzEbnEQqJmogMRj8D0z2wThBGMTUJc80kypkr/e6MkzoV5tpCgyg5Ejcp0gJiyhLlivakNgVEOTExVVynaa2l6SbRIJLJvTwBJ3V/d3lCfoRQg7ACwmIpkijXshbSfGLU81nYJG7e1CBKrtNQwwjyfRAtndSOqUHohjgzrEGYiXIllwVRHkQpimIyI6xmitxqrqX2EuVgYkly4T1siKvFAgXzIETkNOB50dtfKaVmpD/DTDPWwsTULFEOGsNc04ly0e+clazjJPWftBYy2UqomqKlKZyMk7rkOvRWXEpRW9WSH7Q4e3rIK7UR+1TaiGKaSAQTWBOTxaIp0g/iM4Rmpjujn/OibfOOVj6IilHuu5KpWaQnd73aNueWkuPgSL5pxJWwzzUYJqYpS5QrqkGknwvCFXg5yvUYr1THdJDXMKioRgSJgJiIgzq8R2d8LxbLbKPIf9BLgaOUUgGAiHyTsOpqQ4e4uU7LMNcWGoR2UusopmwmdTM/gGNEMVVLU+2knlixPght+OXIB9GJgnV5UUzjNUgy6Sq5VI2cjnZxxZqYLBYoXmpjEbAzer1weobSeVqFuTaW+250Uuf5IBZ2l1nYkz9RuZLkQSzv68KRidvNs1QiU9F4tYiyPgiAfRdW40zqsus1OXP6WNQd9lZY0pv0WOjtKlFypKmPKMu+/VVW5PR/KMLC7nLh+1gs85kis9GngVtF5JeEVSSeR1Rpdb5R91XTFXw5E8VkmmZa+SDOeu46XnX0qtxrmnkQLz50Bee98ED2meCklsVxhCvf/dzcJjkmqX4Q0Yr9n193BEJSbXamWbO0h5++57kctCKJj1jYXean73kuByztLXSNb591/IR9EO978UGpKr0Wy95KkSimS0TkV8CzCAXEB5VSj0z3wDpB2MM437SQLbWRm0ntN/ogertKcYXULK6RSd1VcnjmiqkNGFu3rNhkqtEmpn36pkZITYaD9+1v2PaMfYp/PqsX90z43laDsFhCmhq8ReTg6PcxwErCBkAPAvtF2+YdXtBcg0iZmCIfhDbPZPMgisbem2GunXAGZ2nmf7FYLHsnrTSI9xH2ev5Czj4F/OW0jKhDBFHZ7WZRRGbYqBYAJddhzAta1mJqhSOJiWk2dC9rliRosVj2TpoKCKXU2dHLU5VSNXOfiHTeBjHF1ANdE6m1BpEq2ucIY+TUYmpHg/CC+HWnaRbia7FY9k6KzAi/LbhtTqP9B81W8np7yhcRTagNeRAF7+katZhmgwYxVd3sLBbL/KCpBiEi+wKrgG4ROZpk3usHJu4BnKXohLVmGoSIxKGfGp3fkORBNEYxtUI3DNKvO43tf2CxWExa+SBeArwNWE3oh9CzxwDwkekd1sxTjzSIZlFMEAoEc6Wvj836IIrOs6ZQmKoMaovFYpkqWvkgvgl8U0Reo5T6wQyOqSN4kf+gVcvPUlaD0AKiRR5EK1J9I2ZBFJPFYrGYFJmVjhWRRfqNiCwWkX+cviF1hvF8EBDa6LN9IUSSGkZ5eRCtMJWG2eCDsFgsFpMiAuJUpdQu/UYp9RRhfaZ5hXYWNyu1AVErStNJ7Th0lZLS3xOJYsp7bbFYLLOBIgLCFZEu/UZEuoGuFsfPSRINooWAyPggSq5QMeoyteuDSJcFtwLCYrHMLorUYvo2cJ2IXEiYIPd24JvTOqoOoKOYWjmLKyUnpWGUXYeushsLhHZ9EOa9rICwWCyzjXE1CKXU54B/Ag4BDgX+Ido2LiJyiojcIyLbRKShPHjkz/ihiNwmIjeKyGHR9v1F5JcicpeI3CEi57X3WO2jV/+to5ikIYqpq5TUZZqMBmF9EBaLZbZRqLa0Uupq4Op2LiwiLvBV4EWEdZw2icgVSqk7jcM+AmxWSr0qqv30VeAFgAf8jVLqFhHpA24WkZ9nzp1SxsuDgDDCqZTJgzAFxETyIPJeWywWy2ygSEe5E0Rkk4jsEZExEfFFZKDAtY8Dtiml7lVKjQHfBU7PHLMeuA5AKXU3sFZEViilHlZK3RJtHwTuIkzamzZiE1MLH0SYKJf2QXSVXPQpXuykLnZPN6VB2DBXi8UyuyiiQXwFeANwKbABeAvwjALnrSKs/qrZDhyfOWYL8Grg1yJyHHAAYWLeo/oAEVkLHA3ckHcTETmbsKgga9asKTCsfLwCiXL/34b9494PAK8+ZhWDNa9BgyiakWzmQVQ7WCjv7152CMv75l3cgcVimSRFTUzbRMRVSvnAhSJSpBZT3iyZ7T7zGeBLIrIZ2ErYyjTu1CIiC4AfAO9RSuVqLUqpC4ALADZs2DDh7jZFEuXedMIBqfevOno1ADuHxsJr+O3VYjL9Dgua9IyYCc567tM6dm+LxTJ7KTIrDYtIBdgsIp8DHgaKdKLZDuxvvF8N7DAPiCb9MwEkXHbfF/0gImVC4XCxUuryAvebFGPe+BpEM5wJRjFpDWJBV6mlYLJYLJZOUGRWenN03DnAEOGk/5oC520CDhSRdZGAeQNwhXmAiCyK9gGcBVyvlBqIhMX/A+5SSv1LsUeZHN445b5b4WTyIIq6E7QPYqr6UFssFstU0nJmiiKR/kkp9SagBnyy6IWVUp6InANcA7jA15VSd4jIxmj/+YShs98SER+4E/ir6PTnEAqmrZH5CeAjSqmrCj9ZmxQptdGMifogdORSv21vabFYZiEtBYRSyheR5SJSiSKR2iKa0K/KbDvfeP074MCc835NcVP+lFAkzLUZWqZoLaTowJ1Yg7ACwmKxzD6K2DbuB34jIlcQmpgAmCnTz0yRlPueiICYaB5E+LvPmpgsFssspMjMtCP6cYC+6R1O50iimCZuYvLaFhChhLAmJovFMhtp1VHuIqXUm4FdSqkvzeCYOkKsQUwgYa0xiqnYeVqDsE5qi8UyG2k1Gx4rIgcAb49qJi0xf2ZqgDOFp30QpUloEJGQKeqEiH0QVoOwWCyzkFZL1/OBnwJPA24mPe2paPu8oUipjWZMtJprre4DnU2Ss1gslmY0nQ2VUl9WSh1CGJ76NKXUOuNnXgkHKNaTuhkigiNmLaZi19gzGgqIXisgLBbLLKRIue//PRMD6TReEOA6UjiHIYsj0rYPYmg0rCpiNQiLxTIbsfUdIuq+mpD2oHFE2u4HMTwWCoieijvOkRaLxTLzWAERUfeDCUUwaUTaz6QesiYmi8Uyi7ECIsLz1YRyIDSuI23nQQxFGoQVEBaLZTZiBUSEFwSTqqg6ER/Es5+2FID9FlUnfF+LxWKZLuzSNWLMU1QmISBEzFpMxSTE377kIN564lr26bMCwmKxzD6sBhERahCTMzH5fntO6pLrsN+i7gnf02KxWKYTKyAiPF9NqNS3xoxiKuqDsFgsltmMFRARdT+YUCVXjWNEMU0iGMpisVhmDXYqi5isgBCRuFxHUR+ExWKxzGasgIjwgkmGuU4gislisVhmM1ZAREw2Uc4R8FV7iXIWi8Uym7ECImKyiXJiNQiLxTLPsAIiYtJOaiepCGs1CIvFMh+wAiJissX6XEMoWA3CYrHMB6yAiPCCYELNgjROSkBYCWGxWOY+VkBEeL6iXJpcqY281xaLxTJXsQIiYswPKE8yk1pjfRAWi2U+MK0CQkROEZF7RGSbiHwoZ/9iEfmhiNwmIjeKyGFFz51qpqLct8b6ICwWy3xg2gSEiLjAV4FTgfXAG0VkfeawjwCblVJHAG8BvtTGuVPKZMt9i/VBWCyWecZ0ahDHAduUUvcqpcaA7wKnZ45ZD1wHoJS6G1grIisKnjul1H01SRNT8trKB4vFMh+YTgGxCnjQeL892mayBXg1gIgcBxwArC54LtF5Z4vITSJy0+OPPz7hwY55AV3lifeGtlFMFotlvjGdAiJvllSZ958BFovIZuBc4FbAK3huuFGpC5RSG5RSG5YvXz6hgSqlGPX8STUMcgwVwooHi8UyH5jOjnLbgf2N96uBHeYBSqkB4EwACY3490U/PeOdO5V4gSJQ0DWJMFfTxGQ1CIvFMh+YTg1iE3CgiKwTkQrwBuAK8wARWRTtAzgLuD4SGuOeO5WMemGZ7q6yTZSzWCwWzbRpEEopT0TOAa4BXODrSqk7RGRjtP984BDgWyLiA3cCf9Xq3Oka62jdB6CrNBkfRPJabHaJxWKZB0yniQml1FXAVZlt5xuvfwccWPTc6WIsavQzOROT9UFYLJb5hV3rAqN1a2KyWCyWLFZAYPggJmNiMj5JKyAsFst8wAoIYNTTPogpMjFZ+WCxWOYBVkAwRRqEFRAWi2WeYQUEU+WDMF9bCWGxWOY+VkAw9SYmKyAsFst8wAoIpsbEJLblqMVimWdYAcHUaBBmGSfbMMhiscwHrIAg8UFUpsDEZGWDxWKZL1gBgWlimryAsP4Hi8UyX7ACAsPENIl+ELrlqPU/WCyW+YIVEBhhrpPQIPqqYVkrsZWYLBbLPMEKCEITkyNQmsTyv7+7HL6w8sFiscwTrIAgrObaVXInFX2kNYh6VBnWYrFY5jrTWu57rjBa9yeVRQ3QXw01CJXbGNVimd3U63W2b99OrVbr9FAs00S1WmX16tWUy+XC51gBQWhimoz/AQwTk8UyB9m+fTt9fX2sXbvW5vHMQ5RSPPnkk2zfvp1169YVPs+amNACYuIRTAD9VStrLXOXWq3G0qVLrXCYp4gIS5cubVtDtAKCMMzVahCWvR0rHOY3E/l+rYAgDHOdKh+ExWKxzBesgMCamCyW2cCXv/xlDjnkEM4444xJX+sb3/gGO3bsiN+fddZZ3HnnnZO+7t6GndWwJiaLZTbwb//2b1x99dUNTlTP8yiV2puqvvGNb3DYYYex3377AfC1r31tysY5U/i+j+tObuE6WayAINQgersm91FMVsBYLLOFT/7kDu7cMTCl11y/Xz8ff8WhTfdv3LiRe++9l9NOO423v/3t7N69mx07dnD//fezbNkyPvWpT/HmN7+ZoaEhAL7yla9w4oknAvC5z32Oiy66CMdxOPXUU9mwYQM33XQTZ5xxBt3d3fzud7/j1FNP5fOf/zwbNmzgkksu4VOf+hRKKV72spfx2c9+FoAFCxZw3nnnceWVV9Ld3c2Pf/xjVqxYkRrnjTfeyHve8x5GRkbo7u7mwgsv5KCDDsL3fT74wQ9yzTXXICK84x3v4Nxzz2XTpk2cd955DA0N0dXVxXXXXccPfvADbrrpJr7yla8A8PKXv5z3v//9nHTSSSxYsID3ve99XHPNNXzhC1/gF7/4BT/5yU8YGRnhxBNP5D/+4z8QEbZt28bGjRt5/PHHcV2XSy+9lE984hO89rWv5fTTTwfgjDPO4PWvfz2nnXbahL83O6sR+SAmOcFbB5/FMnHOP/989ttvP375y1/y3ve+F4Cbb76ZH//4x3znO99hn3324ec//zm33HIL3/ve93j3u98NwNVXX82PfvQjbrjhBrZs2cIHPvABXvva17JhwwYuvvhiNm/eTHd3d3yfHTt28MEPfpBf/OIXbN68mU2bNvGjH/0IgKGhIU444QS2bNnC8573PP7zP/+zYZwHH3ww119/Pbfeeit///d/z0c+8hEALrjgAu677z5uvfVWbrvtNs444wzGxsZ4/etfz5e+9CW2bNnCtddemxpLHkNDQxx22GHccMMN/MVf/AXnnHMOmzZt4vbbb2dkZIQrr7wSCCf/d73rXWzZsoXf/va3rFy5krPOOosLL7wQgN27d/Pb3/6Wl770pZP6XqwGgTYxdVaVs1hmC61W+jPJaaedFk+o9Xqdc845h82bN+O6Ln/4wx8AuPbaaznzzDPp6ekBYMmSJS2vuWnTJk466SSWL18OhBPt9ddfzytf+UoqlQovf/nLATj22GP5+c9/3nD+7t27eetb38of//hHRIR6vR6PY+PGjbEpbMmSJWzdupWVK1fyrGc9C4D+/v5xn9l1XV7zmtfE73/5y1/yuc99juHhYXbu3Mmhhx7KSSedxEMPPcSrXvUqIEyAA3j+85/Pu971Lh577DEuv/xyXvOa17RtmssyrRqEiJwiIveIyDYR+VDO/oUi8hMR2SIid4jImca+90bbbheRS0SkOl3jnIpEOYvFMrX09vbGr//1X/+VFStWsGXLFm666SbGxsaAMAGsHe1dtSh1UC6X42u5rovneQ3HfOxjH+Pkk0/m9ttv5yc/+UmcV5A3jmZjK5VKBEFSksfMTahWq7HfoVar8c53vpPLLruMrVu38o53vINardbyGd785jdz8cUXc+GFF3LmmWc2Pa4o0zYriogLfBU4FVgPvFFE1mcOexdwp1LqSOAk4AsiUhGRVcC7gQ1KqcMAF3jDdI111Jt8mKvFYpk+du/ezcqVK3Ech4suugjfD0v0v/jFL+brX/86w8PDAOzcuROAvr4+BgcHG65z/PHH89///d888cQT+L7PJZdcwvOf//y2xrFq1SogdIRrXvziF3P++efHQmXnzp0cfPDB7Nixg02bNgEwODiI53msXbuWzZs3EwQBDz74IDfeeGPuvbTgWLZsGXv27OGyyy4DQk1k9erVsWlsdHQ0fv63ve1tfPGLXwTg0EMnrwlO56x4HLBNKXWvUmoM+C5weuYYBfRJKGYXADsBLbZLQLeIlIAeYAfTxNgUhLlaLJbp453vfCff/OY3OeGEE/jDH/4QaxennHIKp512Ghs2bOCoo47i85//PBBOlBs3buSoo45iZGQkvs7KlSv59Kc/zcknn8yRRx7JMcccEzt1i/CBD3yAD3/4wzznOc+JhRSEYbRr1qzhiCOO4Mgjj+Q73/kOlUqF733ve5x77rkceeSRvOhFL6JWq/Gc5zyHdevWcfjhh/P+97+fY445JvdeixYt4h3veAeHH344r3zlK2NTFcBFF13El7/8ZY444ghOPPFEHnnkEQBWrFjBIYccMiXaA4C0UlcmdWGR1wKnKKXOit6/GTheKXWOcUwfcAVwMNAHvF4p9V/RvvOAfwJGgJ8ppXKDo0XkbOBsgDVr1hz75z//ue2xvue7t/K8Zy7n1cesbvtck9/f+yQP7hzmdRv2n9R1LJaZ5q677uKQQw7p9DAsk2R4eJjDDz+cW265hYULFzbsz/ueReRmpdSGvOtNpwaRZxjMSqOXAJuB/YCjgK+ISL+ILCbUNtZF+3pF5E15N1FKXaCU2qCU2qAdT+3yxTccPWnhAHDC05Za4WCxWDrCtddey8EHH8y5556bKxwmwnRGMW0HzNlyNY1mojOBz6hQjdkmIvcRahMHAPcppR4HEJHLgROBb0/jeC0Wi2XO8sIXvpAHHnhgSq85nRrEJuBAEVknIhVCJ/MVmWMeAF4AICIrgIOAe6PtJ4hIT+SfeAFw1zSO1WLZ65kuc7NldjCR73faNAillCci5wDXEEYhfV0pdYeIbIz2nw/8A/ANEdlKaJL6oFLqCeAJEbkMuIXQaX0rcMF0jdVi2dupVqs8+eSTtuT3PEX3g9A5E0WZNid1J9iwYYO66aabOj0Mi2XOYTvKzX+adZRr5aS2mdQWi4VyudxWpzHL3oHNDrNYLBZLLlZAWCwWiyUXKyAsFovFksu8clKLyONA+6nUIcuAJ6ZwONPBbB/jbB8f2DFOBbN9fGDH2A4HKKVys4znlYCYDCJyUzNP/mxhto9xto8P7Bingtk+PrBjnCqsiclisVgsuVgBYbFYLJZcrIBImAuZ2rN9jLN9fGDHOBXM9vGBHeOUYH0QFovFYsnFahAWi8ViycUKCIvFYrHkstcLCBE5RUTuEZFtIvKhDo5jfxH5pYjcJSJ3RB31EJElIvJzEflj9Huxcc6Ho3HfIyIvmaFxuiJyq4hcOUvHt0hELhORu6PP8tmzcIzvjb7j20XkEhGpdnqMIvJ1EXlMRG43trU9JhE5VkS2Rvu+LFNUGrbJ+P45+p5vE5EfisiiTo2v2RiNfe8XESUiyzo5xrZRSu21P4RlyP8EPA2oAFuA9R0ay0rgmOh1H/AHYD3wOeBD0fYPAZ+NXq+PxttF2HnvT4A7A+N8H/Ad4Mro/Wwb3zeBs6LXFWDRbBojsAq4D+iO3n8feFunxwg8DzgGuN3Y1vaYgBuBZxOW778aOHUax/dioBS9/mwnx9dsjNH2/QnbHvwZWNbJMbb7s7drEMcB25RS9yqlxoDvErY6nXGUUg8rpW6JXg8SNkhaFY3nm9Fh3wReGb0+HfiuUmpUKXUfsI3weaYNEVkNvAz4mrF5No2vn/Cf9P8BKKXGlFK7ZtMYI0pAt4iUgB7CTosdHaNS6npgZ2ZzW2MSkZVAv1Lqdyqc6b5lnDPl41NK/Uwp5UVvf0/YtbIj42s2xoh/BT5AuuVyR8bYLnu7gFgFPGi83x5t6ygishY4GrgBWKGUehhCIQLsEx3WibF/kfAPPTC2zabxPQ14HLgwMoN9TUR6Z9MYlVIPAZ8n7Jr4MLBbKfWz2TRGg3bHtCp6nd0+E7ydcLUNs2h8InIa8JBSaktm16wZYyv2dgGRZ9vraNyviCwAfgC8Ryk10OrQnG3TNnYReTnwmFLq5qKn5Gyb7s+2RKji/7tS6mhgiNA00owZH2Nkxz+d0KywH9ArIm9qdUrOtk7HpjcbU0fGKiIfJew8ebHe1GQcM/0/0wN8FPg/ebubjGVWfd97u4DYTmgf1KwmVPc7goiUCYXDxUqpy6PNj0ZqJ9Hvx6LtMz325wCnicj9hKa4vxSRb8+i8el7bldK3RC9v4xQYMymMb4QuE8p9bhSqg5cDpw4y8aoaXdM20nMPOb2aUNE3gq8HDgjMsnMpvE9nXAhsCX6v1kN3CIi+86iMbZkbxcQm4ADRWSdiFSANwBXdGIgUaTC/wPuUkr9i7HrCuCt0eu3Aj82tr9BRLpEZB1wIKFza1pQSn1YKbVaKbWW8HP6hVLqTbNlfNEYHwEeFJGDok0vAO6cTWMkNC2dICI90Xf+AkJ/02wao6atMUVmqEEROSF6trcY50w5InIK8EHgNKXUcGbcHR+fUmqrUmofpdTa6P9mO2EgyiOzZYzj0inv+Gz5AV5KGDH0J+CjHRzHXxCqkrcBm6OflwJLgeuAP0a/lxjnfDQa9z3MYKQDcBJJFNOsGh9wFHBT9Dn+CFg8C8f4SeBu4HbgIsJIlo6OEbiE0CdSJ5zI/moiYwI2RM/1J+ArRNUapml82wjt+Pr/5fxOja/ZGDP77yeKYurUGNv9saU2LBaLxZLL3m5islgsFksTrICwWCwWSy5WQFgsFoslFysgLBaLxZKLFRAWi8ViycUKCItlEkhYPfadLfb/tsA19kztqCyWqcEKCItlciwCGgSEiLgASqkTZ3pAFstUUer0ACyWOc5ngKeLyGbCBKk9hMlSRwHrRWSPUmpBVGPrx4SJe2Xg75RSncuQtVgKYBPlLJZJEFXevVIpdZiInAT8F3CYCks4YwiIEtCjlBqImsb8HjhQKaX0MR16BIulKVaDsFimlhu1cMggwKdE5HmE5dJXASuAR2ZycBZLO1gBYbFMLUNNtp8BLAeOVUrVo+qe1RkblcUyAayT2mKZHIOELWLHYyFhP426iJwMHDC9w7JYJo/VICyWSaCUelJEfhM1qh8BHm1y6MXAT0TkJsLKo3fP0BAtlgljndQWi8ViycWamCwWi8WSixUQFovFYsnFCgiLxWKx5GIFhMVisVhysQLCYrFYLLlYAWGxWCyWXKyAsFgsFksu/z96UzQqicFoGQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['fraction_accuracy'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"fraction accuracy\")\n", + "ax.legend([\"fraction accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population', 'numerosity']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"pop\", \"num\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df['steps_in_trial'].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps for solution\")\n", + "ax.legend([\"steps\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best action: 0\n", + "1 [1e-06, 1280.3139057920484, 1e-06, 1e-06]\n", + "The best action: 1\n", + "1 [1e-06, 1022.2228432857141, 189.3497017167734, 1e-06]\n", + "The best action: 1\n", + "2 [1e-06, 1e-06, 885.2147964418139, 189.94004576921154]\n", + "The best action: 2\n", + "2 [1e-06, 1e-06, 853.0610656271485, 1e-06]\n", + "The best action: 2\n", + "3 [360.5718274400849, 1e-06, 1e-06, 921.9530187832484]\n", + "The best action: 3\n", + "3 [501.97477909987873, 504.59087383792325, 1e-06, 1100.0509562769134]\n" + ] + } + ], + "source": [ + "moves = [\n", + " {'action': 0, 'exp_state': '1001'}, # 3 -> 0\n", + " {'action': 1, 'exp_state': '1010'}, # 0 -> 1\n", + " {'action': 1, 'exp_state': '1100'}, # 1 -> 2\n", + " {'action': 2, 'exp_state': '0101'}, # 2 -> 5\n", + " {'action': 2, 'exp_state': '0110'}, # 5 -> 8\n", + " {'action': 3, 'exp_state': '0010'}, # 8 -> 7\n", + "]\n", + "\n", + "for step in moves:\n", + " match_set = population.generate_match_set(step['exp_state'], 0)\n", + " print(f\"The best action: {step['action']}\")\n", + " prediction = match_set.prediction_array\n", + " print(f\"{prediction.index(max(prediction))} {prediction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best action: 0\n", + "Cond:1001 - Act:1 - effect:1000 - Num:23 [fit: 1.000, exp: 6536.00, pred: 1280.314, error:981.05]\n", + "Cond:1001 - Act:0 - effect:0101 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:1001 - Act:2 - effect:1001 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:1001 - Act:3 - effect:1100 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "The best action: 1\n", + "Cond:1010 - Act:1 - effect:0011 - Num:28 [fit: 1.000, exp: 5432.00, pred: 1022.223, error:961.05]\n", + "Cond:1010 - Act:2 - effect:0011 - Num:1 [fit: 1.000, exp: 4613.00, pred: 189.350, error:951.61]\n", + "Cond:1010 - Act:3 - effect:0010 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:1010 - Act:0 - effect:1101 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "The best action: 1\n", + "Cond:1100 - Act:2 - effect:1110 - Num:26 [fit: 1.000, exp: 4562.00, pred: 885.215, error:941.05]\n", + "Cond:1100 - Act:3 - effect:1101 - Num:1 [fit: 1.000, exp: 615.00, pred: 189.940, error:1503.88]\n", + "Cond:1100 - Act:1 - effect:1101 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:1100 - Act:0 - effect:1001 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "The best action: 2\n", + "Cond:0101 - Act:2 - effect:0001 - Num:24 [fit: 1.000, exp: 3559.00, pred: 853.061, error:921.05]\n", + "Cond:0101 - Act:1 - effect:0101 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:0101 - Act:3 - effect:1010 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:0101 - Act:0 - effect:1110 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "The best action: 2\n", + "Cond:0110 - Act:0 - effect:0001 - Num:1 [fit: 1.000, exp: 511.00, pred: 360.572, error:1440.44]\n", + "Cond:0110 - Act:3 - effect:1100 - Num:23 [fit: 1.000, exp: 752.00, pred: 921.953, error:901.05]\n", + "Cond:0110 - Act:2 - effect:0101 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "Cond:0110 - Act:1 - effect:1110 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n", + "The best action: 3\n", + "Cond:0010 - Act:3 - effect:0000 - Num:24 [fit: 1.000, exp: 786.00, pred: 1100.051, error:881.05]\n", + "Cond:0010 - Act:0 - effect:0100 - Num:2 [fit: 0.920, exp: 24.00, pred: 501.975, error:1253.14]\n", + "Cond:0010 - Act:1 - effect:1000 - Num:1 [fit: 0.344, exp: 4.00, pred: 504.591, error:250.05]\n", + "Cond:0010 - Act:2 - effect:0001 - Num:1 [fit: 0.000, exp: 0.00, pred: 0.000, error:0.00]\n" + ] + } + ], + "source": [ + "for step in moves:\n", + " match_set = population.generate_match_set(step['exp_state'], 0)\n", + " print(f\"The best action: {step['action']}\")\n", + " for cl in match_set:\n", + " print(cl)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/XCS_Experiments/utils/nxcs_utils.py b/XCS_Experiments/utils/nxcs_utils.py index cfb7e4a..a13632a 100644 --- a/XCS_Experiments/utils/nxcs_utils.py +++ b/XCS_Experiments/utils/nxcs_utils.py @@ -30,6 +30,7 @@ def predicts_successfully(cl: Classifier, p0, action, p1): return True return False + def cl_accuracy(cl, cfg): if cl.error < cfg.epsilon_0: return 1 @@ -74,6 +75,7 @@ def xncs_metrics(xncs: XNCS, environment): 'fraction_accuracy': fraction_accuracy(xncs) } + def avg_experiment(maze, cfg, number_of_tests=1, explore_trials=3000, exploit_trials=1000, pre_generate=False): test_metrics =[] for i in range(number_of_tests): From d5de13c5ccb342058f43e2cb1733b53f43416cba Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 20 Jun 2021 16:10:26 +0200 Subject: [PATCH 16/19] XCS Comparisons --- XCS_Experiments/XCS_XNCS_Maze5.ipynb | 1184 ++++++++++++----- .../XCS_XNCS_Maze5_pre_populated.ipynb | 808 +++++------ XCS_Experiments/XCS_XNCS_Woods1.ipynb | 1013 ++++++++------ .../XCS_XNCS_Woods1_pre_populated.ipynb | 1040 +++++++++++++++ 4 files changed, 2890 insertions(+), 1155 deletions(-) create mode 100644 XCS_Experiments/XCS_XNCS_Woods1_pre_populated.ipynb diff --git a/XCS_Experiments/XCS_XNCS_Maze5.ipynb b/XCS_Experiments/XCS_XNCS_Maze5.ipynb index 28fff3f..9205508 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5.ipynb @@ -23,15 +23,15 @@ "text": [ "This is how maze looks like\n", "\n", - "('0', '0', '0', '1', '0', '0', '1', '0')\n", + "('0', '1', '0', '0', '1', '0', '1', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" ] @@ -111,7 +111,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.07538530000000021, 'population': 143, 'numerosity': 194, 'average_specificity': 6.365979381443299}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 0.056999599999997486, 'population': 114, 'numerosity': 158, 'average_specificity': 7.537974683544304}\n" ] }, { @@ -125,26 +125,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 11, 'reward': 1023.1122292121702, 'perf_time': 0.16061479999999762, 'population': 750, 'numerosity': 7000, 'average_specificity': 6.187285714285714}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 58, 'reward': 1000.0000023605279, 'perf_time': 0.5935101000000031, 'population': 659, 'numerosity': 7000, 'average_specificity': 5.825714285714286}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 100, 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'numerosity': 7000, 'population': 2889, 'average_specificity': 13.62042857142857, 'fraction_accuracy': 1.0, 'knowledge': 0.684931506849315}\n" + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 100, 'reward': 2.385089144282033e-42, 'perf_time': 0.3215463999995336, 'numerosity': 7000, 'population': 299, 'average_specificity': 237.998, 'fraction_accuracy': 1.0, 'knowledge': 0.684931506849315}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 100, 'reward': 1.3360874108190892e-12, 'perf_time': 0.19002030000046943, 'numerosity': 7000, 'population': 180, 'average_specificity': 246.41742857142856, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 100, 'reward': 3.186687579353646e-57, 'perf_time': 0.18601009999929374, 'numerosity': 7000, 'population': 173, 'average_specificity': 246.51342857142856, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 51, 'reward': 1000.0, 'perf_time': 0.10225990000071761, 'numerosity': 7000, 'population': 168, 'average_specificity': 245.83, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2500, 'steps_in_trial': 100, 'reward': 1.3360874108178924e-12, 'perf_time': 0.1794503999999506, 'numerosity': 7000, 'population': 172, 'average_specificity': 245.73857142857142, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3000, 'steps_in_trial': 100, 'reward': 3.2360741395726796e-176, 'perf_time': 0.19106590000046708, 'numerosity': 7000, 'population': 167, 'average_specificity': 246.17742857142858, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 3500, 'steps_in_trial': 76, 'reward': 1000.0, 'perf_time': 0.1504720000002635, 'numerosity': 7000, 'population': 182, 'average_specificity': 243.79457142857143, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 4000, 'steps_in_trial': 70, 'reward': 1000.0, 'perf_time': 0.13572870000007242, 'numerosity': 7000, 'population': 178, 'average_specificity': 244.57171428571428, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 4500, 'steps_in_trial': 12, 'reward': 1000.0, 'perf_time': 0.019764100000429607, 'numerosity': 7000, 'population': 164, 'average_specificity': 245.06942857142857, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 5, 'reward': 1000.0, 'perf_time': 0.009091100000659935, 'numerosity': 7000, 'population': 172, 'average_specificity': 244.95514285714285, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 5, 'reward': 1000.0, 'perf_time': 0.009091100000659935, 'numerosity': 7000, 'population': 172, 'average_specificity': 244.95514285714285, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 100, 'reward': 2.385089144282033e-42, 'perf_time': 0.14370989999952144, 'numerosity': 7000, 'population': 182, 'average_specificity': 243.89, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 100, 'reward': 2.385089144282033e-42, 'perf_time': 0.14370989999952144, 'numerosity': 7000, 'population': 182, 'average_specificity': 243.89, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1000.0000000000005, 'perf_time': 0.004709300000286021, 'numerosity': 7000, 'population': 190, 'average_specificity': 242.86142857142858, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 3, 'reward': 1000.0000000000005, 'perf_time': 0.004709300000286021, 'numerosity': 7000, 'population': 190, 'average_specificity': 242.86142857142858, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 100, 'reward': 2.284709472498595e-12, 'perf_time': 0.1659020999995846, 'numerosity': 7000, 'population': 187, 'average_specificity': 242.55057142857143, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 100, 'reward': 2.284709472498595e-12, 'perf_time': 0.1659020999995846, 'numerosity': 7000, 'population': 187, 'average_specificity': 242.55057142857143, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 85, 'reward': 1000.0000000002275, 'perf_time': 0.1269929999998567, 'numerosity': 7000, 'population': 179, 'average_specificity': 242.50028571428572, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 85, 'reward': 1000.0000000002275, 'perf_time': 0.1269929999998567, 'numerosity': 7000, 'population': 179, 'average_specificity': 242.50028571428572, 'fraction_accuracy': 1.0, 'knowledge': 0.0}\n" ] } ], @@ -185,7 +185,7 @@ "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", "number_of_experiments = 1\n", - "explore = 2000\n", + "explore = 5000\n", "exploit = 500\n", "\n", "df = XCSExp(maze=maze,\n", @@ -259,350 +259,770 @@ " 0\n", " 100\n", " 0.000000e+00\n", - " 0.075385\n", - " 143\n", - " 194\n", - " 6.365979\n", + " 0.057000\n", + " 114\n", + " 158\n", + " 7.537975\n", " 100\n", - " 124\n", - " 173\n", - " 6.693642\n", - " 1.00\n", + " 150\n", + " 201\n", + " 8.656716\n", + " 0.99\n", " \n", " \n", " 100\n", - " 8\n", - " 1.101298e+03\n", - " 0.052353\n", - " 757\n", + " 26\n", + " 1.000184e+03\n", + " 0.548220\n", + " 1148\n", " 7000\n", - " 9.154143\n", - " 1\n", - " 1017\n", - " 5944\n", - " 11.546265\n", + " 22.267143\n", + " 10\n", + " 1855\n", + " 6192\n", + " 43.644703\n", " 1.00\n", " \n", " \n", " 200\n", - " 11\n", - " 1.023112e+03\n", - " 0.160615\n", - " 750\n", + " 7\n", + " 1.107361e+03\n", + " 0.122769\n", + " 1464\n", " 7000\n", - " 6.187286\n", - " 47\n", - " 1396\n", + " 19.244714\n", + " 14\n", + " 2059\n", " 7000\n", - " 10.090143\n", + " 104.774286\n", " 1.00\n", " \n", " \n", " 300\n", - " 9\n", - " 1.046040e+03\n", - " 0.070715\n", - " 687\n", + " 38\n", + " 1.000002e+03\n", + " 1.614283\n", + " 1713\n", " 7000\n", - " 6.249857\n", - " 24\n", - " 1629\n", + " 27.438143\n", + " 100\n", + " 937\n", " 7000\n", - " 9.047286\n", + " 193.871000\n", " 1.00\n", " \n", " \n", " 400\n", - " 58\n", - " 1.000000e+03\n", - " 0.593510\n", - " 659\n", + " 10\n", + " 1.033091e+03\n", + " 0.415734\n", + " 1909\n", " 7000\n", - " 5.825714\n", + " 24.739571\n", " 100\n", - " 2040\n", + " 439\n", " 7000\n", - " 8.935857\n", + " 228.319286\n", " 1.00\n", " \n", " \n", " 500\n", - " 24\n", - " 1.000269e+03\n", - " 0.115538\n", - " 669\n", + " 45\n", + " 1.000000e+03\n", + " 1.820045\n", + " 2024\n", " 7000\n", - " 5.720000\n", - " 3\n", - " 2557\n", + " 33.195000\n", + " 100\n", + " 299\n", " 7000\n", - " 11.392857\n", + " 237.998000\n", " 1.00\n", " \n", " \n", " 600\n", - " 100\n", - " 2.013829e-27\n", - " 0.833867\n", - " 688\n", + " 25\n", + " 1.000209e+03\n", + " 2.665137\n", + " 2100\n", " 7000\n", - " 5.380286\n", - " 100\n", - " 2711\n", + " 38.710000\n", + " 94\n", + " 224\n", " 7000\n", - " 13.068429\n", + " 241.446571\n", " 1.00\n", " \n", " \n", " 700\n", - " 19\n", - " 1.001492e+03\n", - " 0.101426\n", - " 726\n", + " 100\n", + " 1.338896e-12\n", + " 6.220731\n", + " 2208\n", " 7000\n", - " 7.827571\n", - " 4\n", - " 2874\n", + " 44.138143\n", + " 93\n", + " 195\n", " 7000\n", - " 15.086857\n", + " 243.890000\n", " 1.00\n", " \n", " \n", " 800\n", - " 5\n", - " 1.180491e+03\n", - " 0.100662\n", - " 785\n", + " 10\n", + " 1.036617e+03\n", + " 0.279807\n", + " 2174\n", " 7000\n", - " 7.065571\n", - " 54\n", - " 2892\n", + " 38.678714\n", + " 40\n", + " 181\n", " 7000\n", - " 14.202429\n", - " 0.96\n", + " 245.906000\n", + " 1.00\n", " \n", " \n", " 900\n", - " 2\n", - " 1.508270e+03\n", - " 0.005748\n", - " 754\n", + " 96\n", + " 1.000000e+03\n", + " 3.676936\n", + " 2233\n", " 7000\n", - " 6.694000\n", - " 13\n", - " 2915\n", + " 42.429857\n", + " 100\n", + " 169\n", " 7000\n", - " 14.928143\n", + " 246.214000\n", " 1.00\n", " \n", " \n", " 1000\n", - " 48\n", + " 83\n", " 1.000000e+03\n", - " 0.421508\n", - " 687\n", + " 5.506315\n", + " 2214\n", " 7000\n", - " 7.576571\n", - " 34\n", - " 3035\n", + " 42.601857\n", + " 100\n", + " 180\n", " 7000\n", - " 14.731571\n", + " 246.417429\n", " 1.00\n", " \n", " \n", " 1100\n", - " 95\n", - " 1.000000e+03\n", - " 0.888588\n", - " 721\n", + " 3\n", + " 1.362081e+03\n", + " 0.678434\n", + " 2175\n", " 7000\n", - " 6.867429\n", - " 12\n", - " 3067\n", + " 48.369000\n", + " 100\n", + " 177\n", " 7000\n", - " 14.954143\n", + " 246.056286\n", " 1.00\n", " \n", " \n", " 1200\n", - " 21\n", - " 1.000753e+03\n", - " 0.128918\n", - " 724\n", + " 37\n", + " 1.000003e+03\n", + " 2.120263\n", + " 2106\n", " 7000\n", - " 5.954286\n", - " 53\n", - " 2949\n", + " 49.315857\n", + " 100\n", + " 177\n", " 7000\n", - " 14.322857\n", + " 245.320286\n", " 1.00\n", " \n", " \n", " 1300\n", - " 10\n", - " 1.032552e+03\n", - " 0.094600\n", - " 753\n", + " 18\n", + " 1.002151e+03\n", + " 2.292555\n", + " 2034\n", " 7000\n", - " 5.646143\n", - " 30\n", - " 2959\n", + " 46.103857\n", + " 58\n", + " 174\n", " 7000\n", - " 13.953286\n", - " 0.99\n", + " 245.951143\n", + " 1.00\n", " \n", " \n", " 1400\n", - " 24\n", - " 1.000304e+03\n", - " 0.233504\n", - " 759\n", + " 26\n", + " 1.000136e+03\n", + " 1.153210\n", + " 2030\n", " 7000\n", - " 6.648286\n", - " 33\n", - " 3009\n", + " 41.317000\n", + " 100\n", + " 173\n", " 7000\n", - " 14.837429\n", - " 0.98\n", + " 246.088286\n", + " 1.00\n", " \n", " \n", " 1500\n", - " 74\n", - " 1.000000e+03\n", - " 0.682727\n", - " 764\n", - " 7000\n", - " 5.865714\n", " 3\n", - " 2934\n", + " 1.468233e+03\n", + " 0.036700\n", + " 2130\n", + " 7000\n", + " 36.263857\n", + " 100\n", + " 173\n", " 7000\n", - " 15.395571\n", + " 246.513429\n", " 1.00\n", " \n", " \n", " 1600\n", - " 16\n", - " 1.004179e+03\n", - " 0.240538\n", - " 787\n", + " 100\n", + " 1.336801e-12\n", + " 6.072074\n", + " 2193\n", " 7000\n", - " 6.648286\n", - " 91\n", - " 3163\n", + " 41.414571\n", + " 2\n", + " 172\n", " 7000\n", - " 14.119429\n", + " 246.730571\n", " 1.00\n", " \n", " \n", " 1700\n", - " 100\n", - " 1.336087e-12\n", - " 0.680336\n", - " 807\n", + " 43\n", + " 1.000001e+03\n", + " 2.250063\n", + " 2285\n", " 7000\n", - " 5.991143\n", - " 25\n", - " 3074\n", + " 38.557000\n", + " 100\n", + " 173\n", " 7000\n", - " 14.365286\n", + " 246.972857\n", " 1.00\n", " \n", " \n", " 1800\n", - " 3\n", + " 29\n", + " 1.000061e+03\n", + " 1.865515\n", + " 2389\n", + " 7000\n", + " 37.958000\n", + " 100\n", + " 173\n", + " 7000\n", + " 246.406000\n", + " 1.00\n", + " \n", + " \n", + " 1900\n", + " 60\n", " 1.000000e+03\n", - " 0.025747\n", - " 776\n", + " 3.750929\n", + " 2219\n", + " 7000\n", + " 45.477000\n", + " 100\n", + " 177\n", + " 7000\n", + " 245.830000\n", + " 1.00\n", + " \n", + " \n", + " 2000\n", + " 12\n", + " 1.016411e+03\n", + " 0.422333\n", + " 2125\n", + " 7000\n", + " 52.344857\n", + " 51\n", + " 168\n", + " 7000\n", + " 245.830000\n", + " 1.00\n", + " \n", + " \n", + " 2100\n", + " 11\n", + " 1.029134e+03\n", + " 0.366314\n", + " 1990\n", + " 7000\n", + " 53.622286\n", + " 100\n", + " 176\n", + " 7000\n", + " 244.956857\n", + " 1.00\n", + " \n", + " \n", + " 2200\n", + " 11\n", + " 1.039525e+03\n", + " 0.158003\n", + " 1675\n", + " 7000\n", + " 56.014714\n", + " 100\n", + " 177\n", + " 7000\n", + " 244.609429\n", + " 1.00\n", + " \n", + " \n", + " 2300\n", + " 4\n", + " 1.254496e+03\n", + " 0.046373\n", + " 1689\n", + " 7000\n", + " 50.842857\n", + " 67\n", + " 177\n", + " 7000\n", + " 245.692857\n", + " 1.00\n", + " \n", + " \n", + " 2400\n", + " 1\n", + " 1.712102e+03\n", + " 0.008491\n", + " 1689\n", + " 7000\n", + " 51.163714\n", + " 100\n", + " 173\n", + " 7000\n", + " 245.551143\n", + " 1.00\n", + " \n", + " \n", + " 2500\n", + " 16\n", + " 1.004191e+03\n", + " 0.455617\n", + " 1794\n", + " 7000\n", + " 48.983286\n", + " 100\n", + " 172\n", + " 7000\n", + " 245.738571\n", + " 1.00\n", + " \n", + " \n", + " 2600\n", + " 32\n", + " 1.000018e+03\n", + " 0.542778\n", + " 1613\n", + " 7000\n", + " 59.566571\n", + " 100\n", + " 172\n", + " 7000\n", + " 245.674571\n", + " 1.00\n", + " \n", + " \n", + " 2700\n", + " 100\n", + " 1.336447e-12\n", + " 2.178789\n", + " 1608\n", + " 7000\n", + " 55.285714\n", + " 100\n", + " 171\n", + " 7000\n", + " 245.766000\n", + " 1.00\n", + " \n", + " \n", + " 2800\n", + " 65\n", + " 1.000000e+03\n", + " 1.828426\n", + " 1702\n", + " 7000\n", + " 54.020286\n", + " 6\n", + " 182\n", + " 7000\n", + " 245.144286\n", + " 1.00\n", + " \n", + " \n", + " 2900\n", + " 27\n", + " 1.000106e+03\n", + " 0.562149\n", + " 1584\n", + " 7000\n", + " 58.487857\n", + " 100\n", + " 174\n", + " 7000\n", + " 245.926000\n", + " 1.00\n", + " \n", + " \n", + " 3000\n", + " 52\n", + " 1.000000e+03\n", + " 1.480587\n", + " 1613\n", + " 7000\n", + " 54.192429\n", + " 100\n", + " 167\n", + " 7000\n", + " 246.177429\n", + " 1.00\n", + " \n", + " \n", + " 3100\n", + " 31\n", + " 1.000024e+03\n", + " 1.188360\n", + " 1666\n", + " 7000\n", + " 49.768286\n", + " 100\n", + " 178\n", " 7000\n", - " 5.943857\n", + " 244.902000\n", + " 1.00\n", + " \n", + " \n", + " 3200\n", " 28\n", - " 3090\n", + " 1.000068e+03\n", + " 0.856570\n", + " 1890\n", + " 7000\n", + " 44.783143\n", + " 100\n", + " 192\n", " 7000\n", - " 14.499429\n", + " 244.380857\n", " 1.00\n", " \n", " \n", - " 1900\n", + " 3300\n", + " 4\n", + " 1.318694e+03\n", + " 0.038145\n", + " 2007\n", + " 7000\n", + " 41.657000\n", " 100\n", - " 1.336087e-12\n", - " 0.974427\n", - " 805\n", + " 175\n", + " 7000\n", + " 244.705429\n", + " 1.00\n", + " \n", + " \n", + " 3400\n", + " 1\n", + " 1.710012e+03\n", + " 0.009773\n", + " 2099\n", " 7000\n", - " 5.646571\n", + " 40.566857\n", " 100\n", - " 3165\n", + " 179\n", " 7000\n", - " 14.962857\n", + " 243.497429\n", " 1.00\n", " \n", " \n", - " 2000\n", + " 3500\n", + " 2\n", + " 1.524680e+03\n", + " 0.019128\n", + " 2196\n", + " 7000\n", + " 40.054143\n", + " 76\n", + " 182\n", + " 7000\n", + " 243.794571\n", + " 1.00\n", + " \n", + " \n", + " 3600\n", + " 34\n", + " 1.000009e+03\n", + " 1.107027\n", + " 2283\n", + " 7000\n", + " 37.267143\n", + " 2\n", + " 184\n", + " 7000\n", + " 244.393429\n", + " 1.00\n", + " \n", + " \n", + " 3700\n", + " 50\n", + " 1.000000e+03\n", + " 1.365308\n", + " 2355\n", + " 7000\n", + " 38.107429\n", + " 23\n", + " 178\n", + " 7000\n", + " 244.462000\n", + " 1.00\n", + " \n", + " \n", + " 3800\n", + " 31\n", + " 1.000024e+03\n", + " 0.801419\n", + " 2385\n", + " 7000\n", + " 41.131857\n", + " 34\n", + " 174\n", + " 7000\n", + " 244.782000\n", + " 1.00\n", + " \n", + " \n", + " 3900\n", + " 6\n", + " 1.160655e+03\n", + " 0.156220\n", + " 2261\n", + " 7000\n", + " 49.563286\n", + " 100\n", + " 174\n", + " 7000\n", + " 244.416286\n", + " 1.00\n", + " \n", + " \n", + " 4000\n", " 42\n", " 1.000001e+03\n", - " 0.229624\n", - " 775\n", + " 0.827270\n", + " 1816\n", + " 7000\n", + " 51.970571\n", + " 70\n", + " 178\n", + " 7000\n", + " 244.571714\n", + " 1.00\n", + " \n", + " \n", + " 4100\n", + " 10\n", + " 1.044210e+03\n", + " 0.149768\n", + " 1746\n", " 7000\n", - " 6.068000\n", + " 48.629286\n", " 100\n", - " 3114\n", + " 167\n", " 7000\n", - " 14.599429\n", + " 245.326000\n", " 1.00\n", " \n", " \n", - " 2100\n", + " 4200\n", + " 56\n", + " 1.000000e+03\n", + " 1.143407\n", + " 1837\n", + " 7000\n", + " 48.705714\n", + " 12\n", + " 177\n", + " 7000\n", + " 244.622000\n", + " 1.00\n", + " \n", + " \n", + " 4300\n", + " 2\n", + " 1.826022e+03\n", + " 0.028444\n", + " 1797\n", + " 7000\n", + " 51.382714\n", " 100\n", - " 1.336087e-12\n", - " 0.562800\n", - " 738\n", + " 175\n", " 7000\n", - " 19.489571\n", - " 17\n", - " 3039\n", + " 244.968857\n", + " 1.00\n", + " \n", + " \n", + " 4400\n", + " 49\n", + " 1.000000e+03\n", + " 1.503978\n", + " 1941\n", + " 7000\n", + " 45.504714\n", + " 7\n", + " 164\n", " 7000\n", - " 14.158857\n", - " 0.98\n", + " 245.261429\n", + " 1.00\n", " \n", " \n", - " 2200\n", + " 4500\n", " 7\n", - " 1.111330e+03\n", - " 0.131905\n", - " 791\n", + " 1.125770e+03\n", + " 0.157483\n", + " 1925\n", " 7000\n", - " 16.667714\n", - " 16\n", - " 2948\n", + " 46.872000\n", + " 12\n", + " 164\n", " 7000\n", - " 13.590000\n", + " 245.069429\n", " 1.00\n", " \n", " \n", - " 2300\n", - " 10\n", - " 1.000000e+03\n", - " 0.138609\n", - " 772\n", + " 4600\n", + " 4\n", + " 1.254239e+03\n", + " 0.127843\n", + " 1988\n", + " 7000\n", + " 45.354571\n", + " 2\n", + " 171\n", " 7000\n", - " 16.608143\n", - " 9\n", - " 2893\n", + " 244.626000\n", + " 1.00\n", + " \n", + " \n", + " 4700\n", + " 19\n", + " 1.001541e+03\n", + " 0.567645\n", + " 1990\n", " 7000\n", - " 13.632714\n", + " 46.428714\n", + " 44\n", + " 173\n", + " 7000\n", + " 244.653429\n", " 1.00\n", " \n", " \n", - " 2400\n", - " 9\n", - " 1.048945e+03\n", - " 0.070076\n", - " 826\n", + " 4800\n", + " 37\n", + " 1.000004e+03\n", + " 1.400061\n", + " 1990\n", + " 7000\n", + " 47.053571\n", + " 100\n", + " 178\n", + " 7000\n", + " 244.114000\n", + " 1.00\n", + " \n", + " \n", + " 4900\n", + " 2\n", + " 1.571636e+03\n", + " 0.033106\n", + " 2039\n", + " 7000\n", + " 42.514571\n", + " 100\n", + " 174\n", + " 7000\n", + " 244.772286\n", + " 1.00\n", + " \n", + " \n", + " 5000\n", + " 12\n", + " 1.016410e+03\n", + " 0.297559\n", + " 2162\n", " 7000\n", - " 18.742571\n", - " 22\n", - " 2889\n", + " 43.520571\n", + " 5\n", + " 172\n", " 7000\n", - " 13.620429\n", + " 244.955143\n", + " 1.00\n", + " \n", + " \n", + " 5100\n", + " 5\n", + " 1.248632e+03\n", + " 0.127033\n", + " 2211\n", + " 7000\n", + " 41.573857\n", + " 100\n", + " 182\n", + " 7000\n", + " 243.890000\n", + " 1.00\n", + " \n", + " \n", + " 5200\n", + " 12\n", + " 1.019651e+03\n", + " 0.222088\n", + " 2258\n", + " 7000\n", + " 40.680429\n", + " 3\n", + " 190\n", + " 7000\n", + " 242.861429\n", + " 1.00\n", + " \n", + " \n", + " 5300\n", + " 1\n", + " 2.113013e+03\n", + " 0.012375\n", + " 2253\n", + " 7000\n", + " 38.081286\n", + " 100\n", + " 187\n", + " 7000\n", + " 242.550571\n", + " 1.00\n", + " \n", + " \n", + " 5400\n", + " 5\n", + " 1.186229e+03\n", + " 0.072437\n", + " 2237\n", + " 7000\n", + " 34.153286\n", + " 85\n", + " 179\n", + " 7000\n", + " 242.500286\n", " 1.00\n", " \n", " \n", @@ -612,87 +1032,177 @@ "text/plain": [ " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 100 0.000000e+00 0.075385 143 194 \n", - "100 8 1.101298e+03 0.052353 757 7000 \n", - "200 11 1.023112e+03 0.160615 750 7000 \n", - "300 9 1.046040e+03 0.070715 687 7000 \n", - "400 58 1.000000e+03 0.593510 659 7000 \n", - "500 24 1.000269e+03 0.115538 669 7000 \n", - "600 100 2.013829e-27 0.833867 688 7000 \n", - "700 19 1.001492e+03 0.101426 726 7000 \n", - "800 5 1.180491e+03 0.100662 785 7000 \n", - "900 2 1.508270e+03 0.005748 754 7000 \n", - "1000 48 1.000000e+03 0.421508 687 7000 \n", - "1100 95 1.000000e+03 0.888588 721 7000 \n", - "1200 21 1.000753e+03 0.128918 724 7000 \n", - "1300 10 1.032552e+03 0.094600 753 7000 \n", - "1400 24 1.000304e+03 0.233504 759 7000 \n", - "1500 74 1.000000e+03 0.682727 764 7000 \n", - "1600 16 1.004179e+03 0.240538 787 7000 \n", - "1700 100 1.336087e-12 0.680336 807 7000 \n", - "1800 3 1.000000e+03 0.025747 776 7000 \n", - "1900 100 1.336087e-12 0.974427 805 7000 \n", - "2000 42 1.000001e+03 0.229624 775 7000 \n", - "2100 100 1.336087e-12 0.562800 738 7000 \n", - "2200 7 1.111330e+03 0.131905 791 7000 \n", - "2300 10 1.000000e+03 0.138609 772 7000 \n", - "2400 9 1.048945e+03 0.070076 826 7000 \n", + "0 100 0.000000e+00 0.057000 114 158 \n", + "100 26 1.000184e+03 0.548220 1148 7000 \n", + "200 7 1.107361e+03 0.122769 1464 7000 \n", + "300 38 1.000002e+03 1.614283 1713 7000 \n", + "400 10 1.033091e+03 0.415734 1909 7000 \n", + "500 45 1.000000e+03 1.820045 2024 7000 \n", + "600 25 1.000209e+03 2.665137 2100 7000 \n", + "700 100 1.338896e-12 6.220731 2208 7000 \n", + "800 10 1.036617e+03 0.279807 2174 7000 \n", + "900 96 1.000000e+03 3.676936 2233 7000 \n", + "1000 83 1.000000e+03 5.506315 2214 7000 \n", + "1100 3 1.362081e+03 0.678434 2175 7000 \n", + "1200 37 1.000003e+03 2.120263 2106 7000 \n", + "1300 18 1.002151e+03 2.292555 2034 7000 \n", + "1400 26 1.000136e+03 1.153210 2030 7000 \n", + "1500 3 1.468233e+03 0.036700 2130 7000 \n", + "1600 100 1.336801e-12 6.072074 2193 7000 \n", + "1700 43 1.000001e+03 2.250063 2285 7000 \n", + "1800 29 1.000061e+03 1.865515 2389 7000 \n", + "1900 60 1.000000e+03 3.750929 2219 7000 \n", + "2000 12 1.016411e+03 0.422333 2125 7000 \n", + "2100 11 1.029134e+03 0.366314 1990 7000 \n", + "2200 11 1.039525e+03 0.158003 1675 7000 \n", + "2300 4 1.254496e+03 0.046373 1689 7000 \n", + "2400 1 1.712102e+03 0.008491 1689 7000 \n", + "2500 16 1.004191e+03 0.455617 1794 7000 \n", + "2600 32 1.000018e+03 0.542778 1613 7000 \n", + "2700 100 1.336447e-12 2.178789 1608 7000 \n", + "2800 65 1.000000e+03 1.828426 1702 7000 \n", + "2900 27 1.000106e+03 0.562149 1584 7000 \n", + "3000 52 1.000000e+03 1.480587 1613 7000 \n", + "3100 31 1.000024e+03 1.188360 1666 7000 \n", + "3200 28 1.000068e+03 0.856570 1890 7000 \n", + "3300 4 1.318694e+03 0.038145 2007 7000 \n", + "3400 1 1.710012e+03 0.009773 2099 7000 \n", + "3500 2 1.524680e+03 0.019128 2196 7000 \n", + "3600 34 1.000009e+03 1.107027 2283 7000 \n", + "3700 50 1.000000e+03 1.365308 2355 7000 \n", + "3800 31 1.000024e+03 0.801419 2385 7000 \n", + "3900 6 1.160655e+03 0.156220 2261 7000 \n", + "4000 42 1.000001e+03 0.827270 1816 7000 \n", + "4100 10 1.044210e+03 0.149768 1746 7000 \n", + "4200 56 1.000000e+03 1.143407 1837 7000 \n", + "4300 2 1.826022e+03 0.028444 1797 7000 \n", + "4400 49 1.000000e+03 1.503978 1941 7000 \n", + "4500 7 1.125770e+03 0.157483 1925 7000 \n", + "4600 4 1.254239e+03 0.127843 1988 7000 \n", + "4700 19 1.001541e+03 0.567645 1990 7000 \n", + "4800 37 1.000004e+03 1.400061 1990 7000 \n", + "4900 2 1.571636e+03 0.033106 2039 7000 \n", + "5000 12 1.016410e+03 0.297559 2162 7000 \n", + "5100 5 1.248632e+03 0.127033 2211 7000 \n", + "5200 12 1.019651e+03 0.222088 2258 7000 \n", + "5300 1 2.113013e+03 0.012375 2253 7000 \n", + "5400 5 1.186229e+03 0.072437 2237 7000 \n", "\n", " average_specificity steps_in_trial_other population_other \\\n", "trial \n", - "0 6.365979 100 124 \n", - "100 9.154143 1 1017 \n", - "200 6.187286 47 1396 \n", - "300 6.249857 24 1629 \n", - "400 5.825714 100 2040 \n", - "500 5.720000 3 2557 \n", - "600 5.380286 100 2711 \n", - "700 7.827571 4 2874 \n", - "800 7.065571 54 2892 \n", - "900 6.694000 13 2915 \n", - "1000 7.576571 34 3035 \n", - "1100 6.867429 12 3067 \n", - "1200 5.954286 53 2949 \n", - "1300 5.646143 30 2959 \n", - "1400 6.648286 33 3009 \n", - "1500 5.865714 3 2934 \n", - "1600 6.648286 91 3163 \n", - "1700 5.991143 25 3074 \n", - "1800 5.943857 28 3090 \n", - "1900 5.646571 100 3165 \n", - "2000 6.068000 100 3114 \n", - "2100 19.489571 17 3039 \n", - "2200 16.667714 16 2948 \n", - "2300 16.608143 9 2893 \n", - "2400 18.742571 22 2889 \n", + "0 7.537975 100 150 \n", + "100 22.267143 10 1855 \n", + "200 19.244714 14 2059 \n", + "300 27.438143 100 937 \n", + "400 24.739571 100 439 \n", + "500 33.195000 100 299 \n", + "600 38.710000 94 224 \n", + "700 44.138143 93 195 \n", + "800 38.678714 40 181 \n", + "900 42.429857 100 169 \n", + "1000 42.601857 100 180 \n", + "1100 48.369000 100 177 \n", + "1200 49.315857 100 177 \n", + "1300 46.103857 58 174 \n", + "1400 41.317000 100 173 \n", + "1500 36.263857 100 173 \n", + "1600 41.414571 2 172 \n", + "1700 38.557000 100 173 \n", + "1800 37.958000 100 173 \n", + "1900 45.477000 100 177 \n", + "2000 52.344857 51 168 \n", + "2100 53.622286 100 176 \n", + "2200 56.014714 100 177 \n", + "2300 50.842857 67 177 \n", + "2400 51.163714 100 173 \n", + "2500 48.983286 100 172 \n", + "2600 59.566571 100 172 \n", + "2700 55.285714 100 171 \n", + "2800 54.020286 6 182 \n", + "2900 58.487857 100 174 \n", + "3000 54.192429 100 167 \n", + "3100 49.768286 100 178 \n", + "3200 44.783143 100 192 \n", + "3300 41.657000 100 175 \n", + "3400 40.566857 100 179 \n", + "3500 40.054143 76 182 \n", + "3600 37.267143 2 184 \n", + "3700 38.107429 23 178 \n", + "3800 41.131857 34 174 \n", + "3900 49.563286 100 174 \n", + "4000 51.970571 70 178 \n", + "4100 48.629286 100 167 \n", + "4200 48.705714 12 177 \n", + "4300 51.382714 100 175 \n", + "4400 45.504714 7 164 \n", + "4500 46.872000 12 164 \n", + "4600 45.354571 2 171 \n", + "4700 46.428714 44 173 \n", + "4800 47.053571 100 178 \n", + "4900 42.514571 100 174 \n", + "5000 43.520571 5 172 \n", + "5100 41.573857 100 182 \n", + "5200 40.680429 3 190 \n", + "5300 38.081286 100 187 \n", + "5400 34.153286 85 179 \n", "\n", " numerosity_other average_specificity_other fraction_accuracy_other \n", "trial \n", - "0 173 6.693642 1.00 \n", - "100 5944 11.546265 1.00 \n", - "200 7000 10.090143 1.00 \n", - "300 7000 9.047286 1.00 \n", - "400 7000 8.935857 1.00 \n", - "500 7000 11.392857 1.00 \n", - "600 7000 13.068429 1.00 \n", - "700 7000 15.086857 1.00 \n", - "800 7000 14.202429 0.96 \n", - "900 7000 14.928143 1.00 \n", - "1000 7000 14.731571 1.00 \n", - "1100 7000 14.954143 1.00 \n", - "1200 7000 14.322857 1.00 \n", - "1300 7000 13.953286 0.99 \n", - "1400 7000 14.837429 0.98 \n", - "1500 7000 15.395571 1.00 \n", - "1600 7000 14.119429 1.00 \n", - "1700 7000 14.365286 1.00 \n", - "1800 7000 14.499429 1.00 \n", - "1900 7000 14.962857 1.00 \n", - "2000 7000 14.599429 1.00 \n", - "2100 7000 14.158857 0.98 \n", - "2200 7000 13.590000 1.00 \n", - "2300 7000 13.632714 1.00 \n", - "2400 7000 13.620429 1.00 " + "0 201 8.656716 0.99 \n", + "100 6192 43.644703 1.00 \n", + "200 7000 104.774286 1.00 \n", + "300 7000 193.871000 1.00 \n", + "400 7000 228.319286 1.00 \n", + "500 7000 237.998000 1.00 \n", + "600 7000 241.446571 1.00 \n", + "700 7000 243.890000 1.00 \n", + "800 7000 245.906000 1.00 \n", + "900 7000 246.214000 1.00 \n", + "1000 7000 246.417429 1.00 \n", + "1100 7000 246.056286 1.00 \n", + "1200 7000 245.320286 1.00 \n", + "1300 7000 245.951143 1.00 \n", + "1400 7000 246.088286 1.00 \n", + "1500 7000 246.513429 1.00 \n", + "1600 7000 246.730571 1.00 \n", + "1700 7000 246.972857 1.00 \n", + "1800 7000 246.406000 1.00 \n", + "1900 7000 245.830000 1.00 \n", + "2000 7000 245.830000 1.00 \n", + "2100 7000 244.956857 1.00 \n", + "2200 7000 244.609429 1.00 \n", + "2300 7000 245.692857 1.00 \n", + "2400 7000 245.551143 1.00 \n", + "2500 7000 245.738571 1.00 \n", + "2600 7000 245.674571 1.00 \n", + "2700 7000 245.766000 1.00 \n", + "2800 7000 245.144286 1.00 \n", + "2900 7000 245.926000 1.00 \n", + "3000 7000 246.177429 1.00 \n", + "3100 7000 244.902000 1.00 \n", + "3200 7000 244.380857 1.00 \n", + "3300 7000 244.705429 1.00 \n", + "3400 7000 243.497429 1.00 \n", + "3500 7000 243.794571 1.00 \n", + "3600 7000 244.393429 1.00 \n", + "3700 7000 244.462000 1.00 \n", + "3800 7000 244.782000 1.00 \n", + "3900 7000 244.416286 1.00 \n", + "4000 7000 244.571714 1.00 \n", + "4100 7000 245.326000 1.00 \n", + "4200 7000 244.622000 1.00 \n", + "4300 7000 244.968857 1.00 \n", + "4400 7000 245.261429 1.00 \n", + "4500 7000 245.069429 1.00 \n", + "4600 7000 244.626000 1.00 \n", + "4700 7000 244.653429 1.00 \n", + "4800 7000 244.114000 1.00 \n", + "4900 7000 244.772286 1.00 \n", + "5000 7000 244.955143 1.00 \n", + "5100 7000 243.890000 1.00 \n", + "5200 7000 242.861429 1.00 \n", + "5300 7000 242.550571 1.00 \n", + "5400 7000 242.500286 1.00 " ] }, "metadata": {}, @@ -717,7 +1227,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -726,7 +1236,7 @@ }, { "data": { - "image/png": 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\n", 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th34Tgh2FMcYExHFrDKr6E1VNA15X1dQ2j17HSwoi8pSIHBCRdW3W/VJE9onIKvdxXpv3bhORbSKyWUTOPqGSBZL1MRhjwlinB7ip6uwuHH8BcE4H6/+oqlPcxxsAIjIOuBwY7+7ziIhEd+EzA6uxFho9lhiMMWHruIlBRJa4yyoRqTx8eax9VXUxUNbJWGYDz6tqvaruBLYB0zu5b/exUc/GmDDXmaakM9xlr7ZNSJ1pSjqGm0RkjdvU1DJKbCDOaOoW+e66I4jIHBFZJiLLiouLuxhCF9kEesaYMNfppiQRmSEivdq8ThGRU7rwmX/GuQR2ClAI/F/LITvY9ogbAwGo6nxVzVPVvKysrC6EcAJqbToMY0x482USvT8D1W1ee9x1PlHV/ararKpe4HEONRflA7ltNs3Bud90aLEagzEmzPkyp4OoausveFX1iojPc0KISHabm/98FWi5YulV4FkRuQ8YAIwElvp6/IBr7WOwGoMxPUljYyP5+fnU1dUFO5RulZCQQE5ODrGxsZ3ex5cT+w4R+QGHagk3ADuOtYOIPAfMAvqISD5wJzBLRKbgNBPtAq4DUNX1IvICsAFoAm5U1WYf4useHptAz5ieKD8/n169ejFkyJDWud7CnapSWlpKfn4+Q4cO7fR+vk67/SBwB85J/V1gznGCuqKD1U8eY/t5wDwfYup+nlJISIPozmdfY0zw1dXVRVRSAGey08zMTHy9SMeXabcP4IwziGy1ZdbxbEwPFUlJoUVXyuzLVUmjROTdllHMIjJJRO7w+RN7Ok+pdTwbY3y2d+9ehg4dSlmZ0xxdXl7O0KFD2b17N9dddx3Dhw9n/PjxzJw5k08//RSAefPmMX78eCZNmsSUKVNa1weaL1clPY4zkV4jgKquIRJrEDYdhjGmC3Jzc7n++uuZO3cuAHPnzmXOnDn89Kc/JSMjg61bt7J+/XoWLFhASUkJH3/8MQsXLmTFihWsWbOGd955h9zc3ON8in/4khiSVPXwq4Sa/BlMj+AptxqDMaZLfvSjH/HJJ59w//33s2TJEi6++GI+/fRTfvvb3xIV5ZyOhw0bxvnnn09hYSF9+vQhPj4egD59+jBgwIBuidOXzucSERmOO+hMRC7BGaAWWawpyZge71evrWdDwTFn9PHZuAGp3PmV8cfcJjY2lnvuuYdzzjmHt99+m82bNzNlyhSio4+cFu7LX/4yv/71rxk1ahRf/OIXueyyy/j85z/v15iPxpcaw43AY8AYEdkH3IxzpVLkaKyDxhq7VNUY02Vvvvkm2dnZrFu37pjbpaSksHz5cubPn09WVhaXXXYZCxYs6JYYfbkqaQfwRRFJBqJUtSpwYYWoWptAz5hwcLxf9oGyatUqFi1axCeffMIZZ5zBc889x+rVq/F6va1NSW1FR0cza9YsZs2axcSJE3n66af59re/HfA4fbkqKVNEHgQ+BN4XkQdEJLLOkK3TYVjnszHGN6rK9ddfz/3338+gQYP4yU9+wiOPPEJeXh533nknLRNLbN26lVdeeYXNmzezdevW1v1XrVrF4MGDuyVWX5qSngeKga8Bl7jP/x6IoEKWTbltjOmixx9/nEGDBvGlL30JgBtuuIFNmzZx4403UlRUxIgRI5g4cSLXXnstAwYMoLq6mquvvppx48YxadIkNmzYwC9/+ctuidWXzucMVf1Nm9e/FZGL/BxPaLMJ9IwxXTRnzhzmzDk0WUR0dDTLly8HOGqn8kcffdQtsR3OlxrDeyJyuYhEuY9LgdcDFVhIakkMNvLZGBPGfEkM1wHPAvVAA07T0o87cye3sFFb7iytj8EYE8Z8uSqp1/G3CnOeUoi3CfSMMeHNl6uSTncvVUVErhSR+0RkUOBCC0GeMkiyMQzGmPDm6x3cPCIyGbgV2A38NSBRhSob9WyMiQC+JIYm9w5us4EHVPUBILKalzyl1vFsjAl7viSGKhG5DbgSeF1EooHIamyvLbMagzGmS4427fYHH3yAiPCnP/2pddubbrqp3fQX9957L2PGjGHChAlMnjyZv/zlLwAsXLiQqVOnMnnyZMaNG8djjz3ml1h9SQyX4VyR9F1VLQIGAvf4JYqewmOJwRjTNUebdnvw4MH07duXBx54gIaGhiP2e/TRR1m0aBFLly5l3bp1LF68GFWlsbGROXPm8Nprr7F69WpWrlzJrFmz/BJrpxODqhap6n2q+qH7eo+q/qXlfRH52C8Rhaqmemiots5nY0yXHT7t9i233AJAVlYWZ511Fk8//fQR+/zud7/jkUceITU1FYC0tDSuvvpqqqqqaGpqIjPT+bEaHx/P6NGj/RKnLyOfjyfBj8cKPTYdhjHh4825ULTWv8fsPxHOveuYmxw+7XZcXFzre3PnzuXcc8/lO9/5Tuu6qqoqqqqqGD58+BHHysjI4MILL2Tw4MGcddZZXHDBBVxxxRUdTsbnqxM/wiHqx2OFHhv1bIzxg6NNuz106FCmT5/Os88+27pOVY95z+YnnniCd999l+nTp3Pvvfe2Syonwp81hvBmU24bEz6O88s+UA6fdvvyy9vfHflnP/sZl1xyCTNnzgQgNTWV5ORkduzYwbBhwzo85sSJE5k4cSJXXXUVQ4cO9cs9G/xZYzh6WgsHNoGeMeYEdDTt9v/+7/+222bMmDGMGzeOhQsXtq677bbbuPHGG6msdGYeqqysZP78+VRXV/P++++3bufPabl9qjGIyGBgpKq+IyKJQEybG/Zc5ZeIQlVrH4M1JRljfNfRtNsLFixg9+7d7ba7/fbbmTp1auvr66+/nurqak4++WRiY2OJjY3llltuQVX5wx/+wHXXXUdiYiLJycl+u8ObtNwc4rgbilwLzMGZfnu4iIwEHlXVs/wSSRfl5eXpsmXLAv9BH9wD7/0W7iiGmLjjb2+MCSkbN25k7NixwQ4jKDoqu4gsV9W8jrb39Z7PpwOVAKq6FejbxTh7Hk8pxPWypGCMCXu+JIZ6VW0dfSEiMYT7lUht1ZZZM5IxJiL4khg+EJGfAYki8iXgH8BrgQkrBNkEesaYCOFLYpiLc5/ntTg37XkDuCMQQYUkj9UYjOnpOtunGk66UmZfbtTjBR53H5HHUwp9RgY7CmNMFyUkJFBaWkpmZuYxB42FE1WltLSUhATfJqbodGIQkbUc2adQASwDfquqpT59ck9jE+gZ06Pl5OSQn59PcXFxsEPpVgkJCeTk5Pi0jy/jGN4EmnHu+wzQMmSvElgAfMWnT+5JmuqhocqmwzCmB4uNjWXo0KHBDqNH8KWP4XRVvU1V17qP24FZqno3MKSjHUTkKRE5ICLr2qzLEJFFIrLVXfZu895tIrJNRDaLyNldLZTfVRU6y9Ts4MZhjDHdwJfEkCIip7S8EJHpQIr7suko+ywAzjls3VzgXVUdCbzrvkZExuHUQsa7+zzi3gwo+Cr2OcvUAcGNwxhjuoEvieEa4AkR2Skiu4AngGtFJBn4fUc7qOpioOyw1bOBlknHnwYuarP+eVWtV9WdwDZgug/xBU5lS2LwrZ3OGGN6Il+uSvoMmCgiaThTaRxs8/YLPnxmP1UtdI9ZKCIto6cHAp+02S7fXRd8lVZjMMZEDl8n0Tsfp6knoeVyL1X9tZ9i6ej6sQ4vwBWROTjzNjFo0CA/ffwxVOyDhDSITzn+tsYY08N1uilJRB7Fue/z93FO4l8HujLH634RyXaPmQ0ccNfnA7lttssBCjo6gKrOV9U8Vc3LysrqQgg+qiywZiRjTMTwpY/hNFX9FlCuqr8CTqX9ibyzXgWudp9fDbzSZv3lIhIvIkOBkcDSLhzf/yrzrRnJGBMxfEkMde7SIyIDgEbgmBcFi8hzwMfAaBHJF5HvAncBXxKRrcCX3Neo6nqcvooNwL+BG1W12ZfCBEzFPkgLje4OY4wJNF/6GF4TkXTgHmAFTvv/MafHUNUrjvJWh/dwUNV5wDwfYgq8xjrwlECqJQZjTGToVGIQkSicsQcHgRdFZCGQoKoVgQwuJFS53RyWGIwxEaJTTUnuBHr/1+Z1fUQkBXA6nsGakowxEcOXPoa3ReRrEinTErZoHfVsicEYExl86WP4MZAMNItILc4lq6qqqQGJLFRU5jtLuyrJGBMhfBn53CuQgYSsygJISIe45GBHYowx3cKXAW4iIleKyM/d17nuRHrhrWIfpNngNmNM5PClj+ERnEFt33BfVwMP+z2iUFO5z/oXjDERxZfEcIqq3og70E1Vy4G4gEQVSir3Wf+CMSai+JIYGt37IyiAiGQB3oBEFSoaa517PdulqsaYCOJLYngQeBnoKyLzgCXA7wISVaiotMFtxpjI48tVSc+IyHKc6SwEuEhVNwYsslBQaWMYjDGRp9OJQUQeAP6uquHf4dyiddSzXZVkjIkcvjQlrQDuEJFtInKPiOQFKqiQUeEObuuVHdw4jDGmG3U6Majq06p6Hs59mLcAd7tTZ4evyn2QmAFxScGOxBhjuo0vNYYWI4AxwBBgk1+jCTWVBda/YIyJOL6MfG6pIfwaWAecpKpfCVhkocBu0GOMiUC+TKK3EzgNGAbEA5NEBFVdHJDIQkHlPsg9OdhRGGNMt/IlMTQD/wFygFXADJzbdp7p/7BCQIMHasusKckYE3F86WP4AXAysFtVvwBMBYoDElUosEtVjTERypfEUKeqdQAiEq+qm4DRgQkrBLQObrN5kowxkcWXpqR8EUkH/gUsEpFyoCAQQYUEG/VsjIlQvkyJ8VX36S9F5D0gDfh3QKIKBVZjMMZEKF9qDK1U9QN/BxJyKvZBUibEJgY7EmOM6VZdGeAWGewGPcaYCGWJ4Whs1LMxJkJZYjiainwb9WyMiUiWGDrSUAN1B63j2RgTkSwxdKT1zm02uM0c38PvbePOV9Zx0NMQ7FCM8YsuXZUU9lruw2A1BnMcH20v4Z63NgOwcE0hP79gHLOnDEBEOn2MbQequPPV9fRJief288bSNzUhUOEa0ylWY+hI63QY1sdgjq62oZnbXlrL4MwkXrrhNHIykrj576v41lNL2V1ac9z9G5u9PPzeNs57YAlr8yt4c20RZ933AX/7ZDder3ZDCYzpmCWGjrQMbutlNYaeTlVZsaecX766nksf+5glW0v8duz739nC7lIPv794ItMG9eal60/j17PHs3LPQb78x8U8/N42CitqUT3yJL++oIKLHv4v97y1mS+O68u7t8zi3zd/jvEDUrnjX+v4+mMfs2V/ld9iNcYX0tGXtifJy8vTZcuW+fegr/0QNi6EW7f797im22zZX8Urq/bx6uoC9pbVEhcTRUZSHPur6rj5rFHcdOYIoqM639xzuLX5Fcx+eAmX5uVy19cmtXuvqKKOX722njfXFQHQOymWsdmpjMtOZWx2KjtLanj0g+2kJ8Xxm9njOXfioVvHqir/XJ7PvDc2Ul3XxHWfH8b3zxxJQmx0l2M1piMislxVO7xFsyWGjvztEqg5ANeF760mwklxVT0bCyvZUFjJhoJK1u2rYEdJDVECZ4zM4sLJAzh7fD+io4Q7Xl7HSyv38bmRffjjZVPokxLv8+c1Nnu58KH/Ulpdz6Iff560xNgOt1uTf5BVew+yoaCSjYWVbCqqor7JC8DF0wbyiwvGkZ4U1+G+pdX1zHt9Iy+t3MewPsncfckkTh6S4XOsxhxNSCYGEdkFVOHc56FJVfNEJAP4O85tQ3cBl6pq+bGOE5DE8Mip0HsIXPGcf49r/OqpJTv58wfbKa6qb103MD2RsdmpnDEik/MnDSCrV/sTv6ry98/2cuer60lPiuVPV0xj+lDfTrgPv7eNe97azGNXncTZ4/t3er+mZi+7SmtobFbGZqd2ap8PtxZz20tryS+v5VunDubWc8aQEh8e14yUVNezaMN+slLiOXNMX6K6WINTVZ86+40jlBNDnqqWtFn3B6BMVe8SkblAb1X96bGOE5DEcNcgmHgpnH+vf49r/ObRD7Zz15ubOH1EJmeO6cc4t6kmLanjX++H21BQyY3PrmBPmYfzJmajqngamvE0NOFpaKa2oZnxA1I5Z0J/Zo7KIinOORlvL67m3Ac+5Itj+/LIN08KZBFb1dQ3ce/bm1nw0S6yUxOYd/FEvjC6b7d8tr9V1zfx9voiXllVwJJtJTS7nexj+vfipjNHcO6E7E438W0oqOTvn+3h5ZX7+NyoLP7v65Otyc0HPSkxbAZmqWqhiGQD76vqMe/54PfEUF8Nvx8IX/wlnPEj/x03RHm9SlFlHbtKa0hLjGX8gLRgh3Rc8xdv53dvbOIrkwfwx0snExPdtWsoquoaufOV9fx3ewnJcTEkxUeTFBdDclw00VFRLNtdxkFPIwmxUcwcmcU5E/rz/NK9bN5fxaIfz6Rvr+69rHT57nJ++uIath2o5uJpA/nVhePpldC5RBhsu0pquPftzbyzcT91jV4Gpicye8oALpg0gE1FlTz03jZ2FNcwom8K3z9zBBdMGtBhgqiqa+S11YU8/9ke1uRXEBcdxYzhmXy4tZjJOek8/q28I2qJpmOhmhh2AuWAAo+p6nwROaiq6W22KVfV3h3sOweYAzBo0KCTdu/e7b/AirfAwyfDxY/DpEv9d9wQcaCqjic+3Mn2A9XsLvOwp8xDg9vuDXDqsExu+MJwzhjRJySr5098uIPfvr6R8ydl88BlU7qcFDqjqdnL0l1lvLWuiLfW76eosg6AP1wyiUvzcgP2ucdS39TMw//ZxkPvbSM3I4kHL5/K5Nz0oMTSWQc9Dcx++L+UVTdw0dSBzJ4ygJMG9273/Wr2Km+sLeSh/2xj8/4qcnon0i81gWav4lWl2es8dpd6qG1sZnS/Xlw+PZeLpgykd3Icb60v4ofPr6RPSjwL/udkRvTtFcQS9wyhmhgGqGqBiPQFFgHfB17tTGJoy+81hu3/gb9+Fb79Bgw53X/HDQEbCiq55unPKK6uZ3hWCoMzkxicmewsM5LZWFjJE0t2sL+ynokD07h+1nDOHt//hK7e8afWpDAxmwcuD2xSOJzXq6zOP8ieMg8XTvZtAFsgfLarjB8+t5IDVfXces5orjljWJfb6AOpqdnL/yz4jE92lPL8nBmcNPjY/Tler/L2hv28sGwvDU1eoqKEKIFoEaKihH6p8XxtWg5TctOP+DdYk3+Q7yxYRn1TM49deRKnjejT7v26xmY2F1XR2OxlwsC0TjU7qSrV9U2UVDdQUl1PcVU9JdX1jO7Xi1OGZfr+BwkhIZkY2gUh8kugGriWYDclrfgrvHoT/GAVZAz133GD7N2N+/nBcyvplRDLE1fnMWFgx01G9U3NvLxiH48t3sHOkhqG9knm5xeM5cwx/bo54vaeXLKT3yzcwLkT+vPgFVOJ7cakEKoOehr46YtreGv9fma6beyh1owy7/UNPP7hTu7+2kQuO3lQwD8vv9zDdxZ8xo7iGm47byzxMVGs21fBmvwKtuyvosnt04iJEsYNSGXaoN5MHZTOlNx0quub2Hagmu3FNWwvrnZq1W4NpSOX5eVy+wVjSe0hzXmHC7nEICLJQJSqVrnPFwG/Bs4CStt0Pmeo6q3HOpbfE8P7d8P7v4M7DkBM8P6Teb2KCCf8y1RVeXLJTua9sZEJA9J44uo8+nViyoVmr/LW+iLuf2cLW/ZX841TBnHH+WNbO2G703NL93DbS2s5Z3x//vQNSwptqSrPfLqHXy/cQGpCLD+/YCznT8zu1trU0fxr5T5nJPipg/n17And9rmVdY3c+MwKPnQHM6YnxTJxYBoTBqYxcWAaMVHCyr0HWbG7nDX5FUec+KMEcjOSGJ6VwpDMZPqlxpPVK54+Kc6yd1IcT3+8i8c+2E6/1AR+f/FEZvXAiwFCMTEMA152X8YAz6rqPBHJBF4ABgF7gK+ratmxjuX3xPDq92Hzv+EnW/13TB+tL6jghmdW0C81gYe+MbXLnZyNzV5+8cp6nlu6h3PG9+ePl00hMc63qzbqm5q57+0tzP9wB0Myk7n/sind2qb91voirv/bcj43MovHv5VHXEzwT3ihaFNRJTc/v4pNRVUMTE/kms8N5dK8XJKDdGnr2vwKLnn0I6bkpvO3a07p9mTe2Oxl+e5yBqYnktM78ag/sJqavWwqqmJNfgVpibEM75vMkMzkTjUzrdp7kJ/8YzVbD1RzaV4Ot58/jrTEWJqaveSX17KzpIYdJTXUNjRx6vA+TMlND5lmWQjBxOBPfk8MT50L3ia4ZpH/jumDV1bt46cvriE1IZbKukbSE+P485XTmDqo466WZq/y8sp9LN5STEOTl8ZmLw3NXhqavBRX1bOjpIYbZg3nf788+oTaoD/ZUcotL6ymqLKOH541khtmDQ/4r9JPd5Ry1VNLGZudynPXnhKU2kpP4vUq7246wPzF2/lsVzlpibFcNWMwV582pFubmIqr6rnwoSVEifDqTaeT2YVBhD1FXWMzD767lUc/2E5GcjypCTHsKfO0Nlm1lZ4Uy8yRWcwancXMUVldGlzpT5YYOsvrhbtyYfIV3T6GoanZy+/f3MSTS3YyfUgGD39zGsVV9cz56zIOVNbzm4vGt2ujVVUWby3h929sZFNRFdlpCfRKiCEuJorY6CjioqOIi4nikpNymD3FP5MBVtQ2cucr6/jXqgKmDUrngcunkpuRdMx9NhZWcv87WxjdP5VvTB9E/7TO1X42FlZy6WMfk9Urnn9+7zQykjseIWw6tnx3OfMXb+ftDfuJEmFwRhLD+6Ywom8KI7KcZW5GEmmJsX79FbujuJqf/HMN6wsqePH603rE5c/+sHrvQe5/ZwuJcdEMyUxmaJ9khmU5tY/oKGHx1hLe33yAxVuKKal2pmcflJHEoIwkcjMSyc1IIrd3EkMykxk/ILVbLiSwxNBZJdvgoZPgwodg2lX+OWZnPra6npueXcEnO8r49mlDuP38sa1V7/KaBn7w/Eo+3FrClTMG8YsLxrP1QBW/f2MTS7aVMCgjiVvPGc35E7O77UqZV1cXcPvLawH4w9cmtZvrp4Wq8sKyvfzilfXERUdR3dBElAhnj+/HVTOGMGNYxlHj3Vvm4Wt//ogoEV684TQGpicGtDzhbEdxNf9auY8t+6vZVlzNrpKadr9mRSAtMZbeSXH0ToolIzmOz43M4qvTBna6U1VV+XhHKU8t2cm7mw4QGxXFHy+bwvmTjvxeRDqvV1lfUMkHWw6weX81e8s87C3zUFpz6F4eA9MT+dpJOXz9pJzj/vA6EZYYOmvtP+HF78J1H0L2pONv74+PzK/gur8uo7Smgd9fPJGLpx15c6CmZq8zBcPiHeRmJJJfXktaYiw/OHMk35wxiPiY7h/tubfMw03PrWT13oNcNWMwt58/trVd1tPQ1Don0ekjMrn/sql4Gpr42ye7eWFZPhW1jYzql8LF03IYmJ5IZkocWSlO516TV7n0sY8pra7nH987jdH97Xp0f2ps9rK71MO2A9UUHKzloKeBck8j5Z4Gyj0NFFbUsaO4hsTYaGZPGcCVMwYf9Qq2usZmXl9TyJNLdrKhsJLM5DiunDGYK2cMDrmro0JdTX0T+eW1bCys5KWV+/hwazGqzriir+flcO6EbJ/7B4/HEkNnvf1z+PRRuG0fxAS+6eLllfnMfXEtfVLieeyqk476H7DFa6sLuOvNTVwwOZsbZo046uRt3aWhycs9b23i8Q93MjY7lYe/MZVmr3LDMyvYVlzND88ayffPHNmuqaK2oZnXVhfwl092sW5fZYfHjY+J4plrTiHPJo0LijX5B3nmkz28snofdY1eJuekcd7EbCpqG8kvryW/3MO+g7UcqKpHFUb1S+G7Zwxl9pSBNiWFnxQcrOWlFfn8Y3k+u0s9ZCTH8eMvjeKK6YP81vRniaGznr4Q6irgug/8c7yjaGr2ctebm3hiyU5mDMvg4W9M69EddO9u3M8t/1hNQ5MXVUiOj+aBy6dy+mEDjNpSVco9jZRU11NSVU9JTQMlVfWU1TTwhTFZxx0IZQKvoraRl1fk88yne9h6oJqYKGGAe5WPc7VPEicN7s3pIzKDPuAvXKkqn+4s44+LtvDpzjLG9O/FLy4Yd8Tgva6wxNAZqnD3EBg3Gy588MSPdxQHPQ3c9OxKlmwrOaI/oScrrKjl1n+uQUS495JJdnvKMKKqlNY00DspLqQut4wkqsq/1xUx742N5JfXcvb4ftx+3jgGZXa9D8ISQ2cc3AP3T4Tz74OTv3vix+vApqJK5vxlOUUVdfz2oglcenJw5tsxxvRMdY3NPLlkJw+/t42mZuUnZ4/m2pnDunSsYyUGuzC8ReFqZ5k9JSCHX7evgsse+5jk+Biev24G044yLsEYY44mITaaG78wgktOyuHuf28KWCe/JYYWhatBoqHfOL8furS6nuv+upxeCbG8fONpZKfZ5ZfGmK7rl5rAfZdOCdjxLTG0KFwNWWMg1r8n7cZmLzc+u4Li6nr++b1TLSkYY0Jez+/19JfC1ZA92e+Hnff6Rj7ZUcZdF09kUk66349vjDH+ZokBoKoIqvf7nBiamr2sL6jA28G8KAAvLNvLgo928d0zhnY4cM0YY0KRNSVBm47nzicGVeXWf67hpZX7GJSRxGUn5/L1k3JaL9NcuaecO15ex+kjMrnt3DGBiNoYYwLCEgO4iUGgf+fnjH/0gx28tHIfl5yUw77yWu55azP3LdrCWWP6cuGUAfxm4Qb6pcXz0BXTQmJufGOM6SxLDOAkhswREN+5eXneXl/EH95ybkZ/zyWTEBF2ltTw/Gd7eHF5Pm9v2E9ibDQv3XAavW1WUGNMD2OJAZzEkHtKpzbdUFDJzX9fxaSBaa1JAWBon2RuO3cst3xpNP/ZdIC+qfGMzU4NZNTGGBMQ1sZRUwoVe1v7F36zcANfuPd9nvhwB1V1je02La6q59q/LCM1IZbHv5XX4YRhcTFRnDOhvw1gM8b0WJYYilo6nidRWFHL0x/toqqukd++vpFTf/8ffrNwA3vLPNQ1NvO9vy2ntKaeJ67Os7mAjDFhy5qSWq5I6j+JJ/+zEwVevuF0yj0NPLlkJ09/tIv/99+dDOmTzI7iGh755rTjTo9tjDE9mdUYCldD+iAq6MVzS/fwlUnZ5GYkMSnHuXXlhz/9AnNmDqeytpFbzxnNeR3crcwYY8KJ1RjcEc9//WQXNQ3NzJk5vN3b2WmJzD13DHNtLIIxJkJEdo2hrgLKdtDYdxILPtrF50dlMW6AXUlkjIlskZ0Yipwb2n9YPYCS6ga+9/nhx9nBGGPCX2QnBrfj+cGNyUzOSWPGMLudpDHGRHhiWENdQl9WlcXxvc8Pt/vWGmMMEZ4YtHA1a5qHMLRPMl8e3z/Y4RhjTEiI3MTQ4IGSzXxcm8O1nxtmNzk3xhhX5CaG/esR9bInbiQXTxsY7GiMMSZkROw4hh37yyn1jmLSKTM7nPPIGGMiVcTWGKr6TefBwQ9x0azOzapqjDGRImJrDJNz0/nrdy0pGGPM4SK2xmCMMaZjIZcYROQcEdksIttEZG6w4zHGmEgTUolBRKKBh4FzgXHAFSIyLrhRGWNMZAmpxABMB7ap6g5VbQCeB2YHOSZjjIkooZYYBgJ727zOd9e1IyJzRGSZiCwrLi7utuCMMSYShFpi6Gj4sR6xQnW+quapal5WVlY3hGWMMZEj1BJDPpDb5nUOUBCkWIwxJiKFWmL4DBgpIkNFJA64HHg1yDEZY0xEEdUjWmqCSkTOA+4HooGnVHXecbYvBnZ38eP6ACVd3LenCPcyhnv5IPzLaOULjsGq2mFbfMglhu4kIstUNS/YcQRSuJcx3MsH4V9GK1/oCbWmJGOMMUFmicEYY0w7kZ4Y5gc7gG4Q7mUM9/JB+JfRyhdiIrqPwRhjzJEivcZgjDHmMJYYjDHGtBOxiaGnTu8tIk+JyAERWddmXYaILBKRre6yd5v3bnPLuFlEzm6z/iQRWeu+96CIdDQdSbcTkVwReU9ENorIehH5obs+nMqYICJLRWS1W8ZfuevDpozgzJYsIitFZKH7OmzKJyK73LhWicgyd13YlA9VjbgHzuC57cAwIA5YDYwLdlydjH0mMA1Y12bdH4C57vO5wN3u83Fu2eKBoW6Zo933lgKn4sxP9SZwbrDL5saVDUxzn/cCtrjlCKcyCpDiPo8FPgVmhFMZ3dh+DDwLLAzD7+kuoM9h68KmfJFaY+ix03ur6mKg7LDVs4Gn3edPAxe1Wf+8qtar6k5gGzBdRLKBVFX9WJ1v51/a7BNUqlqoqivc51XARpwZdsOpjKqq1e7LWPehhFEZRSQHOB94os3qsCnfUYRN+SI1MXRqeu8epJ+qFoJzYgX6uuuPVs6B7vPD14cUERkCTMX5RR1WZXSbWVYBB4BFqhpuZbwfuBXwtlkXTuVT4G0RWS4ic9x1YVO+mGAHECSdmt47DBytnCFffhFJAV4EblbVymM0vfbIMqpqMzBFRNKBl0VkwjE271FlFJELgAOqulxEZnVmlw7WhWz5XKeraoGI9AUWicimY2zb48oXqTWGcJvee79bLcVdHnDXH62c+e7zw9eHBBGJxUkKz6jqS+7qsCpjC1U9CLwPnEP4lPF04EIR2YXTTHumiPyN8CkfqlrgLg8AL+M0T4dN+SI1MYTb9N6vAle7z68GXmmz/nIRiReRocBIYKlbza0SkRnuVRDfarNPULnxPAlsVNX72rwVTmXMcmsKiEgi8EVgE2FSRlW9TVVzVHUIzv+t/6jqlYRJ+UQkWUR6tTwHvgysI0zKB0TmVUlOPw/n4Vzxsh24Pdjx+BD3c0Ah0Ijzi+O7QCbwLrDVXWa02f52t4ybaXPFA5CH82XeDjyEOwo+2A/gDJzq9Bpglfs4L8zKOAlY6ZZxHfALd33YlLFNfLM4dFVSWJQP52rG1e5jfcv5I1zKp6o2JYYxxpj2IrUpyRhjzFFYYjDGGNOOJQZjjDHtWGIwxhjTjiUGY4wx7VhiMKYLRCRdRG44xvsfdeIY1cfbxphgsMRgTNekA0ckBhGJBlDV07o7IGP8JVLnSjLmRN0FDHcnwmsEqnEGHk4BxolItaqmuHM+vQL0xplF9Q5VDY3RrcYchQ1wM6YL3JlfF6rqBHeiuNeBCepMq0ybxBADJKkzEWAf4BNgpKpqyzZBKoIxR2U1BmP8Y2lLUjiMAL8TkZk4U1APBPoBRd0ZnDG+sMRgjH/UHGX9N4Es4CRVbXRnHE3otqiM6QLrfDama6pwbj16PGk49yZoFJEvAIMDG5YxJ85qDMZ0gaqWish/RWQdUAvsP8qmzwCvuTeMX4UzvbYxIc06n40xxrRjTUnGGGPascRgjDGmHUsMxhhj2rHEYIwxph1LDMYYY9qxxGCMMaYdSwzGGGPa+f/x0wgcCF0eaQAAAABJRU5ErkJggg==\n", 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" ] @@ -750,22 +1260,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -785,22 +1295,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -820,22 +1330,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -855,22 +1365,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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xRFkligiAEGRdQbyFxU0vEjcxn2L3UN+y+4b6vFvT8wgMQv9Yw7BVzDCq1WGrUKc1oYzP/bwMlT1CzVjIgufRQJZ9ZjGDx+lgsOo8QOU87Jkey9nSxgOf6pwWvRaGKYetVlRbQcd6PaSUOPXrorcN5N0hQiQ7XkWzkSgUSzwXT7N7cHnSdCjo2Zo5D18Egtsahq1ixlX5wIpqK+hgl3lCGY9ZGS53sGvGwz7OLmUomBk+5faBu69m2EqX6epeFY0902M1W9t4ON2khb+ySPYK1bpWmg6LIy6mCwSlId2iSxuBktcQR9zE4ZtT8QzFkmTP4HLPYzjoZT6Z3TKS9KTj4A6oxTbYxPMoS5Ms9zygg/0RSaVrlfEM4nQsX9zHwn5KEmbNeoaBwbo5j5X5DlDTBFNdmozYq2xt44GSZXfle0xZNzkLwrnMI+h0wnzO0LUCjCYxhfSFlTjiJjYeWopdV1pphvo8ZPIlUltEkp5UtBLiCW5TxqOO4dRhq8EV1VbQQc/DCFvlqxR1NZZ7PepIlJxeyKzKdwDl4VZ20rzCuhgPIcSvCSGeEEI8LoT4ghDCJ4QYFELcLoQ4atwONN9S+2Rd/Xh7bTpeak59+B1Vb5/OeXQobKW7ywuuPnBWauodASWOuJmNx0S0IsVeTblRcKuErtKxitfZPwaFTN2Lk0Zhq44tuImzZIUXl79/1b8qvR4W8h4rjIeUclV3ucaWKFnNmhsPIcQO4FeAK6WUFwJO4M3ALcAdUspDwB3G310n7+7H12uy7Mm55SErAE8fOFwdC1vNJ7KERZJStXeDUtYNi+SmFgicnE/hcToYW1HrrxsF57ZKl3k6BrpBNDimbuvM9YincjgEyxLZHTceyTkWRLjsBVSz3Wjqs1RxtSJhHk3myBVLq3o8wJYoqcV6ha1cgF8I4QICwCngdcBtxv9vA16/FgeS94QJlhIUe6l6KDlXKTfUCNFRccT5pBJFFDqXYuANDmz6gVCT0RQ7B/04VsTVh/u2mucRrXgeQUMCvU65bjSpRBGrX7Nu5DyiIrKqTFfty4Xf7bTWZb7C86j0ePhXPbwy02OLvPcmWHPjIaV8DvgYMAnMAAtSym8Do1LKGeMxM8C2Ws8XQtwshHhQCPHg7Oxs+8fjDRESSZZ6qXooVcPzgI7O9IgmcoREEucK4+HqG8QlSiQT8Y7sZyMyMZ9iz4qQFVRLlGwhz0PnPPoNz6NOxVU8tVzXCqDP48TpEJ2rzEvOMlcK1TQeQgjGwz5OWcl5ZBaWKQXPrJggWI3teaxmPcJWAygvYx+wHegTQrzV7POllLdKKa+UUl45MlJjAbV6PL4IIVK9FctcKYqo6eBMj/lkjgFHCkdgeepJeyKZXtQDM4GUkqloij0rejygkgzeEo2CWlG37HmMqts6FVcrFXVBLegdlShJzHK62F/TeECLXeZV4oj1GgRBVVuBnfOoZj3CVq8ATkgpZ6WUeeBLwIuBM0KIcQDjtvnQ5A7gDEQIiRTxZI9Mxyvk1BVToJbxiHQ0bBURyeUVXXof9KCki0liqTxL2QK7angePreTfq9ra8z0yC5BqVDRgfKFweltGrZaiTIeHWgSLJWQyVlOl1Y3CGosGQ9tFKtCVzMLGVwOwbBRGFFNx0Nwm4D1MB6TwAuFEAGhOnGuA44AXwFuMh5zE/DltTgYl6Gsm1zqkcWwVoOgpoMzPeYTWfqpTBFctg+g2GCYTi9TlmKvYTwABoOerZHz0MlkvcgKAf2jDRLmy3WtNCGfqzMLbiaOkEXmZahmwhyUx3BmMWMuf1n2PCoe9OmFDKMh36pcF4DTIej3uWzPo4r1yHncD/wb8DDwI+MYbgU+AlwvhDgKXG/83XU8xqCjVK/M9NADeWqGrSId8zwWEin8MlPX86g1C2EzUK/HQzPU59kaMz30+6tzHqAqrmrM9JBSEk3lVkl6QAcHQhlGa6WibjVjYT+FkjSXk6qhbzVTp8dDE/bbEiXVuJo/pPNIKT8EfGjF3VmUF7Km6Ol42USPLIa1uss1OmEupbpSbIOcDkvp/pHqfdCDki4mmZxfPsdjJUNBb3nWx6YmtcLzAFVxNX9s1UPT+SK5Qqlu2Oq5mMkKqEYYDYJzrJYm0egS25mFDNtqlNsuoyzLXuV5LGa4YHuozhOUvpUtUVJhy3eYB4yZHvleMx41cx5hkEXItde3UipJSqm4+mNl2MrwPDz5xU0p1TAZTTEa8uJzO2v+f3ir6FtViyJq6ogj1moQ1HQsYZ6seB6Nch5gstdDe1SG5yGlZGYh3dTzsHMeFba88dBT8gq9EsNvFraCtkNXi5k8/VrXamXYyhtCIgiL5KaM/07UkGKvZqjPSzSZ3fSqwpWwVbXnMaruLywPC9VS1NXosFXbemCJiqJuvbDVuJWJgp4AuPxl47GQzpPJl2r2eGjCfjfxTdwca5Utbzx0WKZ8pb3RqaVrpemQOOJcQkmTLNumxuEg7w4RZnN2mU/Op9g9uLpMVzMU9FCSbP7wRT3jAau8D/05GKgTtiqUZPt6YMlZSsJJjGBd4zHY58HjdDCzaKXLXJ1nox4PTdjv6Uzl2CbBNh69FsPX3eWOGm9dh8QRo8kcIQzjsTLnAZS8Shxxs1UdZfJFTi9mGnseZX2rTZ40T0XB0w/OqoW6v7ZESbOwFdB+o2DyLGl3BIlj2fzyaoQQFns9KvpWp+tMEKxGha1yW0dVuQm28fAEKeLoHVn21HztkBV0TBwxmsxWPI8aHo70hTel5zEdU4nwPXUqrQCGjdDMps97VOtaabREyYqKq0Zhq451ZifnSDgH8LkdeF2181GgFn9ryroqYW7G84gE3OSLknR+i6gqN8E2HkKQdgRx53pEWbdedzl0NGxV9jxWhq1QyrqbUZZdl+nWahDUlD2PHi3X/dcfTPHsrImCimpdK01ZHHGF8dCzPGppThnJ7bZnmSfOsuCorWtVzbjVLvOy55HGIWCkRoOgxlbWXY5tPICMqx9PoUdmeiTnaldaQccS5lqOXbr84Fr9ZXL1KXHE2CYzHhPzzT2Pir5V7517Jl/kA//+Q/7hvonmD67WtdL0jQBiVdgqnsoT8rlwOVcvJ53zPGaJifo9HhodtjIVWqqSZZ9ZyLCt31fzHDQRu8t8GbbxQI1W9RUTvRHLrCXHrvGGANF2zmM+kWXYlUbUyHcAOAMDRESK6CYLW03Mp+jzOBmqEX7RDAQ8CNGbOQ8dljtjJqGcquF5OF3K610RtoomczVDVlCd82gz0ZycbdggqBkP+cgVS+a84sBQWRzx9GLtOR7V2J7HcmzjARQ8IfpJkMlv8L6FQg6yC/XDVg6HmsveZthqPplj2JmuGbICwBchJJLEenABbcRUNMWuwcCq+dXVOB2CwYCHuR70uqaMZr1TcRPGo1oUsZrgWI2EeW1dK+iQ55FNQD7FmTqKutWMWZnrERgCJGTiTbvLwZ4muBLbeGDIspMivtG1+rWUwspZHtV0QKIkmswx6EzVLgcG8IVxUyCR7JFQn0kmoqmGISvNUNDTm56HkdNpmhMoldQFSGBw9f/6R1flPOKpPIN19Kb6fS6EaHPBNbrLZ/L9dRsENeNWJgpWdZnXmyBYTcUQbvB1Yo2wjQeAL0JYJDf+FYXxJaobtoKOiCPOJ9QgqLqeh3F/oVe68k1QKikp9kZlupqhPm9P5jymDc/j7FKGQiN1gOwCyFIdz2N01UyPaDJXs8cDwOEQBL2u9jShjM/9dD5YV5pEU55lbiY0ZxiPVPwMiWyhqedRGQi1wdeJNcI2Hhiy7PRAx3Sj7nKNL9J+ziOZI0iyZo9HeR/0UFe+Cc4uZckWSuyuMcdjJUNBT0/O9Jgych4lCbONPKdaooia4KiSCilVjE+8QdgKOiAoaBiPqVxf07DVUNCLyyHMdZkb5xefV8awUXc5VIZbbfh1Yo2wjQfg7BvAJ/IsLm3wWeaNRBE1vnBbYatSSRJL5QgUE/XDVrqxskPy7xuBZlLs1QwHvT0502M6lsbtVPmchjmBVI3uck1wVM35MKTMs4UiyVyRwb76i3rbmlAmFHU1TodgNGSy18MI/yZiyng08zyEEERsfasytvEAvIYs+4afjlcWRWyQ82gzbLWQzlMqFfEWEw0T5gDewhKZTdIwVZZiNxW28rCUKZAt9Na5T0VTXLhDeZMNcwK1RBE1/YZEiVFxpa/Cm3kench5RAk1DVuBhaFQxvllFtT2x5op8WLoW9nGA7CNBwD+fvUhSifmmzxynUnN1de10rSZMJ9P5ugnhUA29Tw2U5f5ZDSFQ8COgcahC6g0CvZSk2QiWyCWyvP8veqz3vDKfOUgqGrKjYLqar2RrpUm5GvfeBQ8IXK4m3oeYMF4uAPg8pFbUhdlo2aMR8Ce6aGxjQfgNYzHhpdl193ltXStNP4IFLOQb22GwnwiW18UUWMYlc3UZT4ZTbE94sfdoElM04uNgrrH48IdYbwuR+OcQMOchyFRYhiPaFmapLthq5xvuLytZowbYaumfVtClLvMh4NePK7m771S1rWNB9jGAwBhLIYbfrRqcr5+d7lGJ7lbTJorUcTU8m3V2UdYJIklN8cXaWLeXJkuqJkeQE8lzaejyljsGgww3kz/SQ9IqvX+9y/3POJlUcQGYauAuz1hxOQcabcyZGY9j3S+yKIZBVz/II5MrGm+Q2PnPCrYxgPKV9ilDo1w7RqNdK00bUqUzCdzDUURAXA4KXpChEj2rMbTSiZNlumCKtWF3uoy15VWOwf8zcM66ZgyHM4a6rWePqW2u7Q8bFVrBK0m7HeTyZdazxElz5J0qxBaPUXdasZ1o+CiCe87MIg3F2/a46GxB0JVsI0HlK+wRJslrl0nNdfceLQpjlju8ajeVi2M3pjNoG+1lMkTTeYazvGopjfDVmn8biW9Mh72N8951Mp3aILbyo2C+v2P1GkSBAj51ILf8qKbnGXREQHMex5gtst8kL5i3LTnEQ54WMzkN/8wMBPYxgPKxmPDy7KbClsZX/oWPY9oMsuYx7hia5CYdwQihEkS3QTxX11pZTZsFfS68LgczPWQ1zUVTbFzwI8QgvGwjzOLmfoLYC1RxGr6KxIlsVSegMfZUCZdV0i1lGgu5iEdIyYiuJ0Cf53xwNVY6TLPewcIySVLnoeUsNSuVtcmwDYeAC4vOeHFld/AsuyFrKFr1aDHA6o8j9YM4Vwyx5jHWBTr5TwA4Y8w5ExtCs9jykKZLqh6/+E+T895Hlpqfjzso1CS9Y1fLVHEaoKj5VLdWKp+d7mmIuvRwoJrlOnOG4q6jXTHNCP9XhzCnOeRcISJkGQ81NyjgYqy7oaXMloDbONhkHX1497Isuxa16qvQY8HVCXM4y3tJprIMeLKgMOl4tt19xMhIpKbQllXS7HvNul5gCrX7bWcxy6jDFl3Ute9Mk/Havd4aIKjlVLdZK5hpRVUKeu24nkYxuNsMdRU10rjdjoY6fcyE2+e84gRxCEkO33mjq1jEvObANt4GORc/QSKCYobNZZpRtcK2p4mGE3mGHIZooiNrvL8myfnMRFNEQm4TS9O0FsSJQvpPEuZAjsHKp4HNLgyb5bz6B+FXAKyCWKpfFPPI9TOgptQn/vTxX5TDYKasbCf0yb0rWaLQQDG3UlT29W5Hbtc1zYeZQpG9dCGbQAqd5c3yXk43eAJtp4wT2aJiFTjZDmAL0JQJjZFn8dUNGVKlqSaXhJHnCpPSNSeR4OcQKmoQp6Nch5Bo8s8cYa4pbBV657Hc/mgqWS5ZtykRMmZvHrfR5zmpIlsz6NC3bo3IcTljZ4opXy484ezfpR8EcJigng6X3ewzbpSDls1MR7QsjhiqSRVn0dfA1FEjT+CW+ZJbQJZ9on5FJfsilh6znDQw1wii5TSVBx+PdFqutrzGAx48DgdtRdX7bE2y3kAJM4YirrdDFupxPxkNsihbVY8Dx/fOzbX9HFTWWVQvTlz3xc908OWKGlgPIA/a/A/CVzb4WNZV4QvTIgUsY36oSiHrcwYj9bEEePpPCUJQZkA32jzfQClVLwnFtB6FIolnoun+fFLxi09byjoIVsokcwVCXqb9x6sJ7q7fJdhPBwOwWjYW7vLvJGulcZoFCwunmYx422oawUqBxHwOFsMW50Fl4/TaSdXWvE8wj4S2QJLmTz9DcKRE2lDjiZlTpqoLUO4yaj7qZdSvnwtD2S9cQQGCIkUJzZqAjg5p5LYjXStNC2KI0aN6ht/cQn85zZ+sHEc/lKiJxbQepyKZyiWJHtM9nhoqhsFN/q5T8fS9HtdyxrsxkN1ej0a6VppDM8jHTsF7GvYIKhpWd8qOYfsG2FxtmiqQVBTHZprZDyOJYxjT5sTRfW6nPjdTuIbdZ1YQ0zlPIQQFwoh3iiE+B/6p9sHtta4jJkeC6kNWkGTmlP5DjNX+C2KI+oYvrew1NxIGTmREL2dNJ+IqkTpLqs5D6NRcK4H8h5T0RQ7V4zXHQv7aieUG+laafyD4HCRi88AjRsENS13ZifPUgqMUCxJSzmP7RFz42hPLkJeeEx7HmB3mWuaGg8hxIeAvzB+Xg78KfDaLh/XmuMNDuAUklRigzYKJk10l2v8kZZyHqp6SKp+l2Y5D6MZsdfFEa02CGqGg70jUTIdS7NzhVqw1rdaJR5YNh6R+ht0OCA4SnFR9Xo0S5iDMRCqFX2r5Cw531B5G2bR8uqNGgUz+SLRVJ6sO2zJeEQCtjgimPM83gBcB5yWUr4DuATwdvWo1gFvSH1AN+xMj+Rc4zke1fjCLYWt5pM5AmQRpULzaqsqWfZe7vWYnE/hcTpMzXKoZqhHxBGllEaPx3LjOBb2kSuUiK1cBFMmwlYAwW0Io1HQVNjK726tSTAxS8Zj3XhoefVGnscZw/Mq+AYqA7BMELI9D8Cc8UhLKUtAQQgRAs4C+7t7WGuPy1gMcxtVlj0527zHQ+OLqDr8orUP+HwiW9G1aha2qpZl74HQTT0moyl2DvpxOKwl/PWCudE9j1gqTypXrOl5AMysTJqnY4Bo/v4Hx3CmVRGHmbBVyN/CHPNSCVJzFVFEC304HpeD4aCX0w3EEcuGxT9ozfOwjQdgzng8KISIAJ8BHgIeBh5oZ6dCiIgQ4t+EEE8JIY4IIV4khBgUQtwuhDhq3Da59OkwhvHYsLLsqXlrYSuwHLqKJnPs8GWXb6MeWpa9xwdCTcxb7/EAlTjt97k2fM6j0uOx0vOo02Wejqr3vtHMGID+UbyG8TDjebSUJ8jEoVRgyakVdc0bD6Cp9Lw+d1dw2HTCHOych6ap8ZBS/qKUMi6l/GvgeuAmI3zVDp8AvimlPA8VBjsC3ALcIaU8BNxh/L126NLTjSjLXshCdtG88dBXjRaNx3wyx06/sRg2y3k4XUhPPxFH7+Y8pJSWpNhXMhz0bviwVbUUezV1u8ybiSJqgqP48zH8LmlKrDDsd5PIFigUS+YOHMrii3ELirrVNJOe1+fuDY3YOY8WqGs8hBDnGbeX6x9gEHA1ayBshBH6uhr4GwApZU5KGQdeB9xmPOw24PWt7qMlWlxw1wSz3eWaFiVK5hNZxr3Gl81ESbDwRxhxZXrW84il8iSyBXYPWSvT1Qz1eTZ82KrSILjceAwHvTgdYvXi2kwUURMcRSDZ70+Z6vHRC78lNdry7HLDyzURHqumueeRJuRz4e4fVkazZM6whf1u0vliz82w7zSNCqd/HbiZ2s2C7TQJ7gdmgc8JIS5BhcLeC4xKKWcApJQzQohttZ4shLjZOC52797d4iHUYCPLsqcM42E5bGUtBBdN5hjVcuzNwlYAvghDyVTPeh4T8yq/06rnMRT0cHIu1clD6jhThm7Xyl4Hp0Mw2u+t7XkEa371lmP0euzzmpP10PmKBSsKDkZ3+awMIYQk6LHWTzMW9rGQzpPKFQjUeO7MQkYNjgoMgSypMFmj5kiDsFFdtpDOs62/ude1WanreUgpbxZCOIDfkVK+fMVPO93lLuBy4FNSysuAJBZCVFLKW6WUV0oprxwZMZlANoM3RAmxMWXZzYoialqcJjifyDHiNu954FfKur06irbVMl3NYJ93w09SnI6lV1VaacbCvhoJc5Oeh9Flvttj7vvSkiaU4XGfLvQT8rktFzU0m+txejGjmgl1mC5lLu9hd5krGuY8jCqrj3V4n9PAtJTyfuPvf0MZkzNCiHEA4/Zsh/fbGIeDnDOIN7+0uvZ9vUka8VizYasWpgmWSpJYKsegIwUI8IaaP8kXVtVWPRq2mpxfLtthleGgh2gyt3GVmFE5j5UhK8142F8jYR43nfMA2O4yaTwCLRiPxFkQDk7nA5bzHQBjocbS88rz8FVK4E0mzcszPbZ43sOMH/htIcRPAV+SHVhVpZSnhRBTQohzpZRPo3pInjR+bgI+Ytx+ud19WSXn7qcvlyCTL+H3bCB3tEHY6tjZBLc/eYb/+bL9ldhzeaaH+RCc1rUKixT4Qs2rbQD8EfpKiZ7tMJ+IptjW7235vR7q81CSEE/lGApuvNYnKSXPxdK84vzaOmVjYR/feepsRZusmFeFGaZyHiq0NeYwKSior9atNAomz0JgmIVMsSXjUV0UkM/nmZ6eJpNRhkRKyR+/fJB+PxzJjMCN/woxFySOND+XQonPvHYc5+Ipjhw5Y/m41hKfz8fOnTtxu62/fs0wYzx+HehD9XlkAAFIKaWJS9O6/DLwj0IID3AceAfKC/pXIcTPAZPAT7ex/ZYoeMKEk0ni6Rx+T+2rtXUhOQsOd80KqC8+OMWn7z7OT1y2ozJK0+0Hp9dS2EonfvtJmAtZAfgiBEpLxNI5SiVpOayw3kxGUy2HrICywZhPbkzjMbuUJVsoNfA8fKTzRRbTBeUZ6M+Libh/yeFhQQYZknFTx9Jy2Cq4jYV0vjXPQ4etFjNMT0/T39/P3r17EUKQKxQpnF5i54CfQS9wtgSR3aYacbOFIuL0ErsGAhtTgdtASsn8/DzT09Ps27ev49s3U6rbL6V0SCk9UsqQ8Xc7hgMp5aNG3uJiKeXrpZQxKeW8lPI6KeUh43bNW72lN0RIpDaeO6q7y2tUtehSzCdnVlwBWhRH1CWnfaWEuWS5sQ93KYtL5luTnlhnJudTljWtqqnoW23MvMeUUWnVKOcBMKMb6cyIIhosZQqclREGSua+ptUJc9MkzkLfCAvpvCVRRI3P7WQg4GZmIU0mk2FoaKjsneeLKojidjqU4ChAyVwlmNPYxkYOV4Ialzw0NFT2tjqNGW2rO8zctynwRQixQY1HnWS5Tvo+eWpF7NmiOKIWRfSZEUWs3geGREmPha4y+SKnFzOW1XSrqehbbcxzn67T46FZ1etR1rVqbjxiqRyzMkx/wVx/hM/twON0WPQ8Zg3jUWjJ8wBjoqBxftUlxXmj38TtdIBwAMK88TA87MJGy43WoJujEhr1efiEEIPAsBBiwOgAHxRC7AW2d+2I1hFHQI1W3XDdo6m5urPLp6LqqvHIzIqhTL6wpZyHlmP3FJaaNwiW9xEBICR6r8tcL6xtha02uETJyiFQK1nVZW5W1wqIpnKcZYBAzpzxEEIQ8rtZtKJvlZyF4DYWM3nL3eWaer0eFc9DKI/e4VJTFE0ghMDpEJQseB5TU1Ps27ePaFS9xrFYjH379jExMcEv/MIvcODAAS644AKuvvpq7r9f1RJ9+MMf5oILLuDiiy/m0ksvLd+/UWjkC/4C8KsoQ/EQKtcBsAj8VXcPa31w9Rmy7OkNthAm5yCyZ9Xdi5l82dA9ObPC8/BHIGE+mafDVs7sgqWwFWjPY4MZ3CZMzNeW7bBCJODBITauOOJUNMVw0FO3IGBbvxchangeJnIeccPz8GZmQUpTowLCVvStcknIp8j7hsgVSm14Hj4em4qvuj9fLOEQAoc+bofTtOcByvsoWDAeu3bt4t3vfje33HILt956K7fccgs333wzv/mbv8m+ffs4evQoDoeD48ePc+TIEe69916+9rWv8fDDD+P1epmbmyOX21ifs0Z9Hp+QUu4D3iel3C+l3Gf8XCKl/Ms1PMY1wxMcpE9kWUxssMavOmErrVt0/niIk/NJEtmqD38LYatIwI3IxC2HrUKi92Z6tNvjAWoBGezzbFh9KyXFXv/83E4HI8GqiYIWch7RZJ6zMoKjmDWdW7OkRmtIk6Q8ypBZEUWsZjzkYz6ZW1V+ny+WcDsdlbCOw2XNeAhhOefxa7/2a9x33318/OMf53vf+x4/+ZM/yf33388f/uEf4jCqG/fv38+rX/1qZmZmGB4exutVodHh4WG2b99YAZ+mWSgp5V+sxYFsBDx96kuT2UjKuvkM5JZqhq10yOrGC0Y5MrPI06cXuWKPcdVoMWEeTeYYDQCJTEuex0a9+q7HxHyKgMdZDj21ylCfd8OGraZiKS7a0TgEuSysk46BcJrq8VGeh2FkEmdNGZyw320+P2Q0xiackfJzW0EXBVQv9L/31Sd4aCKGQCXVAShkVJe521wYLpMvIqGs63V4e4gP/fgFDZ/jdrv56Ec/yitf+Uq+/e1v8/TTT3PppZfidK72DG+44QZ+//d/n3POOYdXvOIVvOlNb+JlL3uZqWNbK0xNEtwqCOMLkEtuoJke5R6P+p7HjReobt9lSXNfBDKLpvV65hJZdvuNq0KLOY8hZ6ozOY8jX1U/a8CUIYjYbkJxKOjZkIazWJKciqebhuWWiQdqXSsTr0kslWNeRNQfxlyPZlgaCGUYjwXHQPm5rTBu5HWKKzwPFWmrPk+h7jSJECiRJot84xvfYHx8nMcff7zh44LBIA899BC33norIyMjvOlNb+Lv/u7vrO+wi2zs4ctrjbFoFpPx9T2OahqIIk7FUvR7XZw31k8k4F6e9/CFAWk0fUWa7iaazHF+2LiCNhu2MrY75snwTCcW0Lv+BJBw/o+3v60mTERTHBhpvdJKMxT08qPpePsH1GHOLGbIF2XT7vnxsJ97jhlX2+mYqXwHqLBVxjcCRcohpmZYkjI3thkVESDRUc/jf7/mMI8/t8hIv7fSG7V4SuUIxy81ZTynYykW0wUObzfftfDoo49y++23c9999/GSl7yEL3zhCzz22GOUSqVy2Koap9PJNddcwzXXXMNFF13Ebbfdxtvf/nbT++s2ZmeY7xBCvFgIcbX+6faBrQsbUZa9QXf5VFT1KQghODwe4snqiiuLEiXRZK6iqGs2bOV0g7uPEVe6/ZyHlBCfgPljpqteWqVUak+KvRqlrLvi3KWE//5TONu8W7lb1FPTXcl42MdStsBSJm/IsZsboxNP5SgEDAHFhDnPI+Rzs5jOm6tSMi6a5kr96rkdNB6FokQiVaWVRvd6SHOfPZdD5TzMim5IKXn3u9/Nxz/+cXbv3s373/9+PvnJT3LllVfyoQ99qLydo0eP8uUvf5mnn36ao0ePlp//6KOPsmfP6qKZ9cRMn8efAN8Hfgd4v/Hzvi4f1/pgXHGLFka4do1kg7BVLM2uQbU4HB4P8dTMYmVeggVxxGJJEk3l2OYyEqc+C3O4/BGGnOn29a0yceUlFTKwMNXetppwdilLrlBqWYq9muGgh6VsgUy+atFZOg13fhjuWb+6knpDoFaiF9czixnzooiosJU3EAGXz1LYqiQhkTORmE6eBV+YWE6Un9sKQa+Lfp+L6jEiy3o8NFYbBR0CicRszvwzn/kMu3fv5vrrrwfgF3/xF3nqqad4z3vew+nTpzl48CAXXXQR73rXu9i+fTuJRIKbbrqJw4cPc/HFF/Pkk0/yu7/7u+Z2tkaYCVu9HjhXSrkxs4KdxPA8HNkNpKxbDlstT5hLKZmOpbjmHGVUDm8PkS2UODmf5OC2fkvTBOOpHFLCUNl4mMx5APgiRFIdqLaKTVR+nzsGA3vb214D2pVir0bLkkSTObZHjKv8eeOK8dk7TJexdprpWBohYHuk8Wx2nROYWchwMB2H0YtMbT+WzKtKteyopbAVKDXaptVTRne57gsJ+VqPsI+HfRSrcn+NjYc5z0M3ChZLsvx7I26++WZuvvnmyvOdTh566CGAuonwe+65x9SxrBdmwlbHgc6ram1EjAXXvZFk2evoWs0msmTypfKVpY69PqGT5mVxxHjTXeiE74DDwiwPjV/1xrTdYR6frPw+90x722pCuUy3Q2ErWNFlro9/aWbdQldTsRSj/T68rsaij8u6zM0OgkJ5HgMBj5JmNxu2sqJvlZyDPqVrFfS6cDlbr+0ZC/uXJcxz1Q2CGofxOlnsMt/oEiXdxMw7kgIeFUJ8Wgjx5/qn2we2Lrh8FIQbb2HJ2rjMbpKaU/mOFVevlbCEunI8MBLE43RUkuYWwlZ64QtjDPax6Hn0lRIsZgrlK7qWiBueh8u/JsbDIah4Cm2gPY+56rkec0eVwQd49jtt76MVphtIsVezLaSOfza2CPkkBJobDykl8ZQx1Cm4DZbMNaNqfSpzxuMsBEdaFkWsZjzkWxa2KhgNgss8Bsv6VmrpLJqsZtyMmDEeXwH+ALgH1WmufzYfQpBzhwiRYtHKuMxukpyvXWll9Hjo0Ivb6eDQaLBSrmshYa69hiAJ8ARVItwsfqWsC23ON4hPgjcMYxeppHkXmZhPsT3ix+Nqv1J9OFjL8zgKoxfA8DkqdLUOTEWbl+kCeF1OhoMeFqJG6MmE55HMFckVSwwE3BAcM61kYGmIUlnXKk9/GyErUHmdUklSMrwP1SAolpfqtup59IC+Vbcw0yR4W7PHbCaKnhDhtNK3GtwIcsvJ2bqVVrBct+jweIg7nzamDnqCquHLRM5DT8PzF5PWvA4AXwRvXhmPWCrHSH+L0uSxCSWJPXwOHLu9tW2YpF0p9moqOY8VnsfuF6o81UOfg3xayeSvEYViidOLGVOeB6jFNRmfUX+YGASl81tKjnxUXaDkM+BunF+pGI8mC3TRqPzq28bidAc8j7APWUhQKJbwuJzki3J1GEw4UeKI1nMeW5VGwoj/atz+SAjxw5U/a3eIa0vJGyJEkvhGEfrTYasVTMVSjPR7Kx2yqLzHXCLL2aWMCnP5wpbCVt7CovkeD40/gquYwkWhvbxHfBIG9sDwIXUla0HU0SqdKtMF6PM48bocFc8jl4KFSXUeB65V1WMTa5v4nFnIUCw17/HQjIX8ZJeMwgwTnof2MFXOwxg0ZcL7MD3TI1kpT1/sQNhKV5RpMcR8sYRnlfEQlvStbOPROGz1XuP2NcCP1/jZnPgihESS+EZR1q2ra5Vm14ory8PjKmm+LHRlMmw1EHDjyFgQRdRUybK3XHGlezwiu9WiC6riqgssZfJEkzl2tyHFXo0QguGgt6JvFX1W3Q4fgr1XgdOz5nmPqSZS7CsZD/soalUFE02Cuiy7HLYCUxVXfR4XDmHGeBjbamMQVDW6oixfLCGlNDyPGhVSFvStHEK991bEETcbjYQRZ4zbiVo/a3eIa4sjoGZ6bIjh9vkM5BI1p5tNRlcPMjpPG4/qpLkZzyOZVSE6K6KIGq1v1c4s89Q85FNKOXj4HHVfl5LmutKqU54HaIkSI2ylj3v4HPD0we4XwbG1zXtMG/kws4rBY2Ef7lxc/WHK86gKWwXNNwo6HMKcOGLCCL2WB0F1zvMolHSDYI2lz6osuwVxxHqS7P/93/+NEIK/+IuKhOAv/dIvLZMi+djHPsZ5553HhRdeyCWXXMLnP/95AL72ta9x2WWXcckll3D48GE+/elPmzqWTmFrW63AHRggLJIbYyBUne7yfLHEzEJ6VVgi7Hezc8Bv2fOYT+QY6jPG1lr2PFSOJEySaKvqsrrHY2CP6u9wuLpnPObbV9NdybIu87mjgIDB/ervg9fB7BElf7FGTMVUNVlZeqMJ42EfEV1pZyLnocOT5VJdsJQ0b+55KOOR9w+TyrU2v7yakE95PPliqXaPh6YFWXazxqNakh0oS7Lv2bOHbdu28YlPfKKm5Ppf//Vfc/vtt/PAAw/w+OOPc/fddyvvKZ/n5ptv5qtf/SqPPfYYjzzyCNdcc43pY+8EtvFYgTs4oKYJbgSxu2TlCqyamXiGkqx99Xx4PMSRsudhbiCUmsHtUY9tIWEOsM2Tbd3z0GW6kd2q0mtgX6XRrsNMmOy8tsJQsEpZd+6oOg+dID9wrbpdw9DVdCzNeNhfe4GswVjYx4BIUHK4lbfUhFgqjxBGDqNvRE3iM1mua0oc0QhbLTrbE0XU6OFNynjU6PHQWJVlt2A8YLUk+2/8xm8AMDIywnXXXcdtt62uTfqjP/ojPvnJTxIKqahCOBzmpptuYmlpiUKhwNCQikp4vV7OPfdc08fSCSzVwAkhBoBdUspNmzB3BgZwiiKp5FLzBzfjob+D/u1wzg2tPT9pCNatKNUtx7QHV8e0D28PcfuRM6RyBQImw1bRZI7hgENJv7cYthr3ZlrPeVQbD1Ahn7kuGY/5FAMBd9sLUjVDQQ9zxswIMfdMJW8DMHohBEdV6Oqyt3Zsn42Yiprr8dCMh/2cJEHOHcZnohs+nsoR9ruNpLFTfT4t6FuZ8jxcPhYKqtqxE++VUwjyRUm+WGL83t/Dl3iaynw7g2JWVXp5+lb/rwY780VKSHC7VIn5qz7S8PErJdk9nko15y233MKrXvUq3vnOd5bvW1paYmlpiQMHDqza1uDgIK997WvZs2cP1113Ha95zWt4y1veUlNgsVuY0ba6SwgRMkbSPgZ8Tgjxf7p/aOuEceWdS5rT9W/IHX8A3/mD1p9f9jxWGA999VyjmubweAgp4enTS5WwVYNa9GJJEkvlGPcZX+gWE+Zj7jTRVkN98UkVLvEqETyGD8H8s1DsfK/NZDTZEU2raob7vOQKJRKZnOpR0XkbUFU8B66F43d2XfBRMx0z1+OhGQspzyPlNKcQqwosqsrY+61JlJjKefRtY8HotdLNhe1Q8TxKCFHPNFjUWW9Blr2eJPu+fft4/vOfzz/90z+V75NSNhwZ8NnPfpY77riD5z//+XzsYx9bZnjWAjPvSlhKuSiE+Hngc1LKD23mUt2OybJnl1TOIjWnvgzB1RVTTamT85iKpXA6RFlaoprzq5Lml/kiyg3PJcEbrLmLmKFrNebWulYRa8doGJsRdxvKurEJle/QDB+CUl55JEOrr7ra4eRciiv3WhB+NMGQ0SgYPz1Bfz613PMAZTwe+wLMPAo7rujovleSLRQ5s2S+xwPA73Ey7EyxKPoxI8geT+WJBKq8geCYaXFENcfcRNiqb7hsZDrieTgEhaIkXyix8JLfY3ishqFMzasLmW2HwdW8XykaTxNL5bhgu7lQ70pJ9je/+c3L/v9bv/VbvOENb+Dqq5VoeSgUoq+vj+PHj7N///6a27zooou46KKLeNvb3sa+ffvWdOaHGR/HJYQYB94IfK3Lx7P+GIunbFeWPXqi8vvxO1vbRnJOyVysmOw2GU2zPeKrqfezc8BPv8+lkuZlfav6eQ+d/Bx2G3LsVnMeLi+4/Aw60q33ecQnKyErqKq46mzoKldQhQad0LSqRjcKpmcMHauhFcZj/8vV7bHu5z1OxTNIWdsrbcSwI0lMmvPIYqkcg9WeR3DUUsJ8MV1oLGWenIXgtrLKQ6eMh0SSzhdx1wvttKCsa1aWvZYk+/vet1yc/LzzzuPw4cN87WuVZfaDH/wg73nPe1hcVHnMxcVFbr31VhKJBHfddVf5cesh2W7GePw+8C3gWSnlD4QQ+4HuBKQ3AsbiKdttUoseN34RrZdq6h6PGrpW9UpNK7M9Fk1JlMwZid4hpzG33WrYynhOxJFsbZpgqWQYj6oP/tBB4+A6W3E1HUtRkrCnw2ErLY5YPFtVpltNcATGL1mTpHlFecBaR3tYJJgtmDQeyRyRWmErEzpPIb+LXLFEJt/gsYmKNIl6TmeMB0C2UKpfSNCC8QBzjYL1JNknJpZ3Pfz2b/8209PT5b/f/e538/KXv5znPe95XHjhhbzsZS8jEAggpeRP//RPOffcc7n00kv50Ic+tOaTBs3Ik3wR+GLV38eBn+rmQa0rxuLpzLZpPGKG53HOjWrRaEWaOzVXc3b5dCzFK84frfu0w9tD/MsPpih6IzihYdK8XHYpDONhNWxlPCckk6RyRTL54rKu96YkzqhEZbXnERhUSdgOV1xNdKFMF2DY8Dyc0WNKn0v3PlRz4Dq458/VaGCf+elzVtFDoKxWkwVLS5wumTM4sVReNQiWnzyqhiil5puGZ6u7zP2eGp+TUsn43I+Uw1tN5dtNUC2C6HbV+R5aFkesGI8m4sUNJdmr8x+XXHIJpSojLITgAx/4AB/4wAdWbfPrX/+6qePsFmYS5vuFEF8VQswKIc4KIb4shNi3Fge3LhiLpyu/aHpKWE2ix9UCeP5rVQz3TOOZxTWp0V2eyhWYS+QaLg6Hx0OkckVmskbctoHnoY1HqFznH7F+nP4IwZJ6vmXvQ0uxr5zf0YWKq/Icjw4bD62B5l88DsMHa18kHLhWLUon7u7ovlcyFUvhdgpGQ+Z6PADIp/HILDO5wPKhVjXI5Iuk80VD18og2EGJkkxcvU5Gd7nX5bB2MVIHZ9V7Ut/z0OKIFvWttqg4opmw1T8B/wqMA9tRXsg/d/Og1hUjv9BXSpJu8kVqSPSEahTTdf6thK6Ss6vKdM2MF9WzPZ5ZNL4MDUJwc4kcQkCgpBZWyzkPAF8Ef1GVNlvOe6ws09UMH+p42GoimiLgcTISbFG8sQ4el4OQz0U4eWJ1yEqz6wVKrLLLoavpWJrtEb+pAUVl0jEA4vRxdrHxzLdYWZqkOmylGwWbJ83L4oj1ej2qeps6oWulcTgEDsOA1OzxAEMcEVvfyiRmjIeQUv69lLJg/PwDlgvUegini7yrr/0u89hJGNwHoXHYdkFri0ZqflWlle6QbuR5HNwWxOUQPK6rjRuGrbJE/G4c2Tg4va2pv/ojSlQRNWHOEo2MR2peDSjqEBPzKlfUqPyxVXb1FQnlZ1dXWmlcHtj70q5LtFvt8QDKr3FcBplZSDd8qH5/B/uqw1ZGmM5Eo2DZ86j33dIlv32dmeVRjTYadT0PISw1CtrGozl3CiFuEULsFULsEUJ8APhPIcSg0fux6Si4lbKuqaE1NTeQhYVp1SkNcODlMHmvKpk1Sz6tdK1qlOlCY20mr8vJwW1BHjlrxE4bhK3mEzlVLdSKNInGF8GVU8bDcpd5bAL6tq02Wl2ouJqYT3Y836G50GdcMa+stKrmwLXqomL+2a4cAxg9HhYrrSqeR5DTi5mGD9W6VssS5kHzEiU6f1H3u5XsnvHQi33DzvtNZjzaCr03wYzxeBPwC8CdwF3Au4F3ogZCPdi1I1tHSr5we55HbAKQy/WNijk4+X3z2yjPLl/ZIJjG73aWK3zqcXh7iCdmkiqB28DzmE/mWhdF1PgjOPMJnBSt93poKfaVlNV1OxO6KpYkU9E0eztcaaU512mEbOqFrUB9DqBroat0rshcImvd80hXex6NjUe0VtjKE1Dh3k7kPPTn3sh5dKLSCsDn81FILYIEV6OQnsO5aWZ6SCmZn5/H57OQ/7KAmWqrzZscr4cvTEgsEk+32vRmVFoNGi/d7heDy6dCFmalSsoNgssT5lOxFLsG/U1DL4fHQ3zp4ecobgvjbNLncc5osDVdK41hdFqaZR6fqN04F9mj5Mw7VHF1ejFDrljqeLJcs1ecooAD12CDr8vgfnVez34Hnv+ujh/Dc/EWdbsMzyPviXC6ifGI6VkefSsW9eA2U42CoWY5j8RZpZXlH2Qxk+ec0f6m2zTDzp07efSpZynk5nkq0cAgJeeU5zFrzvuYjadJeV3Md9BD6iQ+n4+dO3d2ZdtNjYcQIgD8OrBbSnmzEOIQcK6Usq2GQSGEE+W5PCelfI0RAvsXYC9wEnijlDLWzj5axeEfIMwME62GrXSPhw5buX2w5yprV5zJOt3l0ZSpsIROmqedfQQbhq2yDO4fhDPx2iWmZjDCXbv8OWvGo1RU4b0LfnL1/xxOGDzQsbCVrrTa06E5HivZUZhmqjTCboeHurVBWqrkR1+EQk7lQTqIHk2802rYysh5+ELDJnIeRtjKv+LYg2OmJEqcDkG/19XA8zCKRBwOFlKdC1u53W6ed9F5zR/4lV+BZ74J7zPn8f7cH9/BVQeH+ehPn9/mEfYeZsJWnwNywIuNv6eBP+zAvt8LHKn6+xbgDinlIeAO4+91wdUXoV+kWg9bRU+Ap3/5wn/wOhWCiU+Z20Y5bFXp85BSmtYt0oOhFmRf3bBVoVgins4z2OdtL2xlPG+nz6Ky7uIpdZW3Mlmu6WDFVTek2KvZlp3gWbm9eanywetULmv6Bx0/humY1jyzGraKgcvHQCRswvPIEfS6Vs9/7x81L47YSN/K6C4vlSRL2ULHwlamCQwqY2oyVxDyuzfO4Lg1xozxOCCl/FMgDyClTGNGcrIBQoidwKuBz1bd/TpAaxLfBry+nX20gyswQJg2pgnGTsDg3uX1/gd0vNtktU0NOfZYKk8iWzAV044EPGwP+5grBOomzGOpPFLCcNDTZsJchbvGvVlrOY9yj0cdWYXhQyrBXGz/y3lyXvU/bI90YZZ4qUgoNcmzcntlrkc99l2tSkK7UHU1FUvjdTmsz5FPR8E/wHjY1zTnEU/lV4esQPV6mJRlb6hvlVC6VkuZAlKqWRxrSmBI6aplzalqRwImhB43KWaMR04I4ccozxVCHAAaF4M35+PAB4BqjYLRqumFM0DNGIoQ4mYhxINCiAdnZ2fbPIzaCH+EfpFmIdn4i1SX6PFKyEozci6EdpgPXaXmVMzfW4n5Tlmcgnd4e4hTWW9dz0OHmAYDLiPnETF3bCsxjM42d8Za2KpcplvPeJyjPJNqnbAWmYwm2TkQsNb/YJaFKZylLMfl9spcj3r4wrDr+V2ZLjgdS7FjoHk+bBXpOPgHGQv7mU1kywOTarFKUVcTHIV8ErKJprsL+5uErfq2dVQU0RJ6GFbKnKp22O+uX3a8yTFjPH4X+CawSwjxj6iQ0m+2ukMhxGuAs1LKh1p5vpTyVinllVLKK0dGWlCqNYNxJZ1vRVm3VFTVVrrSSiOEKtk9fpc5qfHkvIr9Vi0EukzXbEL08HiI5zKeujpdenTqNk8ekG0nzEfcaWsd5vFJQEC4TkKvgxVXE/OproWsdF7m2dI4c2aM54FrYeaxSmiyQ6i59i2cY0p5HtvDPqSEs0v1DWA8tULXSmOxy3wxXec7UBZFXCfjocPEaXP9RRG/x/Y86iGl/Dbwk8DbgS8AV0opW5SJBeAq4LVCiJOoTvVrhRD/AJwx1Hsxbs0NCOgGxmJYaKVBbfE55fbWqro5cJ26wj/1cPPtJGdrJMut6RYd3h4iVupDFNKq92QFOsQy7DKSpK2GrYznDTpSxJJ587XlsQnoH68vf617JtqsuJJSKuPRYTXdMobxMOV5gBHClOpCooNMx1poEASV8wgMlMfWnm6QNI+l8gwGaizo/eaNR92BULmkmmXfYTl2S2jjYfK7Hw64W6/K7HHMaFvdIaWcl1L+p5Tya1LKOSFEyz63lPKDUsqdUsq9wJuB70gp3wp8BbjJeNhNwJdb3UfbGFfgpVQL4ogrK62q2X8NIMyFrlJzNRsEBwJugl5zceDD42EWMKqLaoSuyqKIjjZEEUE1+Dm9RESSXLFEMmdS1qVej4fGF1JVPG1WXEWTORLZQseHQJWZewbpHyAu+pvnPAC2Xwr+gY6GrhLZArFUvrXxuuWchzI8jfIeqxR1NbpR0ES5bt2BUOXu8m0dVdS1RECHrUwaD7+bTL7UVBNsM1LXeAghfEb57LAQYkB3lAsh9qI0rjrNR4DrhRBHgeuNv9eHspR5K8ZD93jUGN4SGIQdl5tbNJKzq3s8oilLi8POAT9Zl5EzqZE0n09kEaJNUUSNP0IIVQ5rOmken6hfaaXpQMWVnlu+t4thKzF8DoN9vnIosCEOp7qQ0GrLHUBXWln2PKRUnod/sMrzqG088sUSS9lCWQhyGRbDVul8kVxhRW4lWeltWj/Pw3rOA2g+4GoT0sjz+AVUF/l5xq3++TLwV53YuZTyLinla4zf56WU10kpDxm3nRM1sorhebQkyx47oRLdoTr29cB18NyD5casuuicRxVWjYfDIYgMGtuoYQjnjeSnM6ukRVrOeQD4IvRJZYRMJc2LeRXiq5cs1wwfUp5HG4tsucejW8Zj/igMHWI46GHOjOcB6nOQOA1nn+zIIZRDmlZzHrmkUj/wDxDyuQh4nHU9D126PlArbBUYVIPLzBiPQJ1GQS1NEqzIsa+58fCGVZOiReOxFfMedY2HlPITRnf5+6SU+6WU+4yfS6SUf7mGx7j2GOEbd2GJQoPKk5pEj6sF0VGnVezgdSBLjaW5cylVuVI1y6NYkjwXt54Q3TairghLqdXGKlotTQKth60A/FXKumaS5gvT6nVo6nmco46vjeTyxHwKIVponjNDOq4WzOFDDAU95nIe0J7acg1a9jz0RUxgECEEY2FfXc8jVkvXSiOE6XLduvpWK0QRXQ5BoNbMj27iUN3tphPmhiHcir0eZqqtTgsh+gGEEL8jhPiSEOLyLh/X+mJcgYdJlEdhmiZ6snbISrPjCqUD1GjRqCFNcmYxQ74o2TVobXHYMT6uDmt+df3BfCKnNLJ0PqSdsJWvWlnXhPFo1uOhGWq/4mpyPsV4yNeRuRCrmD+mbofPYajPy7zZkF14B4yc1zGdq6lomoDHWTuk1Ai9SPrVXHfV61E7Ya7f17r7CG6zJMu+ynisCFuF/e6uKCA3JTBk3fPYguW6ZozH/5JSLgkhXgLciGrg+1R3D2ud8fRREk5CIlVWETWFlEaDYAN9I6dbNYo1infXEEXUPR5WPY+9hq7NmTOrv9TzySxDQcPzEE41b6JV/BHcWlnXlPFo0uOhGW6/4urkfLJrmlblZH7Z87DweTlwHUzcozzNNpkyKq2s93gYnofR3zAW8jfwPNQCGakVtgI118OEREmorvE4qy7cXN6OiiJaRneZm0DLtNieR210GcGrgU9JKb8MdFaUZ6MhBAVPyHqXeXJWSU/UqrSq5uB1sDBVv4ooudrzmGpxvOj+XSr3EouubqicT+YY6vNWRBHbucrzRRCZOE6HMNfrEZ9UBiu0o/HjwruUqGQbFVeT0VTX1HSZe0bJeA/sZTjoJZEtmK+8OXitGsE7cU/bh9GSFDtUGY+K53FmKVtTKbbmIKhqgqOmq62gRpK5qkhkMbMO0iSawJClaiuwcx71eE4I8WngjcDXhRBek8/raUreMCGRsvahaFRpVY2Od9cLWZTDVpWcx2RUxe13WJTX8Pn8ZPCSXFjuhheKJeKpvApBtCNNovFHENlFhvwOc55HbEKFbpxNyo4dDhg62HLYKpFVY3u753k8o95vp7ssk286dLXnKjWAq83QlZSS6VaGQEFlkTSqjMbCPoolyVyN3I0p45GabyonE/Kr93yV8Uio7nKg47M8LOEfMB226ve5EAIWrM6x2QSYMQJvBL4FvFJKGQcGgfd386A2BD5Vemoplql7PBqFrUDN6x48UF/fqEbYajqq4varBOlMkHH1k08sv5LSIYhy2KqdZDmUn78rUDAZtppsHrLS6IqrFlilpjt3DL763ppNky0xf6yclxkyxtuaTpq7/bDnxW3rXC2mCyxlCy32eKz2PKB2r0csmcPnduCvl8TuHwVkRZetDvVzHmfLvU2dHEFrmcCQygWZqPBzOET9psdNjpkO85SU8ktSyqPG3zNG1/mmxuHXA6EsXFHETgCieQURqNDVye/VXsSSs+qKtFrXKpZiZ4sd0iVvBHd+cVkiW/cjDPW1OUVQYzx/hy9rbhRtfMKC8ThHPT5vXWtslZruDz4DD/0dnPyu5W2tolhQUwGHtfEwPA8reY+D18HsU6r6rEWmWq20AmU83H3lLv9GXeaxVL6+1wGVXo8moSuvy4nP7ahhPGbLYwEW0vm1F0XUBIZU+XKuuU4XqByQnfOwKeMKDBAixUI9DZ5aRE8YMXoTqqYHrlNSDJP3rf6fnl1erWvVqm4R4DTO5cjMYuVQE1WVM+0MgtIYnse414Qsez4DSzPmjCyoK3tZqnh2FtANgnuGAupK8umvq38cvd3ytlYRn1BSNMb0wOE+9b7XCvnUpay2bFHxZ+kM3PH7cO8nq8p0W/Q8DK8DaNhlHk/VEUXUlMfRNk+ar9K3KubVsfRtQ0q5zp6H9S5z2/OwKeMIRIiIpDXdmuhxJcVuhr0vUU1VtUIWK3StMvkiZ5Yylst0Nf7QIGGR5Mkq46Hj8sOdClsZnseoJ9O8VFdfZTcr09W0UXE1MZ9ksM9Dv88NZ4+ocJnTC0c74DxXVVpBledhRVl42/lK38ts6Cp6Ar72a/Dxi+C7fwZ3fpjpeXWF3I4oomYg4MbjctSsuIomc7Xl2DVlfSsTEwVXhnqqhp+lckUKJbm+YSuwVK7b8uyfHsY2HvXwhekXKWs5j9iJ5pVWGm8Qdr8QjtVIlibnluU7nounkdK8FPtKPMFBBhxJnjxVZTyMq+PBgLszYSvDcxlxpoilcpQazXWOn1S3pj2Pg+q2haT5MjVd7XW8+JeVoZ9/1vL2lqGPxzi+gEeFY0znPKAyXfDZOxvPzj7zBPz7z8NfXAGP/ANc8ma45oOQS5CZOUK/z1Xu3LaEIYpYORxRd65HPJWv3SCoMZLdZhoFV12tl7vL11GOXeO37nnY8iQ2FXwRvORJJc3FPcksqCuVZpVW1Ry4Fs78aPWXbYUoYrnHo1VVWF+EiEgt8zyiyRxCQMRdUKGXDiXMB5xpSrLBjGqoNAiazXl4g6qkt4Wk+TI13We+Cdsvh8veqv5u1/uYe0aVlhphDiGEahS0kvMA9TnIxOHUo6v/N3kf/NOb4FMvhqe+Di98N7z3h/DaPy+P7w3MPtZ693x6uecBMBaq3WUeS+UYbGQ8XB618JrUt1puPCrDz9ZNFFFjVZbdznnYLEPP9EjFzT2+XKZr0vOASsnu8RXx7uRc7R6PVhcIfwS/THHi7ALZgrq6nUuqhcCZ64CulbEPgIhDVTc1rLiKTaiQXf+Y+e23UHGVLRQ5tZBWarqJszD9IJz7KvUeDZ/TvvGoqrQqH2bQYy1sBbD/5Si1ZSN0JaXKyfztq+Bvb4SpB+Ca34Jfexxu/DCElGoAQwfBG2Jk6XHro2c1hihiNeNhH6dWJMyLJUk8na+ta1VN/5j5mR7VFxiJivFYN10rTQviiAtpC6MINgm28aiHsRjW0oSqScwwHmbDVgBjF6vwVLVUiZ5pUDW7fDqawuNysM3qeFGNYRj8pSRHzxjihYlcpccD2g9buQPgcBOShrJuo6R5fBIiu+rrf9Vi+BzLAonTMRXu2zsUgGe+BUhlPAAOXg8nv69e71aZe6aSjzEYCnrNKetW0zekZNqP3g6P/zt8+qXwj29QCflXfkQZjWt+s7KoaRwO5PbLOJB9qjXPo6you8LzCPs5s5hZFnpcTKuRxQPN5E+C20w1Cq6aY67DVuupqKvxaXFE813mxZIkkbUoZdTj2MajHsaCK8wq67bieTgcyvs4fieUDAHGmt3lKXZG/DhaHaFqhJSqk+bRZK7S41H1mJYRAvzVyrqNwlYmpNhXMnQIckumFibNsjLdZ74JoZ0weqH656HrVXf3iRZLdlNRdWVqVFqVD7PPokSJ5sB1MP0A/Ns7VTXa6/4KfuVRFaby1O+OT2+7jHOYZG+4hc9GdkmN+Q2s9jzyRbnMg2raIKgJmpcoWcoUKp3syVmlJODtX3/j4XBaahTcql3mtvGoh7GYOrIL5tzR6HG14Ff1Zpji4HXqi3PmR+rvVKXqRDMVTbfc4wFUKqHc6XLSfC6ZrfR4VD2mLXwR/AWlrNuw4spKg6CmhYqrk0aD4O6QU3Vxn/vKSvnznher/oZWQ1crKq00Q0GV87AcwrjsrXD+a+GNn4f33K/+djVXATrdfwEuUeI8TlrbH6wSRdTUmutRUdRtFrYaVdVWTc5fL7hLOnSlu8uFWP+cB1hS1tWFClut4so2HvUwjEdfKUHKzGS82ElryXLN/perWx26ShpXO1XVVpPRFLtbLNMFyudywYBc5nl0TI5d468o69bt9cgllbG06nnoK3wLFVcT8yn6PE6GZ+9ToUAdsgLVi7P/GhUqaiVWrY9jeHXOI2cMTbLE4D5409/D4ddZCuc961avy57MEWv7g1WiiJpKl3kl76EbP5uq9gZHVYNdjeFj1ay6Wq8qT19M5xEC+k1OzOwKLSjrbrWKK9t41MMIW5nWt4paKNOtpn8URi+q6BuVq06ML1Imz0I633qyHMrncm6kyJFTi+QNXSsVtlpY9pi28EVwZBfwuBz1PY/4lLod2Gtt26HtylOwkDSfjKbYPdSHePobSjF470uXP+DQ9bAwCbNPWzsWUB6Q07PKg2qpy7wNjqX7OSUHGYw/bv3JqSaex+Jqz6N52Ep3mTdOmlcWXMPIJs+Wu8sXMwX6va7Ww7SdwIqybsAOW9lUU57pkWzujuYzaiqelXxHNQevVSWZ2cSqsFXbZbpQDkntDyoNpB89pwzGslkenTAefqWsOxjw1K+2KkuxW/Q8hIDhg5aMx8n5JHsG/CpZfuDa1Z3/h65Xt62EruaOKn2yFV7CUJ9Ffas2mY6lOCIO4Z55yPqTqwZBVTPc58XlEMt6PUyHrYLmGgW19MhCOg/PPaRCmcZnfiGdb61npZNYMB7aEG61cl3beNTD5aHo9KuZHs26zOMTgGwtbAVqYSvlldaVThwaszVaHi9ajRGS2ulX5/G9o8pADQW9KrzgDVurfGq0n3Scgb5GxsNij0c1uuLKBMWSZDqa5vmBKVg6tTxkpQnvhG0XtGg8VldaQcXzMD2Otk2mYmmmAodV2DRpLsxSZoUoosbhEIyu6PWIpfK4nYJgs1BSvzmJkrDfxYsdj3Pet98KnzFK1i/6aWCdFXU1OmxlIqSpZ3rYnodNmZIvTIhk81imrrRqJWwFsPtFqtT12Tsqs8uNxK7WLWpVmgRQ6q1ODyOuNA4B3z2qQmMVOfYOeB2gPJzMAsMBV/2cR+ykMo5GiMISQ4dUmMnE8KTTixlyxRLPy9wPCDh0Q+0HHroeJu+FzGLt/9eimFfnUcN4DGtlXavlui0yHUsRG7hI/XHqYWtPrmM8YPVEwXgqRyTgaT5sqpk4YqkER77K/v94Lf/k+SP6Fo/C9b8Pv/q4ykGhRRHX2Xj4B1U1Xr75Z83nduBxOuyEuU0VPq2s28x4mJRir4fLq7Sunv2O0V2+fI5Hv8/V3pWYEOAL484vsm+4j0cm44ARtuqEKKLGFwEk4/58g5zHpApZtTJ4Si/W0eayIhNzqtJqX/Ru2PWCZdVryzh0gypXPX6X+eOInlDPWVGmC5WcwFrkPEolyXQsTWnsEkCo8I8VUlHw9KvplitYOcs8msw1bxAEVW3o8q9uFCzm4dF/gk++EP7lrbhyC3ww/3P8wwu+Cle9F3yh8kPXVRRRY0HfSghBOLD1xBFt49EAhz9CiFTzWGbshJpLXtXYZ5kD16qO5VOPLu/xiKbYNRBof5azEVI6vD1MwaitL4etOlFpBeXcyrgn0zjnYTXfobFQcTURTTHGPMHoE6pEtx67nq/CdlZCV3UqrQA8Lgdhv3tNch5ziSy5Qoltw8NqHrpV47FC16oarW+lS46byrFrhDDKdQ3jkUvBfX8Nn7gU/uPdylD91N8gfukHfFG+gmhu9RK0McJWrXSZb62BULbxaIAjECHiSDa/oogeV9VD7SzwWpo7eXb57PJYur2QlcYfgUycw+PqCs8hIOLvkCiixjBC2zwZFjMF8sXS6se00uOhGToACFN5j4n5FDe4H1F/nPtj9R/odMOBl8Ox/zJfsqt7TYZWGw9QeY85qxIlLVCe4zEYgB1XKPkVK2XHNbrLNWNhP9lCqex1x5JN5NirCY6p78TdH4WPXwjf/E2lKPAzX4T/+T246A0Ip7uulPnGMB7a8zDbZW57HjZVCF+EsEiZCFudaD1kpRk+pGaBQDnEIqVkOpZqL1muKXseyngM9nlUKWQXPI8Rl4qVr3rdMotqwWrV83D71SJkyngkebXnMZWHqhFeWsahG9R8kTMmy13njqoFsirUUs1wn3dNPI/psuaZH3ZeoZraYifNbyAdXdXjodm+YqJgLJVvLk2iCW5TXtB3/lAZtXd8E975TTjnhmUXWLWMRyZfJFsorW+DIFg2HltRlt02Ho3wRYyBUA2uIosFdTXdaqWVRktzQ9l4zCayZPKl9sp0Nb4wZBbKnke52avjOQ8YdKgr4lX6VrrSyuwcj1oMn2MqbHV6LsrlxR8qr6OZR3jwFerWbOiqTqWVZijYokSJRXQZ984Bw/MAa6Grhp6H7vVII6U0BkGZXNAveytcfpPyMn72i7DnRTUfFqohZa7FEtfdeGijaqHL3PY8bCr4wgRJstCocmZxWpXZtlppVY02HgHd46GuLFud47EMI2w10u9lOOhVxqOQU9UkHQtbKSMUEWpRW5X3KPd4tGE8hg6p3FCpRkjMQErJzuh9uMk3zndo+kdh/BJz0wWlVJ5HM+OxBmGryWiK4aAXn9sJ2w6rKrbnLFRcpaKrxRYNqicKLmULFErSfNjqnBuVZPzYRQ0fVtN4rLeulcYfAYS1nIftediU8UdwIMmlGpRxtiKIWI9D16urtgNKsqTSINiBnIcvoryMUon3XneQn3nBns5Kk0DZCPVjKOuuMh5t9Hhohg8pg7d0qu5D5pM5Xlr6AVlXUJVBm+HQDTB1f6V8tR7JOfW6NQiFDfV5iaVyFGrlfDpEKlfg20+e4co9hufgdCsDaNbzKJXUedTxPEb6vTgdgtMLGeKGNInpsJVJaoWt1l0UUeNwqs+zSeMR8XuUke3ie77RsI1HI4wr6VKjmR5air3dsBUo9dTX/ZVqXmNFWKJd/BE1Bzy3xNtetJfXXrK9ShSx9gJiGU8QhJO+khJHXNXrEZtQj6lztWsKExVXE7NLXOt8hNj2a2qWodbk0A3q9dEyMfVoUGlVPsSgBylVnqBbfPHBaeKpPO+6uuqiZccVMPOoKottRnZBnW+d997pEGzr9zKzkKmSJunsgh72u+oaD92Bvq4EhizkPNTxLma2jiy7bTwaoXMBjWTZo8fVTOz+7R3f/VQsxUi/EZZoF+1dZKrOpZO6VlCWZfcV6yjrttPjodGLdoOk+dKz9zEsFmt3lddjxxVqIW0WumpSaQVGCTTdaxQsliSf/d5xLt8d4Yo9VYZ4xxVQyMDZJ5tvpI4oYjW61yNalibpvOexmCksUyDWWlfr7nmAem3Meh6BNe4y3wCDp2zj0QhjwfXkF2qXnYIhiLhHzeboMFPRdOsT4laiDYT2NqDzYStjW67sAn0e5+qZHvGJ9kJWoDqYvaGGxiNw8nby0snAxRaMh8OpEudHb2+YT2HuqMot6Mq4Ggz1dbdR8FtPnGYqmubmq1d4u1aS5nrIWQOvU3eZxw3j0VRR1yIhn5tiSZKsUq3eMGErUJ6H2YS51rdqNAStk/z7z8Gdf7w2+6qDbTwaoZV1SdWXKGlVit0EU7FUZyqtoJIUr5bK7uQsj+r9ZJS+1bJqKykrnkc7CGGMpK0ftto5+9/80Hk+3n6LTZuHblAd/jOP1H/M3DPK62hwsVDRt+q85yGl5NN3H2fPUIDrD68Y4zuwV10tmzEedUQRqxkL+ZlZyJQvAjoftlqtRrshZnlorISt1lJZd+aHauJkJ/To2mDNjYcQYpcQ4k4hxBEhxBNCiPca9w8KIW4XQhw1bjsUiG8DY1ENiWTtLnMpW5dib0K+WOJUPN2ZSiuoeBdr4HmQjjPYt6LiKB2D7GJ7ZbqaoQbzzGMn2Z49wRPBq6xv98B1gGgcupo7qtR9Gx2eoax7pkrSvFM8OBHjsak4P/+SfThXSpYLobwPMxVXdQZBVTMe9pHKFZmKpnAIOq43VTYeqeXGo8/jxO3cANe1gQ06TfDujyrv+/k3d39fDViPd6gA/IaU8nzghcB7hBCHgVuAO6SUh4A7jL/Xl7LnUafLPHEW8snOVFqtYCaeoSTbVNOtpux5VOc84uq2UzkPvZ+MMh7Lch6tSrHXYviQqrbKLq3+39PfBODM+Mutb7dvCHZeWd945DPqPJo0HUYCbs4ZDfIP902SLZgYJGaBW+8+zkDAzRuuqBM223EFnD1S+7WpxmTOA+DJmUUiAU/H52uUZ3pkKt+txXR+Y3gdoDyPQsaUEGdkrYzH2SNw5Cvwgl/obMSgBdbceEgpZ6SUDxu/LwFHgB3A64DbjIfdBrx+rY9tFZ5+pHCogVC1Kmc6WWm1gor0RIdzHivDVu6AqXGn5vcTUZ7HypkenSjT1ejFe/7Yqn8VjvwnR0s7CI436Sqvx6EbVNhHz5KvJnpcVSg1SJaDEsr7X685zGQ0xd9+72Rrx1GDZ2cT/NeRM7ztRXvxe+qELHZcAUilkdYIHY5pcOGgJwoemVlsPsejBUJ1wlYbIt8BFcNqwvsIlXMeXTYed39MVSy+8Be7ux8TrKtvKITYC1wG3A+MSilnQBkYoKZmtxDiZiHEg0KIB2dnZ7t7gA4HJU9IDYSq1WWu1XS7ELYq93h0yvPw9INwrA5bdTJkBWVZ9oGAe3nOI9ZhzwNWh64yCzgn7+G/SpezZ6jF1+3Q9YCsjAWuRldaNSjT1bz00AivOH+Uv/zOUc52KHz12e+ewO108D9e1MAAm02ap2PKcDjrl8Rqz2MpU2Cww5VWUD/nsaE8DzCVNHc7HQS9q0uPO8rcMXjiS/C8n2uv3L1DrJvxEEIEgX8HflVKaXqYgpTyVinllVLKK0dGRpo/oV184fqeR/SEWpA7sSCuYCqWwukQ5au/tnE4DImSeOW+TooianwRkEVGfXlSuSKpnFH3Hp9U++/E/gb3q9d9pfE49l8IWeC/im0Yj7FLoG9bbakSnaQfapzz0PzOq88nVyzx0W+1MOZ25a4TWf794Wl+6vKd5ZkhNekbUonzpsYj2rS/Z1u/r1xV3ekyXahcrS9uVM/Dgiw7rIG+1Xf/TLUFvOiXu7cPC6yL8RBCuFGG4x+llF8y7j4jhBg3/j8ONB5FtkY4/BHD86gTtgrv7GzYx2AqmmZHxI+rk4lDI6RUJrPQHc8DuGxYrTpf++GMur8dKfaVuLwq/LWy4urpb5J2R3hEHmLPUF9r23Y4lPdx7L+gtCJfMXcUQjvAGzS1qb3DfbzzJfv44kPTPDYVb+14DD5/7wS5Qomff6kJL9dM0jwda5jvACUvrw1VpyutAPq9LoRY7nksboRBUJqyLLv5ct2ueR7RE/DDf4Er3wHBNbhoNsF6VFsJ4G+AI1LK/1P1r68ANxm/3wR8ea2PrRbCH2bAUUdZN3q8KyErULpFHZElqcYQRyyTiXc2WQ5lY3TlGJw31s9n7j6umsDakWKvxcqRtMUCHP02T/a9kIE+X/NxqY04dL16baYfXH5/E02rWvzSyw8yHPTye199YlkznBXSuSJ/f+9JXnH+KAdGTBiuHVcozbV60/xALYgmlAW059vpHg9Q4277va5lnsdiprABPQ8rxqNLfR7f+7/gcMGLf6U722+B9fA8rgLeBlwrhHjU+Pkx4CPA9UKIo8D1xt/rjy9CxFGnz6MTUux16JgUezVGJVSZ9ELnw1bG9kRmgZuv3s/RswnuevpsF4zHITVRUHsHU/dBJs7d4srWQ1aa/S8H4VweuioLIlpLxPf73HzgxnN5eDLOVx6rr8fViH97eJpYKr+6KbAeZvIe6ZipuPlYSBmPboStYLkabaFYIpHdQMZDe+WWBkJ1wfOIT6kpjJe/DULjnd9+i6xHtdX3pJRCSnmxlPJS4+frUsp5KeV1UspDxq05c99tjDnmq8JW6biKG3eh0iqVKzCXyHWuQVCzKmwV73zYqqqf5DUXb2cs5ONf7npEiRl2osdDM3xIlVEuTKm/n/4GOD18PXW49ZCVxh9Ro2urjcfSacgtNa20qsUbrtjJRTvC/PHXn6rkgExSLEn+5rvHuWRXhOftNdn6NH6JMn7NjIcFz6MbYStYvuBqXSitE7XuOF3q8/z4v8Pt/xse+UeY+sHy71AVkUCXch7f/4S6vepXO7/tNtgg79IGxpBlXyU7oMt0uxC20kN+dnZKmkRT7XmUiqppr0ueB5k4HpeDd1y1l29882vgpbOFBWWBxKMqQfz0NyjteQnHjsCr2/U8QIWu7vg9WJxRV3sWKq1W4nAIPvTjh3nDX9/LX9/1LL9+w7mmn3v7k2c4OZ/ik688z/woYrcfRi+obzxKRRW+bJLzADVREDqvqKupNh4bqrtc88J3wxP/Afd+Uo1e0ARH1Wdw+BAMnwvDh9jlcHQ+bLU4Aw9/Hi59ixqEtoGwjUcz/BF8MksytaJRqJNS7CvQZbod6y7X6JyHlJ0XRazeB5S3/5YX7ObodwwnstM5D6gYj+izzF34TuSTtB+2AtXvccfvqcT55W+rUtNtrX/kyr2DvPaS7Xz67uO88Xm7TCslf+a7x9k16OfGC8aaP7iaHVfA419SOl0rpVQyC4C06Hl0x3iEfG7OLCoZlw0zy6Oaa25RP8WCKvqYe6byM/uM8kqMz/p7gLc7vZRuvRDHj38cxi9uf//3/DmUCvCSX29/Wx1mA2gAbHCMMEwpvUJZt4s9HpPlOR5dCFsVc5BPd0eaBFb1k4R8bn5sp1ocTomarTutERhSxz73DDz9dQCOhpUkye7BNsNWoK7c+7fDMaPbfO4ouPsg1Lp68i2vOg8h4I+/8ZSpxz80EeWhiRg//5L9q6VImrHjCqUGXaORspwANpHzuPqcEX7mBbu5aEeHLzIMankeG8p4aJwuGDqglJqveq8anfDzt8NvTsD7jsHbv879F/wv/rl4LXLhOfiHn6qsEa2SmIUHPwcXv7FrudV2sI1HM6pi+MuqZWInVD+AybJNK0xF0/jdzrI6a8eoFkfshigi1OwnecFgknnZz98+0MHqayEqFVdPfxPGLuLpdASAvZ3wPIRQoatn71TzMbSmVRty8tsjft79soP85w9nuP948yTsrXcfJ+x389NX7rS+s0ZJ87I0SXPPY7DPwx/9xEX1O9rbpGeMRz2EUKWze69i7tyf5Q8Kb2Pyx/9Zhbj+/ieVhFGr3PsXKq/30t/o3PF2ENt4NMMIw/TJJKkq6WiiJ7uspus3H+M2S7U4Yrc8D73NqqRiX2qapH87X3hgsrPVKMPnwOkfqUqrc17FZDRF0OvqXFnpoRtUXmjq/pYqrWpx89X72R728XtffZJiqX7p7om5JN9+8gxve+EeAp4Wossj5yoZi5rGQ4sirn+XcsjvJlcokckXe9N4VKGPe9a7B3723yBxRnkgGdM90BVSUXjgs3DhT7WUZ1sLbOPRjHrKutHjXXMlp6JdKNOF5fmIbuU8YHVJcHyS8PhBkrkiX3hgsnP7GT5YmYh37iuZmE+yezDQOaO7/2XgcMOTX4aFyZYqrVbi9zj54I+dz5Mzi/zrg1N1H/c33zuO2+Hgf7y4xTyRwwnbL2vieURa23YHCVd1mWuBxA2VMLdApFqWfeeV8MbPw5kn4F9+FgoW5fnv+6QSXb36fV040s5gG49mGItruLriKp9Wqq5dyHdIKZmOpTuf74C1CVvBcs+jVDKMxwGuOjjE575/glyhQ3OetScQHIPxy5iYT7F3uIOvm7cf9rxY1dhDx64AX3PxOM/bO8DHvvV0TU9sPpHliw9O8xOX7WBbfxvyNDsuV57ZyoVL5zw6NX64DarFERfSeTwuR2cmZ64DqwZCHboeXv9JOHE3fOnm1YoF9UjH4f5Pw/mvhW3nd+dgO4BtPJqhZdlFqvJF1yJ/XQhbxVN5EtlCd4zHWoWtqj2PxBmVpB/Yw7teup8zi1m+2mKz3Cq08TjnRooIpmKpziTLqzl0A+QSy/fXJkIIPvTjFxBN5fiLO1bPJVFS7qXl88lbYccVKvZ++kfL70/HANGd994i1eKIixtJ16oFag6EuuTNcMMfwpP/Ad/4TXPjYx+4VYVLr35/dw60Q9jGoxnGFyxMsiKOqKsouhC2KldadbrHAypXmtrzcHpUT0CnqfY8ynM89vCyc0Y4d7Sfz3z3eMtSHcsYPAAvfA+86D3MLKTJF2VnynSrOXSD8YtQ1TYd4sIdYd505S7+7p6TPDubKN+fyRf5/L0nue68bRzc1t/eTuolzdNRZeC7MDrZKuEVnkcvG4+gx4VD1Jjp8eJfVj8/+Ax892ONN5JdUiGrc17VmVLfLrL+n56NjtuHdHoJiVQl59HFBkE9x6Mrnoc3pG51zsMXbqt6qC7a89CaVgCRPQgh+PmX7uOp00t892iNeRlWcTjglX8EI+cyMa9et44bj+FDqj8lsqvjhvY3bjgXv9vJh//zSPm+Lz38HPPJHO8yK0XSiNAOFdJbZTyaiyKuFdUDoRbTBUK+3m09czhEfYmSV/w+XPxm+M4fwkO3rf6/5gefVe/Pyza21wG28TCHL7J8mmD0BHjDXdHUn4qq7vKuGA+nS/Vh6LBVt8IWvohqbMolq+Z4qO7Y1126g9GQl898t80a+BVUjEeHw1ZCwCt+F67+QGe3C4z0e/mV6w7xnafOcufTZymVJJ/97nEu3hnmBfs68Nkqj6VdYTxMiiKuBdpYLKR63/OABrLsDge87i/h4PXwtV+FI19b/ZhcEu75SzUOWXuNGxjbeJjBHyZSrawbPQ6DextetT80EeOX/ulhfuULj/DVx06RyJrTNJqKpRgIuNtThW2E9gq6Mcujeh+g9hOfUFIOxlW7x+Xg7S/ex3ePzvHkqRZKGOswEU3icTrKQn4d5cKfVF3mXeCmF+9l33Aff/C1J/nmE6c5PpfkXS/d37mKsR2Xq0ZBXWEFpkUR14JKwrywOYxHwFO/HN3phjfeBtsvh397J5z8/vL/P/g5SM3Byzp/odINbONhAuGLMOhMV3RrYidqhqyklPz3M7O86dP38lOfuofvHp3j+8fm+OUvPMLlv3877/jcA3zhgUlml+qX7U1FU52XJalG5yO67XmA2k98YpUsyc+8YDd9HmdHvY+JOdUbY7kTe53xuBz8zqvP5/hskvd98TF2RPy86kKLUiSNKOc9quZ7mBgEtVa4nQ76PM5NkfMAw/No1Mvk6YOf/aISCf3CW1QpL6gKznv+HPa+FHa/cG0Otk16N8C4lvjCDIgT6oqiWFBx/MOvL/+7WJJ88/HTfOq/j/H4c4uMhXz8zqvP5y3P343P7eShiRjfeuI033riNHc+/SN+S/yIK3YPcOMFY9x4wRi7q+L0U9EUF3RJCgJY7nmYnIjX0j7A8DwmYceVy/4d9rt50/N28/l7T/L+G89le6T9XMJENNX5kNUace1527j6nBHufmaW992wr7MDwLZfpm6fexgOXqd+T8c3TM4D9IKbYzGzgUbQtkjE7y5r09UlMAhv/RL8zQ0UP/8TfPrgp/Ce+C9+LnGGrx78fQJHznBgJMiuwcCGvhiyjYcZjFG08VReSYCXCjC4j1yhxP97ZJpP//dxjs8l2Tfcx5/81EW8/rIdeF2VWvXn7xvk+fsG+Z1Xn8+RmSW+/eRpvvXEGT789SN8+OtHOG+snxsuGOP680d5Lp7mlRd2UbPfF1ZhN50w78o+Iuo2NQ8L06pLdgXvuGovt917kr+75yS/9WPt1bJLKZmcT3YmT7AOCCH4w9ddyF/f/Sxvfn6HlVP9EVVirPMexbyhprwxPA9QoatT8TRS9m53uUblPJor654sDPL/xj/CO59+N6985D2EXHke5Tx++b4g3KeGkHmcDvYN93FgWx8HRoIc3BbkwEiQ/SN9rakOdJj1P4JewB8hKBPKeBiVVv857eMPvnknpxczXLA9xF/9zOW88sKxhlcKQggObw9xeHuIX33FOUxFU3zridN8+8kz/OV3jvLnRs1/xycIVuOLqJh3N0bQarTncfaIMrQ1pNh3DQb4sYvG+af7J/mlaw+2NXp0LpEjmSt2RtNqndg9FOCPfuKi7mx8xxVw7A5V/aZLqDdIzgOU8dCFIj3veRjDrUoliaPGWvDEqQU+ddezfP1HM7icHgLnfZR3nfh1HMUMw2/9NI9tfxnPziU4djbBs7MJnj2b5MjMEt964swyOZvtYR+vvXQHt7zqvLU8vWXYxsMMvjCBUpLZpQzfuecHXAv8wT0Z9uwL8CdvuJirDw23lODcNRjg51+6n59/6X7mE1nuOHKWhyZivOL80c6fg8YfUY17stS9hLk2SjOPqds6Uuw3v3Q/X33sFP/ywFRbpamT0STQhUqrzcKOK+CxLygvMG+EVDaQ5xH2u3nwZLT8ey8T9rspSUjkCssuiB44EeWTdx3jrqdnCXpd3Hz1Ad75kr1KQeD4Tpi8Fw5cR1gILt89wOW7l78/2UKRyfkUz85qw5JksG99XyvbeJjBF8FJkWRikWNPP85L3W7+6n++miv2DnVsF0NBL2983i7e+LwuD3zxRZTh0L93A28IEDDzQ/V3nSFQF+0M86L9Q/zt90/w9qv24m4x1q/LdHf3sOfRVXZcrm6fexD6jZDoBjMe+qJ6MxgPUKXH/V4Xdz59lk/e+SwPTsQY6vPw/hvP5a0v3LP8PPe/TP00wOtycmi0n0OjbTaOdhDbeJjByA2863kDvGWpgDuxv6OGY02pznN0K+fhcIAvBIvTgIBwfYN489X7ecff/YD//OEMr79sR0u7OzmfwiG6MHlxszB6oVITeO4h2KNmnmwk41F9hd5O+HIjoI3CFx+c4ttPnuGp00vsiPj5vddewBuv3NU1afv1wC7VNYMR3vm1l4zSn5rqmhT7mlAdquqmqqr2akLbwVVfIv1l54xwaFuQW+9uXbJkcj7JeNi/rEjBpgqXF8YuVhVXFgZBrRXVV+HhLs1KXysixsTFP//OMQolyZ/99CXc9f5ruOnFezeV4QDbeJhDX6GnYxA7uSGnepmmOlTVTWE8bZiajJ51OATveul+npxZ5PvHmg9IqsVEtMNqupuRHVfAqUcgOav+3kCeR9jvqvq9t43HxTvD3PSiPXz6bVfw7V+9mp+6YmfL4diNzuY8q06jjcfcMyrh2AVNqzVjrT2POvmOal532XZG+r3c2mLT4MR8F9R0Nxs7rlCf3cn7QDgrOmcbAO1tOB2Cvh6/Ove5nfze6y7kxgvGalZbbSZs42EGvRCeekTd9nLYai1yHlAxTAPNhxl5XU7e/uK93P3MLEdmrEmWLGXyRJO5zgsibjZ0p/mJu5XX0Q1BzBbReY6Qz9X56Zk2XcM2HmbQi2zZePSw56ENoXAokcRu78eE5wHwsy/YTcDj5Hf+43Eef27B9G50pVUv93isCYP71ec4n9xQ+Q6ohKp6PWS11bCNhxm08Th7RC26DaqHNjzaI/CFuzvPwWTOQxMJePjfrznMM6eXeM1ffI+3/c393PvsfNMkerlM1w5bNcbhUIJ8sKHyHWAbj17FNh5mcBgx4lJBGY4G1UMbHpcXXP7uT5Gz6HkAvPn5u/n+B6/l/Teey5GZRd7ymfv4yU/dw+1PnqFUqm1EJowGQbvHwwQ7DY2xDaRrBRWj0evd5VsNu8/DLL6I0gTq5ZCVxhfubr4D4PDrlFKoBeMBKv79npcf5Odeso8vPjjFp+8+zrs+/yDnjAZ59zUH+PGLty8TDpycTzEc9HZPwn4zofMeG8zzCNnGoyexPQ+z6MW2lyutNP5IdyutQI1svfa3W07M+txO3vaivdz5vmv4v2+6BIBf+5fHuOZjd/H3954kky8CcHI+aSfLzbJBw1Y+txOPy2GHrXoM+3LNLNp49HKllebq92+oUs1GuJ0OfuKynbzukh3c8dRZPnnXMf7Xl5/gE3cc5R1X7eP4bJKXHBxe78PsDfpH1VTEA9et95Gs4rdedR6X7t5YRs2mMbbxMIu+Ut8MYauL3rDeR2AZh0Nw/eFRXnH+Nu47rkTmPvqtpwFbENESL/m19T6Cmrz9qk3wvdpi2MbDLJspbNXDCCF40YEhXnRgiMefW+DfH57m9ZdtX+/DsrHZcmw44yGEeCXwCcAJfFZK+ZF1PiSFrh7aDJ7HJuHCHWEu7ObURRsbm7psKOMhhHACfwVcD0wDPxBCfEVK+eT6HhlwyZshNK5mENvY2NhscTaU8QCeDxyTUh4HEEL8M/A6YP2Nx/jF6sfGxsbGZsOV6u4Apqr+njbuKyOEuFkI8aAQ4sHZ2dk1PTgbGxsbG8VGMx61mgKWtRZLKW+VUl4ppbxyZGRkjQ7LxsbGxqaajWY8poFq4aidwKl1OhYbGxsbmzpsNOPxA+CQEGKfEMIDvBn4yjofk42NjY3NCjZUwlxKWRBC/BLwLVSp7t9KKZ9Y58OysbGxsVnBhjIeAFLKrwNfX+/jsLGxsbGpz0YLW9nY2NjY9AC28bCxsbGxsYxoNqltIyOEmAUm2tjEMDDXocPpNexz37ps5fPfyucOlfPfI6Vsq9ehp41HuwghHpRSXrnex7Ee2Oe+Nc8dtvb5b+Vzh86evx22srGxsbGxjG08bGxsbGwss9WNx63rfQDriH3uW5etfP5b+dyhg+e/pXMeNjY2NjatsdU9DxsbGxubFrCNh42NjY2NZbak8RBCvFII8bQQ4pgQ4pb1Pp5uIIQ4KYT4kRDiUSHEg8Z9g0KI24UQR43bgarHf9B4PZ4WQty4fkfeGkKIvxVCnBVCPF51n+XzFUJcYbxux4QQfy6EqDUmYENR59x/VwjxnPH+PyqE+LGq/22mc98lhLhTCHFECPGEEOK9xv1b5b2vd/7df/+llFvqByW4+CywH/AAjwGH1/u4unCeJ4HhFff9KXCL8fstwJ8Yvx82XgcvsM94fZzrfQ4Wz/dq4HLg8XbOF3gAeBFqtsw3gFet97m1eO6/C7yvxmM327mPA5cbv/cDzxjnuFXe+3rn3/X3fyt6HuVRt1LKHKBH3W4FXgfcZvx+G/D6qvv/WUqZlVKeAI6hXqeeQUp5NxBdcbel8xVCjAMhKeW9Un2bPl/1nA1LnXOvx2Y79xkp5cPG70vAEdT00a3y3tc7/3p07Py3ovFoOup2kyCBbwshHhJC3GzcNyqlnAH1oQO2Gfdv1tfE6vnuMH5feX+v8ktCiB8aYS0dttm05y6E2AtcBtzPFnzvV5w/dPn934rGo+mo203CVVLKy4FXAe8RQlzd4LFb5TXR1DvfzfQ6fAo4AFwKzAB/Zty/Kc9dCBEE/h34VSnlYqOH1rhvM55/19//rWg8tsSoWynlKeP2LPD/UGGoM4Z7inF71nj4Zn1NrJ7vtPH7yvt7DinlGSllUUpZAj5DJQy56c5dCOFGLZz/KKX8knH3lnnva53/Wrz/W9F4bPpRt0KIPiFEv/4duAF4HHWeNxkPuwn4svH7V4A3CyG8Qoh9wCFU8qzXsXS+RnhjSQjxQqPS5H9UPaen0AunwU+g3n/YZOduHOvfAEeklP+n6l9b4r2vd/5r8v6vd7XAevwAP4aqSngW+O31Pp4unN9+VEXFY8AT+hyBIeAO4KhxO1j1nN82Xo+n6YEqkxrn/AWUe55HXUX9XCvnC1xpfNGeBf4SQ4VhI//UOfe/B34E/NBYMMY36bm/BBVe+SHwqPHzY1vova93/l1//215EhsbGxsby2zFsJWNjY2NTZvYxsPGxsbGxjK28bCxsbGxsYxtPGxsbGxsLGMbDxsbGxsby9jGw8bGIkKIiBDiFxv8/x4T20h09qhsbNYW23jY2FgnAqwyHkIIJ4CU8sVrfUA2NmuNa70PwMamB/kIcEAI8SiqMS+BatK7FDgshEhIKYOG3tCXgQHADfyOlHLDdy3b2JjBbhK0sbGIoV76NSnlhUKIa4D/BC6USuKaKuPhAgJSykUhxDBwH3BISin1Y9bpFGxs2sb2PGxs2ucBbThWIIA/MhSNSyiJ61Hg9FoenI1NN7CNh41N+yTr3P+zwAhwhZQyL4Q4CfjW7KhsbLqInTC3sbHOEmrkZzPCwFnDcLwc2NPdw7KxWTtsz8PGxiJSynkhxPeFEI8DaeBMnYf+I/BVIcSDKLXTp9boEG1suo6dMLexsbGxsYwdtrKxsbGxsYxtPGxsbGxsLGMbDxsbGxsby9jGw8bGxsbGMrbxsLGxsbGxjG08bGxsbGwsYxsPGxsbGxvL/H+LmNhhhGiQuAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] diff --git a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb index a33aa3d..63687b3 100644 --- a/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb +++ b/XCS_Experiments/XCS_XNCS_Maze5_pre_populated.ipynb @@ -23,14 +23,14 @@ "text": [ "This is how maze looks like\n", "\n", - "('1', '0', '0', '0', '1', '0', '1', '1')\n", + "('0', '1', '1', '1', '1', '0', '0', '0')\n", "\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[33m$\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[31mA\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[31mA\u001b[0m \u001b[30mâ– \u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[30mâ– \u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m\n" @@ -104,7 +104,7 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -118,16 +118,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 6.438388000000003, 'numerosity': 7000, 'population': 5892, 'average_specificity': 8.564285714285715, 'fraction_accuracy': 1.0, 'knowledge': 10.95890410958904}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1257.3161234796364, 'perf_time': 0.44417859999998655, 'numerosity': 7000, 'population': 5450, 'average_specificity': 7.980571428571428, 'fraction_accuracy': 1.0, 'knowledge': 7.534246575342466}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 11, 'reward': 1023.8709659482762, 'perf_time': 0.3590006999999673, 'numerosity': 7000, 'population': 5286, 'average_specificity': 8.095285714285714, 'fraction_accuracy': 1.0, 'knowledge': 6.8493150684931505}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 9, 'reward': 1048.8876099436375, 'perf_time': 0.3336328999999978, 'numerosity': 7000, 'population': 5268, 'average_specificity': 8.511, 'fraction_accuracy': 1.0, 'knowledge': 6.8493150684931505}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 8, 'reward': 1074.9941221800832, 'perf_time': 0.31316379999992705, 'numerosity': 7000, 'population': 5194, 'average_specificity': 8.718571428571428, 'fraction_accuracy': 1.0, 'knowledge': 6.8493150684931505}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 5, 'reward': 1204.664581528421, 'perf_time': 0.3259860000000572, 'numerosity': 7000, 'population': 5185, 'average_specificity': 9.072, 'fraction_accuracy': 1.0, 'knowledge': 6.8493150684931505}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 7, 'reward': 1188.7702900538752, 'perf_time': 0.34286840000004304, 'numerosity': 7000, 'population': 5190, 'average_specificity': 9.154428571428571, 'fraction_accuracy': 1.0, 'knowledge': 6.8493150684931505}\n", - "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 8, 'reward': 1089.9324523599303, 'perf_time': 0.24491450000004988, 'numerosity': 7000, 'population': 5172, 'average_specificity': 9.311857142857143, 'fraction_accuracy': 0.97, 'knowledge': 6.164383561643835}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 4, 'reward': 1360.8661276785497, 'perf_time': 0.2307184999999663, 'numerosity': 7000, 'population': 5157, 'average_specificity': 9.414285714285715, 'fraction_accuracy': 1.0, 'knowledge': 6.164383561643835}\n", - "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 8, 'reward': 1082.9505835740995, 'perf_time': 0.35617629999990186, 'numerosity': 7000, 'population': 5228, 'average_specificity': 9.921714285714286, 'fraction_accuracy': 1.0, 'knowledge': 6.164383561643835}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 10.801801399999995, 'numerosity': 7000, 'population': 5837, 'average_specificity': 8.09457142857143, 'fraction_accuracy': 1.0, 'knowledge': 8.904109589041095}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 4, 'reward': 1268.4393400342683, 'perf_time': 0.23171939999997448, 'numerosity': 7000, 'population': 5424, 'average_specificity': 11.808, 'fraction_accuracy': 0.98, 'knowledge': 8.904109589041095}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 17, 'reward': 1002.9634080602486, 'perf_time': 1.1649275999999986, 'numerosity': 7000, 'population': 5232, 'average_specificity': 18.535, 'fraction_accuracy': 1.0, 'knowledge': 10.273972602739725}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 3, 'reward': 1640.090458495578, 'perf_time': 0.13137119999998959, 'numerosity': 7000, 'population': 5163, 'average_specificity': 21.890285714285714, 'fraction_accuracy': 1.0, 'knowledge': 10.273972602739725}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 4, 'reward': 1278.945511810267, 'perf_time': 0.19018400000004476, 'numerosity': 7000, 'population': 5103, 'average_specificity': 25.297857142857143, 'fraction_accuracy': 1.0, 'knowledge': 9.58904109589041}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 8, 'reward': 1076.4966296333594, 'perf_time': 0.35427870000000894, 'numerosity': 7000, 'population': 5076, 'average_specificity': 28.026714285714284, 'fraction_accuracy': 1.0, 'knowledge': 9.58904109589041}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 4, 'reward': 1254.4961984638614, 'perf_time': 0.24357809999992242, 'numerosity': 7000, 'population': 5024, 'average_specificity': 30.314714285714285, 'fraction_accuracy': 1.0, 'knowledge': 9.58904109589041}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 6, 'reward': 1130.568313210379, 'perf_time': 0.4939331000000493, 'numerosity': 7000, 'population': 4974, 'average_specificity': 32.60628571428571, 'fraction_accuracy': 0.99, 'knowledge': 9.58904109589041}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 10, 'reward': 1034.6730339429612, 'perf_time': 0.4519010999999864, 'numerosity': 7000, 'population': 4949, 'average_specificity': 34.501, 'fraction_accuracy': 1.0, 'knowledge': 9.58904109589041}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 5, 'reward': 1180.6925347601855, 'perf_time': 0.22202969999989364, 'numerosity': 7000, 'population': 4953, 'average_specificity': 34.76428571428571, 'fraction_accuracy': 1.0, 'knowledge': 9.58904109589041}\n" ] }, { @@ -141,16 +141,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 4.241750100000218, 'population': 5853, 'numerosity': 7000, 'average_specificity': 8.011142857142858, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 6, 'reward': 1132.4085326035574, 'perf_time': 0.30875250000008236, 'population': 5287, 'numerosity': 7000, 'average_specificity': 7.460714285714285, 'knowledge': 99.31506849315068}\n", - "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 4, 'reward': 1301.2749009792358, 'perf_time': 0.39512779999995473, 'population': 5057, 'numerosity': 7000, 'average_specificity': 7.381714285714286, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 5, 'reward': 1180.4229351022, 'perf_time': 0.2696293000001333, 'population': 4927, 'numerosity': 7000, 'average_specificity': 7.288857142857143, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 6, 'reward': 1135.064094309687, 'perf_time': 0.18124489999991056, 'population': 4735, 'numerosity': 7000, 'average_specificity': 7.303142857142857, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 100, 'reward': 1.7135168734605066e-12, 'perf_time': 2.5678250999999364, 'population': 4653, 'numerosity': 7000, 'average_specificity': 7.367571428571429, 'knowledge': 99.31506849315068}\n", - "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 5, 'reward': 1189.4810858153346, 'perf_time': 0.19421860000011293, 'population': 4416, 'numerosity': 7000, 'average_specificity': 7.136857142857143, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 6, 'reward': 1175.7467622693382, 'perf_time': 0.44029089999980897, 'population': 4355, 'numerosity': 7000, 'average_specificity': 7.443142857142857, 'knowledge': 99.31506849315068}\n", - "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 6, 'reward': 1128.1002839222897, 'perf_time': 0.3144142999999531, 'population': 4186, 'numerosity': 7000, 'average_specificity': 8.08457142857143, 'knowledge': 100.0}\n", - "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 3, 'reward': 1000.0000000000006, 'perf_time': 0.2026733000002423, 'population': 4107, 'numerosity': 7000, 'average_specificity': 7.486285714285715, 'knowledge': 100.0}\n" + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 100, 'reward': 0.0, 'perf_time': 8.758626399999912, 'population': 5867, 'numerosity': 7000, 'average_specificity': 8.529142857142856, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 5, 'reward': 1203.5822709802917, 'perf_time': 0.35878309999998237, 'population': 5463, 'numerosity': 7000, 'average_specificity': 7.843571428571429, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 500, 'steps_in_trial': 2, 'reward': 1527.2565917893598, 'perf_time': 0.09319830000004004, 'population': 5302, 'numerosity': 7000, 'average_specificity': 8.445285714285715, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 700, 'steps_in_trial': 10, 'reward': 1058.0534669749482, 'perf_time': 0.7575213999998596, 'population': 5257, 'numerosity': 7000, 'average_specificity': 8.827142857142857, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 4, 'reward': 1293.9856774060174, 'perf_time': 0.509301300000061, 'population': 5218, 'numerosity': 7000, 'average_specificity': 8.955428571428572, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 4, 'reward': 1277.664799811856, 'perf_time': 0.3477192999998806, 'population': 5189, 'numerosity': 7000, 'average_specificity': 8.972428571428571, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1500, 'steps_in_trial': 8, 'reward': 1066.8965579440774, 'perf_time': 0.5528624000003219, 'population': 5196, 'numerosity': 7000, 'average_specificity': 9.646428571428572, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 1700, 'steps_in_trial': 4, 'reward': 1437.7043077017056, 'perf_time': 0.5388333000000785, 'population': 5181, 'numerosity': 7000, 'average_specificity': 9.277142857142858, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2000, 'steps_in_trial': 3, 'reward': 1426.759214002228, 'perf_time': 0.19085350000023027, 'population': 5175, 'numerosity': 7000, 'average_specificity': 9.351, 'knowledge': 100.0}\n", + "INFO:lcs.agents.Agent:{'trial': 2200, 'steps_in_trial': 7, 'reward': 1092.5100166015388, 'perf_time': 0.5086952000001475, 'population': 5169, 'numerosity': 7000, 'average_specificity': 9.467428571428572, 'knowledge': 100.0}\n" ] } ], @@ -159,23 +159,23 @@ "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", "number_of_experiments = 1\n", - "explore = 2000\n", - "exploit = 500\n", + "explore = 0\n", + "exploit = 2500\n", "\n", "df_other = XNCSExp(\n", " maze=maze,\n", " cfg=XNCScfg,\n", " number_of_tests=number_of_experiments,\n", - " explore_trials=0,\n", - " exploit_trials=exploit + explore,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", " pre_generate=True\n", " )\n", "\n", "df = XCSExp(maze=maze,\n", " cfg=XCScfg,\n", " number_of_tests=number_of_experiments,\n", - " explore_trials=0,\n", - " exploit_trials=exploit + explore,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", " pre_generate=True\n", " )\n", "\n" @@ -242,519 +242,519 @@ " \n", " 0\n", " 100\n", - " 0.000000e+00\n", - " 4.241750\n", - " 5853\n", + " 0.000000\n", + " 8.758626\n", + " 5867\n", " 7000\n", - " 8.011143\n", - " 100.000000\n", + " 8.529143\n", + " 100.0\n", " 100\n", - " 5892\n", + " 5837\n", " 7000\n", - " 8.564286\n", + " 8.094571\n", " 1.00\n", - " 10.958904\n", + " 8.904110\n", " \n", " \n", " 100\n", - " 5\n", - " 1.286283e+03\n", - " 0.178884\n", - " 5521\n", + " 6\n", + " 1132.371203\n", + " 0.440872\n", + " 5571\n", " 7000\n", - " 7.266571\n", - " 100.000000\n", - " 2\n", - " 5548\n", + " 8.101857\n", + " 100.0\n", + " 5\n", + " 5532\n", " 7000\n", - " 7.969000\n", + " 8.196571\n", " 1.00\n", - " 7.534247\n", + " 8.904110\n", " \n", " \n", " 200\n", - " 6\n", - " 1.132409e+03\n", - " 0.308753\n", - " 5287\n", + " 5\n", + " 1203.582271\n", + " 0.358783\n", + " 5463\n", " 7000\n", - " 7.460714\n", - " 99.315068\n", - " 6\n", - " 5450\n", + " 7.843571\n", + " 100.0\n", + " 4\n", + " 5424\n", " 7000\n", - " 7.980571\n", - " 1.00\n", - " 7.534247\n", + " 11.808000\n", + " 0.98\n", + " 8.904110\n", " \n", " \n", " 300\n", - " 7\n", - " 1.091801e+03\n", - " 0.221936\n", - " 5220\n", + " 8\n", + " 1102.981508\n", + " 1.255452\n", + " 5400\n", " 7000\n", - " 7.202857\n", - " 100.000000\n", - " 19\n", - " 5341\n", + " 7.811571\n", + " 100.0\n", + " 17\n", + " 5319\n", " 7000\n", - " 7.974000\n", + " 15.260000\n", " 1.00\n", - " 6.849315\n", + " 8.904110\n", " \n", " \n", " 400\n", - " 3\n", - " 1.427822e+03\n", - " 0.371512\n", - " 5107\n", + " 7\n", + " 1103.010512\n", + " 0.268604\n", + " 5348\n", " 7000\n", - " 7.427857\n", - " 100.000000\n", - " 18\n", - " 5317\n", + " 7.952857\n", + " 100.0\n", + " 13\n", + " 5264\n", " 7000\n", - " 8.079571\n", - " 1.00\n", - " 6.849315\n", + " 17.081143\n", + " 0.99\n", + " 8.904110\n", " \n", " \n", " 500\n", - " 4\n", - " 1.301275e+03\n", - " 0.395128\n", - " 5057\n", + " 2\n", + " 1527.256592\n", + " 0.093198\n", + " 5302\n", " 7000\n", - " 7.381714\n", - " 100.000000\n", - " 11\n", - " 5286\n", + " 8.445286\n", + " 100.0\n", + " 17\n", + " 5232\n", " 7000\n", - " 8.095286\n", + " 18.535000\n", " 1.00\n", - " 6.849315\n", + " 10.273973\n", " \n", " \n", " 600\n", " 6\n", - " 1.128100e+03\n", - " 0.532877\n", - " 5016\n", + " 1128.750812\n", + " 0.413180\n", + " 5295\n", " 7000\n", - " 7.463000\n", - " 100.000000\n", - " 6\n", - " 5264\n", + " 8.691000\n", + " 100.0\n", + " 15\n", + " 5191\n", " 7000\n", - " 8.080286\n", + " 20.499143\n", " 1.00\n", - " 6.849315\n", + " 9.589041\n", " \n", " \n", " 700\n", - " 5\n", - " 1.180423e+03\n", - " 0.269629\n", - " 4927\n", + " 10\n", + " 1058.053467\n", + " 0.757521\n", + " 5257\n", " 7000\n", - " 7.288857\n", - " 100.000000\n", - " 9\n", - " 5268\n", + " 8.827143\n", + " 100.0\n", + " 3\n", + " 5163\n", " 7000\n", - " 8.511000\n", + " 21.890286\n", " 1.00\n", - " 6.849315\n", + " 10.273973\n", " \n", " \n", " 800\n", - " 100\n", - " 1.336087e-12\n", - " 2.555656\n", - " 4835\n", + " 2\n", + " 1895.648471\n", + " 0.087332\n", + " 5242\n", " 7000\n", - " 7.322857\n", - " 99.315068\n", - " 5\n", - " 5268\n", + " 8.991714\n", + " 100.0\n", + " 17\n", + " 5139\n", " 7000\n", - " 8.618000\n", + " 22.749429\n", " 1.00\n", - " 6.849315\n", + " 9.589041\n", " \n", " \n", " 900\n", - " 7\n", - " 1.105494e+03\n", - " 0.206130\n", - " 4755\n", + " 9\n", + " 1048.083839\n", + " 0.741097\n", + " 5232\n", " 7000\n", - " 7.140857\n", - " 100.000000\n", - " 5\n", - " 5243\n", + " 8.872571\n", + " 100.0\n", + " 18\n", + " 5118\n", " 7000\n", - " 8.767286\n", + " 23.875571\n", " 1.00\n", - " 6.849315\n", + " 9.589041\n", " \n", " \n", " 1000\n", - " 6\n", - " 1.135064e+03\n", - " 0.181245\n", - " 4735\n", + " 4\n", + " 1293.985677\n", + " 0.509301\n", + " 5218\n", " 7000\n", - " 7.303143\n", - " 100.000000\n", - " 8\n", - " 5194\n", + " 8.955429\n", + " 100.0\n", + " 4\n", + " 5103\n", " 7000\n", - " 8.718571\n", + " 25.297857\n", " 1.00\n", - " 6.849315\n", + " 9.589041\n", " \n", " \n", " 1100\n", - " 5\n", - " 1.206901e+03\n", - " 0.108420\n", - " 4673\n", + " 2\n", + " 1570.370868\n", + " 0.072796\n", + " 5189\n", " 7000\n", - " 7.170714\n", - " 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perf_time population numerosity \\\n", - "trial \n", - "0 100 0.000000e+00 4.241750 5853 7000 \n", - "100 5 1.286283e+03 0.178884 5521 7000 \n", - "200 6 1.132409e+03 0.308753 5287 7000 \n", - "300 7 1.091801e+03 0.221936 5220 7000 \n", - "400 3 1.427822e+03 0.371512 5107 7000 \n", - "500 4 1.301275e+03 0.395128 5057 7000 \n", - "600 6 1.128100e+03 0.532877 5016 7000 \n", - "700 5 1.180423e+03 0.269629 4927 7000 \n", - "800 100 1.336087e-12 2.555656 4835 7000 \n", - "900 7 1.105494e+03 0.206130 4755 7000 \n", - "1000 6 1.135064e+03 0.181245 4735 7000 \n", - "1100 5 1.206901e+03 0.108420 4673 7000 \n", - "1200 100 1.713517e-12 2.567825 4653 7000 \n", - "1300 6 1.000000e+03 0.368133 4558 7000 \n", - "1400 6 1.225702e+03 0.191057 4394 7000 \n", - "1500 5 1.189481e+03 0.194219 4416 7000 \n", - "1600 6 1.165147e+03 0.197963 4360 7000 \n", - "1700 6 1.175747e+03 0.440291 4355 7000 \n", - "1800 5 1.194507e+03 0.512993 4326 7000 \n", - "1900 11 1.023654e+03 0.221673 4265 7000 \n", - "2000 6 1.128100e+03 0.314414 4186 7000 \n", - "2100 9 1.061988e+03 0.325730 4150 7000 \n", - "2200 3 1.000000e+03 0.202673 4107 7000 \n", - "2300 100 1.336087e-12 2.744855 4051 7000 \n", - "2400 6 1.151329e+03 0.234328 4035 7000 \n", + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 100 0.000000 8.758626 5867 7000 \n", + "100 6 1132.371203 0.440872 5571 7000 \n", + "200 5 1203.582271 0.358783 5463 7000 \n", + "300 8 1102.981508 1.255452 5400 7000 \n", + "400 7 1103.010512 0.268604 5348 7000 \n", + "500 2 1527.256592 0.093198 5302 7000 \n", + "600 6 1128.750812 0.413180 5295 7000 \n", + "700 10 1058.053467 0.757521 5257 7000 \n", + "800 2 1895.648471 0.087332 5242 7000 \n", + "900 9 1048.083839 0.741097 5232 7000 \n", + "1000 4 1293.985677 0.509301 5218 7000 \n", + "1100 2 1570.370868 0.072796 5189 7000 \n", + "1200 4 1277.664800 0.347719 5189 7000 \n", + "1300 12 1018.124816 0.691511 5211 7000 \n", + "1400 5 1204.612675 0.338109 5212 7000 \n", + "1500 8 1066.896558 0.552862 5196 7000 \n", + "1600 3 1653.947718 0.179172 5205 7000 \n", + "1700 4 1437.704308 0.538833 5181 7000 \n", + "1800 6 1137.269115 0.655746 5177 7000 \n", + "1900 12 1030.920891 1.295460 5169 7000 \n", + "2000 3 1426.759214 0.190854 5175 7000 \n", + "2100 2 1553.487266 0.109808 5164 7000 \n", + "2200 7 1092.510017 0.508695 5169 7000 \n", + "2300 4 1260.906217 0.275596 5170 7000 \n", + "2400 8 1067.705115 0.796736 5142 7000 \n", "\n", - " average_specificity knowledge steps_in_trial_other \\\n", - "trial \n", - "0 8.011143 100.000000 100 \n", - "100 7.266571 100.000000 2 \n", - "200 7.460714 99.315068 6 \n", - "300 7.202857 100.000000 19 \n", - "400 7.427857 100.000000 18 \n", - "500 7.381714 100.000000 11 \n", - "600 7.463000 100.000000 6 \n", - "700 7.288857 100.000000 9 \n", - "800 7.322857 99.315068 5 \n", - "900 7.140857 100.000000 5 \n", - "1000 7.303143 100.000000 8 \n", - "1100 7.170714 100.000000 7 \n", - "1200 7.367571 99.315068 5 \n", - "1300 7.286429 100.000000 7 \n", - "1400 7.248429 100.000000 7 \n", - "1500 7.136857 100.000000 7 \n", - "1600 7.028714 100.000000 8 \n", - "1700 7.443143 99.315068 8 \n", - "1800 8.047000 98.630137 11 \n", - "1900 7.686000 100.000000 6 \n", - "2000 8.084571 100.000000 4 \n", - "2100 7.908571 100.000000 4 \n", - "2200 7.486286 100.000000 8 \n", - "2300 7.606571 99.315068 8 \n", - "2400 7.732571 100.000000 9 \n", + " average_specificity knowledge steps_in_trial_other population_other \\\n", + "trial \n", + "0 8.529143 100.0 100 5837 \n", + "100 8.101857 100.0 5 5532 \n", + "200 7.843571 100.0 4 5424 \n", + "300 7.811571 100.0 17 5319 \n", + "400 7.952857 100.0 13 5264 \n", + "500 8.445286 100.0 17 5232 \n", + "600 8.691000 100.0 15 5191 \n", + "700 8.827143 100.0 3 5163 \n", + "800 8.991714 100.0 17 5139 \n", + "900 8.872571 100.0 18 5118 \n", + "1000 8.955429 100.0 4 5103 \n", + "1100 9.063857 100.0 7 5084 \n", + "1200 8.972429 100.0 8 5076 \n", + "1300 9.331143 100.0 22 5044 \n", + "1400 9.488143 100.0 18 5032 \n", + "1500 9.646429 100.0 4 5024 \n", + "1600 9.661571 100.0 5 4982 \n", + "1700 9.277143 100.0 6 4974 \n", + "1800 9.140429 100.0 7 4952 \n", + "1900 9.053143 100.0 3 4949 \n", + "2000 9.351000 100.0 10 4949 \n", + "2100 9.483429 100.0 20 4945 \n", + "2200 9.467429 100.0 5 4953 \n", + "2300 9.662571 100.0 2 4941 \n", + "2400 9.390286 100.0 11 4940 \n", "\n", - " population_other numerosity_other average_specificity_other \\\n", - "trial \n", - "0 5892 7000 8.564286 \n", - "100 5548 7000 7.969000 \n", - "200 5450 7000 7.980571 \n", - "300 5341 7000 7.974000 \n", - "400 5317 7000 8.079571 \n", - "500 5286 7000 8.095286 \n", - "600 5264 7000 8.080286 \n", - "700 5268 7000 8.511000 \n", - "800 5268 7000 8.618000 \n", - "900 5243 7000 8.767286 \n", - "1000 5194 7000 8.718571 \n", - "1100 5187 7000 8.862143 \n", - "1200 5185 7000 9.072000 \n", - "1300 5194 7000 9.215857 \n", - "1400 5192 7000 9.093857 \n", - "1500 5190 7000 9.154429 \n", - "1600 5218 7000 9.329429 \n", - "1700 5172 7000 9.311857 \n", - "1800 5166 7000 8.916143 \n", - "1900 5169 7000 9.170429 \n", - "2000 5157 7000 9.414286 \n", - "2100 5175 7000 9.630429 \n", - "2200 5228 7000 9.921714 \n", - "2300 5215 7000 10.057286 \n", - "2400 5220 7000 10.158571 \n", + " numerosity_other average_specificity_other fraction_accuracy_other \\\n", + "trial \n", + "0 7000 8.094571 1.00 \n", + "100 7000 8.196571 1.00 \n", + "200 7000 11.808000 0.98 \n", + "300 7000 15.260000 1.00 \n", + "400 7000 17.081143 0.99 \n", + "500 7000 18.535000 1.00 \n", + "600 7000 20.499143 1.00 \n", + "700 7000 21.890286 1.00 \n", + "800 7000 22.749429 1.00 \n", + "900 7000 23.875571 1.00 \n", + "1000 7000 25.297857 1.00 \n", + "1100 7000 26.676286 1.00 \n", + "1200 7000 28.026714 1.00 \n", + "1300 7000 28.889714 1.00 \n", + "1400 7000 29.586714 0.99 \n", + "1500 7000 30.314714 1.00 \n", + "1600 7000 31.800000 1.00 \n", + "1700 7000 32.606286 0.99 \n", + "1800 7000 33.040714 1.00 \n", + "1900 7000 33.979571 1.00 \n", + "2000 7000 34.501000 1.00 \n", + "2100 7000 34.571857 1.00 \n", + "2200 7000 34.764286 1.00 \n", + "2300 7000 34.967571 1.00 \n", + "2400 7000 34.874286 1.00 \n", "\n", - " fraction_accuracy_other knowledge_other \n", - "trial \n", - "0 1.00 10.958904 \n", - "100 1.00 7.534247 \n", - "200 1.00 7.534247 \n", - "300 1.00 6.849315 \n", - "400 1.00 6.849315 \n", - "500 1.00 6.849315 \n", - "600 1.00 6.849315 \n", - "700 1.00 6.849315 \n", - "800 1.00 6.849315 \n", - "900 1.00 6.849315 \n", - "1000 1.00 6.849315 \n", - "1100 1.00 6.849315 \n", - "1200 1.00 6.849315 \n", - "1300 1.00 6.849315 \n", - "1400 1.00 6.849315 \n", - "1500 1.00 6.849315 \n", - "1600 1.00 6.849315 \n", - "1700 0.97 6.164384 \n", - "1800 0.94 6.164384 \n", - "1900 1.00 6.164384 \n", - "2000 1.00 6.164384 \n", - "2100 1.00 6.164384 \n", - "2200 1.00 6.164384 \n", - "2300 1.00 6.849315 \n", - "2400 1.00 6.849315 " + " knowledge_other \n", + "trial \n", + "0 8.904110 \n", + "100 8.904110 \n", + "200 8.904110 \n", + "300 8.904110 \n", + "400 8.904110 \n", + "500 10.273973 \n", + "600 9.589041 \n", + "700 10.273973 \n", + "800 9.589041 \n", + "900 9.589041 \n", + "1000 9.589041 \n", + "1100 9.589041 \n", + "1200 9.589041 \n", + "1300 9.589041 \n", + "1400 9.589041 \n", + "1500 9.589041 \n", + "1600 9.589041 \n", + "1700 9.589041 \n", + "1800 10.273973 \n", + "1900 10.958904 \n", + "2000 9.589041 \n", + "2100 10.273973 \n", + "2200 9.589041 \n", + "2300 9.589041 \n", + "2400 9.589041 " ] }, "metadata": {}, @@ -780,7 +780,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -789,7 +789,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -958,7 +958,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -967,7 +967,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -1029,8 +1029,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "523.0\n", - "288.0\n" + "239.0\n", + "341.0\n" ] } ], diff --git a/XCS_Experiments/XCS_XNCS_Woods1.ipynb b/XCS_Experiments/XCS_XNCS_Woods1.ipynb index c59500f..e390200 100644 --- a/XCS_Experiments/XCS_XNCS_Woods1.ipynb +++ b/XCS_Experiments/XCS_XNCS_Woods1.ipynb @@ -24,10 +24,10 @@ "This is how maze looks like:\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n" ] } ], @@ -110,7 +110,7 @@ " initial_fitness = 10, # f_i\n", " user_metrics_collector_fcn=xncs_metrics,\n", " lmc=10,\n", - " lem=200)\n" + " lem=20)\n" ] }, { @@ -120,6 +120,13 @@ "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1000.0, 'perf_time': 0.0008067999999994413, 'population': 16, 'numerosity': 16, 'average_specificity': 7.8125}\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -131,16 +138,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 34, 'reward': 1000.0, 'perf_time': 0.3281767000000002, 'population': 1620, 'numerosity': 1800, 'average_specificity': 7.988888888888889}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 31, 'reward': 1000.0000000015988, 'perf_time': 0.3438484000000024, 'population': 1455, 'numerosity': 1800, 'average_specificity': 7.92}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 1.1838409489159387e-49, 'perf_time': 0.3812447999999975, 'population': 1358, 'numerosity': 1800, 'average_specificity': 8.216666666666667}\n", - "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 1, 'reward': 1882.3185923996884, 'perf_time': 0.0075286000000005515, 'population': 1288, 'numerosity': 1800, 'average_specificity': 11.762222222222222}\n", - "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 4, 'reward': 1340.357011710357, 'perf_time': 0.02330560000000048, 'population': 1276, 'numerosity': 1800, 'average_specificity': 10.543888888888889}\n", - "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 6, 'reward': 1143.3603838274303, 'perf_time': 0.07492340000000297, 'population': 1204, 'numerosity': 1800, 'average_specificity': 9.35388888888889}\n", - "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 6, 'reward': 1142.1957109245309, 'perf_time': 0.05842979999999898, 'population': 1157, 'numerosity': 1800, 'average_specificity': 8.884444444444444}\n", - "INFO:lcs.agents.Agent:{'trial': 1400, 'steps_in_trial': 50, 'reward': 1.04145034561349e-19, 'perf_time': 0.3994035999999994, 'population': 1095, 'numerosity': 1800, 'average_specificity': 9.163333333333334}\n", - "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 3, 'reward': 1590.98010356131, 'perf_time': 0.061321399999997084, 'population': 1076, 'numerosity': 1800, 'average_specificity': 9.036111111111111}\n", - "INFO:lcs.agents.Agent:{'trial': 1800, 'steps_in_trial': 50, 'reward': 3.6552529472225224e-05, 'perf_time': 0.31517619999999624, 'population': 1102, 'numerosity': 1800, 'average_specificity': 9.441666666666666}\n" + 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"INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 30, 'reward': 1000.0, 'perf_time': 0.02990180000000464, 'numerosity': 75, 'population': 75, 'average_specificity': 7.84, 'fraction_accuracy': 1.0}\n" ] }, { @@ -154,16 +171,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000.0, 'perf_time': 0.009655500000008033, 'numerosity': 1800, 'population': 1604, 'average_specificity': 8.03888888888889, 'fraction_accuracy': 1.0}\n", - "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 4, 'reward': 1328.6306750731378, 'perf_time': 0.05837580000002163, 'numerosity': 1800, 'population': 1557, 'average_specificity': 8.656666666666666, 'fraction_accuracy': 0.65}\n", - "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 4, 'reward': 1305.7042391746443, 'perf_time': 0.049973099999988335, 'numerosity': 1800, 'population': 1537, 'average_specificity': 9.525555555555556, 'fraction_accuracy': 0.78}\n", - 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0.14407840000012584, 'numerosity': 1800, 'population': 1143, 'average_specificity': 17.824444444444445, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 12, 'reward': 1016.8376339359654, 'perf_time': 0.3346696000000975, 'numerosity': 1800, 'population': 1320, 'average_specificity': 20.779444444444444, 'fraction_accuracy': 0.98}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 12, 'reward': 1018.5353804965479, 'perf_time': 0.5161216999999851, 'numerosity': 1800, 'population': 1380, 'average_specificity': 25.29111111111111, 'fraction_accuracy': 1.0}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 11, 'reward': 1028.9865580629587, 'perf_time': 0.2975836000000527, 'numerosity': 1800, 'population': 1363, 'average_specificity': 29.932222222222222, 'fraction_accuracy': 0.98}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 8, 'reward': 1114.6309899952016, 'perf_time': 0.25611449999996694, 'numerosity': 1800, 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28.54611111111111, 'fraction_accuracy': 0.95}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1660.379484786392, 'perf_time': 0.020633099999940896, 'numerosity': 1800, 'population': 1454, 'average_specificity': 29.829444444444444, 'fraction_accuracy': 0.97}\n", + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 2, 'reward': 1660.379484786392, 'perf_time': 0.020633099999940896, 'numerosity': 1800, 'population': 1454, 'average_specificity': 29.829444444444444, 'fraction_accuracy': 0.97}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': 3.6552644154800746e-05, 'perf_time': 0.6723105999999461, 'numerosity': 1800, 'population': 1440, 'average_specificity': 36.025, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 100, 'steps_in_trial': 50, 'reward': 3.6552644154800746e-05, 'perf_time': 0.6723105999999461, 'numerosity': 1800, 'population': 1440, 'average_specificity': 36.025, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 22, 'reward': 1000.0000000195255, 'perf_time': 0.2429108000001179, 'numerosity': 1800, 'population': 1430, 'average_specificity': 38.919444444444444, 'fraction_accuracy': 0.98}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 22, 'reward': 1000.0000000195255, 'perf_time': 0.2429108000001179, 'numerosity': 1800, 'population': 1430, 'average_specificity': 38.919444444444444, 'fraction_accuracy': 0.98}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 47, 'reward': 1000.0001021274282, 'perf_time': 0.531361299999844, 'numerosity': 1800, 'population': 1385, 'average_specificity': 44.06055555555555, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 300, 'steps_in_trial': 47, 'reward': 1000.0001021274282, 'perf_time': 0.531361299999844, 'numerosity': 1800, 'population': 1385, 'average_specificity': 44.06055555555555, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 2.385089144282033e-42, 'perf_time': 0.7609427000002142, 'numerosity': 1800, 'population': 1385, 'average_specificity': 42.263888888888886, 'fraction_accuracy': 0.99}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 50, 'reward': 2.385089144282033e-42, 'perf_time': 0.7609427000002142, 'numerosity': 1800, 'population': 1385, 'average_specificity': 42.263888888888886, 'fraction_accuracy': 0.99}\n" ] } ], @@ -195,30 +231,29 @@ "from utils.nxcs_utils import avg_experiment as XNCSExp\n", "\n", "number_of_experiments = 1\n", - "explore = 0\n", - "exploit = 2000\n", + "explore = 2000\n", + "exploit = 500\n", "\n", "df = XCSExp(maze=maze,\n", " cfg=XCScfg,\n", " number_of_tests=number_of_experiments,\n", " explore_trials=explore,\n", " exploit_trials=exploit,\n", - " pre_generate=True\n", - " )\n", + " pre_generate=False)\n", "\n", "df20 = XNCSExp(maze=maze,\n", " cfg=XNCScfg20,\n", " number_of_tests=number_of_experiments,\n", " explore_trials=explore,\n", " exploit_trials=exploit,\n", - " pre_generate=True)\n", + " pre_generate=False)\n", "\n", "df200 = XNCSExp(maze=maze,\n", " cfg=XNCScfg200,\n", " number_of_tests=number_of_experiments,\n", " explore_trials=explore,\n", " exploit_trials=exploit,\n", - " pre_generate=True)\n", + " pre_generate=False)\n", "\n" ] }, @@ -290,503 +325,623 @@ " \n", " \n", " 0\n", - " 34\n", - " 1.000000e+03\n", - " 0.328177\n", - " 1620\n", - " 1800\n", - " 7.988889\n", - " 1\n", - " 1604\n", - " 1800\n", - " 8.038889\n", - " 1.00\n", - " 8\n", - " 1601\n", - " 1800\n", - " 7.603889\n", + " 2\n", + " 1000.000000\n", + " 0.000807\n", + " 16\n", + " 16\n", + " 7.812500\n", + " 30\n", + " 75\n", + " 75\n", + " 7.840000\n", " 1.00\n", + " 50\n", + " 105\n", + " 111\n", + " 7.846847\n", + " 0.96\n", " \n", " \n", " 100\n", - " 9\n", - " 1.051363e+03\n", - " 0.086761\n", - " 1542\n", - " 1800\n", - " 7.676111\n", - " 6\n", - " 1555\n", - " 1800\n", - " 8.598333\n", - " 0.90\n", - " 3\n", - " 1553\n", + " 1\n", + " 1782.852673\n", + " 0.003133\n", + " 679\n", " 1800\n", - " 7.540556\n", + " 18.994444\n", + " 14\n", + " 679\n", + " 1144\n", + " 15.145979\n", + " 0.99\n", + " 1\n", + " 751\n", + " 1262\n", + " 13.617274\n", " 1.00\n", " \n", " \n", " 200\n", - " 31\n", - " 1.000000e+03\n", - " 0.343848\n", - " 1455\n", + " 6\n", + " 1157.585744\n", + " 0.107963\n", + " 821\n", " 1800\n", - " 7.920000\n", - " 4\n", - " 1557\n", + " 16.940000\n", + " 20\n", + " 1180\n", " 1800\n", - " 8.656667\n", - " 0.65\n", - " 4\n", - " 1547\n", + " 15.915000\n", + " 0.99\n", + " 6\n", + " 1143\n", " 1800\n", - " 7.685000\n", - " 0.74\n", + " 17.824444\n", + " 1.00\n", " \n", " \n", " 300\n", - " 5\n", - " 1.224458e+03\n", - " 0.089136\n", - " 1425\n", + " 2\n", + " 1000.000023\n", + " 0.032814\n", + " 990\n", " 1800\n", - " 8.066111\n", - " 3\n", - " 1527\n", + " 23.004444\n", + " 4\n", + " 1260\n", " 1800\n", - " 9.138889\n", - " 0.81\n", - " 5\n", - " 1564\n", + " 18.256667\n", 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1507\n", " 1800\n", - " 13.750000\n", - " 0.79\n", - " 5\n", + " 27.080000\n", + " 0.99\n", + " 2\n", + " 1454\n", + " 1800\n", + " 29.829444\n", + " 0.97\n", + " \n", + " \n", + " 2100\n", + " 8\n", + " 1071.208752\n", + " 0.071233\n", + " 985\n", + " 1800\n", + " 43.791667\n", + " 2\n", " 1473\n", " 1800\n", - " 12.501667\n", + " 30.498333\n", + " 1.00\n", + " 50\n", + " 1440\n", + " 1800\n", + " 36.025000\n", + " 0.99\n", + " \n", + " \n", + " 2200\n", + " 1\n", + " 2279.192959\n", + " 0.010671\n", + " 964\n", + " 1800\n", + " 44.405556\n", + " 30\n", + " 1429\n", + " 1800\n", + " 34.997222\n", " 1.00\n", + " 22\n", + " 1430\n", + " 1800\n", + " 38.919444\n", + " 0.98\n", + " \n", + " \n", + " 2300\n", + " 2\n", + " 1901.420188\n", + " 0.017654\n", + " 948\n", + " 1800\n", + " 42.927778\n", + " 1\n", + " 1456\n", + " 1800\n", + " 34.772778\n", + " 0.99\n", + " 47\n", + " 1385\n", + " 1800\n", + " 44.060556\n", + " 0.99\n", + " \n", + " \n", + " 2400\n", + " 2\n", + " 1775.748043\n", + " 0.021717\n", + " 945\n", + " 1800\n", + " 40.914444\n", + " 50\n", + " 1437\n", + " 1800\n", + " 38.423889\n", + " 1.00\n", + " 50\n", + " 1385\n", + " 1800\n", + " 42.263889\n", + " 0.99\n", " \n", " \n", "\n", "" ], "text/plain": [ - " steps_in_trial reward perf_time population numerosity \\\n", - "trial \n", - "0 34 1.000000e+03 0.328177 1620 1800 \n", - "100 9 1.051363e+03 0.086761 1542 1800 \n", - "200 31 1.000000e+03 0.343848 1455 1800 \n", - "300 5 1.224458e+03 0.089136 1425 1800 \n", - "400 50 1.183841e-49 0.381245 1358 1800 \n", - "500 50 6.525100e-35 0.481674 1300 1800 \n", - "600 1 1.882319e+03 0.007529 1288 1800 \n", - "700 50 1.336087e-12 0.349070 1262 1800 \n", - "800 4 1.340357e+03 0.023306 1276 1800 \n", - "900 8 1.109848e+03 0.100791 1220 1800 \n", - "1000 6 1.143360e+03 0.074923 1204 1800 \n", - "1100 50 4.883737e-20 0.486568 1195 1800 \n", - "1200 6 1.142196e+03 0.058430 1157 1800 \n", - "1300 7 1.123776e+03 0.063389 1145 1800 \n", - "1400 50 1.041450e-19 0.399404 1095 1800 \n", - "1500 1 1.000000e+03 0.008488 1086 1800 \n", - "1600 3 1.590980e+03 0.061321 1076 1800 \n", - "1700 2 2.082703e+03 0.024753 1091 1800 \n", - "1800 50 3.655253e-05 0.315176 1102 1800 \n", - "1900 50 2.153124e-79 0.563533 1097 1800 \n", + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 2 1000.000000 0.000807 16 16 \n", + "100 1 1782.852673 0.003133 679 1800 \n", + "200 6 1157.585744 0.107963 821 1800 \n", + "300 2 1000.000023 0.032814 990 1800 \n", + "400 8 1086.095610 0.119559 1005 1800 \n", + "500 19 1002.465335 0.456329 1047 1800 \n", + "600 3 1404.274583 0.077144 1074 1800 \n", + "700 8 1095.754950 0.156363 1073 1800 \n", + "800 3 1357.911028 0.025112 1005 1800 \n", + "900 11 1023.121803 0.229623 1029 1800 \n", + "1000 3 1374.389255 0.033363 1001 1800 \n", + "1100 4 1633.088701 0.075050 991 1800 \n", + "1200 17 1003.483119 0.150379 1007 1800 \n", + "1300 5 1210.345678 0.048048 969 1800 \n", + "1400 6 1222.105490 0.064024 965 1800 \n", + "1500 5 1212.975371 0.023662 980 1800 \n", + "1600 3 1427.716897 0.025214 963 1800 \n", + "1700 1 1853.922278 0.005750 1012 1800 \n", + "1800 4 1266.043339 0.038609 965 1800 \n", + "1900 2 1515.882544 0.010527 1016 1800 \n", + "2000 1 2280.366921 0.006330 1002 1800 \n", + "2100 8 1071.208752 0.071233 985 1800 \n", + "2200 1 2279.192959 0.010671 964 1800 \n", + "2300 2 1901.420188 0.017654 948 1800 \n", + "2400 2 1775.748043 0.021717 945 1800 \n", "\n", " average_specificity steps_in_trial_20 population_20 numerosity_20 \\\n", "trial \n", - "0 7.988889 1 1604 1800 \n", - "100 7.676111 6 1555 1800 \n", - "200 7.920000 4 1557 1800 \n", - "300 8.066111 3 1527 1800 \n", - "400 8.216667 4 1537 1800 \n", - "500 10.144444 50 1525 1800 \n", - "600 11.762222 3 1504 1800 \n", - "700 10.932778 2 1508 1800 \n", - "800 10.543889 8 1523 1800 \n", - "900 10.381667 6 1521 1800 \n", - "1000 9.353889 33 1532 1800 \n", - "1100 9.064444 4 1527 1800 \n", - "1200 8.884444 10 1553 1800 \n", - "1300 8.892778 9 1543 1800 \n", - "1400 9.163333 4 1552 1800 \n", - "1500 9.376667 1 1509 1800 \n", - "1600 9.036111 1 1540 1800 \n", - "1700 8.953889 3 1533 1800 \n", - "1800 9.441667 1 1527 1800 \n", - "1900 9.522222 4 1531 1800 \n", + "0 7.812500 30 75 75 \n", + "100 18.994444 14 679 1144 \n", + "200 16.940000 20 1180 1800 \n", + "300 23.004444 4 1260 1800 \n", + "400 27.981667 3 1317 1800 \n", + "500 30.340000 10 1343 1800 \n", + "600 34.626111 7 1384 1800 \n", + "700 35.105000 2 1392 1800 \n", + "800 39.952222 2 1399 1800 \n", + "900 37.428889 24 1415 1800 \n", + "1000 40.721667 50 1423 1800 \n", + "1100 41.140000 34 1431 1800 \n", + "1200 41.773889 1 1463 1800 \n", + "1300 41.268333 2 1443 1800 \n", + "1400 46.108333 2 1432 1800 \n", + "1500 49.860556 3 1431 1800 \n", + "1600 48.046111 7 1483 1800 \n", + "1700 41.710556 2 1476 1800 \n", + "1800 42.016111 42 1470 1800 \n", + "1900 41.471111 11 1469 1800 \n", + "2000 42.435000 4 1507 1800 \n", + "2100 43.791667 2 1473 1800 \n", + "2200 44.405556 30 1429 1800 \n", + "2300 42.927778 1 1456 1800 \n", + "2400 40.914444 50 1437 1800 \n", "\n", " average_specificity_20 fraction_accuracy_20 steps_in_trial_200 \\\n", "trial \n", - "0 8.038889 1.00 8 \n", - "100 8.598333 0.90 3 \n", - "200 8.656667 0.65 4 \n", - "300 9.138889 0.81 5 \n", - "400 9.525556 0.78 7 \n", - "500 9.927222 0.97 6 \n", - "600 9.568889 0.97 12 \n", - "700 9.852778 0.88 16 \n", - "800 10.219444 0.99 3 \n", - "900 10.721667 0.91 5 \n", - "1000 11.747222 0.88 5 \n", - "1100 11.754444 0.89 2 \n", - "1200 11.956111 0.86 2 \n", - "1300 12.176667 0.89 5 \n", - "1400 11.974444 0.97 5 \n", - "1500 12.362778 1.00 1 \n", - "1600 12.092222 0.68 3 \n", - "1700 13.438889 0.79 3 \n", - "1800 13.238889 0.71 3 \n", - "1900 13.750000 0.79 5 \n", + "0 7.840000 1.00 50 \n", + "100 15.145979 0.99 1 \n", + "200 15.915000 0.99 6 \n", + "300 18.256667 0.99 1 \n", + "400 21.812778 0.98 12 \n", + "500 23.564444 0.99 1 \n", + "600 22.591111 0.99 12 \n", + "700 25.903333 1.00 23 \n", + "800 28.865556 1.00 11 \n", + "900 28.578889 1.00 3 \n", + "1000 30.850556 0.99 8 \n", + "1100 29.767778 0.98 39 \n", + "1200 27.950000 1.00 9 \n", + "1300 31.535000 0.99 15 \n", + "1400 31.779444 0.98 17 \n", + "1500 30.883889 1.00 4 \n", + "1600 28.786667 1.00 1 \n", + "1700 27.389444 1.00 50 \n", + "1800 28.207222 0.98 3 \n", + "1900 29.913889 0.98 17 \n", + "2000 27.080000 0.99 2 \n", + "2100 30.498333 1.00 50 \n", + "2200 34.997222 1.00 22 \n", + "2300 34.772778 0.99 47 \n", + "2400 38.423889 1.00 50 \n", "\n", " population_200 numerosity_200 average_specificity_200 \\\n", "trial \n", - "0 1601 1800 7.603889 \n", - "100 1553 1800 7.540556 \n", - "200 1547 1800 7.685000 \n", - "300 1564 1800 8.555556 \n", - "400 1516 1800 8.445556 \n", - "500 1518 1800 9.269444 \n", - "600 1514 1800 11.503333 \n", - "700 1510 1800 11.192222 \n", - "800 1525 1800 11.952222 \n", - "900 1536 1800 12.186111 \n", - "1000 1550 1800 12.157778 \n", - "1100 1549 1800 12.073333 \n", - "1200 1550 1800 11.833889 \n", - "1300 1547 1800 11.262778 \n", - "1400 1551 1800 13.501111 \n", - "1500 1549 1800 12.729444 \n", - "1600 1542 1800 12.456667 \n", - "1700 1537 1800 12.912222 \n", - "1800 1534 1800 13.870556 \n", - "1900 1473 1800 12.501667 \n", + "0 105 111 7.846847 \n", + "100 751 1262 13.617274 \n", + "200 1143 1800 17.824444 \n", + "300 1252 1800 16.866111 \n", + "400 1320 1800 20.779444 \n", + "500 1362 1800 23.400000 \n", + "600 1380 1800 25.291111 \n", + "700 1377 1800 24.647222 \n", + "800 1363 1800 29.932222 \n", + "900 1402 1800 26.406111 \n", + "1000 1437 1800 25.500556 \n", + "1100 1435 1800 26.309444 \n", + "1200 1432 1800 26.684444 \n", + "1300 1466 1800 29.200000 \n", + "1400 1477 1800 26.985000 \n", + "1500 1465 1800 27.093333 \n", + "1600 1472 1800 27.922222 \n", + "1700 1482 1800 27.795556 \n", + "1800 1478 1800 28.546111 \n", + "1900 1472 1800 28.298333 \n", + "2000 1454 1800 29.829444 \n", + "2100 1440 1800 36.025000 \n", + "2200 1430 1800 38.919444 \n", + "2300 1385 1800 44.060556 \n", + "2400 1385 1800 42.263889 \n", "\n", " fraction_accuracy_200 \n", "trial \n", - "0 1.00 \n", + "0 0.96 \n", "100 1.00 \n", - "200 0.74 \n", + "200 1.00 \n", "300 1.00 \n", - "400 0.93 \n", - "500 0.93 \n", - "600 0.75 \n", - "700 0.69 \n", - "800 0.64 \n", - "900 0.96 \n", - "1000 0.43 \n", - "1100 0.73 \n", - "1200 0.75 \n", - "1300 0.56 \n", - "1400 0.84 \n", - "1500 0.22 \n", - "1600 0.99 \n", - "1700 0.92 \n", - "1800 1.00 \n", - "1900 1.00 " + "400 0.98 \n", + "500 1.00 \n", + "600 1.00 \n", + "700 1.00 \n", + "800 0.98 \n", + "900 0.94 \n", + "1000 1.00 \n", + "1100 0.98 \n", + "1200 0.95 \n", + "1300 0.96 \n", + "1400 0.97 \n", + "1500 0.97 \n", + "1600 0.98 \n", + "1700 0.96 \n", + "1800 0.95 \n", + "1900 0.94 \n", + "2000 0.97 \n", + "2100 0.99 \n", + "2200 0.98 \n", + "2300 0.99 \n", + "2400 0.99 " ] }, "metadata": {}, @@ -817,7 +972,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -826,7 +981,7 @@ }, { "data": { - 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\n", 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\n", 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\n", 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\n", 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" ] @@ -884,22 +1039,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -919,9 +1074,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df[['numerosity_20', 'numerosity_200']].plot()\n", "ax.set_xlabel(\"trial\")\n", @@ -931,22 +1109,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -966,24 +1144,31 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "467.0\n", - "157.0\n", - "103.0\n" + "127\n", + "357\n", + "454\n" ] } ], "source": [ - "print(sum(df['steps_in_trial'])/number_of_experiments)\n", - "print(sum(df['steps_in_trial_20'])/number_of_experiments)\n", - "print(sum(df['steps_in_trial_200'])/number_of_experiments)" + "print(sum(df['steps_in_trial']))\n", + "print(sum(df['steps_in_trial_20']))\n", + "print(sum(df['steps_in_trial_200']))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/XCS_Experiments/XCS_XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/XCS_XNCS_Woods1_pre_populated.ipynb new file mode 100644 index 0000000..7e294c4 --- /dev/null +++ b/XCS_Experiments/XCS_XNCS_Woods1_pre_populated.ipynb @@ -0,0 +1,1040 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# logging \n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is how maze looks like:\n", + "\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n" + ] + } + ], + "source": [ + "# environment setup\n", + "import gym\n", + "# noinspection PyUnresolvedReferences\n", + "import gym_woods\n", + "\n", + "maze = gym.make('Woods1-v0')\n", + "print(\"This is how maze looks like:\")\n", + "situation = maze.reset()\n", + "maze.render()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lcs.agents.xcs import XCS\n", + "from lcs.agents.xcs import Configuration as XCSConfig\n", + "from lcs.agents.xncs import XNCS\n", + "from lcs.agents.xncs import Configuration as XNCSConfig\n", + "\n", + "from utils.xcs_utils import xcs_metrics\n", + "from utils.nxcs_utils import xncs_metrics\n", + "\n", + "XCScfg = XCSConfig(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08,\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover chi\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xcs_metrics)\n", + "\n", + "XNCScfg200 = XNCSConfig(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=200)\n", + "\n", + "XNCScfg20 = XNCSConfig(number_of_actions=8,\n", + " max_population=1800,\n", + " learning_rate=0.2,\n", + " alpha=0.1,\n", + " gamma=0.71,\n", + " mutation_chance=0.08, # mu\n", + " delta=0.1,\n", + " ga_threshold=25,\n", + " deletion_threshold=25,\n", + " covering_wildcard_chance = 0.7,\n", + " chi=1, # crossover\n", + " metrics_trial_frequency=100,\n", + " initial_prediction =10, # p_i\n", + " initial_error = 0.1, # epsilon_i\n", + " initial_fitness = 10, # f_i\n", + " user_metrics_collector_fcn=xncs_metrics,\n", + " lmc=10,\n", + " lem=20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000.0, 'perf_time': 0.007115500000004715, 'population': 1608, 'numerosity': 1800, 'average_specificity': 8.09388888888889}\n", + "INFO:lcs.agents.Agent:{'trial': 200, 'steps_in_trial': 4, 'reward': 1453.624725184914, 'perf_time': 0.05274989999999491, 'population': 1521, 'numerosity': 1800, 'average_specificity': 8.33611111111111}\n", + "INFO:lcs.agents.Agent:{'trial': 400, 'steps_in_trial': 2, 'reward': 1628.8421748810078, 'perf_time': 0.013923000000005459, 'population': 1497, 'numerosity': 1800, 'average_specificity': 8.47888888888889}\n", + "INFO:lcs.agents.Agent:{'trial': 600, 'steps_in_trial': 7, 'reward': 1116.7679302783154, 'perf_time': 0.06485539999999901, 'population': 1511, 'numerosity': 1800, 'average_specificity': 9.2}\n", + "INFO:lcs.agents.Agent:{'trial': 800, 'steps_in_trial': 2, 'reward': 1711.1722449879185, 'perf_time': 0.0130927000000014, 'population': 1510, 'numerosity': 1800, 'average_specificity': 9.747222222222222}\n", + "INFO:lcs.agents.Agent:{'trial': 1000, 'steps_in_trial': 6, 'reward': 1149.9895471736977, 'perf_time': 0.10961669999998946, 'population': 1489, 'numerosity': 1800, 'average_specificity': 9.466666666666667}\n", + "INFO:lcs.agents.Agent:{'trial': 1200, 'steps_in_trial': 6, 'reward': 1203.6806328691923, 'perf_time': 0.11548569999999359, 'population': 1503, 'numerosity': 1800, 'average_specificity': 9.88}\n", + "INFO:lcs.agents.Agent:{'trial': 1400, 'steps_in_trial': 4, 'reward': 1340.6472952635195, 'perf_time': 0.05446240000000557, 'population': 1503, 'numerosity': 1800, 'average_specificity': 10.29888888888889}\n", + "INFO:lcs.agents.Agent:{'trial': 1600, 'steps_in_trial': 6, 'reward': 1155.0517641686956, 'perf_time': 0.11012420000000134, 'population': 1501, 'numerosity': 1800, 'average_specificity': 10.531666666666666}\n", + "INFO:lcs.agents.Agent:{'trial': 1800, 'steps_in_trial': 6, 'reward': 1184.0276804345788, 'perf_time': 0.2541136000000108, 'population': 1502, 'numerosity': 1800, 'average_specificity': 9.36}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing 0 experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 7, 'reward': 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+ "\n", + "number_of_experiments = 1\n", + "explore = 0\n", + "exploit = 2000\n", + "\n", + "df = XCSExp(maze=maze,\n", + " cfg=XCScfg,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=True\n", + " )\n", + "\n", + "df20 = XNCSExp(maze=maze,\n", + " cfg=XNCScfg20,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=True)\n", + "\n", + "df200 = XNCSExp(maze=maze,\n", + " cfg=XNCScfg200,\n", + " number_of_tests=number_of_experiments,\n", + " explore_trials=explore,\n", + " exploit_trials=exploit,\n", + " pre_generate=True)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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190012107.2612120.007106149618009.49888961520180019.4533331.0021549180023.9055560.94
\n", + "
" + ], + "text/plain": [ + " steps_in_trial reward perf_time population numerosity \\\n", + "trial \n", + "0 1 1000.000000 0.007116 1608 1800 \n", + "100 7 1091.042147 0.080965 1538 1800 \n", + "200 4 1453.624725 0.052750 1521 1800 \n", + "300 3 1429.272738 0.019097 1508 1800 \n", + "400 2 1628.842175 0.013923 1497 1800 \n", + "500 6 1202.014122 0.070977 1515 1800 \n", + "600 7 1116.767930 0.064855 1511 1800 \n", + "700 6 1144.512439 0.062709 1509 1800 \n", + "800 2 1711.172245 0.013093 1510 1800 \n", + "900 3 1576.655662 0.019542 1495 1800 \n", + "1000 6 1149.989547 0.109617 1489 1800 \n", + "1100 7 1101.632805 0.101002 1512 1800 \n", + "1200 6 1203.680633 0.115486 1503 1800 \n", + "1300 2 1902.288722 0.029495 1502 1800 \n", + "1400 4 1340.647295 0.054462 1503 1800 \n", + "1500 7 1121.590436 0.088064 1494 1800 \n", + "1600 6 1155.051764 0.110124 1501 1800 \n", + "1700 4 1282.650979 0.102158 1491 1800 \n", + "1800 6 1184.027680 0.254114 1502 1800 \n", + "1900 1 2107.261212 0.007106 1496 1800 \n", + "\n", + " average_specificity steps_in_trial_20 population_20 numerosity_20 \\\n", + "trial \n", + "0 8.093889 7 1592 1800 \n", + "100 8.143333 4 1538 1800 \n", + "200 8.336111 3 1539 1800 \n", + "300 8.667778 3 1533 1800 \n", + "400 8.478889 1 1539 1800 \n", + "500 8.500556 2 1547 1800 \n", + "600 9.200000 5 1537 1800 \n", + "700 9.953333 4 1522 1800 \n", + "800 9.747222 16 1529 1800 \n", + "900 9.290000 6 1531 1800 \n", + "1000 9.466667 12 1527 1800 \n", + "1100 9.171111 16 1516 1800 \n", + "1200 9.880000 5 1519 1800 \n", + "1300 9.847778 1 1510 1800 \n", + "1400 10.298889 5 1516 1800 \n", + "1500 10.144444 1 1526 1800 \n", + "1600 10.531667 3 1524 1800 \n", + "1700 10.047222 9 1506 1800 \n", + "1800 9.360000 1 1517 1800 \n", + "1900 9.498889 6 1520 1800 \n", + "\n", + " average_specificity_20 fraction_accuracy_20 steps_in_trial_200 \\\n", + "trial \n", + "0 8.056111 1.00 50 \n", + "100 8.396111 0.99 3 \n", + "200 9.983333 1.00 4 \n", + "300 10.541667 1.00 46 \n", + "400 10.966111 1.00 3 \n", + "500 11.560556 1.00 38 \n", + "600 13.433333 1.00 50 \n", + "700 15.464444 1.00 20 \n", + "800 16.426667 1.00 1 \n", + "900 16.401111 1.00 18 \n", + "1000 17.814444 1.00 3 \n", + "1100 19.023333 1.00 5 \n", + "1200 19.356667 1.00 2 \n", + "1300 19.372222 1.00 1 \n", + "1400 19.933333 1.00 4 \n", + "1500 19.471111 1.00 6 \n", + "1600 19.217778 1.00 41 \n", + "1700 20.028889 1.00 20 \n", + "1800 19.828889 1.00 4 \n", + "1900 19.453333 1.00 2 \n", + "\n", + " population_200 numerosity_200 average_specificity_200 \\\n", + "trial \n", + "0 1619 1800 8.398333 \n", + "100 1547 1800 9.474444 \n", + "200 1544 1800 9.857778 \n", + "300 1550 1800 12.838889 \n", + "400 1527 1800 15.620556 \n", + "500 1535 1800 16.774444 \n", + "600 1526 1800 17.574444 \n", + "700 1541 1800 18.971667 \n", + "800 1527 1800 21.519444 \n", + "900 1512 1800 24.141667 \n", + "1000 1522 1800 23.775000 \n", + "1100 1507 1800 26.031667 \n", + "1200 1499 1800 27.473889 \n", + "1300 1508 1800 27.152778 \n", + "1400 1500 1800 28.292778 \n", + "1500 1494 1800 27.946111 \n", + "1600 1506 1800 26.201667 \n", + "1700 1509 1800 26.765000 \n", + "1800 1538 1800 25.401111 \n", + "1900 1549 1800 23.905556 \n", + "\n", + " fraction_accuracy_200 \n", + "trial \n", + "0 0.94 \n", + "100 1.00 \n", + "200 0.51 \n", + "300 0.59 \n", + "400 0.91 \n", + "500 0.34 \n", + "600 0.64 \n", + "700 0.92 \n", + "800 0.95 \n", + "900 0.99 \n", + "1000 0.86 \n", + "1100 0.76 \n", + "1200 0.82 \n", + "1300 0.93 \n", + "1400 0.77 \n", + "1500 0.94 \n", + "1600 0.93 \n", + "1700 0.89 \n", + "1800 0.89 \n", + "1900 0.94 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['steps_in_trial_20']=df20['steps_in_trial']\n", + "df['population_20']=df20['population']\n", + "df['numerosity_20']=df20['numerosity']\n", + "df['average_specificity_20']=df20['average_specificity']\n", + "df['fraction_accuracy_20']=df20['fraction_accuracy']\n", + "\n", + "df['steps_in_trial_200']=df200['steps_in_trial']\n", + "df['population_200']=df200['population']\n", + "df['numerosity_200']=df200['numerosity']\n", + "df['average_specificity_200']=df200['average_specificity']\n", + "df['fraction_accuracy_200']=df200['fraction_accuracy']\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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mhQ3X77zzjg4ZMqSw4TotLU2nTJmiGRkZunDhwsL95s+ff8pxTrd27Vpt1qyZ7thxakNbcnKyRkVFaUpKiqakpGhUVJQmJye79Nmqwvmt7banbNduM7vpLT/cosknXPv7GZVj7q652nVmVx369VDdnbrb7nDOgGm4tldJ60mcPq35k08+SdeuXQufjx49mszMTHr06IGnpyeenp48/PDDqCovvfQSo0aNok6dOvj5+ZV6FfHoo4+SmZnJDTfcAEBkZCRz584lODiY//znP/To0QOAp556qrAR26geTuSf4LFFjxHgFcCbF79JsI/5+1VFVzS/giYBTbh/4f3c+qPVoH0uU6LYodLWk6gMMTExunr16lPKzHoHFcucX3s9u/xZvtzxJVMGTzllLiWjakrITGDsb2PZeWwnj8Y8yq1tb60S7YClrSdhBtMZRjU1f/98vtzxJf/o8A+TIKqJhv4NmTl0JgMiBvDiqhd5Zvkz5BVU7WVWTZKoQSZOnEiXLl1OuU2cONHusIwKkJCZwNPLnqZjaEfGdh1rdziGC3w9fXl94Ovc3fFuvt75NSPnj+RY9jG7wyqRqW4yzos5v5Uv35HPP3/5J9tTt/Pl5V+ankzV2A97fuDppU8T7hvO24Petm0lP1PdZBg1yNQNU1mbuJb/9P6PSRDV3OXNLuejIR+RXZDNbT/dxtoja8++UyWr0CQhIk1EZKGIbBWRzSJyv7N8gogcEpFY521YCfsPEZHtIrJLRMZVZKyGUR2sPryaKRumcGXzK/lbs7/ZHY5RDjqFdWL2sNmE+IQwcv5Ifj/4u90hnaKiryTygYdVtS3QGxgjIu2cr72uql2ctx9P31FE3IF3gKFAO2B4kX0No9ZJy0lj3JJxNAlowvhe4+0OxyhHjfwbMXPoTFoGteSBhQ/wzc5v7A6pUIUmCVVNUNW1zscZwFagcRl37wnsUtU9qpoL/Be4qmIiNYyqTVV5etnTJGcn82L/F/Hz9LM7JKOc1fOpx7TLptGzQU+eWvYU0zdNtzskoBLbJEQkCugKrHQW3SsiG0RkuojUK2aXxsDBIs/jKCbBiMhIEVktIquPHj1a3mGXi+q4nsTevXvp1asXLVu25KabbiI3N7e8TodxDr7c8SULDizggW4P0D6kvd3hGBXE19OXdwa9w9Cooby+5nVeWfUKDnXYG1RJQ7HL8wb4A2uAa53P6wPuWElqIjC9mH1uAD4s8vw24K3S3qeqTsuhWv3Wk7jhhhv0s88+U1XVUaNG6bvvvlvssavK+a3JdqTs0O6fdNdR80eZdSFqiQJHgU5aMUk7fNxBn1j8hOYW5Fbo+2HntBwi4gl8DcxW1TnOxHSkyOsfAD8Us2scULTrRgQQfz6xvPjni2xL2XY+hzhDm+A2PN7z8bNuV53Wk7j55pv57bffCmelHTFiBBMmTGD06NEunBmjPJzIP8Fjix/D39Of5/o9V+bpqo3qzU3cGNdzHCF1Qnhr3VscyznGqwNepY5HncqPpSIPLtZ482nAVlV9rUh5wyKbXQNsKmb3VUBLEYkWES/gZmBuRcZbkU6uJ/Hggw8yefLkM9aTePXVVykoKCgsK+t6EsOHD2f27Nk4HGW7JC3LehLJyckEBQXh4eFxSrlR+V5Z9Qq7ju1i0gWTCK0Tanc4RiUSEUZ2GsnTfZ5mafxS7p53ty3Lolb0lUQ/rGqijSIS6ywbj9VTqQugwD5gFICINMKqYhqmqvkici/wC1bV1HRV3Xw+wZTlF39Fqi7rSZRUblSuX/f/yhc7vrCm3ajmq5sZ5+76VtdTz7sejy1+jBE/jeD9we/TwK9Bpb1/Rfdu+kNVRVU7aZHurqp6m6p2dJZfqaoJzu3jVXVYkf1/VNVWqtpcVav1/BJF15N4/fXXSUhIOOX18ePH8+KLLxZeERRdT6IkHTt25MEHH2T+/Pl8/fXXpb7/yfUk5s6de8p6EgcP/tU3IC4ujkaNGhEaGsqxY8fIz88/pdyoPAmZCTy17Ck6hHRgbBcz7UZtN6jpIN4f/D5Hso5w20+3sSet5O+F8mYqOCuBqjJ69GgmT55MZGQkjz76KI888sgp27Rp04Z27drxww9/Nc888cQTjBkzhvT0dADS09OZOnUqmZmZpywWFBsbS9OmTUt8/3Xr1jFq1Cjmzp1LeHh4Yflll13GvHnzSE1NJTU1lXnz5nHZZZchIgwcOJCvvvoKsHpAXXWV6X1cWfId+YxbMg6HOnip/0vltgiOUb31aNCDj4Z8RF5BHiN+GsHGoxsr541LatGujreq2rtpypQphT2KVFXz8/O1W7du+vvvv5+yWFBsbKyKSGHvJofDoS+++KK2atVK27dvr126dNFPPvlE09PTdejQodqqVSvt3Lmz9u3bV1etWlXi+w8aNEjDw8O1c+fO2rlzZ73iiisKX5s2bZo2b95cmzdvrtOnTy8s3717t/bo0UObN2+u119/vWZnZxd77Kpwfmuad9e9qx0+7qDf7/7e7lCMKuhA2gEd+vVQ7TGrhy6NW1oux6SU3k1mgj/jvJjzC3mOPEbNH0XSiSQ6h3Wmc1hnuoR1oVlQM5d7I605soY7f7mTy5tdzsQLqnUNq1GBkk4kMfrX0exK3cXECyYyrFmxMxuVWWkT/JmV6QzjPH2y5RNWHV5FTP0YFh5cyLe7vgUgwDOAjmEdC5NGx7COBHgFlHick9NuRPhHmGk3jFKF1gll+mXTuX/h/Ty+5HFSc1K5te2tFfJeJknUIBMnTuTLL788peyGG27gySeftCmimi8uI473Yt9jYJOBvHnxm6gq+9P3s/7oemKPxrL+6HreX/8+iiIIzYOaF15tdA7vTHRgdGGPsgnLJpB0IolZw2aZaTeMswrwCuC9S95j3OJxvPDnCySfSGZs17Hl3hOxVlQ3tWnTxnThrACqyrZt22ptdZOqMnrBaNYeWcvcq+eW2C0xMzeTjUkbC5PGhqMbyMjNACDQK5DOYZ2p51OPubvn8kjMI4xoP6IyP4ZRzRU4Cnhu5XMkZSXx+sDX8XBz/bd/ra5u8vHxITk5mZCQEJMoypGqkpycjI+Pj92h2OaX/b+w9NBSHuvxWKn91v29/OnTqA99GvUBwKEO9qXt++tqI3E9Sw4t4YLGF3Bbu9sqK3yjhnB3c+ep3k+R78g/pwRxNjX+SiIvL4+4uDiys7Ntiqrm8vHxISIiAk/P2tdFMz03nau+vYqwOmF8+rdPz/s/5/G843i5e+HpVvvOpWG/Wn0l4enpSXR0tN1hGDXMm2vfJCU7hbcHvV0uv95MG4RRVZnBdIbhotjEWL7Y/gW3tLnFTNtt1HgmSRiGC/IceTy74lnCfcO5t+u9dodjGBWuxlc3GUZ5+mTLJ+xM3ckbA98wVURGrWCuJAyjjE6Oibi4ycVcHHmx3eEYRqUwScIwykBVeW6ltejPE72esDscw6g0JkkYRhn8ss8aEzG269hKncvfMOxW5iQhIsEVGYhhVFXpuem88OcLtAtpx/A2w+0OxzAqlStXEitF5EsRGSZlHLosIk1EZKGIbBWRzSJyv7P8ZRHZJiIbROQbEQkqYf99IrJRRGJFZHVx2xhGRXtjzRuk5qTydJ+ncXdztzscw6hUriSJVsBUrOVId4nIJBFpdZZ98oGHVbUt0BsYIyLtgPlAB1XtBOwASqvkHajWinbFjgY0jIoUmxjLFzu+4Na2t9IupJ3d4RhGpStzknCuTTFfVYcDdwEjgD9FZJGI9ClhnwRVXet8nAFsBRqr6jxVzXdutgKIOK9PYRgVIM+RxzPLn6GBXwPu7WLGRBi1kyttEiEicr+z2ucRYCwQCjwMfFqG/aOArsDK0166E/iphN0UmCcia0RkZAnHHSkiq0Vk9dGjR8v2YQyjDGZunsmuY7sY33M8vp6+dodjGLZwZTDdcuAT4GpVjStSvlpE3i9tRxHxB74GHlDV9CLlT2JVSc0uYdd+qhovIuHAfBHZpqqLi26gqlOxqsGIiYmpObMVGrY6mHGQ99e/z6DIQQyMHGh3OIZhG1faJP6tqv9XNEGIyA0AqvpiSTuJiCdWgpitqnOKlI8ALgdu1RKmolXVeOd9IvAN0NOFeA3jnKgqE1dMxE3cGNdznN3hGIatXEkSxf1vKXVUkbMX1DRgq6q+VqR8CPA4cKWqZpWwr5+IBJx8DFwKbHIhXsM4J7/s+4Wl8Uu5r9t9ZkyEUeudtbpJRIYCw4DGIvJmkZcCsaqKStMPqzfURhGJdZaNB94EvLGqkABWqOq/RKQR8KGqDgPqA984X/cAPlXVn8v6wQzjXJwcE9E+pD03t77Z7nAMw3ZlaZOIB1YDVwJripRnAA+WtqOq/gEUN6bixxK2j8dKSKjqHqBzGeIzjHIzec1kUnNSee+S98yYCMOgDElCVdcD60VkdpFuq4ZR48QmxvLlji+5vd3ttA2pnet2G8bpylLd9IWq3gisE5EzGpidA+IMo1orOiZiTJcxdodjGFVGWaqb7nfeX16RgRiGnWZsnsGuY7t46+K3zJgIwyiiLNVNCc6HbkCCqmYDiEgdrMZlw6jWDqQfKBwTMaDJALvDMYwqxZUusF8CjiLPC5xlhlFtqSoTlk/Ay82L8b3G2x2OYVQ5riQJD1XNPfnE+dir/EMyjMozZ+ccVh1exUMxDxHuG253OIZR5biSJI6KyJUnn4jIVUBS+YdkGJUjMSuRV1e/So8GPbiu5XV2h2MYVZIrczf9C5gtIm9jjX04CNxeIVEZRiWYtHISuY5cnu7zNGVcIsUwap0yJwlV3Q30dk7WJ86pvw2jWpq/fz4LDizggW4P0DSwqd3hGEaVVZZxEn9X1Vki8tBp5QAUnZPJMKqDtJw0Jq2cRNvgtoxoP8LucAyjSivLlcTJTuMBFRmIYVSW19a8Rmp2Ku8MegcPN1dqXA2j9inL/5Dmzvstqmq6vBrV2sqElczZOYd/dPiHWY7UMMqgLL2bhjnXhCh1WnDDqOpO5J9gwrIJRAZEck/ne+wOxzCqhbJcSfyM1dXVT0TSi5QL1tLXgRUSmWGUs3dj3yUuM47pl03Hx8PH7nAMo1o465WEqj6qqnWB/6lqYJFbgEkQRnWxOXkzM7fM5LqW19GjQQ+7wzGMaqPMg+lU9SpXDy4iTURkoYhsFZHNInK/szxYROaLyE7nfb0S9h8iIttFZJeImHUkjXOS58jj6aVPE+ITwkMxD519B8MwCp01SYjIH877DBFJP/3+LLvnAw+ralugNzBGRNphLYW6QFVbAgsoZmlUEXEH3gGGAu2A4c59DcMlMzbPYHvqdp7s9SSBXubi1zBcUZZZYC9w3rvcBdY5g2yC83GGiGwFGgNXAQOcm80Afsda87qonsAu5wp1iMh/nfttcTUOo/bam7aX92LfY3DTwQxqOsjucIyaKGUP/DEZju2HuhEQ1BTqNoGgJtZ9YGNwr75drcscuYj0BjafHGntHHndXlVXlnH/KKArsBKof3IKclVNEJHiZlZrjDX1x0lxQK9ijjsSGAkQGRlZ1o9j1AIOdTBh2QS8PbzNDK9VVVYKbJ0LW74DdUDzQdByMIS1gao+VUryblj8Cmz4HNw9IbwdJM6HzCOnbiduVqIomjiCIp2PI63E4ll1O1K4kt7eA7oVeZ5VTFmxnAnla+ABVU0v4zw5xW1U3Mp4U4GpADExMWe8btReX+34irWJa3mm7zOE1gm1OxzjpOw02PYjbPoa9iwERz4ENwd3L5j/H+sW2BhaDIIWl0D0RVAnyO6o/3J0Byx5BTZ+Ce7e0Otf0O8+CGhgvZ6XDemHrCuLYwch7eBf9/uXQXo8aMGpx/QLh7DW0KQnNOkFET3AN7jyP1sxXEkSoqqFX8Kq6hCRskzr4YmVIGar6hxn8RERaei8imgIJBazaxzQpMjzCCDehXiNWuzI8SO8vuZ1ejXoxTUtrrE7HCP3OOz4GTbNgZ3zoSDH+hXdZwx0uA4adLKuHNLiYNcC2PUrbP4W1s4Ecbe+PFtcYt0adAI3VyawLieJ22Dxy1Zy86wDve+BvvdBwGlrr3n6QEhz61acgnzIiLcSx7EDziRyAI5ssqqtTiaQsDbOpNEbIntDcDNbrq6kyPd+6RuKzMFqO3jPWXQPMFBVry5lH8Fqc0hR1QeKlL8MJKvqC85eS8Gq+thp+3oAO4BBwCFgFXCLqm4u6f1iYmJ09erVZfo8Rs2lqty38D5WxK9gzpVzaBLY5Ow7GeUvL9v6st/0tZUg8rLAvwF0uBbaXwsRMaV/6RXkQdxq2DXfOk7CeqvcL8yqlmpxCTS/GPxCKvZzHNkCi1+ykpanL/S8C/qMBf+w8n+v3ONwaC0cXAEHVkLcn9aVF4BvqHWVEdnLum/YpdyqqURkjarGFPuaC0kiHHgTuBir2mcBVvVRcVcBJ/e5AFgCbOSvVe3GY7VLfAFEAgeAG1Q1RUQaAR+q6jDn/sOAyYA7MF1VJ5YWo0kSBsDP+37m0UWP8nD3h7mjwx12h1O7FOTB7oWweQ5s+x/kpINvCLS72koOkX3Azf3cjp2ZCLt/s65Edv8GJ1IAgcbdrIQR2RtCW1lVVeXxi/vwJlj0otVm4uUPPUdCn3srPikV5XBA0nY4uNJKGgdXQspu6zV3LytRRPayrjaa9DrnxFUuSaI6MEnCSMtJ48pvr6SBXwNmD5ttJvArC4fD6qGTlWw1HmuBde9w3qsWU3ZyO7XKHPnWF9jWuXAiFXzqQtsrrCuG6IvKv3ePowDiY60rjF2/wqHVVkxg/doPaQGhLa2kEdLir3sv31IPC1hXLItegm0/gFcA9BplVYtVkTYCMo9a5/rkLX4dFORa7Tr3rT2nQ5aWJFzp3dQKq6qpvqp2EJFOwJWq+tw5RWUYFeDlVS+TlpPGlMFTTIIojiqk7rO+WE7eEtZbv/jPl5c/tB5mtTE0vxg8KnB1Yzd3iOhu3QY8bvWSOrIJknZat+SdELfKagMp2t+lbhMreYS0dCYRZyIJaAgJsVZy2P4jeNeFix6H3qOhTrFjfe3jHwZtL7duAPk5VsIsj79hMVz5X/QB8CgwBUBVN4jIp4BJEkaVsCx+Gd/t/o67Ot5Fm+A2dodjP1Wrl038Oque+2RSyD5mve7uBfU7QMcboFFXCGxoNRKLm/UlLG5Fnrs5n7sVs43zFtDAatC1g28wRPe3bkXlnbC6qibv/CuBJO2Ag7MhN/Ov7Tz9IO+4dQU0YLx19VCVelSVxsPbqnKqqMO7sK2vqv55WvfV/HKOxzDOSVZeFs8uf5aowCj+1flfdodjj4zDp14hxK+D40et19w8rH787a6yEkKjrtbzivy1XxV41oEGHaxbUaqQkfBX0kjeZSW5mDutRGEUciVJJIlIc5zXbiJyPc7R1IZht7fWvcWhzEN8dNlHeLt72x1O5Tq6A+bcbVWXgPWrPqwttLwMGnWBRt2gfvsqPWCr0olAYCPr1uwiu6Op0lxJEmOwBq21EZFDwF7g1gqJyjDKqMBRwCurX2HW1lnc1PomYhoU2/ZWc8V+Cv972PrFfOlEaxBWg45la6A1jDIoc5JwzqF0iYj4AW4np+cwDLsczzvO44sfZ1HcIv7e9u88EvOI3SFVnpwM+N8jsOG/EHUhXDvV+lVsGOXMld5NIcDTwAWAOmeHfVZVkysqOMMoyeHjh7l3wb3sOraLJ3s9yc1tbrY7pMqTsB6+/Aek7rUaWfs/cu5jDwzjLFypbvovsBi4zvn8VuBz4JLyDsowSrM5eTNjF4wlKz+Ltwe9zQWNL7A7pMqhCn9OhXn/tgaojfgeomrJZzds40qSCFbV/yvy/DkRubqc4zGMUi04sIAnljxBkHcQM4fOpFW9VnaHVDmyUmDuWGuAV8vL4Or3Knfkr1FruZIkForIzVjTaQBcD/yv/EMyjDOpKjO3zOTV1a/SIbQDb178Zu2Z2fXACvjqn9YU1JdOtEb/VvVptI0aw5UkMQp4CPgEaxpvN+C4iDwEqFnv2qgoeY48Jq2cxFc7vmJw08FMvGAidTxsGrRVmRwO+OM1WDjJWnvgn79A4+52R2XUMq70bnJ5ZTrDOF/puek8/PvDrEhYwV0d72Js17G4iQ3TRFe2jCPwzUjY87s1/9EVk80gL8MWrvRu6gfEqupxEfk71mJDk1X1QIVFZ9RqcRlxjFkwhgPpB3i277Nc07KWrAuxawF8MwpyMuGKN6Hb7aZ6ybCNKz/J3gOyRKQz8BiwH6vqyTDKXWxiLLf+eCtJJ5KYMnhK7UgQBXnw6wSYda3Ve2nkQug+wiQIw1auJIl858p0VwFvqOobgKmCMsrdT3t/4p+//BM/Tz9mDZtFz4Y97Q6p4h07AB8Ngz9eh24j4O6FEN7W7qgMw6WG6wwReQL4O9BfRNwBz4oJy6iNVJWpG6byduzbdAvvxuSBk6nnU8Wmaa4I+5fDf2+xriSun25NtW0YVYQrSeIm4Bbgn6p6WEQigZdL20FEpgOXA4mq2sFZ9jnQ2rlJEHBMVbsUs+8+IAMowLqKqWWT8tQuuQW5TFg2ge/3fM/lzS7nmb7P4OVew2coBdj4FXw72lrn4NYvS14X2TBs4krvpsPAa0WeHwBmnnwuIstVtc9pu30MvF10O1W9qcg+rwJppbztQFVNKmuMRvWjqvx5+E/eWPsGG5M2MqbLGEZ1GoXU9Hp4VVjyKvz2fxDZF26eXXVWPjOMIspz6a4z5iFW1cUiElXcxmJ9C9yItWa2UcuoKkvjlzJl/RRij8YSVieMly96mSFRQ+wOreIV5MEPD8C6WdaCP1e9Yy0cYxhVUHkmCVcXy74QOKKqO0s53jwRUWCKqk4tbiMRGQmMBIiMjHQxBKOyqSq/H/ydKRumsDl5Mw38GvBkrye5puU1tWMdiBPH4IvbYe8i6P8YDBxvei8ZVZqdiwAPBz4r5fV+qhovIuHAfBHZpqqLT9/ImTymAsTExLiaqIxK4lAH8/fPZ+qGqexI3UGEfwQT+kzgyuZX4uleS/o/pO6HT2+0ltO8+j3ocovdERnGWZVnkijzzyER8QCuBUqcY0BV4533iSLyDdATaxZaoxrJd+Tz876f+WDDB+xJ20NUYBQTL5jIsOhheLjZ+Rulkh1aA5/eDAU5cNucM9diNowqyqX/pSLSFGipqr+KSB3Ao8jiQ7e5cKhLgG2qGlfC+xQubOR8fCnwrCuxGvbKc+Txw+4f+HDjhxzIOECLoBa83P9lBjcdjHttW/tg6/fw9d3gHw53/ABhrc++j2FUEa5My3E3Vt1/MNAciADeBwYBqOqmYvb5DBgAhIpIHPC0qk4Dbua0qiYRaQR8qKrDgPrAN84eLh7Ap6r6s6sfzqh8uQW5fLvrW6Zvms6hzEO0DW7L5AGTGRg5sHbMuVSUKix/x1r/oXF3GP5f8A+zOyrDcImra1z3BFYCqOpOZ3tBiVR1eAnldxRTFg8Mcz7eA3R2ITbDZtn52Xy982umb5pOYlYinUI7Mb7XeC5sfGHN785anIJ8+PlxWPUhtL3SWl7UsxbMXGvUOK4kiRxVzT35H97ZrmAaig1WJKzg33/8myNZR+gW3o3/6/d/9GnYp3YmB7DWn/7qTtg5D/reB5c8A2617CrKqDFcSRKLRGQ8UEdEBgP3AN9XTFhGdZBTkMOba99k5paZRAVGMf2y6fRo0MPusOyVHm/1YDqyBS5/HWLutDsiwzgvriSJccA/gY1YCxD9CHxYEUEZVd+O1B2MWzKOnak7uan1TTwc83DtWAioNIc3wuwbrSuJW7+AFmb5d6P6c2VaDgfwgfNm1FIOdTBryywmr51MgFcA7wx6h/4R1bw7Z+5xWDkF0g6Cm4fz5l7kcXHPTyvLTrem2PCpC3f+DA062P2pDKNcuNK7aSNntkGkAauB51Q1uTwDM6qeI8eP8O+l/2ZFwgoGNBnAhD4TCKkTYndY504VNn0N85+C9EPgGwpaAI4CcOT/dVNH2Y7XoBPc8gUENqzYuA2jErlS3fQT1oysnzqf3+y8T8eayO+K8gvLqGrm7ZvHM8ufIc+Rx9N9nua6ltdV74bpwxvhp8dh/1Lry/26adD09PkpnRwOZ/IokjhOTySOAghqCu61aICgUSu48i+6n6r2K/J8o4gsVdV+zuVMjRooMzeT5/98nrm759IhpAPPX/g8UXWj7A7r3GWlwMKJsHo6+ATB5ZOt5UFLG+Dn5ga4QW2ZPsQwinAlSfiLSC9VXQkgIj0Bf+dr+eUemWG7tUfWMv6P8SQcT2BUp1GM6jwKT7dq+kXpKIA1H1vtBtlp0OMuGPCEmZ7bMM7ClSRxFzBdRPyx5mlKB+5yTpvxfEUEZ9gjz5HHe7HvMW3TNBr6NWTGkBl0Ce9id1jnbv8y+PExOLIRoi6EIS+YhmXDKCNXejetAjqKSF1AVPVYkZe/KO/ADHvsS9vHuCXj2Jy8matbXM24nuPw8/SzO6xzkx4P8/4Dm76CwAi44WNod7WZmtswXODqBH9/A9oDPicbLVXVTLxXA6gqX+74kldWv4KXuxevDXiNwU0H2x3WucnPgeVvw+JXrUbl/o/BBQ+AVzVNdoZhI1e6wL4P+AIDsQbRXQ/8WUFxGZXIoQ4eWfQI8/fPp0/DPjx3wXOE+5Y6LVfVpAo7foGfx0HqXmhzOVw2EepF2R2ZYVRbrlxJ9FXVTiKyQVWfca5PPaeiAjMqz3e7vmP+/vmM6TKGkZ1GVs/ZWpN2Wclh13wIbQ23fQPNzcq4hnG+XEkS2c77LOe03slAdPmHZFSmjNwMJq+dTOewztU4QeyEDwYBCpdNgp4jTXdVwygnriSJ70UkCHgZWIs1+tpM0VHNvRv7LqnZqbx3yXvVM0Fkp8Fnw62kcPdvUK+p3REZRo1Spm8FEXEDFqjqMVX9GmgKtFHVp86y33QRSRSRTUXKJojIIRGJdd6GlbDvEBHZLiK7RGScC5/JKKNdqbv4bNtnXNfqOtqFtLM7HNc5HDBnpNX+cONMkyAMowKUKUk4J/d7tcjzHFVNK8OuHwNDiil/XVW7OG8/nv6iiLgD7wBDgXbAcBGpht9iVZeq8sKfL+Dn6cd9Xe+zO5xz8/sk2PGzNe4hqt/ZtzcMw2Wu1C/ME5HrxIUJe1R1MZDielj0BHap6h5VzQX+C1x1DscxSjB//3xWHl7JvV3vpZ5PPbvDcd2W72Dxy9D1Nmv0tGEYFcKVJPEQ8CWQKyLpIpIhIunn+L73isgGZ3VUcd9QjYGDRZ7HOcvOICIjRWS1iKw+evToOYZTu5zIP8Erq1+hZb2W3NDqBrvDcd2RzfDNaIjoAX971QyOM4wKVOYkoaoBquqmqp6qGuh8HngO7/ke0BzoAiRQpBqriOL+1xe7VKqqTlXVGFWNCQszi8yXxfRN00k4nsATPZ/Aw62azVqalWI1VHsHwI2fgIe33REZRo1W5iQhlr+LyH+cz5s4J/lziaoeUdWCIosYFXeMOKBJkecRQLyr72WcKS4jjukbpzM0amj1W2q0IB+++gdkJMDNs826DYZRCVypbnoX6APc4nyeidW47BIRKfo/+xpgUzGbrQJaiki0iHhhrV0x19X3Ms708qqXcXdz56GYh+wOxXW/Pg17frfWjo6IsTsaw6gVXKlr6KWq3URkHYCqpjq/wEskIp8BA4BQEYkDngYGiEgXrOqjfVjrZeMcoPehqg5T1XwRuRf4BXAHpqvqZpc+mXGGZYeW8dvB37i/2/008GtgdziuWf9faz6mnqOgq1m+xDAqiytJIs/ZNVUBRCQMKHVdR1UdXkzxtBK2jQeGFXn+I3BG91jj3OQV5PH8n88TGRDJ7e1utzsc1xxaC3Pvs6b5vmyi3dEYRq3iSnXTm8A3QLiITAT+ACZVSFRGuZu9dTb70vfxeM/H8XIv9QKwaslMhM//Dv71ram+zXQbhlGpXFlPYraIrAEGYfU+ulpVt1ZYZEa5OZp1lPfWv0f/iP70j+hvdzhll58LX9xu9Wj65zzwC7U7IsOodVyZKvwN4HNVdbmx2rDX62teJ8+Rx+M9Hrc7FNf8/DgcWA7XTYOGneyOxjBqJVeqm9YC/3bOpfSyiJjuJdVAbGIs3+/5nhHtRxAZGGl3OGW3+iNYPR363Q8dr7c7GsOotVwZTDdDVYdhjWvYAbwoIjsrLDLjvBU4Cpi0chLhvuHc3fFuu8MpuwMr4MdHocUlMOhpu6MxjFrtXOaGbgG0AaKAbeUajVGu5uyaw9aUrTzc/WF8PX3tDqds0g7B57dBUBO47kNwc7c7IsOo1VwZcX3yyuFZrAFw3VX1igqLzDgvaTlpvLn2TbrX787Q6KF2h1M2edlWT6a8LLj5U6hTDSceNIwaxpVxEnuBvkAzwBvoJCInZ3o1qpi3171Nem46T/R8Ahcm7rWPKvzwIMSvtRJEeFu7IzIMA9eSRAHwG9Y8SrFAb2A5YBYSrmK2p2znix1fcGOrG2kd3NrucMpm5fuw/lMY8AS0+Zvd0RiG4eRKm8R9QA9gv6oOBLoCZm7uKkZVmbRyEoFegdzb9V67wymb7T/DL+OhzeXQ/zG7ozEMowhXkkS2qmYDiIi3qm4DqsnP1Nrjp70/sTZxLfd1u4+63nXtDufsDq21ZnZt0AmunQpu1XCdbcOowVypbooTkSDgW2C+iKRipu+uUrLysnh19au0DW7LtS2utTucs0vdB5/eaI2kvuUL8PKzOyLDME7jyrQc1zgfThCRhUBd4OcKico4J1M3TCXxRCKvDngV96redTQrBWbfAAV5cMf/IKC+3REZhlGMc1qWTFUXlXcgxvnZn76fGVtmcGXzK+kS3sXucEqXn2N1dU3dB7d9C2Gm1tIwqqpqtnalUZKXVr2Et7s3D3R7wO5QSudwwLejYf9Sa06mqH52R2QYRikqtJVQRKaLSKKIbCpS9rKIbBORDSLyjbOdo7h994nIRhGJFZHVFRlndbcsfhmL4xYzqtMownyr+DrfCybApq/hkmfMnEyGUQ1UdFeSj4Ehp5XNBzqoaiesOaCeKGX/garaRVXNZIIlcKiD11a/RmP/xtza9la7wyndnx/A0jcg5p/WxH2GYVR5FZoknKOxU04rm6eq+c6nK7AG5xnn6Ic9P7A9dTv3db2vai8mtP0n+OkxaDUUhr4E1WEUuGEYFX4lcTZ3Aj+V8JoC80RkjYiMrMSYqo3s/GzeWvcW7UPaMyT69Au2KuTQGvjqTmjYGa6fBu6mKcwwqgvb/reKyJNAPjC7hE36qWq8iIRjjcvYVtw8Uc4EMhIgMrIarZdQDmZtncXh44eZdMEk3MTufF+C1H3w6U1mLIRhVFO2fLOIyAjgcuBWVdXitlHVeOd9Itba2j1L2G6qqsaoakxYWBVvtC1HKdkpTNs4jYsiLqJHgx52h1O8rBSYdb01FuLWr8E/3O6IDMNwUaUnCREZAjwOXKmqWSVs4yciAScfA5diTU9uOE1ZP4Ws/Cwe7P6g3aEULy8b/nsLHNsPwz+DsFZ2R2QYxjmo6C6wn2HNFNtaROJE5J/A20AAVhVSrIi879y2kYj86Ny1PvCHiKwH/gT+p6pmdLfTgfQDfLH9C65teS3Ng5rbHc6ZTo6FOLAcrnkfmva1OyLDMM5RhbZJqOrwYoqnlbBtPDDM+XgP0LkCQ6vWJq+djKe7J2O6jLE7lOL9+jRsngODn4UO19kdjWEY56GKtnYaJYlNjGX+/vn8o/0/CK0Tanc4Z/rzA1j2JvS4G/reZ3c0hmGcJ5MkqhFV5dXVrxJaJ5QR7UfYHc6Ztv1YZCzEi2YshGHUACZJVCMLDiwg9mgsY7qMwdfT1+5wTlU4FqKLNRaiqs9CaxhGmZhRTdVEniOPyWsn06xuM65ucbXd4VgK8mHPQtjwOWz9weriesvnZiyEYdQgJklUE1/t+Ir96ft5++K38XCz8c+mCgnrrcSw8Ss4ngg+QdBlOPR7wIyFMIwaxiSJaiAzN5P3179PjwY96B/R354gjh2EjV/Ahi/g6DZw94JWl0Gnm6HlYPDwticuwzAqlEkS1cD0TdNJyU7h3Zh3kcpsDM5Ogy3fWYlh3xKrLLIPXP46tL8G6tSrvFgMw7CFSRJV3OHjh5m5ZSbDoofRPqR9xb9hQR7sWgAb/mvN3JqfDcHNYeCT0PEGCI6u+BgMw6gyTJKo4t5e9zYOdXBftwocc1CQb/VO2vSVtSBQVjL4hkC3263qpMbdTHdWw6ilTJKowranbGfu7rnc3u52Gvs3Lr8DOxyQuAX2Loa9i2DfUsjNAHdvaDMMOt0ELS4Bd8/ye0/DMKolkySqsNfXvE6AVwB3d7r7/A6kCil7rISwZ5HVvpCVbL0W3MxaRjT6Qisx+NQ9/8ANw6gxTJKoopbFL2Np/FIeiXmEut7n8MWdduivK4W9iyH9kFUe0AhaXgrR/SHqQghqUr6BG4ZRo5gkUQUVOAoK160e3qa4ORKLcTwZ9i22EsKeRZCy2yqvE2wlhOiHIfoiCGlu2hcMwygzkySqoJPrVr/U/6XS163OSoGt31szru5dDOoArwCI6gc9/mklh/D24GZmXzGMovIKHGyOT2fV3hRW7Uth9f5UPNyEga3DubhtOBe0CMXP23w9gkkSVc7Jdas7hHRgSFQx61Znp8G2/8GmOdaUGI58q13hgoeg1RBo1NWsIW0Ypzmek8/aA6ms2pfK6n0prDtwjBN5BQA0DfFlQOswcvIc/Lgxgc9XH8TL3Y1ezYIZ1Caci9vUJzKkis2VVonMt0kVM2vrLI5kHeH5C5//a+BcTibs+NlKDLvmQ0Eu1I2EPmOg/bXQsLOpQjKqpcT0bJbsTOKPXUlkZOcRFuBNmL+3dX/y5u9DWIA3dbzKPmnk0YwcVu9LYdW+VFbtS2FLQjoFDsVNoG3DQG7q0YQeUcHERNWjfqBP4X55BQ5W7Uvht62J/LY9kQnfb2HC91toEe7PxW3CubhNON2b1sPTvfZcnUsJS0yXz8FFpmOtZZ2oqh2cZcHA50AUsA+4UVVTi9l3CPAG4A58qKovnO39YmJidPXq1eUWf2VLyU7hb3P+Rkz9GN7q/xLsnGclhh2/QP4JCGhojXRufy1ExJjEYFQ72XkFrNqXwpKdSSzecZRthzMACPHzIizAm6TMXJKP51Dc15K/twfhAd6EFiaPU5PJ0YwcVu21qo72Jh0HwNvDjS5NgugZHUxMVDDdIoMI8Cl71+69Scf5bVsiC7clsnJvMnkFSqCPB/1bhTGobTgXtQon2K+UKuFqQkTWqGpMsa9VcJLoD2QCM4skiZeAFFV9QUTGAfVU9fHT9nMHdgCDgThgFTBcVbeU9n7VPUk8v+I5Pt/+JXN8O9Bs5++Qmwl+YdDuauhwLTTpbdoXjGpFVdmZmMniHUdZvDOJlXuSycl34OXuRkxUPS5sGcaFLUNp1zAQNzfrR09+gYOUrFyOZuQU3hJPPs607pOczzNy8k95vyBfT2KaBtMjqh49ooPp0KguXh7l838mIzuPpbuSWLA1kYXbE0nKzMVNoGtkPS5uE86QDg1oHuZfLu9V2WxLEs43jwJ+KJIktgMDVDVBRBoCv6tq69P26QNMUNXLnM+fAFDV50t7r2qbJPKy2f/TA1yd8gfXZGTyVBbQ9korMTS9wLQxnIXDoazYm0xOnoOIenVoXK8Ovl7mnNkl5Xguf+xKYsmOoyzZmcTh9GwAmof50b9VGP1bhtGrWXC5/I1O5BaQlJlDYkY2gT6eNA/zL0w2FcnhUDYeSmPBtkR+23aETYfSAejXIoTb+0QxqE04HtWoSqq0JGHH/6T6qpoA4EwUxc0t3Rg4WOR5HNCruIOJyEhgJEBkZGQ5h1rxHDkZ7P3vjbyctRNPXz/uufg1aHOFGe1cBrn5Duauj2fq4t3sOJJ5ymshfl5EBPsSUa+O82Y9blKvDo2DfF2q367p4lKz+OzPAxxKPYGHuxue7oKHmxse7oKnuxsebmKVn7x3l7/Kimy780gmi3ceZeOhNFShbh1PLmgRyoUtQ7mwVRiNg+qUe+x1vNxpEuxLk+DKbVh2cxM6Nwmic5MgHhrcisNp2Xy15iCzVx5g1CdraFTXh1t7N+WmHk0I9a/eMyTbcSVxTFWDiryeqqr1TtvnBuAyVb3L+fw2oKeqji3tvarDlURuQS6bkzez9sha1h1eReyh5aSJA4CHuz/MHR3usDfAaiAjO4/P/jzA9D/2cTg9mzYNAhjZvxlNQ/yIS80iLvVEkfsTHEo9QW6B45RjhPp7n5FAOkXUpUOjupXyS9Ruqsqqfal8tHQvv2w+DEDjenUoKFDyHEp+gYP8AiXPYd3nO87+PeHuJnSLDCqsQuoUEYR7LTiXReUXOPh16xFmLt/Pst3JeLm78bdODbmtT1O6Ngmq3FmcXVDVriSOiEjDItVNicVsEwcUHQocAcRXSnTlLC0njdjEWNYlrmNd4jo2JW0i15ELQJS6Mygzg64dbqFbt5FEBla/K6HKdCQ9m+lL9/LpigNk5OTTt3kIL1zXkYtahRX+5+ve9Mzpyx0OJTEj54wEcjA1i42H0vhl82HyCqwvwWA/Ly5sGUr/lmFc2CqU8ACfM45XnWXnFfD9+ng+XraPzfHp1K3jyd39m3Fb76ZE1Cv517iqlShOSRwFjsKEkleghAd6E+hCo3BN5OHuxpAODRnSoSG7EjP4ZPl+vl57iG/WHaJj47rc1qcpV3ZuhI9n9bmSteNK4mUguUjDdbCqPnbaPh5YDdeDgENYDde3qOrm0t7L7isJVeVQ5qHChLAucR27ju0CwMPNg3Yh7egW3o2udVvS5beXCD6yDW74CNpeYVvM1cGuxAymLt7DN+sOUeBQhnZsyKj+zegUEVQuxy9wKAlpJ1i1L4XFO5JYsvMoSZlWIm/XMJD+rcK4qFUY3ZvWK7dG0Mp2JD2bWSv28+nKAyQfz6VluD939Ivimq6NTftNBcvMyeebtXHMXL6fnYmZBPl6clNME/7eu2mlV5OVxM7eTZ8BA4BQ4AjwNPAt8AUQCRwAblDVFBFphNXVdZhz32HAZKwusNNVdeLZ3s+uJHHk+BHeWvcWy+OXk3jCujDy9/SnS3gXuoZ3pWt4VzqEdqCORx3IPAozr4LkXXDTLGh1aaXHWx2crA6Zung3v25NxMfTjRtjmnDXBc0qfGCTw6FsSUhn0Y6jLN5xlDX7U8l3KH5e7vRpHsJFrcLo3yqMpiHntpZ3Vm4+h9OyOZyezeG0bBLSsjmSnk2AjwdtGwbSpkEgUSG+5dLwGXvwGB8t3cv/NiRQoMqgNuHc0Teafi1CqmzVR02lqqzYk8LM5fuYt+UIDlUGtg7n9j5N6d8yrNhqzhO5BSQfzyHZ2TXYus8lOTPHef9XeZN6vnzxrz7nFJutvZsqU2UnCVXl213f8vKql8l15HJxk4vpVr8bXcO70iKoBe5up11SpifAzCutpUCHfwbNB1ZarNVFgUOZv+UwUxbvYd2BY9Tz9WRE3yhu7xNlW3/0jOw8lu9OZvHOoyzacZSDKScAa6TuRc7eOn2ah+Dr5U7aiTwSiiSAk7eE9GyOpGWTkHaC9Oz8M94jwMeDrNwCCpx1/94ebrSqH0CbBgG0aRhI24YBtGkQWKZzkJvv4KdNCXy0dB+xB4/h7+3BDTERjOgTRVTouSU2o3wlpJ3gs5UH+PTPgyRl5hAV4ktMVDCpx51JwPnFn5VbUOz+3h5uhPp7E+LvRYifFyH+3jQP82f0gObnFI9JEhUgPjOeCcsmsDxhOTH1Y3im7zOltykcO2gliMxEuOULa34lo1B2XgFz1h7igyV72Jt0nMhgX+6+MJrruzepUj2RVJV9yVks3mEljOW7kzmRV4Cnu+DuJmTnndpALmI1kjes60P9QJ9T7hvU9aFBoHXv6+VBTn4BuxIz2ZaQwbbD6WxNyGBrQjrJx3MLj1c/0Js2DQJp0zCAts775mH+eLq7kZyZw6crD/DJiv0kZuQQHerHiD5NuT6mCf5mHqIq6WRCn73iAAdSsgjx9yLYz4tQf2+C/bwI8fci1K/IY2e5r5d7uV4JmiRRjhzq4IvtX/D6mtcBeKj7Q9zQ+gbcpJSqgdR9MOMKOJEGf/8amvSo0Birk8NpVl35Z39adeWdIuoyqn9zhnRoUC16xuTkF7BmXypLdiWRl++gQV0fGtatQ4O63jSoW4fwAO/znsLhaEYO2w6nsy0hg63O+12JmYU9tjzdhWah/uxNPk5uvoMLW4ZyZ79oLmpVfBWGYZzOJIlyciD9AE8te4o1R9bQt1Ffnu7zNI38G5W+U9Iu6woiLwtu+8aagK+WU1VW70/l42X7+HnTYRyqDGpTnzsviKJPM1NXXhZ5BQ72Jh1na4J1xbH9cDoR9XwZ0bcpLcID7A7PqGaqWhfYaqfAUcCsrbN4e93beLp58mzfZ7m6xdVn/zJL3GYlCEcBjPgBGnSonICrqOy8AubGWt0vtySkE+jjwT8viOa2KtTLo7rwdLfaLFrVD+CqLnZHY9RkJkmcxe5ju3lq6VNsSNrAgIgB/KfPfwj3LW6Q+GkOb7R6Mbl5wj9+hLDWZ9+nhjp07ASfLN/P56sOkJqVR+v6AUy6piNXd21kul8aRhVn/oeWIM+Rx8ebPua99e/h5+nHixe+yNDooWWrCjm0Bj65Frz8YcRcazW4WuZkd78Zy/Yxb4s1ondwu/rc0Tea3s2CTZWSYVQTJkkUY1vKNp5a+hRbU7ZyadNLGd9rPCF1Qsq284GVMPt6qFMPRnwP9ZpWSIwFDmX30Uw2xqWx8VAam+PT8PZwp2FdHxoF1aFRkHXfsK71uLJ+sZ/ILeCbdYeYuXwf2w5nEOTrycj+zfl778hSR/QahlE1mSRRRG5BLlM3TGXaxmnU9a7LawNeY3DTwWU/wN4l8OlNENDAShB1G5dLXA6HsifpOJsOpbEhLo2Nh46xOT69sA+1r5c77RoGkpmTz+KdR0nMOHM+/iBfTxrVPTN5WAmlDvUDvAsHb6kqBQ5rGgaH83HhrchzhwPyHQ4cqpzIdfD9hng+X3WQtBN5tGsYyEvXdeLKLtVrCgLDME5lkoTTxqMbeWrZU+w6tosrml3BYz0eI8gnqGw7q8LO+fDFbVAvCm7/zkoU58DhUPanZLEh7lhhUtgcn06mc958H083OjSqy40xTegUUZdOEXWJDvU/pbtobr6DI+nZxB87QUJaNoeOnSAh7QTxx7KJSz3Bn3tTzhjQJQIebmJ9+Z9jhzd3N2FI+wbc0S+KmKb1TJWSYdQAJkkAcRlx3PbTbYTUCeGdQe/QP6J/6TtkHIH4tXBorXUfvw6ykqF+R7j9W/ALLXV3VSU1K4/4Yyc4dOwE8ces2Uq3JqSz8VAaGc4vcG8PN9o1CuS6bo3p0LgunSKCaB7md9bpGrw83M46fXJmTj4Jx04Qn/ZXMskvcODuJriJNRW0m5s1QMzjZJm7de/uLHcv8tjDTegSGUTDuuU/HbRhGPYx4yScftjzAxdFXESA12l9zLPTrCRwMiEcWgfpcdZr4gZhbaFxV2jcHTpcBz51yckv4LDzF3z8MetLuGhCiD+WXbgI+0k+nm60rh9Ax4i6dGxcl46Ng2hZ379WraVrGIY9zDiJs8gvcNDU60J2HMjA6+gyfBLX45e8gcCUjfhn7ivc7lidJhz2a8+hyOvZX6c1BzxbkKne5GQ5yN5SQOLKTcQfO8HRjJwz3iMswJtGQXVo3SCAga3DaRRkraDW2NkmUM/X01TPGIZR5ZgkAWQfO4zb1CF0loN4OBcAOqz1WOZoxnpHTzZoMzY4mpGW7Q+pVnWOt7sb3p5WjyJvDze8PNwIC/CmTetwGterU9jDqHFQHRrU9cHbwzTeGoZR/ZgkAdQJqk9YRAvi6/2N7LDO5DboiltgQ5p7utHOw407PNytxODhhpe7m5kPxzCMWsMkCcDd3Z3wkXPsDsMwDKPKsaVVVERai0hskVu6iDxw2jYDRCStyDZP2RGrYRhGbWbLlYSqbge6AIiIO9YSpd8Us+kSVb28EkMzDMMwiqgK/SsHAbtVdb/dgRiGYRinqgpJ4mbgsxJe6yMi60XkJxFpX9wGIjJSRFaLyOqjR49WXJSGYRi1kK1JQkS8gCuBL4t5eS3QVFU7A28B3xZ3DFWdqqoxqhoTFhZWYbEahmHURnZfSQwF1qrqkdNfUNV0Vc10Pv4R8BSR0ue7MAzDMMqV3UliOCVUNYlIA3EOQRaRnlixJldibIZhGLWebeMkRMQXGAyMKlL2LwBVfR+4HhgtIvnACeBmrUkTTRmGYVQDNWqCPxE5CpxPL6lQIKmcwqkIJr7zY+I7Pya+81OV42uqqsU26taoJHG+RGR1STMhVgUmvvNj4js/Jr7zU9XjK4ndbRKGYRhGFWaShGEYhlEikyRONdXuAM7CxHd+THznx8R3fqp6fMUybRKGYRhGicyVhGEYhlEikyQMwzCMEpkkAYjIEBHZLiK7RGScTTE0EZGFIrJVRDaLyP3O8gkicqjIuhrDiuzzhDPm7SJyWSXEuE9ENjrjWO0sCxaR+SKy03lfz474SlqjxM7zJyLTRSRRRDYVKXP5fIlId+d53yUib56ciaCC4ntZRLaJyAYR+UZEgpzlUSJyosh5fN+m+Fz+e1ZyfJ8XiW2fiMQ6yyv9/JUbVa3VN8Ad2A00A7yA9UA7G+JoCHRzPg4AdgDtgAnAI8Vs384ZqzcQ7fwM7hUc4z4g9LSyl4BxzsfjgBftiu+0v+lhoKmd5w/oD3QDNp3P+QL+BPoAAvwEDK3A+C4FPJyPXywSX1TR7U47TmXG5/LfszLjO+31V4Gn7Dp/5XUzVxLQE9ilqntUNRf4L3BVZQehqgmqutb5OAPYCjQuZZergP+qao6q7gV2YX2WynYVMMP5eAZwdZFyu+IryxolFR6fqi4GUop53zKfLxFpCASq6nK1vlFmFtmn3ONT1Xmqmu98ugKIKO0YlR1fKarE+TvJeTVwIyUvg3ByuwqLr7yYJGF9ER8s8jyO0r+cK5yIRAFdgZXOonudl//Ti1RP2BG3AvNEZI2IjHSW1VfVBLASHRBuY3wnnb5GSVU5f+D6+WrsfFzZcQLcifXL9qRoEVknIotE5EJnmR3xufL3tOv8XQgcUdWdRcqqyvlziUkS1iXe6WzrFywi/sDXwAOqmg68BzTHWu41AesSFuyJu5+qdsOa4n2MiPQvZVtbzqucuUZJVTp/pSkpHrvO45NAPjDbWZQARKpqV+Ah4FMRCbQhPlf/nnb9nU+f4bqqnD+XmSRhZe4mRZ5HAPF2BCIinlgJYraqzgFQ1SOqWqCqDuAD/qoSqfS4VTXeeZ+ItSZ5T+CI85L55KVzol3xOZ2yRklVOn9Orp6vOE6t8qnwOEVkBHA5cKuzCgRnNU6y8/EarDr/VpUd3zn8Pe04fx7AtcDnReKuEufvXJgkAauAliIS7fwVejMwt7KDcNZhTgO2quprRcobFtnsGuBkT4q5wM0i4i0i0UBLrAawiorPT0QCTj7GauDc5IxjhHOzEcB3dsRXxCm/4KrK+SvCpfPlrJLKEJHezn8jtxfZp9yJyBDgceBKVc0qUh4mIu7Ox82c8e2xIT6X/p6VHZ/TJcA2VS2sRqoq5++c2N1yXhVuwDCs3kS7gSdtiuECrMvMDUCs8zYM+ATY6CyfCzQsss+Tzpi3U8E9IrB6f6133jafPE9ACLAA2Om8D7YjPuf7+WItTFW3SJlt5w8rWSUAeVi/GP95LucLiMH6MtwNvI1zpoQKim8XVt3+yX+D7zu3vc75d1+PtbTwFTbF5/LfszLjc5Z/DPzrtG0r/fyV181My2EYhmGUyFQ3GYZhGCUyScIwDMMokUkShmEYRolMkjAMwzBKZJKEYRiGUSKTJAzjPIlIkIjcU8rry8pwjMzyjcowyodJEoZx/oKAM5LEycFTqtq3sgMyjPLiYXcAhlEDvAA0d64dkAdkYg2y6gK0E5FMVfV3zsv1HVAP8AT+rapVa3StYZzGDKYzjPPknLX3B1XtICIDgP8BHdSaspoiScID8FXVdBEJxZqKu6Wq6sltbPoIhlEicyVhGOXvz5MJ4jQCTHLOnuvAmhK6PtYCSYZRJZkkYRjl73gJ5bcCYUB3Vc0TkX2AT6VFZRjnwDRcG8b5y8BacvZs6gKJzgQxEGt5VcOo0syVhGGcJ1VNFpGlIrIJOAEcKWHT2cD3IrIaa4bVbZUUomGcM9NwbRiGYZTIVDcZhmEYJTJJwjAMwyiRSRKGYRhGiUySMAzDMEpkkoRhGIZRIpMkDMMwjBKZJGEYhmGU6P8B0Z76aA2qTrQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "ax = df[['average_specificity', \"average_specificity_20\",\"average_specificity_200\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"average_specificity\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['population', \"population_20\", \"population_200\"]].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"population\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['fraction_accuracy_20', 'fraction_accuracy_200']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"Fraction Accuracy\")\n", + "ax.legend([\"XNCS_20\",\"XNCS_200\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['numerosity_20', 'numerosity_200']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"numerosity\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['steps_in_trial', 'steps_in_trial_20', 'steps_in_trial_200']].plot()\n", + "ax.set_xlabel(\"trial\")\n", + "ax.set_ylabel(\"steps in trial\")\n", + "ax.legend([\"XCS\",\"XNCS_20\",\"XNCS_200\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "90\n", + "110\n", + "321\n" + ] + } + ], + "source": [ + "print(sum(df['steps_in_trial']))\n", + "print(sum(df['steps_in_trial_20']))\n", + "print(sum(df['steps_in_trial_200']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From cc7cb2ba960802d6e40a9e8df4c42125907915f7 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 20 Jun 2021 16:21:40 +0200 Subject: [PATCH 17/19] Experiments Woods --- .../Experiment_XCS_XNCS_Woods1.ipynb | 2442 ++++++++-------- ...riment_XCS_XNCS_Woods1_pre_populated.ipynb | 2466 +---------------- 2 files changed, 1237 insertions(+), 3671 deletions(-) diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb index 09be353..988e3a8 100644 --- a/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -27,13 +27,13 @@ "text": [ "This is how maze looks like\n", "\n", - "['.', 'O', 'O', '.', '.', '.', '.', '.']\n", + "['.', '.', '.', '.', '.', '.', '.', 'O']\n", "\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m\n" + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } ], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": { "scrolled": false }, @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -314,22 +314,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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ThYTwZnrla1OV8iX8cD8ExsLkxRDZ2dMRKefInfFnfwohvhZCXC7cP5P2AJBa4/l0YLmUsj2w3PVcaaGOFFdw99zNTPpwIzaH5MPJ/fjw1v4q8TcmDjssfQq+uwvaDISpK1Tib+Lc+S7dAbgImALMEkLMBz6RUu4700ZCiNbAFcALwEOuxVcBI1yPPwVWAo+5EYvSDFRZ7cxZnc47K/cD8Ojojtw+NFH15zc2VaWw8HbYvwz6T4VLXwJtLebgVRq1Wid/6az9vAxYJoQYCcwF7hFCbAOmSynXn2bTN4H/A2pe3x0lpcx2vW62ECLyVBsKIaYB0wDi4uJqG6rSyEkp+S01j/8s2U1mUQVXdI/hiSs60yrY9+wbKw2rOAPmXecswTzmv9BviqcjUuqIO33+YcDNwCQgF7gPWAz0Ar4GEk+xzRggT0q5WQgxwt3gpJRzgDngrOfv7vZK43OwwMS/f9jFyr35tIs0MO+OgQxpF+7psJRTMebCZ1dBZQlM+g4Sh3k6IqUOudPtsx74HLhaSnmkxvJNQoh3T7PNEGCsEOJywAcIFELMBXKFEDGuVn8MkHcuwStNh8ls4+0V+/lwzUG8dBqeuqIzky9IaBllD5qiyhKYey2U5ztP7LY+aS4QpYlzJ/k/JaVcUHOBEOI6KeXXUspXTrWBlPJx4HHXuiOAR6SUNwshXgMmAy+77r8/h9iVJkBKyZLt2bzwYyo5ZVVc06cV0y/rVOfzlCp1yFrprNGTvwdunK8SfzPlTvKfDiw4YdnjOLt83PUysEAIcTuQCVx3Dq+hNAGv/LKXd1cdoGtsIP+7qTd940M9HZJyJnYbLJwCmevh2g+g3ShPR6TUk7MmfyHEZcDlQCshxFs1fhQI2Gr7RlLKlThH9SClLATUX1Uzl5pdxvtr0rm2T2teHd8DrUZdAdqoSekcw7/3J7h8hqrK2czVpuWfhfMCrbHA5hrLjcCD9RGU0vQ5HJKnv9tJkK+ep8d0Vom/KVj2NKTMg+HTYcBUT0ej1LOzJn8p5TZgmxBinpSy1i19pWVbtOUImw4V8+q1PQj2U0XXGr0/ZsK6Wc5x/CPUNZctQW26fRZIKa8HtgohThpuKaXsUS+RKU1WSYWFl37eQ5+4YMb3be3pcJSz2ToXlv0Lul4Dl72qCrS1ELXp9nnAdT+mPgNRmo/Xft1LSYWF528fiEZ19zRue36ExfdB2wth3HtqysUW5Ky/6WNX4rrWzZVSHpJSHsI5Nl/9ZyvH2Xa4hC82ZjL5ggS6xAZ6OhzlTDLWwte3QWwfuP5z0KnuucZCOhxUbt9O3syZpF89DlthYZ2/hztDPb8GLqjx3O5a1r9OI1KaLLtD8vT3Owk3ePPgxR08HY5yJtnbnGP5QxLgpq/B2+DpiFo8R1UVpvXrKf99BcaVK7DnF4BWi1+fPtiLitCFhdXp+7mT/HVSSsuxJ1JKixBCNRWUal9uzGT7kVJmTuxFoI8q/NVoFR5wXr3rHQiTvgE/de2Fp9gKCihftQrj7ysw/fEHsqoKjb8//snDCLjwQgzDhqENDq6X93Yn+ecLIcZKKRcDCCGuAgrqJSqlySkoN/Par3sZnBTG2J6xng5HOR1jDnw+zlmi+dZvIUidkG9IUkosBw5g/H0F5b//TuW2bSAlutgYgq+9FsOFI/Hv3x/hVf/taneS/13APCHE2zj7+g8Dt9RLVEqT8/LPezCZbTx3VVc1cXpjdaxej6kAJv8AEaprriFIm42KzVso//13jCtWYM3MBMCnWzfC77uXgAsvxLtjxwb/v3GnpPMBYJAQwgAIKaWx/sJSmpJNGUUs3HyEu4a3pX1UwNk3UBqepQK+nAj5e519/K37ejqiRs9WUED52rVUbtuGNFuQNivY7EibzXmz28B67LHd+XPrscc2cK1nLynBYTIhvLzwGzyIsClTMIwcgT4qyqOfrzbj/G+WUs4VQjx0wnIApJRv1FNsShNgszt46rudxAb5cP+odp4ORzkVuxUW3gaZG+C6j6HtSE9H1ChJm43KbdsoX70G05o1VO3eDYAmIACNvz9Cp0NotaDXIXR6hFaL0Olcz3VofHz+fq51LhM6HRp/P/wGD8ZwwQVo/BvP7HS1afn7ue5Vk045yafrD7Enx8i7N/dRk6w3Rg6Hcxz/vl/gijeg6zhPR9SoWHNzMa1ZQ/matZjWrcNhNIJWi2/vXkT8858Ykofh3akTohle/1Cb/9a2rvvdUspzqeCpNFO5ZVX8d9k+hneIYHTXaE+Ho5yoLAuWPAT7foaRT0L/2z0dkcdJi4WKrSmY1qymfM1azHv3AqCLiiJg9CUYhiXjP3gQ2sDmf41KbZL/5UKIpzj38s1KM/XCj6lY7A7+PVad5G1UHA7Y8qmzZIPdCqNfgkF3ezoqj7FmZVG+eg3la9dQsX4DDpMJ9Hr8+vQh8pGH8R+WjHeH9i3ub7g2yf8XnEM6/YUQZTWWC5xT+zb/Q6RyknX7C1i8LYv7R7UnIbzx9GO2eIUH4IcHIGMNJAyDsW9BaJKno2pQjooKKv76i/K1f2D64w8s6ekA6GJjCBwzBkPyMPwGDkJraNl/t7Wp6vko8KgQ4nsp5VUNEJPSyFlsDp7+fidxoX7cM6Lt2TdQ6p/dBhv+ByteBK03jJ0FvSe1iCJt0uHAvGcP5X/8gWntH1Ru2YK0WhHe3vj170/wdddhSB6GV1JSi2vdn4k7Qz1V4lcA+GBtOgfyTXx8a3989FpPh6Pk7IDv74XsFOh4BVzxOgTGeDqqemXNy8O0bh2mtX9gWrcOe1ERAN4dOxJyyyQMQ4bg27cvGm9vD0faeNVmqOdaKeVQIYQRkLi6e1DdPi3S0ZJKZi3fzyVdohjZKdLT4bRsNjOsfg3W/hd8Q+C6T6DL1c2yte+oqqJi82ZMf6zDtHYt5n37ANCGheE/ZAiGoUPwGzwYfaT6m6yt2nT7DHXdq6GeCs/9sAuJ5F9XdvF0KC1b5p+w+F4o2Ac9b4DRLza7Gj3SYqHs16WUfv89FX/9hTSbEXo9vn37EvHwQxiGDnVeGdsMh2E2hFp3+wghBgG7jl3Z67rSt6uU8s/6Ck5pXFbszePXXbk8OrojrUP8zr6BUvfM5bD8Odg4x1mX5+ZF0O4iT0dVp6x5eZR8NZ/iBQuwFxSgb9OG4AnXYxgyBL/+/dH4qb+9uuDOVTmzgT41nlecYpnSTFVZ7Tzz/S6SIvyZOqxljR5pNPb/Bj88CKWHYcA0GPU0eDePL+RSSiq3plA8dy5lS5eC3Y5/8jBCb74Z/yFDVOu+HriT/IWUsnoaRymlQwihLulsIWavPEBmUQXz7hiIl079IzaoiiL49QnY9iWEd4Apv0LcQE9HVSccZjNlP/5E8dy5VO3ejcZgIPSmGwm58Ua84uM9HV6z5k7yThdC3I+ztQ9wD5Be9yEpjc2hQhOzVx3gyp6xDGkX7ulwWpZD62DBLVBZDMmPwrBHQO/j6ajOmzU7m+Ivv6Lk66+xFxfj1a4t0c/8i6CxYxtV/ZvmzN2Szm8BT+Ec7bMcmFYfQSmNh5SSZxbvwkur4akrOns6nJYlf6+zEqd/JEz6FqK7ezqi8yKlpOKvvyieOw/j8uUgJYYLRxJ68834DRyoxuA3MHfG+ecBE+sxFqURWro7l5V783nqis5EBTb9FmeTUZ4P865zXrB18yIIabpdII7KSkp/+IHiufMw79uHJiiI0FsnE3LDjXi1buXp8Fosd0b7dMDZ5RMlpewmhOgBjJVSPl9v0SkeZXdIXvt1L+0iDdx6QYKnw2k5rJXOFn95Htz2Y5NN/LbiYoo++4ziL77EUVqKd6dOxDz/HwKvuAKNr6+nw2vx3On2eR94FHgPQEq5XQjxBaCSfzP1045s9ueVM+uG3ui06iRvg3A44JtpcHQzTJgLrZrepCu2/HwKP/6E4q++QlZUEHDxRYROnoxv376qa6cRcSf5+0kpN57wy7PVcTxKI+FwSGb9nka7SAOXd2/epQIald/+BamLnRdtdR7j6WjcYs3OpvDDjyj5+muk1UrgFVcQPm0q3u3bezo05RTcSf4FQoi2OE/2IoQYD2TXS1SKx/28M4d9ueXMnNgLrUa11hrEXx/CulnQfyoMusfT0dSa5fBhCue8T8l334GUBF01lvCpU/FKSPB0aMoZuJP8/wHMAToJIY4CB4Gb6iUqxaMcDslby9NoG+HPmB6xng6nZUj7DX56FNqPhktfbhL1ecwHDlA4Zw6lS35EaLWEXDeesNtvR99KncRtCtwZ7ZMOXCSE8Ac0agL35mvp7hz25hp5c4Jq9TeInB3w9WSI6grjPwJt4752smrPHgrefQ/jr78ivL0JvflmQqdMQR+liqo1Je6M9gkDngGGAlIIsRZ4TkpZWF/BKQ3P4ZDMXL6fpHB/ruypWv31riwL5l0P3oFw43zwNng6otOq3L6dgtnvUr5iBRp/f8KmTiX01snoQptXQbmWwp0mxlfAauBa1/ObgPlA86oq1cItS80lNbuMN67vqVr99c1shC+uB3MZTPkFAhvnwbZi0yYKZr+L6Y8/0AQFEX7fvYTefDPaoCBPh6acB3eSf6iU8j81nj8vhLi6juNRPEhKZ19/QpgfY1Wrv37ZbbBwCuTuhhsXNMqrd+1GIznP/puyH39EGxZG5CMPEzzxhhY//WFz4U7yXyGEmAgscD0fD/xY9yEpnvJbah67ssp4bXwPNa6/PkkJvzwGaUthzH+hfeP78lyZksLRRx7Fmp1N+L33Enb7FHVhVjPjTvK/E3gI+BznLF4awCSEeIjTzOglhPDB2VXk7XqvhVLKZ4QQoTi7jBKADOB6KWXxeXwO5TxJKZm5fB9xoX6M661Ga9SrDe/AXx/ABfdDvymejuY40m6n8P0PyJ81C31UFPFzP8evd29Ph6XUg1o376SUAVJKjZRSL6XUuR4HuG6nm8rRDFwopewJ9AIudU0KMx1YLqVsj7NA3PTz/BzKefp9Tx47j5Zx78h2qtVfn1J/gF+fhM5j4aJ/ezqa41hzc8mccjv5b75J4OhLSPzuW5X4m7Fa/5cLIYa4hnkihLhZCPGGECLuTNtIp3LXU73rJoGrgE9dyz8FrnY3cKXuOFv9abQJ9WVcH9XqrzdHNsOiqc6SDdfMgUY0QYnx9985OPYqKnfsIOaFF4h9/XW0gWp67ubMnb++2UCFEKIn8H/AIZxdQGckhNAKIVKAPGCZa9rHKCllNoDr/pQDhIUQ04QQm4QQm/Lz890IVXHHyn35bD9Syj9GtEOvWv31o/gQfDkBDJFww1egbxz9546qKnKe+w9H7vkHulaxJC5aSPC116gaPC2AO//pNtdMXlcBM6WUM4GzziEnpbRLKXsBrYEBQohutX1DKeUcKWU/KWW/iIgIN0JVaktKyczf0mgV7Ms1fVp7OpzmqbLEWZ7ZboGbvgZD4/hbNqelkXHd9RR/8QWht95Kwldf4Z2Y6OmwlAbizglfoxDiceBmIFkIocXZjVMrUsoSIcRK4FIgVwgRI6XMFkLE4PxWoHjA6rQCUg6X8OK47mp6xvpgqYAFk6Ao3TkhS0RHT0eElJKS+fPJfellNAYDbd6fg2HYME+HpTQwd5L/BOBG4HYpZY6rv/+1M20ghIgArK7E74vzgrBXgMXAZOBl1/335xK8cn6crf59xAb5ML6vavWfM5sZijOg8AAUHahxnw5lRwEJV78LiZ5PsPaSErKeeory35bjP3QosS+/hC5cTc3ZGEmHpDS/kvxMI4k9w9F5aev09d2p7ZMDvFHjeSbw2bHnQoj1UsrBJ2wWA3zq+pagARZIKZcIIdYDC4QQtwOZwHXn8RmUc7R2fwFbMkv4z9XdVKv/bOxWZ7/9ccnddV96BKTj73V9QyC0LSQMcd63GQBtR3oudhfTxo1k/d9j2AoLiXzsMUIn34JoRCedmzqr3UphVSGFlYUUVBZQUFlAYVUhZeYyfHQ++Ov9q28GvQE/vV/1Yx+tL7YiDWVHLRQcLic/00j+YSPWKjsA1z7Wl+jEur2iui4rSJ00x5+Ucjtw0lgxVz2gUXX43oqbjvX1xwT5cH2/Ft7qt1RAeQ4Ya96y/74vPQIlmSDtf2/jHQRhSdB6APS8wZnkw9pCaBL4Na5aN9Jmo+CddyiY/S5e8fEkfPUlvl27ejqsJsEhHRRVFTkT+QlJveaywqpCSs2lp3wNH60PZrsZ6ayGj5AaQiqiiTC1JtzUmghTG8JMrdA7vAGwaSyUBeRjiizEHFqCPbSCyuAkoPEmf1mHr6XUs3UHCtl0qJjnruqKt65uv042KtYqyN52fDI/dl+e67yvOsU/rdYbAqIhIAZie0G3a13J3ZXk/cKaRNnlqt27yfnP81Ru3UrQNdcQ/eQTaPxVeYZjTFYT2eXZ5FTkkG3Kdj42OR/nleVTWWTHt8o55FUikUIiceCl8yLQO5Ag71C6eMcT6BdIcGgwwT5BBPkEEeITQohPMEG+QWgdOvIyy8g5VEJ+ppGSrCocNteBQC/xinQg2pqxheVhDinF5F9Ild2E2VpBubWcCmsFOm3d/4827tqxSr041uqPCvTm+n5tPB1O/bDbYPtXsOIlKDvy93KN3pnQA6IhvAMkJv+d5I/dG6KcXTdNILmfirRYKFu2jOK586jcuhVNQACxr88g6IorzrqtwyGpNFowlZgpLzZjKjFTZbLi5aPDy1eLl6/O9ViHt6/z3stXi05fd8lJOiQ2qwOb1Y7N4kA6JD4GPXpvrdtDUIuqisgozXAmdpMzsR9L7tmmbIxmIz42f4IrowipjCKkMppISwLdKgfiXWlAyNq/nwMoct0OUgwcX7TAy0dLRFwAbUaEExkXQERcAEGRfmg8VECxLpN/0/xPaYE2pBexMaOIZ6/sgk8d/tM2ClLCniWw/D9QsBdi+8ClL0JYOzBEO7tkmmhSPxtrXh4l8xdQvGA+9vwC9HFxRD0+naBx49AGBmK3OjCV/p3Uq+9LqqqfV5RacDjc/xKv0YkTDgra6ud6Ly02mwO7xY7V4sButTuTu8WBzep67npss9qrW8Un0uo0+Abo8Q3wwtegx6fGY4tXJSWigFxHFodtGaSb09hv2kuJpQRwdrUEVoURa02ktS2RPua+GEyh6Mr8wPz3/4BWryE4yo+QRD9CovwIjvYjMMwXoRFIKZEOZ+MJ12OHlOBaJqXzwFX92LWORiMIbeVPULjzdRoLt5K/ECIeaC+l/M01ekdXY1KXSXUenVIvZi7fR2SANxMHnPEC7abn4Br47Vk4ugnC2sP1n0PnK5ttsgdngqncupXiufMo/m0FVbpAZL8L4ZaRmINj2VdQhXHWHkwlZiqN1pO213lpMIT44B/sTasOIfiHeGMI9sY/2BtDiPPex1+P1WzHUmnDXGnDWmXDXOl8frplliobZQWVmCtt2CwOdHoNWr0GnV6LzkuDzkuDX6CXc7mXa7neuVxb47FOr0VooKrchqmsiqKSUopLjBQUmLEeciCqdGjtNUecRxBKBKH0p6/GgcbPgd5Lh7X0+FM2foFehET7EdzZvzrJh0T5ERDq06gSdH1yZzKXqcA0IBRoi/OirXdxnbiVUu6sjwCVuvVneiEb0ov415hm1OrP3ga//RsOLIeAWBg7C3re2OhnxHKXwyExlZgxFlZSkmWk4M8dFO4+hMmsp9J3GJYLanTrbHag1WcRGOZDQJgvkQmBfyf1YO/qJO/lq6tVV4pWp8HHv9aX9ZwXKSU5phz2Fe9jX/E+0orTSK9K52DlQSx6C0QAERDhG0FSUBJJhnbE65OI1cYRLiLRVflQVW6jstxCZbkVm9lOYLivM9m7kry3X8N8lsbM3Tl8BwB/Akgp04QQat62Jmbm8jTCDd7cOLAZtPoLD8CKF2DnIvAJhov/AwOmNprSCefLXGlj74YcDu0ooLSgEmNR1fFdItIbH10sgVE+RHeIJSg6gMBwX9fNB79Ar0ZfpsFkNZFWnHZcok8rTsNo/XuW2Fj/WJKCkxgUM4ik4CRnwg9OItBL1R46H+4kf7OU0nLsj0kIoUON8GlS/sooYt2BQp66onPTbvUbc2DVq7DlU9B6wbBH4IL7wDfY05HVicKj5exYeYS9G3Oxme2ExPgR7GsmwrYH7b6t+JiLCO/TgdgbriJg8KhGn+AB7A47h42Hj0vy+4r3caT875Px/np/OoR04PKky+kQ0oH2Ie1pF9yOAK+zVpFRzoE7yX+VEOIJwFcIcTFwD/BD/YSl1IeZv6URbvDipoHxng7l3FSWwB8zYcNscFih762Q/H8QEOXpyM6b3eYgfWs+O1YdIXt/KVq9hvZ9I0nQpMNXr2NJT0cbEkLw9dcTMnEC+pgYT4d8kgprBVnlWWSZssguz66+zzRmcqDkAFX2KgA0QkN8YDxdw7syrv042ge3p0NoB2L9Y5vEgay5cCf5TwduB3bgnNjlJ+CD+ghKqXubDxWxdn8BT1zeCd86vky83lkrYeMcWPMGVJVAt/Fw4ZPOC6qaOGNRFbvWHGX32iwqjVYCw3244Jp2xHkdpeytp6navRvvzp2JefklAi+7DI23d61eV0pJibmEbFM2JeYS9Bo93lpvvLXe6LU1HruWe2m90IjTX+0rpaTUXHpcYs8qzyLblF2d8E+8yEmn0RHtF02rgFaM7zCeDiEd6BDagbZBbfHRnXRNqNLA3Cnv4ADed92UJmbm8v2E+Xtx86Am0Op3OKD4IGRthewU2LEIjFnQ7mIY9S+I6eHpCM+LdEiO7C1mx8ojZGwvQAIJ3cPpNrwVUV5F5M94kbxVq9DFxhD72qsEXnHFSWUYKqwV5FTkkFOeU32B0rHx67mmXHJMOdUt7drSaXTOA4HGCy+tV/VBwSEdZJuyqbRVHre+r86XWP9YYgwxdA/vTowhhlj/WGINscT4xxDhF3HGA4riWe6M9tnByX38pcAm4HlXyQalEdqaWczqfflMv6wTfl6NbASMwwGF+50jdrJTICsFcraDucz5c623szbOte9DwlBPRnreqkxW9m7IYefqo5TkVuBj0NP7kni6DovFT5aTP+stDi5ciMbfn8hHHibwphvZUbaHrbs+PukCpTJL2XGvLRBE+EYQbYimQ0gHhrceTrR/NDH+MQT7BGNz2LDYLVjsFsx2MxaH5fjndgsWR43HNZYDXBB7ATH+Mc7E7krywd7BqpumCXMnE/wM2IEvXM8nuu7LgE+AK+suLKUuzVyeRqi/F5M83ep32KEg7eREb3FN9qbzgahu0ON6iOkJMb0gsjNom/awvPxMIztXHWHfxlxsVgfRSYFcdFsX2vaJQGM1U/jxR2R9+BHSYsEw8TpSr+rGJ2WbWfP9JdVdKYFegdXJvFdkL6L9o6ufR/tHE+kbib6J7yelYbmT/IdIKYfUeL5DCPGHlHKIEOLmug5MqRsph0tYuTef/7u0I/7eHmj15+6CLZ+5Ev0OsJqcy3W+EN0det3oTPIxPZ217pt4ApNSUlZQRVZaCVlpxWSllVBWUIVOr6HDgCi6DW9NRFwA0m6n9NtvyZ/5Frb8fIoGdeTbUf4sl99jS1lEsHcwya2SSW6TzOCYwQR5121RL0VxJxsYhBADXdMwIoQYABhcP7PVeWTKeXM4JC/8uJtgPz23DE5o+ABSvoQl/wShgege0GeSM9HH9nJegVuHF2FJKSk2F3Oo7BAZpRkcLEnHvHUbkev3EZFdiT0iBO+YWIITOhDTtgcxbXvgFRuLxs/vvN+3JLfCleydt/JiMwDe/jpi2wXT66I4OgyIwttP7zxxunIFR155Ec3BIxyK8+H9SVr2tT5A24C23NLmFoa3Hk7PiJ5oNU3sxLzSpLjz33cH8JEQwoCzjk8ZcIdrUveX6iM45fx8vuEQf2UUM+O6nhgastVvs8Cvj8NfH0DCMBj/cZ1NXWi2m8ksyySjLIOM0gznvetxmbmUxBwYkurgglQIL5NY9RpK2gSh3V9E0KZctHIrVcznoOv1LAZvHFHh+LZuQ1BcO7xatUIfG4s+thX62Bi0ISHH9WtLh6Qo23Rcsq8oc/aL+wZ6EdsumD6jg4ltH0xojH91qQCjxcja3z6D/31KbGo+ecHw1TV6HMP7ck3cSJJbJ9MmoJkW2VMaJeGclteNDYQIcm1XUi8RnUa/fv3kpk2bGvItm7TDRRWMfnM1/RNC+eS2/g13Yq4sCxZMhiMb4YL7YdQzbrfwpZQUVhVysPQg6SXppJemO1v0ZRlklWdV10UHiPSNpG9lJAN32Uj6Kwuf7CLQ6fAfNpSgy6/AMHIkWoOzhHGl2cSB/X9xKG0T+empmI5kQE4ewSV2IsokEaXgY3WOapBCi0OjR/oZsEUnURLcnmLfNhRporDgBYC3zkJQYDn+YZV4RVViD7VR6ScwejkwejswOaowWU0UH95P54VbGbbDQYWvIPWq7kTddAtD4odj8DKcYg8oSt0RQmyWUvY7abk7yV8IcQXQlRoTt0gpn6uTCM9CJf/ak1Iy6cONpBwu4dcHk2kV3EDlDjLWwte3OsflX/U/6Hr1GVd3SAc5phwOlBwgvTTdmexL0zlQcuC40Sy+Ol8SAhOctyDXfbkPIX/spvLnZZj37QONBr9Bg9BfeDn0GESFVU95cRXlJWYqjRbsVgd2m6yuKOl87KwiabZYsFht2K12pA00jlMfrLSWAvzL0wgp2U9kQRqGisIzlrKt9IIqHw2GCokWgeO6y+n4wBN4BYe4v28V5RydLvm7M9TzXcAPGInz4q7xwMY6i1CpMws2HWbt/gKev7pbwyR+KWHDO7D0aeeFV7f+eNxE5VaHlcPGwxwscSX30gOkl6STUZZx3NjxEO8QEoMSuSThEtoGta2u4RLlFwUSytKPkvfrGgrWreZITikHvEOwxlyJtVNrqoQ/FUYbjrUS1u6tfk2dXoNfkBdavRatTlRXl9R7a9HqXJUmXffaGvcmWU6hJZ/cqhxKRCGaGDN+wXo0+giELh6r7kr8HDr8q8C30oFflQPvChv6Cit6kxmNqRJpLMdeVobG15fQ227Dq3Wr+v9dKEotufN9/AIpZQ8hxHYp5b+FEK8D39RXYMq5ySmt4vkfUxmUFMqNDVGy2VwOi++DXd9ApzFw9WzwcRbcKqgs4PVNr/NLxi/YHH+PCYjyiyIpKIlr219LYlBidZIP9XFOf+iwO8g/XE52agmbdx4mP2MHFZXOrhiIgoBLIAC0WoF/qA8BId6EhfhgCPHGEOqDIdgbQ6g3hhAfvP1qV7VSUVoad5L/scsFK4QQsUAhkFj3ISnnSkrJU9/twGp38Mq1Pep/hqDCA/DVTc5JUy56Fob8E4TA7rAzf+98Zm2dhdluZnz78XQL70bb4LYkBiXirz9+GkGrxU7uwTIO7D/I0dQCcg+WYbM7Y/epzCew7BCRvg6CO8URPqgHIZ3iMYR442PQN2hit1qtHDlyhKoq966cVZSG4OPjQ+vWrdHrazdc2p3k/4MQIhh4DdiC87yYKvXQiCzelsVvqXk8dUVn4sPqeZ7WPT/Bt3eCRgc3fwNtRwKwPX87z294ntSiVAbFDOLJgU+SEJRw3KZVJis5B0rJ2l9C1r5i8jONOByAlBhMWUSV7iek8jAxSQbChvfGf8jVeHdo7/EW/JEjRwgICCAhIcHjsShKTVJKCgsLOXLkCImJtWuT1yr5CyE0wHLXCJ9FQoglgI+U8tTT1SsNrqDczLOLd9E7LpjbhtTjFzKHHVa8CGtmOMfsT/gcguMoNZcyc8tMFu5bSIRvBK8Nf43R8aMRQlBebCZ7f4kz2aeVUJTlvNBLSDuBxkO0LtlPsPEg0XH+BA/ui//gsfh2747w8qq/z3EOqqqqVOJXGiUhBGFhYeTn59d6m1olfymlw9XHP9j13AyYzylKpV48s3gXJrOd18b3QFtf3T0VRbDoDueMWb0nweUzcOi8WLz/O97Y9AZlljJu7nIz9/S8B4OXgcpyCyvm7uFgSgEAWqwElaaTWLSP4JL9RMT6EDioP/6DL8evb180/vX8baUOqMSvNFbu/m260+2zVAhxLfCNdPfiAKVe/bIzhx+3Z/Po6I60i6yniS+yUmDBJOdEKlfOhL63sq94Hy9seIEteVvoFdGLpwY9RcdQ5yifw6mFLHsvhapKBwmHfiG8YAchYRoCBg3Cf/Bo/Ab+C12IGvKoKJ7iTvJ/CPAH7EKISpxX+UoppZpLzYNKK6w8/f1OusQEMi25nurbb50HPz4EfmFw2y+Yojox+68ZzE2dS4BXAM9d8BxXtbsKjdBgs9hYO2slu/YJ/CpyGVTyM4m3XoFh2EPoW6mhjufj8OHDJCcns3nzZkJDQykuLqZPnz6sXLmSF198kd9++w0fHx/CwsJ47bXXGDhwIC+88AJffPEFWq0WjUbDe++9x8CBAz39UZRGwJ16/moutUboPz/upthk4ZPb+qPX1nHtdClh6VOw/m1ITEZe+xFLC7bw6ndjyavIY3yH8TzQ+wGCfYKRDgdHFv7Cyp+LKPWKpnX5NoZek0ToVR8jdI2sjHQT1aZNG+6++26mT5/OnDlzmD59OtOmTeOxxx4jMTGRtLQ0NBoN6enppKamsn79epYsWcKWLVvw9vamoKAAi8Xi6Y+hNBLuXOQlgJuARCnlf4QQbYAYKaW60MtDVu7NY+HmI9x3YTu6xtZx1UeH3VmUbctn0H8qhy64mxfXP8W6rHV0Cu3EGyPeoGdET6TdTsmSH9n22SpSg0ai0QaR3MdMt9vvR2ibb2Gyf/+wi91ZZWdf0Q1dYgN55squZ1znwQcfpG/fvrz55pusXbuWhx56iDlz5jBv3jw0rglfkpKSSEpK4ptvviE8PBxv1+xf4eHhdRqv0rS50yR7B3AAFwL/AcqB/wH96yEu5SyMVVae+GYH7SMN3Hthu7p9cbvVOYxz5yLMQx/kw7AIPlxyPV5aL6YPmM6EjhPQSkHpD0vIfu9jdngNJC/yUqLC7Ix+cAgB4edXKVM5Pb1ez2uvvcall17K0qVL2bt3L7169UJ7igPtJZdcwnPPPUeHDh246KKLmDBhAsOHD/dA1Epj5E7yHyil7COE2AogpSwWQjSusXgtyCu/7CGnrIpFd1+At64OW9jWKlh4G+z9iXUXTOX5so0cPnqYyxMv55F+jxDuFULZkp8omP0ueUUadne/A7M+gIFXJtLn0oT6v7CskThbC70+/fzzz8TExLBz507atm172vUMBgObN29mzZo1rFixggkTJvDyyy9z6623NlywSqPlTvK3CiG0uKZyFEJE4PwmoDSw9QcKmbshkzuGJtI7rg5HzFhM8OUNFGSu4dWeF/Fz9q/EB8bz/iXvMzCiH6VLlpA++12qMg9zpM9NHIgfSEC4L1fc3pXoRDXZSENISUlh2bJlbNiwgaFDh/Lll1+ybds2HA5HdbdPTVqtlhEjRjBixAi6d+/Op59+qpK/AriX/N8CvgUihRAv4Czs9lS9RKWcVqXFzvRvthMf5sfDl3Q8+wa1fuESHF9cx8LSVN5MaEtV+UHu7nk3UzpPpurHpRx47wqshzKxd+3P7nGPkl8k6DgomuQJHfDyVSd0G4KUkrvvvps333yTuLg4Hn30Ud555x369evHM888w3PPPYcQgrS0NHbv3k2nTp3QaDS0b98ecB444uM9PJWn0mi4M9pnnhBiMzAK5zDPq6WUqfUWmXJKry/dy6HCCr6aNghfrzrq7jEVsHfuWJ4ThWwPC2FAZE+eGvQUrU3eHLllClXbtuPdpTPWx2bx5zY9VMDFUzrSYUB03by/Uivvv/8+cXFxXHzxxQDcc889fPLJJ7zxxhvMnTuXdu3a4efnVz3Us7y8nPvuu4+SkhJ0Oh3t2rVjzpw5Hv4USmNR63r+QoiZwHwp5br6DenUVD1/2JJZzLWz13HTwDiev7p7nbxmRVE6sxddy+d6K4F6A48OepIxSWMwrf2DrEcfRVqthD39b1JKEtm7IYfopEAuntKVwPAGmiOgEUlNTaVz586eDkNRTutUf6PnXc8fZzG3p4QQHXB2/8yXUrbsbNyAzDY7/7dwO7FBvky/rG4S0KrUr3lhw3Nke8G10UN5cMQrBOoMFMx6m4LZs9G374Djvhf4eXUZxoIc+l2RQP/LE9DU9fUEiqI0OHe6fT4FPhVChALXAq8IIeKklO3rLTql2qzl+9mfV86nUwac93y8OaYcXln7NL/lbKCd3c6n/Z+iT7cbsRUVcfiRaeSlHKDokkc46tWeikW5BIT6cPVDfYhtH1w3H0ZRFI87lyzSDugEJAC7z7Si60Kwz4BonCOD5kgpZ7oOIPNdr5EBXC+lLD6HWFqEnUdLmb3qAOP7tmZ4h3OfCN3usPPlni+ZtWUmdlslD5hsTB73FfrYXhSv38zWl+dx1DCEsgE3obEK4jsF0WlwDPHdw9Cq1r6iNCvuXOH7CnANcABn4v5PLSZxtwEPSym3CCECgM1CiGXArThLRL8shJgOTAceO4f4mz2r3cH/LdxOqL8XT1/R5ZxfZ1fBLv69/t+kFqUypMrKk5UaWt+4mMMFYWz/3w8cztPjiBlDSJiOISMT6DAgGr9AdRmHojRX7rT8DwIXAEmAN9BDCIGUcvXpNpBSZgPZrsdGIUQq0Aq4ChjhWu1TYCUq+Z/SzN/S2J1dxnuT+hLkV7sZemoyWoy8vfVtvtr7FaF6A68VGhlkT2Rv3Bv8/kYe5SVH0FkFcV6H6XPvGKK7RKuyxYrSAriT/O3A70BrIAUYBKzHWe7hrIQQCUBv4E8gynVgQEqZLYSIPM0204BpAHFxDTAfbSPz+YZDvL1iP9f3a83oru4NqzRajHyR+gWf7f4Mo8XIhLBkLl+XR4b5NuaZkuBAMeGVB4nPWEmXm0cScccdKukrSgviTkfu/Tjr+BySUo7EmchrNW2MEMIALAL+KaWsdTUsKeUcKWU/KWW/iIhz7+tuipZsz+Jf3+9kVKdIXhhX+2GdRouRd7e9y+hFo3k75W0G+Qzn2YJnCf/xctYV3YXJtxM921cxZMtz9E7/hAEz7idy6hSV+JuAw4cPk5iYSFFREQDFxcUkJiayatUqhBDMmjWret17772XTz75pPr5jBkz6NSpE926daNnz5589tlnACxZsoTevXvTs2dPunTpwnvvvdegn0nxHLcmcJdSVgkhEEJ4Syn3CCHOeompEEKPM/HPk1J+41qcK4SIcbX6Y4C8c4i92VqTls+D81PoFx/C/27qU6tSzWWWMualzuPz3Z9jtBgZGXshowtvIHOpiQKqaB+2l47XXY34+mNK3/8avwEDaPX6DHQt7KDalJ2upHN8fDyRkZHMnDmTO++8E68Tpr989913WbZsGRs3biQwMJDS0lK+++47rFYr06ZNY+PGjbRu3Rqz2UxGRoZnPpzS4NxJ/kdcE7h/BywTQhQDWWfawFUG+kMgVUr5Ro0fLQYmAy+77r93I45mLeVwCXd+vpm2EQY+mNwfH/2Zr+Its5Qxb7cr6VuNjGwzkpvCb+fgwmIy8iro4LOaob0z0A57giOPPIx5dyphU6cS8cD9qs7++fh5OuTsqNvXjO4Ol718xlVOLOk8a9YssrKyiIiIYMiQIXz66adMnTr1uG1efPFFVqxYQWCgc96loKAgJk+eTFFRETabjbCwMAC8vb3p2LEOS4YojZo74/zHuR4+K4RYAQQBv5xlsyHAJGCHECLFtewJnEl/gRDidiATuM6doJur/XlGbvt4I+EGbz67fQBBvqc/wVtmKWPu7rnM3T0Xo9XIhW0uZGqXOzGucbD56xx8RQmXxywgcfyNGHOSyZwwCYSg9TvvEHDhyAb8VEpdOrGkc81W/vTp07nsssuYMmVK9TKj0YjRaDxl9c/Q0FDGjh1LfHw8o0aNYsyYMdxwww2nLBCnND/n1PSTUq6q5XprcdYBOpVR5/LezdXRkkomfbgRrUbD57cPIDLA55TrlZpLmZvqTPrl1nJGxY3irp53EVwcxe9vbqS4RE8n35UMHaVD0+sVsud8QsmCBfh06UKrt2bi1bp1A3+yZuosLfT6VLOk87E6PwCJiYkMGDCAL774onqZlPKM53M++OADduzYwW+//caMGTNYtmzZcecKlOZLfe9vBIpMFiZ9+CflZhvzpw0mPsz/pHVKzaV8vvtz5qXOo9xazkVxF3FXz7tIMrTjzy/+ZPmGoxg0JVzZYSWtxk6hcPFaip66BmmzETr5FiIeegiNa0Ynpek6saTzxIkTj/v5E088wfjx40lOTgYgMDAQf39/0tPTSUo69RzP3bt3p3v37kyaNInExESV/FsI9f3Ow8rNNm77eCNHiyv5cHJ/usQGHvfzvIo8Zm2dxehFo3lv+3sMjh3MwisX8t+R/yUg25/5j//Etg1VdA1cxYSbrPgH9WP/TfdROGcOAaNG0fanH4l6/HGV+JuBU5V0fuSRR45bp1OnTnTp0oUlS5ZUL3v88cf5xz/+QVmZc6BdWVkZc+bMoby8nJUrV1avp0o+tyyq5e9BZpuduz7fzM6sMt67uS8DEkOpsFawKXcT67PWsyF7A/tL9gNwcfzF3NnjTjqGdsRSaWX1/35kxw5fArVGxg7YToBPOw4/+SG2nBz8hw0j8qEH8VEVKJuV05V0PnTo0HHrPfnkk/Tu3bv6+d133015eTn9+/dHr9ej1+t5+OGHkVLy6quvcuedd+Lr64u/v79q9bcgtS7p7GmeKulstdipKLUQEOpdp9Us7Q7J/V9u5ccdR3nwCl98g9JZn7WelPwUbA4bXhov+kT1YXDsYEa0HkFSsPMr++EN21jxZTpGcwA9IjbSpXMEJfO+x3LgAD49exD50MP4DxxQZ3Eqf1MlnZXGrr5KOrdIv328m/St+Wh0gqBwX4Kj/AiJ9iM4yo/gKH9CovzwMbhXdiGzLJPHf17E5qI/Ce+awQfpJgA6h3ZmUudJDIodRJ/IPvjo/j7pay4tZd3sH9idEUuwroLLuh1ErN5G3tfb8UpKotWstwi46CJ1sZaiKLWikv8ZlBebObg1jyjzQQKwYioOo6A4gEPbvXDIv5Osj7+O4Ch/gqP9CI70JSTKn+AoP4IifNHqNZSaS/kz+0/WZ69nfdZ6jpYfBSAoOIzRSZcwOHYwA2MGEuoTenIQUnLol59Z+WMlJlsU3SN20ObodqreXocuOpqYF54n6Kqr1Jh9RVHcojLGGaSuOYxE0D7rZwJ8Hdjy8rAXFeEQGqp8wqjwi6LCN4oK/ygq81pxcH8EZk3NkToObLpSjNoiHEKg0cQxQtMeh1VLkNabMG8v2CUpcdj4xbEG6ZDOm3TdHBIpwWQPJURfQjK/oP36JyxBQUQ++ighN92IxufUQ0IVRVHORCX/05AOya4VGYQUp9Gu42b8Y+wgHUibA1sl2CqKsFakYavQUFWpIb9YR1mODovZH+GIQmhdBwa/KCx6fwQSIR0gyxFCIoQDjXC47iVCC0IDGp1AaDVodBo0eh1Cp8W3cj/hK79CqxWETptG2B23ow0MPPuHUBRFOQ2V/E/j8J4iTJUa2havxe/CBIgfDEJT3ae+21rCRksBG815bDMXYsWBFkFX7xAG+IYxQBdGX4s/OmMxdlMeh/PNrNpVQLivN0PahCCsDhwVFuyVZhzlJuxlRhzGchxGI46KiuOD0WoJvm484ffcgz7ylAVQFUVR3KKS/2ns/GUfems5HUPXYB/9Fbv9DWzM2cjG7I2k5KdQaatEIOgU2omb2l1B/+j+9I3qi7/+5Au0Nh8q4qYP/qTdUANfTh1EgM+ZTxBLux1HeTl2YzmOciPakBD0UVH19VEVRWmB1EVep1BRZiFjn4no3D9ZOTicoese4qafbmLmlpkUVBVwTftreHPkm6yZuIYFVy7g4X4Pk9w6uTrxSykpqbCw82gpP2zL4raP/yI2yJdPbhtw1sQPILRatEFBeLVuhU+nTirxK8DpSzofOnSIjIwMj5Z13rRpE/fffz8AK1euZN26ddU/u/XWW1m4cOG5fGSP++STT7j33nvPuM6Jn7epUC3/U0hdk4lEQwK/c0eIpHNIR27sfCP9ovoR5huG3SHJLatiX3YlWSVHOVJcydGSSrJKKjla7Lw3WezVrxcT5MOnUwYQblBX2Srn7kwlnTMyMjxa1rlfv3706+ccSr5y5UoMBgMXXHBBXXzsRq+pfl6V/E8gpWTX8oMElWawuXseZmkgzjGZX/4M5cOSfRwtriSnrAq74/iL40L9vYgN9iEpwp9h7SOIDfahdYgvrYL9aBvpj5+X2tXNySsbX2FP0Z46fc1OoZ14bMCZZzM9VUnnY+qzrHP37t1Zs2YNQUFBhIeH89///pdbbrmFSZMmMXnyZHQ6HTNmzODtt9/m3XffRavVMnfu3Or4Vq9ezRtvvEFOTg6vvvoq48ePP+k95s6dy1tvvYXFYmHgwIG88847aLVaDAYDDzzwAEuWLMHX15fvv/8eHx8fevbsSXp6OhqNhoqKCjp27Eh6ejp6/d/frm+99VbGjBlT/X4Gg6G6rMW//vUvwsLC2Lt3L8nJybzzzjtoNBo+/vhjXnrpJWJiYujQoQPertIoP/zwA88//zwWi4WwsDDmzZtHZWXlSZ+3U6dO3HXXXWRmZgLw5ptvMmTIkDP/8j1AZaQTZKWVYKzQ0rVkLY929MfL3Jd5ayuJCSqiVbAv/RNCaOVK6scSfGywr0ruSoM4U0lnqL+yzkOGDOGPP/4gPj6epKQk1qxZwy233MKGDRuYPXs2x66+T0hI4K677sJgMFTXHfrwww/Jzs5m7dq17Nmzh7Fjx56U/FNTU5k/fz5//PEHer2ee+65h3nz5nHLLbdgMpkYNGgQL7zwAv/3f//H+++/z1NPPUXPnj1ZtWoVI0eO5IcffmD06NHHJf6z2bhxI7t37yY+Pp5LL72Ub775hiFDhvDMM8+wefNmgoKCGDlyZHWpjKFDh7JhwwaEEHzwwQe8+uqrvP766yd93htvvJEHH3yQoUOHkpmZyejRo0lNTa11XA1FZawT7PgxFZ2tgoLYjZiFHuORZN6c0IurerXydGhKI3K2Fnp9Ol1JZ6i/ss7Dhg1j9erVxMfHc/fddzNnzhyOHj1KaGgoBoPhrDFfffXVaDQaunTpQm5u7kk/X758OZs3b6Z///4AVFZWEuka2ebl5cWYMWMA6Nu3L8uWLQNgwoQJzJ8/n5EjR/LVV19xzz33nDWOmgYMGFBd6fSGG25g7dq16HQ6RowYwbFpYydMmMC+ffsAOHLkCBMmTCA7OxuLxUJiYuIpX/e3335j9+7d1c/LysowGo0EBAS4FV99Uyd8a6gyWTm4t4KovL+Y1V+Lv7U/rQ1xXNE9xtOhKQpwfEnn//73v2RnZ5+0zhNPPMErr7yCw+EAji/rfDrdu3fnwQcfZNmyZSxatOiknycnJ7NmzRrWrFlTnRwXLlzIsGHDahW3d42qsqeqJyalZPLkyaSkpJCSksLevXt59tlnAee3nWMHL61Wi81mA2Ds2LH8/PPPFBUVsXnzZi688MKTXlen01XvByklFoul+mcnHhCPPT/dgfK+++7j3nvvZceOHbz33ntUVVWdcj2Hw8H69eurP8vRo0cbXeIHlfyPs2d1Jg40aPWrKfHVkJM5jGnJbdHVYUE3RTlXtSnpDPVT1rlNmzYUFBSQlpZGUlISQ4cOZcaMGadM/gEBARiNRrc+26hRo1i4cCF5ec7pvIuKik6qVnoig8HAgAEDeOCBBxgzZgxa7clTniYkJLB582YAvv/+e6xWa/XPNm7cyMGDB3E4HMyfP5+hQ4cycOBAVq5cSWFhIVarla+//rp6/dLSUlq1cvYAfPrpp6f9vJdccglvv/129fOUlBQ39kTDUVnNRUrJzt8OEFB2iHm98wiz9yXMqxXX9VUzXymNw6lKOu/Zs4dVq06eWO/JJ5/kyJEj1c/vvvtuRo4cSf/+/enWrRvDhw/Hz8+vuqxzx44d6dWrF88888xpyzoPHDiQDh06AM5uoKNHjzJ06NCT1rvyyiv59ttv6dWrF2vWrKnVZ+vSpQvPP/88l1xyCT169ODiiy8+5beaE02YMIG5c+cyYcKEU/586tSprFq1igEDBvDnn3/i7//3dTiDBw9m+vTpdOvWjcTERMaNG0dMTAzPPvssgwcP5qKLLqJPnz7V6z/77LNcd911DBs2jPDw8NN+3rfeeotNmzbRo0cPunTpwrvvvlurfdDQVElnl+wDJXzz2hbij8zjyfF/Upb+CI+MHMo/Rrart/dUmhZV0rn5WLlyJTNmzDju21Fz4E5JZ9Xyd9nxwy60djNrE7eQKLtiEDFMGqxmNVIUpXlSyR+wVNpI31NBRMEmVnazsuPgZdw0KJ7AWlyNqyhK0zNixIhm1+p3l0r+wN41h7CjJcd3HR30cWgdUUwZmuDpsBRFUeqNSv7AzqVp+JcfZVn3THZljuW6vq2JDFB18hVFab5afPLPyyyjqNwLb9MfxIaHUGxuw7TkJE+HpSiKUq9afPLf8d12NHYLm+L/oiD7Esb0iCU+7OSyzIqiKM1Ji07+VrOd/btNhBal4JVkYWNFH+4afnL9E0VpDFRJZ6Uutejkv2/NQWzoyfH7A71xECM7RtIlVk2PqDRONUs6A8eVdAaqSzrXLGFwTM2Szjt37mT16tVIKatLOv/www9s27aNrVu3MmLECLdj69evH2+99RbQdOvbnw8pZXUZiaaiRRd22/bzHvwqyshvm8r3xpt5f4S6oEupnZwXX8ScWrclnb07dyL6iSfOuI4q6exeSefc3Fzuuuuu6rpGs2fP5oILLuCNN97go48+AuCOO+7gn//8J4899hjx8fHVBeKeffZZAgICePjhh3nttddYsGABZrOZcePG8e9//5uMjAwuu+wyRo4cyfr16/nuu+9OWRqjsWqxLf/Co+UUm/zQm9YR69WedvFt6J8Q4umwFOWMjpV0fvDBB3nzzTdPWdL59ddfx27/ezKh2pZ0vuGGG5g3b94pW7DHSjrv2rWruqQzwIYNGxg0aFD1esdKOj/44IOkpKRU1/45VtJ5yZIl1d9caqpZ0jklJQWtVsu8efMAqks6b9u2jeTkZN5//32CgoKqSzoDpy3pfP/99zN8+HC2bdvGli1b6Nq1K5s3b+bjjz/mzz//ZMOGDbz//vts3bqViRMnMn/+/OptFyxYwHXXXcfSpUtJS0tj48aNpKSksHnzZlavXg3A3r17ueWWW9i6dWuTSvzQglv+fy1Yj3BIqqLX8KvxFu4Z1/aMZW8VpaaztdDrkyrpXPuSzr///nv1uQ2tVktQUBBr165l3Lhx1XV+rrnmGtasWcP9999PXl4eWVlZ5OfnExISQlxcHG+99RZLly6trutfXl5OWloacXFxxMfHH3fwa0paZPK3We0c3GMhpDiVhO42vjb0Y2THSE+HpShnVbOk89ChQ5k4cSIxMceXHH/iiScYP348ycnJwPElnY/Vrz9R9+7d6d69O5MmTSIxMfGk5J+cnMz//vc/MjMzeeGFF/j222/rpaTzSy+9dNLPzlTS+fHHHz9jSedTOVM9s/Hjx7Nw4UJycnKYOHFi9fqPP/44d95553HrZmRkHFcorqlpkd0+W5fvwCF8wWs1y8wjuXtkOzQa1epXGjdV0vl4tSnpPGrUKGbPng2A3W6nrKyM5ORkvvvuOyoqKjCZTHz77bfVn2PixIl89dVXLFy4sPq8xOjRo/noo48oLy8H4OjRo9VxNmUtMvmn/LgTn8oC+sVsYkPARYzpoSZrURo/VdL5ZGcr6Txz5kxWrFhB9+7d6du3L7t27aJPnz7ceuutDBgwgIEDB3LHHXdUd+l07doVo9FIq1atqr9RXXLJJdx4440MHjyY7t27M378eLcPbo1RiyvpnJ6Ryc8v7yeyYDG6zvswXv4OtwxOOP8AlWZPlXRWGrtGU9JZCPGRECJPCLGzxrJQIcQyIUSa675Bh9gs++QnhLTTNXgpS/Sjub5fm4Z8e0VRlEahvrt9PgEuPWHZdGC5lLI9sNz1vEHkGHOxH40muGQX+ihJn6GX46M/uZ9QURSluavX5C+lXA0UnbD4KuDYBJifAlfXZww1zf96PlIbSIJYzje6i7hZdfcoitJCeeKEb5SUMhvAdX/aMZZCiGlCiE1CiE35+fnn9aZ5FXk4/tTibS6mW8xfePe9iSBfNVmLoigtU6Me7SOlnCOl7Cel7BcREXFer/Xxus/woiMxxj9YEdCXG0f0qpsgFUVRmiBPJP9cIUQMgOu+3gfM5ppyMf7k/ObQO+hXcttPJDJQTdaiKErL5YnkvxiY7Ho8Gfi+vt/wo+0fEVs2gJDSPZhiHFxy6bj6fktFqXPNuaRzRkYG3bp1c+s933zzTSoqKqqf16bMRH25/PLLKSkpqdPXLCkp4Z133qnT16ypvod6fgmsBzoKIY4IIW4HXgYuFkKkARe7ntebXFMuu1fuQOpC6CR+Y3vM1SREeO6PRFHOlSrpfLwTk78n/fTTTwQHB9fpa9Z38q/X2j5SyhtO86NR9fm+NX208yP6HhyA3lZG29gN+F32ZkO9tdKMrVmwj4LD5XX6muFtDAy7vsMZ12nOJZ1tNhuTJ09m69atdOjQgc8++ww/Pz+WL1/OI488gs1mo3///syePZv33nuPrKwsRo4cSXh4OCtWrACcVzbXLP0cFRV13HuYTCbuu+8+duzYgc1m49lnn+Wqq67ik08+YfHixVRUVHDgwAHGjRvHq6++yuzZszl48CCvvvoqAJ988gmbN28+br+Ds5rppk2bKC8v57LLLmPo0KGsW7eOVq1a8f333+Pr68uIESPo1asXGzdupKysjI8++ogBAwbw7LPPYjAYqkt1dOvWrbr66YEDB+jVqxcXX3wxDz30EBMmTKCsrAybzcbs2bNrXVvpVBr1Cd+6EEc8etGV1sZ17IoaTKekBE+HpCjnrLmWdAZneeRp06axfft2AgMDeeedd6iqquLWW29l/vz51Ql79uzZ3H///cTGxrJixYrqxH+q0s8neuGFF7jwwgv566+/WLFiBY8++igmkwlw1jU69j7z58/n8OHDjB8/nm+++aZ6+/nz55+2lMQxaWlp/OMf/2DXrl0EBwezaNGi6p+ZTCbWrVvHO++8w5QpU874Oi+//DJt27YlJSWF1157jS+++ILRo0eTkpLCtm3b6NWr1xm3P5tmX9UzYWMkKUJDz+BfKB1arz1MSgtythZ6fWqOJZ3B2a01ZMgQAG6++WbeeustLr74YhITE6trCk2ePJn//e9//POf/zxp+9OVfq5p6dKlLF68mBkzZgBQVVVFZmYm4CwCFxQUBDhrDR06dIihQ4eSlJTEhg0baN++PXv37q2O8XQSExOrE3Pfvn3JyMio/tkNNzg7Q5KTkykrK3PrPEH//v2ZMmUKVquVq6+++ryTf7Nv+RfuOEhw2V4crW10HnTixcaK0rTULOn83//+95TFz5544gleeeWV6hZ8zZLOp9O9e3cefPBBli1bdlxL9Zjk5GTWrFnDmjVrGDFiBBEREXVa0hk46QAlhDhj+eUTna70c01SShYtWkRKSgopKSlkZmZW18KpGWPN7SdMmMCCBQtYtGgR48aNO+u8H6d7ndN9Rp1Od9y3raqqqlO+bnJyMqtXr6ZVq1ZMmjSp+oT9uWr2yT9wdChXBj5HRY9JCE2z/7hKM9acSzoDZGZmsn79egC+/PJLhg4dSqdOncjIyGD//v0AfP755wwfPvyc32f06NHMmjWr+qCydevWs25zzTXX8N133/Hll1+etcvnbI7NFLZ27VqCgoIICgoiISGBLVu2ALBlyxYOHjwInPz5Dh06RGRkJFOnTuX222+v3uZcNftsaD64GJ9oC+0vnnr2lRWlEWvOJZ0BOnfuzKeffkqPHj0oKiri7rvvxsfHh48//pjrrruO7t27o9FouOuuuwCYNm1a9Ry6tfX0009jtVrp0aMH3bp14+mnnz7rNiEhIdXdQAMGDKj1e53utS644ALuuusuPvzwQwCuvfZaioqK6NWrF7Nnz67ex2FhYQwZMoRu3brx6KOPsnLlSnr16kXv3r1ZtGgRDzzwwHnF0uxLOtv+eJuK/EwCr361HqJSWhJV0lk5HyNGjGDGjBn063dSdeU6405J52Z/wlc35F4CPR2EoihKI9Psk7+iKEpjUPPcSmPQ7Pv8FaUuNZVuUqXlcfdvUyV/RaklHx8fCgsL1QFAaXSklBQWFuLjU/uClarbR1FqqXXr1hw5coTznVtCUeqDj48PrVu3rvX6KvkrSi3p9XoSExM9HYai1AnV7aMoitICqeSvKIrSAqnkryiK0gI1mSt8hRD5wKFz3DwcKKjDcOqaiu/8qPjOj4rv/DT2+OKllCdNgt5kkv/5EEJsOtXlzY2Fiu/8qPjOj4rv/DT2+E5HdfsoiqK0QCr5K4qitEAtJfnP8XQAZ6HiOz8qvvOj4js/jT2+U2oRff6KoijK8VpKy19RFEWpQSV/RVGUFqjZJ38hxKVCiL1CiP1CiOkeeP82QogVQohUIcQuIcQDruXPCiGOCiFSXLfLa2zzuCvevUKI0Q0QY4YQYocrjk2uZaFCiGVCiDTXfYgn4hNCdKyxj1KEEGVCiH96ev8JIT4SQuQJIXbWWOb2PhNC9HXt+/1CiLfE2WYHP7/4XhNC7BFCbBdCfCuECHYtTxBCVNbYl+96KD63f6cNHN/8GrFlCCFSXMsbfP/VCSlls70BWuAAkAR4AduALg0cQwzQx/U4ANgHdAGeBR45xfpdXHF6A4mu+LX1HGMGEH7CsleB6a7H04FXPBXfCb/PHCDe0/sPSAb6ADvPZ58BG4HBgAB+Bi6rx/guAXSux6/UiC+h5nonvE5Dxuf277Qh4zvh568D//LU/quLW3Nv+Q8A9ksp06WUFuAr4KqGDEBKmS2l3OJ6bARSgVZn2OQq4CsppVlKeRDYj/NzNLSrgE9djz8Frq6x3FPxjQIOSCnPdKV3g8QnpVwNFJ3ivWu9z4QQMUCglHK9dGaKz2psU+fxSSmXSiltrqcbgDPW/23o+M6gUey/Y1yt9+uBL8/0GvUZX11o7sm/FXC4xvMjnDnx1ishRALQG/jTtehe11fwj2p0EXgiZgksFUJsFkJMcy2LklJmg/MABkR6ML5jJnL8P1xj2X/HuLvPWrken7i8IUzB2RI9JlEIsVUIsUoIMcy1zBPxufM79dT+GwbkSinTaixrLPuv1pp78j9V/5pHxrYKIQzAIuCfUsoyYDbQFugFZOP8GgmeiXmIlLIPcBnwDyFE8hnW9cg+FUJ4AWOBr12LGtP+O5vTxeSpffkkYAPmuRZlA3FSyt7AQ8AXQohAD8Tn7u/UU7/rGzi+EdJY9p9bmnvyPwK0qfG8NZDV0EEIIfQ4E/88KeU3AFLKXCmlXUrpAN7n766JBo9ZSpnlus8DvnXFkuv62nrs62uep+JzuQzYIqXMdcXaaPZfDe7usyMc3/VS77EKISYDY4CbXF0RuLpTCl2PN+PsU+/Q0PGdw+/UE/tPB1wDzK8Rd6PYf+5q7sn/L6C9ECLR1XKcCCxuyABc/YMfAqlSyjdqLI+psdo44NiogsXARCGEtxAiEWiP86RRfcXnL4QIOPYY50nBna44JrtWmwx874n4ajiutdVY9t8J3Npnrq4hoxBikOvv5JYa29Q5IcSlwGPAWCllRY3lEUIIretxkiu+dA/E59bvtKHjc7kI2COlrO7OaSz7z22ePuNc3zfgcpwjbA4AT3rg/Yfi/Kq3HUhx3S4HPgd2uJYvBmJqbPOkK9691PPoAJwjoba5bruO7SMgDFgOpLnuQz0Rn+v9/IBCIKjGMo/uP5wHomzAirOFd/u57DOgH84kdwB4G9dV9/UU336cfefH/g7fda17ret3vw3YAlzpofjc/p02ZHyu5Z8Ad52wboPvv7q4qfIOiqIoLVBz7/ZRFEVRTkElf0VRlBZIJX9FUZQWSCV/RVGUFkglf0VRlBZIJX9FOQMhRLAQ4p4z/HxdLV6jvG6jUpTzp5K/opxZMHBS8j92UY+U8oKGDkhR6oLO0wEoSiP3MtDWVbvdCpTjvPinF9BFCFEupTS4ajd9D4QAeuApKWXjuZpTUU6gLvJSlDNwVWJdIqXsJoQYAfwIdJPO0sLUSP46wE9KWSaECMdZMrm9lFIeW8dDH0FRTkm1/BXFPRuPJf4TCOBFV0VUB87SvVE4J59RlEZHJX9FcY/pNMtvAiKAvlJKqxAiA/BpsKgUxU3qhK+inJkR5/SbZxME5LkS/0icU00qSqOlWv6KcgZSykIhxB+uibwrgdzTrDoP+EEIsQlnxcw9DRSiopwTdcJXURSlBVLdPoqiKC2QSv6KoigtkEr+iqIoLZBK/oqiKC2QSv6KoigtkEr+iqIoLZBK/oqiKC3Q/wNGC/ik6Di6ywAAAABJRU5ErkJggg==\n", 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" ] @@ -353,22 +353,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -391,22 +391,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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3yvMZXj+Dc3ZOwojayS50ctq5hZRPzCOrMDnRYQsh6MGhs4kmQ2f7Bq01vk8+oeFvC/EsWwYKGoc7WDRWU50ylXG1M8n0DcJqh+FTixg9q4i8IWmJDluIU1avD50Vp7aIx0vzG6/TuHAhwZ27INnKpkkRXhozhFzfLEbUT2b4AQfZxcmMubSY4VMLsCfJP0ch+ir5v1N0q8DOXTS+sJDmVxcT9fkJ5mmWXmDh06JpnH5gBmdXDsGwwfCpBYyeVUx+WZpUNBWiH5BkIU5aNBikZckSml58Ad+qNWDAwfIQ/3PGEEJqNiMOTmF2pZO0AifjLxnMiGkFOFzdfDd5IUSPkmQhTlhwzx6aXn6ZpsWLiTQ2YaREWT/d4O3TzmRQ0wxG1JWhLJrTJuUzZvYgCoemSy9CiH5KkoU4LjoUomXZMppeWoT344/BUDiLA6w8U/PPgnOZsPffmFyVjivHysSLShk5vRBnivQihOjvJFmILgnt20fjK6/Q/MpiwgcOYE13kDHWw78mWFicNplxlV9jxs7BZAx2MPsbpzNoRKb0IoQYQCRZiKPSkQie9z+gadEiPB98AEDKaSmkjqxn6chknk8dw7BdF3Le3nE4MizMvmEEwyblo6QQnxADjiQLcYRQbS1Nr7xC0yuLCVdXY8lMI3t6Ghk5W3m/OIsnM8aRVzmDr2yfhdVmMPWycsafOxirXQr1CTFQSbIQcVGvl5pf/pLm19+ASITkcUPJPyOF1OTNfJoziMdyJ6EqRzJ7+8XYIk5GzShi2tfLcaXJvR6EGOgkWQgA/Fu2su/73ye4ezdZ508kM/tz7NHlbMgbxqN5s6nZk8bMlZeT0prFoJGZzLxqGNnFKYkOWwjRSyRZnOK01jQvXkzN//0FhstByb9BcvLf2VU4nscLxrBmTyOzP7ucCc2lZBQkMfOG4ZSMzpKT10KcYiRZnMKiXi/VP/857jf/TvLQdIrGbqFh8FAeHjqPJXs3M/WziVxZNwlnipVpV5czamYRhkWqvgpxKpJkcYqKH3aqrCRnYpCskTtYfMYVPNa4jRGrM7l6/39gwcqECwdzxkWlOKRukxCnNNkDnGLih53+v/+LYYtQMucADVMnclN2Fk1bInyj6v9g97s4bXIeZ142lLScpESHLIToAyRZnEKiXi/V992H+61/kFwQJP/sEC+dNY8Xd+1l2ocXMsk9hNySVGbNHU7h0PREhyuE6EMkWZwi/Fu3su/2WwhWVZMzxk3TlbNYYLGT+lE+l9RdhjPFylnXDOP0MwvlojohxBEkWQxwWmuaX/obNb98EMMSoujfbCy86Ft8vDrApD0XYdcOxp9XwpRLyuS8hBDiqGTvMIBFPR5qfnATzR9UkJwfoOWW8/ih18mQf0zkzNYCCkamcs7cUXLrUiFEpyRZDFD+1R+w7447CDb4yTgzmRevmEv1J5lMbBiLLQPO/844Ssdmy/USQogukWQxwOhwiObf3EnN8//CsGu8t5/HU95BlP5zEkMsBhMuKWbahadhtUkdJyFE10myGEB0cx3VN36V5vVeHGUpvH7pXEIbh3JaMIOscRa+dvV0UjIdiQ5TCNEPSbIYIKI126mafzne3WEOXjyHZfbpZFcMxprt5avXjqZsRH6iQxRC9GOSLAaA8LZV7L3+GloPwsdfvQG/bwKpET9DL3VxwYXnYMhQWCHESeqxQj9KqaeVUnVKqS/atWUppZYopbaZz5nt3rtXKbVdKbVFKXVhu/ZJSqn15nuPKTkje4jQ6v9l97f/HX8DLL/0ewR8ZxAecZAbHjiHiy6eLolCCNEterIq3LPARYe13QMs1VoPA5aar1FKjQLmAaPNzzyhlGo7A/sksAAYZj4OX+Ypy//u81TeeCehVgsffuP/EG4aSXDcfu6485ukpcpwWCFE9+mxZKG1/gBoOKz5UuA5c/o54LJ27S9prQNa613AdmCqUqoQSNNaf6K11sDz7T5zSvO99DC773oAbbHx0Td/RrBuCC1jKrnjlqsxDKkMK4ToXr19ziJfa10NoLWuVkrlme3FwIp281WZbSFz+vD2DimlFhDrhVBSUtKNYfct7sd/yP4n/oE1w8mHl92Pf2c69aO2cO+tN2IxZEisEKL79ZWfoB0dWNfHaO+Q1voprfVkrfXk3Nzcbguuz9CaxvuvYd8f/4GjKJUVV/0K7850akZ+wT2334DNYkt0hEKIAaq3exa1SqlCs1dRCNSZ7VXA4HbzDQL2m+2DOmg/5ehwiIN3XcbBJTtJHpnHR+feR/NGxd4Ra/jP22/DbpH7YAshek5v9yzeBOab0/OBN9q1z1NKOZRSZcROZK80D1m1KKWmm6Ogrm33mVOG9nuomX8uB5fsJG1aOSvOf4CGjYqdwz7l3ttuwmVzJTpEIcQA12M9C6XUi8AcIEcpVQXcBzwIvKyUugHYA1wFoLXeoJR6GdgIhIHbtdYRc1G3EhtZlQS8bT5OGdGmWvZf+1VatvrI/OoUPhx2KwfWtbLltA/5z9tuI90h950QQvQ8FRtkNPBMnjxZr1q1KtFhnJTI/u1UXXM5vn1hcuf/G8szv8n+Cg8bhr7Hf9x6C0UpRYkOUQgxwCilVmutJx/eLldw91GhzSvZe/18Ak2agh9fzwehc9lf4ebzsne5e8GNkiiEEL2qr4yGEu0EVvyT3d++lpBbM+iX9/Jh6CtUVbhZU/a/3HnjtylPL090iEKIU4wkiz6m9X+fY/fNdxGNKAY98TverxnHnoomPiv9B7ddN5dR2aMSHaIQ4hQkyaIP0T43+/7jVxh2CyXPPcd7GwvZXdHIp0Pe5IZvX8ak/EmJDlEIcYqSZNGHtPz3fYQ8ipw7b2Ppxw4qKxr4eMhrfGveRcwaNCvR4QkhTmGSLPqKoI/GV/+Jke7gI/dMKivq+aj0VS7/xtlcVCa1E4UQiSXJoo/wv/4wvmqD7bN/TOXnDXxYupgLLpnMlcOvTHRoQgghyaJPCPpoXPgC/uQcKlsKWFe4jDMvGMF1Y65LdGRCCAFIsugTIu89QfN2qJ42lyhRsqfB9yZ+L9FhCSFEnCSLRAv6aPrrU4RIYpcxnJ3Z67hp+neQGwIKIfoSSRYJplf+F40bNPUTL4GwFTW2kfIMuehOCNG3SLmPRAr68Lz4OEGflW25Z1Jn7OGyGRd2/jkhhOhl0rNIpNXP0LA+jLt0CsHWJGrKNzJz0MxERyWEEEeQZJEoQR+Bvz+Kr87BrtFfw2drYc7ZZ2Ao+ZMIIfoe2TMlyupnaFgXoDW1kAZvNtsLP+PSEV9PdFRCCNEhSRaJEPQRWfoozXtS2H/mt4iqCMNm5Mgd74QQfZYki0RY/QxNX3gJRe3sjpSwI7uCqyddleiohBDiqCRZ9LagD/3BozRWZnNw8jcgYsUxwUtxSnGiIxNCiKOSobO9bfWzeLY3E2yysS1zIrXWSq6aeUmioxJCiGOSnkVvCrXCR4/SuHcQzeVnEvK7qD9tu9ynQgjR50my6E2rniGwrx5vZSs7R1yI19bMV86ZKqU9hBB9XpeThVLKopQqUkqVtD16MrABx+xVNNScRmtaMU2tOewqXsNXT7s40ZEJIUSnupQslFLfA2qBJcA/zMdbJ7pSpdRdSqkNSqkvlFIvKqWcSqkspdQSpdQ28zmz3fz3KqW2K6W2KKX6Zz2MVc8QaaijeVOAfVPnElFhxswuxmFxJDoyIYToVFd7FncCI7TWo7XWY83HuBNZoVKqGLgDmKy1HgNYgHnAPcBSrfUwYKn5GqXUKPP90cBFwBNKKcuJrDthzF5FU9MYQiHFHj2EnTkVzJ0oNzYSQvQPXU0We4HmblyvFUhSSlkBF7AfuBR4znz/OeAyc/pS4CWtdUBrvQvYDkztxlh63qpn0O5aGjcZ1E26AiJW0iZFyEnKSXRkQgjRJV0dOrsTeE8p9Q8g0Naotf7t8a5Qa71PKfUIsAdoBd7RWr+jlMrXWleb81QrpfLMjxQDK9otospsO4JSagGwAKCkpI+cUjF7FZ7oGQSra9k+biI1tl3MmymlPYQQ/UdXexZ7iJ2vsAOp7R7HzTwXcSlQBhQByUqpbx/rIx206Y5m1Fo/pbWerLWenJubeyLhdb/Vz4KnlsadWTSWn0U44KJlxG5G54xOdGRCCNFlXepZaK1/DqCUSo291J6TWOdXgF1a6wPmMl8FzgJqlVKFZq+iEKgz568CBrf7/CBih636vlArfPg7AqnT8K7ZyM6L7scbaeKic2ckOjIhhDguXR0NNUYptRb4AtiglFqtlDrRn8Z7gOlKKZeKXWBwHrAJeBOYb84zH3jDnH4TmKeUciilyoBhwMoTXHfvautV1JyGL20Qbn8uuwev4ytl5yU6MiGEOC5dPWfxFPADrfUyAKXUHOAvxHoEx0Vr/alS6hVgDRAG1prLTwFeVkrdQCyhXGXOv0Ep9TKw0Zz/dq115HjX2+vMXkWk4Cyan/qUvWfeTDgaYuKcUqyGVFkRQvQvXd1rJbclCgCt9XtKqeQTXanW+j7gvsOaA8R6GR3N/wDwwImuLyHMXkWz9VsEgm9SpUvZlVvBz8ffnOjIhBDiuHX1BPdOpdTPlFKl5uM/gF09GVi/ZvYq9JCZNLz9CXUTL0NFreROtZDuSE90dEIIcdy6miyuB3KBV4HXzOnv9FRQ/d7q58BTi9f1bwR372V75kSqU3cwb+ZliY5MCCFOSFdHQzUSu+padMbsVVA6i4Z3K2gsO5NIMJnAhBqGZgxNdHRCCHFCjtmzUEo9aj7/XSn15uGPXomwv1n9HHhqCAy9Du/y5ewYfj4eeyOXnDc70ZEJIcQJ66xn8Vfz+ZGeDmTAWPM8DJ5O4/tb8KQNpiWQx75hy5lV8rNERyaEECfsmD0LrfVqc3KC1vr99g9gQo9H199EI1C/nUjuGTS/9hp7J3+DsAoy9ZzhGEpuHSKE6L+6ugeb30Hbdd0Yx8DQXAWRAM1feAgEouynjMr8z7l8rNSBEkL0b8c8DKWUuhr4FlB22DmKVKC+JwPrl+q3ozU0Ll1PzYTLUVErxdOTSLad8CUpQgjRJ3R2zuJjoBrIAX7Trr0F+Lynguq36nfgrXEQ2FfHjlET2J+0nVtmXJboqIQQ4qQdM1lorXcDu4Ezeyecfq5+O4070mkoO4toKBmmNDA4dXDnnxNCiD6uq4UEpyulPlNKeZRSQaVURCnl7ung+ht9cButB23sKJ1Di72BS887N9EhCSFEt+jqCe7HgauBbUAScCPwh54Kqr+K7NtBs6UQjy6itmwzU4qmJDokIYToFl0ez6m13g5YtNYRrfUzwDk9F1Y/FA4Q2FtLVfHZRAgy87wxxCqwCyFE/9fVqrM+pZQdqFBKPUTspLcM8WmvsZJAs0F91mjqMrZw8+hbEh2REEJ0m672LK4BLMB3AS+xO9d9o6eC6pfqt+NtSSXgzCS1NBmHxZHoiIQQott0tZDgbnOyFfh5z4XTj9VvpzYQG/mUU5SW4GCEEKcCXzDMwZYgB70B6j1B6j0BGnxBbj17aLcfBu/sorz1gD7a+1rrcd0aTX9Wv4OmaCxZlJYWJjgYIUR/FIlqGrxBDnrMnb83wEEzCdR7Yu0HvV++bg11fNPQa88sJcXRvXfk7Gxpl3Tr2gawcNVWWm0T0IQYOWRkosMRQvQhgXCEAy0B6loC1LkDHGjxx6fr2qZbAtR7AkQ7+HluNRRZyXZyUhxkp9gpz0kmO9lOtvk613zOTnGQnWzHabN0+3foykV5oguCO3fhdV2M33qQojTpWQgx0Git8YeitARCeAMRPP7wl9OBEB5/GE8gQlNrkANuMzGYiaDJFzpieUpBdrKDvFQHeWkORhelkZfqJDfVQU6Kgxxz55+TYifNacMwEju6skv9FKVUC18ejrIDNsCrtZaD8wCBFgI1brzJBQTSm2TI7CmouTXElpoWttS4CYSjjCpM4/TCNDKT7YkOrVPRqGZ3g48N+5vZuN+N2x+iJMtFSVYyQ7JdDMl24bJ37yGNRAtHotR7g+avfb/5Cz/AgZYADd4gLYEwHn9bIgjT4g/hDUaIdPSz/zB2i0FuqoPcVAel2clMLcsiL9UZTwpt01nJdqyW/lONuqsnuFPbv1ZKXQZM7YmA+qX6HbS6k/AXZmMUyYXtA1koEmXnAS+ba9xsrmlhS00Lm6vd7G/2dzh/YbqTUYVpjCqKJY9RhWmUZLkS9isxEI6wrdbDxv1uNuxvZsN+N5uq3XiDsWPfVkOR7LDS3HroL+HcVAdDslyUZLsY0i6JDMlOJtNl69UfSNGoJqI1kagm2vYchXA0SkRr/MFo/Bd9R8mgriVAvTeA7mC/n+GykZVsJ9VpI9VhJTfVQYrDRqrTSrLDQorDRorTSkrbtMMaezit8WmnzRiQPxhP6OeC1vp1pdQ93R1Mv9Wwg7rAYFAG2YWpnc8v+jytNTVuP5trWthcHesxbK5pYccBD6FIbC9jsyiG5qYwpSyLkQVpjCxIZWRhKjaLwaZqNxvNHfHGajfvbT0Q/1WabLcw0kwcbUlkRH4qSfbuPc7s9ofYuN9tJoZYHNtqWwi3i+P0wjSunDSIUUVpjC5KZ1h+Cg6rhebWEHvqfexu8LK73sfu+tjzJzvqeXXNvkPWk+qwxpJIdqw3kpNiJxiJEghF48+BcIRgOEogHDWfI0fME3uOEIx8mQTiD63jSaKjnfyxWA1FTkrsV31hupPxg9PJNQ/35JmPtp6Aw9r9x/oHiq4ehrqi3UsDmMwxRkl1YXkZwH8BY8zlXA9sARYBpUAl8E3z3t8ope4FbgAiwB1a6/93ouvuEfU7aIwOAmBwSV6CgxEnot4T4LPKRlZVNvD5vmY2V7tx+8Px94vSnYwoSOWckXmMLEhlREEq5Tkp2K0dH0aYNSyXWcNy46/9IfMXfXWzmURaeG3tPv66InZa0FBQnpvC6YVpFGckAaDRmP+hzZ1kbDr2XttOU2t9SPvBliAbq93safDF15+TEjsmPmdELqPNxDDkGD2c9CQbYwelM3ZQ+hHv+UMR9jb4Ykmkwceeei+V9T42VbewZGNtPJlC7JCMw2pgt7Z/thzyOsVpNactOKwGNouBxQCrYWAohcUAw1BYDYVFKYx2z1ZDYTGUOV/s4bRZDkkEmS57wo/3DwRd7Vl8rd10mNjO/NKTWO/vgf/VWl9pXhnuAn4CLNVaP2j2Wu4B7lZKjQLmAaOBIuBdpdRwrXXHY8YSILJvM15rEVpHGFk+PNHhiE5oralqbOWzygY+q2xg5a4GdhzwAmC3GowpSuOS8UWcXpDKiILYr/50l+2k1um0WY7Y+UajsTg2mr2PjfvdrNndyP9+UY1CYf6HUqBQ5jMopVDQ7v1D30tzWhlTnMbcKYNjPYbCNPLSnCcV/+HfZVh+KsPyj+xFR6IabzCM3WJgtxiykx5AunrO4jvdtUKlVBowG/NOe1rrIBBUSl0KzDFnew54D7ibWFJ6SWsdAHYppbYTO1/ySXfFdLKC27fic11BwHqQkgypNNvXRKOarXUtfLargZWVjXy2q4Ead+wcQ6rTyuQhmXxj0iCmlmYxdlB6rx2KMAxFSXbsPMBFYwp6ZZ09zWIo0pwnl1hF39TVw1DlxHoD04n1hD8B7tJa7zyBdZYDB4BnlFLjgdXAnUC+1roaQGtdrZRqO55TDKxo9/kqs62jOBcACwBKSkpOILQToDWB3fvxugoIpLZgMeSYZ6IFw1HW72uO9Rx2NbBqd2P8hG1+moMppVlMLctiSmkWI/JT5devEF3Q1cNQLwB/BC43X88DXgSmneA6zwC+p7X+VCn1e2KHnI6mo/+TOzxforV+CngKYPLkySd8TuW4+BporY/iy8+FQk+vrFLEegs1bj+7632x4+fmidg9DT621LQQCEcBKM9N5qLRBUwpy2JqaRaDs5IG5EgVIXpaV5OF0lr/td3rvymlvnuC66wCqrTWn5qvXyGWLGqVUoVmr6IQqGs3f/vbzQ0C9p/gurtf/XYO+otBWcgokkK83enwE6mxaS+7G3xUNbQSjETj81oNRXFmEiVZLv592hCmlmUyuTSLnBQp6ChEd+hqslhmnnR+idiv+rnAP5RSWQBa64aurlBrXaOU2quUGqG13gKcB2w0H/OBB83nN8yPvAm8oJT6LbET3MOAlV1dX4+r3059JJbLZCRU1wXDsbHwte4AtW4/tW4/Ne7YePh9ja3sbvBS6w4c8pkUh5WSLBcj8lM5f1Q+JVlfjvkvTHf2qwuchOhvupos5prPNx/Wfj2x5FF+nOv9HrDQHAm1E/gOsSG5LyulbgD2AFcBaK03KKVeJpZMwsDtfWkkVHT/ZnxGEVpHGVFWmuhwjovWOja2PRzFH4rEx8NHdWwop1LmkERztI1hKAyF+To2bZhDGOPTSuENhqlp9lPX4qem+ctkEEsIAercfuq9wSPisVsM8tIcFKUnMWtYbvwisJKsxFz8JYT4UldHQ5V150q11hXErtU43HlHmf8B4IHujKG7BLZuwJs8naClgbLss09oGVprdh708unOBlbuqueAJ4Bh7hSNth21OVxSxV/HhlMaRrthleYOPBzVBEKRL5PAMZ6P9wKnE9FWA6cg3UFRupOJJRnkpzopSHeQl+akIM1JfppTkoEQfVhXR0PZgFuJDXmF2LDWP2utj6yOdYoJ7tqD13U5/pRmbJauDRnUWrOtzsOnO+tZsauBT3c2cNATO+SSl+pgcJYrfqFV1LzaKmpecBWNHnqRVlTHrnZtuygrqjVWQ+GwWnDaYhdApSRbD3nttBk4bBac1tizo92z02bBotSXy9XEyyq0LT/SFk/8qlpz2mxPslnIT3OSn+agIN1JTooDmxwiEqJf6+phqCeJFQ98wnx9jdl2Y08E1W9Eo7Tua8CXnYcuqDzGbJrNNS18uqs+1nuobKDBPAxTmO5k5mnZTCvPZnp5NqXZLvl1LYToc7qaLKZorce3e/0vpdS6ngioX2nZT703B51rI63QFW+ORDWbqt2s2FnPp7tiVwm3lSgelJnEOSPymFaexfSybBnKKYToF7qaLCJKqaFa6x0Qv0ivz5xkTpj67dSHYxf/FQ/OAeC372zhmY8raTHrCg3JdnHBqHymlWUzrTyLQZmuoy5OCCH6qq4mix8TGz7bdsV2KbERTKe0aPVmvEbsYvLh5UOIRjXPfFTJ0LwUvjOjlKllWRSmJyU4SiGEOHldPev4EfBnIGo+/kwfqs2UKMEtn+NzFRBUTZyWV87Og15aAmG+Na2ESycUS6IQQgwYXU0WzwNlwP81H2XAX4/5iVNAYNt2vMkF+JMbcFqdrNvbBMCEwRkJjUsIIbpbVw9DjTjsBPcyOcENgb01eF0FRPL2ALCuqolku4WhuSkJjkwIIbpXV3sWa5VS09teKKWmETs0deqKhGistxO1OEgpit0roGJvE+MGZWCRKqZCiAGmqz2LacC1Sqk95usSYJNSaj2gtdbjeiS6vqxxNwfCsbvjFQ3Kxh+KsKnazQ0zj7fyiRiIQqEQVVVV+P0d35tbiERzOp0MGjQIm61rFxN3NVlcdOIhDUy6ZjMe87Yaw8pL2FTtJhTRTBh85G0oxamnqqqK1NRUSktL5Toa0edoramvr6eqqoqysq5Vc+pqbajdJxXZABTcvBafq5CwcjO8cBr/szJWUX28nNwWgN/vl0Qh+iylFNnZ2Rw4cKDLn5GCPScosGUjXlcBrUkNJNuSWVfVTF6qg4JuvNex6N8kUYi+7Hj/fUqyOEH+yj14kwsI58bKeKzb28T4wRmygxBCDEiSLE5Qc02EiNWFq9BOsy/EzoNeub5C9Cl79+6lrKyMhobYvckaGxspKytj9+7d3HzzzQwdOpTRo0cze/ZsPv00duPKBx54gNGjRzNu3DgmTJgQbxeiqye4RXtBHwdacwEoGJTJuqomQC7GE33L4MGDufXWW7nnnnt46qmnuOeee1iwYAF33303ZWVlbNu2DcMw2LlzJ5s2beKTTz7hrbfeYs2aNTgcDg4ePEgweORNqsSpSZLFCdB12/Co2EiooWWDWVnZBMDYQTISShzp53/fwMb97m5d5qiiNO772uhO57vrrruYNGkSjz76KB9++CE/+MEPeOqpp1i4cCGGETuwUF5eTnl5Oa+++io5OTk4HLH7lufk5HRrzKJ/k8NQJyC06TN8zkIi+BhRPJR1VU0MzU0mzdm18cpC9BabzcbDDz/MXXfdxaOPPsqWLVuYMGECFovliHkvuOAC9u7dy/Dhw7ntttt4//33ExCx6KukZ3ECApvW4U0eg895gHRHOhV7m5k9XH6FiY51pQfQk95++20KCwv54osvGDp06FHnS0lJYfXq1Sxfvpxly5Yxd+5cHnzwQa677rreC1b0WdKzOAGBHTvwugoI5QTZ3+znoCcg5ytEn1RRUcGSJUtYsWIFv/vd78jOzmbdunVEo9EO57dYLMyZM4ef//znPP744yxevLiXIxZ9lSSLE+CuaiFkTyWp0BavNDt+UEZCYxLicFprbr31Vh599FFKSkr48Y9/zBNPPMHkyZO577770FoDsG3bNt544w22bNnCtm3b4p+vqKhgyJAhiQpf9DGSLE7AAXcaALnF6VTsbcJuMTi9MC3BUQlxqL/85S+UlJRw/vnnA3DbbbexefNmbr/9dmpqajjttNMYO3YsN910E0VFRXg8HubPn8+oUaMYN24cGzdu5P7770/slxB9RsLOWSilLMAqYJ/W+hKlVBawiNhd+CqBb2qtG8157wVuIHYr1zu01v8vIUED2nMQd7QIgPLSYv7xfhOjitKwWyXvir5lwYIFLFiwIP7aYrGwevVqAM4+++wOP/Pxxx/3Smyi/0nkHu5OYFO71/cAS7XWw4Cl5muUUqOAecBoYgUNnzATTUKENq3El1RAFD/DistZX9Us5yuEEANeQpKFUmoQ8G/Af7VrvhR4zpx+DrisXftLWuuA1noXsB2Y2kuhHiHwxSq8rgJ89gM0ehy0hiKMl0qzQogBLlE9i0eB/0Psft5t8rXW1QDmc57ZXgzsbTdfldmWEMGtm/EmFxLMbuXzqmZATm4LIQa+Xk8WSqlLgDqt9equfqSDNn2UZS9QSq1SSq06ntK7x8NTWUfQkYG90ErF3mbSnFZKs5N7ZF1CCNFXJKJnMQP4ulKqEngJOFcp9TegVilVCGA+15nzVwGD231+ELC/owVrrZ/SWk/WWk/Ozc3tkeDrGmIlyHOLYiOhxg/OwJDbqAohBrheTxZa63u11oO01qXETlz/S2v9beBNYL4523zgDXP6TWCeUsqhlCoDhgErezlsAHQ0ijscS0JFxXlsrW2Rk9tCiFNCXxrv+SBwvlJqG3C++Rqt9QbgZWAj8L/A7VrrSCICDO9Yj9dZSJQQOPKIRLWcrxB91tFKlL///vsopfjDH/4Qn/e73/0uzz77bPz1I488wsiRIxkzZgzjx4/n+eefB+Ctt95i4sSJjB8/nlGjRvHnP/+5V7+TSJyEJgut9Xta60vM6Xqt9Xla62Hmc0O7+R7QWg/VWo/QWr+dqHgD6z7B5yqg1VbH3oN2AMbJSCjRR7UvUQ7ES5QPGTKEvLw8fv/733dYgvxPf/oTS5YsYeXKlXzxxRd88MEHaK0JhUIsWLCAv//976xbt461a9cyZ86cXv5WIlGkkOBxCG5ej9d1NoGMetZVNVOckUReqtxGVXTi7XugZn33LrNgLFz8YKezHV6i/A9/+AP79+8nNzeXGTNm8Nxzz3HTTTcd8plf/vKXLFu2jLS0WFWC9PR05s+fT0NDA+FwmOzsbAAcDgcjRozo3u8l+qy+dBiqz/Pu3Is/KQdroZV1VU1yfYXo8w4vUW632+Pv3XPPPfzmN78hEvnyqG5LSwstLS0dVqfNysri61//OkOGDOHqq69m4cKFRy1IKAYe6VkchwO1wGBIzXOxd08r354mRdZEF3ShB9CT2pcob6sTBVBWVsbUqVN54YUX4m1a62PeR/6//uu/WL9+Pe+++y6PPPIIS5YsOeRchxi4pGfRRVprmvwZAESTUwG5jaro+w4vUV5dXX3I+z/5yU/49a9/He8hpKWlkZyczM6dO4+6zLFjx3LXXXexZMkSKWF+CpFk0UWR2lp89kI0ERqi6RgKxhTLYSjRd3VUovxHP/rRIfOMHDmSUaNG8dZbb8Xb7r33Xm6//Xbc7titYN1uN0899RQej4f33nsvPp+UMD+1yGGoLgp8/gne5AL81gPsrC1neL4i2SGbT/RdHZUof/bZZ9m9e/ch8/30pz9l4sSJ8de33norHo+HKVOmYLPZsNls/PCHP0RrzUMPPcTNN99MUlISycnJcgjqFKLaboAy0EyePFmvWrWq25bX8Luf8fraUewrbmRx0mlcOKqAX185rtuWLwaWTZs2cfrppyc6DCGOqaN/p0qp1VrryYfPK4ehusi3dRutSblEcjVNvhDj5XyFEOIUIsmii+r3B0AZhNMcADJsVghxSpFk0UUN3hQA3FYnTpvBiPzUBEckhBC9R5JFF4QbGvBZ89BEqfQ6GVucjtUim04IceqQPV4XBLdswucqJGA5yOZqpxQPFEKcciRZdEHgi8/wJhfgS24gGFZyclsIccqRZNEFrZs24EvKw5vuB+TKbdH3Ha08+e7du6msrExoifJVq1Zxxx13APDee+/x8ccfx9+77rrreOWVV07kKyfcs88+y3e/+91jznP49+1PJFl0QcOeJrRhpcVpISvZzqDMpESHJMQxHas8OZDQEuWTJ0/mscceA/r3zvNE9OfvK5cgd0FDsx1yYG/UxvhB6ccstCbE4X698tdsbtjcrcscmTWSu6fefcx5OipP3qYnS5SPHTuW5cuXk56eTk5ODr/73e+49tprueaaa5g/fz5Wq5VHHnmExx9/nD/96U9YLBb+9re/xeP74IMP+O1vf0tNTQ0PPfQQV1555RHr+Nvf/sZjjz1GMBhk2rRpPPHEE1gsFlJSUrjzzjt56623SEpK4o033sDpdDJ+/Hh27tyJYRj4fD5GjBjBzp07sdls8WVed911XHLJJfH1paSkxEuc/Od//ifZ2dls2bKF2bNn88QTT2AYBs888wy/+tWvKCwsZPjw4TgcsaH1f//73/nFL35BMBgkOzubhQsX0traesT3HTlyJLfccgt79uwB4NFHH2XGjBnH/uMniPQsOhFxu/Gq2K1Ut7bYmTA4M8ERCdE1xypPDj1XonzGjBl89NFHbNiwgfLycpYvXw7AihUrmD59eny+0tJSbrnlFu666y4qKiqYNWsWANXV1Xz44Ye89dZb8Z5Re5s2bWLRokV89NFHVFRUYLFYWLhwIQBer5fp06ezbt06Zs+ezV/+8hfS09MZP34877//PhDbkV944YWHJIrOrFy5kt/85jesX7+eHTt28Oqrr1JdXc19993HRx99xJIlS9i4cWN8/pkzZ7JixQrWrl3LvHnzeOihhzr8vnfeeSd33XUXn332GYsXL+bGG2/scky9TXoWnQjs2IE3uZCA0YA/lCMX44nj1lkPoCcdrTw59FyJ8lmzZvHBBx8wZMgQbr31Vp566in27dtHVlYWKSkpncZ82WWXYRgGo0aNora29oj3ly5dyurVq5kyZQoAra2t5OXlAWC327nkkksAmDRpEkuWLAFg7ty5LFq0iHPOOYeXXnqJ2267rdM42ps6dSrl5eUAXH311Xz44YdYrVbmzJlDbm5ufB1bt24FoKqqirlz51JdXU0wGKSsrKzD5b777ruHJBm3201LSwupqX3vOi7pWXQiuPkLvK58vEkHQNtk2KzoNzorTw49U6J89uzZLF++nOXLl8d3pq+88kq859CZtkM5EEteh9NaM3/+fCoqKqioqGDLli3cf//9QKw31ZbsLBYL4XAYgK9//eu8/fbbNDQ0sHr1as4999wjlmu1WuPbQWt9yPmcwxNo2+ujJdbvfe97fPe732X9+vX8+c9/xu/3dzhfNBrlk08+iX+Xffv29clEAZIsOuX/Yi0+VwHulBaGZLvITLZ3/iEhEqwr5cmhZ0qUDx48mIMHD7Jt2zbKy8uZOXMmjzzySIfJIjU1lZaWluP6bueddx6vvPIKdXV1ADQ0NBxRSfdwKSkpTJ06lTvvvJNLLrkEi8VyxDylpaWsXr0agDfeeINQKBR/b+XKlezatYtoNMqiRYuYOXMm06ZN47333qO+vp5QKMT//M//xOdvbm6muLgYgOeee+6o3/eCCy7g8ccfj7+uqKg4ji3RuyRZdKJhVx1Ri516S1h6FaLf6Kg8+ebNm+PH7dv76U9/SlVVVfz1rbfeyjnnnMOUKVMYM2YMZ599Ni6XK16ifMSIEUyYMIH77rvvqCXKp02bxvDhw4HYYal9+/Yxc+bMI+b72te+xmuvvcaECRPi5zY6M2rUKH7xi19wwQUXMG7cOM4///wOe02Hmzt3Ln/729+YO3duh+/fdNNNvP/++0ydOpVPP/2U5OTk+Htnnnkm99xzD2PGjKGsrIzLL7+cwsJC7r//fs4880y+8pWvcMYZZ8Tnv//++7nqqquYNWsWOTk5R/2+jz32GKtWrWLcuHGMGjWKP/3pT13aBokgJco7sfyCf+fz8ht4JX8t3z7/Cm6Y2fGxRyHakxLlA8d7773HI488ckjva6CQEuXdJOLx4tVZANToNCbIyW0hxCmq15OFUmqwUmqZUmqTUmqDUupOsz1LKbVEKbXNfM5s95l7lVLblVJblFIX9laswV078boKCKlmQpEcRhdJshDiVDNnzpwB2as4XonoWYSBH2qtTwemA7crpUYB9wBLtdbDgKXma8z35gGjgYuAJ5RSR56d6gGB7dvxJRfS4qxlZEEOTluvrFYIIfqcXk8WWutqrfUac7oF2AQUA5cCbcMGngMuM6cvBV7SWge01ruA7cDU3og1sHE9Xlc+za5GObkthDilJfSchVKqFJgIfArka62rIZZQgDxztmJgb7uPVZltHS1vgVJqlVJq1YEDB046vqatlUSsSTRZ/FJpVghxSktYslBKpQCLge9rrd3HmrWDtg6HcGmtn9JaT9ZaT267qvJkHKyLXZRTq2xSaVYIcUpLSLJQStmIJYqFWutXzeZapVSh+X4hUGe2VwGD2318ELC/p2OM+v14QrFCajWWXIbmdl6mQIi+QkqUi+6WiNFQCvhvYJPW+rft3noTmG9OzwfeaNc+TynlUEqVAcOAlT0dZ3DXLnyuAsLKQ1n+aVgMqTQr+g8pUd53aa07LMDY1yWikOAM4BpgvVKqwmz7CfAg8LJS6gZgD3AVgNZ6g1LqZWAjsZFUt2utI0cstZsFtu/A6yrAY6/ljJKu1bQRoiM1v/wlgU3dW6LccfpICn7yk2POIyXKj69EeW1tLbfccku8LtaTTz7JWWedxW9/+1uefvppAG688Ua+//3vc/fddzNkyJB4QcL777+f1NRUfvjDH/Lwww/z8ssvEwgEuPzyy/n5z39OZWUlF198Meeccw6ffPIJr7/+eoelUvqyRIyG+lBrrbTW47TWE8zHP7XW9Vrr87TWw8znhnafeUBrPVRrPUJr/XZvxOnftg1vcgFu10G5GE/0S1Ki/PhKlN9xxx2cffbZrFu3jjVr1jB69GhWr17NM888w6effsqKFSv4y1/+Ei87vmjRovhnX375Za666ireeecdtm3bxsqVK6moqGD16tV88MEHAGzZsoVrr72WtWvX9rtEAVKi/KhatmwjbBuPx+aVkVDipHTWA+hJUqK86yXK//Wvf8XPzVgsFtLT0/nwww+5/PLL43WirrjiCpYvX84dd9xBXV0d+/fv58CBA2RmZlJSUsJjjz3GO++8w8SJEwHweDxs27aNkpIShgwZckiy7G8kWRzFgX0tMAh8doOCNGeiwxHiuLUvUT5z5kzmzZtHYWHhIfP85Cc/4corr2T27NnAoSXK2+7fcLixY8cyduxYrrnmGsrKyo5IFrNnz+aPf/wje/bs4YEHHuC1117rkRLlv/rVr45471glyu+9995jlijvyLFq51155ZW88sor1NTUMG/evPj89957LzfffPMh81ZWVh5SmLA/ktpQHYgGg3haXQDYc4rlNqqi35ES5YfqSony8847jyeffBKASCSC2+1m9uzZvP766/h8PrxeL6+99lr8e8ybN4+XXnqJV155JX5e5cILL+Tpp5/G4/EAsG/fvnic/Z0kiw4EKyvxugqI0Mqw8omJDkeI4yYlyo/UWYny3//+9yxbtoyxY8cyadIkNmzYwBlnnMF1113H1KlTmTZtGjfeeGP8ENPo0aNpaWmhuLg43mO74IIL+Na3vsWZZ57J2LFjufLKK487GfZVUqK8A+633+atZyvZne9g1ve+xsxhJ3+Bnzi1SIly0R9IifKT1DZstsVVxzg5uS2EEJIsOtKydRdBRzphZwtpTlvnHxBCiAFOkkUHDu6pB8CaKie2hRACJFkcQYfDuFtiI4rzCvMTHI0QQvQNkiwOE9yzF19SPlGCTBjXfy+gEUKI7iTJ4jCBHdvxJhfitdcyfYQMmxVCCJBkcYTgjthIKJ+rFqdNLnAX/dNALlFeWVnJmDFjjmudjz76KD6fL/66K2VHespXv/pVmpqaunWZTU1NPPHEE926zMNJsjhMy9ZdBJxZ4GpKdChCnDApUX6ow5NFIv3zn/8kIyOjW5fZG8lCfjofpmbnASiAtPQer4IuThHLX97Kwb2ebl1mzuAUZn1z+DHnGcglysPhMPPnz2ft2rUMHz6c559/HpfLxdKlS/nRj35EOBxmypQpPPnkk/z5z39m//79nHPOOeTk5LBs2TIgduV6+1Lm+fmHDmjxer1873vfY/369YTDYe6//34uvfRSnn32Wd588018Ph87duzg8ssv56GHHuLJJ59k165dPPTQQwA8++yzrF69+pDtDrFqu6tWrcLj8XDxxRczc+ZMPv74Y4qLi3njjTdISkpizpw5TJgwgZUrV+J2u3n66aeZOnUq999/PykpKfHSLWPGjIlX592xYwcTJkzg/PPP5wc/+AFz587F7XYTDod58sknu1yb62ikZ3GYnSNGAVA6WK7aFv3bQC1RDrFy3wsWLODzzz8nLS2NJ554Ar/fz3XXXceiRYviO/gnn3ySO+64g6KiIpYtWxZPFB2VMj/cAw88wLnnnstnn33GsmXL+PGPf4zX6wVidbHa1rNo0SL27t3LlVdeyauvvhr//KJFi45aWqTNtm3buP3229mwYQMZGRksXrw4/p7X6+Xjjz/miSee4Prrrz/mch588EGGDh1KRUUFDz/8MC+88AIXXnghFRUVrFu3jgkTJhzz810hPYvD7Ha5SHGHmTx2QqJDEQNEZz2AnjQQS5RD7DDbjBkzAPj2t7/NY489xvnnn09ZWVm8JtX8+fP54x//yPe///0jPn+0UubtvfPOO7z55ps88sgjAPj9fvbs2QPEig6mp8fuczNq1Ch2797NzJkzKS8vZ8WKFQwbNowtW7bEYzyasrKy+I580qRJVFZWxt+7+uqrgVgVX7fbfVznOaZMmcL1119PKBTisssu65ZkIT2Lw2iPFbezjsGDpyU6FCFOSvsS5b/73e86LLb3k5/8hF//+tfxHkL7EuVHM3bsWO666y6WLFlyyC/hNrNnz2b58uUsX76cOXPmkJub260lyoEjEppS6pjlxA93tFLm7WmtWbx4MRUVFVRUVLBnz554HaX2Mbb//Ny5c3n55ZdZvHgxl19+eacVq4+2nKN9R6vVekhvzu/3d7jc2bNn88EHH1BcXMw111wTH6BwMiRZHMYZzCTorMHiTE10KEKcsIFcohxgz549fPLJJwC8+OKLzJw5k5EjR1JZWcn27dsB+Otf/8rZZ599wuu58MIL+cMf/hBPQmvXru30M1dccQWvv/46L774YqeHoDrTdie+Dz/8kPT0dNLT0yktLWXNmjUArFmzhl27dgFHfr/du3eTl5fHTTfdxA033BD/zMmQZHGYj0Y/Rrj03USHIcRJGcglygFOP/10nnvuOcaNG0dDQwO33norTqeTZ555hquuuoqxY8diGAa33HILAAsWLIjfA7urfvaznxEKhRg3bhxjxozhZz/7WaefyczMjB+Wmjp1apfXdbRlnXXWWdxyyy3893//NwDf+MY3aGhoYMKECTz55JPxbZydnc2MGTMYM2YMP/7xj3nvvfeYMGECEydOZPHixdx5550nFQtIifIjhN++B19KLmmzftgDUYlThZQoFydjzpw5PPLII0yefESl8G51PCXK5QT3YawXP0haooMQQog+RpKFEEL0Me3PDfUV/eachVLqIqXUFqXUdqVUx4OvhehDBuohXjEwHO+/z36RLJRSFuCPwMXAKOBqpdSoxEYlxNE5nU7q6+slYYg+SWtNfX09Tqezy5/pL4ehpgLbtdY7AZRSLwGXAhsTGpUQRzFo0CCqqqo4cOBAokMRokNOp5NBgwZ1ef7+kiyKgb3tXlcBR1w1p5RaACwAKCkp6Z3IhOiAzWajrKws0WEI0W36xWEooKPLII/o32utn9JaT9ZaT87NldpOQgjRXfpLsqgCBrd7PQjYn6BYhBDilNNfksVnwDClVJlSyg7MA95McExCCHHK6DdXcCulvgo8CliAp7XWD3Qy/wFg9wmuLgc4eIKf7Q0S38mR+E6OxHdy+np8Q7TWRxzH7zfJojcppVZ1dLl7XyHxnRyJ7+RIfCenr8d3NP3lMJQQQogEkmQhhBCiU5IsOvZUogPohMR3ciS+kyPxnZy+Hl+H5JyFEEKITknPQgghRKckWQghhOiUJIt2+kIZdKXUYKXUMqXUJqXUBqXUnWb7/UqpfUqpCvPx1XafudeMeYtS6sJeiLFSKbXejGOV2ZallFqilNpmPmcmIj6l1Ih226hCKeVWSn0/kdtPKfW0UqpOKfVFu7bj3l5KqUnmdt+ulHpMKdVRGZzujPFhpdRmpdTnSqnXlFIZZnupUqq13bb8U0/HeJT4jvtv2svxLWoXW6VSqsJs7/Xt1y201vKInbexADuAcsAOrANGJSCOQuAMczoV2EqsLPv9wI86mH+UGasDKDO/g6WHY6wEcg5rewi4x5y+B/h1ouI77G9aAwxJ5PYDZgNnAF+czPYCVgJnEquV9jZwcQ/HeAFgNad/3S7G0vbzHbacHonxKPEd99+0N+M77P3fAP+ZqO3XHQ/pWXwpXgZdax0E2sqg9yqtdbXWeo053QJsIlZ192guBV7SWge01ruA7cS+S2+7FHjOnH4OuKxde6LiOw/YobU+1pX8PR6f1voDoKGD9XZ5eymlCoE0rfUnOrZXeb7dZ3okRq31O1rrsPlyBbGabEfVkzEeZRseTa9vw2PFZ/YOvgm8eKxl9PTf+GRJsvhSR2XQj7WT7nFKqVJgIvCp2fRd85DA0+0OWyQibg28o5RarWJl4QHytdbVEEt4QF4C42szj0P/B+0r2w+Of3sVm9O9HWeb64n90m1TppRaq5R6Xyk1y2xLRIzH8zdN1DacBdRqrbe1a+sr26/LJFl8qUtl0HuLUioFWAx8X2vtBp4EhgITgGpi3VpITNwztNZnELtz4e1KqdnHmDch21XFCk5+Hfgfs6kvbb9jOVo8CYtTKfVTIAwsNJuqgRKt9UTgB8ALSqm0BMR4vH/TRG3Dqzn0R0tf2X7HRZLFl/pMGXSllI1YoliotX4VQGtdq7WOaK2jwF/48lBJr8ettd5vPtcBr5mx1Jrd6LbudF2i4jNdDKzRWteasfaZ7Wc63u1VxaGHgXolTqXUfOAS4N/NQyOYh3fqzenVxM4JDO/tGE/gb9rr21ApZQWuABa1i7tPbL/jJcniS32iDLp5fPO/gU1a69+2ay9sN9vlQNuoizeBeUoph1KqDBhG7CRZT8WXrJRKbZsmdhL0CzOO+eZs84E3EhFfO4f8musr26+d49pe5qGqFqXUdPPfyLXtPtMjlFIXAXcDX9da+9q15yqlLOZ0uRnjzt6O8Xj/ponYhsBXgM1a6/jhpb6y/Y5bos+w96UH8FVio492AD9NUAwziXU9PwcqzMdXgb8C6832N4HCdp/5qRnzFnp49ASx0WLrzMeGtu0EZANLgW3mc1Yi4jPX5wLqgfR2bQnbfsSSVjUQIvbr8YYT2V7AZGI7xB3A45gVGHowxu3Ejv23/Tv8kznvN8y//TpgDfC1no7xKPEd99+0N+Mz258Fbjls3l7fft3xkHIfQgghOiWHoYQQQnRKkoUQQohOSbIQQgjRKUkWQgghOiXJQgghRKckWQjRTZRSGUqp247x/sddWIane6MSontIshCi+2QARySLtguwtNZn9XZAQnQXa6IDEGIAeRAYat63IAR4iF2oNQEYpZTyaK1TzLpfbwCZgA34D61137lSV4gOyEV5QnQTs0rwW1rrMUqpOcA/gDE6ViabdsnCCri01m6lVA6x8t/DtNa6bZ4EfQUhjkp6FkL0nJVtieIwCvilWa03SqwMdT6xGzUJ0SdJshCi53iP0v7vQC4wSWsdUkpVAs5ei0qIEyAnuIXoPi3EboXbmXSgzkwU5xC77asQfZr0LIToJlrreqXUR0qpL4BWoPYosy4E/q6UWkWsmuvmXgpRiBMmJ7iFEEJ0Sg5DCSGE6JQkCyGEEJ2SZCGEEKJTkiyEEEJ0SpKFEEKITkmyEEII0SlJFkIIITr1/wOtDZjuPE+jUAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -431,22 +431,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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1525.747005\n", + " 0.038475\n", + " 818.7\n", " 1600.0\n", - " 15.903625\n", - " 8.5\n", - " 6.4\n", - " 6.9\n", - " 4.2\n", + " 44.800250\n", + " 17.2\n", + " 7.8\n", + " 5.6\n", + " 4.5\n", " ...\n", - " 1287.5\n", - " 1269.2\n", - " 17.087438\n", - " 17.220063\n", - " 17.363813\n", - " 17.169125\n", - " 0.997\n", - " 0.767\n", - " 0.911000\n", - " 0.77000\n", + " 1237.2\n", + " 1322.3\n", + " 45.982375\n", + " 28.050875\n", + " 35.333875\n", + " 27.729750\n", + " 0.971000\n", + " 0.631000\n", + " 0.709000\n", + " 0.631000\n", " \n", " \n", " 1700\n", - " 12.8\n", - " 1140.256537\n", - " 0.067335\n", - " 638.8\n", + " 3.5\n", + " 1506.859642\n", + " 0.033605\n", + " 823.1\n", " 1600.0\n", - " 14.540062\n", - " 6.7\n", - " 5.2\n", - " 3.1\n", - " 6.3\n", + " 44.215687\n", + " 23.0\n", + " 4.8\n", + " 8.3\n", + " 5.0\n", " ...\n", - " 1292.3\n", - " 1278.1\n", - " 16.943250\n", - " 16.955437\n", - " 17.465250\n", - " 17.085000\n", - " 0.990\n", - " 0.811\n", - " 0.853000\n", - " 0.73800\n", + " 1234.4\n", + " 1319.7\n", + " 48.890937\n", + " 28.477812\n", + " 36.372313\n", + " 28.299687\n", + " 0.960000\n", + " 0.652000\n", + " 0.737000\n", + " 0.662000\n", " \n", " \n", " 1800\n", - " 9.8\n", - " 1295.255838\n", - " 0.058703\n", - " 648.8\n", + " 3.7\n", + " 1614.462372\n", + " 0.027231\n", + " 821.6\n", " 1600.0\n", - " 13.296687\n", - " 3.6\n", - " 6.0\n", - " 9.5\n", - " 3.3\n", + " 44.326000\n", + " 16.7\n", + " 5.3\n", + " 5.7\n", + " 5.1\n", " ...\n", - " 1299.7\n", - " 1279.8\n", - " 16.776437\n", - " 16.628813\n", - " 17.829438\n", - " 16.890812\n", - " 0.990\n", - " 0.711\n", - " 0.940000\n", - " 0.77500\n", + " 1239.4\n", + " 1320.2\n", + " 51.224688\n", + " 28.816625\n", + " 36.439438\n", + " 28.267500\n", + " 0.933000\n", + " 0.669000\n", + " 0.664000\n", + " 0.618000\n", " \n", " \n", " 1900\n", - " 17.1\n", - " 1026.096222\n", - " 0.120938\n", - " 657.7\n", - " 1600.0\n", - " 12.757625\n", - " 7.4\n", - " 6.4\n", - " 10.5\n", - " 5.1\n", + " 5.4\n", + " 1470.754653\n", + " 0.028873\n", + " 818.3\n", + " 1600.0\n", + " 43.230125\n", + " 9.5\n", + " 4.1\n", + " 6.1\n", + " 5.0\n", " ...\n", - " 1305.1\n", - " 1289.3\n", - " 17.105250\n", - " 16.893750\n", - " 17.536437\n", - " 17.039687\n", - " 0.995\n", - " 0.825\n", - " 0.868000\n", - " 0.66600\n", + " 1234.1\n", + " 1318.2\n", + " 52.821875\n", + " 28.625250\n", + " 37.555188\n", + " 28.989500\n", + " 0.942000\n", + " 0.618000\n", + " 0.663000\n", + " 0.595000\n", " \n", " \n", "\n", @@ -1058,187 +1058,187 @@ "text/plain": [ " steps_in_trial reward perf_time population numerosity \\\n", "trial \n", - "0 18.8 1000.000000 0.009595 52.9 53.5 \n", - "100 9.5 1401.484342 0.077549 538.1 1600.0 \n", - "200 9.5 1249.588408 0.065521 563.4 1600.0 \n", - "300 22.2 989.924691 0.173920 562.8 1600.0 \n", - "400 9.3 1278.489603 0.086630 566.2 1600.0 \n", - "500 10.0 1346.944083 0.075711 565.3 1600.0 \n", - "600 9.6 1267.189177 0.079716 575.9 1600.0 \n", - "700 12.9 1135.043588 0.115464 589.8 1600.0 \n", - "800 9.0 1224.173095 0.093796 614.2 1600.0 \n", - "900 9.1 1200.203022 0.087108 632.4 1600.0 \n", - "1000 10.9 1315.337806 0.076018 613.3 1600.0 \n", - "1100 19.9 946.891106 0.164149 613.7 1600.0 \n", - "1200 24.1 896.214958 0.176361 633.2 1600.0 \n", - "1300 22.5 793.767865 0.154064 619.1 1600.0 \n", - "1400 20.4 1009.440264 0.121407 622.1 1600.0 \n", - "1500 25.2 820.787529 0.123551 623.4 1600.0 \n", - "1600 8.3 1277.109552 0.051780 633.3 1600.0 \n", - "1700 12.8 1140.256537 0.067335 638.8 1600.0 \n", - "1800 9.8 1295.255838 0.058703 648.8 1600.0 \n", - "1900 17.1 1026.096222 0.120938 657.7 1600.0 \n", + "0 19.1 1000.000000 0.009435 56.2 56.9 \n", + "100 11.1 1301.414589 0.087160 596.1 1600.0 \n", + "200 11.0 1123.465230 0.097362 736.8 1600.0 \n", + "300 7.6 1201.184712 0.074454 858.7 1600.0 \n", + "400 10.2 1216.605330 0.118807 919.9 1600.0 \n", + "500 16.2 1178.278956 0.197529 933.6 1600.0 \n", + "600 10.5 1241.655991 0.098874 914.2 1600.0 \n", + "700 15.8 1108.009438 0.225334 901.2 1600.0 \n", + "800 11.2 1290.739758 0.128304 883.2 1600.0 \n", + "900 10.8 1157.014855 0.106913 875.8 1600.0 \n", + "1000 8.2 1332.964597 0.063133 879.0 1600.0 \n", + "1100 9.7 1304.352254 0.079629 865.8 1600.0 \n", + "1200 4.5 1447.547424 0.029774 841.1 1600.0 \n", + "1300 6.1 1481.795415 0.039967 832.6 1600.0 \n", + "1400 8.5 1286.080818 0.059691 826.1 1600.0 \n", + "1500 3.6 1528.372602 0.024416 820.0 1600.0 \n", + "1600 5.8 1525.747005 0.038475 818.7 1600.0 \n", + "1700 3.5 1506.859642 0.033605 823.1 1600.0 \n", + "1800 3.7 1614.462372 0.027231 821.6 1600.0 \n", + "1900 5.4 1470.754653 0.028873 818.3 1600.0 \n", "\n", " average_specificity steps_in_trial_no_mods steps_in_trial_update \\\n", "trial \n", - "0 8.699444 14.5 15.2 \n", - "100 12.256375 9.7 6.5 \n", - "200 9.480813 14.1 11.2 \n", - "300 9.344188 25.4 17.1 \n", - "400 9.400562 8.9 14.5 \n", - "500 9.881688 14.6 15.1 \n", - "600 10.083562 18.4 16.0 \n", - "700 10.168500 11.7 11.3 \n", - "800 10.228938 14.2 16.4 \n", - "900 9.521000 12.7 14.0 \n", - "1000 9.579812 8.4 9.8 \n", - "1100 9.410687 9.0 4.8 \n", - "1200 13.232125 5.5 3.6 \n", - "1300 17.300625 4.8 9.6 \n", - "1400 16.340438 13.0 6.6 \n", - "1500 14.922250 8.5 7.0 \n", - "1600 15.903625 8.5 6.4 \n", - "1700 14.540062 6.7 5.2 \n", - "1800 13.296687 3.6 6.0 \n", - "1900 12.757625 7.4 6.4 \n", + "0 7.735548 20.5 12.4 \n", + "100 15.526375 9.5 9.1 \n", + "200 15.965187 10.8 6.8 \n", + "300 19.953500 9.0 13.5 \n", + "400 25.550250 13.1 6.8 \n", + "500 32.943188 13.8 10.4 \n", + "600 38.988625 13.2 12.7 \n", + "700 41.160063 15.1 7.2 \n", + "800 42.012312 25.2 12.6 \n", + "900 42.685750 11.1 6.0 \n", + "1000 42.284438 9.3 10.9 \n", + "1100 44.075875 9.9 3.3 \n", + "1200 45.001125 15.3 4.2 \n", + "1300 44.188063 11.9 5.2 \n", + "1400 44.917875 10.7 5.9 \n", + "1500 45.357062 9.9 5.7 \n", + "1600 44.800250 17.2 7.8 \n", + "1700 44.215687 23.0 4.8 \n", + "1800 44.326000 16.7 5.3 \n", + "1900 43.230125 9.5 4.1 \n", "\n", " steps_in_trial_cover steps_in_trial_both ... population_cover \\\n", "trial ... \n", - "0 26.4 16.4 ... 72.1 \n", - "100 8.4 7.9 ... 698.6 \n", - "200 10.4 12.9 ... 938.7 \n", - "300 13.8 7.0 ... 1031.5 \n", - "400 13.3 11.6 ... 1108.5 \n", - "500 16.7 12.8 ... 1150.0 \n", - "600 9.1 7.8 ... 1193.5 \n", - "700 13.4 17.0 ... 1225.5 \n", - "800 8.0 6.5 ... 1243.4 \n", - "900 15.9 5.1 ... 1250.3 \n", - "1000 13.7 14.9 ... 1266.4 \n", - "1100 5.2 3.6 ... 1270.2 \n", - "1200 5.5 4.0 ... 1273.9 \n", - "1300 3.8 3.6 ... 1278.1 \n", - "1400 4.0 5.5 ... 1279.8 \n", - "1500 4.3 8.5 ... 1282.8 \n", - "1600 6.9 4.2 ... 1287.5 \n", - "1700 3.1 6.3 ... 1292.3 \n", - "1800 9.5 3.3 ... 1299.7 \n", - "1900 10.5 5.1 ... 1305.1 \n", + "0 19.0 27.4 ... 53.5 \n", + "100 5.1 11.3 ... 734.3 \n", + "200 12.4 8.7 ... 1022.1 \n", + "300 10.5 4.3 ... 1096.3 \n", + "400 10.6 8.5 ... 1151.5 \n", + "500 9.0 13.4 ... 1194.4 \n", + "600 8.1 13.6 ... 1220.7 \n", + "700 9.2 12.2 ... 1236.5 \n", + "800 14.8 8.8 ... 1238.8 \n", + "900 9.7 5.1 ... 1242.8 \n", + "1000 9.3 7.7 ... 1256.3 \n", + "1100 6.9 8.2 ... 1257.8 \n", + "1200 4.0 3.7 ... 1259.2 \n", + "1300 8.2 4.1 ... 1250.9 \n", + "1400 6.9 4.1 ... 1242.2 \n", + "1500 7.7 4.2 ... 1239.1 \n", + "1600 5.6 4.5 ... 1237.2 \n", + "1700 8.3 5.0 ... 1234.4 \n", + "1800 5.7 5.1 ... 1239.4 \n", + "1900 6.1 5.0 ... 1234.1 \n", "\n", " population_both average_specificity_no_mods \\\n", "trial \n", - "0 48.4 7.493962 \n", - "100 677.9 12.336967 \n", - "200 916.5 10.230375 \n", - "300 1018.4 11.016188 \n", - "400 1078.3 12.619125 \n", - "500 1136.0 13.650687 \n", - "600 1177.4 14.837688 \n", - "700 1210.9 15.848250 \n", - "800 1215.5 16.376875 \n", - "900 1236.8 16.939062 \n", - "1000 1255.5 17.750375 \n", - "1100 1250.6 17.268687 \n", - "1200 1258.9 17.268812 \n", - "1300 1255.3 17.105187 \n", - "1400 1258.5 17.380875 \n", - "1500 1262.7 17.311500 \n", - "1600 1269.2 17.087438 \n", - "1700 1278.1 16.943250 \n", - "1800 1279.8 16.776437 \n", - "1900 1289.3 17.105250 \n", + "0 70.7 7.606516 \n", + "100 714.5 16.777873 \n", + "200 1062.2 16.806250 \n", + "300 1119.2 17.013625 \n", + "400 1181.2 19.240125 \n", + "500 1226.7 22.719563 \n", + "600 1253.3 25.597875 \n", + "700 1273.2 26.324687 \n", + "800 1269.3 28.275750 \n", + "900 1281.8 28.645312 \n", + "1000 1286.9 29.174437 \n", + "1100 1290.9 33.421750 \n", + "1200 1297.2 35.794312 \n", + "1300 1298.8 39.788375 \n", + "1400 1303.5 42.805438 \n", + "1500 1313.3 44.624688 \n", + "1600 1322.3 45.982375 \n", + "1700 1319.7 48.890937 \n", + "1800 1320.2 51.224688 \n", + "1900 1318.2 52.821875 \n", "\n", " average_specificity_update average_specificity_cover \\\n", "trial \n", - "0 8.191041 8.589860 \n", - "100 13.160747 13.018353 \n", - "200 10.394875 10.879812 \n", - "300 11.113500 11.515813 \n", - "400 12.565438 13.236938 \n", - "500 13.976313 14.756000 \n", - "600 14.664937 15.825563 \n", - "700 16.135125 16.670250 \n", - "800 16.664500 17.884625 \n", - "900 17.134812 17.755312 \n", - "1000 17.468687 17.890375 \n", - "1100 16.498875 17.685312 \n", - "1200 16.722125 17.481125 \n", - "1300 16.612687 17.551313 \n", - "1400 16.805500 17.526500 \n", - "1500 17.058312 17.444000 \n", - "1600 17.220063 17.363813 \n", - "1700 16.955437 17.465250 \n", - "1800 16.628813 17.829438 \n", - "1900 16.893750 17.536437 \n", + "0 8.755512 8.150237 \n", + "100 16.068753 17.213004 \n", + "200 17.005375 16.843875 \n", + "300 17.991688 16.629188 \n", + "400 19.817188 18.585563 \n", + "500 22.093750 21.396625 \n", + "600 24.326375 23.212938 \n", + "700 24.917125 24.173188 \n", + "800 25.471312 25.437000 \n", + "900 24.768250 24.997250 \n", + "1000 24.944938 24.761750 \n", + "1100 26.042625 26.786063 \n", + "1200 26.465063 28.337375 \n", + "1300 26.355062 30.991000 \n", + "1400 26.800250 32.767187 \n", + "1500 27.456625 34.327000 \n", + "1600 28.050875 35.333875 \n", + "1700 28.477812 36.372313 \n", + "1800 28.816625 36.439438 \n", + "1900 28.625250 37.555188 \n", "\n", " average_specificity_both fraction_accuracy_no_mods \\\n", "trial \n", - "0 10.134139 0.998 \n", - "100 12.852362 0.996 \n", - "200 10.530125 0.987 \n", - "300 11.579125 0.992 \n", - "400 12.902125 0.996 \n", - "500 14.264000 0.992 \n", - "600 14.810937 0.998 \n", - "700 16.049812 0.993 \n", - "800 16.925250 0.996 \n", - "900 16.842250 0.995 \n", - "1000 17.152937 0.994 \n", - "1100 16.990438 0.998 \n", - "1200 16.922125 0.985 \n", - "1300 17.115687 0.998 \n", - "1400 17.046500 0.981 \n", - "1500 17.092625 1.000 \n", - "1600 17.169125 0.997 \n", - "1700 17.085000 0.990 \n", - "1800 16.890812 0.990 \n", - "1900 17.039687 0.995 \n", + "0 8.237262 0.992515 \n", + "100 17.523287 0.990000 \n", + "200 16.966438 0.995000 \n", + "300 16.998188 0.984000 \n", + "400 19.408687 0.996000 \n", + "500 20.795437 0.989000 \n", + "600 23.428875 0.978000 \n", + "700 24.303125 0.983000 \n", + "800 24.714750 0.980000 \n", + "900 24.823875 0.981000 \n", + "1000 25.721125 0.962000 \n", + "1100 27.243250 0.924000 \n", + "1200 27.722625 0.959000 \n", + "1300 28.116688 0.905000 \n", + "1400 27.904750 0.957000 \n", + "1500 28.034375 0.972000 \n", + "1600 27.729750 0.971000 \n", + "1700 28.299687 0.960000 \n", + "1800 28.267500 0.933000 \n", + "1900 28.989500 0.942000 \n", "\n", " fraction_accuracy_update fraction_accuracy_cover \\\n", "trial \n", - "0 1.000 0.116699 \n", - "100 0.963 0.802000 \n", - "200 0.953 0.838000 \n", - "300 0.922 0.874000 \n", - "400 0.898 0.883000 \n", - "500 0.885 0.917000 \n", - "600 0.850 0.915000 \n", - "700 0.870 0.915000 \n", - "800 0.861 0.927000 \n", - "900 0.882 0.921000 \n", - "1000 0.857 0.916000 \n", - "1100 0.739 0.905000 \n", - "1200 0.757 0.926000 \n", - "1300 0.772 0.922000 \n", - "1400 0.762 0.894000 \n", - "1500 0.742 0.929000 \n", - "1600 0.767 0.911000 \n", - "1700 0.811 0.853000 \n", - "1800 0.711 0.940000 \n", - "1900 0.825 0.868000 \n", + "0 0.992308 0.094839 \n", + "100 0.900000 0.810000 \n", + "200 0.882000 0.799000 \n", + "300 0.865000 0.797000 \n", + "400 0.825000 0.812000 \n", + "500 0.849000 0.828000 \n", + "600 0.844000 0.841000 \n", + "700 0.823000 0.819000 \n", + "800 0.851000 0.815000 \n", + "900 0.838000 0.820000 \n", + "1000 0.796000 0.836000 \n", + "1100 0.717000 0.705000 \n", + "1200 0.637000 0.662000 \n", + "1300 0.627000 0.718000 \n", + "1400 0.665000 0.711000 \n", + "1500 0.620000 0.730000 \n", + "1600 0.631000 0.709000 \n", + "1700 0.652000 0.737000 \n", + "1800 0.669000 0.664000 \n", + "1900 0.618000 0.663000 \n", "\n", " fraction_accuracy_both \n", "trial \n", - "0 0.07085 \n", - "100 0.77100 \n", - "200 0.81900 \n", - "300 0.82900 \n", - "400 0.84300 \n", - "500 0.83100 \n", - "600 0.83300 \n", - "700 0.84900 \n", - "800 0.83400 \n", - "900 0.87800 \n", - "1000 0.85000 \n", - "1100 0.79300 \n", - "1200 0.77700 \n", - "1300 0.79200 \n", - "1400 0.70700 \n", - "1500 0.72200 \n", - "1600 0.77000 \n", - "1700 0.73800 \n", - "1800 0.77500 \n", - "1900 0.66600 \n", + "0 0.115279 \n", + "100 0.785000 \n", + "200 0.797000 \n", + "300 0.783000 \n", + "400 0.828000 \n", + "500 0.836000 \n", + "600 0.841000 \n", + "700 0.812000 \n", + "800 0.829000 \n", + "900 0.847000 \n", + "1000 0.800000 \n", + "1100 0.689000 \n", + "1200 0.633000 \n", + "1300 0.638000 \n", + "1400 0.604000 \n", + "1500 0.599000 \n", + "1600 0.631000 \n", + "1700 0.662000 \n", + "1800 0.618000 \n", + "1900 0.595000 \n", "\n", "[20 rows x 22 columns]" ] @@ -1289,203 +1289,203 @@ " \n", " \n", " 0\n", - " 14.5\n", + " 20.5\n", " 900.000000\n", - " 0.009549\n", - " 39.9\n", - " 38.7\n", - " 7.493962\n", - " 0.998\n", + " 0.015873\n", + " 63.2\n", + " 60.9\n", + " 7.606516\n", + " 0.992515\n", " \n", " \n", " 100\n", - " 9.7\n", - " 1447.022405\n", - " 0.077211\n", - " 1333.5\n", - " 733.6\n", - " 12.336967\n", - " 0.996\n", + " 9.5\n", + " 1227.423987\n", + " 0.078016\n", + " 1213.6\n", + " 716.4\n", + " 16.777873\n", + " 0.990000\n", " \n", " \n", " 200\n", - " 14.1\n", - " 1254.887166\n", - " 0.179498\n", + " 10.8\n", + " 1421.401580\n", + " 0.166206\n", " 1600.0\n", - " 939.1\n", - " 10.230375\n", - " 0.987\n", + " 1032.6\n", + " 16.806250\n", + " 0.995000\n", " \n", " \n", " 300\n", - " 25.4\n", - " 1081.838483\n", - " 0.412204\n", + " 9.0\n", + " 1199.453545\n", + " 0.163911\n", " 1600.0\n", - " 1032.6\n", - " 11.016188\n", - " 0.992\n", + " 1115.3\n", + " 17.013625\n", + " 0.984000\n", " \n", " \n", " 400\n", - " 8.9\n", - " 1214.170493\n", - " 0.140844\n", + " 13.1\n", + " 1261.426106\n", + " 0.243658\n", " 1600.0\n", - " 1097.4\n", - " 12.619125\n", - " 0.996\n", + " 1169.1\n", + " 19.240125\n", + " 0.996000\n", " \n", " \n", " 500\n", - " 14.6\n", - " 1095.214666\n", - " 0.234140\n", + " 13.8\n", + " 1122.521038\n", + " 0.273922\n", " 1600.0\n", - " 1145.7\n", - " 13.650687\n", - " 0.992\n", + " 1208.1\n", + " 22.719563\n", + " 0.989000\n", " \n", " \n", " 600\n", - " 18.4\n", - " 1144.253687\n", - " 0.317876\n", + " 13.2\n", + " 1100.041260\n", + " 0.266519\n", " 1600.0\n", - " 1182.4\n", - " 14.837688\n", - " 0.998\n", + " 1221.5\n", + " 25.597875\n", + " 0.978000\n", " \n", " \n", " 700\n", - " 11.7\n", - " 1191.811579\n", - " 0.216060\n", + " 15.1\n", + " 1213.301119\n", + " 0.304208\n", " 1600.0\n", - " 1216.0\n", - " 15.848250\n", - " 0.993\n", + " 1232.9\n", + " 26.324687\n", + " 0.983000\n", " \n", " \n", " 800\n", - " 14.2\n", - " 1141.992875\n", - " 0.252691\n", + " 25.2\n", + " 777.720326\n", + " 0.433987\n", " 1600.0\n", - " 1236.9\n", - " 16.376875\n", - " 0.996\n", + " 1238.5\n", + " 28.275750\n", + " 0.980000\n", " \n", " \n", " 900\n", - " 12.7\n", - " 1078.066398\n", - " 0.235643\n", + " 11.1\n", + " 1273.841102\n", + " 0.192230\n", " 1600.0\n", - " 1255.2\n", - " 16.939062\n", - " 0.995\n", + " 1249.3\n", + " 28.645312\n", + " 0.981000\n", " \n", " \n", " 1000\n", - " 8.4\n", - " 1304.620794\n", - " 0.130436\n", + " 9.3\n", + " 1389.702556\n", + " 0.137407\n", " 1600.0\n", - " 1265.3\n", - " 17.750375\n", - " 0.994\n", + " 1247.3\n", + " 29.174437\n", + " 0.962000\n", " \n", " \n", " 1100\n", - " 9.0\n", - " 1498.141530\n", - " 0.128202\n", + " 9.9\n", + " 1366.185858\n", + " 0.123258\n", " 1600.0\n", - " 1259.8\n", - " 17.268687\n", - " 0.998\n", + " 1231.5\n", + " 33.421750\n", + " 0.924000\n", " \n", " \n", " 1200\n", - " 5.5\n", - " 1361.676745\n", - " 0.069905\n", + " 15.3\n", + " 1120.049810\n", + " 0.179822\n", " 1600.0\n", - " 1264.8\n", - " 17.268812\n", - " 0.985\n", + " 1220.7\n", + " 35.794312\n", + " 0.959000\n", " \n", " \n", " 1300\n", - " 4.8\n", - " 1733.544005\n", - " 0.075803\n", + " 11.9\n", + " 1134.378787\n", + " 0.133168\n", " 1600.0\n", - " 1265.5\n", - " 17.105187\n", - " 0.998\n", + " 1198.1\n", + " 39.788375\n", + " 0.905000\n", " \n", " \n", " 1400\n", - " 13.0\n", - " 1232.817278\n", - " 0.192882\n", + " 10.7\n", + " 1193.525644\n", + " 0.123407\n", " 1600.0\n", - " 1277.3\n", - " 17.380875\n", - " 0.981\n", + " 1176.6\n", + " 42.805438\n", + " 0.957000\n", " \n", " \n", " 1500\n", - " 8.5\n", - " 1387.444910\n", - " 0.099938\n", + " 9.9\n", + " 1268.484244\n", + " 0.116908\n", " 1600.0\n", - " 1280.0\n", - " 17.311500\n", - " 1.000\n", + " 1169.7\n", + " 44.624688\n", + " 0.972000\n", " \n", " \n", " 1600\n", - " 8.5\n", - " 1353.072799\n", - " 0.130752\n", + " 17.2\n", + " 1287.526281\n", + " 0.208801\n", " 1600.0\n", - " 1288.5\n", - " 17.087438\n", - " 0.997\n", + " 1165.9\n", + " 45.982375\n", + " 0.971000\n", " \n", " \n", " 1700\n", - " 6.7\n", - " 1366.059677\n", - " 0.114054\n", + " 23.0\n", + " 821.306593\n", + " 0.219798\n", " 1600.0\n", - " 1289.9\n", - " 16.943250\n", - " 0.990\n", + " 1148.5\n", + " 48.890937\n", + " 0.960000\n", " \n", " \n", " 1800\n", - " 3.6\n", - " 1590.723526\n", - " 0.050111\n", + " 16.7\n", + " 1273.907250\n", + " 0.177473\n", " 1600.0\n", - " 1292.8\n", - " 16.776437\n", - " 0.990\n", + " 1141.5\n", + " 51.224688\n", + " 0.933000\n", " \n", " \n", " 1900\n", - " 7.4\n", - " 1218.893178\n", - " 0.116085\n", + " 9.5\n", + " 1154.489796\n", + " 0.115964\n", " 1600.0\n", - " 1300.4\n", - " 17.105250\n", - " 0.995\n", + " 1138.6\n", + " 52.821875\n", + " 0.942000\n", " \n", " \n", "\n", @@ -1494,49 +1494,49 @@ "text/plain": [ " steps_in_trial reward perf_time numerosity population \\\n", "trial \n", - "0 14.5 900.000000 0.009549 39.9 38.7 \n", - "100 9.7 1447.022405 0.077211 1333.5 733.6 \n", - "200 14.1 1254.887166 0.179498 1600.0 939.1 \n", - "300 25.4 1081.838483 0.412204 1600.0 1032.6 \n", - "400 8.9 1214.170493 0.140844 1600.0 1097.4 \n", - "500 14.6 1095.214666 0.234140 1600.0 1145.7 \n", - "600 18.4 1144.253687 0.317876 1600.0 1182.4 \n", - "700 11.7 1191.811579 0.216060 1600.0 1216.0 \n", - "800 14.2 1141.992875 0.252691 1600.0 1236.9 \n", - "900 12.7 1078.066398 0.235643 1600.0 1255.2 \n", - "1000 8.4 1304.620794 0.130436 1600.0 1265.3 \n", - "1100 9.0 1498.141530 0.128202 1600.0 1259.8 \n", - "1200 5.5 1361.676745 0.069905 1600.0 1264.8 \n", - "1300 4.8 1733.544005 0.075803 1600.0 1265.5 \n", - "1400 13.0 1232.817278 0.192882 1600.0 1277.3 \n", - "1500 8.5 1387.444910 0.099938 1600.0 1280.0 \n", - "1600 8.5 1353.072799 0.130752 1600.0 1288.5 \n", - "1700 6.7 1366.059677 0.114054 1600.0 1289.9 \n", - "1800 3.6 1590.723526 0.050111 1600.0 1292.8 \n", - "1900 7.4 1218.893178 0.116085 1600.0 1300.4 \n", + "0 20.5 900.000000 0.015873 63.2 60.9 \n", + "100 9.5 1227.423987 0.078016 1213.6 716.4 \n", + "200 10.8 1421.401580 0.166206 1600.0 1032.6 \n", + "300 9.0 1199.453545 0.163911 1600.0 1115.3 \n", + "400 13.1 1261.426106 0.243658 1600.0 1169.1 \n", + "500 13.8 1122.521038 0.273922 1600.0 1208.1 \n", + "600 13.2 1100.041260 0.266519 1600.0 1221.5 \n", + "700 15.1 1213.301119 0.304208 1600.0 1232.9 \n", + "800 25.2 777.720326 0.433987 1600.0 1238.5 \n", + "900 11.1 1273.841102 0.192230 1600.0 1249.3 \n", + "1000 9.3 1389.702556 0.137407 1600.0 1247.3 \n", + "1100 9.9 1366.185858 0.123258 1600.0 1231.5 \n", + "1200 15.3 1120.049810 0.179822 1600.0 1220.7 \n", + "1300 11.9 1134.378787 0.133168 1600.0 1198.1 \n", + "1400 10.7 1193.525644 0.123407 1600.0 1176.6 \n", + "1500 9.9 1268.484244 0.116908 1600.0 1169.7 \n", + "1600 17.2 1287.526281 0.208801 1600.0 1165.9 \n", + "1700 23.0 821.306593 0.219798 1600.0 1148.5 \n", + "1800 16.7 1273.907250 0.177473 1600.0 1141.5 \n", + "1900 9.5 1154.489796 0.115964 1600.0 1138.6 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 7.493962 0.998 \n", - "100 12.336967 0.996 \n", - "200 10.230375 0.987 \n", - "300 11.016188 0.992 \n", - "400 12.619125 0.996 \n", - "500 13.650687 0.992 \n", - "600 14.837688 0.998 \n", - "700 15.848250 0.993 \n", - "800 16.376875 0.996 \n", - "900 16.939062 0.995 \n", - "1000 17.750375 0.994 \n", - "1100 17.268687 0.998 \n", - "1200 17.268812 0.985 \n", - "1300 17.105187 0.998 \n", - "1400 17.380875 0.981 \n", - "1500 17.311500 1.000 \n", - "1600 17.087438 0.997 \n", - "1700 16.943250 0.990 \n", - "1800 16.776437 0.990 \n", - "1900 17.105250 0.995 " + "0 7.606516 0.992515 \n", + "100 16.777873 0.990000 \n", + "200 16.806250 0.995000 \n", + "300 17.013625 0.984000 \n", + "400 19.240125 0.996000 \n", + "500 22.719563 0.989000 \n", + "600 25.597875 0.978000 \n", + "700 26.324687 0.983000 \n", + "800 28.275750 0.980000 \n", + "900 28.645312 0.981000 \n", + "1000 29.174437 0.962000 \n", + "1100 33.421750 0.924000 \n", + "1200 35.794312 0.959000 \n", + "1300 39.788375 0.905000 \n", + "1400 42.805438 0.957000 \n", + "1500 44.624688 0.972000 \n", + "1600 45.982375 0.971000 \n", + "1700 48.890937 0.960000 \n", + "1800 51.224688 0.933000 \n", + "1900 52.821875 0.942000 " ] }, "metadata": {}, @@ -1585,203 +1585,203 @@ " \n", " \n", " 0\n", - " 15.2\n", + " 12.4\n", " 1000.000000\n", - " 0.009437\n", - " 43.4\n", - " 42.7\n", - " 8.191041\n", - " 1.000\n", + " 0.007646\n", + " 48.4\n", + " 48.4\n", + " 8.755512\n", + " 0.992308\n", " \n", " \n", " 100\n", - " 6.5\n", - " 1275.621012\n", - " 0.038644\n", - " 1230.8\n", - " 672.8\n", - " 13.160747\n", - " 0.963\n", + " 9.1\n", + " 1324.661978\n", + " 0.069879\n", + " 1216.8\n", + " 720.2\n", + " 16.068753\n", + " 0.900000\n", " \n", " \n", " 200\n", - " 11.2\n", - " 1211.859556\n", - " 0.121210\n", + " 6.8\n", + " 1558.597326\n", + " 0.102525\n", " 1600.0\n", - " 909.0\n", - " 10.394875\n", - " 0.953\n", + " 1076.0\n", + " 17.005375\n", + " 0.882000\n", " \n", " \n", " 300\n", - " 17.1\n", - " 1006.874827\n", - " 0.225228\n", + " 13.5\n", + " 1195.968338\n", + " 0.267367\n", " 1600.0\n", - " 1018.6\n", - " 11.113500\n", - " 0.922\n", + " 1135.1\n", + " 17.991688\n", + " 0.865000\n", " \n", " \n", " 400\n", - " 14.5\n", - " 1224.684568\n", - " 0.202540\n", + " 6.8\n", + " 1363.705578\n", + " 0.118301\n", " 1600.0\n", - " 1085.5\n", - " 12.565438\n", - " 0.898\n", + " 1191.0\n", + " 19.817188\n", + " 0.825000\n", " \n", " \n", " 500\n", - " 15.1\n", - " 1085.700859\n", - " 0.223366\n", + " 10.4\n", + " 1204.884402\n", + " 0.250377\n", " 1600.0\n", - " 1140.1\n", - " 13.976313\n", - " 0.885\n", + " 1229.9\n", + " 22.093750\n", + " 0.849000\n", " \n", " \n", " 600\n", - " 16.0\n", - " 1002.165613\n", - " 0.249547\n", + " 12.7\n", + " 1285.448742\n", + " 0.247564\n", " 1600.0\n", - " 1183.8\n", - " 14.664937\n", - " 0.850\n", + " 1254.7\n", + " 24.326375\n", + " 0.844000\n", " \n", " \n", " 700\n", - " 11.3\n", - " 1236.477106\n", - " 0.175480\n", + " 7.2\n", + " 1309.232862\n", + " 0.141729\n", " 1600.0\n", - " 1199.6\n", - " 16.135125\n", - " 0.870\n", + " 1271.6\n", + " 24.917125\n", + " 0.823000\n", " \n", " \n", " 800\n", - " 16.4\n", - " 1095.041727\n", - " 0.257612\n", + " 12.6\n", + " 1249.459743\n", + " 0.276387\n", " 1600.0\n", - " 1228.9\n", - " 16.664500\n", - " 0.861\n", + " 1279.0\n", + " 25.471312\n", + " 0.851000\n", " \n", " \n", " 900\n", - " 14.0\n", - " 1312.518464\n", - " 0.210517\n", + " 6.0\n", + " 1307.623437\n", + " 0.144991\n", " 1600.0\n", - " 1231.6\n", - " 17.134812\n", - " 0.882\n", + " 1285.6\n", + " 24.768250\n", + " 0.838000\n", " \n", " \n", " 1000\n", - " 9.8\n", - " 1328.188123\n", - " 0.138156\n", + " 10.9\n", + " 1283.865231\n", + " 0.193909\n", " 1600.0\n", - " 1258.7\n", - " 17.468687\n", - " 0.857\n", + " 1296.1\n", + " 24.944938\n", + " 0.796000\n", " \n", " \n", " 1100\n", - " 4.8\n", - " 1650.958596\n", - " 0.063207\n", + " 3.3\n", + " 1584.204691\n", + " 0.050299\n", " 1600.0\n", - " 1255.7\n", - " 16.498875\n", - " 0.739\n", + " 1293.4\n", + " 26.042625\n", + " 0.717000\n", " \n", " \n", " 1200\n", - " 3.6\n", - " 1438.425735\n", - " 0.045298\n", + " 4.2\n", + " 1486.345715\n", + " 0.058364\n", " 1600.0\n", - " 1251.1\n", - " 16.722125\n", - " 0.757\n", + " 1298.3\n", + " 26.465063\n", + " 0.637000\n", " \n", " \n", " 1300\n", - " 9.6\n", - " 1227.483325\n", - " 0.120556\n", + " 5.2\n", + " 1361.129288\n", + " 0.085101\n", " 1600.0\n", - " 1262.3\n", - " 16.612687\n", - " 0.772\n", + " 1298.8\n", + " 26.355062\n", + " 0.627000\n", " \n", " \n", " 1400\n", - " 6.6\n", - " 1423.351286\n", - " 0.089249\n", + " 5.9\n", + " 1368.003294\n", + " 0.089700\n", " 1600.0\n", - " 1265.3\n", - " 16.805500\n", - " 0.762\n", + " 1304.7\n", + " 26.800250\n", + " 0.665000\n", " \n", " \n", " 1500\n", - " 7.0\n", - " 1568.724338\n", - " 0.089971\n", + " 5.7\n", + " 1425.958516\n", + " 0.087835\n", " 1600.0\n", - " 1263.4\n", - " 17.058312\n", - " 0.742\n", + " 1304.4\n", + " 27.456625\n", + " 0.620000\n", " \n", " \n", " 1600\n", - " 6.4\n", - " 1435.448680\n", - " 0.071525\n", + " 7.8\n", + " 1300.077623\n", + " 0.113463\n", " 1600.0\n", - " 1271.0\n", - " 17.220063\n", - " 0.767\n", + " 1304.8\n", + " 28.050875\n", + " 0.631000\n", " \n", " \n", " 1700\n", - " 5.2\n", - " 1403.153191\n", - " 0.066743\n", + " 4.8\n", + " 1432.350454\n", + " 0.074378\n", " 1600.0\n", - " 1281.7\n", - " 16.955437\n", - " 0.811\n", + " 1311.2\n", + " 28.477812\n", + " 0.652000\n", " \n", " \n", " 1800\n", - " 6.0\n", - " 1485.071104\n", - " 0.076047\n", + " 5.3\n", + " 1350.697305\n", + " 0.088388\n", " 1600.0\n", - " 1285.3\n", - " 16.628813\n", - " 0.711\n", + " 1315.3\n", + " 28.816625\n", + " 0.669000\n", " \n", " \n", " 1900\n", - " 6.4\n", - " 1265.459446\n", - " 0.083462\n", + " 4.1\n", + " 1421.114976\n", + " 0.070564\n", " 1600.0\n", - " 1287.0\n", - " 16.893750\n", - " 0.825\n", + " 1313.8\n", + " 28.625250\n", + " 0.618000\n", " \n", " \n", "\n", @@ -1790,49 +1790,49 @@ "text/plain": [ " steps_in_trial reward perf_time numerosity population \\\n", "trial \n", - "0 15.2 1000.000000 0.009437 43.4 42.7 \n", - "100 6.5 1275.621012 0.038644 1230.8 672.8 \n", - "200 11.2 1211.859556 0.121210 1600.0 909.0 \n", - "300 17.1 1006.874827 0.225228 1600.0 1018.6 \n", - "400 14.5 1224.684568 0.202540 1600.0 1085.5 \n", - "500 15.1 1085.700859 0.223366 1600.0 1140.1 \n", - "600 16.0 1002.165613 0.249547 1600.0 1183.8 \n", - "700 11.3 1236.477106 0.175480 1600.0 1199.6 \n", - "800 16.4 1095.041727 0.257612 1600.0 1228.9 \n", - "900 14.0 1312.518464 0.210517 1600.0 1231.6 \n", - "1000 9.8 1328.188123 0.138156 1600.0 1258.7 \n", - "1100 4.8 1650.958596 0.063207 1600.0 1255.7 \n", - "1200 3.6 1438.425735 0.045298 1600.0 1251.1 \n", - "1300 9.6 1227.483325 0.120556 1600.0 1262.3 \n", - "1400 6.6 1423.351286 0.089249 1600.0 1265.3 \n", - "1500 7.0 1568.724338 0.089971 1600.0 1263.4 \n", - "1600 6.4 1435.448680 0.071525 1600.0 1271.0 \n", - "1700 5.2 1403.153191 0.066743 1600.0 1281.7 \n", - "1800 6.0 1485.071104 0.076047 1600.0 1285.3 \n", - "1900 6.4 1265.459446 0.083462 1600.0 1287.0 \n", + "0 12.4 1000.000000 0.007646 48.4 48.4 \n", + "100 9.1 1324.661978 0.069879 1216.8 720.2 \n", + "200 6.8 1558.597326 0.102525 1600.0 1076.0 \n", + "300 13.5 1195.968338 0.267367 1600.0 1135.1 \n", + "400 6.8 1363.705578 0.118301 1600.0 1191.0 \n", + "500 10.4 1204.884402 0.250377 1600.0 1229.9 \n", + "600 12.7 1285.448742 0.247564 1600.0 1254.7 \n", + "700 7.2 1309.232862 0.141729 1600.0 1271.6 \n", + "800 12.6 1249.459743 0.276387 1600.0 1279.0 \n", + "900 6.0 1307.623437 0.144991 1600.0 1285.6 \n", + "1000 10.9 1283.865231 0.193909 1600.0 1296.1 \n", + "1100 3.3 1584.204691 0.050299 1600.0 1293.4 \n", + "1200 4.2 1486.345715 0.058364 1600.0 1298.3 \n", + "1300 5.2 1361.129288 0.085101 1600.0 1298.8 \n", + "1400 5.9 1368.003294 0.089700 1600.0 1304.7 \n", + "1500 5.7 1425.958516 0.087835 1600.0 1304.4 \n", + "1600 7.8 1300.077623 0.113463 1600.0 1304.8 \n", + "1700 4.8 1432.350454 0.074378 1600.0 1311.2 \n", + "1800 5.3 1350.697305 0.088388 1600.0 1315.3 \n", + "1900 4.1 1421.114976 0.070564 1600.0 1313.8 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 8.191041 1.000 \n", - "100 13.160747 0.963 \n", - "200 10.394875 0.953 \n", - "300 11.113500 0.922 \n", - "400 12.565438 0.898 \n", - "500 13.976313 0.885 \n", - "600 14.664937 0.850 \n", - "700 16.135125 0.870 \n", - "800 16.664500 0.861 \n", - "900 17.134812 0.882 \n", - "1000 17.468687 0.857 \n", - "1100 16.498875 0.739 \n", - "1200 16.722125 0.757 \n", - "1300 16.612687 0.772 \n", - "1400 16.805500 0.762 \n", - "1500 17.058312 0.742 \n", - "1600 17.220063 0.767 \n", - "1700 16.955437 0.811 \n", - "1800 16.628813 0.711 \n", - "1900 16.893750 0.825 " + "0 8.755512 0.992308 \n", + "100 16.068753 0.900000 \n", + "200 17.005375 0.882000 \n", + "300 17.991688 0.865000 \n", + "400 19.817188 0.825000 \n", + "500 22.093750 0.849000 \n", + "600 24.326375 0.844000 \n", + "700 24.917125 0.823000 \n", + "800 25.471312 0.851000 \n", + "900 24.768250 0.838000 \n", + "1000 24.944938 0.796000 \n", + "1100 26.042625 0.717000 \n", + "1200 26.465063 0.637000 \n", + "1300 26.355062 0.627000 \n", + "1400 26.800250 0.665000 \n", + "1500 27.456625 0.620000 \n", + "1600 28.050875 0.631000 \n", + "1700 28.477812 0.652000 \n", + "1800 28.816625 0.669000 \n", + "1900 28.625250 0.618000 " ] }, "metadata": {}, @@ -1881,203 +1881,203 @@ " \n", " \n", " 0\n", - " 26.4\n", + " 19.0\n", " 1000.000000\n", - " 0.016119\n", - " 73.6\n", - " 72.1\n", - " 8.589860\n", - " 0.116699\n", + " 0.009044\n", + " 54.2\n", + " 53.5\n", + " 8.150237\n", + " 0.094839\n", " \n", " \n", " 100\n", - " 8.4\n", - " 1221.669451\n", - " 0.057126\n", - " 1273.9\n", - " 698.6\n", - " 13.018353\n", - " 0.802000\n", + " 5.1\n", + " 1458.221347\n", + " 0.030004\n", + " 1265.9\n", + " 734.3\n", + " 17.213004\n", + " 0.810000\n", " \n", " \n", " 200\n", - " 10.4\n", - " 1246.272006\n", - " 0.138206\n", + " 12.4\n", + " 1164.970553\n", + " 0.153219\n", " 1600.0\n", - " 938.7\n", - " 10.879812\n", - " 0.838000\n", + " 1022.1\n", + " 16.843875\n", + " 0.799000\n", " \n", " \n", " 300\n", - " 13.8\n", - " 1358.774050\n", - " 0.169118\n", + " 10.5\n", + " 1202.146656\n", + " 0.133908\n", " 1600.0\n", - " 1031.5\n", - " 11.515813\n", - " 0.874000\n", + " 1096.3\n", + " 16.629188\n", + " 0.797000\n", " \n", " \n", " 400\n", - " 13.3\n", - " 1150.131556\n", - " 0.193538\n", + " 10.6\n", + " 1326.370793\n", + " 0.137508\n", " 1600.0\n", - " 1108.5\n", - " 13.236938\n", - " 0.883000\n", + " 1151.5\n", + " 18.585563\n", + " 0.812000\n", " \n", " \n", " 500\n", - " 16.7\n", - " 1138.237237\n", - " 0.272251\n", + " 9.0\n", + " 1260.933829\n", + " 0.128201\n", " 1600.0\n", - " 1150.0\n", - " 14.756000\n", - " 0.917000\n", + " 1194.4\n", + " 21.396625\n", + " 0.828000\n", " \n", " \n", " 600\n", - " 9.1\n", - " 1343.110549\n", - " 0.185532\n", + " 8.1\n", + " 1456.110729\n", + " 0.114719\n", " 1600.0\n", - " 1193.5\n", - " 15.825563\n", - " 0.915000\n", + " 1220.7\n", + " 23.212938\n", + " 0.841000\n", " \n", " \n", " 700\n", - " 13.4\n", - " 1103.825693\n", - " 0.237226\n", + " 9.2\n", + " 1244.340769\n", + " 0.137950\n", " 1600.0\n", - " 1225.5\n", - " 16.670250\n", - " 0.915000\n", + " 1236.5\n", + " 24.173188\n", + " 0.819000\n", " \n", " \n", " 800\n", - " 8.0\n", - " 1232.469702\n", - " 0.124556\n", + " 14.8\n", + " 1247.902166\n", + " 0.201304\n", " 1600.0\n", - " 1243.4\n", - " 17.884625\n", - " 0.927000\n", + " 1238.8\n", + " 25.437000\n", + " 0.815000\n", " \n", " \n", " 900\n", - " 15.9\n", - " 1191.095129\n", - " 0.268067\n", + " 9.7\n", + " 1174.495519\n", + " 0.153809\n", " 1600.0\n", - " 1250.3\n", - " 17.755312\n", - " 0.921000\n", + " 1242.8\n", + " 24.997250\n", + " 0.820000\n", " \n", " \n", " 1000\n", - " 13.7\n", - " 1283.696213\n", - " 0.216740\n", + " 9.3\n", + " 1262.913827\n", + " 0.116464\n", " 1600.0\n", - " 1266.4\n", - " 17.890375\n", - " 0.916000\n", + " 1256.3\n", + " 24.761750\n", + " 0.836000\n", " \n", " \n", " 1100\n", - " 5.2\n", - " 1386.589812\n", - " 0.069840\n", + " 6.9\n", + " 1353.825305\n", + " 0.078169\n", " 1600.0\n", - " 1270.2\n", - " 17.685312\n", - " 0.905000\n", + " 1257.8\n", + " 26.786063\n", + " 0.705000\n", " \n", " \n", " 1200\n", - " 5.5\n", - " 1456.445839\n", - " 0.073675\n", + " 4.0\n", + " 1650.097169\n", + " 0.044516\n", " 1600.0\n", - " 1273.9\n", - " 17.481125\n", - " 0.926000\n", + " 1259.2\n", + " 28.337375\n", + " 0.662000\n", " \n", " \n", " 1300\n", - " 3.8\n", - " 1498.786752\n", - " 0.054212\n", + " 8.2\n", + " 1262.565146\n", + " 0.092396\n", " 1600.0\n", - " 1278.1\n", - " 17.551313\n", - " 0.922000\n", + " 1250.9\n", + " 30.991000\n", + " 0.718000\n", " \n", " \n", " 1400\n", - " 4.0\n", - " 1563.874124\n", - " 0.051957\n", + " 6.9\n", + " 1418.447547\n", + " 0.075790\n", " 1600.0\n", - " 1279.8\n", - " 17.526500\n", - " 0.894000\n", + " 1242.2\n", + " 32.767187\n", + " 0.711000\n", " \n", " \n", " 1500\n", - " 4.3\n", - " 1599.786984\n", - " 0.056483\n", + " 7.7\n", + " 1411.299491\n", + " 0.082853\n", " 1600.0\n", - " 1282.8\n", - " 17.444000\n", - " 0.929000\n", + " 1239.1\n", + " 34.327000\n", + " 0.730000\n", " \n", " \n", " 1600\n", - " 6.9\n", - " 1347.869673\n", - " 0.082054\n", + " 5.6\n", + " 1505.630341\n", + " 0.057217\n", " 1600.0\n", - " 1287.5\n", - " 17.363813\n", - " 0.911000\n", + " 1237.2\n", + " 35.333875\n", + " 0.709000\n", " \n", " \n", " 1700\n", - " 3.1\n", - " 1580.921525\n", - " 0.049898\n", + " 8.3\n", + " 1307.387000\n", + " 0.082784\n", " 1600.0\n", - " 1292.3\n", - " 17.465250\n", - " 0.853000\n", + " 1234.4\n", + " 36.372313\n", + " 0.737000\n", " \n", " \n", " 1800\n", - " 9.5\n", - " 1358.356212\n", - " 0.121344\n", + " 5.7\n", + " 1346.594829\n", + " 0.063727\n", " 1600.0\n", - " 1299.7\n", - " 17.829438\n", - " 0.940000\n", + " 1239.4\n", + " 36.439438\n", + " 0.664000\n", " \n", " \n", " 1900\n", - " 10.5\n", - " 1419.560940\n", - " 0.132001\n", + " 6.1\n", + " 1473.722092\n", + " 0.062220\n", " 1600.0\n", - " 1305.1\n", - " 17.536437\n", - " 0.868000\n", + " 1234.1\n", + " 37.555188\n", + " 0.663000\n", " \n", " \n", "\n", @@ -2086,49 +2086,49 @@ "text/plain": [ " steps_in_trial reward perf_time numerosity population \\\n", "trial \n", - "0 26.4 1000.000000 0.016119 73.6 72.1 \n", - "100 8.4 1221.669451 0.057126 1273.9 698.6 \n", - "200 10.4 1246.272006 0.138206 1600.0 938.7 \n", - "300 13.8 1358.774050 0.169118 1600.0 1031.5 \n", - "400 13.3 1150.131556 0.193538 1600.0 1108.5 \n", - "500 16.7 1138.237237 0.272251 1600.0 1150.0 \n", - "600 9.1 1343.110549 0.185532 1600.0 1193.5 \n", - "700 13.4 1103.825693 0.237226 1600.0 1225.5 \n", - "800 8.0 1232.469702 0.124556 1600.0 1243.4 \n", - "900 15.9 1191.095129 0.268067 1600.0 1250.3 \n", - "1000 13.7 1283.696213 0.216740 1600.0 1266.4 \n", - "1100 5.2 1386.589812 0.069840 1600.0 1270.2 \n", - "1200 5.5 1456.445839 0.073675 1600.0 1273.9 \n", - "1300 3.8 1498.786752 0.054212 1600.0 1278.1 \n", - "1400 4.0 1563.874124 0.051957 1600.0 1279.8 \n", - "1500 4.3 1599.786984 0.056483 1600.0 1282.8 \n", - "1600 6.9 1347.869673 0.082054 1600.0 1287.5 \n", - "1700 3.1 1580.921525 0.049898 1600.0 1292.3 \n", - "1800 9.5 1358.356212 0.121344 1600.0 1299.7 \n", - "1900 10.5 1419.560940 0.132001 1600.0 1305.1 \n", + "0 19.0 1000.000000 0.009044 54.2 53.5 \n", + "100 5.1 1458.221347 0.030004 1265.9 734.3 \n", + "200 12.4 1164.970553 0.153219 1600.0 1022.1 \n", + "300 10.5 1202.146656 0.133908 1600.0 1096.3 \n", + "400 10.6 1326.370793 0.137508 1600.0 1151.5 \n", + "500 9.0 1260.933829 0.128201 1600.0 1194.4 \n", + "600 8.1 1456.110729 0.114719 1600.0 1220.7 \n", + "700 9.2 1244.340769 0.137950 1600.0 1236.5 \n", + "800 14.8 1247.902166 0.201304 1600.0 1238.8 \n", + "900 9.7 1174.495519 0.153809 1600.0 1242.8 \n", + "1000 9.3 1262.913827 0.116464 1600.0 1256.3 \n", + "1100 6.9 1353.825305 0.078169 1600.0 1257.8 \n", + "1200 4.0 1650.097169 0.044516 1600.0 1259.2 \n", + "1300 8.2 1262.565146 0.092396 1600.0 1250.9 \n", + "1400 6.9 1418.447547 0.075790 1600.0 1242.2 \n", + "1500 7.7 1411.299491 0.082853 1600.0 1239.1 \n", + "1600 5.6 1505.630341 0.057217 1600.0 1237.2 \n", + "1700 8.3 1307.387000 0.082784 1600.0 1234.4 \n", + "1800 5.7 1346.594829 0.063727 1600.0 1239.4 \n", + "1900 6.1 1473.722092 0.062220 1600.0 1234.1 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 8.589860 0.116699 \n", - "100 13.018353 0.802000 \n", - "200 10.879812 0.838000 \n", - "300 11.515813 0.874000 \n", - "400 13.236938 0.883000 \n", - "500 14.756000 0.917000 \n", - "600 15.825563 0.915000 \n", - "700 16.670250 0.915000 \n", - "800 17.884625 0.927000 \n", - "900 17.755312 0.921000 \n", - "1000 17.890375 0.916000 \n", - "1100 17.685312 0.905000 \n", - "1200 17.481125 0.926000 \n", - "1300 17.551313 0.922000 \n", - "1400 17.526500 0.894000 \n", - "1500 17.444000 0.929000 \n", - "1600 17.363813 0.911000 \n", - "1700 17.465250 0.853000 \n", - "1800 17.829438 0.940000 \n", - "1900 17.536437 0.868000 " + "0 8.150237 0.094839 \n", + "100 17.213004 0.810000 \n", + "200 16.843875 0.799000 \n", + "300 16.629188 0.797000 \n", + "400 18.585563 0.812000 \n", + "500 21.396625 0.828000 \n", + "600 23.212938 0.841000 \n", + "700 24.173188 0.819000 \n", + "800 25.437000 0.815000 \n", + "900 24.997250 0.820000 \n", + "1000 24.761750 0.836000 \n", + "1100 26.786063 0.705000 \n", + "1200 28.337375 0.662000 \n", + "1300 30.991000 0.718000 \n", + "1400 32.767187 0.711000 \n", + "1500 34.327000 0.730000 \n", + "1600 35.333875 0.709000 \n", + "1700 36.372313 0.737000 \n", + "1800 36.439438 0.664000 \n", + "1900 37.555188 0.663000 " ] }, "metadata": {}, @@ -2177,203 +2177,203 @@ " \n", " \n", " 0\n", - " 16.4\n", - " 1000.000000\n", - " 0.009913\n", - " 49.3\n", - " 48.4\n", - " 10.134139\n", - " 0.07085\n", + " 27.4\n", + " 800.000000\n", + " 0.020166\n", + " 73.1\n", + " 70.7\n", + " 8.237262\n", + " 0.115279\n", " \n", " \n", " 100\n", - " 7.9\n", - " 1361.271487\n", - " 0.060853\n", - " 1211.2\n", - " 677.9\n", - " 12.852362\n", - " 0.77100\n", + " 11.3\n", + " 1138.264495\n", + " 0.106833\n", + " 1210.2\n", + " 714.5\n", + " 17.523287\n", + " 0.785000\n", " \n", " \n", " 200\n", - " 12.9\n", - " 1212.254701\n", - " 0.161913\n", + " 8.7\n", + " 1284.205520\n", + " 0.145641\n", " 1600.0\n", - " 916.5\n", - " 10.530125\n", - " 0.81900\n", + " 1062.2\n", + " 16.966438\n", + " 0.797000\n", " \n", " \n", " 300\n", - " 7.0\n", - " 1391.110547\n", - " 0.101733\n", + " 4.3\n", + " 1373.006410\n", + " 0.079344\n", " 1600.0\n", - " 1018.4\n", - " 11.579125\n", - " 0.82900\n", + " 1119.2\n", + " 16.998188\n", + " 0.783000\n", " \n", " \n", " 400\n", - " 11.6\n", - " 1189.616061\n", - " 0.196893\n", + " 8.5\n", + " 1270.097351\n", + " 0.155063\n", " 1600.0\n", - " 1078.3\n", - " 12.902125\n", - " 0.84300\n", + " 1181.2\n", + " 19.408687\n", + " 0.828000\n", " \n", " \n", " 500\n", - " 12.8\n", - " 1080.610024\n", - " 0.213625\n", + " 13.4\n", + " 1077.686409\n", + " 0.246428\n", " 1600.0\n", - " 1136.0\n", - " 14.264000\n", - " 0.83100\n", + " 1226.7\n", + " 20.795437\n", + " 0.836000\n", " \n", " \n", " 600\n", - " 7.8\n", - " 1296.385204\n", - " 0.137334\n", + " 13.6\n", + " 1249.219204\n", + " 0.348517\n", " 1600.0\n", - " 1177.4\n", - " 14.810937\n", - " 0.83300\n", + " 1253.3\n", + " 23.428875\n", + " 0.841000\n", " \n", " \n", " 700\n", - " 17.0\n", - " 1107.833345\n", - " 0.321735\n", + " 12.2\n", + " 1341.153410\n", + " 0.254647\n", " 1600.0\n", - " 1210.9\n", - " 16.049812\n", - " 0.84900\n", + " 1273.2\n", + " 24.303125\n", + " 0.812000\n", " \n", " \n", " 800\n", - " 6.5\n", - " 1355.103457\n", - " 0.094710\n", + " 8.8\n", + " 1253.539500\n", + " 0.190762\n", " 1600.0\n", - " 1215.5\n", - " 16.925250\n", - " 0.83400\n", + " 1269.3\n", + " 24.714750\n", + " 0.829000\n", " \n", " \n", " 900\n", " 5.1\n", - " 1379.515701\n", - " 0.092159\n", + " 1348.031566\n", + " 0.105990\n", " 1600.0\n", - " 1236.8\n", - " 16.842250\n", - " 0.87800\n", + " 1281.8\n", + " 24.823875\n", + " 0.847000\n", " \n", " \n", " 1000\n", - " 14.9\n", - " 1115.686736\n", - " 0.213837\n", + " 7.7\n", + " 1344.826061\n", + " 0.125655\n", " 1600.0\n", - " 1255.5\n", - " 17.152937\n", - " 0.85000\n", + " 1286.9\n", + " 25.721125\n", + " 0.800000\n", " \n", " \n", " 1100\n", - " 3.6\n", - " 1535.159016\n", - " 0.044629\n", + " 8.2\n", + " 1317.527097\n", + " 0.122233\n", " 1600.0\n", - " 1250.6\n", - " 16.990438\n", - " 0.79300\n", + " 1290.9\n", + " 27.243250\n", + " 0.689000\n", " \n", " \n", " 1200\n", - " 4.0\n", - " 1545.666275\n", - " 0.058915\n", + " 3.7\n", + " 1466.537411\n", + " 0.053515\n", " 1600.0\n", - " 1258.9\n", - " 16.922125\n", - " 0.77700\n", + " 1297.2\n", + " 27.722625\n", + " 0.633000\n", " \n", " \n", " 1300\n", - " 3.6\n", - " 1604.892079\n", - " 0.057837\n", + " 4.1\n", + " 1451.611026\n", + " 0.075580\n", " 1600.0\n", - " 1255.3\n", - " 17.115687\n", - " 0.79200\n", + " 1298.8\n", + " 28.116688\n", + " 0.638000\n", " \n", " \n", " 1400\n", - " 5.5\n", - " 1406.009543\n", - " 0.080619\n", + " 4.1\n", + " 1539.839643\n", + " 0.063969\n", " 1600.0\n", - " 1258.5\n", - " 17.046500\n", - " 0.70700\n", + " 1303.5\n", + " 27.904750\n", + " 0.604000\n", " \n", " \n", " 1500\n", - " 8.5\n", - " 1440.822189\n", - " 0.119539\n", + " 4.2\n", + " 1564.309914\n", + " 0.068538\n", " 1600.0\n", - " 1262.7\n", - " 17.092625\n", - " 0.72200\n", + " 1313.3\n", + " 28.034375\n", + " 0.599000\n", " \n", " \n", " 1600\n", - " 4.2\n", - " 1589.038677\n", - " 0.067650\n", + " 4.5\n", + " 1468.101814\n", + " 0.085229\n", " 1600.0\n", - " 1269.2\n", - " 17.169125\n", - " 0.77000\n", + " 1322.3\n", + " 27.729750\n", + " 0.631000\n", " \n", " \n", " 1700\n", - " 6.3\n", - " 1350.584447\n", - " 0.081440\n", + " 5.0\n", + " 1589.611027\n", + " 0.065388\n", " 1600.0\n", - " 1278.1\n", - " 17.085000\n", - " 0.73800\n", + " 1319.7\n", + " 28.299687\n", + " 0.662000\n", " \n", " \n", " 1800\n", - " 3.3\n", - " 1525.365672\n", - " 0.048491\n", + " 5.1\n", + " 1636.405049\n", + " 0.071383\n", " 1600.0\n", - " 1279.8\n", - " 16.890812\n", - " 0.77500\n", + " 1320.2\n", + " 28.267500\n", + " 0.618000\n", " \n", " \n", " 1900\n", - " 5.1\n", - " 1564.625838\n", - " 0.073603\n", + " 5.0\n", + " 1378.947507\n", + " 0.079519\n", " 1600.0\n", - " 1289.3\n", - " 17.039687\n", - " 0.66600\n", + " 1318.2\n", + " 28.989500\n", + " 0.595000\n", " \n", " \n", "\n", @@ -2382,49 +2382,49 @@ "text/plain": [ " steps_in_trial reward perf_time numerosity population \\\n", "trial \n", - "0 16.4 1000.000000 0.009913 49.3 48.4 \n", - "100 7.9 1361.271487 0.060853 1211.2 677.9 \n", - "200 12.9 1212.254701 0.161913 1600.0 916.5 \n", - "300 7.0 1391.110547 0.101733 1600.0 1018.4 \n", - "400 11.6 1189.616061 0.196893 1600.0 1078.3 \n", - "500 12.8 1080.610024 0.213625 1600.0 1136.0 \n", - "600 7.8 1296.385204 0.137334 1600.0 1177.4 \n", - "700 17.0 1107.833345 0.321735 1600.0 1210.9 \n", - "800 6.5 1355.103457 0.094710 1600.0 1215.5 \n", - "900 5.1 1379.515701 0.092159 1600.0 1236.8 \n", - "1000 14.9 1115.686736 0.213837 1600.0 1255.5 \n", - "1100 3.6 1535.159016 0.044629 1600.0 1250.6 \n", - "1200 4.0 1545.666275 0.058915 1600.0 1258.9 \n", - "1300 3.6 1604.892079 0.057837 1600.0 1255.3 \n", - "1400 5.5 1406.009543 0.080619 1600.0 1258.5 \n", - "1500 8.5 1440.822189 0.119539 1600.0 1262.7 \n", - "1600 4.2 1589.038677 0.067650 1600.0 1269.2 \n", - "1700 6.3 1350.584447 0.081440 1600.0 1278.1 \n", - "1800 3.3 1525.365672 0.048491 1600.0 1279.8 \n", - "1900 5.1 1564.625838 0.073603 1600.0 1289.3 \n", + "0 27.4 800.000000 0.020166 73.1 70.7 \n", + "100 11.3 1138.264495 0.106833 1210.2 714.5 \n", + "200 8.7 1284.205520 0.145641 1600.0 1062.2 \n", + "300 4.3 1373.006410 0.079344 1600.0 1119.2 \n", + "400 8.5 1270.097351 0.155063 1600.0 1181.2 \n", + "500 13.4 1077.686409 0.246428 1600.0 1226.7 \n", + "600 13.6 1249.219204 0.348517 1600.0 1253.3 \n", + "700 12.2 1341.153410 0.254647 1600.0 1273.2 \n", + "800 8.8 1253.539500 0.190762 1600.0 1269.3 \n", + "900 5.1 1348.031566 0.105990 1600.0 1281.8 \n", + "1000 7.7 1344.826061 0.125655 1600.0 1286.9 \n", + "1100 8.2 1317.527097 0.122233 1600.0 1290.9 \n", + "1200 3.7 1466.537411 0.053515 1600.0 1297.2 \n", + "1300 4.1 1451.611026 0.075580 1600.0 1298.8 \n", + "1400 4.1 1539.839643 0.063969 1600.0 1303.5 \n", + "1500 4.2 1564.309914 0.068538 1600.0 1313.3 \n", + "1600 4.5 1468.101814 0.085229 1600.0 1322.3 \n", + "1700 5.0 1589.611027 0.065388 1600.0 1319.7 \n", + "1800 5.1 1636.405049 0.071383 1600.0 1320.2 \n", + "1900 5.0 1378.947507 0.079519 1600.0 1318.2 \n", "\n", " average_specificity fraction_accuracy \n", "trial \n", - "0 10.134139 0.07085 \n", - "100 12.852362 0.77100 \n", - "200 10.530125 0.81900 \n", - "300 11.579125 0.82900 \n", - "400 12.902125 0.84300 \n", - "500 14.264000 0.83100 \n", - "600 14.810937 0.83300 \n", - "700 16.049812 0.84900 \n", - "800 16.925250 0.83400 \n", - "900 16.842250 0.87800 \n", - "1000 17.152937 0.85000 \n", - "1100 16.990438 0.79300 \n", - "1200 16.922125 0.77700 \n", - "1300 17.115687 0.79200 \n", - "1400 17.046500 0.70700 \n", - "1500 17.092625 0.72200 \n", - "1600 17.169125 0.77000 \n", - "1700 17.085000 0.73800 \n", - "1800 16.890812 0.77500 \n", - "1900 17.039687 0.66600 " + "0 8.237262 0.115279 \n", + "100 17.523287 0.785000 \n", + "200 16.966438 0.797000 \n", + "300 16.998188 0.783000 \n", + "400 19.408687 0.828000 \n", + "500 20.795437 0.836000 \n", + "600 23.428875 0.841000 \n", + "700 24.303125 0.812000 \n", + "800 24.714750 0.829000 \n", + "900 24.823875 0.847000 \n", + "1000 25.721125 0.800000 \n", + "1100 27.243250 0.689000 \n", + "1200 27.722625 0.633000 \n", + "1300 28.116688 0.638000 \n", + "1400 27.904750 0.604000 \n", + "1500 28.034375 0.599000 \n", + "1600 27.729750 0.631000 \n", + "1700 28.299687 0.662000 \n", + "1800 28.267500 0.618000 \n", + "1900 28.989500 0.595000 " ] }, "metadata": {}, diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb index c62b1c4..cb85911 100644 --- a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb @@ -27,12 +27,12 @@ "text": [ "This is how maze looks like\n", "\n", - "['.', '.', '.', '.', '.', 'O', 'O', 'F']\n", + "['.', '.', '.', 'F', 'O', 'O', '.', '.']\n", "\n", "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[37mâ–¡\u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[33m$\u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", - "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[36mX\u001b[0m \u001b[37mâ–¡\u001b[0m\n", + "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n", "\u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[30mâ– \u001b[0m \u001b[37mâ–¡\u001b[0m \u001b[37mâ–¡\u001b[0m\n" ] } @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "scrolled": true }, @@ -313,32 +313,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df[['average_specificity',\n", " \"average_specificity_no_mods\",\n", @@ -352,32 +329,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df[[\"fraction_accuracy_no_mods\",\n", " \"fraction_accuracy_update\",\n", @@ -390,32 +344,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = df[['population',\n", " \"population_no_mods\",\n", @@ -430,32 +361,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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6009.21177.5671770.1321041600.01379.510.3543750.685000
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8009.21188.4136720.1351591600.01380.910.5633750.685000
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10006.21381.3275340.1144951600.01378.511.1600000.637000
11007.51445.5706690.1112001600.01374.111.8643750.596000
120010.91280.6849150.1830981600.01379.611.9753130.619000
130017.31077.2683160.2625311600.01372.611.7810630.631000
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150010.21193.1852170.1793271600.01369.712.3449370.605000
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170014.81260.0745810.2286481600.01379.012.7742500.657000
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20007.01167.9519070.1312391600.01368.012.8683120.623000
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220022.9781.2204730.4514361600.01366.013.4358750.598000
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24009.51109.3736770.1638101600.01365.113.2625000.614000
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1260.074581 0.228648 1600.0 1379.0 \n", - "1800 7.5 1310.813923 0.134034 1600.0 1375.1 \n", - "1900 9.6 1246.832134 0.157212 1600.0 1380.4 \n", - "2000 7.0 1167.951907 0.131239 1600.0 1368.0 \n", - "2100 7.2 1505.131104 0.094823 1600.0 1361.8 \n", - "2200 22.9 781.220473 0.451436 1600.0 1366.0 \n", - "2300 14.2 1210.008992 0.219051 1600.0 1365.2 \n", - "2400 9.5 1109.373677 0.163810 1600.0 1365.1 \n", - "\n", - " average_specificity fraction_accuracy \n", - "trial \n", - "0 8.211000 0.982192 \n", - "100 8.346563 0.674000 \n", - "200 8.763875 0.720000 \n", - "300 9.188562 0.734000 \n", - "400 9.580437 0.658000 \n", - "500 9.951937 0.662000 \n", - "600 10.354375 0.685000 \n", - "700 10.754500 0.602000 \n", - "800 10.563375 0.685000 \n", - "900 10.921750 0.678000 \n", - "1000 11.160000 0.637000 \n", - "1100 11.864375 0.596000 \n", - "1200 11.975313 0.619000 \n", - "1300 11.781063 0.631000 \n", - "1400 12.324125 0.619000 \n", - "1500 12.344937 0.605000 \n", - "1600 12.828188 0.641000 \n", - "1700 12.774250 0.657000 \n", - "1800 12.986562 0.550000 \n", - "1900 13.043375 0.635000 \n", - "2000 12.868312 0.623000 \n", - "2100 13.188063 0.660000 \n", - "2200 13.435875 0.598000 \n", - "2300 13.260812 0.580000 \n", - "2400 13.262500 0.614000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(df)\n", "display(df_no_mods)\n", From 18417ea7b15c6085e1dc4367b0f361743eda7270 Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 20 Jun 2021 16:31:57 +0200 Subject: [PATCH 18/19] Woods1 Experiments --- .../Experiment_XCS_XNCS_Woods1.ipynb | 24 +- ...riment_XCS_XNCS_Woods1_pre_populated.ipynb | 2464 ++++++++++++++++- 2 files changed, 2462 insertions(+), 26 deletions(-) diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb index 988e3a8..ba8da7a 100644 --- a/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1.ipynb @@ -471,34 +471,34 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "18.249999999999996\n", - "27.47\n", - "15.470000000000002\n", - "17.709999999999997\n", - "16.489999999999995\n" + "182.49999999999997\n", + "274.7\n", + "154.70000000000002\n", + "177.09999999999997\n", + "164.89999999999995\n" ] } ], "source": [ - "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_no_mods\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_update\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_cover\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_both\"])/number_of_experiments)" + "print(sum(df[\"steps_in_trial\"]))\n", + "print(sum(df[\"steps_in_trial_no_mods\"]))\n", + "print(sum(df[\"steps_in_trial_update\"]))\n", + "print(sum(df[\"steps_in_trial_cover\"]))\n", + "print(sum(df[\"steps_in_trial_both\"]))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ { diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb index cb85911..7d9a009 100644 --- a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -313,9 +313,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df[['average_specificity',\n", " \"average_specificity_no_mods\",\n", @@ -329,9 +352,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df[[\"fraction_accuracy_no_mods\",\n", " \"fraction_accuracy_update\",\n", @@ -344,9 +390,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = df[['population',\n", " \"population_no_mods\",\n", @@ -361,9 +430,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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10011.61081.4094320.1268241378.61600.09.1474376.99.59.16.1...1401.11401.59.4720008.3646259.7540628.6804380.9840.2730000.9500000.320000
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11004.81637.4022620.0692431600.01399.616.7461250.923
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steps_in_trialrewardperf_timenumerositypopulationaverage_specificityfraction_accuracy
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24005.21449.2273700.1058541600.01391.015.5719370.362000
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" + ], + "text/plain": [ + " steps_in_trial reward perf_time numerosity population \\\n", + "trial \n", + "0 17.7 800.000000 0.363859 1600.0 1448.1 \n", + "100 6.1 1311.344022 0.112446 1600.0 1401.5 \n", + "200 5.0 1405.412021 0.093146 1600.0 1393.7 \n", + "300 9.6 1452.419037 0.177421 1600.0 1394.6 \n", + "400 3.4 1549.959119 0.060312 1600.0 1404.2 \n", + "500 5.5 1270.033639 0.120645 1600.0 1403.5 \n", + "600 5.2 1285.804782 0.099456 1600.0 1406.2 \n", + "700 9.3 1222.694849 0.187226 1600.0 1406.6 \n", + "800 8.8 1266.459666 0.182160 1600.0 1403.1 \n", + "900 7.1 1334.622903 0.133413 1600.0 1402.9 \n", + "1000 4.9 1386.245880 0.100514 1600.0 1405.4 \n", + "1100 5.1 1339.279385 0.073045 1600.0 1407.1 \n", + "1200 6.1 1276.383220 0.123140 1600.0 1409.4 \n", + "1300 7.1 1300.071115 0.131486 1600.0 1403.8 \n", + "1400 6.4 1343.739910 0.102307 1600.0 1405.7 \n", + "1500 8.8 1235.965927 0.170057 1600.0 1401.8 \n", + "1600 6.0 1430.046747 0.120761 1600.0 1399.7 \n", + "1700 12.1 1189.275281 0.201908 1600.0 1398.9 \n", + "1800 5.8 1343.098770 0.106744 1600.0 1396.4 \n", + "1900 8.8 1305.213986 0.166900 1600.0 1397.0 \n", + "2000 5.9 1359.623625 0.104277 1600.0 1392.8 \n", + "2100 7.4 1197.212865 0.135463 1600.0 1389.5 \n", + "2200 7.8 1275.521142 0.140057 1600.0 1390.3 \n", + "2300 4.3 1356.440714 0.086864 1600.0 1395.3 \n", + "2400 5.2 1449.227370 0.105854 1600.0 1391.0 \n", + "\n", + " average_specificity fraction_accuracy \n", + "trial \n", + "0 8.266500 0.952294 \n", + "100 8.680438 0.320000 \n", + "200 9.236875 0.193000 \n", + "300 9.892125 0.204000 \n", + "400 10.351062 0.172000 \n", + "500 10.825375 0.186000 \n", + "600 11.424375 0.139000 \n", + "700 11.715312 0.256000 \n", + "800 12.170625 0.187000 \n", + "900 12.290625 0.223000 \n", + "1000 12.550312 0.257000 \n", + "1100 12.695562 0.162000 \n", + "1200 13.018875 0.203000 \n", + "1300 13.189250 0.245000 \n", + "1400 13.414188 0.293000 \n", + "1500 13.605500 0.277000 \n", + "1600 13.834875 0.288000 \n", + "1700 14.299312 0.291000 \n", + "1800 14.374437 0.352000 \n", + "1900 14.555625 0.335000 \n", + "2000 14.755562 0.370000 \n", + "2100 14.859313 0.385000 \n", + "2200 15.126437 0.318000 \n", + "2300 15.253688 0.328000 \n", + "2400 15.571937 0.362000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(df)\n", "display(df_no_mods)\n", From dca0c472d42164306707aa72221f261b0dad09cc Mon Sep 17 00:00:00 2001 From: Marek Chmielewski Date: Sun, 20 Jun 2021 16:35:02 +0200 Subject: [PATCH 19/19] Experiments Woods not division by number of experiments --- ...riment_XCS_XNCS_Woods1_pre_populated.ipynb | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb index 7d9a009..e6c03ec 100644 --- a/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb +++ b/XCS_Experiments/Experiment_XCS_XNCS_Woods1_pre_populated.ipynb @@ -470,27 +470,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "18.82\n", - "20.690000000000005\n", - "14.520000000000001\n", - "22.64\n", - "17.940000000000005\n" + "188.20000000000002\n", + "206.90000000000003\n", + "145.20000000000002\n", + "226.4\n", + "179.40000000000003\n" ] } ], "source": [ - "print(sum(df[\"steps_in_trial\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_no_mods\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_update\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_cover\"])/number_of_experiments)\n", - "print(sum(df[\"steps_in_trial_both\"])/number_of_experiments)" + "print(sum(df[\"steps_in_trial\"]))\n", + "print(sum(df[\"steps_in_trial_no_mods\"]))\n", + "print(sum(df[\"steps_in_trial_update\"]))\n", + "print(sum(df[\"steps_in_trial_cover\"]))\n", + "print(sum(df[\"steps_in_trial_both\"]))" ] }, {