policy_value_sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import time\n",
    "import connect_four as cccc\n",
    "import sys\n",
    "import sklearn\n",
    "from sklearn import *\n",
    "from numpy import random\n",
    "\n",
    "from sknn import *\n",
    "import sknn\n",
    "from sknn import mlp\n",
    "\n",
    "from sknn.mlp import *\n",
    "\n",
    "from sknn.mlp import Layer, Regressor, Classifier\n",
    "import matplotlib as mpl\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from itertools import izip_longest as izip\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "matplotlib.rcParams['savefig.dpi'] = matplotlib.rcParams['savefig.dpi']\n",
    "\n",
    "def progress_bar(percent, size = 20, message=''):\n",
    "    percent = int(percent)\n",
    "    fill = int(percent * size/100)\n",
    "    sys.stdout.write('\\r[' + 'X'*fill + ' '*(size-fill) + '] {:3d}% complete. '.format(percent) + message)\n",
    "    sys.stdout.flush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('value.pkl','rb') as in_strm:\n",
    "    v = pickle.load(in_strm)\n",
    "with open('policy.pkl','rb') as in_strm:\n",
    "    p = pickle.load(in_strm)\n",
    "with open('parameters.pkl','rb') as in_strm:\n",
    "    (training_data, target_data, current_write_index, temperature) = pickle.load(in_strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_units=50\n",
    "num_layers=1\n",
    "dropout_rate = 0.25\n",
    "batch_size = 128\n",
    "\n",
    "policy = mlp.Regressor(layers = [Layer(\"Rectifier\", units = num_units, dropout = dropout_rate)\n",
    "                              for _ in range(num_layers)]+[Layer(\"Rectifier\",units = 7)],\n",
    "          learning_rule = \"nesterov\", learning_rate = 0.01, batch_size = batch_size, valid_size = 0.2,n_iter = 1)\n",
    "value = mlp.Regressor(layers = [Layer(\"Tanh\", units = num_units, dropout = dropout_rate) \n",
    "                              for _ in range(num_layers)]+[Layer(\"Tanh\")],\n",
    "          learning_rule = \"nesterov\", learning_rate = 0.01, batch_size = batch_size, valid_size = 0.2,n_iter = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saved_p = p.get_parameters()\n",
    "saved_v = v.get_parameters()\n",
    "saved_p = [[array for array in layer[:2]] for layer in saved_p]\n",
    "saved_v = [[array for array in layer[:2]] for layer in saved_v]\n",
    "policy.set_parameters(saved_p)\n",
    "value.set_parameters(saved_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class rand_est():\n",
    "    def __init__(self):\n",
    "        None\n",
    "    def predict(self,board):\n",
    "        return np.random.rand(7)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regressor(batch_size=128, callback=None, debug=False, dropout_rate=None,\n",
       "     f_stable=0.001,\n",
       "     hidden0=<sknn.nn.Layer `Tanh`: units=50, name=u'hidden0', frozen=False, dropout=0.25>,\n",
       "     layers=[<sknn.nn.Layer `Tanh`: units=50, name=u'hidden0', frozen=False, dropout=0.25>, <sknn.nn.Layer `Tanh`: units=1L, name=u'output', frozen=False>],\n",
       "     learning_momentum=0.9, learning_rate=0.01, learning_rule='nesterov',\n",
       "     loss_type=None, n_iter=1, n_stable=10,\n",
       "     output=<sknn.nn.Layer `Tanh`: units=1L, name=u'output', frozen=False>,\n",
       "     random_state=42, regularize=None,\n",
       "     valid_set=(array([[ 0.74624,  0.76745, ...,  0.84413,  0.97539],\n",
       "       [ 0.1692 ,  0.07922, ...,  0.65778,  0.7157 ],\n",
       "       ...,\n",
       "       [ 0.29927,  0.06333, ...,  0.73697,  0.26702],\n",
       "       [ 0.62526,  0.28288, ...,  0.12404,  0.43028]]), array([[ 0.10931],\n",
       "       [-0.46766],\n",
       "       [-0.38773],\n",
       " ... ],\n",
       "       [ 0.61837],\n",
       "       [-0.04806],\n",
       "       [ 0.76885],\n",
       "       [-0.63439],\n",
       "       [-0.56145]])),\n",
       "     valid_size=0.2, verbose=None, warning=None, weight_decay=None,\n",
       "     weights=None)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    \n",
    "policy.random_state=42\n",
    "value.random_state=42\n",
    "  \n",
    "X = np.random.rand(batch_size,42)\n",
    "# y = np.random.randint(0,7,(1000,1))\n",
    "\n",
    "# policy.fit(X,y)\n",
    "y = np.random.randint(0,1,(batch_size,7))\n",
    "policy.fit(X,y)\n",
    "\n",
    "y = np.random.rand(batch_size,1)*2-1\n",
    "\n",
    "value.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42L, 150L)\n",
      "(150L,)\n",
      "(150L, 7L)\n",
      "(7L,)\n"
     ]
    }
   ],
   "source": [
    "len(policy.get_parameters())\n",
    "for item in policy.get_parameters():\n",
    "    print item.weights.shape\n",
    "    print item.biases.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class rand_est():\n",
    "    def __init__(self):\n",
    "        None\n",
    "    def predict(self,board):\n",
    "        return np.random.rand(7)\n",
    "\n",
    "inf = float(\"inf\")\n",
    "def policy_move(board, turn, policy = rand_est()):\n",
    "    dummy_board = np.copy(board) * turn\n",
    "    options = cccc.available_moves(dummy_board)  \n",
    "    recommend = policy.predict(dummy_board.reshape(1,42))[0]\n",
    "#     print recommend\n",
    "    return sorted(options, key = lambda x:-1* recommend[x])\n",
    "\n",
    "\n",
    "def partition(temp, total):\n",
    "    return np.sum(np.exp(-1/float(temp) * np.arange(total)))\n",
    "\n",
    "def probs(temp,total):\n",
    "    if temp ==0:\n",
    "        return np.ones(total)\n",
    "    \n",
    "    return np.cumsum(np.exp(-1/float(temp) * np.arange(total)) / partition(temp,total))\n",
    "\n",
    "#returns the index of a sorted list of moves\n",
    "def boltz_move(temp,total):\n",
    "    x = np.random.random()\n",
    "    return np.argmax(x < probs(temp,total))\n",
    "\n",
    "#get self-play moves with policy_move(board,turn,policy)[boltz_move(temp,len(options))]\n",
    "\n",
    "class nn_ai:\n",
    "    \n",
    "    def __init__(self,policy,temp = 0):\n",
    "        self.temp = temp\n",
    "        self.policy = policy\n",
    "    \n",
    "    def make_move(self,board,active_turn):\n",
    "        options = cccc.available_moves(board)\n",
    "        return policy_move(board, active_turn, self.policy)[boltz_move(self.temp, len(options))]+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def alpha_beta_move(board, turn, depth = 0, alpha = -inf, beta = inf, evaluation = lambda x: 0):\n",
    "    dummy_board = np.copy(board) # we don't want to change the board state\n",
    "\n",
    "    swap_player = {1:-1,-1:1} # So we can change whose turn\n",
    "    options = cccc.available_moves(board) # get legal moves\n",
    "    random.shuffle(options) # should inherit move order instead of randomizing\n",
    "\n",
    "    best_value = -inf\n",
    "    \n",
    "    if not options:\n",
    "        print board, cccc.game_over(board)\n",
    "        print 'oops, no available moves'\n",
    "    cand_move = options[0]\n",
    "    if depth == 0: \n",
    "        for x in options:\n",
    "            update_move(dummy_board,x,turn)\n",
    "            op_value = evaluation(dummy_board*swap_player[turn])\n",
    "\n",
    "            if -op_value > best_value:\n",
    "                cand_move = x\n",
    "                best_value = -op_value\n",
    "                alpha = max(alpha, best_value)\n",
    "            if alpha >= beta:\n",
    "                break   #alpha-beta cutoff\n",
    "            unupdate_move(dummy_board,x)\n",
    "    else:\n",
    "    \n",
    "        for x in options:\n",
    "            update_move(dummy_board,x,turn)\n",
    "        \n",
    "            if cccc.winner(dummy_board): #should check over and tied too\n",
    "                return(inf, x)\n",
    "            \n",
    "            if cccc.is_full(dummy_board): #This assumes you can't lose on your turn\n",
    "                return(0 , x)\n",
    "            \n",
    "            op_value,_ = alpha_beta_move( dummy_board,\n",
    "                                            swap_player[turn],\n",
    "                                            depth-1,\n",
    "                                            alpha = - beta,\n",
    "                                            beta = - alpha,\n",
    "                                            evaluation = evaluation)\n",
    "\n",
    "            if -op_value > best_value:\n",
    "                cand_move = x\n",
    "                best_value = -op_value\n",
    "                alpha = max(alpha, best_value)\n",
    "            if alpha >= beta:\n",
    "                break   #alpha-beta cutoff\n",
    "            unupdate_move(dummy_board,x)\n",
    "    return (best_value, cand_move)\n",
    "\n",
    "class Alpha_Beta:\n",
    "    def __init__(self,depth):\n",
    "        self.depth = depth\n",
    "    def make_move(self,board,active_turn):\n",
    "        #print (board,active_turn,self.depth)\n",
    "        return alpha_beta_move(board,active_turn,self.depth)[1]+1\n",
    "\n",
    "def one_where(num):\n",
    "    array = np.zeros(7)\n",
    "    array[num] = 1\n",
    "    return array\n",
    "    \n",
    "\n",
    "def get_inputs(log):\n",
    "    boards = []\n",
    "    piece = 1\n",
    "    board = np.zeros((6,7))\n",
    "    boards.append(np.copy(board))\n",
    "    for move in log:\n",
    "        board[np.where(board[:,move-1]==0)[0][-1],move-1] = piece\n",
    "        piece = -piece\n",
    "        boards.append(np.copy(board))\n",
    "    return boards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def update_move(board, move, turn):\n",
    "    board[np.where(board[:,move]==0)[0][-1], move] = turn\n",
    "    return None\n",
    "    \n",
    "def unupdate_move(board, move):\n",
    "    if 0 in board[:,move]:\n",
    "        board[np.where(board[:,move]==0)[0][-1]+1, move] = 0\n",
    "    else:\n",
    "        board[0,move]=0\n",
    "    return None\n",
    "\n",
    "def value_move(board,active_turn):\n",
    "    dummy_board = np.copy(board).reshape((6,7))*active_turn\n",
    "    options = cccc.available_moves(dummy_board)\n",
    "    if not options:\n",
    "        return 0\n",
    "    \n",
    "    moves = np.zeros(7)\n",
    "    move_values = np.zeros(7)\n",
    "    if len(options )== 1:\n",
    "        return options[0]\n",
    "    for choice in options:\n",
    "        update_move(dummy_board, choice, 1)\n",
    "        if cccc.winner(dummy_board):\n",
    "            move_values[choice] =  cccc.winner(dummy_board)\n",
    "            unupdate_move(dummy_board,choice)\n",
    "            continue\n",
    "        reshapeable = np.copy(dummy_board)\n",
    "        move_values[choice] = -1 * value.predict(-1 * reshapeable.reshape((1,42)))\n",
    "        unupdate_move(dummy_board, choice)\n",
    "#    print move_values\n",
    "#     available_move_values = np.array([move_values[move] for move in options])\n",
    "\n",
    "#     move = options[available_move_values.argmax(-1)]\n",
    "#     moves[move] += 1\n",
    "\n",
    "#     move = np.argmax(moves)\n",
    "    return max(options, key = lambda x: move_values[x])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validate(validation_str = 0):\n",
    "\n",
    "    test_result = {'wins':0,'ties':0,'losses':0}\n",
    "    for j in range(100):\n",
    "#             sys.stdout.write('\\r playing validation game {:4d} of {:4d}'.format(j+1, 1000))\n",
    "#             sys.stdout.flush()\n",
    "        result = cccc.play(nn_ai(player_1,temp=0),Alpha_Beta(0))\n",
    "        if result.winner ==1:\n",
    "            test_result['wins'] +=1\n",
    "        if result.winner == 0:\n",
    "            test_result['ties'] +=1\n",
    "        if result.winner == -1:\n",
    "            test_result['losses'] +=1\n",
    "\n",
    "         \n",
    "    print 'As X, neural network has a score of {:3d}-{:3d}-{:3d} vs {}-depth minimax'.format(test_result['wins'],\n",
    "                                                           test_result['ties'],test_result['losses'],validation_str)\n",
    "\n",
    "    test_result = {'wins':0,'ties':0,'losses':0}\n",
    "\n",
    "    for j in range(100):\n",
    "#             sys.stdout.write('\\r playing validation game {:4d} of {:4d}'.format(j+1, 1000))\n",
    "#             sys.stdout.flush()\n",
    "        result = cccc.play(Alpha_Beta(0),nn_ai(player_2,temp=0))\n",
    "        if result.winner ==1:\n",
    "            test_result['losses'] +=1\n",
    "        if result.winner == 0:\n",
    "            test_result['ties'] +=1\n",
    "        if result.winner == -1:\n",
    "            test_result['wins'] +=1\n",
    "#         print '\\n'\n",
    "    print 'As O, neural network has a score of {:3d}-{:3d}-{:3d} vs {}-depth minimax'.format(test_result['wins'],\n",
    "                                                        test_result['ties'],test_result['losses'],validation_str)\n",
    "    print 'epoch time: {:3.3f}s'.format(time.clock()-t0)\n",
    "    # save the chromes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6601"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(item[0].size+item[1].size for item in value.get_parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((6,7)).size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110050\n"
     ]
    }
   ],
   "source": [
    "training_size = sum(item[0].size+item[1].size for item in value.get_parameters()) * 50\n",
    "training_data = [np.zeros((6,7))]*training_size\n",
    "target_data = [0]*training_size\n",
    "current_write_index = 0\n",
    "temperature = 2\n",
    "print training_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temperature = 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Epoch 0, temperature = 2.00\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 110050\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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D/3mnkphZTRDr/WPRp1XjcWnpL1uIuzcejd4BGg+61GO4mrPwBH7F93hPLr95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\ncBdCiJMWeeHeVQ0gLXchhJiAiAv3up5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1ie3ZWTohSNYtm6vQ7J80/dLdj2V/BhqI3jSp\n5zSzucDvgL3u/iQB1jRPxmDrCeDua4HrgJ3AjDyZgniN5uR8IeSajlfazf0wsAIgz9yaEKwDfgAQ\nz8ipBF6I1+YAlgM9Bfadaq+a2S3xz9lcfwSWmtl0M5sJLABeSytg7GBi+e3TRP+tTT1nvKZ6EHjA\n3ffGdx8LqaYFMoZazy+Z2bfim2eA/wFH87x3Us2aJ+cw8LSZLYrvC6amE5X2mvszwO1mdji+fVea\nYfLYBew2sx6iX/paoqP3nfH8nNeBzvTiXeB+YEcyl7uPmNmjRB/+lAGb3f29NEMSLW/9xMzeA/4F\n3OvupwPIuQmoAh40s+8AI8CGOGsoNc2XcSPw4wDr+TTRe+f3RH3m68BfyXnvBPAazc25gegMpG0B\n1nRCNFtGRKQEpb0sIyIik0DNXUSkBKm5i4iUIDV3EZESpOYuIlKC1NxFREqQmruISAlScxcRKUH/\nBweINT4U9NNVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfd6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.437960021256, policy = 0.461520709798 epoch 0 complete\n",
      "As X, neural network has a score of  98-  0-  2 vs 0-depth minimax\n",
      "As O, neural network has a score of  89-  0- 11 vs 0-depth minimax\n",
      "epoch time: 3745.799s\n",
      "Starting Epoch 1, temperature = 1.83\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 220100\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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IExr0FbI5F16FuAfTrNBUEW/u8PC9B9aw/L2Dim3b9vewu6mPHz2yjsa2IVKJ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\nuP7661iw4EYWL/4ZkybFePTRAWeoRESGLJdG3QlEM5YL3D2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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6afb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.434267066641, policy = 0.454092142411 epoch 1 complete\n",
      "As X, neural network has a score of  99-  0-  1 vs 0-depth minimax\n",
      "As O, neural network has a score of  93-  0-  7 vs 0-depth minimax\n",
      "epoch time: 3591.807s\n",
      "Starting Epoch 2, temperature = 1.68\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 330150\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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cX0qk+9juZxR5RJbVfMg3x32NPFuuNFSu6jzEkMxSjvYK2nF5VhmV2cMxYFAk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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf20bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.422065114634, policy = 0.464121473853 epoch 2 complete\n",
      "As X, neural network has a score of  98-  0-  2 vs 0-depth minimax\n",
      "As O, neural network has a score of  97-  0-  3 vs 0-depth minimax\n",
      "epoch time: 3379.598s\n",
      "Starting Epoch 3, temperature = 1.54\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 440200\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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Sbjrql7p6I2Yx9R7RchfJXdjvmilXcnnNNzXn4qnVJwL0WwflonHncuLQmXxv\n4iUZtzmQcNgdnDFiVlpCk4UvHwbjUM0HxLmu4j6fz+73+yWfz1cJ/A7Fur+ElP+nFxgFbAFKgANf\niN3CZ4JKssaZ5o1ISAme3viCQNYKIYsz8Gxq9zO3LmXBx6SYjtzVYlXK3638Z/vbhGJhFjcu4+rJ\nVzCpdIKuHktHuAuX3YXL7kyziB02B4eXTNBK1xYayCcqxJCPyB+uFd4yps/PGnY8k0sPozSZ8n5U\npVLP3y1Y5uayTEpzFyUXsU7JhBI9iV845hzOGnGqaX1xFVnOLC7x7XvZWgsWBkPuPYDYkux+v18V\nTi9GIe/ZQBWQ5fP5NqPo83P8fv/NPp9vKDDP5/NN8vv9/VrwZWXm1di+bDgUr3Pp3tWMLBxGRW7/\nWmm4VZE3JHu83/vc0LxFN+uN2+ukrCyPYLxXt11jOBW/vaF9izaHpopcdw4JOUFtYDdrhOO9s/t9\nZk04itBu5XqG5Vext6eR+t5GxpWMoqJckSk8Tg+ReIRCbz7Dy8thM1TllVNeVATCBEEVuWXa/dTE\nR0JShi8vT7dAy+nfKq2uLKcsX/9sStpTvysrUuQ+ekgV9gxhhV8UDsXv88uMQ+U6jRgMuS9Csbxf\n9fl8xwBaC/T7/Q8ADwD4fL7vAz6/3/+cz+f7LSkppit5ngEDnFtb09Osv2woK8s75K6zOdjC3Usf\nx2FzcP+sP/W735725MTEwYDuPht6myjNKtYs1LZO/TPoC0VoaemhM6y3+Le17er3fCXZRdgku06i\nAch15tLQ1EFjl6LXj8itZm+Pcm1V3irt2tx2FxEiZDuzqbIP43sTLmFiiU83H2eJt5irJ/1A26fC\nPpTpFUcwo2LqoN+l2MCjAWiN6PeLhVJSUmtrgPNHn0lnuIv29v0zafVgcSh+n19mHArXmanzGQy5\nvwac7vP51EyLK3w+37eBHL/fnz4vl4J7gad8Pt8ngAu40e/3Dzxdi4UvBKoUkpATA2wJ7UlNW5x7\nsrmvlduX3cP4wjFcO+1HAPRG9aTVFwsRiofSKhMGYnpL3oiS7CKyyE4j903tfm5Y+AdlxneHh7GF\no1iUDG2szhuqbadOKp3nzsVms2llCMTQu3NHna4LD3TZnVxx2GX9XpcZvj/xUnZ07zKVZUTNHZRY\ndgsWDiYGJHe/3y8Dxpmc08Ia/X7/s8LfQRQN3sKXAD3RdIJ9p/YDvA6P5tRT0ZaceCEUS/XFqhNT\nLT87v26Yc7zxAAAgAElEQVQRr257U7dfMNanOTb3BeXZJQzxDNWIW0Q42cFU5lRwZPkU/rn5FRJy\ngpEFqakN1WSaPEMEi1iytj9Ne19wVOU0TYM3wmNST8WChYMJK0P1vwBmztHZtR8AcHTVkXxct4hT\nqk/A4/DQmcwAVS337V21rErW5AYlYUgtmCWiL96XVvgqE8qzS7l0/EXs7qnjjJrj6O1K7ffHmTfx\n2PpnqROyRE8dfgIOu4M/zLyJ3T17dOGCUUlR/4xzT4o1Wfa1IuFnwectq2DBwv6GRe7/BRDn8gzH\nI7p61f/c/C/Wt22mLx7itOqTNOkmFA8jyzL3rnpEd6w9GbJJg7E+XUSKEfnuPHqiAew2O7ccfR02\nmw1f8VjKcvKgL8DQ3CoiiShF3kJtBDA8byjfm3AJQ3IrASUhaHLZYbrjqklRYola0Fvr6sTGXyQG\nmt3egoUDDYvc/wsghhN2R3t0koU6p+f6ts26lHhJlkyrGe4O7E1bBklyN6Ski/W8Czz59EQDeBwe\n0zop109PzS+Z786lLdTOxGKfRuwDwViHWyT0A2G5W7DwZYNF7gcJ7aFOolL0c88oE0lEeX/XXGZV\nn2Dq6JNkSZfledund5lfT7iD9W2bdMt6TZyhZqVpQUmTX9q4QrdsbOFobbJkV3I2okzatJix+f2J\n32Ze3QLOSpYvGAyMsx2JcenZ+3lWeTNMLPYxoXi8rs6LBQsHE1bhsIOEW5b8iT8uvXtQ27618z3e\nTab3h+IhNgqx4u/vnsec3XN5asPzpvs+vfEFXcVEM0wsVmbmWZuc8b4sS4ksae1rT9tWLBlgxOLG\n5brfTruD/z3yJ1w3/RqNfDMV5hJRmlXMxeMvMJ30wYjxRcosSMOECBojDoQe7nK4uOaIKwdd6dKC\nhS8aFrkfZBilDDPM2fURb9e+D8Aja5/m4bVPaZMdqzVStnbuYEH9EuYIMwUBWiZmf5iRLNmqRsqo\nRNkSatNtl+vK0aZ1g1QRqUwRJHZsjC4Yycj8alwOhdTt+3nKtB8e/j1+feQ1jC4YkbZuculh+IrG\nmuxlwcJXH5Ysc5ARjAVxO1IZjbIsI8kSH+yZz9GVR6ZN0abO77knsJehuVU4ktOXycja/JlqjLU2\nYYQ7jx8cdjn/3PwvRuQPZ6UQ/QIwqSRVs9uGjaE5laxmnVbNUUV5dim93Up8+zkjT+Pc0WfQFGym\nPLuMaCLKmqTlrx1LnPzBliT3/WxPZDm9jBJCI0X8aPL39+u5LFg4lGCR+0FGMNZHkVCL5MaFf9AS\nfxY3LOOnU/7HdL83drzLGzve5Ziq6Wnr1NGAWo1xesURjCsaza0zb2BLx7Y0chcjS0q8ReQmC2O1\nGiz30qwSTeJRk4TUkrNmEopNIHK1dK01I48FCwcGVks7yNjU4Wd1y3qiiSiSLOkyOtvDnewVHJgJ\nKT3D1C/UKlfRF1csdrUIWKYStA6bg8trLgZgRoUizXzL93Xyk+SuxprbbXa+PvZcTYaBdCelMUMT\n9Ja7lAyxtMjdgoUDA8tyPwgQS9q+seNdQClRa1b976mNKUdpn8n8oWJJXG1ZuJtnVi+hpVvR0MXJ\nI8RytT+f+kPGFo4C4PIJF/PNcV8j152jTQmnTkH38yN+yLii0TpHrjG80CwKxoZI7qrlvn81dwsW\nLJjDIveDALPZfrZ0buPWDGGKKlSZxYgsp1eX+v/2zvfY0rlN+y2GW4qkLFrfLrsTl1v5HEqzinHb\nXVpSUl4y+3NUfkrbNqb0i7JMZU4FTcFmRgh109V4d7vNyuS0YOFAwCL3A4xwPMISQ8jgpb6vs6xp\n1YAhi8ZsURVFnkJC8Sbtt9gJVOVU6AhdlEWMzlpxm8qccm2iaFXKEYtxeQ0ZoaLlfsn4C+mL9enC\nAmXLcrdg4YDCEkAPMB5a+2Ra0a0hOVUcN+TotG1tJpMim6E72sMvpv6I4uTEE6JUMyx3SMb9svpJ\n7lEzPMcVjtaR+vSKIwB0TmDQa+45rmyOKD9cF1+uWu4265OzYOGAwGppBxg7k6GMIsqyS6gwmXD4\nnpP+wBkjZumWiXHbBUmLOhjrY1zRGL41/oK0YwzNrUpbdlTlNIo8hf1WMjxv9JlMK5/M/0z6jm75\n9ydeyt9OviPN6heP5TJkiwJIKOTusByqFiwcEFiyzH5GdyQAyLo5Jhc3LGNF8xrGJJ2XRuS5crV4\ndRFuh5sLxpyNx+HhrZ1zAIWY/cmU/hOHHcdbO+doce1iPZXhBUOYVjKFWcOPTzvu9ydeOuB9jCqo\n5n8KvpO23G6zm0a8iOTudqST+wlDj2V7Vy3HDz1mwHNbsGDh88Mi9/2Mmxb9AYCHTvkL3ZEeVrWs\n471dcwnEejVSNsJms/Vb3EokTtEJOqZgBHefeJvmzMwRnJyTKyZwxjC91f9FQpRlGlrDFA4r0K2f\nXnEEk0om4HWmh0xasGBh/8MaI+9HiCGOCSnBPSsf5tVtb2acjejw0onccvSvtd+ZYsB1RbCETiDX\nnYvX6dX2E9dNGzLps91EBgT6ojw1ezOtXeYTaonRMn993rzkgUXsFiwcOFjkvh8hxqH/ecX9Wq0W\ngFOHn8jXRp/FFRO/rS0bXzhaF4N++3E3c+Wk76Yd12M3t9yNiUTi7wml+7emyusLalm4rpEn3tpk\nul6n38vWZ2XBwsGGJcugRHKsbd3IpJKatLrg/WFjux+nzYGvWCFScVIMY2ncoblVHF11pG4bl8Gh\nme/OY1zRaABG5acKYYlWcY4rm+un/4z63ibmLWujuiLCtPGKM9ZpdzKz6ijKs0txOvbvq+2LKLH5\n3cGI6Xq9c/a/K9xxT3OAWEJizJCCgTe2YOEAwTKxgA92z+fvG/7BazveybhNbyxIKGmZS7JETIrz\n8NonuX/N41qYnzgpBsC3fRdpf1cmLXQx/NBYgxyUyou3H3czP596VWo7gTgjYTu1O+zMKJ/Gm4t2\n8eB/1uv2v3zCNzl9xMkD3fI+Q6VrQXnSwaz8wH8Lfv/0cm5/bqWWhWvBwpcBFrmjlMsF2JVhCjmA\n3yy4lf9b/CfWNm3ilx//lntWPqStaww2A3rLHZQY8QvHnEN13lCGJEMSxSnuMtUrL/QU6NaJxPn0\n21t57j0/7y/PfK37gtrGHn5+3wL2NAf63U7NPcpM7gPXXg+GY6zZ3pa2fKW/lSff2aTzWRyKaOk0\n90dYsLC/EOiLsnlXx8AbYpE7kNLKM83YE06m9ofiYZbsWUlcimvZm5DqHIwTUZdll3L6iJP5zYxr\nNVIXi2mZhQyaQSTOjoByLau3pZPkZ8ELH2ylNxTj5bnmkTwqUtdtTsBmse1G3PPyGu5/dR2bDB/n\nQ6+tZ9H6Jh05yrJ8yJH9rsb0ici/LEhIEtFYeuE5C4cWbn9uJXe9tIa9reZBGiIscgdNbvEKiTkN\nvU3csPA21rZuoFOQW9Y0KsWzitxF2rItHVvpiQZ4f/c83XGVOUQzE5TbPrC1C3oLv7pcSVza2bB/\niMTtUuLro/H+G75muWdcP7DOXtuojA46A4puL8sydcKIwWZPHeOV+Tv434cWEYkeOoS0q6n/0c/B\nxN0vreHHd39MPGFN5H0ooyUZrdbRY+77EmGRO2hFt0R6em/3XALRXh5f/xztoZSl2RHuICtaScPi\n6fxy0v8yLHcIG9q38Meld2sTSp9WfRJXT76CD1fUceWf59HcoZTglWWZHQ2pjiKT5d7aFSIUSRUX\nE61ip/Pzv7KX527j9n+sQJZlXMnjxWL9N3q1FILYV8myzOZdHcTiyr4nFJ5FZKv5rEwiEpJykIXr\nGvnJX+amlieJR5Jl5izdQ1dvlNbuzFJHXzj2uaz7tq4QL364TfesPwvUZzgYayoT4gmJZ97dPKA8\nNhAy3cuWPUpJikBfjDXb2ugNDTwD2FcVc5bu4Yd3zTuoz2D2p7t58cNtX6jxYpE7KVkmHFd6w2gi\nxoa2VHnbR9Y9rdu+pzUHJAfxkFurtRKM9VGdN4zLar7B18eey6TSCbzwoVKZUZVQ1mxr4/bnVmrH\nMbN2A31RfvPoEu74Z2o7tQLjhOLxGpF+Hqzd3s6O+h5icUmz3COG40qSTJsQ0x6XUsSr4tONzdz1\n0hr+8Z4fgJGeiUhd5ZhB3C/Qp3SC63fq52hV721bXao2TjAU44UPt7J5d6du29rGHq752wL+/fHO\nQdyxOV74cBsfrKjjlfk7TNev39nOpl0dvL5gJ03twYzHcTqUZmTWUNftaKOrd2Ara8WWFj5Z28jv\nn14+4LaZMG91Pd+66R02JmWvupbeNAL7eE099/97HY+8vsHsEJ8J8YSk62RlWeapdzbzj/f9GTsb\nWZb3y7f8WfCveduJJ2S2GL6pA4lX5+/ggxV1vDx328Abf0b815J7XIoTiPYSioe0aBfVgm/uayGc\nCDOl9DCqTSZelnqVolkul51jqqbjKxrLN8d9jeun/8y0AFiWRyHQvW2ZCUJFT5/SGOtbU9u67E7u\nO/kOfjLlBwPKJwNBlmXae5I+hGhCG63EDMd9ds4Wrn90CTvqlZFGNGnZi4ZybZMiDS33tySPkbmx\ntgp6eiB5jx6XvuRCPCHTGYjwsEA8y7a08OGKvdz14mrdthuSHcPsT/uvpNkfesPKdazfYe6/uPdf\na/nrS2t4c9Eurrt/QcbjSMmRiPH+G9uD/O2Vdfzf35eyuynQ7yjD4/78pZBnL1GexeL1TbR0hfjd\nU8u4919rdNus3KpUDDV2lvsKSZIJReJEYwl+eNd8HhfyH7p6oyxc38i8VfW8t2yP6f5/e2k1P/rr\nfPrCn2/UpCISTbB+Z7tmOAwGMROJSpZl/vPJDlPH//6E26VQb1NyVP9F4L+W3F/2v84NC29jdu2H\n2rLant3Mq1tIR1ixHEcVjOCisefr9pPlFLknEjJ57ly+Mfxyjq+amVF39ibrpKvMGNk8g5mVxzI8\nN73jyOT0ctqd2G12U/nk5bnbNB3bDLF4QiOenmBU+zscjWuWVdRw3AXrlDh9VduPqNclEJQrabHG\nk8czayygyEw3Pv6p9rsn2QDdbiO5S/j3dBLoi5HjVZ7Z1j3pk5EAKSfA54DaybT3RGjvDve7bX/W\nd0Iyv3/1+MFwnFufWc7yLS1p+772yU7uenH1oHwWAyF1CJmNtYr1Xtuo71RUoyE/Z3D+nkx44cOt\n/PTeT7TOf+mmZm2dmMXcluG5zl1Rl1y/fyKM5q7ey73/Wsu19y8ctNRhZoz0BKO8vXg397+aeWL5\n7mBUu+/PjOQriSe+uKCB/1pyVyezmFunt8he3fYmWzq2AlDsLWJM4UjdejmUCwlFA08kJLbXd/N/\nf1/Kk++YZ24C2JOOQrWxS4ESahzHmzboPsMw9qOVe3nk9Q1aA42afJDvLavjqdmbM57/D8+u5PE3\nNwLQKjS2cCRBKKqcLxSJmzp/VT1ZJXfxY1TliIQk09oVyvjBG5dnstxjcUlbd/iYEgDqM4x27IZH\nt72+W+fPyIRdTT1s2d1JLC7R0pmymoz7mlnZKomnLU+YW+52w0VuN3k+by3exebdnQQF+SQyyKiW\nQF/U1LEuk3rmOV4nYROyy8seOLpJkmQWrG0wta7nrlKixTbUpofliYQdHEDXVr+hzwvRwdjcOThr\n2Exz7x3ESOL3Ty/j9n+s/MwdkyzLWjv+rNJUpm9RxFeG3GVZpqUrNCgHmyzL9MYySyQNQWXii2Jv\nIXabnasnX8EYeSZSXy6JtpS1HZdS0R7LNqdbZdp2SYuuRxgy1reZO99CwsclyTLPf7CV5VtaNLnG\nKJ+oaOnng25sD7IrKaGIH6RiuSvHS0iyaSNWyT2aJIhILKE9Y7Fv+s2jS1iyMWW9ie+hoV25ttOn\nD8fpsBEIKs/B2LBjCYlASFlXWaQvpGa32TT5Qzm3njjv+MdKnT/DiI6eMK8v2Mltz6zgLy+uTsok\nUFWinMdIkmaNrrEt/RlLkqxFEBk7XvF6Ib0zEyGS4EDx8vGExIJ1Dfz+6eX88bkVNJr4A1RyryzJ\nNn2vAznQAV5fWMvT727hlfmZw2RViU9EW1dqmSp9ZcL+SvwS73GgUZiKnmC6hNM7CFmnu1fZpr/R\ncn8QI5YyRS+t2dbG1Xd/nLFdD8bi/8qQ+9uLd3HDo0v6JVlQerzuaA/RROaXqMawqxNSTCqdQG5w\nHJENxxNvSpXtTSTkNK1UkmX++tJq3lxYqy1TZQvVKgVleJyQJB78z3pWCMN10XIXP9iu5IdkZrlD\n5pcdT0gkJJnuoBJZIn74oWhC5/DqNpEeVOtTtSYTkqydy8wiNF5PJJpgdzJE8KyjqynM9RBIElnC\n8GHH4xK9yWdUUawnd0mW6RYa476qGPe9uo43F+3Sfn+4UpEFjplYgd1mY6chRt3Meq41iWNPCARu\n7BCM/hEjuYsdYEAg96aOPpZvaeHTjYqR0RuK8emmJm37D1fs5enZWzRyaUx2nuIz6UiuiydkUwIO\nDCJSRJWRzEhM1YzNQvJadZZ7/5awSG6SJH/mWHzxO24zuV8ziO3RbFltYw93v7CSSExpJzc8uoQF\naxu09UYpc7AQ23Amy/2JtzcSiSX4YMVe0/WDCWn9ypD74g1KQ1i9LX2e0eZgC5va/bR2hbjqL/N5\nfVn/kQLRRBSHzaFNLwcpq/vCk8ZQWqDEw8cTEg67/hH2hmJs2tXJ6yK5Jwmgpy9KlseB1+2goS1I\nW3eYVVtbtWsH/UcqWhadSeLN9DF0BiLMW7U3beSifoDxhEQokqBVsKpEzR0gYvKxqh2TSHbhpJSj\n/m8G9Tr/9M+VrN/ZjsNuozDXTV62m55g1DRaIpZIyTKVxeklkEXdW5ylajBDW5UAVahGQGVJDlWl\n2dQ197JkQ5MWtmrWcM1kFXF4nHY/hmMYDQF11AR6y33Lnk4eeX0Dj7+lZO0+/Np6Hn9zE0uSZG90\nwqn7qs8kFpe0a9ndFODO51elXXcoEtcRRFtXiDv/uZK6ltSIUn0WZQXpyX15WYpmL3Yc6rNo6wpj\nA0oLvAQHsNxFo+Sp2Zv58d0f60a4A6GnL8rP71ugc4D2Z7nLhqitjp4w4Wicj9fU89N7P9GFsy7f\n3ML8lXvZ1djDpl2dtHSFePrdVBTdQPdmRGtXiHteXqN7f5n8VFoUW4bOLhaXePHDbVpggRm+MuSu\nWpgJKd2CvW3pX3lo7ZOs2K5Ya4u2KsPMAoG8jSj0FOhK8PYEY2R5HPzP1yZx9jFKUa+4JKU1aDNr\nQG1Egb4Y+dluhpbl0NTRRzjZuDuEBiJa66LnX7Pc+7Fs/vH+Vj4VpBHQW489fVHaBasqFEnorG8z\nHU/9+ESyUx1W/VvuEqFInD1JskhIMjabjdwsFwlJJhRJpH3YsbhEIBTDBpQXpQilKE8pv5BpGCyO\ndjLJcu4M+QGFuW5yvS4isQRPvL1Jc/ware4sj5MF6xqZv7pet1z83tSQwLXb23h69mbCsf4td/H9\nipb0+h2pBtsZiLC9XhkxqM5Qh0M/bJm7ql6JfkkuVmWDTDh8dIl2bJVI//3JTrbu7eaJtxTfjGhY\nmBFMblKzF9+J+u22dYcozPNQmOshGIqnvRNdaWzhG1CNnB17u7nznyv5RLCSM8EsZj+TExf032xj\ne5BfP7yYu15czbNzlLDNj4VzqhJhJCZpEW8izNp6f1i7vY0NtR0sF9QFI3/sauqhvTusfSuZ2ntd\ncy8frKjjnn+tzXi+r0xVSEeS3I06pxrmCNASU16czaP0nKMKRrCm1dyKL/EW6X4H+qLkZSvWilPt\nSBKyjgTeX7aHf3+SHncdT0hIskygL0p5UQHDy3LZUd+DPxnPLVo/oiUtfjxqIxrISp29dDdt3SFO\nnjGCXJdNkzlAabDih28ky4SJtBMztdwHR+7ifX33TB+QGs5f87dPTPcJ9EXJ9jrxClaub3ghn25q\n1l2v2BH1CRZUQpJxOtI1G5fTDiZ9Q2GuR/MriDBa7tUVefj3dPLce36OnVSpNT6jMRFPSNyXjLQY\nyFkoykzqe8ryOHTvqL4tiMdlJ56QtHfgMDhqdzcHuOvF1VqHaCbDAEwZU8Jxh1dpYZC3PbOcYDjO\ng784UWs3qiUtknvIZITmNHq0UaKCsjxOOgIRxg4tIMfrQpKVjjzbm6Ia8bsxs1yfeHsT4WiC9p4I\nJ07JPAcw6DuKknwPgb6YqaNzZ0MPWR6HroNtT0pKauY06DtG9e9oLIHXnR5dJHYqn25q4uPVDVzz\njcPJ9jjZsqcLh93GuGEFmn8omOz8xGginSwly9z2zAoAqstzAfPcCTB/J0Z85Sx3I7m39KVkmoaQ\nYrnbvcnhpit9flEVlUKddYWYFasb9FEiIgm8NHe7KfnGEzLBUAxZhrwsF4eNKgZgVTLmOBiOE4km\n+GRtAx+uTGlsojXXGYjovOyZUN8a5LUFtVx7z3yeemcztzy1TFvX1RvRNfzOgJ4EVKISrYWX527n\n6dmbdSSmWqThfjI7YwlJszS/d6aPWVMVR7QZkWr7xCV6QzHyst06h+moIcqUhaJ0IT7noDDaWbiu\n0XRYn0mjLMx1p12TJMlpHd/ZM0dqf4sdXdwoxQi/jeGTkiQTjsa1Bita7qrj0Vg2eKW/BYdDH7Hk\ntJs/Q/XcmUY4J04ZwvSacnKzFKtbfW7NnX1CYbikP8WkM890nyqC4RgdPWFkGcoKs7RwVqNTVTRg\n1M5EJGn1fPFBRISI0la210VxvjfND9AXjvHH51bw2yeW7lM2svr+IrFEmn8I4I2FtbyWNOZe/HAb\n/rouXpm3nZ2NPdz14mrufH6VLlFP7QxEco9l0N/VZ5ZJlhmMdPWVIXfVmkkIH0lHT5gn56Xiq9sT\nSuy2zRtETjhob0mFg10z5Ury3Lnab3ESjWAohiTLWmywOiyOJwZXjCmRkDQrLT/HzYQRRTjsNrYK\nmZgdgTDPCHqeev0qOnsjWkPojyBFLBK0fFAyFuMJmbJCxWfQZSABlQCDhugKNeZdhWrR9Gu5xyUa\nklEcQ0pztOWZ5BFQLM5AX0wb8qtQ99c5ohLmlvtz7/m51zBUjcUTunsaMzQ1v63L6Uh7nk/P3sz9\n/07FOX/3jPGcOqOa4yZVKteRvO/3lu3h1w8vNpwrdV3NhqiXhCTzk3s+4VcPLQT01rHa8MsK9fr2\nJ2sbte1Un4hRllHR3/uAVISR8fl29Ua0dWrz0Y3UTAjRzOIOhuJaqG1pgZcctRMxyCaijLZgbQOr\nt7byxNvpocTdvdGMfp2u3gi7mnp0HWi2x0m215m2zwq/YkTJ6DuDgdDVqz73hOY3M+KtxbsAGFam\ncMei9U06zX97fTc76rvZ3RTQNPoWgdwTkowky7z44TZ+ek9qNKt2UGZ+MICeAaQ3+ArJMqLlHo3H\n+c3bj9PbUoC9qBl13uiQvQ332NXYs3uRgvnIQkGuCSXj+dNx/8c1834DQGV2itzVMMT8ZKNQnajx\nhEwkQ2jiVedPZPnmFtZsbyOWkLTwsNICL1keJ8PKctkt1BExizoQP5KuQEQLg8zNcn2mMCw13G9Y\nWS6tXWHtI3O77ERjUlILj/PH51aY7p/jdRIMx6lrCeBx2TU93Qz1bUFNWxTJ3eXIHA74oRoZkGxH\n3zhpNK1dYXK9ynMXJTCRRI2hfmp0zrNzlM7y3GNG6NaPG1rIjvpU5Ivbqb8msVP89qnjmDVtmLJd\nckgfiib417ztzFmann0pkmKzwfGphv2FIko4qZksYyR3EdEMsoy6rD8HN8CwMuU9ZHv0zb4rEBEK\nw6WinFSEBmm5b6/vYsFaxRAoK8zSInaMjkcx+3r1trZ+K5w2d4QYUZnuG/vVg4sAOGJsqbbM63Zg\nj9uIJ2TiCUkbYS/PEI02ENQONxJNDBidoioGCUnWyal7mnt5e7GSOaz6OoyS35ptbXywwryEt/g9\niaObbpMwTiMGJHefz2cDHgamAGHgSr/fnyYs+3y+x4B2v99/U/L3DcDXABfwsN/vf9q4z/6EI/l1\nxuUoaxq2E83fhTtpoEnBfOSIF0dxC45ixeEohbORPW7dExClAFGWUeOyNc3doTpvpYzhUENKcvja\n8SNZs72NRELWhmLlyfhtUYMEc51UXBaOJjTLNe9zkvvw8lxWb2vTImdGVebjr+siIcms2d6W8djj\nhhWyZnsbizc08VYyrLCiOJsrzq5h2eZmLbEFFIumvSfMyVOHajIAmI86bvrukdzxj1SMutqYzz12\nJIAWx721rotX5m/nGyeN0UVZGEcaoISxfbxG8bHMTFrc031lHHtYZZpO7nJlHk24hXXq30s2NDEn\nQ1r9n19IlUkwnkeUDHtDMV2YoLqtGokFSuio2IGohGtG7k6nvd/MzNt+cBSlyY7D+A7+8f5W7W9Z\nVjpmse6KWadhJg+qJKbeh7pNVyBFRBt3dfBYMqFuMGju7DMld3G9CklG89VEYgmaO0O8taiW7XuV\nKKcsj0OTZapKstMiqDJBkWX6jysXR03iiFtM4MtUqMw44Y4IcdQjfk+DIffBjO8vBDx+v38mcCNw\nj3EDn8/3I2CS8Psk4NjkPicDwwdxns8F1XJvLHmPZ7c/pVsX2z0BKZyTto8cTZHO9r3dumJdBe7U\n0F3Vt1RZRrUI4gk5Y1JRXrZL2E7SyF2VRIyREx0DkHs8IWmNxTisHixUK0C0pAF81Wo5BYmGfurf\njBmaT1Geh7busJa443LYGD+8UJvqT4Wa8HPpKfq5XI1VLd1Ou1bGAJRRydeOG2nYRnlWe5p7effT\nPazf0a577n0mIWlzV6V8F2op3rHDCpk6vkyzjtV34erH8ekW3pN6HV0ZphqE/hNbxMbZ0hkylTZE\ny90YDqo60cQ+wze8kOHluabELsa9F+SmRqn9OXplWeb//r5U5/sJRRKKMz6DVmyGssIsRlTkYQNe\n+mibJi35M5WTyACjtKVcT6qzEUN7Y/GEFm4aiSZ44q2NLNvcon33YSGvQ5VRzHD6dD1dRWL9W+6S\nLIC9TSoAACAASURBVOs6QLXd5me7dIbHvoZOgtIhzFu1l4deW582D8JAGIwsczwwB8Dv9y/1+XzT\nxZU+n+9YYAbwGFCTXHwmsMHn870O5AHX7dNVDQJtXSF2NPRw9MSK1EJbgrhTLxXE26sYkjWcxoh+\neaJtKKEyOHLkFKrzh7FuZxvb93bzgyOvZsJIxcPd3NFHV29EmxQ6T5Nl1GiZzJZ7XrZbI+OdDT0C\nuSuN12uIeTaSqsNu00k18YRELJaSZczgdTsG1F3FawAoyfdSkq+QXEKS+61JPnFkMTsbeugMRMjP\ndtHTF9OuMcsw1Jdkmfxsl44cId1qdDntOsKfXlOujZC0bQyWdU9fdEDLXfQnqFaoahWPqMzjV9+a\nwvBkRIK7P8tdkGzU7QaT3WkG0fna0hUyddIVCiRcnK+fulC1/MRIIbfLkdHJXpDj1nRj8T2YRROp\nMJOWw9E4v3pwEZIs8+RvZhEMxzMaNQAjK/MozPNQnO/lrGOqeffTPexs7OGIsaVpklAmqBKgWfkC\nMU5cn+0pa3WcQtFEWicmyykn9tCyHJbrXVycfXQ102vKCYZjOpkkEpP6zQgNG0KKVTm1ZkSRLqly\noFIMZkhIsjay2tdSBYOx3PMBMXsj7vP57AA+n68S+B1wDfpy6KXAkcA3gauBF/bpqgaB/3tqGY+9\nuZG9Sd03Gpew5+mtgljDaGK1k/jFxZPJtqUs8dDKU5C6y+jujfKDSZdzWvVJWsx5IuKmJKuYZZub\nufHxT/nzC6s1i6sgJz1aRrUKrjpvIjVJCxiSpJXsBHY1BTQCyknqx8aElrqkDjl2aAF3//Q4zRml\nIp6QBVkmw/R8uYObx7RAKBo1aXSxzkG8q7GH0gIvV5xTk7bfiIo8vnHSGP7f2TWcPkOxblQN00ju\nACUmyS9GK1mS9YRvFk/scaYn//QXleKw23QljNUSuOVCpzZpdAkFyeflcmb2A3h0sow6scnnz0xs\n7QyZOunEaynM9egkGFWXF4nG47LjykDWBTmp70F8xv1Z7sZn6XbalWJ5Sb13zfY2fn7fgn7J7teX\nTsWeHDaoFnJn0prtz3oVR2xqOxEt4rXb22ju6Ms4upSR8bpSlrsocalQ/WdDStJH8tUVeYyqyifb\no297kWgiLXLnzKOGa3JfXySmI3c1Q3bCCH04tdE3NNiOTsW+Zu8Ohtx7UKxvbR+/36/e6cVACTAb\nuAG4zOfzfQ9oB97z+/1xv9+/FQj7fL5S9iPUYWh3nxqLKuEo1JceSLQMA8lBlsdJgVN40AmF3ETd\nSn056ss3c5apFqUuWibZYKfXlHP9ZdMYVZXPqCrlcfU3sYZRllGdb9UVuRTlebQwMhXxRCphKstr\nTkZqss9AyM9xc/zkKo6aUM6lp4zTGnswHCcYjjOkNIfpPn1d9vNmjsRutzGkNIcTpwxhqGFYa07u\n6Y3LaCUnJFlH+GYfvNFyN2ZXGpN2HA6briGoo6tMzsrByjLqO+uLpBPUYKohiA7hlq6Q6VBfJOEc\nr37kE41LaRKB2+XI+J2JUoxd0Gj6l2UyHwPgkzUDJxZ53KnjFye/yZRzNZ68hvQnJho8qnHTlzS6\nNu/u5L5X1/HQa+t1WbSgGB1jhxZwxdkTBFkmbjrCVS13431ByrAw+sPMNHePy6F988FQXOf4VL/H\nGgO5G7tD43kGQqbImUwYzNEXAecBr/p8vmMATf33+/0PAA8A+Hy+7wM+v9//nM/nOxf4OXCvz+cb\nAmSjEH6/KCvL7Dgxg6OkgU2hBCeXXUTU1YmjQk/Ickwhl2FDCqkoKKW5dShSIPXAe4JRSkpysdtt\nyHbVIatcx26TGXFGVSvx6WWlCrG53E5kFG2zqjIfm83G/b+epW3vydaT7a1XHavdY3FhSk+121NF\nscqKcygry6Mo36tz+CQkmazk8QpNLGKA6RMrM9bpdjntxOISToeNqsoCfvP9o7R1e9TzJKOA8nM9\nVFWm4q2/dsJorrrwcN3xTi3JpbEzxFETKykryyO/MN2qqK7MT3un4n0DSJJEZUVqVFVWkmv6HTgd\ndo3UoglZJygbK/m5nA6MvFmY52HY0ELMUFyUXupARUV5nnY9pcWKtdcXTr/XLK9Ts8x+ddk0Vm5u\n4ePVim6dnVznEKzySFzCYRI5VFWZz1UXTmLVlhbGjCwhy+PUaczPzPGzfHMqC7kg30tMGAGcecwI\n3kvWuK8oyYFktqv4TDsFR+6Q0hzcLkfG+V/LirL1JSsEgnHYbWlOY6fDTmVF6ttJJL+pvmiCd5bu\n0TJ8r710Gnc/ry/0lp+X+q6LC7zUNvaQkGTKyvK48wWlhMLe1mCadDRqaAH/e/mRANQmid+d5cbl\nTtHbsPJc9rb0aiO68rI8/nLNCfz+70u091ZRprxrl1dP/DLgNXQUJUXZZCXfi2TSWWZ7nRw2Tskp\nEB2ponSan+vpN5vWiH0tsjYYcn8NON3n8y1K/r7C5/N9G8jx+/1/N9vB7/e/4/P5TvD5fMtQjJqf\n+P3+Aa+stXXfphhzj1nH/Eb4evMsQu56bDaZ+I4pOMck45xl5aG3t/eS5XIQ26wnqIQks3tvJ7lZ\nLrqTCT0t7UF213WkWTAAoWCY/Bw3PckMuLeT9WM8LgdtJlUexUY5Zkg+w0uytHtMxFPrSgu8WiVA\nOSHR2hrAbfLBNLYoDTBmEs71kwsnMWl0Mf9417z0b0m+l6aOPmQ5/TkHk0PxNjXyQJLp7Ejdj5RI\nmL6bs5PSTGur+UQUWS572n7hkN7KTkgy3V2pYXYiFjc9l9uZIvfG1l6dE9VY8Mxhg1AkRlGeh55g\nlIQkk5/lyvh9RcKZIw/6esO0tgYoK8sjkrz2jkB6g3Q77ahdcXVpNusFy/RnFx3On19YTY+wX0d3\nOG105nLaaWvr5diaco6tKaejvTdNclm6UZ+7kIglkISebGRVPuOHF9IViGAXbEXx3gM9KSelzQb9\nJdLmGEZSHcK++TnuNAeyx/DOpSSZ7mroYZ7gpDUbZYb6xNpBSufRFYiweXuLrq6/UZaRkm0GIJaU\ncbbuaqdDcABXFmezt6WX9mRHFQyEGVKaw+gh+WzYqch24b4ora0Bk5IiUboMWa/xaBzVgthVl25Q\nZbmdtLX1Mn54oZasCFBVkqMVoPMII65vnzqOFz/Sz8rkdtp1Up5Z4EB/GJDck6R8tWHxVpPtnjX8\nvmGfrmQAPPSf9cQSEr+4eIqywCnUXYl0k3AoTSsezGOifBLrt+uLPBVkmJygLxIn25Oqed0TjJp6\n6H9zWUpHNA4pMznkxCG2UWP3CsPtyuJsjdzVBi8OKW2oCRjKh2uWCDS9phxZVlLvzfTQ4nwPTR19\nptUUVZlJ/Xg8LocuLLS/UrXaNZoceEhJukVsdKjKBs09kw7pcqXKB3T1RnRDXGOImcOhxO0X5Lg5\nfHQJn6xt0LI8TY/dj3ymd6im9FwjFEdeVLuH/BxX2n5iQw2GY5qer75fM7lkoGdvt9l0spLH5eC6\nbx+BDZuueJ0I8TxOu133LRphlC96hBjuAoHcVSvezIGen+PWcg9A+X6NksS4YQU6/4LTbiPL46S2\nsYfrH1mSdl0jKlKja7E9qPfyyrzU1Im/uHgyu5oCrNiSqhejtlnx2alBDi6nXUes0aiJLON2aM9R\nlZxUJzBATpZyf/9z7gSisYRW935ISbZG7uK3biYTlRVm6eYzMAZvVJVkc+6xI4y7aTgkMlQTksTK\nra2sEwoq2bNTw8jfLfkzcokiychRL67ACKTOSt0xMjkbb3h0CX94boXmUA30xdLCEiuKsvBVp+Qc\nYyM0ljxQIX6sxkYqkr3o3FE/evXjUP5WiEIlMbfLwV1Xz+Tso6t1x7TZbDx+3SxuuDx9kur+SEJ1\n/KrOUWNnNRhyB3j8upP56ddTo6Oxw9JlEDMiFStrmmn3oG/AXb2RfkPTbDZFJ3W77Hz71HGcMm0o\nl50+LuP2xiQm3TqTOHcziNFPToddF/Gjfge6kgmhGHFJxmZL3bPZszGSpREysu579LgdOOx27HZb\nxk7LqXOu2vo9h9EoEjs2cZ16D2bfSnGeRycp2Ow23TWfMLmKX35rim5yE7vdltbRT05O4AJw7GGp\nKDnx+r3u9O9n7NACjfQDwWQbSr5z/b6pv7OEzkfJUNV/b4mErLXVd5LTG4ocozqEszxOLb8AlPr6\nKsQOzhghZrwGSHfkF+S4mTkpcwmVQ4LcxYw29SMRyV2FHHeC5EyrmQKZLXdQMhojMYXYAn1RrSdW\n4TV8ZMYkErNQPNBbs0bL3eNKHfOIcSlfc3byo1A/DkiRu2ol5ee4KSnw6j4UEWYE2V8DVq1adWTQ\nX0fUH5wOuy66wYxc+otMgcyWu3j93cFovxX5ojHF+ex2OvC4HXznDF9azRb9Ne1bnLsZjKGt+UJj\nVUlLdPL2hePEYkoWpdrIzRy7JvlKaRDJWiS3TI5icbnDYU+7dhH9RXSI1qZ6DLNvpTj//7d37lFy\n1Ned/1T1Y6bnpdFI85I0Agmhn4Rk9BYCxNMIQ4wx9hoHbBwQxsYkHILXsY+B2E58jpNN4rCO4dhm\nzWYD2ex6Yx87Xq8J2CdPosRsbAM2a/gJB/OwCSBA6DXv6d4/qqvmV9VV3dU9NdPV4/v5R+ruqu47\nv6r61q37u/f+/BPrE5MzvjFfNdBFez5bIe7tgcypXylXGr95+yrvOgH/TTfs93NZ23vf1Q/396Oe\nrs2/e3xqpuJpuFgqVYyNmcZqhtxMx6SnI+9N3JrXuHkM3O1LAacxmC1Ta6nElhB3M/fazTG2CpUx\n7tKkcxIFZ9Oh9kCMT0Z77oXACVPtET+K4KOveTDd7BqYFdbl5QKbVf2drC1/7uaUBxuYBQnaC7Br\ng5P9cknA24fZm5U7sRS8EcT13MHJhe/tynPj5ZtCP6/VFyfKc88YN8pSKXwVHZeovyOKYJjL1FPT\n3uqeu9/ubiMs446v6XmVcEIE2YzlZfGUKvIpwsNdJqWSP0wY51iZvWmyGavqPtVuxl1GSq471mHf\nFZaSaJ677vibTlMm4Ln/2lsU60d6ufPms7l636n+JyrDxrAbVSZjV4i+F5bxHV9D3A1xnjQylG5+\n55u45IzVnP2m4QrP2vTcTW/dtLW9LettZ/6GOW6uMzlTLPHp63fzkau2eq9NekK8fZOW6C1jzuRP\nTDuP21YuxHOz3K6GlY/s1Tx3mBX3Yqnk5c67BC/cakUgUVR47oHH+GsuXs/f/vAXrFjueOO7Nw6y\nYlknqwa6+POHNDBbxRqslA0SfNIAp6/FZ3/9rNDwlBdz9zx3//dW8+yCLO1u486b90Z+Xi3tEKLT\nw9zz2oxrmphZCa53Fvw7Im0yxKG7I8cHL9/ESH8XYxPTvhTCaiIYPL7hnrv/vDxyfJJCW5b+3gJP\nPnc4tA97nBWncplw7zPsZhHcPhsifOYYV7uhmcLo2hlX3H2iWh7/oOfu3uiXL2nn/HJXUff89c8z\nVMbNXbIZC9uyaDeelDO25YUCzRu7eazNXPeJydkipsGlBa8aO3gt9RqTxGaoNRe4+fR1O/NfZi2L\nLySUz3D0hBPuXTXQxVBECLKWw9oS4m62yHz52Kv8yY/+J5neyhWXMvnoR/VaA2HGQ597+bhzQuQz\njE5MVxTWBFdfikPwpDc9ecuyuHD7Ki4sN6gC50RbPVjOly+fyG5Zs1spG/XIHibG2YxV8Xjs4v49\nYwl47rWo5blHhYBcoVq3cgkHf/5GRXe/YMoZxPfcTZv+4ENnejfz4DkT9/vAL+7uU0dwARC397zb\nBiFsoRnXc3f6ooQXsQRj7i5RmXPm9hm70nMfWFrwepxXC0XNFEvc8q7TyWdtvlLO9Ajre7M8pL7A\ntCHnee5+u1zCxj0XUlwGleeq+93muJjHO+p8NJ2MYqnExKSbnz+7fW9XG5/5wBnc8eVHAP+NeHj5\nbMjUN+Gbz/Cu89fx76+f8N1M8hGeO/hvOiaLIixz2PBonnj9SV4adfJ8S5NtFCaHyOL8kf3F9RWP\n9bZ3cWT5nf27vNnlat738bEpervznicRPGGCLVfXrYqO57pU89xr4dp6YtwpzHBPsDgXLzjNvao9\n3rvf74YNGo25x6GWuEedyK4Tms3YoX1Bwjz+RsIy1cSs2pNAUJjdMetsn40luw5ENuA5V+sE6Q5H\nWLiqpyPHhdtX+uPGOVPcIyb6fWGZSs/drCyu1lRtanqGreuWc9rJfVQr4+o3PHc10svHrt4WGu4y\nj33GtozzMWTuJhMu7pWeu13xft4n7uHHOxhPd59kgtf+8LJO7/o3K4KH+8LbXBfyWU4a6mbPaUO+\nm1nQc4fZcyrqklgUnruZS/viidkKudJ0jtefcOJRVuEYJ29cz8CaGf71qVc4c9MQ45PTXmdBcMqL\nVw10sXvjII8+fYi/ejg8VQyc8IK3Cn1gdM2T8LytK7jqzdGZGC7BmHt94m5MyBgHNE5Jw76dI1z1\n5nVVtwl6WxXpbA3MMUQRjG9vKz/ifujtm6q2Y3VDLZblFN48/XN/quvyJe2sGerhb4yGYdV6x5uY\nQmNXmcHMZmwvbTHI+lVL+P5Tr3CZsajH527ZSy5je9WL7oTYks689xSWydhV20ZYhnMSXErqc7ec\n49nl0pbPUAxpMGYSrFYN3sy7Cjn2X7qBvp72qmNoPu0GF/swWW7cLG69cgtt+UzAe3Z+PxMIy7jj\nFnbDNUW5Wsqx57mbE+O5cKE3CToLbtuEsFDore86nQNPvMT521bwF989WLF/MCzjYmYQmd+7aqCL\np55/g8HyylqWZWFZlc5cy8fcJ4xObgAvjc0uHFGano1Zlca6yZLhA29TvO8tKrK5lm1ZjAx08f9+\nVtlhzXz07eueLSyqxkBvIVbYIiiY1XKLg/jE3egIGeWZmeSyds1JuWCYKegpNTKBXM0el1uvPJ2z\nt49w7MgYuzcOVtlrVqgsy2IwpKI0n8vw3ovXc2J8iu/95GXvvXptqoZlOWmDYavj9Ha1ce/HLvDd\nHNyLz+3+6Irhkq5Zcc9mLK/7Y1ius3voqt1gg5672z3SLZIJFkuZZDNWxQ1t9UAX55SXt3uuSiM5\nn7iX/w07I03Bdf9vnnOuwNqBCVX3ZhjmCJmibBYDZWyb87au8No9h3nucfrsBD330fHKsIy3bXvO\n6yR56RmrK9IagxOqLmGN4wDeee5aujvynGcsMWhblm8hIsBXSxFG6sX9sFl9aE9zeMroYjDtH8Rn\nXz5KNmPTVah9sYatZtPfW+D5l53J1L6etlj9QuJ64MHHxbiCAv4Qkum5xylHjuO9BsfCFcWNJy3l\nyecO+1K85or5d69buYT2fJY4dcklw3M3F892cZuL+YUu3hhXC8UEyWXtUHHPhIik91kgW8b0uLK2\nU+TzO/t3eY3MTFwvu9qR9mXL5DOMlTOHz9u6kp8fOuE1eQvf1/Z58vsv3cDe02dzp6tNqJreaS0H\n4soLTvFqScAv5LMxd1PcbS45YzX/5X//hIt2zs5FBfdxbPQfv2sv2cAzLx7lhVeOe9t1G06Rebyj\nHCQ3E8Z1+LywTI3c1CsvqHxKzgVi7i5hcyzONlneZjwBgjNewe1rJYmkX9yN6q+x7GFfBoDrub/1\nzJP49r88x5ZT4vcmC/OEBkxx7zYmH6uIaNzJxuBNwLIsNp60tKK3ehh+z91IP4shStXCDGHfD7MX\ny0eu2srE5ExoYUij+CfS4ouqexHalsWaYacXzeDSgldNXCu1rRr13Gh7u/IcH5tip+rnvG0r+eOv\nPAZUn2QPziOYnrQrzO7keRDPI65yDgZj+C5t+QzXv3Vj5H5QvikZ9p0TWJA6amz27RzxC5AXlgn/\nnUvPiK6kdI9TMFtmz2lD7FjfH3qe1DrO7vngjkcum/H6upjzCFEFiBtWL+XUVUsY6OvkwI9e9Kq3\nay16HoZ5nZr7uy2ng2shhGGeQltOWUZfT3vN6yf14u725R7q6+DZGX/hUmk6Ry5r845z13L2m4ZD\nU66iCPPczVn9pd1t9Ha1cXR0qiKf1SRummCYEH/06m2x9jVPCDN9asu6ZVy0YxVnbh4K2w2IPnlN\ngt5ImzHBFZV33iiWL94bP6W05IVlnGPzBx86k7Z8hls/76xH6l7g5jjHvfHGuQG6fOSqbbzw8jHW\nrujxFdJU8+iC32+eT7VCXme/aZjH/+01Lti2kpXLu7jvwad8JekQTEmsL003m7GplvwVdYO8+iL/\nPFOcm1AUrqMVzHOHaAegVv2Bl15ppje2O21GzKfZqMtjqK+D267ZwT/8+CUO/OhFLxUyav3aakTd\nIFcPdvP7H9zjdU+9/X07vDUbgph/x0W7Rth0cl/N3029uLthmYGlHTw/6p9E27Smj5vfcy62ZVWs\nWlOLMM/9ZGM5r76edm56x2b++nvPVazBaRJXQOrxDoOYImgWKGVsm/fsW19136iFfU1qTajOF/UK\nEcwKZTDDxD0O/vzp5JPBlnTmWbJ2WcX71cQ9+JkZz611g9u5YYC7bz3Hu5Gcvm5Zhbg34k2av+/O\nT4V9jy/To0o65prhHp596Rgjg9ErHEXhetI+z73GuRGWJ28ym1Fm9r4vnyPG3zlTjG5jAX7nLWNb\nNe0Ko1poa9DQrXUro7PuzN+Na0Nqxf2HBw+xfEm7t6pJb98M2c4XyZBlplQEq4hllRrKOYfwE3lk\noMsr4OjrcTz36y6t/lgbN+ZeK1ZXDdPWsAKlqH2mZ4qRkzY+2zJBz31hxL0ebrpiM//9Owd5+941\noZ/PJSwD8Ec3nVX1IqxFNY8u6LmbHn8cYTa3D3NK5iTuts3mtcu44pw17Aj08Ae/cHYX8oxNhCcZ\nvPuCdawZ7mH3xsrvqEVUhWo1annus9k7s++557UZu3adhChhNUOSjXjtjq1zv56Ck81xSKW4T0zO\neIvGOiuGl3hs5gEArLGlzExNk+k5THeuvv7vJmEHamBpgZ7OPOOTMzXTjFwajbnXg0/cY/d5sZie\niZ60MbEty5fiNx8e71xZM9zDJ67dGfm51wgqZruAIGELi9RDrZi7Ob6m517vTT+0oGcOx8uNuV9+\ndvhN0xSV7o4cr7wRLu5t+YxvIrYewkIotYTUn21TOSbeRLSh7u61M26EPnZuGOADMyU2rw0Pc5j9\nbbINOpJzOT4u5mnS0p672S/7uZeO0rH0OG9Mv0ZxtItjT20GLLLLf8EZ5+9p+DfCPKCMbfP2vWs4\nNjoVOw5bS7Q//t7t6OcPh6bvxcUXlonpue/YOMiBx1+MNWFrWRaZcqvgfK526uRc+ejV2+peeKAW\n3gRazIKkpKkl0ma2Q8EXlqnvwg9txjYHz72efaPSi+eKe62Zgl7PPEhYaMs9hYshYRmzs6VtWVXn\nrEzPvZG2I0lh2fWPTSrF3Vzc+OjoFH2rX2YMmHpBwbSTLvbuzRez4aTakwpRRE1k1cq3DlIrX339\nSC/rR8JXAIqLKQCFmJkrt7x7K1vXLmPbqfEyiDK2zfTMzIKEZIJrSyaB68mZTzbmHMp8U4+457KN\nTSo7+4Y4JXMQHasOEe3qSFbcgwVhvrBMHQ5GmDNieZ777HveEnx1rEVqOm+N1nss62ljVX8XZ5xW\nf8jKxRdzj2lGKsU92HLXLjgTSMVjjpjnczYX7ljV0OSGi+mxrBnu5v1vPa2h71mIyUd/zD3e73W0\n59ihaqdYubgXVhrj7XFwPbRtp/Zz1YVT7Nk8tGATw1BbYH0xU19vl/oEIyw0kPBDUCRJZ07d/eFz\nfYVQwVTIuWCHeO7ujT9ssZUo/H36G7MpY9t8+v27a29YhUYmVNMXXIWKpbtm7HHaMm1QdAZ6uK9z\nTsIO/ovx8rPXxApfmLhtepPsuxKFP1tmfu7H7nh0ztOj93zjtmQttGW5ePfq2HMmSVFLjExPNGs3\n7rmH3QuSDnFFkfSNv9CW9RXlmWMUZy7iop2rvG6RQVzP3Zxycusj4vSCcvGHZZonl+Zxb+mwjCvu\nW9ct57GfvkopO05Pvht3FcV6hTgMX9vTBiY87njfTqami3O+ycTBF5aJ6bnXi5sP353wo/d8c+6W\nYf7x8X9n7XBP7Y3nkThhGW/bwGIZ9RB2vrkVxIMhlbs1qeO+ELZaUJJEjVEU77koOg3Y/S5zQvXc\nLSvobM+xaU38cK4vW6bBCdUkCDZVi0Oqxf36t27k2ZeO8KWfPURPfnbSI6wcuV5Mz72RCSnbthbE\na4dgtsz8HDK3aVd3i3nu116ygXedv27eJvuSIiqVrX7PPaT4bkmBT163k4Eq3SWjqMfnP3mom/9w\n3tpyF8jk8TUOm6PTFNbIzLIsdm6oL+7ty5Zp5oSqL+bewuL++rFx2nIZOtuzjKzIU/pZiZ62bq7Y\nu4al3W3e49VciCrZTiPZBrMI6sG9BubbO0say7JSIey1IiN+QXf6uRRLpbqPZ5TonTy0ME8uZpfV\npGkklzsKi8oJ1UYIro3bLOop8PK2my9j5sLRE5P0dOawLIujk06vl558N5fvXVPR+6JR/OLevDty\nHBoJGzVKGoSylThlhSOqtcYt+FjtPjmWateY+Ug8TbUO9WuktUA9BFv+zoX1I05c3VxUuxHSohPm\ncLRsWKZUKnF8bJqRASeufnTS6RnYk082rc08UEkUGcwnjRZPNEKrxdybzcev2c7YxEzNTJJgKMYV\nr+ka5e9B+nqcVOBaCzXMBzHq4eaEP+Y+NyE9Z8sKhvo6WFtlYfQ4mDfTkxYwtTaI6RzEvcGnTtwn\np4pMzxS9rI35E/cWCsssqOfeWmGZZpOx47WY9occbLK2xQTxGruZrBnu4aYrNntPDI3ittmIk954\nw2Ub+ftHX6za+yQJGs1zD8O2LNTqZOsptq6L33U2aawGQlapE3d3xZOucj+NYxOuuNffkKgarSTu\nC/kwKJ77/GAHPHc3GyROe4ggu+qcFAzjtmt28PCPXqxanely1uZhztrcWGuBekgyz30+qCeFZ0z2\n8AAADqJJREFUMmnsxTCh6i5y7Hrur084CZBL2+dW5RmklcIynYUsy5e0J3JR10Ji7vODv2+K7Xlf\nMzMLVIEUYMXyTn71wtrLQy4kjaT7LQR/eNOZFEtNToU089xbNSzjirsrMq+OOSsvLWtP9hGr0b7i\nzSBj2/zhTWctyG+FLTQtzJ1gx0NP3Oc7kN1CNJLutxCYa8A2i0XRfsBdzspdrea18dfpynXSnp1b\n175qpD0ssxD82iWKJ589XHPpLqExgmEZ2xP3OtNlfklIk+eeBlq6iGl8Yppv/8uz3qNPVyFHsVTk\n9bHDrOxKJv0xCjmR4PytKzl/a3gptzB3guuDqpFeXjk85i21JviRa9KPGYlpuZj7XV99jH989Bfe\n665CjiMTR5kuzbCskHwXQZP5bnErCO71aFnOxfmei9azaU1frPUzfxlJU1gmDTRSxJQacX/s4CHf\n685CjlfHXgVgeWFuhQiC0Gzci9Nt/NaWz9TdXvqXCfHc/TSS556aYPPRE5O+152FHM8dewGAgQ7x\nboTWxhUryUaKh3jufhoZj9R47kG62nP84OnHsS2bzcs2zMtvfPy92339pAVhvnBzYlq1pfJC08y0\nwzTSSCO1VIp7NmMxWjzC88d+zsa+9XQnXMDkMtcVkgQhLm4WmHju8ZCwjJ9GpgVTc3vctHY2rt7d\nkefgG/8GwOblG5tlkiAkxgmvfiOV/lTqkLCMn0bGIzXi/vu/frZXTNTTmefpN54BYH3vKc00SxAS\n4Vig8lqojnjufhoJy6RG3C3L8uJsPR15nj78DJ25DoY657/kXhDmG3fdTgnLxEPE3c+8TKgqpSzg\nC8AWYBy4QWv9TMh29wCvaa1vN94bAL4PXKS1Pljrt9x1MDs7S/x04g1OW6awrdTcfwRhzoi4x0PC\nMn4aGY44ynkF0Ka1Pgu4DbgzuIFS6kZgc+C9LPAlYDSuMW6fjVzBSYvsa5MJT2FxIeIeDxF3P40U\nWsYR973AgwBa60eAneaHSqkzgV3APYH9Pgt8EXixXqOK2XEAetua12JTEJLkgu1Oa4dTV4nDEoeF\nWHi+lZivmHsPcMR4Pa2UsgGUUkPAp4CbMdqOK6WuA17RWn+XBtqRT1knABF3YfFwzb71fOkj57G0\nu63ZpqSaKy84hT2bpHI3SCNp/3Hyso4C5jJIttbarfy5ElgGPAAMAwWl1FPAfqColNoHbAXuV0pd\nrrV+JY5Rk5TFvV3EXVgcWJZFPpepveEvOZeecVKzTUgl81XEdAC4DPiaUmoP8GP3A631XcBdAEqp\nawGltb4fuN/dRin1d8CNcYT9t/fv5r4HnmRw+Aj6eVg7vIL+nuatWxhFf3/6bApD7EwWsTNZxM74\ndHQ4rbgtK749ccT9G8A+pdSB8uv9SqmrgU6t9b0x9o+9GsHawS5+d/8uvvC4c/8onshyqLzMXlro\n7+/m0KF02RSG2JksYmeyiJ31MTHhVDhTosKeKLGvKe5a6xJwU+DtirRGrfV9EftfWOs3grwxcYT2\nTBuFeVygQxAEoVXwwjJ1RGdSl0Q+U5zh5dFD9Hc0b6VxQRCENNHIhGrqxP2l0VeYLk4z0iWrAgmC\nIMCs527V4bqnTtxfOOasxjTSLeIuCIIAs0VM9STNiLgLgiAsQlIn7q+Ul9YbloZhgiAIPlracx+f\nHse2bNoyUsknCIIAUIqfUe6RQnGfoD3T1lCjHEEQhMVNC0+ojk2PS367IAiCSf2Oe/rEfXxmnHYR\nd0EQhApaNuZeLBXLYRkRd0EQBJcGHPd0ifvkzCQlShSyMpkqCILgUVb3emYiUyXuY9POIh0SlhEE\nQZil5bNlxmcmABF3QRAEk5LrudcRdE+VuLuee0Fi7oIgCJW06oTquIRlBEEQEiFV4u557iLugiAI\nFbTshOr4TNlzl9YDgiAIHqVWL2ISz10QBCGali1iGp+WbBlBEIQgrZ8KKZ67IAhCFVo1FdKLuYu4\nC4IgeLR6zF08d0EQhEpcbW/dbBkv5i7ZMoIgCB5ehWr8XVIl7mPT4+TsLFk722xTBEEQUoNa3QvA\nntOGYu+TKhUdnxmXeLsgCEKAHaqfT79/N8PLOmLvkypxl1WYBEEQKrEsi1X9XXXtk6qwzPj0uMTb\nBUEQEiA14j5dnGGyOEV7ttBsUwRBEFqe1Ij7+JTb7lc8d0EQhLmSGnEfnRoDpPWAIAhCEqRI3KWX\nuyAIQlKkSNwdz12yZQRBEOZOasTdWxxbYu6CIAhzJjXiPjopnrsgCEJSpEfcZUJVEAQhMVIj7rIK\nkyAIQnKkRtw9z116ywiCIMyZmr1llFIW8AVgCzAO3KC1fiZku3uA17TWtyulssCfAicDeeAzWutv\nVfsdyZYRBEFIjjie+xVAm9b6LOA24M7gBkqpG4HNxlvXAK9qrc8FLgXurvUjs3nuki0jCIIwV+KI\n+17gQQCt9SPATvNDpdSZwC7gHuPtvwQ+YfzGVK0fGRPPXRAEITHiiHsPcMR4Pa2UsgGUUkPAp4Cb\nMVaA0lqPaq1PKKW6ga8Cd9T6Eddzb5M8d0EQhDkTp5/7UaDbeG1rrYvl/18JLAMeAIaBglLqKa31\n/UqpEeDrwN1a6/9V60fGpsYoZNsZHFhS31/QBPr7u2tvlALEzmQRO5NF7Jxf4oj7AeAy4GtKqT3A\nj90PtNZ3AXcBKKWuBVRZ2AeBh4Df0Fr/XRxDRqfGaMu0cejQsXr/hgWlv7879TaC2Jk0YmeyiJ3J\nEXXziSPu3wD2KaUOlF/vV0pdDXRqre+N2Oc2oBf4hFLqkzjLu16qtZ6I+pGxqXE6c/WtNCIIgiCE\nU1PctdYl4KbA2wdDtrvP+P+twK31GDI6Pc7y9mX17CIIgiBEkJoippnijLQeEARBSIjUiDtIXxlB\nEISkSJW4F6T1gCAIQiKkStylOlUQBCEZUiXuOTvXbBMEQRAWBakS96ydabYJgiAIi4J0ibsVJ+1e\nEARBqEWqxD0jnrsgCEIiiLgLgiAsQlIl7jkJywiCICRCqsRdPHdBEIRkSJW4Zy0Rd0EQhCRIlbhn\nbAnLCIIgJEGqxF3y3AVBEJIhXeIuE6qCIAiJkCpxlwlVQRCEZEiVuGcl5i4IgpAI6RJ3yZYRBEFI\nhFSJu4RlBEEQkiFV4i6euyAIQjKkS9wl5i4IgpAIqRJ3CcsIgiAkQ6rEXfLcBUEQkiFV4i6euyAI\nQjKkStwl5i4IgpAMqRL3jJUqcwRBEFqW1KhpxrKxRdwFQRASITVqKiEZQRCE5EiPuGdE3AVBEJIi\nPeIunrsgCEJipEjcJQ1SEAQhKVIk7uK5C4IgJEVqxD0n4i4IgpAYqRF3CcsIgiAkR4rEXTx3QRCE\npEiNuOckFVIQBCExaiqqUsoCvgBsAcaBG7TWz4Rsdw/wmtb69rj7+AyRsIwgCEJixPHcrwDatNZn\nAbcBdwY3UErdCGyuZ58gEpYRBEFIjjjivhd4EEBr/Qiw0/xQKXUmsAu4J+4+YexcsSWexYIgCEJN\n4oh7D3DEeD2tlLIBlFJDwKeAmwErzj5R7Ft3TiyDBUEQhNrEiYUcBbqN17bWulj+/5XAMuABYBgo\nKKWewhH2qH0EQRCEeSaOuB8ALgO+ppTaA/zY/UBrfRdwF4BS6lpAaa3vV0q9M2qfKlj9/d21t0oB\nYmeyiJ3JInYmS6vYGSSOuH8D2KeUOlB+vV8pdTXQqbW+N+4+c7RTEARBqAOrVCo12wZBEAQhYVJT\nxCQIgiAkh4i7IAjCIkTEXRAEYREi4i4IgrAIaWrNfyM9aBYapdQPmC3I+hnwe8CfAUXgCa31bzTJ\nNJRSZwD/SWt9gVLqlDC7lFIfAD4ITAGf0Vp/u8l2bgX+D3Cw/PEXtdZfbbadSqks8KfAyUAe+Azw\nE1I0phE2vkA6x9MGvgwonPH7EDBBisazip15Ujim9dLUbBml1DuAt2mtry8LwG1a6yuaZlAApVQb\n8M9a6x3Ge98EPqu1flgp9UXgQa31N5tg20eB9wHHtdZnhdkFfA/4LrAd6AD+CdihtZ5qop3vB3q0\n1v/Z2GYwBXZeB5yutf6PSqle4HHgMVI0pgEbl5bt+11gSQrH8+041/YNSqnzgA/jVLGnZjyr2Pkt\nUniO1kuzu3X5etAopWr2oFlgtgCdSqmHgAxwB7Bda/1w+fO/BvYBCy7uwE+BdwB/Xn69I2DXxTie\nyD9praeBo0qpp4HTgR80005gvVLqChzP6MPA7hTY+ZfAV8v/zwDTVB7rZo+paaON40HuADakbTy1\n1t9USn2r/PIk4DBwUcrGM2jnyWU7dwAqbWNaL82Oudfdg2aBGQX+SGv9FuAm4C/w99A5BixphmFa\n62/gCJBL0K4enBYQ5vgeZ4HtDbHzEeCjWuvzgGdwehMFz4Nm2DmqtT6hlOrGEdA7SNmYhtj428D/\nBX4rbeNZtreolPoz4PPA/yBl4+li2PknONf4I6R0TOuh2UJarW9NGjiIc7DRWj8NvAYMGp93A280\nwa4wzHFz7TqKc1IG328mf6W1ftT9P7AV56Jpup1KqRHgb4H7tNZfIYVjGmJjascTQGt9HbAeuBco\nhNiUinM0YOd30jymcWm2uB8AfgWgjh40C8n1wB8DKKVW4Bzc75RjcwCXAg9H7LvQ/FApdW75/65d\n/wrsVUrllVJLgA3AE80ysMxDRvjtzTiPtU23sxxTfQj4mNb6vvLbj6ZpTCNsTOt4XqOU+nj55Tgw\nA3w/5Nppqq0hdhaBryuldpXfS82Y1kuzY+5p70HzX4H/ppR6GOegX4fjvd+rlMoBTwJfa555Pn4L\n+LJpl9a6pJT6PM7kjwXcrrWebKaROOGtu5RSk8BLwAe11sdTYOdtQC/wCaXUJ4ES8JtlW9MypmE2\nfhj4XArH8+s4184/4OjMLcBTBK6dFJyjQTt/EycD6e4UjmldSG8ZQRCERUizwzKCIAjCPCDiLgiC\nsAgRcRcEQViEiLgLgiAsQkTcBUEQFiEi7oIgCIsQEXdBEIRFiIi7IAjCIuT/A7tLAGF9FQRDAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf5c1860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.459230552006, policy = 0.491218487806 epoch 3 complete\n",
      "As X, neural network has a score of  95-  0-  5 vs 0-depth minimax\n",
      "As O, neural network has a score of  90-  0- 10 vs 0-depth minimax\n",
      "epoch time: 3197.746s\n",
      "Starting Epoch 4, temperature = 1.42\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 550250\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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Z94ztCI40jqpmJBkO9jVo+7jr9yP6hk3EboWD/fogEnPpg8+BvsO0hUfeF2B3\nm7L/h2d9kOpAFdu7dnPrxm+xu1uxhuNJCdFvjkxqyJyz1FNComEBqWN1TAvMojfWR5wBxIDynqwo\nO2Pkk2fkoc0dWzkSD+EoVmYa59Qq+7UNd+CS/UgDFWww9MfB4RRytBAxMHLhP5/TT7JpHnJap2aP\nQ+lPrskhZEkg1TUJOTV6RvFbntw/ftfz3PHbrdrfqkwSjado6VJIRnXCReIpJFlm/5G+DJmZvepG\n680ofURiyRHrhOw42GXSaNUoEDV5xkjuqnVaHNAtd1dmum0k9/5wnIefPpATUQKKBv2Pl48gy7Kp\nguBr+zt54G+vs/2g7oj57m+35pDzaHHADW3mAUF1fFrFThvbPBRNsudwD7c/sDlnO+NU3GrAcjoE\n/F4XBT4Xuxp6+PSPXwRg9uSSnBnX1WdPpSjg1u6lCmNUidft4NrzpvOeC2ZSW2FO0f7fFw4jZwYz\nFeogaZwVWWH34R6e3Zob+pZKyVoJAE8mR+CotIdemnFP32l5rMFIQvPlDAcUCzhdmCEEI2nP3UzC\n20m/L7e2jeDU76sgyjirm3DP2kqLb5P2vXf+S7gWrGfeWbqDP35wMTWRNRQOz0RwpHFPU6S2ZJt5\nebc211YClX30+/fSETmG4Fba5Sjs42j542zu2Mp40TjUZPl923A7B3obTd99ZektJI7MMX0Xdyj9\nc8feiCZ3ADq5J9KIvqywU1l5ru+dfS3pnlqSR+YxyTMNgETRURxlymBY55lJqsdsFEx2zabSqyQr\numXrdP9VE5dpnz3hekA0+U/6h+JIEbNBkuqqxXFkBVJcT+QKuALI0UJiWy/Svjundg1O2Ut6qAQ5\ndBZypBg5eZqTezKlWHcNbTopbzNUgnttf6dp+3gixYadbdz5yHYeW99gerlf2NlmiuowWq+HWgdG\nJHfVkfbZdy6kvEjPhrQid/VzSaHycPrDBsvdcI6nXz3Ks9taNKL+1vUruXzNFO33dZubOdYXZSiS\n1Ky/1xt7efn1XMvH6CwNR5PsPjzyykcNLcr9VC1pNcU/O447G0ORJNsO5kY/gNlyt3K0qn6HCaVK\nR1cHoJJCDz/45Bptu4DXyeVnTkUQBG3b4swsqNWQ1OR1OxEFgYtX1PHF9yxmcVYKdyyRJp5MU1ro\nMb2E5UWZ6a4ggZhC8A2ZiDYf1m9vpaVrGJ/HiStzn7oFhbDluN9yn2g8jUMUSEtpYi6l38oJ5Z4b\nSVtF2qG7x+uwAAAgAElEQVQ8B8EdZensciqKvZyzzHxdgieCozjXIRdPxzgcUfwEyaOzkPqqqXUE\nqYot184JkO421wlqFXfSP+FFHLUhNnY9b26PM0I8naDWOcNSJ86HA+HXc69tQPFThAaUqBEp5ifZ\nMgMx7SPdmVu7SE47aGqNsPll3eJwihmfTTqsDULZKDREsFS7puAUnYjVDThKuimSJ+JOlpJsWET8\n4GJtu4Dbz6zSzEAg6MaW8b5NLpxEuVfRwbsPK8+kyFCvpz+cgKjuJ7qi6v2IrYsJHytHjujZwYVa\nrRj9umoKqpkbeReJfaspc2dKWKRGz5Z9S5P7wHAuSRitzpYsbTQaT9PaqTycZ7YeNYW1HW4zT5eM\n0RctXcN59WvQicvvdVJbqT8ojdzdFuSesdwHwrpDVY17lmWZbYaMyoDXyaQJBVy0oo53rK7n7IUT\nSaYkXtunEHlF8cj6W9hArD94ZPuoC2Kos5+JZWZSyiZ3Y/YlKKGh+aoWGsNGrTR3jdyz0tG9LgdO\nh6gNYMZzquQ+eYJiEbUbCkYZy+kWF3j4zDsXMqe+VPtuKJIgkUk8Ml7ndefP4IZL5+Ce/Rq+5c/g\nXbAJz9yXcWtlgLP6gaBEpah19t2BGE+3rgNHkqiQGUTTTi7IU1zN4RA5Gm5FEjJxzJkklVULcmsb\npcUYgmcYz8INCNUhvv/xM6ibqPSjGcnzkWI+TSbIxpyyoPZZ1Wv9Hhdel4v0gD5AyDHrgQjgaPwQ\nsiQgDZud2VOcizQH32gBWpLh+FJc77fpvir9+0gh8V3nkGqbobyHsoNEw0KE/lrTNexv7kceLtGI\neEPrS3x3892Ehfyx6y5BP6cj7ePzSz9BqrMOKVxMbWxlJmlPQOrTrfeAy0ddUcaxgcjM9FpSPdUk\n2/VZjiiIrCm5kETjXOS4QtDGmWV/OE5hbBrisdkkGudSF6ijIBNAICf1QaLEm1snpsAVIJl5fcoy\nnCLH/STbphHftyJne61NeX95C8AqbXognNAiALJD4qLxlGYxp9LmabkqPVx6Rr1yHAO5b9rdzo48\nFinokRp+j9MkAeiWu040voxzVbU2+8NxLQoilZbp7IvwelOvyYmpVhss8Ll453nTtXo2uzJZpQUW\n4XNGGFPNrZyB2TiSkXqqy8xT0GxyL8iq/yKDFl2UDXV2I8kynf25Gr4aNZJdAU8lc/XcxjZUZSrq\nqclPVvsZ8Ymr5jN/mkJCg5EkiWQat0s0nbMo4ObMBRNxFOohdqI3SmGBG+ekEJ6FG5SwP0AsPYZ3\n6bN4F70ATuUeJ4uO8PKxV3BWNevGlzPJ5WdO4TLDzAtALO4kUrOJ3d37tO8EZ5JZ9X5WLSglGykx\niqOsA0GU2RvZwudeuF1zaLoEL3Ikf+XCYNl07bNqcfq8TjwuB1JY8Q3JskDA6zKRbjakoVKMA9zS\nwrMoEatJNM4n2T6F2LYLWOO5Nu/+8V1ns9r5TpKt00m16W0qSOnEbZQv1Pcw3VNDvL1G+97oUJT6\nqhAyN7ttuIOhgOI7SDTOpXJ4Gcm2afp+BjkjnkxT468l2TSP+N4zIF6k5WqYjAOfn9XVy6C3jsK2\nsxD6a0g2LDbNyGRZZqCjmHTXZO27hCGwYXA4QWkgwLcvfz8fXH4xc+pLtVo1UsYJfnH9Wkp9uc/Q\n6/RqEvKUiervAqmWWUhD5Tnbq3jLkvuexh7Les2DkQQlBW6lqmGWthxNpEzZlcZyqx0Zq68kE8Vi\ndL6290T420tNpmN98qr5TMpY6eog4/e6qK3UR21vxmL3W2juBT4XTodA76DZoXrr/a9w9x/NGm12\n4avptcWIgqDJUYE8RbZUjHWRiWxUlHhNIaDurCqGVoWUskMKVah1W3oHY5ZZnqqunh3+qJG6O5fc\n508rw+dxsjw4IccfYBXjX+BzMbc+Q+7DCRJJCbfLQTLQiqOqSTu+ZX5A7R7FGemN4qxRCNVZeRTB\nISE4UzgrFR0+klZmhsakIsGRxOUUKfC5lCSUjMzjqt9Hyn+Mp5qeBSDdp0y5ZXeE4WTufYwJAzjK\ndNktKaXY2aVo5S48OZquERMDumWsWop+j0Lu6Z4aUt0TSexfzhfesxjHwfNINgctjyMNF5PqVJLN\n4gcXs+nZAmW2lnKTOjob0i7K3LmEI0sCyZYZgIAUKSLVOhNpUN/uq9edpW8bKdT6ndGAS0f1Qdwo\niYCAlNBJO+VTLPdSoYb2UBVSWI+sihlsj0QqbZr9xxIpbeZprJJZUViAy+Ei0LWM5GCJlpkuDZST\n7q8kfnAxqbScI3fGM8ZlZ1+UtCRTXuSlpMDDOYtqEASBgkwht3RXHf85+8tcOf3tpv4txhUiL/UU\nawPF2QsnMla8Zcn97j/uNEknoDycRFKiKODG43JwpCNscrZG4ylTpEhThy7FhKNJBMhx3uVDWZGX\nieXKyK12EMVyVzpFqUF7t9LcBUGgbkIBLV1hbVRO54kOySZ3n8dJVZnBCTMquVtLJWqqu9Mh5MRQ\nq+cpMdSRz674aDVjyFf0q6F1gG//egvbM5FExk58yco6zl2sWG5vWzVZixMHndTVzE2jRTVzUgk/\n/9w51FcXMiGrLnY8T5mAooDS5r6hOJIs43GKNLjX467fj+AN86uG+9jelWs0RAJ6nLRzQjM4EziL\n+5DiXuS0A+fERsSSY6QEhT2M4W6CK8n27u0Irhie+ZvwLngRsagb0nq/cBPQpI0h/yEO9eeuNCWR\nzhtt4cKjkG48ALJAbPeZGTJVUOkzSC8Zq3d2fakyYEsOkocXcfs1lzCluogSfwDJQKQ3zP6Ivm+k\niHRXHbEd52rSRVNW8aoib4CPLfgw7oa12nep1hmk2pT2qIQtx31I4SIcfVMoL9b7sxT3a/6efqP0\nmtL7ot/l5a5PruEXXziX+qpCLbPTiHk1k0imJNOMxigbxhNpkxM+Gk9rlntJVvw/KLJrJJ4ipgZb\nyA4SB5Yh9VXTPRClrXuY2ZNLtP57tGuY9dtb2Znx5c3NKiNunDFWFCr325goVnZsLel9Z1PqLdFm\nmRNK/bpfaBS8Jcl90ELXlWVZS0Ev9rvxuh1IsmxaJi4aT5ukmp6srEy/12kiD0GA1fOqsILH7dB0\n2IFwAoco4HaJTCz3E/A6qTNo71bkDjC9ppi0pIcrGpNqjE4+q5K1RkIfrRRpPstdHchkWT+HcXAr\nK/SYJA9JkjEOAcbzqjKTER6Xg+vOV17o15v6aGwf5JGM3m887nXnz9QGGq/byfWX6tER3mydP49T\nd3nQXCvdqojZs80bWNf3OxBTmnzkdOpX5Jqyl554N7/KpMzng+BI457yOrKQZknlQpJN80CQcc/c\nnhVXruPh0KP8tfcBBGcKwZXEM3sLYkAhxbdPuZCFjotI91Uhp5wMeA6NOwrFJfgg5UE8eB5zo9ci\nRwuV7NQMyrwl2udPX72Euz65htqKgOl+qv3Z7RRNA8+SibO1z1KkCBCQEzoZH85a11eSZRZWzmP5\nlOlYQS+nIBDfu4aKIUU3vnLq5UgxP9JgGWUZKTKnHk/G8epyOCkr8uJ2OfjcuxfhdJkHc2esnKkT\nM3JTQhmAi6QaE3ckUhLrt+lr3cYSKYaiSURB0GbdAOlMLRq/x0k8kTaVYFahSqTzp5Xzn+9aRHHA\nTTSe4rf/DPHH5xTDYME084zGSO7q+26cKfucXhJDAb738DYSKQl3ppjfd25cxV2ZIIPsfm/EKU/u\nvYMxntjUaCqva7U0WFqSNUu+KOA2kbSKaDxlkmqy9eGAz6WRDCiO0A9dEtSsCCO8LocWFTEwnMDn\ncSIIAm6Xg2/dsIoPXqJPa31eoyyjt2t6rTJdVK3dYUMFxq98QA+tsppN+A3HNNbptkI+clcHGkmW\nqSj2IQB1BtItL/Yyc5JOCilJNt0fo+ZeVphrTcyqK+G8xbWWIZT1VfklBKM0o1ruamKJlZYOsGqO\neRC2im7a13uA3mQXYkE//2p4GcEdMRViEr0WySVAbJcuGUiZyAZVHllauZh0Tw2J0HLL65SyHJRy\nwkPi0CL9+uJVXDbtYia4a5DjfuL7zItP+J0KidYX1mnfqQ7EszILVRS7C3GoC53JIoVOxVJV5Rdp\nuAiH6GCCr4JJBTUsmVlJWcb6cxvJPfPZ6RSRwiUkW6ezNnAdgiDgSGSOaeFwzc4fUVWta86ZhouM\nAzAzWLicosnxDWhRYBdNOYv4rnMUaSfTvv6s2XmiYRGpjnpKh+dr3xUF3FqmZlV6Don9y6kcWE1V\nqToACcS2XsCxLQt46Ek9s7h3MMb2A11MqS6kothLLJFmKJKkwOfk6nOmEUgqEki1X/Fxqe9L31Cu\ncalG5gUnK+9Ldj+dU1+aY6QZ3x91VjynvhRBgHetnc5Axlg9cLSfeCKtyaIel4OyIi/3f/FcPn7l\nfPLhlM9Q/eEfd9DeE6HQ72btEmXq3m3htEumJBO5W5FAJJ4yJd1k11MJeF2mzu7zOPC6nUyqLMiJ\n8PC4HaYiT0ZdPfshGn8zShtTshJx1JjxlXMmUGWI4Miu6ZF9zHyWu9/jJJGS6A8rdTCyl5vzqcQp\nw7vXzuBYX4SDLQPsQdEOy4q8WmcFJZrHmMxlHFSqy/xaxIgKj9uBx+1gTn2pqQ6Mx+1g1dwqntna\nwoq5uTMjNWsTwONyam0E8wIXaSlN42Az04rrmTShgJuumIvb6eDPu1+gpWI9fbEplBosVrVglLOq\nGUdpJ7IMUvJi7XfBbX7Gq6uXs7xyORujYXag1P6WwqWke6txTTrE6onLqQlMBJqRwiWIOJBI4xQc\npDJx1XK0ALw6mUnDxaR7JwKKX8WVVgYLtS/J0UKmJs+m0aXE+Re6C4mkojhFB/M8Z7KjsQ2pr5rP\nzb6VGTVlXDbtEhLpJP96SXf4q3043TORhJhWIlEuh6+t/lLOvTa+Jxq5i8pa96nWmVQsUKSXmr6L\nOdDSizFEb/XcKraEOrVks7IiD72DcW1W5vM4uWXlzbzQ8hL/ek0ZHKpKfVr+yQVLJ3HxyjoqMxFS\nxv6pGlQD2YZJyk2yeQ5indngmVxQR+NQE5F+P+nBCionVZjeISs79nDbIDKKXLLncA+d/VEi8RSV\nxT5KCz1864JPMCD2UiUqjly/wRhSQlhl07EmlvuZmlmOzzjjvOnyuSyZlWthW723hX43v/qysvjG\no+v1BLFwLElpgZlX1LLc+XDKW+7qKG+sW2K10koybSb37Ok8wFObm7XpE0BPFmEHfE6z5Z55mFZ1\nZbxuh8nB6BuhDopRijF2YOP3Rqgv2Zr5yos1rSZ3GTvj+fKR+88+dw7zp5YRjae16o6mazCcv766\nkJVzqky1Rjwuh0k+SWaRu3Hqqg5UYlG3VrlOTeZZmWVVz59axvTaYr51/Upu/VD+UC6AuDDEUCKM\n5IzgrD1ATBzg1Y5tbD22k/UtG/nRtvt49MATAKyeW83SWZVMnjlMPB2ncbAZWZZ5qe019vceZDCh\nyCBiiWJlCQK0uvIv6ryieglzKqdx46ULte/khIdU2wxiu87ifcFrcWr9RcAnKvdqanE9ctqhDB5R\nc8SRGkYopx1aGwBtFghQKOjTdzXuWZIl5vqXk2pRZoR+tyfzewHlvlIuWTmZKdWF/L/rVxkMFIF0\nVx0XLp6WOZeQM8B7DH1YlWWchndA7ftepwfS5r5x0xXztGxcgI9fMZ87blrNFMN6ozUF1bx39jV8\n96Nn8K3rV5oIt7zYqxG7iqvPnsrKORM0IlWjxrLrBGXPkm5a+EFSR+bR2aD4FsqLvKYZ76evWaBF\nSqlQjcTaigBet0PJfUiktdBir9PL/CrrGfiE0twVpN6xul5LSnNnBs0Cn4vV86ot5cR877+Kj185\nT/tstNzHilPecreCKq18/rpF/P2lIxw42s9gOKGVLFUs9/FfWoHXZYhn1snPitydDtE0EGRnSxqR\nl8Sd1g9LHZj+4+2zueacadoU1Qjj+UZyqE6s8LPjELy0pyOnpKtVu7IlIIco8sAt53H7LzcrziZn\nAsETQR4uMXW2qlI/gjuKZ/YW5LRIbOvFWnja2Qsn4naKPLu1hab0bvor95OW5jJpQoFppmSFP7U+\nyMMtCZiuLMYb4jChrETNDa0v8Y6pF1LoVsi1eUjRUbsjPTQMNPHw/kdN2xuJISLkT+iq9FlEfWRC\n6eRYAQ7RPHtzix6GJXCKTtIdU8EVz0k2kWMKWad7anBOOIpXUkIejX2h2KmTkCuTmJOWJVN/c2b1\nndJCD1/7jxVUVhby2h49UueL71nM7Mm5YZVamw3Wn+ZgN8yc1JlmNjmpEUVGR7zH7aC6zDpOfmK5\nct3G3z0WZHX5mUrsuFp4L5GScDpEzlpQzfMjLFhT5CmkODaTbhTCLi/2mgayQr9bG0jcLtEUsVVT\nETAZOvkclsZ3rqrUnyMvzajVjTD1HbaSh1WMVrV15Zwq9jb1smFne6bdI78r2TjlLXcVg8O6E0O1\n3MuLvFRnokbWb2/VJJfSAs+INzVftUFBME911OQjR551QY0vhn8kyz1PW9x5plXqVNnpEC2JHfI7\nVLOjXmoyL5VVrW41YchohVgt4e0QRRwOgWgihWfeS3jnvcL/++i8XFkqE+KnLDoga4lfgiCwel41\nn3nnQtz1+2hLNGkErCKcHNbqZWsQUyTlXH2zwBXAJZrv92vHttMabiecHKYvs/RaZ7Sb/b35E7ZS\nnXUEYnXU+GtI9+vT5kvqz+c/l3yccp/FIumZ8rlnZmZVJnITFWs6lopx17X/wRLfWnDoMqBDcJDO\nhP8lj8whfnAxZQnFMjQSt9vpotRTQoW3jClFStz0lKI60zZWdd+1dhhIoKLYa1muWtvW0DdVIrey\n3LOtRjlrH7Aui52NKkNU00hkZfytrMjD+XmSwIwwRnapBK22L+BzcunqeuomFPCZa/WZmABMLPeb\n+KI8T1KgyaDyOXnXedNN1UmN+6nPypXHgAO9BtFIEXrG2bFnhGNZ4ZS23I2FnfqH4/z0sV1MrirQ\nRl2XU8TlUB6K6oFfu7SWmopAXscbKMQoyXJOHZH+cMIstWScn1alY9XzqxjJcnc5RSqKvcyqKzF9\nL4qCtiCyEWMZoU0OVcPn4ORSXm/s5bKzFAuoujx/xuHapbU4RIHV8/VsvImZJKx5U8zWntMhIssg\nZjIoRXeC4Zjebp/XaVo4wLfyn4Sj7wDmat8Zp/BNg81MLdYTPr666bskpRQ/P/9O7Tvj8cRwFVKB\n4sSs9FUQSUU5FtEjU/734N8A+PTiG7XvuqLdOSvtlHvL6In1IuIg2TSX6XOruX75HG7+w/04SpRt\nr5j+tpx7NbW0jsa+o8hxH7d/aBnTM1KZkdy8Tg8klJV4Cv1uasoDbBtQ+k6Zt5RvnXErD0X2U1zg\n5h8vH0Hqq8ZZlQn1zCLub57xZe3vSl85SycsZHeDXilwJNIw9p/RFiy3kgtchgFL9X9k98mFmcgP\n4/GtZrjZMPpwRurnxusrL/IyaUIBX/ngMp7Y1Miew705oa+gkKsaHacS7R03reJIR1ibOXzz+pVa\nu1NpicoSH26XwzSLtQqgAPNM1+t28vbV9XjdDi273Xgv1Hc6nwEHsGhGOe+/aBaLZuRPRDKeczSN\nPRuntOVudGL2DsbZcaibJzY1aaTvdjpwZchYTZ65dLWSYWqluauQZJlCn040alxqZYnPND1WrQwr\nhyZgqc9bQRAE7vzEGj562dyc36we2Gg1XMA8mBi1wPIiD/99y1puumoBABPLrAsdqW2+4qypppT/\nGbXF3P7BZdx8zQLTttkv7lAirNQqF1MUFyhFvLLXdOx2mmueG3F4oEn7nEgnSUqZmvqZBRK+/L4l\nrF6kDDAXTj4XT7te66PEW0y5z1pqaBzQS7se6m+kabCZ+iI90mTphIUICFQHKnnP+TN5/0WzcDlF\n5HiuhmrE7ed+hviBpUhD5aZyEsY+sLbqQip85bxv9jsBRdZLdUwh1T2RTy3+KIIgcP2lc7j2XD1E\nUC33bCQBp1PEITq0f6smLsPlMEdyWeUlqPCcILkbZ6rqeYzbXXPONC1c1diOsVjuRo19pDV3je+h\nSrYzaou5+aoFXHvuNN574cycfYwRUxUZy72i2Mcyi3BBVRKqyRgzxncgn+VuNE5Uwy9fySmVo1wj\n6OSCIHDBsklaGXArGHllvJr7KU3u3YZoFmPmo5qtpVjuyiV098cQQHMIjmS5x5NpUwLOdefP4IMX\nz+K682eYXoazMtlg+SwSYwe0WkFnLLDS3UeSlFQYCT1bIxVF3XHm9zqZP62MeVNzJYZ8mv/02mLT\ndFA9rpp2DzCYCDNvthPf8mdYubYPn8eRs+SXKOfeE1VO2dG1h8cb1vGt9T/mN3v/oP2uLnsWnFzK\nygWKdVzoLsAh6y9AqadYK9KUjV3dSsbmhEzSjiRLXFJ/vvZ7kaeQD855N++adQUXr5ysSVrvO3dh\n7sEMKPIUIPUrIXHG52PsG9UFE/jmGV/WZiQ+twMkJ8nDi6jyW8cji9pKTKNLLsa+OZLlbib3kQnX\nijCM5zcuJqJiWbBSDzYwyjKjczsAN1w6h4DPZbLic9pgGOyMGrjH7eDSM6ZYDkpWyW/5oM4a1Izy\nsxfppQ0q8kihEyv02YI6wOcLM1al45Hks7HAKOmO5oDNxiktyxhrohjX51RHfLdLd2rKQJHfpb0A\n2Zb7mfOrGYom2dXQQzyRNlVs83ucrDVoerPqSijyuzQrIx+5Gztg0RgzW3OPkfvwxzJCGy1348zC\nylL7/LsVq/f67z0HwNtXTeYdZ9QrCxukE7zcvoU1NStzdGwVsiwTKTiIo9hQuz0xhENQzrXx2Itc\nMPWMnOqJbq/SwZPpJE8fWc/CyvkkpRRu0YUoiDx9ZH3OuY4NdzKjZGrmHIoGX+Qu5BNXzeAHe/8K\nKOQ+mK3PZ9A81IrP6eODc6/jueYNVAcmsKhyHnUFNRwNtxFw+k3lWVXMqp4IzRYHtIBRTjA67bL7\n3EhWmwq1b02q1GdYVguNg7mvGMNFs2F0VB6f5W6stJjrUDUe0ynmSjij4cwFE7nq/Fl0dQ3l3aay\nRCfYfH6nbIiiwI8+deaI6wCoUJ+harkX+Fx84OJZNLYN5n2XKw0WtuqAnTuljH+8fISrzjaXSlYN\n0HwG1FhhJPTsUMjRcEqTu1UmKiijokMUcIjmiJViw8ULWYRcV1XIcIbcEynJlL6frf3d+v6lJk92\nvk5rOndgfDc+37lhjLKMwXI3Dj4jWXSiICBl4t1Vh+xfG57khZaX6Ip0885ZV1ju1xJuo694G25D\nROZgYgifU3/p9vaGcix3f6EitTx39EWebHqGdZkaKgsr5yHJEts6d+WcqzncirrulUruha4CppQX\nQSZKpthTTCRlXcMGoK6wlmnF9Uxb8EHtu5sXf5Stx3ayonqJ5T61BRP5wOx3McXgB8jGZ965kMNt\ng3lDT7OtRecYyE61fP1eF+csmsiGne05lTFVjNUKHI/mbtX/LC33fOQ+TofqWDGxPMCnr13Axl3t\nzLeYdeZD8RgJUHVOqgEHgOK0XZp/H+P1qQPonPpSfnjzmdriOyrUmXzpGAemfPAakh5LLDLVR8Ip\nK8ukDHHrE7OcgomUpFm3ZnLXb3B2hqLbKZpevkmG8gBWhGi0yMYiy4y1Js1Ix1AxXs3d2NaRXmZV\nJzyQ2sxdW35GIp2kPbPyTcNAY979uqK5GcFDibCJYJsHW5g/06zv98cHSEkpnm3eAOhZpgGXnxkl\n07Tt3j/7ncwuVTTUja2v8PfMsmyqda6GOGrXKDo5Z9IaagLVfHLRDTltqy/MjawodBdwXt2ZiEL+\n+3NGzQpTga1sLJ5RwTXnTMv7e7aclh2uaAWH4dl9+G2z+X8fXs6SmdYSzliOB+b+Mxrhjq65qw5V\na71/vA7V8WDJzEo+fe3CMVvu40GBXyncl80tY4VxveXSQk9O/sCNl83lgmWTePda6xIMY4XRv2NV\nhmQknJLkHkuk+PzPNvHPV5VY16osz3gkltIkESMxG0Ohssnd5RQ17a600GMi99Gsm7wOVUOHP15Z\nxlTPI4PR9ELQLffssM6RNFa13EGruIPGwWaeaX4ej1O5Z+FkhGgqSmekm1DvIW7f9F1CvUr4ZE9U\niQWX0w5tpZ7BxBARQ+XCpsGjRNIRXKKT76z5CvWFdfTHB+iJ9jKcilDk1rNx/U4/K6oWM624ni+c\neRNralby6SU38raMNv5M8wsMxAcZSqrkruz7kbnvZVpxPbPLZlLkLuT2VZ9nXnlu9cK1dWePev/e\nCGT3o9H0bjD3LUFQ1knNR8gjzcqMcI+h/xiP+f6LZvGl9+gOa3O0TK5D1WnhcIX8UWWnIt574Sw+\n9+7F444df+d5ClnPHWU2UVzg4f0XzcrxXY0X3tNNltm4q92ksWdngw1FE5pmbiZ3nWCzKxY6HSIr\n50ygbyjOsmBlzjRqJOQNhTR08uN1qKqZiQV+lxYdNBbL3SGK3PnxM4gzTFrKaOGOJJJo7eBpDbdz\nsPxhVk88nwa5gHAyzIutr2iOyd5YH19+8VvacmQAP93xS+48+xu0hJXkkfjeVcjRIvy17Qwmwgyn\nlPDTMm+pFpZY6imh1Kv8OzJ0VFuYeEHFHDa1vQoolrvf5ecLy26msrJQ014vn/42Sr0lPBL6M/fu\nfJCWcBsCAgUuZXBfXr2E5RayytdX38JAfICmwaNMKqyh2JO/bs0biWzrLVhXwvSaItYurbXYVimp\nMB5rd6yyzHjjoS9YZp7pWMa5O0eXZU625f5GorYikLP84ljw9lWTuXDZpHEPCscLo0N1vLLMKUXu\nyVSajt4oL+7S13p0OcUcyUOtww1oce6gL2oBcMa8avqG4vz1RUVuULXmt63Kr6nmQ7blrq7oMx5t\nM++xMy+E3+OklziCN0xEGgRyF6DIhuQe5o5X7mRl9VJgAt5FG3hq+FkuR48VX390o2Z5A4TSm3AI\nSmyOH+oAAB4sSURBVLsHE0NaSj5gInYVt7z4De2zujiBTwzQF++nNKbMBC6oO4e/Hf4nsXRMCzus\nL5zEjq7d/G7fnwCYUjTZRO75sLJ6GY+E/qwNKMuqFuEQR36RJvgrmOCvYGbpiU2Bjxdr5lebjBEV\nLqeD2z+03HIfh5ib3zAaxmy5nyDxWGWoGmUZ4/vgGGco5FsdanHANws+j3U+y1hwSpH7Q0/u55W9\n5jVA02nZNDVRYZUBZly/1OkQueLMqTq5WwSk/vSzZ4+4fJ4Ko0Vy4YrJXLFGiaU/UU846LMCxeqT\n8S7cyE9e38jPq+5kT/c+Xmh5iRvmf0BJkMmCSoCvdmwD13naupvh5DCVKNbrYweVuivLJizKXIvD\nJKeMhKUTFpqdnpLyHCZ7ZrAnspn9fUr25zmTzuCcSWcQSUW1KoYXTD6HUN8hbZsyrx6X7swTlQPg\ndrg4q2YVG9s2c/m0S3jblAvG1NZ/J6zyF0aDKAqQlsfU/1SMReaBsc38RjyPheVu0vHz+HjeSpb7\nWwVGiTZ7ZjgaRmWnYDAoBIPB+4LB4EvBYPC5YDBo6VEKBoP3B4PBOwx/35rZ57VgMPgRq32ykU3s\noFjcVun7bgtyH8nxYvUSFfhcppDIfDBGy5y/ok6LljgZlop6DEmWTeVnU1KK+3Y9xN7eEAf69NIB\nDf1NPLD7t8RSccKGcEBHsV4VsDOifI6l9OMdzaT7p6U0MjKlHj3GeNmERdx97ndy2nbD/A9w64r/\nzPl+QeEK3JmaJz6nF1EQEQWRAldAc1g6RAfzKvQ64KXeEqYVK4NisTv/cnAAV8+4jBvnf5CL69eO\nuN1bGSpBStZRj5YYq+V+ov3SFN6oWe7WA4aR3N9CkvtbBqIgML2mSCt3Ma59x7DNVYAnFAqtAW4D\n7s7eIBgMfgyYb/j7XOCMzD7nAXXZ+4wHVpa7lSPSquDPuYuV5ARjUZ/xwiHmOphAiZBZM7+aG4/D\ncgMlSzPhUFLKY76jeBe/oP22I7N8Gig1UlTcve1ednTtZkfXbnpjhnT0en0dTjXlvn24M+cYCUmx\n7meUTGNqpm5JdWACHoebK6e/nRKP+T7VFdZw1fR3MDutl8YNuH3UFigJXh5Hfh1wiiEztNRTwicX\n3cCnF9/I9JIpefcBJY1/8YQFI0a2vNWh9iOrGWX+fZT7YbUwysmEyXLXQiHzJVaZHcI2Tj5u/9By\nbjgOjhmLLHMW8BRAKBTaHAwGTSJiMBg8A1gB3A+optolwJ5gMPhXoBDILSQ9CmorArR2D+MQBWvL\n3ZUbLWOVwfWhS4Jce+70UVcrGgliHnIXBOG4puSgZE7+cOu9yt0RLyJS9Zrpd2OCz/7eg5S4i5hU\nqGfRdUd76YnpWrrgSCPFvYieGJ0auVvNhBRTscDl50NzP0mo7xBTixSL+uL6tZxZs4qf7/yVKavz\novrz6Dl0EFCil1xOkZqCahoHm3OLfRkwqUB3JLodLsDF7LLctPH/i1D7VHocsgwoUqJVNcVs3Pr+\npcctz7gskpTyHet4fU023niMhdyLgAHD36lgMCiGQiEpGAxWA19Hse6vM2xTAUwGLgOmAU+gE78l\nsstfXrBsEp19UVbNrbKc7o2l6hqQWYj2+IkdshxIY8zCA0X73tsTYkXVkhyrxujIdE5oRkh5kV26\nFt4a1p3K+3oPsK/3gGn/dU3PaJ9XVS9j46th0r3VeBe/QNNAM2kpTftwR962+Vw+REFkTtks0/cB\nl59bln86Z3vjoBbwupjgUGKxrZywKtwOF2+bcgEe8Y21NN+K0GWZ8ZH7WPtydpG68cDsMFVnyNbk\nbuvspy7GQu6DgDG2TAyFQqpS+C6gHHgSmAj4gsHgfqAH2BcKhVLAgWAwGAsGgxWhUKibPBjOijaY\nWlfKuy5WxoOOntzlz4oKvFRWFhJJ6S9HZeUbEwJXUqSHYoqiMObz/OL5B9l9bD8FBR7Onbpa+75l\noJ1/NujkLBb1IgsKSc4om8Kh3iYAqgoqORY2VzW0whfO/SjPP/E4IDPBW8WB/ga++swPaBnqwOVw\nUeDy0xcbMO0zoaRkXPeryBCJVD+plFS4GjKugJGOc33lO0c99hv13E42TmY7VenD7Xae9Os/0eNV\n9OhGRnVVkVZB0er4xYZ3Y7zn/b/43N9MjIXcN6FY4I8Fg8HVgLY0fCgUuge4ByAYDH4YCIZCod8E\ng8FLgc8APwoGgzWAH4Xw8yJ7PVNRkrT454hFGYJ0Ok1X1xBeUVmWbnlwwoi1Kk4EkYgeO+4QhTGf\nZ2+nEinyetshmrraqPJXsqxqMb/c/ogWRQIg+IbAmWBO2Sw+tfij/GTb/Rzob6DMXcpl8y+hceAI\n75h6IeuanmVJ5UL+fOjvWlXF6cVTDO0RuKb2fWw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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf677b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.478291548814, policy = 0.475572269547 epoch 4 complete\n",
      "As X, neural network has a score of  94-  0-  6 vs 0-depth minimax\n",
      "As O, neural network has a score of  97-  0-  3 vs 0-depth minimax\n",
      "epoch time: 3320.314s\n",
      "Starting Epoch 5, temperature = 1.30\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 660300\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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LfLGQbKLJMrl1QdTEoyKnem5zG0o9Q0+u4S/Q5qFIdFRRLd+ddyFlmfotbrv7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      "text/plain": [
       "<matplotlib.figure.Figure at 0xca5e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.435488702901, policy = 0.471397118134 epoch 5 complete\n",
      "As X, neural network has a score of  95-  0-  5 vs 0-depth minimax\n",
      "As O, neural network has a score of  92-  0-  8 vs 0-depth minimax\n",
      "epoch time: 3266.131s\n",
      "Starting Epoch 6, temperature = 1.20\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 770350\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training policy net"
     ]
    },
    {
     "data": {
      "image/png": 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usPFwIudyqvD5AyPeIyHuCSJwhn+Jqbt/kPte2E1TR/DFrWvu4+l3HAA89Zbj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\niUP/D9xY6axE0+DsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b7e8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.403497477123, policy = 0.484498338167 epoch 6 complete\n",
      "As X, neural network has a score of  92-  0-  8 vs 0-depth minimax\n",
      "As O, neural network has a score of  92-  0-  8 vs 0-depth minimax\n",
      "epoch time: 3303.699s\n",
      "Starting Epoch 7, temperature = 1.11\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. games played: 880400\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. training value net\n",
      "\n",
      "[XXXXXXXXXXXXXXXXXXXX] 100% complete. building policy targets\n",
      "\n",
      "[                    ]   1% complete. training policy net"
     ]
    },
    {
     "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<ipython-input-75-d7a1997dc5e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[0mprogress_bar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmini_epoch\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mmini_epoch_policy\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"training policy net\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m         \u001b[0mpolicy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m         \u001b[0mpolicy_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpolicy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[0mprogress_bar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"training policy net\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# ascthetic only\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\mlp.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    291\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalid_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalid_set\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 293\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRegressor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\mlp.pyc\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 232\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m             log.error(\"\\n{}{}{}\\n\\n{}\\n\".format(\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\mlp.pyc\u001b[0m in \u001b[0;36m_train\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m             \u001b[0mis_best_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m             \u001b[0mavg_train_error\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_train_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mavg_train_error\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mavg_train_error\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\backend\\lasagne\\mlp.pyc\u001b[0m in \u001b[0;36m_train_impl\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_train_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 305\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    307\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_valid_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\backend\\lasagne\\mlp.pyc\u001b[0m in \u001b[0;36m_batch_impl\u001b[1;34m(self, X, y, w, processor, mode, output, shuffle)\u001b[0m\n\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'train'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 290\u001b[1;33m                 \u001b[0mloss\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwb\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mwb\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    291\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\compile\\function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mt0_fn\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[0m\n\u001b[0;32m    606\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\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    608\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'position_of_error'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\gof\\op.pyc\u001b[0m in \u001b[0;36mrval\u001b[1;34m(p, i, o, n)\u001b[0m\n\u001b[0;32m    759\u001b[0m             \u001b[1;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 761\u001b[1;33m                 \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    762\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    763\u001b[0m                     \u001b[0mcompute_map\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\tensor\\blas.pyc\u001b[0m in \u001b[0;36mperform\u001b[1;34m(self, node, inp, out)\u001b[0m\n\u001b[0;32m   1664\u001b[0m         \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1665\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1666\u001b[1;33m             \u001b[0mz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\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   1667\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m             \u001b[1;31m# The error raised by numpy has no shape information, we mean to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_duration = 100\n",
    "min_temperature = 0.3\n",
    "self_play = 0\n",
    "temporal_index = 0.8\n",
    "mini_epoch_policy = 400\n",
    "mini_epoch_value = 400\n",
    "refresh_rate = 1\n",
    "for epoch in range(train_duration):\n",
    "    t0 = time.clock()\n",
    "    print \"Starting Epoch {}, temperature = {:.2f}\".format(epoch,temperature)\n",
    "    for train_i in range(training_size/refresh_rate):\n",
    "        t1 = time.clock()\n",
    "\n",
    "        player_1 = policy\n",
    "        player_2 = policy\n",
    "        play_result = cccc.play(nn_ai(player_1,temp = temperature),nn_ai(player_2,temp=temperature))\n",
    "        learning_selection = random.choice(range(len(play_result.log)+1))\n",
    "        boards = get_inputs(play_result.log)[learning_selection:]\n",
    "        learning_board = boards[0]\n",
    "\n",
    "        if learning_selection % 2 ==0:\n",
    "            learning_player = 1\n",
    "        else:\n",
    "            learning_player = -1\n",
    "        self_play +=1\n",
    "        learning_pool = []\n",
    "        value_history = []\n",
    "\n",
    "#        current_state = learning_board * learning_player\n",
    "        current_player = learning_player\n",
    "        learning_board = learning_player * learning_board\n",
    "    #    print play_result.log\n",
    "        for board in boards[:-1]:\n",
    "            board = board * current_player\n",
    "    #        print board\n",
    "    #        board = board * current_player\n",
    "    #        next_value , next_state , explore = play_one_move(current_state,value_funs)\n",
    "    #        next_value = value.predict(current_state)\n",
    "            learning_pool.append(board.reshape((1,42)))\n",
    "            value_history.append(value.predict(board.reshape((1,42)))[0])\n",
    "            current_player *= -1\n",
    "\n",
    "        learning_pool.append(boards[-1]*current_player)\n",
    "\n",
    "        value_history.append(cccc.winner(boards[-1]*current_player))\n",
    "\n",
    "\n",
    "        future_board = learning_pool.pop()\n",
    "        learning_target = value_history.pop()\n",
    "        while(value_history):\n",
    "            learning_target = -1 * temporal_index * learning_target + (1-temporal_index)*value_history.pop()\n",
    "        training_data[current_write_index%training_size] = learning_board\n",
    "        target_data[current_write_index%training_size] = learning_target   \n",
    "        current_write_index +=1\n",
    "\n",
    "        if train_i%(training_size / refresh_rate / 10)==0:\n",
    "            progress_bar(100.*float(train_i+1) / (training_size / refresh_rate),message = \"games played: {}\".format(current_write_index))\n",
    "            \n",
    "    #    if train_i %(training_size/10)==0:\n",
    "    #        print \"\\n\"\n",
    "    progress_bar(100,message = \"games played: {}\".format(current_write_index)) #ascthetic only\n",
    "    if current_write_index < training_size:\n",
    "        \n",
    "        print '\\n'\n",
    "        continue\n",
    "    \n",
    "    X_train =np.array(training_data[:3*training_size/4] ).reshape(3*training_size/4,42)\n",
    "    X_test = np.array(training_data[3*training_size/4:]).reshape(training_size-3*training_size/4,42)\n",
    "    y_train = np.array(target_data[:3*training_size/4]).reshape(3*training_size/4,1)\n",
    "    y_test = np.array(target_data[3*training_size/4:]).reshape(training_size - 3*training_size/4,1)\n",
    "    print '\\n'\n",
    "    policy_scores = []\n",
    "    value_scores = []\n",
    "    for mini_epoch in range(mini_epoch_value):\n",
    "        progress_bar(float(mini_epoch+1) / mini_epoch_value*100, message = \"training value net\")\n",
    "        value.fit(X_train,y_train)\n",
    "        value_scores.append(value.score(X_test,y_test))\n",
    "    zeros = np.zeros(7)\n",
    "    progress_bar(100, message = \"training value net\")#ascthetic only\n",
    "    print '\\n'\n",
    "    policy_targets = [progress_bar(float(i) / training_size*100, message = \"building policy targets\")\n",
    "                      or one_where(value_move(board, 1)) for i,board in enumerate(training_data)]\n",
    "    z_train = np.array(policy_targets[:3*training_size/4]).reshape(3*training_size/4,7)\n",
    "    z_test = np.array(policy_targets[3*training_size/4:]).reshape(training_size - 3*training_size/4,7)\n",
    "    progress_bar(100, message = \"building policy targets\")#ascthetic only\n",
    "    print '\\n'\n",
    "    for mini_epoch in range(mini_epoch_policy):\n",
    "        \n",
    "        progress_bar(float(mini_epoch+1) / mini_epoch_policy*100,message = \"training policy net\")\n",
    "        policy.fit(X_train,z_train)\n",
    "        policy_scores.append(policy.score(X_test,z_test))\n",
    "    progress_bar(100,message = \"training policy net\") # ascthetic only\n",
    "#     print \"\\n\"\n",
    "    pd.DataFrame(list(izip(value_scores,policy_scores, fillvalue = float(\"nan\"))),columns=['value','policy']).plot()\n",
    "    plt.show()\n",
    "    print \"value = {}, policy = {} epoch {} complete\".format(value.score(X_test,y_test),policy.score(X_test,z_test), epoch)\n",
    "    validate()\n",
    "    temperature = (temperature - min_temperature)*0.9 + min_temperature\n",
    "           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('value.pkl','wb') as out_strm:\n",
    "    pickle.dump(value,out_strm)\n",
    "with open('policy.pkl','wb') as out_strm:\n",
    "    pickle.dump(policy,out_strm)\n",
    "with open('parameters.pkl','wb') as out_strm:\n",
    "    pickle.dump((training_data, target_data, current_write_index, temperature),out_strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('value.pkl','rb') as in_strm:\n",
    "    value = pickle.load(in_strm)\n",
    "with open('policy.pkl','rb') as in_strm:\n",
    "    policy = pickle.load(in_strm)\n",
    "with open('parameters.pkl','rb') as in_strm:\n",
    "    (training_data, target_data, current_write_index, temperature) = pickle.load(in_strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-ae145b31b881>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtraining_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\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[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "training_data.count(training_data[0],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stuff = [str(array) for array in training_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for thing in stuff:\n",
    "    print stuff.count(thing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "saved_p = [[array for array in layer[:2]] for layer in saved_p]\n",
    "saved_v = [[array for array in layer[:2]] for layer in saved_v]\n",
    "policy.set_parameters(saved_p)\n",
    "value.set_parameters(saved_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True]\n"
     ]
    }
   ],
   "source": [
    "print policy.get_parameters()[0][1]==saved_p[0][1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ..., \n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]], dtype=bool)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(p.get_parameters())[0][0]==np.array(policy.get_parameters())[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v_para = value.get_parameters()\n",
    "p_para = policy.get_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v_para = [[array for array in layer[:2]] for layer in v_para]\n",
    "p_para = [[array for array in layer[:2]] for layer in p_para]\n",
    "\n",
    "value.set_parameters(v_para) \n",
    "policy.set_parameters(p_para)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('parameters2', 'rb') as in_strm:\n",
    "        v,p,training_data,target_data,current_write_index = pickle.load(in_strm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-253242aa06c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpolicy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_parameters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\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[0m",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\nn.pyc\u001b[0m in \u001b[0;36mset_parameters\u001b[1;34m(self, storage)\u001b[0m\n\u001b[0;32m    564\u001b[0m             \u001b[0mlayers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 566\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_array_to_mlp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\backend\\lasagne\\mlp.pyc\u001b[0m in \u001b[0;36m_array_to_mlp\u001b[1;34m(self, array, nn)\u001b[0m\n\u001b[0;32m    325\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 327\u001b[1;33m             \u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbiases\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'W'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack"
     ]
    }
   ],
   "source": [
    "policy.set_parameters(list(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.40694909]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value.predict(-boards[0].reshape((1,42)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1., -1.,  0., -1.,  0.],\n",
       "       [ 0.,  0.,  1.,  1.,  0., -1.,  0.],\n",
       "       [-1.,  0.,  1.,  1.,  0., -1.,  0.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boards[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameters(weights=array([[ 0.10713747, -0.11199324,  0.10277964, ..., -0.03107075,\n",
       "        -0.1648835 , -0.05584284],\n",
       "       [ 0.04986459,  0.06363781,  0.01551032, ...,  0.16507727,\n",
       "        -0.01411279,  0.11935348],\n",
       "       [-0.10634266, -0.03136429,  0.07384725, ..., -0.05273866,\n",
       "         0.07376527, -0.00728419],\n",
       "       ..., \n",
       "       [ 0.16502712, -0.10894664,  0.0820191 , ..., -0.1020957 ,\n",
       "        -0.06141815,  0.11160652],\n",
       "       [ 0.01521472,  0.1271042 , -0.09782301, ..., -0.03381845,\n",
       "         0.1753141 , -0.11578896],\n",
       "       [ 0.06263408, -0.11556657,  0.08329605, ...,  0.071731  ,\n",
       "        -0.04514209, -0.17116802]]), biases=array([  3.77970966e-03,  -2.10656906e-04,   7.74439963e-03,\n",
       "        -8.34223206e-04,   3.78463112e-03,  -3.16353173e-03,\n",
       "        -2.83451546e-04,  -1.95067843e-03,  -5.73721080e-03,\n",
       "        -1.13304378e-03,  -3.90153449e-04,  -3.22483137e-03,\n",
       "        -4.15094938e-03,  -1.76362650e-03,  -7.08357228e-03,\n",
       "        -3.57063931e-05,  -1.15433647e-03,  -1.60776157e-03,\n",
       "        -2.66882938e-03,  -3.76510472e-03,   2.80317041e-04,\n",
       "         4.63123329e-03,  -3.82427413e-03,  -4.49561125e-03,\n",
       "        -4.80987404e-03,  -5.73485398e-03,   1.29983867e-03,\n",
       "         5.44253864e-04,  -7.25816585e-04,   5.17503235e-06,\n",
       "        -2.68216086e-03,  -1.80754708e-03,   3.85854027e-03,\n",
       "        -5.56540078e-03,  -1.52986364e-03,  -1.38803238e-03,\n",
       "         5.08491563e-03,  -3.82990383e-03,   3.06611609e-05,\n",
       "         4.38493849e-04,  -9.90029969e-04,  -1.40858648e-03,\n",
       "        -7.59886654e-03,   3.59886604e-04,   9.24207137e-04,\n",
       "         8.10180232e-04,   1.38232831e-03,  -9.67838459e-03,\n",
       "         3.28343574e-03,   2.50617543e-03,   2.00173521e-03,\n",
       "        -4.57919608e-03,  -5.45122066e-03,  -3.25475842e-03,\n",
       "         6.02577353e-03,  -4.72763330e-03,   7.09891251e-04,\n",
       "         1.86217173e-03,   3.25641527e-03,   5.82850830e-03,\n",
       "         9.48471869e-04,   4.98406480e-03,  -1.60922341e-03,\n",
       "        -5.77936308e-03,  -1.93266209e-03,   9.69846245e-03,\n",
       "        -6.38234625e-04,   1.81270564e-03,   1.70943300e-03,\n",
       "        -8.91813883e-03,   3.21516848e-04,  -8.65315769e-04,\n",
       "        -3.10603845e-04,   2.69735284e-03,  -4.08154576e-03,\n",
       "         9.12792495e-04,   7.33187140e-03,  -1.79647155e-03,\n",
       "        -5.10223613e-04,  -3.47487933e-03,   1.41543408e-03,\n",
       "        -1.45553143e-04,   2.54090439e-03,  -1.71526527e-03,\n",
       "        -3.25415364e-03,  -1.65076823e-03,   8.78296217e-04,\n",
       "        -1.90069455e-03,  -7.46378577e-03,  -6.42802452e-04,\n",
       "         2.21095497e-03,   4.93958033e-03,  -1.12247203e-02,\n",
       "        -7.57200869e-04,  -1.09152728e-02,   5.31735562e-03,\n",
       "         2.45712103e-03,   1.03081691e-03,  -6.41707689e-03,\n",
       "        -4.38648674e-04,   1.86465110e-04,   2.95297060e-03,\n",
       "        -6.80543699e-03,   1.82156502e-03,  -1.69596170e-03,\n",
       "         4.40653139e-04,  -4.85194029e-03,   2.39092453e-03,\n",
       "         7.79432286e-03,   3.22665479e-03,  -5.49499806e-03,\n",
       "         4.16693781e-03,   1.34599589e-03,   1.85467794e-03,\n",
       "        -7.18325121e-03,  -3.01470125e-03,   3.47228337e-03,\n",
       "        -3.41298337e-03,  -6.17662760e-03,  -1.76110158e-03,\n",
       "        -2.08290008e-03,   1.91456286e-03,   1.71460594e-03,\n",
       "        -1.93161767e-03,  -2.08792850e-03,   3.64784721e-03,\n",
       "         9.26927397e-03,  -6.36163068e-03,  -4.26087908e-04,\n",
       "        -1.63015191e-03,  -1.43186216e-03,  -1.17145382e-03,\n",
       "         2.19501655e-03,   1.26638342e-03,  -2.87553043e-04,\n",
       "         2.76258619e-03,  -7.36099498e-03,   1.84608706e-03,\n",
       "         1.07921452e-02,  -6.60280492e-03,  -1.07648829e-02,\n",
       "        -1.87350706e-03,  -1.35411706e-02,   4.07304410e-03,\n",
       "         6.06005580e-03,  -7.62712293e-03,   3.35675614e-04,\n",
       "         5.20659777e-04,   8.75921020e-04,  -2.07235153e-03]), layer=u'hidden0'),\n",
       " Parameters(weights=array([[ 0.07855731, -0.18857013,  0.04971826, ...,  0.01476237,\n",
       "        -0.00107107, -0.19460563],\n",
       "       [-0.05395177,  0.03932808,  0.13626533, ..., -0.00879228,\n",
       "         0.08885262, -0.07293174],\n",
       "       [-0.06225829, -0.12177251,  0.02245206, ..., -0.1826568 ,\n",
       "         0.06890089,  0.07506852],\n",
       "       ..., \n",
       "       [ 0.02959527,  0.02089928, -0.12191042, ..., -0.16525104,\n",
       "         0.19191569,  0.11025531],\n",
       "       [ 0.16365469, -0.12253921,  0.17829902, ..., -0.01454385,\n",
       "         0.12539755, -0.06551677],\n",
       "       [-0.02946534, -0.14618075, -0.14774627, ...,  0.05905916,\n",
       "         0.10764649,  0.0181425 ]]), biases=array([-0.00289542, -0.00891585, -0.00464961, -0.01540573, -0.06768468,\n",
       "       -0.0128858 , -0.02526067]), layer=u'output')]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "policy.get_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.00000000e+00   1.05718265e-01   2.05222898e-02   0.00000000e+00\n",
      "    5.55111512e-17   0.00000000e+00   6.81224115e-01]]\n",
      "[1, 2, 4, 0, 3]\n",
      "[ 0.06019007  0.04614589 -1.          0.00267254  0.05088697  0.          0.        ]\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print policy.predict(X_train[i].reshape((1,42)))\n",
    "print policy_move(X_train[i].reshape((6,7)),1,policy)\n",
    "print value_move(X_train[i].reshape((6,7)),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4]\n",
      "[ 0.06019007  0.04614589  0.17373381  0.00267254  0.05088697  0.          0.        ]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "dummy_board = X_train[14].reshape((6,7))\n",
    "options = cccc.available_moves(dummy_board)\n",
    "print options\n",
    "\n",
    "moves = np.zeros(7)\n",
    "move_values = np.zeros(7)\n",
    "\n",
    "for choice in options:\n",
    "    update_move(dummy_board, choice, 1)\n",
    "    reshapeable = np.copy(dummy_board)\n",
    "    move_values[choice] = -1 * value.predict(-1 * reshapeable.reshape((1,42)))\n",
    "    unupdate_move(dummy_board, choice)\n",
    "print move_values\n",
    "print max(options, key = lambda x:  move_values[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 0.120798554732, policy = -0.437051063332 epoch 0 complete\n",
      "value = 0.101803571257, policy = -0.284918307083 epoch 1 complete\n",
      "value = 0.09770518044, policy = -0.251997171308 epoch 2 complete\n",
      "value = 0.0860769768841, policy = -0.2462004443 epoch 3 complete\n",
      "value = 0.113245520518, policy = -0.31231018018 epoch 4 complete\n",
      "value = 0.115281285356, policy = -0.300428208949 epoch 5 complete\n",
      "value = 0.0599270240985, policy = -0.293244037285 epoch 6 complete\n",
      "value = 0.0996128519659, policy = -0.270490540291 epoch 7 complete\n",
      "value = 0.0959149105556, policy = -0.26012062539 epoch 8 complete\n",
      "value = 0.101433792129, policy = -0.218059443375 epoch 9 complete\n",
      "value = 0.0986670612722, policy = -0.233956916815 epoch 10 complete\n",
      "value = 0.0932080401872, policy = -0.203944635185 epoch 11 complete\n",
      "value = 0.0940497570382, policy = -0.184805294441 epoch 12 complete\n",
      "value = 0.084254668461, policy = -0.191887068805 epoch 13 complete\n",
      "value = 0.0949794012475, policy = -0.186291115696 epoch 14 complete\n",
      "value = 0.095449393021, policy = -0.174000242701 epoch 15 complete\n",
      "value = 0.0968017853041, policy = -0.178450742049 epoch 16 complete\n",
      "value = 0.100613023182, policy = -0.168662488204 epoch 17 complete\n",
      "value = 0.0958879350734, policy = -0.167480724581 epoch 18 complete\n",
      "value = 0.1016378116, policy = -0.160790500255 epoch 19 complete\n",
      "value = 0.0994382673532, policy = -0.164414759912 epoch 20 complete\n",
      "value = 0.105044686148, policy = -0.202602243885 epoch 21 complete\n",
      "value = 0.0828720129013, policy = -0.230053104775 epoch 22 complete\n",
      "value = 0.0875082553509, policy = -0.220174443248 epoch 23 complete\n",
      "value = 0.0871483246298, policy = -0.221558837709 epoch 24 complete\n",
      "value = 0.0866073494324, policy = -0.220393484809 epoch 25 complete\n",
      "value = 0.0947331607409, policy = -0.256845872184 epoch 26 complete\n",
      "value = 0.106598125188, policy = -0.25627406536 epoch 27 complete\n",
      "value = 0.122173194415, policy = -0.221278524423 epoch 28 complete\n",
      "value = 0.124905264063, policy = -0.222947143827 epoch 29 complete\n",
      "value = 0.109140221448, policy = -0.243051968948 epoch 30 complete\n",
      "value = 0.117639881918, policy = -0.249711134058 epoch 31 complete\n",
      "value = 0.117168502539, policy = -0.251860888169 epoch 32 complete\n",
      "value = 0.136658369751, policy = -0.218063589526 epoch 33 complete\n",
      "value = 0.094938027556, policy = -0.256156314819 epoch 34 complete\n",
      "value = 0.0575717540234, policy = -0.214529668725 epoch 35 complete\n",
      "value = 0.0747648268016, policy = -0.22517600533 epoch 36 complete\n",
      "value = 0.0657758530553, policy = -0.239662421473 epoch 37 complete\n",
      "value = 0.0669236269845, policy = -0.227467185666 epoch 38 complete\n",
      "value = 0.0734508607136, policy = -0.222500568359 epoch 39 complete\n",
      "value = 0.0817809846697, policy = -0.225042460856 epoch 40 complete\n",
      "value = 0.0790331288471, policy = -0.192996448048 epoch 41 complete\n",
      "value = 0.0788467179161, policy = -0.158075797577 epoch 42 complete\n",
      "value = 0.0778607259032, policy = -0.159202605386 epoch 43 complete\n",
      "value = 0.0789780437079, policy = -0.249677352662 epoch 44 complete\n",
      "value = 0.0757963576098, policy = -0.231892418696 epoch 45 complete\n",
      "value = 0.0755854352819, policy = -0.234795205472 epoch 46 complete\n",
      "value = 0.0720255642555, policy = -0.287087538604 epoch 47 complete\n",
      "value = 0.0739534458311, policy = -0.261409272066 epoch 48 complete\n",
      "value = 0.0805412281351, policy = -0.274925625937 epoch 49 complete\n",
      "value = 0.0945501122899, policy = -0.151275215355 epoch 50 complete\n",
      "value = 0.0902892826169, policy = -0.180672949239 epoch 51 complete\n",
      "value = 0.0981806064964, policy = -0.178054966164 epoch 52 complete\n",
      "value = 0.0900654379121, policy = -0.206246453372 epoch 53 complete\n",
      "value = 0.0974580007008, policy = -0.153836409734 epoch 54 complete\n",
      "value = 0.114904689653, policy = -0.149863120747 epoch 55 complete\n",
      "value = 0.105830822689, policy = -0.151296695959 epoch 56 complete\n",
      "value = 0.0831381337143, policy = -0.152911876356 epoch 57 complete\n",
      "value = 0.0706982134025, policy = -0.210024267693 epoch 58 complete\n",
      "value = 0.0697407154281, policy = -0.230419534827 epoch 59 complete\n",
      "value = 0.0674189617015, policy = -0.230549363639 epoch 60 complete\n",
      "value = 0.0856262235676, policy = -0.235336850166 epoch 61 complete\n",
      "value = 0.0821594014761, policy = -0.235122665043 epoch 62 complete\n",
      "value = 0.0769765979669, policy = -0.22084992891 epoch 63 complete\n",
      "value = 0.0890973720876, policy = -0.215759998078 epoch 64 complete\n",
      "value = 0.0814455838813, policy = -0.221635514001 epoch 65 complete\n",
      "value = 0.0800916701593, policy = -0.195389830649 epoch 66 complete\n",
      "value = 0.0739074168956, policy = -0.190766182638 epoch 67 complete\n",
      "value = 0.0745867634236, policy = -0.206773852972 epoch 68 complete\n",
      "value = 0.0594015020982, policy = -0.246547777668 epoch 69 complete\n",
      "value = 0.0630738092368, policy = -0.230844174587 epoch 70 complete\n",
      "value = 0.0762496252715, policy = -0.216253671949 epoch 71 complete\n",
      "value = 0.0570375977476, policy = -0.201079616743 epoch 72 complete\n",
      "value = 0.0751168879343, policy = -0.195734998219 epoch 73 complete\n",
      "value = 0.0742359777314, policy = -0.212234602599 epoch 74 complete\n",
      "value = 0.0799485677261, policy = -0.219403018174 epoch 75 complete\n",
      "value = 0.083532048742, policy = -0.218966807153 epoch 76 complete\n",
      "value = 0.0864330244178, policy = -0.24380362428 epoch 77 complete\n",
      "value = 0.0968672142387, policy = -0.239918004168 epoch 78 complete\n",
      "value = 0.0978088332396, policy = -0.209986166628 epoch 79 complete\n",
      "value = 0.0942173703216, policy = -0.209674318998 epoch 80 complete\n",
      "value = 0.0612897972725, policy = -0.182130220277 epoch 81 complete\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<ipython-input-30-8d611af9527f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mpolicy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mz_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\mlp.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    291\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalid_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalid_set\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 293\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRegressor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\mlp.pyc\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 232\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m             log.error(\"\\n{}{}{}\\n\\n{}\\n\".format(\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\mlp.pyc\u001b[0m in \u001b[0;36m_train\u001b[1;34m(self, X, y, w)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_callback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'on_train_finish'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocals\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--> 202\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_array_to_mlp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbest_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\sknn\\backend\\lasagne\\mlp.pyc\u001b[0m in \u001b[0;36m_array_to_mlp\u001b[1;34m(self, array, nn)\u001b[0m\n\u001b[0;32m    330\u001b[0m                 \u001b[0mlayer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m             \u001b[0mws\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mW\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\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    333\u001b[0m             \u001b[1;32massert\u001b[0m \u001b[0mws\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Layer weights shape mismatch: %r != %r\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m                                         \u001b[1;33m(\u001b[0m\u001b[0mws\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\gof\\graph.pyc\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, inputs_to_values)\u001b[0m\n\u001b[0;32m    410\u001b[0m         \u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs_to_values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    411\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fn_cache\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 412\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fn_cache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    413\u001b[0m         \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0minputs_to_values\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\compile\\function.pyc\u001b[0m in \u001b[0;36mfunction\u001b[1;34m(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)\u001b[0m\n\u001b[0;32m    314\u001b[0m                    \u001b[0mon_unused_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m                    \u001b[0mprofile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 316\u001b[1;33m                    output_keys=output_keys)\n\u001b[0m\u001b[0;32m    317\u001b[0m     \u001b[1;31m# We need to add the flag check_aliased inputs if we have any mutable or\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m     \u001b[1;31m# borrowed used defined inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\compile\\pfunc.pyc\u001b[0m in \u001b[0;36mpfunc\u001b[1;34m(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[0;32m    521\u001b[0m                          \u001b[0maccept_inplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_inplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m                          \u001b[0mprofile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 523\u001b[1;33m                          output_keys=output_keys)\n\u001b[0m\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\compile\\function_module.pyc\u001b[0m in \u001b[0;36morig_function\u001b[1;34m(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[0;32m   1523\u001b[0m                    \u001b[0mprofile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1524\u001b[0m                    \u001b[0mon_unused_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1525\u001b[1;33m                    \u001b[0moutput_keys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1526\u001b[0m             defaults)\n\u001b[0;32m   1527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\compile\\function_module.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys)\u001b[0m\n\u001b[0;32m   1169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1170\u001b[0m         \u001b[1;31m# Check if some input variables are unused\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1171\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_unused_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1172\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1173\u001b[0m         \u001b[1;31m# Make a list of (SymbolicInput|SymblicInputKits, indices,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\compile\\function_module.pyc\u001b[0m in \u001b[0;36m_check_unused_inputs\u001b[1;34m(self, inputs, outputs, on_unused_input)\u001b[0m\n\u001b[0;32m   1283\u001b[0m             ([o.variable for o in outputs] +\n\u001b[0;32m   1284\u001b[0m              [i.update for i in inputs if getattr(i, 'update', False)]),\n\u001b[1;32m-> 1285\u001b[1;33m             blockers=[i.variable for i in inputs])\n\u001b[0m\u001b[0;32m   1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1287\u001b[0m         msg = (\"theano.function was asked to create a function computing \"\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\gof\\graph.pyc\u001b[0m in \u001b[0;36mancestors\u001b[1;34m(variable_list, blockers)\u001b[0m\n\u001b[0;32m    548\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mblockers\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblockers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mreversed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mowner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m     \u001b[0mdfs_variables\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstack_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeque\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvariable_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpand\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'dfs'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdfs_variables\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Todd\\Anaconda2\\lib\\site-packages\\theano\\gof\\graph.pyc\u001b[0m in \u001b[0;36mstack_search\u001b[1;34m(start, expand, mode, build_inv)\u001b[0m\n\u001b[0;32m    526\u001b[0m                     \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mexpand_l\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m                         \u001b[0mexpand_inv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 528\u001b[1;33m                 \u001b[0mstart\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpand_l\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    529\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrval_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrval_set\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mbuild_inv\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_duration = 100\n",
    "scores = []\n",
    "for epoch in range(100):\n",
    "    \n",
    "    value.fit(X_train,y_train)\n",
    "    policy.fit(X_train,z_train)\n",
    "    \n",
    "    scores.append((value.score(X_test,y_test),policy.score(X_test,z_test)))\n",
    "    print \"value = {}, policy = {} epoch {} complete\".format(scores[-1][0],scores[-1][1], epoch)\n",
    "           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcac4f60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o2DmcLpyr6MThc01o7R4/j8a5ii4Gz0QTYPDsYwfqP8Cp1rP+bsaEqvvrAACf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\nmK6c54oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe753588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "(pd.DataFrame(scores)).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
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