Create_animation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dependencies.NN import *\n",
    "from dependencies.LTL import *\n",
    "\n",
    "\n",
    "from dependencies.csrl.mdp import GridMDP\n",
    "from dependencies.csrl.oa import OmegaAutomaton\n",
    "from dependencies.csrl import ControlSynthesis\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time \n",
    "\n",
    "from ipywidgets.widgets import IntSlider\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### from the Nursary case ###\n",
    "# LTL Specification\n",
    "# ltl = ('G ('\n",
    "#     '(!d) & '\n",
    "#     '((b & (!(X b)))->(X ((!b) U (a|c)))) & '\n",
    "#     '(((!b) & (X b) & (!(X X b)))->((!a) U c)) & '\n",
    "#     '(a->(X ((!a) U b))) & '\n",
    "#     '(c->((!a) U b)) & '\n",
    "#     '((b & (X b))->(F a))'\n",
    "# ')')\n",
    "\n",
    "ltl = (\"(G !d) & ((!c) U b) & ((!b) U a) & (F G c)\")\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa = OmegaAutomaton(ltl)\n",
    "print('Number of Omega-automaton states (including the trap state):',oa.shape[1])\n",
    "\n",
    "# MDP Description\n",
    "shape = (4,4)\n",
    "# E: Empty, T: Trap, B: Obstacle\n",
    "structure = np.array([\n",
    "['E',  'E',  'E',  'E'],\n",
    "['E',  'B',  'E',  'B'],\n",
    "['E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E']\n",
    "])\n",
    "\n",
    "# Labels of the states\n",
    "label = np.array([\n",
    "[(),    (),    ('a',),    ()],\n",
    "[(),    (),    (),()],\n",
    "[('c',),('d',),    (),('d',)],\n",
    "[(),    (),    ('b',),()]\n",
    "],dtype=object)\n",
    "# Colors of the labels\n",
    "lcmap={\n",
    "    ('a',):'yellow',\n",
    "    ('b',):'greenyellow',\n",
    "    ('c',):'turquoise',\n",
    "    ('d',):'pink'\n",
    "}\n",
    "p=1\n",
    "grid_mdp = GridMDP(shape=shape,structure=structure,label=label,lcmap=lcmap, p=p, figsize=5)  # Use figsize=4 for smaller figures\n",
    "grid_mdp.plot(save=\"env.pdf\")\n",
    "\n",
    "# Construct the product MDP\n",
    "csrl = ControlSynthesis(grid_mdp,oa)\n",
    "max_rew = round(csrl.reward.max(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### from the Nursary case ###\n",
    "# LTL Specification\n",
    "# ltl = ('G ('\n",
    "#     '(!d) & '\n",
    "#     '((b & (!(X b)))->(X ((!b) U (a|c)))) & '\n",
    "#     '(((!b) & (X b) & (!(X X b)))->((!a) U c)) & '\n",
    "#     '(a->(X ((!a) U b))) & '\n",
    "#     '(c->((!a) U b)) & '\n",
    "#     '((b & (X b))->(F a))'\n",
    "# ')')\n",
    "\n",
    "ltl = (\"(G !d) & ((!c) U b) & ((!b) U a) & (F G c)\")\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa = OmegaAutomaton(ltl)\n",
    "print('Number of Omega-automaton states (including the trap state):',oa.shape[1])\n",
    "\n",
    "# MDP Description\n",
    "shape = (5,5)\n",
    "# E: Empty, T: Trap, B: Obstacle\n",
    "structure = np.array([\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'B',  'E',  'B',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E']\n",
    "])\n",
    "\n",
    "# Labels of the states\n",
    "label = np.array([\n",
    "[(),    (),    ('a',),    (),    ()],\n",
    "[(),    (),    ('d',),(),    ()],\n",
    "[(),    (),    (),    (),    ()],\n",
    "[('c',),('d',),    ('d',),('d',),()],\n",
    "[(),    ('d',),    ('b',),(),    ()]\n",
    "],dtype=object)\n",
    "# Colors of the labels \n",
    "lcmap={\n",
    "    ('a',):'yellow',\n",
    "    ('b',):'greenyellow',\n",
    "    ('c',):'turquoise',\n",
    "    ('d',):'pink'\n",
    "}\n",
    "\n",
    "p= 0.8\n",
    "grid_mdp = GridMDP(shape=shape,structure=structure,label=label,lcmap=lcmap, p=p, figsize=5)  # Use figsize=4 for smaller figures\n",
    "grid_mdp.plot(save=\"env.pdf\")\n",
    "\n",
    "# Construct the product MDP\n",
    "csrl = ControlSynthesis(grid_mdp,oa)\n",
    "max_rew = round(csrl.reward.max(), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### from the Nursary case ###\n",
    "# LTL Specification\n",
    "# ltl = ('G ('\n",
    "#     '(!d) & '\n",
    "#     '((b & (!(X b)))->(X ((!b) U (a|c)))) & '\n",
    "#     '(((!b) & (X b) & (!(X X b)))->((!a) U c)) & '\n",
    "#     '(a->(X ((!a) U b))) & '\n",
    "#     '(c->((!a) U b)) & '\n",
    "#     '((b & (X b))->(F a))'\n",
    "# ')')\n",
    "\n",
    "ltl = (\"(G !d) & ((!c) U b) & ((!b) U a) & (F G c)\")\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa = OmegaAutomaton(ltl)\n",
    "print('Number of Omega-automaton states (including the trap state):',oa.shape[1])\n",
    "\n",
    "# MDP Description\n",
    "shape = (6,6)\n",
    "# E: Empty, T: Trap, B: Obstacle\n",
    "structure = np.array([\n",
    "['E',  'E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'B',  'E',  'B',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E',  'E'],\n",
    "])\n",
    "\n",
    "# Labels of the states\n",
    "label = np.array([\n",
    "[(),    (),    ('a',),    (),    (),    ()],\n",
    "[(),    (),    ('d',),(),    (),    ()],\n",
    "[(),    (),    (),    (),    (),    ()],\n",
    "[(),    (),    (),    (),    (),    ()],\n",
    "[('c',),('d',),    ('d',),('d',),(),    ()],\n",
    "[(),    (),    ('b',),(),    (),    ()]\n",
    "],dtype=object)\n",
    "# Colors of the labels\n",
    "lcmap={\n",
    "    ('a',):'yellow',\n",
    "    ('b',):'greenyellow',\n",
    "    ('c',):'turquoise',\n",
    "    ('d',):'pink'\n",
    "}\n",
    "p=1\n",
    "grid_mdp = GridMDP(shape=shape,structure=structure,label=label,lcmap=lcmap, p=p, figsize=5)  # Use figsize=4 for smaller figures\n",
    "grid_mdp.plot(save=\"env.pdf\")\n",
    "\n",
    "# Construct the product MDP\n",
    "csrl = ControlSynthesis(grid_mdp,oa)\n",
    "max_rew = round(csrl.reward.max(), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [2], 'b': [22], 'c': [15], 'd': [7, 16, 17, 18, 21]}\n"
     ]
    }
   ],
   "source": [
    "t = \"[] ~d /\\ (~c % b) /\\ (~b % a) /\\ (<> [] c)\"\n",
    "\n",
    "LTL_formula = parser.parse(t)\n",
    "predicates=grid_mdp.get_predicates()\n",
    "print(predicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 5, 5, 6)]    0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 5, 5, 32)     800         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 5, 5, 8)      1032        ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 200)          0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 32)           6432        ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 16)           528         ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 10)           170         ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 1)            17          ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 8,979\n",
      "Trainable params: 8,979\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model(csrl.ch_states[(0,0,0,0)].shape, csrl.shape[-1])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model(csrl.ch_states[(0,0,0,0)][np.newaxis])\n",
    "\n",
    "N, W, Q, P, visited = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x21918b61c00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(f'models\\p=0.8\\d100_s100_t100')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train wins: 0 / 0\n",
      "--- : [0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 19, 24, 24, 23, 23, 22, 23, 24, 19, 18]\n",
      "--- : [19, 18]\n",
      "--- : [10, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 1, 2, 1, 1, 2, 1, 0, 1, 2, 3, 4, 3, 4, 4]\n",
      "--- : [10, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 10, 5, 5, 5]\n",
      "--- : [9, 9, 9, 9, 4, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19]\n",
      "--- : [19, 14, 14, 9, 9, 4, 3, 2, 3, 3, 4, 9, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24]\n",
      "--- : [3, 2, 3, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 18]\n",
      "--- : [10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 1, 2, 3, 4]\n",
      "--- : [13, 13, 18]\n",
      "--- : [13, 12, 17]\n",
      "--- : [23, 23, 24, 24, 19, 19, 14, 9, 9, 9, 4, 3, 3, 2, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9]\n",
      "--- : [0, 0, 0, 1, 2, 3, 3, 4, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 23, 23, 24, 19, 14, 14]\n",
      "--- : [11, 10, 5, 0, 0, 1, 2, 2, 3, 4, 9, 9, 4, 9, 14, 14, 19, 19, 24, 23, 22, 23, 24, 23, 24]\n",
      "--- : [14, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 19, 18]\n",
      "--- : [3, 3, 2, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 17]\n",
      "--- : [5, 10, 5, 5, 5, 5, 0, 0, 1, 2, 3, 3, 4, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 24, 19]\n",
      "--- : [11, 10, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9]\n",
      "--- : [4, 4, 3, 3, 2, 3, 3, 4, 4, 9, 9, 9, 14, 19, 18]\n",
      "--- : [10, 10, 10, 5, 0, 0, 1, 2, 7]\n",
      "--- : [19, 14, 9, 9, 9, 9, 9, 9, 9, 4, 9, 4, 3, 3, 2, 2, 2, 1, 0, 0, 5, 10, 15, 15, 20]\n",
      "--- : [20, 15, 15, 20, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15]\n",
      "--- : [14, 9, 9, 9, 9, 4, 3, 3, 2, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 18]\n",
      "--- : [23, 23, 23, 23, 23, 23, 23, 23, 22, 23, 22, 22, 17]\n",
      "--- : [9, 4, 4, 3, 2, 3, 4, 9, 4, 9, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 14, 19]\n",
      "--- : [20, 20, 21]\n",
      "--- : [19, 14, 13, 12, 11, 10, 10, 5, 0, 0, 0, 0, 1, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [23, 24, 19, 14, 9, 4, 3, 3, 4, 3, 3, 2, 3, 4, 9, 4, 3, 4, 9, 9, 9, 9, 9, 9, 14]\n",
      "--- : [0, 0, 0, 0, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 5, 5, 10, 10]\n",
      "--- : [0, 1, 2, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 10]\n",
      "--- : [20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 20, 15, 15, 15, 20, 15, 20, 20, 15]\n",
      "--- : [11, 11, 11, 10, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 14, 19]\n",
      "--- : [10, 10, 10, 5, 5, 5, 10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 1, 2]\n",
      "--- : [9, 9, 9, 14, 13, 13, 12, 17]\n",
      "--- : [14, 9, 9, 4, 4, 3, 2, 3, 3, 4, 9, 14, 13, 12, 11, 11, 11, 10, 15, 20, 21]\n",
      "--- : [19, 14, 14, 19, 14, 9, 9, 9, 14, 9, 9, 4, 9, 9, 4, 4, 3, 4, 3, 2, 3, 4, 9, 9, 14]\n",
      "--- : [23, 23, 23, 23, 23, 23, 23, 23, 24, 19, 24, 19, 14, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 18]\n",
      "--- : [12, 11, 12, 7]\n",
      "--- : [9, 4, 3, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 2, 3, 3]\n",
      "--- : [9, 9, 14, 9, 9, 4, 3, 2, 3, 4, 9, 9, 9, 14, 14, 13, 12, 11, 10, 11, 10, 15, 10, 15, 15]\n",
      "--- : [9, 9, 14, 13, 13, 12, 17]\n",
      "--- : [12, 7]\n",
      "--- : [11, 11, 10, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 1, 1, 2, 3, 4, 9, 14, 19, 24, 23, 23, 18]\n",
      "--- : [9, 4, 3, 2, 1, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2]\n",
      "--- : [23, 23, 23, 23, 24, 24, 19, 14, 9, 9, 4, 9, 9, 4, 9, 9, 9, 4, 3, 3, 2, 1, 0, 1, 0]\n",
      "--- : [24, 19, 18]\n",
      "--- : [1, 2, 3, 4, 9, 9, 9, 9, 14, 13, 14, 19, 24, 23, 22, 23, 23, 24, 23, 24, 19, 14, 14, 9, 14]\n",
      "--- : [24, 24, 23, 23, 23, 24, 24, 19, 14, 13, 12, 11, 11, 11, 10, 10, 5, 10, 5, 5, 5, 0, 1, 1, 2]\n",
      "--- : [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 1, 1, 2, 3, 4, 9, 14]\n",
      "--- : [20, 20, 20, 21]\n",
      "--- : [11, 11, 10, 5, 5, 5, 5, 5, 5, 0, 1, 2, 1, 0, 1, 2, 3, 4, 9, 9, 9, 9, 9, 14, 19]\n",
      "--- : [11, 10, 5, 10, 5, 5, 5, 5, 5, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [12, 11, 11, 10, 5, 5, 5, 10, 5, 5, 0, 0, 0, 0, 0, 0, 1, 2, 7]\n",
      "--- : [12, 11, 10, 5, 5, 5, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 5, 5, 10, 15, 15]\n",
      "--- : [11, 10, 5, 0, 0, 5, 5, 0, 1, 2, 7]\n",
      "--- : [13, 13, 12, 11, 11, 11, 10, 5, 0, 1, 2, 7]\n",
      "--- : [1, 2, 3, 3, 3, 4, 9, 14, 19, 18]\n",
      "--- : [10, 5, 5, 10, 5, 5, 5, 10, 5, 5, 5, 10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23]\n",
      "--- : [24, 23, 24, 19, 14, 9, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 19, 24, 23, 22, 23, 23, 18]\n",
      "--- : [12, 11, 11, 11, 10, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5]\n",
      "--- : [23, 23, 23, 23, 23, 23, 23, 23, 22, 23, 22, 17]\n",
      "--- : [9, 4, 4, 4, 3, 2, 3, 4, 3, 4, 4, 9, 14, 19, 19, 18]\n",
      "--- : [23, 23, 24, 24, 19, 14, 9, 4, 3, 3, 3, 2, 1, 0, 0, 0, 1, 0, 0, 0, 5, 5, 5, 10, 11]\n",
      "--- : [9, 4, 3, 2, 2, 3, 4, 9, 9, 9, 14, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12]\n",
      "--- : [14, 13, 13, 18]\n",
      "--- : [0, 0, 1, 1, 2, 7]\n",
      "--- : [10, 5, 5, 5, 5, 5, 0, 0, 1, 2, 7]\n",
      "--- : [0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "--- : [1, 2, 7]\n",
      "--- : [0, 0, 1, 2, 3, 4, 4, 9, 14, 13, 12, 11, 10, 15, 10, 10, 15, 15, 15, 15, 10, 15, 15, 15, 15]\n",
      "--- : [19, 14, 9, 9, 9, 14, 9, 9, 4, 3, 2, 1, 0, 0, 1, 0, 0, 5, 5, 0, 1, 1, 0, 5, 10]\n",
      "--- : [24, 24, 19, 14, 13, 12, 11, 11, 10, 5, 0, 0, 0, 1, 2, 3, 4, 9, 9, 4, 4, 9, 14, 19, 24]\n",
      "--- : [20, 20, 20, 15, 16]\n",
      "--- : [12, 7]\n",
      "--- : [19, 19, 14, 9, 9, 9, 9, 4, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 14, 19, 19, 18]\n",
      "--- : [3, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 18]\n",
      "--- : [4, 3, 3, 3, 2, 3, 4, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 23, 24, 24, 19, 14, 14, 9, 14]\n",
      "--- : [11, 10, 5, 0, 0, 0, 0, 1, 2, 3, 4, 4, 9, 14, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14]\n",
      "--- : [13, 18]\n",
      "--- : [13, 13, 13, 13, 12, 11, 10, 5, 5, 0, 0, 1, 2, 3, 3, 4, 4, 9, 14, 13, 12, 11, 10, 15, 20]\n",
      "--- : [14, 13, 13, 12, 11, 10, 5, 10, 5, 5, 5, 0, 1, 2, 3, 4, 9, 9, 9, 9, 14, 14, 19, 24, 23]\n",
      "--- : [5, 5, 10, 10, 5, 5, 0, 1, 2, 3, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14]\n",
      "--- : [1, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 18]\n",
      "--- : [5, 5, 5, 5, 0, 0, 1, 2, 3, 4, 3, 4, 9, 14, 19, 24, 23, 22, 22, 22, 17]\n",
      "--- : [24, 19, 14, 13, 13, 12, 7]\n",
      "--- : [23, 23, 24, 19, 14, 9, 4, 3, 3, 3, 3, 2, 3, 4, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24]\n",
      "--- : [23, 23, 23, 22, 23, 23, 23, 23, 23, 22, 21]\n",
      "--- : [13, 13, 12, 11, 11, 11, 11, 10, 5, 5, 0, 1, 2, 7]\n",
      "--- : [23, 22, 23, 22, 17]\n",
      "--- : [13, 13, 12, 11, 10, 5, 5, 5, 10, 5, 5, 5, 5, 0, 1, 2, 3, 4, 3, 4, 9, 9, 14, 19, 24]\n",
      "--- : [10, 5, 5, 5, 5, 5, 0, 1, 1, 2, 3, 3, 4, 4, 9, 14, 19, 19, 18]\n",
      "--- : [11, 11, 11, 11, 11, 10, 5, 0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 23, 22, 23, 24, 23, 24, 19]\n",
      "--- : [24, 23, 23, 23, 24, 19, 19, 14, 14, 9, 4, 3, 2, 2, 3, 3, 4, 9, 14, 19, 19, 18]\n",
      "--- : [13, 12, 11, 11, 11, 16]\n",
      "--- : [20, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 10, 15, 20]\n",
      "--- : [1, 2, 7]\n",
      "--- : [10, 5, 5, 5, 5, 5, 0, 1, 2, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "--- : [19, 18]\n",
      "--- : [14, 13, 13, 13, 12, 11, 16]\n",
      "--- : [12, 11, 10, 5, 5, 0, 5, 5, 5, 5, 5, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24]\n",
      "--- : [11, 10, 5, 5, 0, 1, 2, 2, 2, 2, 2, 7]\n",
      "Test wins: 0 / 100\n",
      "Train wins: 0 / 0\n",
      "--- : [23, 23, 23, 24, 19, 14, 14, 9, 9, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 9, 14, 19, 18]\n",
      "--- : [14, 9, 4, 3, 2, 3, 3, 3, 4, 9, 9, 9, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 23, 24, 24, 19, 14, 14, 9, 14, 13, 18]\n",
      "--- : [0, 1, 1, 2, 3, 4, 9, 9, 9, 9, 9, 9, 14, 19, 19, 24, 23, 18]\n",
      "--- : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 19, 14, 14, 14, 13, 12, 7]\n",
      "--- : [10, 5, 10, 5, 5, 5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 20, 15, 15, 15, 20, 15, 15, 20, 15, 15, 15, 15, 10, 10]\n",
      "--- : [14, 9, 9, 4, 4, 3, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 14, 9, 9, 9, 14, 13, 12, 11, 10, 15, 15, 10, 11, 10, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 20]\n",
      "--- : [13, 13, 13, 12, 7]\n",
      "--- : [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [14, 9, 9, 9, 4, 4, 3, 3, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 10, 10, 15]\n",
      "--- : [9, 4, 9, 4, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 18]\n",
      "--- : [3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 18]\n",
      "--- : [24, 24, 24, 19, 18]\n",
      "--- : [10, 5, 0, 0, 1, 2, 2, 3, 4, 9, 9, 14, 19, 24, 19, 24, 19, 19, 24, 23, 22, 22, 23, 18]\n",
      "--- : [9, 4, 9, 4, 3, 3, 2, 3, 4, 9, 14, 19, 24, 24, 23, 18]\n",
      "--- : [10, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 7]\n",
      "--- : [9, 4, 4, 3, 2, 3, 4, 9, 14, 19, 18]\n",
      "--- : [23, 24, 24, 24, 23, 23, 23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 19, 24, 23, 23, 18]\n",
      "--- : [5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 19, 14, 13, 18]\n",
      "--- : [12, 7]\n",
      "--- : [13, 13, 14, 9, 4, 9, 9, 9, 4, 3, 4, 3, 2, 7]\n",
      "--- : [1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 18]\n",
      "--- : [14, 13, 12, 7]\n",
      "--- : [13, 13, 13, 13, 13, 12, 11, 16]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15]\n",
      "--- : [20, 20, 20, 20, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 20, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15]\n",
      "--- : [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 9, 14, 13, 14, 19, 24, 23, 18]\n",
      "--- : [0, 0, 0, 1, 1, 1, 2, 3, 4, 3, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 10, 15, 15, 20, 15, 15, 15, 15, 20, 20, 15, 10, 11, 11, 16]\n",
      "--- : [12, 11, 12, 7]\n",
      "+++ : [14, 9, 4, 3, 3, 4, 3, 3, 2, 3, 4, 9, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 19, 14, 19, 14, 13, 12, 11, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15]\n",
      "+++ : [13, 13, 13, 13, 13, 12, 11, 11, 11, 10, 5, 5, 5, 5, 0, 1, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 13, 12, 11, 11, 10, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15]\n",
      "+++ : [12, 11, 10, 10, 5, 5, 5, 0, 0, 0, 0, 1, 1, 2, 3, 4, 9, 14, 13, 14, 14, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 12, 11, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15]\n",
      "--- : [9, 4, 3, 2, 3, 4, 4, 9, 14, 19, 18]\n",
      "--- : [9, 4, 4, 3, 2, 3, 3, 4, 3, 3, 4, 4, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [14, 9, 4, 3, 2, 3, 4, 3, 4, 9, 14, 13, 14, 19, 18]\n",
      "--- : [20, 20, 15, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 10, 11, 10, 11, 10, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 20, 20, 15, 20, 15, 15, 15, 15, 10, 15]\n",
      "--- : [3, 2, 3, 4, 9, 9, 9, 9, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 9, 14, 13, 12, 7]\n",
      "--- : [19, 18]\n",
      "--- : [4, 3, 3, 2, 3, 4, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 14, 14, 19, 14, 13, 12, 17]\n",
      "--- : [11, 16]\n",
      "+++ : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 11, 10, 15, 15, 15, 10, 15, 20, 15, 20, 15, 15, 10, 15, 15, 15, 15, 15, 10, 11, 10, 15, 15, 15, 15]\n",
      "--- : [19, 14, 19, 14, 13, 13, 12, 17]\n",
      "+++ : [24, 19, 19, 14, 9, 4, 3, 2, 3, 3, 4, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 11, 10, 15, 20, 15, 15, 15, 15, 15, 15, 10, 15, 10, 11, 10, 15, 15, 15, 15, 15, 15]\n",
      "--- : [24, 19, 14, 13, 14, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "+++ : [0, 0, 0, 1, 2, 3, 4, 4, 3, 4, 9, 9, 14, 19, 24, 23, 23, 22, 23, 24, 24, 19, 14, 14, 9, 14, 9, 9, 14, 13, 12, 11, 10, 10, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 0, 0, 1, 2, 3, 3, 4, 9, 9, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 14, 13, 12, 11, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 11, 11, 10, 15, 15]\n",
      "--- : [4, 3, 3, 2, 7]\n",
      "--- : [5, 0, 0, 0, 0, 0, 1, 1, 2, 7]\n",
      "--- : [9, 4, 3, 2, 7]\n",
      "--- : [11, 10, 5, 5, 5, 0, 0, 0, 1, 2, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 14, 13, 18]\n",
      "+++ : [10, 5, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 24, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 15, 15, 15]\n",
      "--- : [20, 21]\n",
      "--- : [11, 10, 5, 0, 0, 0, 0, 0, 5, 0, 0, 1, 2, 7]\n",
      "--- : [13, 13, 13, 12, 11, 10, 5, 5, 5, 5, 0, 0, 1, 2, 3, 4, 3, 4, 9, 14, 19, 24, 19, 24, 23, 22, 17]\n",
      "+++ : [10, 5, 0, 0, 1, 2, 3, 4, 9, 14, 14, 19, 24, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 10, 11, 10, 15, 15, 15, 15, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [11, 10, 5, 5, 0, 1, 2, 3, 4, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 14, 14, 13, 12, 17]\n",
      "--- : [9, 4, 3, 3, 2, 3, 3, 4, 9, 9, 14, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 14, 13, 12, 7]\n",
      "--- : [23, 23, 24, 19, 14, 9, 4, 3, 2, 3, 3, 3, 4, 9, 14, 19, 18]\n",
      "--- : [23, 23, 23, 18]\n",
      "--- : [0, 0, 0, 0, 0, 1, 2, 7]\n",
      "--- : [23, 24, 23, 24, 19, 18]\n",
      "--- : [9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 18]\n",
      "--- : [0, 0, 1, 2, 7]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 3, 3, 3, 2, 3, 4, 9, 14, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 12, 11, 10, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 10, 15, 20, 15, 15, 10, 15, 20, 15, 15, 15]\n",
      "--- : [4, 3, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "+++ : [23, 24, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 24, 23, 23, 22, 23, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [11, 10, 5, 0, 1, 2, 3, 3, 4, 9, 14, 19, 18]\n",
      "--- : [5, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 3, 3, 4, 9, 9, 14, 19, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 13, 13, 12, 11, 16]\n",
      "--- : [20, 15, 15, 15, 20, 15, 15, 20, 15, 15, 10, 15, 15, 15, 20, 15, 10, 15, 20, 15, 10, 15, 10, 15, 15, 15, 15, 10, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15]\n",
      "--- : [0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [10, 5, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 18]\n",
      "--- : [10, 5, 10, 5, 5, 0, 1, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 18]\n",
      "--- : [11, 10, 5, 0, 0, 1, 2, 3, 4, 9, 14, 13, 14, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 18]\n",
      "--- : [1, 2, 3, 4, 9, 14, 19, 24, 19, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 20, 21]\n",
      "--- : [11, 10, 5, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 16]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 19, 24, 23, 22, 23, 24, 19, 19, 19, 14, 14, 9, 14, 13, 12, 11, 10, 11, 10, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15]\n",
      "--- : [23, 23, 23, 24, 19, 14, 14, 9, 4, 9, 4, 3, 2, 3, 3, 3, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [24, 23, 24, 23, 24, 23, 23, 23, 24, 23, 24, 19, 14, 9, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 11, 10, 15, 15, 15, 15, 15, 15, 15, 10, 11, 10]\n",
      "--- : [1, 2, 3, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [23, 24, 19, 14, 14, 14, 9, 4, 3, 2, 3, 4, 9, 9, 14, 13, 14, 14, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 14, 13, 18]\n",
      "--- : [12, 11, 10, 10, 5, 5, 0, 0, 0, 0, 1, 2, 3, 4, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 14, 13, 12, 11, 10, 15, 15, 20, 15, 15, 15, 10, 10, 15, 15, 10, 15, 15, 15, 20]\n",
      "--- : [19, 14, 9, 4, 9, 9, 4, 3, 2, 2, 3, 4, 9, 14, 13, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 20, 15, 15, 15, 15, 15, 10, 15, 10, 15, 15, 15, 10, 15, 15, 20]\n",
      "--- : [11, 10, 5, 0, 1, 2, 2, 3, 3, 4, 9, 14, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 18]\n",
      "--- : [24, 19, 14, 9, 4, 9, 4, 3, 2, 2, 2, 3, 4, 9, 14, 19, 18]\n",
      "+++ : [11, 10, 5, 5, 0, 1, 1, 2, 3, 4, 4, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 10, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 15, 15]\n",
      "--- : [11, 16]\n",
      "--- : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [4, 3, 2, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [12, 17]\n",
      "--- : [14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 17]\n",
      "--- : [19, 18]\n",
      "--- : [9, 4, 3, 3, 3, 2, 2, 7]\n",
      "+++ : [0, 0, 1, 2, 2, 3, 4, 9, 9, 14, 13, 14, 19, 19, 24, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [4, 9, 4, 3, 2, 3, 4, 9, 14, 14, 19, 24, 23, 18]\n",
      "--- : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 10, 11, 10, 15, 15, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 20]\n",
      "--- : [11, 10, 11, 10, 5, 5, 0, 0, 0, 1, 2, 2, 3, 4, 9, 14, 19, 18]\n",
      "--- : [5, 0, 1, 2, 1, 2, 3, 4, 9, 14, 19, 19, 18]\n",
      "--- : [24, 19, 14, 9, 4, 9, 9, 4, 3, 2, 3, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 13, 18]\n",
      "--- : [19, 14, 9, 4, 3, 2, 3, 4, 3, 3, 4, 3, 4, 9, 14, 13, 14, 19, 24, 19, 24, 19, 18]\n",
      "Test wins: 16 / 100\n",
      "Train wins: 0 / 0\n",
      "--- : [3, 3, 3, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [13, 13, 14, 14, 19, 14, 9, 4, 3, 3, 3, 2, 3, 4, 4, 9, 9, 9, 9, 9, 9, 14, 14, 19, 18]\n",
      "--- : [14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 4, 9, 14, 19, 18]\n",
      "+++ : [12, 11, 11, 11, 11, 11, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [13, 13, 13, 12, 7]\n",
      "--- : [9, 9, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 14, 19, 18]\n",
      "--- : [19, 14, 9, 4, 3, 2, 3, 4, 4, 3, 3, 4, 9, 14, 19, 18]\n",
      "+++ : [9, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 3, 3, 3, 3, 3, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 12, 11, 10, 10, 15, 10, 15, 20, 15, 15, 15, 10, 15, 15, 15, 15, 15, 10, 15, 15]\n",
      "+++ : [9, 9, 9, 4, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 20, 15, 15, 15, 10, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 20, 20, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15]\n",
      "--- : [24, 19, 14, 14, 13, 14, 9, 4, 9, 4, 4, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 20, 21]\n",
      "+++ : [13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 9, 4, 3, 3, 3, 3, 3, 4, 3, 3, 4, 9, 4, 9, 9, 4, 3, 4, 4, 3, 3, 3, 2, 2, 3, 4, 9, 9, 14, 19, 19, 24, 23, 23, 22, 23, 24, 23, 24, 19, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [23, 22, 23, 23, 18]\n",
      "--- : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 3, 4, 9, 14, 19, 24, 23, 22, 23, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 16]\n",
      "--- : [20, 20, 20, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 20, 15, 15, 20, 15, 15, 15, 20, 15, 10, 15, 20, 15, 20, 15, 15, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 20, 15, 20, 21]\n",
      "--- : [11, 11, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 1, 2, 3, 4, 9, 9, 14, 14, 13, 13, 14, 19, 24, 23, 22, 23, 24, 23, 23, 18]\n",
      "+++ : [9, 9, 4, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 9, 14, 14, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 10, 11, 10, 15, 15, 10, 15, 15, 15, 15]\n",
      "--- : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 4, 9, 9, 14, 19, 24, 19, 24, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 19, 18]\n",
      "--- : [1, 2, 3, 4, 9, 9, 9, 14, 13, 13, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 20, 21]\n",
      "--- : [12, 11, 10, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 5, 5, 5, 5, 5, 5, 0, 1, 2, 7]\n",
      "--- : [19, 14, 9, 9, 9, 4, 3, 2, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 23, 24, 19, 14, 14, 14, 13, 12, 17]\n",
      "--- : [4, 3, 3, 3, 3, 2, 3, 4, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [19, 14, 9, 4, 3, 3, 4, 9, 4, 3, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 3, 4, 4, 3, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 13, 12, 17]\n",
      "--- : [10, 5, 5, 10, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 7]\n",
      "--- : [24, 19, 14, 9, 4, 9, 9, 4, 3, 3, 3, 3, 3, 3, 2, 7]\n",
      "--- : [1, 2, 3, 4, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 16]\n",
      "--- : [20, 21]\n",
      "--- : [0, 0, 1, 2, 2, 3, 4, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 23, 24, 19, 19, 19, 19, 18]\n",
      "--- : [13, 13, 13, 13, 13, 13, 13, 13, 12, 11, 16]\n",
      "+++ : [13, 13, 13, 13, 14, 13, 13, 14, 9, 4, 3, 3, 3, 4, 4, 3, 2, 3, 4, 9, 9, 4, 9, 14, 19, 24, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 13, 12, 11, 10, 10, 11, 10, 15, 15, 15, 15, 15, 15, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 9, 4, 3, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 15, 15, 15, 20, 15, 15, 15]\n",
      "--- : [19, 14, 9, 9, 4, 4, 9, 4, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 9, 14, 19, 19, 18]\n",
      "--- : [4, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [10, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 4, 4, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 24, 24, 23, 22, 23, 24, 19, 14, 14, 13, 13, 12, 11, 10, 15, 15, 20, 20, 20, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 10, 15, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 20, 20, 15, 10, 15]\n",
      "--- : [24, 19, 19, 14, 9, 4, 3, 4, 3, 3, 3, 3, 4, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 2, 3, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "--- : [11, 11, 16]\n",
      "--- : [5, 10, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [12, 11, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 9, 14, 13, 14, 13, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 10, 10, 15, 15, 15, 15, 15, 15, 10, 15]\n",
      "--- : [20, 20, 15, 16]\n",
      "--- : [20, 20, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 15, 10, 15, 15, 10, 10, 11, 16]\n",
      "+++ : [10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 14, 19, 24, 23, 23, 23, 22, 23, 24, 24, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 15, 15, 10, 11, 10, 15, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15]\n",
      "--- : [24, 24, 19, 18]\n",
      "--- : [24, 19, 14, 13, 14, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 23, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "--- : [5, 5, 5, 10, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [4, 3, 3, 2, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 23, 24, 23, 24, 19, 18]\n",
      "--- : [24, 19, 14, 14, 13, 14, 9, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 4, 9, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [9, 9, 4, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 7]\n",
      "--- : [10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [20, 15, 15, 10, 15, 15, 15, 15, 15, 20, 15, 15, 20, 15, 20, 15, 15, 10, 10, 11, 10, 15, 20, 21]\n",
      "--- : [4, 3, 3, 3, 4, 3, 2, 3, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 14, 13, 12, 11, 16]\n",
      "--- : [20, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 20, 15, 15, 10, 11, 10, 10, 15, 15, 10, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 20, 21]\n",
      "+++ : [1, 2, 3, 4, 4, 9, 9, 4, 9, 14, 13, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 15, 10, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 20, 20, 15, 10, 11, 10, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 10, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 11, 10, 5, 5, 5, 5, 0, 0, 0, 0, 1, 2, 3, 4, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 10, 15, 10, 10, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 20, 15, 15, 15, 15, 15]\n",
      "--- : [1, 2, 3, 4, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "--- : [23, 23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 3, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [14, 19, 14, 9, 9, 9, 9, 4, 9, 4, 4, 3, 3, 3, 2, 3, 4, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 19, 14, 14, 9, 14, 19, 14, 13, 12, 11, 10, 10, 15, 15, 10, 10, 15, 15, 15, 15, 20, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 20, 15, 15, 15, 10, 15, 15]\n",
      "--- : [9, 9, 4, 3, 3, 2, 3, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 11, 16]\n",
      "--- : [19, 14, 9, 4, 3, 3, 4, 3, 2, 3, 4, 9, 9, 9, 9, 14, 19, 18]\n",
      "--- : [0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 23, 23, 24, 23, 24, 23, 24, 19, 14, 14, 9, 14, 9, 9, 9, 14, 13, 12, 11, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 20, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 20]\n",
      "--- : [10, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10]\n",
      "--- : [13, 13, 12, 11, 10, 11, 11, 10, 11, 10, 10, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 14, 19, 24, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 10, 15, 15, 15, 15, 20, 21]\n",
      "--- : [0, 0, 1, 2, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 18]\n",
      "--- : [5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 4, 9, 9, 14, 19, 24, 19, 24, 23, 22, 17]\n",
      "+++ : [9, 4, 3, 2, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 10, 15]\n",
      "+++ : [11, 11, 10, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 3, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 10, 15, 15, 15]\n",
      "--- : [14, 13, 13, 13, 14, 13, 13, 12, 11, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 10, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 1, 2, 3, 4, 4, 9, 14, 19, 19, 24, 23, 18]\n",
      "--- : [11, 11, 10, 11, 10, 5, 5, 5, 5, 10, 5, 5, 10, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 10, 11, 10, 5, 5, 5, 0, 1, 2, 7]\n",
      "--- : [14, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 9, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [10, 5, 5, 5, 5, 5, 5, 5, 10, 5, 10, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 7]\n",
      "+++ : [4, 3, 3, 3, 3, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [9, 4, 3, 3, 3, 4, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 20, 15, 15, 20, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 15, 10, 15]\n",
      "--- : [23, 23, 24, 23, 24, 24, 19, 14, 9, 4, 3, 3, 4, 3, 3, 3, 3, 4, 3, 4, 3, 2, 2, 7]\n",
      "--- : [14, 9, 9, 9, 9, 14, 13, 13, 13, 13, 13, 13, 12, 11, 10, 11, 10, 5, 10, 10, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 19, 24, 23, 22, 23, 18]\n",
      "--- : [1, 2, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [23, 24, 19, 19, 14, 9, 9, 14, 9, 4, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 4, 9, 9, 9, 9, 14, 19, 18]\n",
      "--- : [11, 16]\n",
      "--- : [9, 4, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 9, 14, 19, 18]\n",
      "--- : [19, 14, 9, 9, 9, 4, 4, 9, 9, 4, 4, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 14, 13, 14, 19, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 13, 12, 17]\n",
      "--- : [1, 2, 3, 3, 3, 4, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "--- : [12, 11, 11, 10, 5, 5, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 10, 15, 15, 10, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 15, 20, 21]\n",
      "--- : [19, 19, 14, 13, 13, 14, 13, 13, 13, 12, 17]\n",
      "--- : [1, 2, 2, 3, 4, 9, 14, 19, 24, 19, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "--- : [14, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 9, 9, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 14, 19, 14, 19, 14, 13, 12, 17]\n",
      "--- : [9, 4, 3, 3, 3, 2, 7]\n",
      "--- : [9, 9, 9, 4, 3, 3, 3, 2, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 20, 15, 10, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20]\n",
      "--- : [13, 13, 13, 12, 11, 10, 5, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 4, 9, 14, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 10, 15, 10, 15, 15, 15, 15, 10, 11, 10, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 15, 15, 15, 20, 21]\n",
      "--- : [12, 7]\n",
      "--- : [19, 14, 9, 9, 9, 4, 9, 9, 9, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 18]\n",
      "--- : [20, 15, 10, 15, 15, 15, 15, 10, 11, 10, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 20, 15, 15, 20, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15]\n",
      "--- : [14, 9, 4, 3, 3, 3, 4, 3, 3, 3, 3, 4, 9, 9, 4, 9, 4, 9, 4, 9, 4, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 10, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [11, 10, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 4, 4, 3, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [24, 19, 14, 14, 13, 13, 13, 14, 9, 4, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 16]\n",
      "+++ : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 4, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15]\n",
      "--- : [5, 10, 5, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 14, 19, 24, 23, 18]\n",
      "--- : [3, 3, 2, 7]\n",
      "--- : [20, 15, 16]\n",
      "--- : [11, 10, 5, 5, 10, 5, 5, 5, 5, 0, 0, 0, 1, 1, 2, 3, 3, 4, 9, 14, 19, 24, 19, 24, 23, 22, 23, 23, 24, 23, 23, 24, 24, 19, 14, 14, 9, 14, 13, 12, 7]\n",
      "+++ : [19, 14, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 13, 12, 11, 10, 15, 15, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 10, 11, 11, 10, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15]\n",
      "Test wins: 21 / 100\n",
      "Train wins: 0 / 0\n",
      "--- : [13, 13, 13, 13, 12, 11, 10, 5, 10, 11, 11, 10, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 14, 13, 14, 14, 19, 24, 23, 22, 17]\n",
      "--- : [13, 13, 13, 13, 12, 11, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 7]\n",
      "--- : [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 14, 9, 9, 14, 9, 9, 9, 9, 9, 14, 14, 13, 13, 13, 13, 12, 11, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 14, 13, 14, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "--- : [4, 3, 2, 3, 4, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 4, 9, 9, 14, 19, 18]\n",
      "--- : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 3, 4, 9, 14, 19, 24, 23, 24, 19, 18]\n",
      "+++ : [24, 24, 24, 24, 24, 24, 24, 24, 19, 19, 14, 14, 9, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 14, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 14, 13, 12, 11, 10, 11, 10, 11, 10, 11, 10, 15, 20, 15, 15, 15, 20, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 10, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 20, 15, 15, 15, 20, 15, 20, 15, 15, 15, 15]\n",
      "--- : [24, 19, 14, 9, 9, 9, 4, 9, 4, 4, 3, 4, 3, 3, 3, 2, 3, 4, 9, 14, 13, 12, 7]\n",
      "--- : [23, 23, 23, 23, 23, 23, 23, 24, 23, 23, 23, 23, 23, 23, 22, 22, 21]\n",
      "--- : [3, 3, 3, 2, 3, 4, 4, 9, 9, 9, 9, 9, 4, 9, 9, 14, 19, 19, 19, 24, 23, 18]\n",
      "+++ : [14, 9, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 14, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 10, 15, 20, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [23, 23, 23, 18]\n",
      "--- : [13, 13, 13, 13, 13, 13, 12, 11, 12, 7]\n",
      "--- : [23, 23, 24, 24, 23, 24, 24, 19, 14, 9, 4, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 4, 3, 3, 3, 4, 3, 3, 4, 9, 9, 4, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 9, 9, 4, 9, 9, 9, 9, 9, 14, 19, 18]\n",
      "+++ : [1, 2, 3, 4, 3, 4, 3, 3, 4, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 20, 15, 10, 15, 20, 15, 20, 15]\n",
      "--- : [0, 0, 0, 1, 1, 2, 3, 3, 4, 9, 9, 9, 9, 14, 19, 24, 19, 18]\n",
      "--- : [3, 2, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "--- : [4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 4, 9, 14, 19, 24, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "--- : [14, 9, 9, 4, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 3, 3, 4, 9, 4, 4, 3, 3, 4, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [23, 24, 19, 14, 9, 9, 4, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 14, 19, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 17]\n",
      "--- : [4, 3, 3, 2, 3, 4, 3, 4, 4, 4, 9, 9, 9, 4, 9, 9, 9, 14, 19, 19, 18]\n",
      "--- : [19, 14, 9, 4, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 4, 9, 9, 9, 14, 19, 24, 24, 23, 18]\n",
      "--- : [19, 14, 9, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 9, 9, 9, 9, 4, 4, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 9, 14, 13, 12, 11, 10, 11, 10, 15, 15, 20, 15, 20, 21]\n",
      "--- : [20, 20, 15, 20, 15, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 10, 15, 10, 15, 15, 10, 15, 15, 15, 15, 10, 15, 15, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20]\n",
      "--- : [1, 2, 3, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [13, 13, 13, 12, 11, 10, 11, 10, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 1, 2, 3, 4, 3, 4, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 14, 13, 14, 19, 18]\n",
      "--- : [24, 19, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 4, 9, 9, 4, 9, 9, 9, 9, 9, 14, 19, 24, 24, 23, 22, 17]\n",
      "--- : [13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 11, 11, 10, 5, 5, 5, 10, 5, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3, 4, 9, 9, 9, 9, 9, 4, 9, 14, 19, 19, 24, 23, 22, 23, 18]\n",
      "--- : [19, 14, 9, 9, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 4, 4, 4, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 21]\n",
      "+++ : [19, 14, 9, 4, 3, 4, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 2, 3, 4, 9, 9, 9, 9, 4, 3, 3, 3, 3, 4, 3, 4, 3, 4, 4, 3, 4, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 9, 14, 13, 12, 11, 10, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [13, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 19, 24, 19, 24, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 18]\n",
      "--- : [5, 10, 5, 5, 0, 1, 2, 3, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 4, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [24, 19, 14, 9, 4, 3, 3, 2, 3, 3, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 17]\n",
      "--- : [19, 14, 9, 9, 4, 9, 4, 4, 3, 3, 4, 4, 3, 2, 3, 4, 9, 9, 4, 9, 9, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 15, 15, 15, 10, 15, 15, 15, 10, 15, 15, 10, 11, 10, 10, 15, 20, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 10, 15, 15, 15, 10, 11, 11]\n",
      "--- : [19, 24, 19, 14, 9, 4, 3, 3, 2, 3, 4, 9, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 17]\n",
      "--- : [13, 12, 13, 13, 12, 11, 11, 11, 10, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 1, 1, 1, 1, 2, 3, 4, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 4, 9, 9, 14, 19, 24, 23, 18]\n",
      "--- : [14, 9, 4, 3, 2, 3, 4, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 18]\n",
      "--- : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 3, 4, 9, 9, 9, 9, 4, 9, 9, 9, 14, 19, 24, 23, 22, 22, 23, 18]\n",
      "--- : [24, 19, 14, 9, 9, 4, 3, 3, 2, 2, 3, 4, 9, 9, 9, 9, 4, 9, 9, 9, 9, 14, 19, 18]\n",
      "--- : [14, 13, 14, 14, 9, 4, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 19, 14, 14, 9, 14, 13, 12, 17]\n",
      "--- : [19, 14, 9, 9, 4, 3, 2, 3, 4, 4, 9, 9, 9, 14, 14, 19, 24, 23, 22, 17]\n",
      "--- : [1, 2, 7]\n",
      "--- : [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 7]\n",
      "--- : [19, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 19, 18]\n",
      "--- : [10, 5, 5, 5, 5, 5, 10, 10, 5, 5, 5, 5, 10, 5, 10, 5, 5, 5, 5, 0, 0, 0, 1, 1, 2, 3, 4, 9, 4, 9, 4, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 14, 19, 18]\n",
      "--- : [20, 20, 20, 20, 15, 15, 15, 15, 10, 15, 15, 20, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 10, 15, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 15, 10, 15, 10, 15, 15, 15, 15, 15, 10, 11, 10, 15, 20, 15, 15, 15, 15, 15, 15, 10, 11, 11, 10, 15, 20, 15, 15, 20, 15, 15, 15, 15, 15, 20, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15]\n",
      "--- : [3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "--- : [4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [12, 11, 10, 5, 5, 5, 5, 5, 10, 10, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 24, 23, 22, 23, 18]\n",
      "--- : [23, 23, 24, 19, 14, 9, 4, 9, 9, 4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 4, 9, 9, 9, 9, 14, 19, 18]\n",
      "--- : [1, 2, 2, 2, 3, 4, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 21]\n",
      "--- : [20, 20, 15, 16]\n",
      "--- : [20, 15, 10, 11, 16]\n",
      "--- : [10, 10, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 3, 3, 4, 9, 9, 4, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [13, 12, 11, 11, 11, 10, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 23, 22, 23, 24, 19, 18]\n",
      "--- : [11, 10, 5, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 0, 0, 5, 10, 15, 20, 20, 15, 10, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 10, 15, 15, 20, 20, 15, 20, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15]\n",
      "--- : [20, 20, 15, 10, 15, 15, 20, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 10, 15, 20, 20, 20, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 20, 15, 15, 15, 10, 15, 20, 15, 15, 15, 15]\n",
      "--- : [14, 9, 4, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 11, 10, 15, 15, 10, 15, 20, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 21]\n",
      "+++ : [5, 10, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 19, 19, 14, 14, 14, 13, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 11, 10, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15]\n",
      "+++ : [19, 19, 19, 14, 9, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 13, 14, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 10, 15, 15, 15, 15, 15, 10, 15, 15, 15]\n",
      "--- : [13, 12, 11, 10, 5, 5, 5, 10, 5, 5, 5, 10, 10, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 3, 3, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 22, 23, 23, 18]\n",
      "--- : [19, 18]\n",
      "--- : [23, 23, 24, 19, 19, 14, 9, 4, 4, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 4, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 13, 12, 7]\n",
      "--- : [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 4, 4, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [3, 4, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 14, 19, 24, 24, 23, 23, 22, 17]\n",
      "--- : [19, 14, 14, 9, 9, 9, 4, 3, 3, 3, 3, 2, 3, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 4, 3, 4, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 23, 18]\n",
      "--- : [20, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 20, 15, 15, 20, 15, 15, 15, 10, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 20, 15, 20, 21]\n",
      "+++ : [1, 2, 3, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 20, 15, 15, 15, 15, 15, 20, 15, 10, 15, 15, 15, 10, 11, 10, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 20, 15, 15, 15, 15, 15, 20, 15, 20, 15, 10, 15, 15]\n",
      "--- : [5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 4, 9, 9, 9, 9, 4, 4, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 19, 24, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 10, 11, 10, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 10, 15, 15, 15, 15, 15, 15, 15, 20, 21]\n",
      "--- : [10, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [23, 23, 24, 19, 14, 9, 9, 9, 4, 4, 3, 4, 4, 9, 4, 3, 3, 3, 3, 2, 7]\n",
      "--- : [10, 5, 5, 5, 5, 5, 10, 5, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 9, 9, 9, 9, 9, 14, 13, 14, 13, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 14, 13, 12, 7]\n",
      "--- : [13, 13, 13, 13, 13, 13, 13, 13, 12, 11, 10, 10, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 4, 4, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [11, 10, 5, 5, 5, 10, 5, 5, 5, 5, 5, 10, 15, 15, 15, 15, 15, 20, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 10, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 20, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 10, 15, 15, 20, 15, 15]\n",
      "--- : [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 0, 1, 2, 3, 4, 3, 4, 9, 9, 9, 9, 9, 4, 9, 9, 9, 4, 9, 4, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 9, 9, 14, 13, 12, 17]\n",
      "--- : [1, 1, 2, 3, 4, 9, 9, 9, 4, 3, 4, 9, 9, 9, 4, 9, 14, 14, 19, 24, 19, 24, 23, 22, 22, 23, 24, 24, 23, 23, 24, 19, 14, 14, 14, 13, 12, 17]\n",
      "--- : [1, 2, 3, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 4, 4, 9, 9, 9, 4, 9, 9, 14, 19, 19, 18]\n",
      "--- : [13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 14, 13, 13, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 20, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 10, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 20, 20, 15, 15, 15, 20, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 20, 21]\n",
      "--- : [9, 4, 3, 3, 3, 2, 7]\n",
      "+++ : [9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 4, 9, 9, 9, 4, 3, 3, 3, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 11, 10, 15, 15, 20, 15, 15, 15, 15, 15, 10, 15, 15, 15, 10, 15, 20, 15, 15, 15, 15, 15, 10, 15, 15, 20, 15, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 10, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 19, 14, 13, 12, 11, 10, 15, 15, 15, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 10, 15, 20, 15, 15, 15, 15, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15]\n",
      "--- : [12, 11, 11, 10, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [24, 19, 14, 14, 9, 4, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 14, 14, 9, 14, 13, 13, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 21]\n",
      "+++ : [23, 23, 24, 19, 14, 9, 4, 4, 3, 3, 4, 3, 3, 2, 3, 3, 4, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 23, 22, 23, 23, 24, 23, 24, 24, 19, 14, 14, 9, 14, 19, 19, 14, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 20, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 15, 20, 15, 20, 15, 15, 15, 15, 15, 15, 10, 11, 10, 15, 20, 15, 15, 20, 15, 15, 15, 15, 15, 15, 10, 10, 15]\n",
      "--- : [9, 9, 4, 3, 3, 4, 9, 4, 3, 3, 3, 3, 3, 2, 2, 3, 4, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 23, 22, 17]\n",
      "+++ : [13, 13, 13, 13, 13, 13, 13, 13, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 14, 13, 12, 11, 11, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 10, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 10, 10, 15, 15, 15, 15, 15]\n",
      "--- : [12, 17]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 4, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 14, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 20, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15]\n",
      "--- : [0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 3, 4, 9, 14, 19, 24, 23, 22, 23, 18]\n",
      "--- : [1, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 12, 11, 10, 15, 15, 15, 15, 10, 11, 10, 10, 10, 11, 16]\n",
      "--- : [13, 12, 11, 11, 10, 11, 10, 5, 5, 5, 5, 5, 10, 5, 5, 5, 5, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 19, 24, 23, 22, 23, 24, 19]\n",
      "+++ : [1, 2, 3, 4, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 14, 9, 9, 9, 9, 14, 19, 14, 19, 14, 13, 12, 11, 10, 15, 15, 10, 15, 10, 15, 10, 15, 15, 15, 10, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 10, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 11, 10, 15, 15, 15, 15, 10, 15, 15, 20, 15, 15, 15, 10, 15]\n",
      "--- : [1, 1, 2, 2, 2, 3, 4, 3, 4, 9, 9, 9, 9, 9, 9, 4, 3, 4, 9, 4, 9, 9, 9, 9, 4, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 14, 9, 14, 13, 13, 12, 17]\n",
      "--- : [4, 3, 3, 3, 3, 2, 7]\n",
      "--- : [4, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 14, 19, 18]\n",
      "--- : [4, 3, 4, 3, 3, 3, 2, 3, 3, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 24, 23, 22, 23, 24, 23, 24, 19, 18]\n",
      "--- : [3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 18]\n",
      "--- : [11, 10, 5, 0, 0, 0, 1, 2, 3, 3, 4, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 22, 23, 18]\n",
      "--- : [14, 9, 9, 9, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 14, 19, 18]\n",
      "--- : [20, 15, 10, 10, 15, 15, 15, 20, 20, 15, 15, 15, 20, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 10, 15, 20, 15, 15, 15, 10, 15, 15, 15, 15, 10, 11, 10, 15, 10, 15, 10, 10, 15, 15, 15, 15, 15, 10, 15, 15, 15, 10, 15, 10, 11, 10, 15, 15, 10, 15, 20, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 21]\n",
      "Test wins: 13 / 100\n"
     ]
    }
   ],
   "source": [
    "search_depth = 400\n",
    "MCTS_samples = 100\n",
    "\n",
    "num_training_epochs =  0\n",
    "num_test_epochs = 100\n",
    "training = True\n",
    "epochs = 20\n",
    "T = [25, 50, 75, 100]\n",
    "K = 1\n",
    "\n",
    "success_rates = []\n",
    "succes_std = []\n",
    "win_hist = []\n",
    "train_history = []\n",
    "\n",
    "for i in T:\n",
    "    # TRAIN ##############################\n",
    "    train_wins = 0\n",
    "    # num_training_epochs = int(200 - 1.9*i)\n",
    "    # model = build_model(csrl.ch_states[(0,0,0,0)].shape, csrl.shape[-1])\n",
    "    N, W, Q, P, visited = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), set()\n",
    "    for epoch in range(num_training_epochs):\n",
    "        t1 = time.time()\n",
    "        state_history, channeled_states, trajectory, action_history, reward_history, better_policy = csrl.MC_learning(model, LTL_formula, predicates,\n",
    "                N = N, W = W, Q = Q, P = P, C=3, tow=1, n_samples=MCTS_samples, visited=set(), start=None, search_depth=search_depth,\n",
    "                verbose=0, T=i, K=K)\n",
    "        # print('N',N[(0,0,0,0)])\n",
    "        t2 = time.time()\n",
    "        # print(t2-t1, \" run episode\")\n",
    "\n",
    "        win = check_LTL(LTL_formula, trajectory, predicates)[0]\n",
    "        if win: train_wins+=1\n",
    "\n",
    "        t3= time.time()\n",
    "        if training and len(action_history)>0:\n",
    "            if epoch==0:\n",
    "                x_train = np.array(channeled_states)[:-1]\n",
    "                y1_train = np.array(better_policy)\n",
    "                y2_train = np.array(reward_history) + (check_LTL(LTL_formula, trajectory, predicates)[0]*max_rew)\n",
    "                y2_train = y2_train[:-1]\n",
    "            else:\n",
    "                x_train = np.concatenate((x_train, np.array(channeled_states)[:-1]),0)\n",
    "                y1_train = np.concatenate((y1_train, np.array(better_policy)),0)\n",
    "                y2_train_curr = np.array(reward_history) + (check_LTL(LTL_formula, trajectory, predicates)[0]*max_rew)\n",
    "                y2_train = np.concatenate((y2_train, y2_train_curr[:-1]),0)\n",
    "            tr_hist = model.fit(x_train, [y1_train, y2_train], epochs=epochs, verbose=0)\n",
    "            train_history += tr_hist.history['loss']\n",
    "        win_hist.append(win)\n",
    "        t4 = time.time()\n",
    "        # print(t4-t3, \" fit\")\n",
    "    print(\"Train wins:\",train_wins,\"/\", num_training_epochs)\n",
    "\n",
    "    # TEST ##############################\n",
    "    test_wins = 0\n",
    "    N, W, Q, P, visited = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), set()\n",
    "    for epoch in range(num_test_epochs):\n",
    "        \n",
    "        state_history, channeled_states, trajectory, action_history, reward_history, better_policy = csrl.MC_learning(model, LTL_formula, predicates,\n",
    "                N = N, W = W, Q = Q, P = P, C=3, tow=1, n_samples=100, visited=set(), start=None, search_depth=search_depth, verbose=0, T=i, K=1)\n",
    "\n",
    "        win = check_LTL(LTL_formula, trajectory, predicates)[0]\n",
    "        if win: test_wins+=1\n",
    "        win_hist.append(win)\n",
    "\n",
    "    success_rates.append(100*test_wins/num_test_epochs)\n",
    "    temp = np.zeros(num_test_epochs)\n",
    "    temp[:test_wins]=1\n",
    "    std = np.sqrt(num_test_epochs*np.var(temp))\n",
    "    succes_std.append((success_rates[-1]-std, success_rates[-1]+std))\n",
    "    ###############################################################\n",
    "    print(\"Test wins:\",test_wins,\"/\",num_test_epochs)\n",
    "    # print(\"last reward:\", reward_history[-1], \"  | trajectory:\", trajectory)\n",
    "    # print(\"Actions:\", action_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d75_s100_t50'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'd{search_depth}_s{MCTS_samples}_t{num_training_epochs}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(f'models/ind/p={p}/d{search_depth}_s{MCTS_samples}_t{num_training_epochs}', overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27a1a0fd360>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(win_hist) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[89.0, 94.0, 91.0, 88.0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RFNHb27vhsUKhEEKhUOJ7r9eb3sETpck99wJu2H1YjmR88fkxC0sRTM8FML2q/ERjTTlKWX6CiChpWXkFFUURw8PDGBkZgdvtTgRgOp0uEXiZTCYIgoCBgQH09fVteqz3338fGo0m8dXe3p6p0yBKCX9oGZdmnJi6782JgGs9dzCMqfteXLgxh9/c9WDeH0IWshKIiPJeVnK64iRJgtvthiiK0Gq1cLlcuHTpEvr6+uByuR4NUqHA5OQkjEbjY8fYaKWrvb2dOV2U8yJRGdPzftx2BlOeKJ9uLD9BRBST8zldwNrkeKvVCqPRCEEQdpy/VV5ejvLy8nQMkSht5v0hXHvow0IGE+VTaX35iWaNGk0aNctPEBFtIWtBV1dXF6anpyEIAsxmM4aHhwHEth6PHTsGt9sNQRASwdlGq1xE+WYxHMENux92b+YT5dPFv7iMG4t+3Jz1Q1dVhmZNLP+L5SeIiNbK2vbiyMhIIodLFMU1xU/dbjeGhobQ1dWFyclJDA0NsWQE5TVZlnHXtQDbnD8n87ZSjeUniKhY7CTuyGpOVzow6KJc41sMY+qBD96FcLaHkhXx8hPNGjUqy9h5jIgKS17kdBEVukhUhjQXS5QvrI82O7O+/ESTRg19rZrlJ4io6DDoIkqDWd8irj/0YzGcn4ny6eIOhuEOhnHd7kNDdSz5vr66DAoF87+IqPAx6CJKocVwBNce+jDnCz35wUUsGgXs3kXYvYuJ8hNNGjVqWX6CiAoYgy6iFJBlGXecC7DN+xEpgkT5VNqo/IS+Vg11KctPEFFhYdBFtEuehTCuPvDCt7ic7aHkvdXlJ7RVZWjRVKChphwqlp8gogLAoIsoScuRKGxzAdx1FXeifDrIMuD0L8HpX4JKpYC+Jnb1o7aK5SeIKH8x6CJKwqx3EdfsPoTCeda/Jw9FIjLuuxdw373A8hNElNf4qkW0A4vhCK4+9GGeifJZwfITRJTPGHQRbYMsy7jlCGJ6PoBIlHuJuWB1+Yn66nI0aypYfoKIchqDLqIn8ATDmHrohZ+J8jkpGgVmvSHMekMsP0FEOY1BF9EmwpEobs76cc+1kO2h0DatLj9RVV6CFoHlJ4godzDoItrAQ88irtt9WFpmony+CoSWccPO8hNElDsYdBGtElxaxtWHPjj9S9keCqXI+vITjTXlaNFUsPwEEWUcgy4iANGojFvOIKbn/YhycatgRSIyHrgX8cC9iIoyFfS1arQILD9BRJnBVxoqeq7AEqYeehEMsTl1MVlYimBmPoCZ+QA0laWJ9kMsP0FE6cKgi4rW0nIUN2Z9eOBezPZQKMs8wTA8LD9BRGnGoIuK0n33Am7M+hFmojytsrr8RGmJEk21ajQLLD9BRKnBoIuKSiC0jKsPvXAFwtkeCuW48HIUd5xB3HHGyk80r9T/YvkJIkoWgy4qCtGojGlHALccASbK044FQsu4OeuHbY7lJ4goeQy6qOA5A0u4+sCL4BIT5Wl3Nio/0aypgI7lJ4hoGxh0UcEKLUdww+7HQw8T5Sn1VpefUJeq0KRh+Qki2hpfHagg3XUFcXPWj+UIm1NT+i2G15afaKqN5X+x/AQRrcagiwqKP7SMqw+8cAeZKE/ZES8/cWM2Vn6iSaNGfVU5lMz/Iip6DLqoIESiMqbn/bjtDDJRnnLCRuUnmjRqaCpYfoKoWDHoorw37w/h2kMfFpgoTzmK5SeICGDQRXlsMRxLlLd7mShP+WN9+YlmjRqNNWqWnyAqAgy6KO/Isoy7rgXcnPMjwkR5ylOry09cVflYfoKoCDDoorziWwxj6oEP3gUmylPh2Kj8RLNGjapyvkQTFRL+RVNeWI5EIc0HcMcZhMzFLSpgq8tP1FaUJvK/WH6CKP8x6KKcN+tbxPWHfiyGmShPxcW7EIZ3IVZ+oq6qHM0Cy08Q5TMGXZSzFsMRXHvow5wvlO2hEGVVNArM+UKY87H8BFE+Y9BFOUeWZdxxLsA2z0R5ovVYfoIofzHoopziWQjj6gMvfIvL2R4KUc5bXX5CqCxDi8DyE0S5jEEX5YTlSBQ35/y451pgojzRDsky4AoswRWIlZ9oqC5Hi1ABbWUpFAoGYES5gkEXZZ3du4jrdh9CYfbvIdqtSETGQ88iHnpYfoIo1/CvkLJmYSmCqw+9cPiXsj0UooK0UfkJfa0aZSUsP0GUDQy6KOOiURm3nUFMzwcQiXIvkSgTWH6CKPuy9nFHkiQMDQ1hZGQEQ0NDcLvdGz5uq/so/7iDS/jVtBM3Z/0MuIiyIF5+4td3PLhwcx5XH3rhYYcHooxQyHJ20pYNBgMmJychCAKsVivMZjPMZvOax1itVnR1dcHlckEQhG0d1+v1QqPRwOPxoLa2Ng0jp2SEI1HcsPtx372Q7aEQ0QYqy1Vo1lSgmeUniHZkJ3FHVla6LBYLACQCKaPRiJGRkcceJ0kSRFHM5NAoDR56FvGlzcGAiyiHBUMR2Gb9+PzmPCZvuXDfvYDlCC9uIUqlrARdm20XWq3WxP+PjY2ht7c3QyOidAguLcN624Vv73mwtMwX73RxBZfg8LNqP6VGvPzElfteXLgxj2/veeDwh5ClTRGigpKVRHqj0QhJkhLfx4Mtp9MJIBaUbXc7MRQKIRR69Ibj9XpTN1BKSjQqY8YRwIwjgChjrbSRZRmf3pjHX1+8jeWIjOfaNOg5rMcBfQ1rM1FKRKLry0+Uo1lTwfITREna1V/OzMxMYtVKFMVt51CJoojh4WGMjIygv78/EYDpdDoAwLlz5zAwMLCtY73//vv48Y9/vPPBU1q4AkuYeuhFMMTm1OkUXFrGX3xxC5O3XYnbvrnrwTd3PWjXVsB0SI8XOnUoVbE0AKVGrPxEEDPzQZafIErSjhLpvV5vIuF9enoagiBAq9UCiOVfGQwG9Pb24vTp09sKwCRJgtvthiiK0Gq1cLlcuHTpEo4dO5ZY6VqdcL+RjVa62tvbmUifYUvLUdyY9eGBezHbQyl4tjk/Rj6V4AgsQaVQ4B1jK55r0+D81Vl8bnMktnJr1CV480AjvvtUA2rZGJnSQKlErPyERo36apafoOK0k0T6ba90ffzxxxgaGkJfXx/Gx8fR2dn52GMuX74Mi8WCrq4uDA8P45133tn0eKuT5K1WK4xGYyKwOnfu3JrHvf/++zhx4gSMRuNjxykvL0d5efl2T4PS4L57ATdm/QgzbyutorKM//ntQ/yXr+8hKgMN1eUYeENEZ30VAOAPXtyLf3q0FRduzOH81Vm4gmH87Tf38Xe/eYAXO3UwHdajXVuZ5bOgQhIvPzHnC6G0RAl9bTmaayugqWSQT7SRba10Xb58GWazGR988MG2D/zee+/h937v93D06NEN79dqtYnVssHBQfT19cFkMj0+QIUCNptt21cxsmRE5gRCy7j60AtXgDV+0s2zEMaHn0mYeuADALzQocMfvrQXFWUbX9q/HI3CesuN8Sk7pucDidsPNtWg57Aez7ZqoGTeF6VJZbkKLZoKNAtqlJew/AQVtp3EHVmr0zUyMgKdTgen0wlRFB8LuNxud6Jw6sDAAAYHBzdc6VqPQVf6RaMypPkAbjuZKJ8J397z4M8+n4ZvcRllJUr8sxf24FVD3baT5W1zfoxfscN624V4PVp9TTm6D+nxiqGONZkobRQKoK66HC2CGg3V5bzAgwpS2oOun/70p+jt7UVHRwfOnz+PgYEBKBQKjIyM4M0330x64KnAoCu9HP4Qrj30IbjERPl0W45E8TeX7+HnV+wAgDZtBQbfENGsqUjqeA5/COevzeLT6/NYCMd+f5VlKry+vx5vHWhEXTW36Sl9ykqUaNKo0SJUoJpXP1IBSXvQtX//fty4cQMAUFdXh+HhYbz11lt4++23E7dnC4Ou9AgtR3DD7sdDDxPlM2HOF4L5UxtmHEEAwJsHGtB/rD0lVyMuhiP4wubAxJQddl/sIhSlAjDu0aLnsB6Ghupd/wyirdRWlKJFUKOpVo0SXmFLeS7tQde+fftw8+ZNTExMoKenB9GVPab4dmE2MehKvbuuIG7O+rEcYXHETPhq2on/+5czWAxHUVmmwh+90oHn92hT/nOisozf3PNg/IodVx/6EreL9VUwHdLDuFdAiZJviJQ+KqUCDTXlaBUqoK0qy/ZwiJKSlqsXVxNFEadPn8bo6Giintb09DRb9hQY32IY1x764A4yUT4TQuEI/v8X7+Czm/MAgP2N1Xj3tc60bfspFQocaRNwpE3AHVcQlit2/GraCWk+gJELErSTpXjrYCPe2N/AYpiUFquLr1aWqdAssPcjFbakVro8Hk+irMPJkycBxK5wlCQJP/jBD1I7wh3iStfuRaIypuf9uOUIgp0/MuOOKwjzpxIeehahAPCPn2vGP3muBaoM1z3yLITxi+tz+OTaLHyLywBiuTivGurQfVCPJo06o+Oh4qNQANqqMrQKFWhg7S/KA3lx9WK6MOjanTlfCNftPiwwUT4jZFnGP1ybw0eX7mA5KkOoKMW7r3fiYFN2n7vhSBRfTTsxPmXHXdejRuXPtWpgOqTHoWa2GqL0K1Ep0KypQIugRo2atb8oN6U96PrZz36GU6dOYXp6OnGbLMtQKBSIRLL7Zs2gKzmL4Qiu232Y9bJxcqb4Q8v4iy9ncPm2G0AsoPmjVzty6s1FlmVcs/swfsWOX9/1IP5i0SpUwHSoES+JdWw1RBlRrS5Bq1CBJo2azznKKWkPuurq6jAyMrJhMVONRrPTw6UUg66dkWUZd10LuDnnR4SJ8hlzY9aHs59OwxlcgkqpQK+xDaZDjTm9emT3LmJiahaf2+YRWtVq6DtPNeDNA43QsNUQZYBSCTRUq9EiqKGrKsvpvxkqDmkPur73ve/h7//+75MeYDox6No+72IYVx/44F1gonymRKMy/u7bB/jbb+4jKgONNbFWPh11Vdke2rYFl5Zx4cY8Jq7OwhlYAhC7Cu2FDh16DuuxR8dWQ5QZ6lIVmjRqtAoVm3ZnIEq3tAddZ8+ehcViwYkTJyCK4ppm1B0dHTs9XEox6Hqy5UgU0nwAd5xMlM8kd3AJH342nSjP8JKowz9/cW/eXqkVicq4fNuF8Sk7bHOPWg09pa9GzyE9jrQJTIKmjNFWlaJFqEBjjTrjF6BQcctIna6N6nEpFAo4HI6dHi6lGHRtbda3iGsPfQiF2b8nk359143/8PkM/KFllJco8Qcv7sErhvpsDytlpHk/LFdmMXnLhcjKS0pDTTm6DzbitX31eRtYUv4pUSmgr1WjRcPG25QZvHqRQddjFsMRXHvow5yPifKZtByJ4uPL9zC+0sqnXVuBwe8Y0FRbmKUXnIElfHJtFr+4PpdoFVVRqsJr++vRfbAR9Ww1RBlUVV4Sq3yvYeNtSh8GXQy6EmRZxm1nENJcAJFoQf2qc57du4iRCxJurbTy6T7YiN6utqK48ioUjuBLyQHL1CweemOtoxQKwNiuhelwI/Y1VDMBmjJGqQTqqsrRIlSgvprJ95RaaQ+6lErlhk9aURTZezGHeBbCmHrghX+lyCVlzi8lB/7yl7cQWo6iqkyFP3q1E0fbhWwPK+Oisozf3vdi/IodVx54E7d31FWi55AeXXu17L1HGVVeqkSzRo1mTQU7LVBKpD3o8ng8a76XZRkjIyNQKpX4kz/5k50eLqUYdMW2tG7O+XHPtcBE+QxbDEfwV1/dxhe2WG7jU/pqvPuaCB37yuGeawGWKTu+lBxYXll1FSoetRqqVvMNkDJLqCxFs1ABfU05g39KWta2F99++238/Oc/T9XhklLsQZfdu4jrdibKZ8NtZxAjn0p46F2EQgH8k+da8DvPNvMKvnV8i/FWQ3PwrJQrKVMp8bKhDt0HG9EiVGR5hFRsVEoFGmtjjbeFSn5Aop1Je8PrmZmZx26TJAmSJCVzOEqBhaUIrj70wuFfyvZQio4sy/jk2hzOrbTy0VaW4uTrIp7S12R7aDmpRl2K33muBf/o6SZcnImVnLjtDOIX1+fwi+tzeKalFqZDejzdUsvcG8qISFTGA/ciHrjZeJvSK6mgy2g0QqFQYPUiWV1dHQYGBlI2MNqeaDSWKD89z0T5bPAvLuPPv5zB13fcAIAjbRr80Sud3CrbhpKV1a2XRB1uzPoxfsWOr++48e19L76970WLRo3uQ3q8LNahrIRbP5QZwaUIbLN+SHN+6FYab9ez8TalCK9ezGPu4BKmHvgQCDFRPhuu2304e0GCKxhGiVKBvq42vHUwt1v55Lo5XwgTV+24cONRq6Hq8niroQZu/VBWlJbEku9bhApUM/me1kl7TtdPf/rTrCfMb6YYgq5wJIobdj/uuxeyPZSiFI3K+O+/eYD/9uv7kGVAX1uOwdcN2FPH9jepElxaxmc353H+6izmV7bMVQoFjnVo0XNYn1dtk6iw1FaUolmjZuNtSkh70NXf34/f//3fx/e///2kB5kuhR50PfAs4Ibdj6VlJspngzOwhA8/k3Dd7gcAvGKowz97YQ9zP9IkGpVx+Y4blik7bsz6E7fvb6yG6ZAez7ez1RBlh0qpQENNrPYXr04ubmkPui5fvoz+/n786Ec/Qnd3N3svZkBwaRlTD3xwBZgony1f33Hjz7941Mrnn7+0Fy+LddkeVtGYcQRgmbLj4vSjVkP11WV462AjXt/XwIbHlDUVZarE9iM/gBWftAddx44dg9vtfvxgCgWLo6ZYNCpjxhHAjCOAKBe3siIcieJj611YpmYBAHt0lRh8Q4S+QFv55Dp3cAnnr83iF9fmEFhpNaQuVeK1ffXoPqhHQw1bDVF2KBSAtqoMLZoKNNYw+b5YsA1QgQRdrsASph56EQxFsj2UovXQu4iRTyXcdsZa+fQc0uMdYytzOXJAaDmCX0pOWKbseOBZaTUE4Gi7gJ7DeuxvZKshyp4SlQJNK6tftWo23i5kDLryPOhaWo7ixqwPD9yL2R5KUfvCNo///KvbCC1HUV1egn/9ageeaxOyPSxaR15pNWSZsuPb+49aDe3RxVoNHe9gqyHKrmp1CVo0FWjSqFn+pAAx6MrjoOueewE3Z/0IM1E+axbDEfznX93Gl1Kslc8BfQ3efb0TWpYryHn33QuYuDqLL20OLEVif0OailK8eaAB33mqATVccaAsUiqB+upY8n1dFRtvFwoGXXkYdAVCy7j60AtXIJztoRS1W44ARj6VYPeFoFAA//RIC/7XZ9jKJ9/4F5fx6Y05nL86C/dKq6FSlQIvddbBdFiPVrYaoiyLNd6uQIugRmUZa3/lMwZdeRR0RaMypPkAbjuZKJ9Nsixj4uosRifvIhKVoassw8nXO7GfrXzy2nIkislbsVZDM45g4vbDzbXoORxrNaTkagNlmVBZihahAvpaNVT8gJd30h50eb1eSJIEp9MJnU4HURRzJsDJp6DL4Q/h2kMfgktMlM8m32IY//GLGfz6rgcA8Hy7gH/5SgcrTxcQWZZxc84Py5VZWO+4EH/Va9KoYTrYiJfFOpTzUn/KMpVKAX2NGq1CBTSV3ArPF2kNur73ve9hcnISnZ2dAGKNrj0eD0wmE0ZHR7Me6ORD0BVajuCG3Y+HHibKZ9vVh158eGEa7oVYK58Tx9rx3QMNzLUoYPP+ECauzuKzG/NYCMc+8FSWqVZaDTWy0CXlhMpyFVo0FWgW1Cgv4QeCXJa2oOvEiRMwmUw4efLkY/eNjIxgYmICH3300c5HnEK5HnTddQVxc9aP5UhB7ermnUhUxn/79X38j18/gAygqVaNwTdEtOvYyqdYLIYj+OzmPCamZjHnDwGItRrq2quF6XAjxPrqLI+QKFb7q666HC2CGg3V5fxAmIPSFnTt379/y+KnT7o/E3I16PIthnH1oQ+eIBPls80ZWMLZC1Kircxr++rx+8fbub1UpKJRGd/cdWN8yp5o7wQAhoYq9BzS4/k9WubZUE4oW9V4u4rpDzljJ3HHjn5rnZ2d+OSTT/Dmm28+dt/PfvazxJYjPRKJypDm/LjtDKKwLlnIT5dvu/Afv5hBcCkCdakSf/jSXrzYyVY+xUypVOD5PVo8v0eL244gxqfs+GrGCdtcALY5CbqqMnQfbMTr++t5lRll1dJyFLccQdxyBKGpXGm8XatmHbo8sqOVrunpafT09EChUEAURQiCALfbDZvNBoVCgfHxcfZeXGXOF0uUXwwzUT7bwpEoRi/dxflrsVY+HXWVGHhDRGMNW/nQ4zwLYXxybRb/cG0O/tAyAKC8RIlX99Wj+2AjW0BRzog33m4VKqBlPmJWZKTh9fqrF59//vmkB5xKuRB0LYYjuG73YdYbysrPp7UeeBYw8qmEO64FAMDbh/X4/vOt/HRITxSORPFLyQHL1CzuuWPPHwWAI20CTIcbcUBfwxwbyhmVZSo0CxVo1qjZeDuDWDIiS0GXLMu461rAzTk/IkyUzzpZlvG5zYG/+uo2lpajqFGX4F+/2olnWzXZHhrlGVmWMfXAB8uUHb++50nc3q6tgOmQHi906tiPk3JGvPF2q1CBhmo23k63jJSMEEURsiyzZET85y6GcfWBD94FJsrngoWlCP7Tr27hV9NOAMChphr88LVOCGzlQ7v00LOIiat2fG5zYGmlXVeNugRvHmjEd59qQG0F6ytR7igtUaKpVo0WQc02WGnCkhEZDLqWI1FI8wHcYaJ8zpiZD8B8QcKcLwSlAvjdo634R0838dMepZQ/tIwLK62GXCtXJZcoFXixUwfTYT3atSw/QrmlRl2CFiHWeJsrs6mTFyUjJEmC2WyGwWCAzWbD6dOnIQgCAMBqtcJisQAALl68iLNnzybue5JMBl2zvkVce+hDKMz+PbkgKsuwTNnxsfUeIlEZdVVlOPm6iH2NrLdE6bMcjcJ6K1ZyYno+kLj9YFMNeg7r8Wyrhq2GKKcolUBDdWz1S8fG27uWFyUjenp6MDk5CUEQYLVaMTQ0BLPZDACwWCw4deoUAODMmTPo7u7G5OTkToaaVovhCK4+9GHex0T5XOFbDOPPPp/Gt/e8AICuPVr8i5f3spYNpV2JUokXOnV4oVMH25wf41fssN524epDH64+9EFfU47uQ3q8YqhjcjPlhGgUsHsXYfcuQl2qQrOgRoumAhVlfH6mW1ZKRlgsFgwODsJmsz0aiEIBWZZhtVrR3d0Nl8sFILYiFl8NE0XxicfOxErXPfcCpu5703Js2rmpB158+Nk0PAthlKpirXy+8xRb+VD2OPwhnL82i0+vr2019Pr+erx1oBF11eVZHiHR47RVZWgR1GisYePtnUjrStfNmzd3XTLC7XZveLvVaoXRaMTZs2cfe6xOp9vJUKkIRKIy/vab+/i738Ra+TRrYq182phLQ1lWV12Ovq52/JPnWvCFzYGJKTvsvhB+/ls7xq/YYdyjRc9hPQwN3Pqm3OEKLMEVWMI1lQ/62ljlew0vDEmppPZenn/++V3V5TIajZAkKfG91WoFADidsSvNent7E/d99NFHMJlMm+Z0hUIhhEKPtvm8Xq5AFQOHP4SRCxJsc7Ecmjf21+PE8XY2hqWcoi5V4a2DjfjugQb85p4H41fsuPrQh0u3XLh0ywWxvgqmQ3oY9wooUTKxmXLDckTGPdcC7rkWUFVegtaV5PuyEj5HdyupOl2bOXHixLavXjxz5gwEQUB/fz8sFgv6+vowOTkJo9GYeIzb7UZXV1ci92sjf/qnf4of//jHj93O7cXCNXnLhb/4MtbKp6JUhX/x8l4c7+BKKOWHO64gLFfs+NW0E8vR2MuvtrIUbx1sxBv7G5iHSDlJqQTqqsrRIlSgvprJ96ul7erFJ60idXV17ajhtSRJcLvdEEURWq0WLpdrTXA1ODiIoaGhLXO5Nlrpam9vZ9BVgJaWozh36Q7+4focAECsr8LJ10U01DA/hvKPZyGMX1yfwyfXZuFbjLUaKitR4lVDHboP6tGkYashyk3lpY8ab7MfaRqDru9973uYmJgAEKvQnDjIShK8QqFAJLK9PoOSJCWCKavVipMnT665QvHMmTPo7e2FKIqJvK7tlI1gIn1huu9egPlTKdGK5R893YTffb6FWzKU98KRKL6admJ8yo67K62qAOC5Vg1Mh/Q41MxWQ5S7hMpSNAsV0NeUF21rtbQFXVarFT/5yU9w7ty5De8/duwYLl26tK1jabVaTE9PQxAEDA4Ooq+vDyaTCQAwNjYGQRBgMpngdrtx7tw5DAwMbOu4DLoKiyzLuHBzHn/91R0sRWKtfN59rRNPt7CVDxUWWZZxze7D+BU7fn3Xg/gLc6tQgZ5DerwostUQ5S6VUoHG2ljj7WLr/JG2qxeNRuOWVxHGg6btGB4ehsVigdPpXBNwSZKEvr6+NY8VBGHbQRcVjuDSMv7yl7dwcSZWPuRwcy1++Fonr6ahgqRQKHCwqRYHm2ph9y5iYmoWn9vmcc+9gD//cgYfX76L7z7VgO8eaOTfAOWcSFTGA/ciHrgXUVmmSlS+Z226tVKaSJ8LuNJVGKR5P0Y+lTDvX4JKocDvPt+Ct59uYmVvKirBpWVcuDGPiauzcAaWAMRaDb3QqYPpkB57dCyPQrlLoYiVT2nRqFFfwI2309rwOtcx6MpvUVnG3//Wjr+5fA8RWUZ9dayVD+sZUTGLRGVcvu3C+JQ9USYFAA7oa2A61IgjbULBvqFRYSgteZR8X11gV+gy6GLQlZc8C2H8h8+n8duVuT22N9bKh1fHED0izfthuTKLS7ecWKk4gYaacnQfbMRr++q5nUM5r7aiFM0adcE03mbQxaAr7/z2vgd/9tk0vIvLKFMp8XvH2/H6/npetUW0CWdgCZ9cm8Uvrs8huBS7aryiVIXX9tej+2Aj6tlqiHKcSqlAQ02s9peuKn+T7xl0MejKG8vRKP7r1/fxP799CBlAi6DG4BsGtAoV2R5aXlAogKf0NaguL4E074crEM72kCjDQuEIvpQcsEzN4qF3EUDseWFs18J0uBH7Gqr54YVyXkWZKrH9mG+rtWkJut5++238/Oc/T8kA04lBV/6Y94cw8qkEaT6Wo/Kdpxpw4lg7W01sU4lKgWdbNWuaJzv8IUjzAXiCDL6KTVSW8dv7XoxfsePKg0evTx11leg5pEdXh5Z17SjnKRQrjbc1FWisyY/k+7QEXfv27cPNmzcBAMePH8fFixd3P9I0YNCVHy7NOPEXX97CQjjWyudfvdKBrr3abA8rb1SUqXC0Xdi0Zcy8PwRpLgDvAoOvYnTPtQDLlB1fSo5EqyGh4lGroWo18yQp95WoFGhaWf2qVedumZS0BF39/f0wGAwwGAw4deoUzpw5s+Hj3n333Z2POIUYdOW20HIEH128g09vzAMADA2xVj7MP9k+obIUz7UJ21oRnPUtQpoLwL/SZoaKi28x3mpoDp6VALxMpcTLhjp0H2xEC7fxKU9Uq0vQosnNxttpy+n6d//u38Fms+HcuXPo7+9//GAKBf79v//3Ox9xCjHoyl333Aswf2rDffciFAD+l2ea8L8dZSufnWgW1DjUVLvjJfdZ7yJscwEEQgy+ilE4EsWlmVjJidvOYOL2Z1pq0XNYj8PNtcz7orygVAL11bHk+7qq3Gi8nfZE+vfeew8/+clPkh5gOjHoyj2yLOPTG/P464u3EY7I0FSU4oevduJwS3p+P4XK0FiNzvqqpP+9LMuwe0OQ5vyJq92ouMiyjBuzfoxfsePrO+5Eq6EWjRqmQ3q8JNbl3CoC0WZijbcr0CKos1paKGNXL54/fz7RuPqtt95K9jApxaArtwSXlvF/f3kLl27FWvk801KLf/1qJ2rZxmTbVEoFnm6pRWOtOiXHk2UZDzyLmJ4PYIHBV9Ga84UwcdWOCzfmEVqOAgCqy0vwnaca8OaBhqLrn0f5TVtVimZNBfS1aqgynHyf9qDL4/Ggq6sLWq0WnZ2dkCQJHo8H4+Pj6OjoSHbcKcGgK3fY5mKtfByBWCufd4yt6DmsZyufHSgvVeJIu5CWJNJoVMYD7yKm5wJYDDP4KlbBpWV8dnMe56/OYt4fazWkUipwvEML0yE9OuqSX10lyjSVSgF9jRqtQgU0lZn5cJ/2oOvtt9/GqVOn0N3dnbhtbGwMZ8+ezXpZCQZd2ReVZfzPbx/iv3x9D1EZaKgux8k3OiHWs5XPTtSoS3CkXUh7zZpoVMY99wJmHAGEwtG0/izKXdGojMt33LBM2XFj1p+4fX9jNUyH9Hi+na2GKL9UlqvQutJ4u7wkfa+jaQ+6VpePWK2urg4Oh2Onh0spBl3Z5VkI48PPJEw98AEAXujQ4Q9f2ouKsvwqdpdtDTXleKZVk9Fl8mhUxl1XLPhaWmbwVcxm5gOwXLXj4rQLkZW3iPrqMrx1sBGv72vg3zPlldqKUrzQqUvb8XcSdySVeSaKIr755hscOXIkcdvXX3+Nzs7OZA5HBeLbex782efT8C0uo6xEiX92fA9e3VeXE1eX5JOO+krsa6zJ+M9VKhXYU1eJVm0F7jiDuOUMIszgqyh11Ffh3ddE9BqXcP7aLH5xbQ7z/iWcu3QXf/vNfby2rx7dB/VoqGGpF6KdSGqlS5IkHDt2DD09PRBFETabDRMTE5iYmMDRo0fTMMzt40pX5i1Hovibr+/h57+1AwDatBUYeF1kDaAdUiqBg021OTNvy5Eo7rgWcMsRwHKkoLqF0Q6FliP4peSEZcqOB55HrYaOtgvoOaTH/ka2GqLclUsrXbu6evHs2bOw2WwwGAzo7++HRqNJ9lApw6Ars+Z8IYxckDC90srnzQMN6OtiK5+dKi1R4kibJievGAtHorjtDOK2M4gIg6+iJq+0GrJM2fHtqtfAPbpYq6HjHVqUqPi3T7mlYIKuXMSgK3O+mnbiL38Za+VTWRZr5WPcw1Y+O1VZHmvpk806M9sRjkRxyxHAHecCItGCetmgJNx3L2Di6iy+tDmwFIltQ2sqSvHmgQZ856kG1ORw2xYqLgy60ohBV/qFliP466/u4MLNWCuffQ3VOPl655rGy7Q9uuoyPNuqQWkerQ6EliO45QjinovBFwH+xWV8emMO56/Owr3SaqhUpcBLnXUwHdajNUe2y6l4MehKIwZd6XXXFYT5UwkPPLFWPv/42Wb8kyMtGS9GVwhatRU42FSTt7kwi+GV4MsdRJT59kVvORLF5K1Yq6EZx6NWQ4ebY62Gnm6pZY0+yopcCrpyez+DcoYsy/iH63P46OIdLEdjrXxOvt6Jg01s5bNTCgXwlL4G7brKbA9lV9SlKhxoqsHeukpMzwfwwLPA4KuIlaiUeFGswwudOtyc88NyZRbWOy5ceeDFlQdeNGnUMB1sxMuGurTWTCLKZVzpSkKxrXQFQsv4iy9nYL3tBgA826rBv361gzkbSVCpFHi2VYP6AtyKXViKJIKvwnpVoWTN+0OYuDqLz27MY2Gl60FlmQrfeaoBbx1shDYHLxyhwpNLK11JBV3nz59/rNeix+PBxMQE3nnnnZ0eLqUYdKXWjVkfzl6YhjOwBJVSgR8YW9FzSJ+3W2LZVFGmwpF2AdXlhb3AHFxahjQXgN27yOCLAMS2oj+7OY+JqVnM+UMAAJVCga69WvQc1u+qkTvRk+R90HX8+HFcvHhx27dnEoOu1IhGZfzdtw/wt9/cR1QGGmvKMfCGyD5sSdJUluK5Nk1RbasEQo+CLyIg9rryzV03xqfsuG5/1GrI0FCFnsN6PN+uZX4opVwuBV07+sj94YcfAogVR43/f9zNmzcxOTm5w6FSLnIHl/DhZ9O4+jDWyufFzlgrn3T3ACxUTRo1DjfXFl3fuqryEjzbpkHHYiWkuQDmfKFsD4myTKlU4Pk9Wjy/R4vbjiDGp+z4asYJ21wAtl9IqKtaaTW0vz7nS6gQJWNHz+p4v0VZlnHp0qU19wmCwKCrAPzmngd/9tk0/KFYK58/eHEPXhHZyidZYkMVxIbibvRdoy7FkXYB3sUwpLkA5hl8EYA9dZX44Wud6O1qwyfXZvEP1+bgCCxhdDLWaujVffXoPtgIfa0620MlSpmkthd/9KMf4YMPPkjHeHaN24vJWY5E8fHlexi/Emvl066twOAbBjRp+IKXDJVSgcMttXzD2IAnGIZt3g+nfynbQ6EcEo5E8UvJAcvULO65FwAACgBH2gSYDjfigD5/y6tQduXS9iKvXkxCoQVddu8izl6QErV13jrYiL6utrwq2JlLykqUONIuQFPBqzu34g4uwTbnhysQzvZQKIfIsoypBz6MT9nxm3uexO3t2gqYDunxQqeOr020I3kfdCmVyg0/cYiiiBs3buz0cCnFoGtnfiU58Je/uoXFcBRVK618nmcrn6RVq0twtF1g/tsOOANLkOb8cAcZfNFaDz2LmLhqx+c2B5aWY0XgatQlePNAI777VANq+cGGtiGXgq6kMhVdLtea72VZxsjICJRKfvrIF6FwBH/11W18bnMAAPY3VuPk6yJ0Vaybk6z6mnI801LLhr87pKsqg65KB4c/BNtcAN4FBl8U06RR4w9e3It/erQVF1ZaDbmCYfztN/fxd795gJfEOpgONaJNm9+Fhql4pHR78e2338bPf/7zVB0uKVzperLbziBGPpXw0LsIhQL4nWeb8TvPsZXPbuytq8S+xmrmnKTAnC8E25wf/sXlbA+FcsxyNArrrVjJien5QOL2Q001MB3W49lWDVsN0WPyfqVrZmbmsdskSYIkSckcjjJElmV8cm0O5y7FWvloK0vx7msiDjTVZHtoeUupBA401bKpbwo11JSjoaYcs95F2OYCCIQYfFFMiVKJFzp1eKFTB9ucH+NX7LDedmHqoQ9TD33Q15Sj+5AerxjquMVPOSmpoMtoNEKhUGD1IlldXR0GBgZSNjBKLX9oGX/+xQy+vuMGABxp0+BfvcJWPrtRolLguTaBW7Jp0lirRkNNOezeEKR5P4KhSLaHRDnE0FANw3eq4fCHcP7aLD69Pg+7L4S/+uo2/svX9/D6/nq8daARdQXYcovyF69eTEK+bS9et/tw9oIEVzCMEqUCvV1t6D7YyK2wXagsU+HoHoEFHDNElmU89C5iei6A4BKDL3rcYjiCL2wOTEzZYV+pBadUAMY9sVZDhiKvl1fM8n57Me78+fOQJAkGgwFvvvnmbg5FaRCNyvgfK618ZBnQ15Rj8A0D9tQx6XQ3tFVleK5Nw8vWM0ihUKBZU4GmWjXue2LB12KYwRc9oi5V4a2DjfjugQb85p4H41fsuPrQh0u3XLh0ywWxvgqmQ3oY9woo4UVflCVJBV0ejwddXV3QarXo7OyEJEnweDwYHx9HR0dHiodIyXAFl/DhhWlcs8da+bws1uEPXtzDPIddahEqcLCppuha+uQKhUKBVqECzbVq3HMvYMYRQCgczfawKIcoFQocaRNwpE3AHVcQlit2/GraCWk+gJELErSTpXjrYCPe2N+AqgJvPk+5J6ntxbfffhunTp1Cd3d34raxsTGcPXuWVy/mgG/uuvEfP5+BP7SM8hIl/vmLe/GyoS7bw8prCgWwr7Eae9nwO6dEozLuuRcwPR9I1HEiWs+zEMYvrs/hk2uz8K1cFVtWosSrhjp0H9KjiZ0jCloubS8mFXTt27cv0Ydxtbq6Ojgcjp0eLqWKOegKR6L42HoXlqlZAMAeXSUG3xDZimaXVCoFnmnRoKGGCbm5KhKVcdcVxIwjiDCDL9pEOBLFV9NOjE/Zcde1kLj9uVYNeg7rcbCJrYYKUS4FXUmtrYqiiG+++QZHjhxJ3Pb111+js7Nz28eQJAlmsxkGgwE2mw2nT5+GIAiJ+8bGxiCKIiRJwsDAQOI+2pjduwjzpxJuO2OtfEyHGvEDI1v57Ja6VIUj7Rpe5ZnjVEoF9tZVoVWowB3XAm45AliOFNQ1QpQCpSolXt1Xj1cMdbhm92H8ih2/vuvBr+/FvlqFCvQc0uNFka2GKD2SWumSJAnHjh1DT08PRFGEzWbDxMQEJiYmcPTo0W0dw2AwYHJyEoIgwGq1wmw2w2w2AwC6urowOTmZ+FlDQ0MYHR3d1nGLcaXrS5sD/+lXtxBajqK6vAR/9GoHjrQJ2R5W3qutKMWRdg3KS5gHl2+WI1HcdgZx2xlk8EVbsnsXMTE1i89t8witajX03aca8N0DjeyhWgByaaVrVyUjzp49C5vNBoPBgP7+fmg0mm39O4vFgsHBQdhstkcDWan7JUkS+vr6EkEXAGi12sdaD22mmIKuxXAE//lXt/GlFNvSPaCvwbuvd0JbybpRu6WvVeNwSy2r9Oe5cCSKW44g7riCiDD4oi0El5Zx4cY8Jq7OwhlYAgCUKBV4oVMH0yE99uh41Xe+yqWga1vbix6PBwqF4rGDnTx5cst/NzMzs+HVjG63e8PHW61WXLp0CTrd2snR6XSwWq0wGo3bGW5RuO0IwvypDXZfCAoF8L8dacE/fqaZV9WlQGdDFWv6FIhSlRL7GquxR1eJW44A7roWEIky+KLHVZaV4O2nm2A6pMfl2y6MT9lhmwvgC5sDX9gcOKCvgelQI460CXydpaRtO6ert7cXp0+f3lY9romJCbz33nuYmJjY8H6j0bimZZDVagUAOJ3OTQMyp9O54e2hUAihUCjxvdeb/RWodJJlGRNXZzE2eTfRyufk6yKe0rOVz24plcDhZg2aNLzwoNCUlSixX1+DPXWVuOUI4q4riCjz7WkDKqUCxzp0ONahgzTvh+XKLCZvuXDN7sM1uw8NNeXoPtiI1/bVswQP7di2gi6NRoNz586hv78fAwMD6O3txfHjxyGKIgRBgCRJcLvduHjxIkZHR2EwGGCxWDZdZhNFEcPDwxgZGUF/f38iAFu/wrXaZsHY+++/jx//+MfbOY2851sM48+/mME3dz0AgKPtAv7VKx2oZq2ZXSsrUeK5Ng0Ebs0WtPISFZ7S12CPrhIzjgDuuxcYfNGmxPpqDLxRDWdgCZ9cm8Uvrs9hzhfCX1+8g//69X28tr8e3QcbUc9WQ7RNO87pmp6extjYGMbHxyFJEpxOJ0RRhE6ng9FoxODg4LavYowHa6IoJvK2zp07B7PZ/FhO1+joKEwm02PH2Gilq729veByuq499OHDzx618uk/1o43DzTw8uYUqCovwdF2ARVl/NRabBbDEUhzATzwLKCwGqJROoTCEXwpOWCZmsVD7yKAWA0/Y7sWpsON2NdQzdfkHJRLOV1Z670oSRJEUQQQ2148efIkJicnN02kn56e3lbZiEJLpI9EZfz3X9/Hf//NA8gy0FSrxuAbItqZ1JkSddVleLZVgxJeHl7UgkvLkOYCsHsXGXzRE0VlGb+978X4FTuuPHj0XtBRV4meQ3p0dWjZaiiH5FLQlbV9qa6urkQgZTabMTw8DACJQCwuXp6iGOt0OQNLOHtBwo1ZPwDgVUMd/tkLe1DOPIKUaNdV4ik9P5lSLIn6mVYNOuurMD3P4Iu2plQo8GyrBs+2anDPtQDLlB1fSg7MOII4+9k0RifvJloNVauZ/kGPZG2la2RkBDqdLrE9uXrrMF449fjx47h48eKawqlPUigrXV/fceM/fj6NwFIE6lIl/vDFvXhRZCufVFAogKf0NVwtpE35Q8uQ5vyY9Yae/GAixHJuY62G5uBZCAMAylRKvGyoQ/fBRrQIFVkeYfHKpZWurAVd6ZLvQVc4EsXo5F2cvxpr5dNRV4mBN0Q01vCKulQoUcU+odYx8ZW2wbcYhjQXwJyPwRdtz3IkioszsZIT8Q4hAPBMSy16DutxuLmWq+sZlktBF9c9c8hDzyLMn9pwZ6Un2PcO6/HO863MN0qRyjIVjrQLqOLVnrRNNepSHGkX4FkIQ5rzw+FfyvaQKMeVrKxuvSTqcGPWj/Erdnx9x41v73vx7X0vWjRqmA7p8ZJYh7ISvrYXm6RXus6fP49z585Bq9Xi/fffx8TEBBQKBd56661Uj3FH8nGlS5ZlfCE58Fe/up1o5fPD1zrxbOv2KvzTkwmVpXiuTeCLHO2KO7gE21wArgCDL9q+OV8IE1ftuHDjUauh6vISfOepBrx5oIGlatIsl1a6kgq6Tp8+DUmS8N5772FgYAAXL16Ex+OByWTCxYsXkx54KuRb0LUYjuAvf3kLv5qOFX892FSDd1/r5B9hCjULahxqqmUVaUoZV2AJtjk/3MFwtodCeSS4tIzPbs7j/NVZzK+smqqUChzv0MJ0SI+Ouqosj7Aw5VLQldQ+y9jYGG7cuAEgtkoDxAqoblbAlDY24whg5FMJs74QlArgnx5txf/ydBODgxTa11iNjnq+kFFqaavKcKxKB4c/BGk+AA+DL9qGyrISfO9wE0wH9bh8xw3LlB03Zv34peTELyUn9jdWw3RIj+fb2WqoUCUVdGk0GnzzzTc4cuRIIiHw8uXL2254XeyisgzLlB0fW+8hEpWhqyrDwOsi9jWy31+qqJQKPN1aywsQKK3qqstRV12OOV8I0pwfvsXlbA+J8oBSqUDXXi269mox4wjAMmXHxWkXbsz6cWPWj/rqMrx1sBGv72tg0eYCk9T2Yrx21vHjx2Gz2dDT0wOLxYLR0VEcPXo0DcPcvlzfXvQthvEfPp/Bb+7FWvkY9wj4ly93MLk7hcpLlTjSLqBWXZrtoVCRmfUtQpoLwM/gi3bIHVzC+Wuz+MW1OQSWIgAAdakSr+2rR/dBPRpqeMV1snJpe3FXJSM+/vhjSJIEQRDQ39+fEytduRx0XX3oxdkL0/AsxFr5/N7xdnznKbbySaUadQmOtAtsREtZZfcuwjbnRzAUyfZQKM+EliP4peSEZcqOB56VVkOI9drtOazH/kYWdN6pvA+6zp8//9hVih6PBxMTE3jnnXd2eriUysWgKxKV8d++uY//8ZsHkAE0a2KtfNq0LM6ZSo215Xi6RQMVcyEoB8iyjIfeRUzPBRBcYvBFOyOvtBqyTNnx7ar3mz26WKuh4x1alhPaprwPuuKV4rd7eyblWtDl8Idw9sI0bs7FWvm8vq8ev3e8na18UqyjvhL7GmuyPQyix8iyjAeeRUzPB7DA4IuScN+9gImrs/jS5sBSJFZyQlNRijcPNOA7TzWghqkUW8qloGtHiUQffvghgFhOV/z/427evLmmSTUB1tsu/PkXMwguRVBRqsIfvrQ3rb/4YqRUAgebatlig3KWQqFAi1CBplo17nsWMDMfxGKYwRdtX4tQgT98aS++f7QVn96Yw/mrs3AvhPFfvo7toLzUWQfTYT1a+TqY83YUdN28eRNA7JPbpUuX1twnCAKDrhXhSBTnLt3BJ9fmAACd9VUYeF1kImSKlZYocaRNw5pmlBeUSgXatJVo0VTgnnsBM44AQuFotodFeaRaXYL/9dlmfO+wHpO3Yq2GZhxBXLg5jws353G4OdZq6OmWWiiZ95WTdhR0/eQnPwEAuN1ufPDBB2kZUL67717AyAUJd1da+fyjp5vwu0dbuPeeYpXlKjzfruXl1JR3lEoF2nWVaBEqcM8VC76Wlhl80faVqJR4UazDC5063Jzzw3JlFtY7Llx54MWVB140adQwHWzEy2IdU1lyDBteJ2GjnC5ZlvH5TQf+6uJtLC1HUaMuwQ9f7cQzbOWTcrrqMjzbqkEpA1kqAJGojDvOIG45gwgz+KIkzftDmLg6i89uzGNhZfu6skyF7zzVgLcONkJbxDsCuZTTtavei1arFQ6HI3FbXV0d/uRP/iSZw6VMNoKu4NIy/tMvb+OrmVgrn0PNNXj3NRGaCiY3plqbrgIH9DW8ZJoKznIkitvOIG47g1iOFNRnYcqgxXAEn92cx8TULOb8IQCAShErxtpzWI/OIuzQkUtBV1IVOd977z2MjIygv78fFosFJpMJly5dgsFgSGrA+Wx6PtbKZ84fa+Xz/edb8fbTTdxPTzGFAnhKX4N2HctsUGEqUSkhNlSjXVeZCL4iDL5oh9SlKpgO6fHWgUZ8c9eN8Sk7rtv9+GrGia9mnDA0VKHnsB7Pt2tZXicLkgq6Pv74YzidsVWdH/3oRzhz5gxqa2tx4sSJlA4ul0VlGeNX7PiZ9R4isoz66jKcfF2EoYGtfFJNpVLg2VYN6qt5IQIVvlKVEoaGarRrK3HbGcAd5wIiUQZftDNKpQLP79Hi+T1a3HYEMT5lx1czTtjmArD9QkJd1Uqrof31qCxjR5RMSWqmV+9IGgwGTExM4Pvf/z4sFkvKBpbLXIEl/H8nbuC3K1uMx/Zq8S9e3ssnbhpUlKlwpF1ANdskUZEpK1FiX2NsdfeWI4i7riCiTPmiJOypq8QPX+tEb1cbPrk2i3+4NgdHYAmjk3fxt9/cx6v76tF9sBH6WvaqTbekcrree+89vPDCC3jnnXfg8XjQ1dUFrVa7YSmJTEt3TtdnN+bx//nry3AEllCqUuD3ju/BG/vrmWOUBkJlKZ5rE1BWwoR5osVwBLccQdxzM/ii3QlHovil5IBlahb33LEr7RUAjrQJMB1uLLi82VzK6UrJ1YvT09OQJAnd3d27PdSupTvo+v+dv4Gf/v11tAhqDL5hYDG6NGnSqHG4uRZK5hwQrbEYjmB6PoAHngUGX7Qrsixj6oEPlik7fn3Pk7i9XVsB0yE9XujUFcRV4nkfdP30pz/N+lWKm0l30BWJyvi/LNdxsKkG5SWsf5IOYkMVRObGEW1pYSkCad6Ph55FFFbhH8qGh55FTFy143ObI1E3rkZdgjcPNOK7TzWgNo+vxs/7oKu/vx+///u/j+9///tJDzJdcq33Im2fSqnA4ZZa5hUQ7UBwaRnSXAB2L4Mv2j1/aBkXVloNuYJhAECJUoGXxDp0H2pEuzb/riDP+6Dr8uXL6O/vx49+9CN0d3dDEITEfR0dHTs9XEox6MpPZSVKHGkXWNuMKEn+0DKmV4Ivot1ajkZhvRUrOTE9H0jcfrCpBj2H9Xi2VZM3pZHyPug6duwY3G734wdTKHDjxo2dHi6lGHTln2p1CY62C1CzXQXRrvkWw5DmApjzhbI9FCoQtjk/xq/YYb3tQrx6ib6mHN2H9HjFUJfzr915H3TlMgZd+aWhphxPt9SyNyVRinkXw7DN+uHwL2V7KFQgHP4Qzl+bxafX17Yaen1/Pd460Ii6HK2lyKArjRh05Y+9dZXY11hdUJcmE+UaTzAM27wfTgZflCKL4Qi+sDkwMWWHfWVFVakAjHtirYZyrUh4LgVdrDhJGadUAgeaallugygDNJWlMO7Rwh1cgm3OD1cgnO0hUZ5Tl6rw1sFGfPdAA35zz4PxK3ZcfejDpVsuXLrlglhfBdMhPYx7BZQouYuxGoMuyqgSlQJH2gRoq4q34z1RNgiVZejaq4MzsARpzg93kMEX7Y5SEXs9P9Im4I4rCMsVO3417YQ0H8DIBQnayVK8dbARb+xvQBW7igDg9mJSuL2YnMoyFY7uEdguiSgHzPtDkOYC8C4w+KLU8SyE8Yvrc/jk2ix8i8sAYlenv2qoQ/dBPZo0mS8JlEvbiwy6ksCga+e0VWV4rk1TENWNiQrJrG8R0lwA/pU3SKJUCEei+GraifEpO+66FhK3P9eqgemQHoeaM9dqKJeCLi45UNq1CBU42FTDlj5EOaixRo3GGjVmvYuwzQUQCDH4ot0rVSnx6r56vGKowzW7D+NX7Pj1XQ9+fS/21SpUoOeQHi+KhdFqaLuSOtOf/vSnmJmZAQCcP38e+/btw/79+/HJJ5+kcmyU5xQK4Cl9DQ63sIciUa5rrFXjJVGHZ1o1qCzL7bpLlD8UCgUONtXi//3Wfvyfv/sM3jrYiPISJe65F/DnX87g1Me/xn/9+h48RbLNndT24v79+xNFUOvq6jA8PIy33noLb7/9NoujEgBApVLgmRYNGmpys24LEW1OlmU88Cxiej6AhaVItodDBSa4tIwLN+YxcXUWzkCslEmJUoEXOnUwHdJjjy61rYbyfnsxHqdNTEzA5XLh3XffBQA4HI5kDkcFRl2qwpF2DWrUbOlDlI8UCgVahAo0a9S471nE9FwAi2EGX5QalWUlePvpJpgO6XH5tgvjU3bY5gL4wubAFzYHDuhrYDrUiCNtQsHtkiQVdImiiNOnT2N0dBQDAwMAgOnpaYiimNLBUf6prSjFkXYNyku4PUGU7xQKBVqFCjTXqnHPvYAZRwChcDTbw6ICoVIqcKxDh2MdOkjzfliuzGLylgvX7D5cs/vQUFOO7oONeG1ffc63GtqupLYXPR4Pzp07B4VCkVjlunz5MiRJwg9+8IOUD3InuL2YPfpaNZ5m/hZRwYpGZdx1xYKvpWUGX5R6zsASPrk2i19cn0NwZWu7olSF1/bXo/tgI+qTaDWUS9uLSSXSazQaiKKIS5cu4d/8m38DAHA6ndBqtckcjgpAZ0MVnm3TMOAiKmBKpQJ76irx6r567NdXo7SkeK46o8zQVZXhB8Y2/LsfPId//uIeNNWqsRCOYPyKHaf/5jf49/9gw41ZH/K12lVSK12nT5+GJEl47733MDAwgIsXL8Lj8cBkMuHixYvpGOe2caUrs5RK4HCzJisF74gou5YjUdxxLeCWI4DlSH6+CVJui8oyfnvfi/Erdlx58Oh9t6OuEj2H9Ojq0D6x1VAurXQlldM1NjaWuEoxHrNpNBq43e5tH0OSJFgsFuh0OkiShN7e3kRO2Fb3Ue4oK1HiSJsATSUT5omKUYlKic76KrRpK3DbGcRtZxARBl+UQkqFAs+2avBsqwb3XAuwTNnxpeTAjCOIs59NY3TybqLVULU690uPJjVCjUaDb775BkeOHElUlL18+TI0Gs22jzE2NoZTp04lvh8cHITZbH7ifZQbqspL8PweoWCSG4koeaUqJQwN1dijq8QtRxB3XAy+KPVatRX4l6904B1j60qroTm4F8L42eV7+O+/foCXDXXoPtiIFqEi20PdVFIb8ufOncObb76Jt99+Gy6XC3/8x3+M/v5+fPjhh9s+xkcffZTUfZR9ddVlON6hZcBFRGuUqpTY11iNVw316KivhErFHE9KvRp1KX7nuRYMv/MsfvhqJ/boKrEUieIX1+fwf/ztb/F/Wa7jt/c9OZn3lXTJCKfTiY8//hiSJEEQBPzkJz/Z0UqXTqdDV1cXRkdHIUkSenp6tnUfZdeeukrsb6zOWM8sIso/ZSVK7GuswR5dFW47ufJF6VGiUuJlQx1eEnW4MevH+JQdX99249v7Xnx734sWjRqmQ3p872l9toeakFQi/c9+9jOIooijR48mbvv444+hUCjwzjvvbOsYbrcb3d3dsFqtGBgYWLN9uNV964VCIYRCocT3Xq8X7e3tTKRPMYUCONBUgzZtaisFE1HhC0ei3HakjJjzhTBx1Y4LN+YRWilrUqMuwb98uQP/r7f2pWWHZieJ9Em3AZqcnFxzcLfbjePHj2+7DdDY2BgEQYAkSRgcHFwTXG1133p/+qd/ih//+MeP3c6gK3VKVLFExrok6qMQEcWFI9HYypczyKsdKa2CS8v47OY8zl+dxbx/CWJDFSz/+3fSUtYo7UGXTqeD0+nc9u3rSZIEs9mM4eHhxPddXV2YnJwEgE3v2+gKRq50pVdlmQpH2gVUlef+VSFElB8YfFGmRKMyrtl9ONhci57D6dlmTHvJiGPHjuFv/uZv8P3vfz9x2/nz57dd1sFqteL48eOJ7+NthdxuNyRJ2vS+jZSXl6O8nCsw6aCtKsWzrQLKWACRiFJo9dWOd1ZKTTD4onRQKhV4UaxLa52unUjq3fSDDz7AD3/4Q+zfvx9//Md/jO9973vo6+vD2NjYtv690Wh8rIiqw+GA0Wjc8j7KnGZBjefbtQy4iChtSlVKiA3VeG1fPQyN1Sjh1Y5U4JLaXoyLX70oiuKOey5aLBZYrVYIggAAMJlMiZWyre57Elak3719jdXoqK/K9jCIqMjEK9zfdgYRZm9HSpFcqkiftasX04VBV/JUSgWebq1FYw1b+hBR9jD4olTKpaArqb2joaGhx1aeuru7MTQ0lMzhKAeUlyrR1aFlwEVEWRdvL/SqoQ77GtlYmwpHUon0DofjsWhOEAQ4HI6UDIoyq0ZdgiPtbOlDRLmlRKVER30V2nWVuOsK4pYjiCWufFEeS+rjQ/zqxdV2cvUi5Y7G2nIc69Ax4CKinKVSKrC3rgqv7qvHfn01L/ChvJXUStcHH3yAY8eO4dSpUzCZTLDZbJicnEzU2aL80FFfhX2N1dkeBhHRtsSDrzZtJe65FjDjCHDli/LKrnovjo2NYXp6GiaTacdXL1L2KJXAoeZaNGtytxM7EdFmVEoF9tRVolVbgXuuBdxyBhAKM/ii3LerMuO9vb2pGgdlSGmJEkfaNBAqy7I9FCKiXYkHX23aCtxzx1a+GHxRLktqY1ypVEKlUj32tX///lSPj1KoslyFFzp0DLiIqKAolQq06yrxqqEeB5pqUF7KnC/KTUmtdLlcrjXfy7KMkZERKJV8oucqXXUZnm3VoFTF3xERFaZ48NUqxFa+bjmCWAxHsj0sooSk3oE1Gs2aL0EQcOrUKYyPj6d6fJQCbboKPN8uMOAioqIQD75eMdThYHMNr86mnJHUStfMzMxjt0mSBEmSdjseSiGFAnhKX4N2XWW2h0JElHFKpQJt2kq0aCpw37OAmXmufFF2JRV0GY1GKBQKrO4gpNPpMDg4mLKB0e6oVAo826pBfXV5todCRJRVq4OvB95FzMwHsLDE4IsyL6mgy+l0pnoclEIVZSocaRdQXb6ri1OJiAqKUqlAq1CBFo0a9z0Mvijzkkry+elPf5rYYjx//jz279+P/fv345NPPknl2CgJQmUpjnfoGHAREW1CoYgFX68Y6nC4pRaVZcz5osxIKugym83o6OgAAPT19WFoaAg///nPMTAwkMqx0Q41adQw7tGyRQYR0TYoFAq0CBV42VCHp1sZfFH6JbUcEs/lmpiYgMvlwrvvvgsAbHidRYbGanTWV2V7GEREeUehUKBZU4GmWjUeehcxPRdAkNuOlAZJtwE6ffo0RkdHE6tb09PTbHidBSqlAodbaqGvVWd7KEREeW118GX3hiDN+xEMMfii1ElqH2p0dBSiKGJoaAgffPABAMDtduP06dMpHRxtraxECeNeLQMuIqIUUigUaNKo8bJYh2daNags57YjpYZCXl33oQB4vV5oNBp4PB7U1tam5Wfccy9g6r43Lcfermp1CY62Cyz6R0SUZrIsY9YXgjQXQCC0nO3h0A7VVpTihU5d2o6/k7iDl7jloYaacjzTqoFKqcj2UIiICp5CoYC+Vg19rRp27yKDL0oag648s7euEvsaq6FQMOAiIsq0ePA1612ENB+Af5HBF20fg648oVQCB5pq0SpUZHsoRERFr7FWjcZaNWZ9sZUvBl+0HQy68kCJSoEjbQK0VWXZHgoREa3SWKNGY00s+JqeC8DH4Iu2wKArx1WWqXB0j4DKMv6qiIhyFYMv2g6+k+cwbVUZnmvToFTFCvNERPkgHnzN+UKYng/AuxDO9pAohzDoylEtQgUONdcwYZ6IKA811JSjoaYc8/5YqQkGXwQw6Mo5CgWwv7EGe+oqsz0UIiLapfrqctRXx4Kv6fkAPEEGX8WMQVcOUakUeKZFg4aa8mwPhYiIUigefDlWgi83g6+ixKArR6hLVTjSrkGNujTbQyEiojSpqy5HHYOvosWgKwdoKkvxXJsG5SVs6UNEVAziwZczsITpeT9cAQZfxYBBV5bpa9V4uqUWSrb0ISIqOrqqMuiqdAy+igSDrizqbKiCoaE628MgIqIsiwdfrsASpPkAXIGlbA+J0oBBVxYolcDhZg2aNOpsD4WIiHKItqoMXVVlcAeXYJtj8FVoGHRlWFmJEkfaBGgqmTBPREQbEyrL0LU3FnxJ8wE4/Qy+CgGDrgyqVpfgaLsAdSkT5omI6MmEyjIY95TBEwzDNu9n8JXnGHRlSF11GZ5t1aCELX2IiGiHNJWlMO7RwhMMQ5r3w8HgKy8x6MqAPXWV2N9YzZY+RES0K5rKUjy/RwvPQhjSHIOvfMOgK40UCuBAUw3atGzpQ0REqaOpeBR8Tc8HMO8LZXtItA0MutKkRKXAs60a1FWzpQ8REaWHpqIUR9sFeBfDmJ4LYI7BV07LWtAlSRIsFgt0Oh0kSUJvby9EUUzcb7FYIElS4jaTyZStoe5YZZkKR9oFVJUzpiUiovSrVZfiCIOvnJe1qGBsbAynTp1KfD84OAiz2QwgFnCNjo7CbDZDkiT09PTAZrNla6g7oq0qxbOtAspKmDBPRESZFQ++fIuxbcdZL4OvXJK1yOCjjz7a9L7BwUEMDw8DAERRxPj4eKaGtSvNghrPt2sZcBERUVbVqEvxXJuAF0UdGmuZ5pIrsrbSpdPp0NXVhdHR0cRqFhDbdnQ6nRAEAVarFaIortl2XC8UCiEUehTJe73etI99I/saq9FRX5WVn01ERLSRePDlWwxjZj4Iu3cx20MqallbkhkdHQUAGAwGjI6Oore3FwBgtVqh0+kwNjYGURQxMjKCsbGxTY/z/vvvQ6PRJL7a29szMv44lVKB59o1DLiIiChn1ahL8WybBi8Z6qCvVYMVjLJDIcuynI0fPDY2BkEQIEkSBgcHMTAwALPZjJGREQwODsLlckEQBLjdbmi1Wmw2zI1Wutrb2+HxeFBbW5uWsd9zL2DqvhflpUocaRdQq2ZLHyIiyh+B0DKm5wOwexeRnSggc2orSvFCpy5tx/d6vdBoNNuKO7Ky0iVJEi5evAiTyYSBgQHYbDacO3cucbWiIAgQBAEAEv+1Wq0bHqu8vBy1tbVrvjKhRl2C4x06BlxERJR3qspL8EyrBi+JdWjScOUrU7KS02W1WnH8+PHE96Io4vTp03C73Vvmb+UKTUUpmjp0UCn5LCUiovwVD77EhipIc8Wx8pVNWVnpMhqNuHjx4prbHA4HjEYjRFHEsWPH4Ha7ASCx+mU0GrMw0o1Vl5cw4CIiooJRWRYLvl421KFZ4MpXumRlpUsURfT09ODMmTOJ7cPBwcHE/aOjoxgaGkJXVxcmJyfzpmQEERFRPqssK8HTLRp01ldhej6Ahx6ufKVS1hLp02UnCW1ERES0uYWlCKbnA3jgWcjb4KvoE+mJiIgo91WUqXC4pRavGOrRIlRAyahhVzh9REREtKXVwVerlsFXsjhtREREtC3qUhUONTP4Shani4iIiHZkdfDVpmPwtV2cJiIiIkqKulSFg02x4KtdV8ng6wk4PURERLQr6lIVDjTVMPh6gqzU6SIiIqLCEw++9tZV4rYziHuuBUSieVprIg0YixIREVFKqUtVeEpfg1f21WFPXSW7uKxg0EVERERpUV7yKPjay+CL24tERESUXuUlKuzX12BPXSVuO4K4W6Tbjgy6iIiIKCPiwdfeuircdgZwx7WASKR4gi8GXURERJRRZSVK7GuswR5dFW47g7jjChZF8MWgi4iIiLIiFnxVY4+usiiCLwZdRERElFXx4CteauK2szCDLwZdRERElBNKVUoYGlatfDmDWC6g4ItBFxEREeWU1cHXnZWVr0IIvhh0ERERUU4qVSkhNlSjvUCCLwZdRERElNPiwdceXSXuuBZwyxHIy+CLQRcRERHlhRKVEp31VWjXVuCOawG3nUGEl6PZHta2MegiIiKivLI6+LrrWsCtPAm+GHQRERFRXipRKdFRX5XI+cr14ItBFxEREeU1lVKRCL7uuoK45QhiKQeDLwZdREREVBBUSgX21lWhTVuJe64FzDgC2R7SGgy6iIiIqKColArsqatEq7YCjkAo28NJUGZ7AERERETpoFIq0FijzvYwEhh0EREREWUAgy4iIiKiDGDQRURERJQBDLqIiIiIMoBBFxEREVEGMOgiIiIiygAGXUREREQZwKCLiIiIKAMYdBERERFlAIMuIiIiogxg0EVERESUAQy6iIiIiDKgJNsDSDVZlgEAXq83yyMhIiKiQhePN+Lxx1YKLujy+XwAgPb29iyPhIiIiIqFz+eDRqPZ8jEKeTuhWR6JRqO4f/8+ampqoFAosj2ctPB6vWhvb8edO3dQW1ub7eFkDechhvMQw3mI4TxwDuI4DzHpngdZluHz+dDS0gKlcuusrYJb6VIqlWhra8v2MDKitra2qP+Q4jgPMZyHGM5DDOeBcxDHeYhJ5zw8aYUrjon0RERERBnAoIuIiIgoAxh05aHy8nL823/7b1FeXp7toWQV5yGG8xDDeYjhPHAO4jgPMbk0DwWXSE9ERESUi7jSRURERJQBDLqIiIiIMqDgSkYUIovFAgBwu924ePEiTpw4AaPRCACQJAljY2MQRRGSJGFgYACCIGRxtOk3NDSE06dPJ86zmObAarUCAIxGIyRJgtvtLtrngsVigSRJEEURAGAymQAU1zyMjY0lznv9ORbLPEiSBIvFAp1OB0mS0Nvbm3hOFMscALFzNZvNMBgMsNlsRfMaabVacfLkSUxOTq65fatzzup8yJTzBEGQJycnZVmWZbPZLIuimLjPaDQm/t9ms8m9vb0ZH18mTU5OygBkl8uVuK2Y5mBgYEAGIAOQTSZT0c7D+Pi4PDAwIMty7FyL9W8i/lxY/TU8PCzLcvHMQ/x84+LPC1kunjmQZVkWRTHxejA5OVkU8zA6Opp4T1hvq3PO5nww6MoD4+Pjif83m82JJ4zNZlvz5JHlWIBWyEZHR9e8uBTbHJjNZtnlcq0JtmS5+OZh9XNAlmPnH/9vscyDy+WSR0dH19wWD0CKaR7Wn+fqYLxY5mB8fHzNBw9ZlhOBSDHMw/qga6tzzvZ8MKcrD8S3DwBgdHQUg4ODAJBYUl9Np9MltqAKzdjYGHp7e9fcVmxzAMS2kdYvhRfTPEiSBKfTCUEQYLVa4Xa7E9tJxTQPANb8Paz++yimedDpdOjq6kpsM/b09AAorjlwu90b3m61WotqHuK2OudszweDrjxhtVoxNDSEnp4eDAwMANj8D83pdGZwZJnhdrs33HMvpjkAYuc7NjaGsbExDA0NQZKkxO0bKcR5sFqt0Ol0iZyMkZERjI2NASiueVj99+B2u+F0OhPBZzHNw+joKADAYDBgdHQ0EXgW0xzEczzj4gGE0+ksqnmI2+qcsz0fTKTPE0ajEaIoYmhoaMMVn9U2e1Lls3PnziWCze0oxDkAsCbhUxRF9PT0wGazbfr4QpwHp9MJSZJgMpkgCAIGBgag1Wohb1FysBDnYbWhoSEMDw8/8XGFOA8WiwXDw8OQJCmxC2A2mzd9fCHOgSiKGB4exsjICPr7+xMB2PoVndUKcR6eZKtzztR8cKUrjwiCgL6+PvT19SVWftZH5/Ftl0JisVjQ39+/4X3FMgdxqz/Nxq+8kSSpqOZBFMU1W6zx/1qt1qKahzi32w2LxbLmHItlHiRJwsWLF2EymTAwMACbzYZz584V3d8EAJw6dQomkynxgQR49LdSTPMAbP38z/Z8MOjKcRaLBVqtNvH96kuhV+d6rXbs2LGMjC2Tzp07h5GREYyMjECSJLz//vuwWq1FNQdWqxXd3d2P3a7T6YpqHuJ/AxsppnmIu3Tp0mNvGMUyD1arFcePH098L4oiTp8+DbfbXTRzEBcvnxLfajQajRAEoejmAdj6+Z/t+eD2Yo5b/4Ya/zQfr820miRJOHbsWMF9gln/RzI4OIjBwcEN33wLdQ6AR1sIcRaLBb29vRsm1hf6PBw7diyx2rv6zWa9Qp6HuHiO22rr/zYKdR6MRiPMZvOadAuHw1GUz4Wuri5MT09DEASYzebEa0WxPBdW5/1udc7Zfq1k0JXjjEYjTpw4gZGREQDA+Pj4miJwo6OjGBoawvHjx3Hx4sVEUmkhcrvdiXkYHh7G4OAgjEZj0cyBIAg4duwYzpw5A0EQYLPZ1pxrscwD8Ohcu7q6MDk5ifHx8cfuK4Z5iNvoA0gxzEM8rzH+NwEgkdcFFMccxA0PD8NiscDpdKKvr++xq94LcR4sFkvib//999/H8ePHEwH4Vueczflgw2siIiKiDGBOFxEREVEGMOgiIiIiygAGXUREREQZwKCLiIiIKAMYdBERERFlAIMuIiIiogxg0EVEtAVJkvKqT53b7d7ReOPNkYko/Rh0ERUxrVa7pp9jvv28vr4+GAwG9PT0pOyY6w0NDeH9999P2/FTye12o6+vDwBw5swZaLVaaLVaGAyGxP/Hv7q6ugDEmkOPjY1lc9hERYPFUYmKmFarxeTk5Jb9DJM1NDQEg8GAgYGBtPy8M2fOwOFwrGmNtJ0xZFomx9DT04OhoaEN+8tptVqMjo5ueJ/BYMDk5GTBtYYhyjVc6SKivGSz2WAwGLI9jJwhSRIkSdq0oe9Went7Ey22iCh9GHQREYDYm3ZXVxcMBgOGhoYStxsMBoyMjKCnpwdarXbNVpTFYoHBYEBXVxcGBwcTKy2Dg4MYGRlJ9Ee0WCyJfzM2Noaurq7HjrXZmHp6ehJbiPFcpaGhIZw7dy5x/I1sNoauri5YrVb09fXhzJkzieMZDIbHzh2Iraitvm2zeYqPNb6dNzY2tukYNjuv9WMbHBxMjBGIbR9qtdoNz3dsbGxN4+edGBwchNlsTurfEtEOyERUtARBkG02W+L/JycnZVmW5d7eXnl8fDxxe29vryzLsjw6OiqLorjm37tcLtnlciX+P25gYEA2m82P/bzNjrXZ+OJjGh8fX/P4jY6/3kaPEUVRFkVRHh0dTdy2+v8FQUicuyzL8vDwsHzq1KkNx7R+nuLHcblcicdsNg8bndf6sU1OTq45Z7PZLA8MDGx4riaTac15rLf+vNbj2wFR+nGli4gwNjaGY8eOwWg0AgBOnz6N0dHRxP0nTpwAAJhMpkQivNvthk6ngyAIEAQBx44d21aS/EbH2sjIyAhMJlNiTCaTCYIgrFk1S9bg4OCaVaHV/9/f37/pFX2bzdPY2Bh0Ol3iOIIgJB6z0/NaPbb4Y+LjMZvNGBwc3PC4kiTtKldOEISMXlRBVIxKsj0AIsq+eD7Q6q261blBG72Zx5Ou3W534g17O2/62w0MbDbbY48VRTElgcH6vCe3242RkRHYbDZYLJZNE8o3m6ed5FI96bzWHye+9Tc8PAy3271pMOd0OneVCK/T6fKqNAZRPmLQRUQQBAEmk2nTvJ7N3swFQUB3dzfcbjeGh4e39aa/3cDAYDBgfHx8zW27Xc3ZaAzx/Krh4WEMDAw8ltO1/t9tNE9jY2OPjXUzTzqv9fMzMDCArq4unDt3bsucrXjglo4rUYkoNbi9SETo7++HxWJJbGO53e5tbePpdDpMTk7CZrM9FhAIggCbzZY43m7HNDY2BrfbvaOr87YzBkmSIAgCent7IQgCLl26tO0xxeept7cXly5dSsyZ2+1OXCSwfgw7Pa/4VuVWW4vA7leqGLARpR+DLiKCIAgYHR3FyZMnYTAY0N3dva1/J0kSFAoFFAoFtFrtmlWiEydO4MyZM49dvbiTMU1MTODkyZPQarUwm83bXk3ayRjiwY5Wq0VfXx9EUURdXd2mY9psniYnJzE0NLSm8OhGY0jmvOLB1lZBkdFoxMWLF7c8zmasVitEUWSdLqI0Y3FUIkrKmTNnYLPZElttbrcb3d3dOH36dNKlC3JRPJDcqghruo2NjUGSJJw6dWrTx8S3SeOrajuxnUKzRLR7XOkiopQQBAE6nS7bw0iZeGK7JElZL8JqNpufWNFeFEWIopjUqqLZbMbp06eTHR4RbRODLiJKyqlTpyAIQqI4arzQZ6Gscg0PDyd6RWarjdDIyAi6urrQ09Ozra2/0dHRLS8E2MjQ0NC2L4Igot3h9iIRUQFxu91wOp3bToq3Wq2blqEgotRi0EVERESUAdxeJCIiIsoABl1EREREGcCgi4iIiCgDGHQRERERZQCDLiIiIqIMYNBFRERElAEMuoiIiIgygEEXERERUQYw6CIiIiLKgP8HDs9x7IdstfEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "succes_std = np.array(succes_std)\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.fill_between(T, succes_std[:,0], succes_std[:,1], alpha=0.3, label=\"MCTS area of 1 STD\")\n",
    "\n",
    "plt.plot(T, success_rates)\n",
    "plt.xlabel(\"length of trajectory (T)\")\n",
    "plt.ylabel(f\"succes rate (out of {num_test_epochs} runs)\")\n",
    "plt.title(\"MCTS performance results on the custom gridworld, P=\"+str(p))\n",
    "success_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'training loss')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval = 400\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.plot(train_history)\n",
    "plt.title(\"training loss\")\n",
    "# plt.xticks(range(1,num_training_epochs*epochs,interval), range(0,num_training_epochs,interval//epochs));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- : [9, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 4, 3, 2, 7]\n",
      "--- : [1, 0, 5, 10, 5, 5, 5, 5, 10, 10, 15, 15, 20, 20, 20, 20, 20, 20, 20, 21]\n",
      "--- : [20, 15, 10, 11, 10, 11, 10, 15, 10, 15, 15, 10, 10, 11, 11, 16]\n",
      "--- : [10, 15, 16]\n",
      "--- : [14, 9, 4, 9, 14, 13, 12, 17]\n",
      "--- : [3, 3, 2, 1, 2, 3, 4, 4, 4, 4, 4, 4, 3, 2, 2, 7]\n",
      "--- : [20, 21]\n",
      "--- : [11, 16]\n",
      "--- : [13, 12, 11, 10, 10, 11, 11, 16]\n",
      "--- : [20, 20, 15, 10, 15, 16]\n",
      "--- : [24, 19, 18]\n",
      "--- : [12, 11, 10, 5, 0, 5, 10, 5, 0, 1, 2, 7]\n",
      "--- : [19, 19, 14, 13, 12, 7]\n",
      "--- : [13, 14, 13, 12, 11, 10, 10, 5, 0, 0, 1, 0, 1, 0, 0, 1, 2, 3, 2, 3, 4, 9, 14, 19, 24]\n",
      "--- : [24, 23, 18]\n",
      "--- : [11, 10, 10, 15, 10, 5, 5, 5, 10, 15, 10, 10, 5, 10, 11, 11, 12, 7]\n",
      "--- : [19, 14, 13, 12, 7]\n",
      "--- : [4, 4, 3, 2, 1, 1, 1, 0, 1, 1, 0, 5, 10, 5, 10, 10, 5, 10, 15, 15, 15, 15, 15, 20, 15]\n",
      "--- : [23, 24, 19, 14, 13, 12, 11, 11, 16]\n",
      "--- : [11, 10, 11, 10, 5, 10, 10, 5, 0, 0, 0, 5, 5, 0, 1, 0, 0, 0, 1, 2, 3, 4, 4, 4, 3]\n",
      "--- : [20, 20, 21]\n",
      "--- : [13, 12, 11, 10, 11, 10, 5, 0, 1, 2, 3, 4, 9, 9, 9, 9, 4, 3, 3, 3, 3, 4, 9, 4, 4]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 2, 3, 4, 9, 14, 13, 12, 11, 12, 13, 18]\n",
      "--- : [19, 14, 14, 14, 9, 9, 4, 4, 9, 4, 3, 2, 1, 1, 0, 0, 1, 0, 5, 0, 1, 2, 3, 2, 2]\n",
      "--- : [3, 2, 1, 0, 5, 5, 0, 0, 5, 0, 5, 10, 11, 12, 17]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 3, 2, 3, 4, 3, 3, 4, 4, 3]\n",
      "--- : [3, 2, 2, 3, 3, 3, 4, 4, 9, 14, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [23, 22, 17]\n",
      "--- : [10, 5, 0, 5, 0, 1, 2, 1, 1, 0, 0, 0, 1, 1, 1, 2, 3, 3, 2, 7]\n",
      "--- : [5, 0, 1, 2, 2, 3, 3, 4, 9, 4, 4, 4, 9, 14, 9, 14, 9, 14, 13, 18]\n",
      "--- : [20, 21]\n",
      "--- : [11, 10, 5, 0, 0, 0, 1, 2, 2, 1, 1, 1, 1, 2, 3, 2, 2, 3, 4, 9, 14, 13, 14, 19, 14]\n",
      "--- : [10, 5, 10, 5, 5, 0, 1, 2, 3, 3, 4, 9, 14, 19, 24, 24, 24, 24, 24, 19, 14, 13, 12, 13, 13]\n",
      "--- : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 19, 19, 24, 19, 19, 24, 24, 23, 24, 19, 19, 18]\n",
      "--- : [14, 9, 4, 3, 2, 1, 2, 2, 2, 7]\n",
      "--- : [12, 11, 10, 5, 0, 1, 2, 1, 0, 5, 0, 5, 10, 10, 10, 5, 5, 5, 5, 0, 5, 0, 0, 0, 5]\n",
      "--- : [4, 3, 2, 1, 0, 5, 10, 15, 15, 16]\n",
      "--- : [14, 13, 12, 11, 10, 5, 0, 1, 2, 1, 1, 1, 1, 1, 2, 7]\n",
      "--- : [4, 3, 2, 1, 1, 0, 5, 5, 0, 0, 0, 5, 0, 1, 2, 1, 1, 2, 1, 0, 5, 10, 5, 10, 15]\n",
      "--- : [0, 1, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 22, 23, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 10]\n",
      "+++ : [9, 4, 3, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "--- : [20, 21]\n",
      "--- : [1, 2, 3, 4, 9, 4, 9, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 10]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 2, 2, 3, 4, 4, 9, 14, 14, 19, 14, 14, 14, 9, 9, 14, 19, 14, 9]\n",
      "--- : [11, 16]\n",
      "--- : [1, 2, 1, 0, 1, 0, 5, 10, 11, 11, 16]\n",
      "--- : [24, 19, 14, 13, 13, 12, 11, 10, 5, 0, 0, 1, 2, 3, 4, 9, 14, 19, 14, 19, 24, 23, 22, 22, 23]\n",
      "--- : [12, 11, 10, 15, 15, 10, 10, 15, 10, 11, 11, 11, 16]\n",
      "--- : [11, 10, 11, 10, 10, 5, 0, 1, 1, 0, 1, 2, 1, 0, 5, 10, 11, 16]\n",
      "+++ : [14, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "Train wins: 2 / 50\n",
      "--- : [19, 14, 13, 12, 11, 10, 5, 0, 1, 2, 3, 4, 4, 3, 4, 9, 14, 14, 14, 19, 19, 24, 23, 23, 24]\n",
      "--- : [23, 24, 19, 14, 13, 12, 11, 10, 5, 0, 0, 1, 2, 3, 4, 4, 9, 14, 19, 24, 23, 23, 24, 19, 14]\n",
      "+++ : [5, 0, 1, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "--- : [10, 15, 10, 11, 11, 10, 5, 10, 15, 16]\n",
      "--- : [23, 24, 19, 14, 13, 12, 11, 10, 15, 10, 15, 20, 20, 20, 20, 21]\n",
      "--- : [12, 11, 10, 5, 0, 1, 2, 1, 0, 5, 10, 11, 12, 12, 13, 13, 14, 13, 13, 14, 13, 14, 19, 18]\n",
      "--- : [13, 12, 11, 10, 15, 20, 20, 15, 16]\n",
      "+++ : [3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 10, 15, 15, 15]\n",
      "+++ : [0, 1, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "--- : [20, 15, 10, 5, 0, 0, 0, 5, 10, 11, 12, 11, 12, 7]\n",
      "+++ : [1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 10, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "--- : [20, 15, 10, 5, 5, 10, 5, 10, 15, 10, 11, 12, 13, 18]\n",
      "+++ : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "+++ : [14, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "--- : [23, 24, 19, 14, 13, 12, 11, 10, 5, 0, 1, 2, 3, 4, 9, 14, 13, 12, 13, 14, 19, 24, 24, 24, 19]\n",
      "+++ : [10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "--- : [19, 14, 9, 4, 3, 2, 2, 1, 0, 5, 10, 5, 10, 10, 10, 11, 12, 7]\n",
      "--- : [12, 11, 16]\n",
      "+++ : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "--- : [19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 16]\n",
      "+++ : [10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "+++ : [1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13]\n",
      "--- : [23, 22, 23, 24, 23, 22, 22, 17]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 10, 15, 20, 20, 20, 21]\n",
      "+++ : [10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "--- : [23, 24, 19, 14, 9, 14, 9, 4, 3, 2, 2, 1, 0, 5, 5, 10, 15, 15, 20, 15, 16]\n",
      "--- : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 24, 23, 18]\n",
      "--- : [20, 15, 10, 15, 16]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "--- : [24, 19, 24, 19, 14, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14]\n",
      "+++ : [3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "+++ : [11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10]\n",
      "+++ : [1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15]\n",
      "--- : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 10]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10]\n",
      "+++ : [3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 2, 3, 4, 3, 4, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "--- : [20, 15, 20, 20, 21]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10]\n",
      "--- : [23, 24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "--- : [12, 11, 10, 11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12]\n",
      "--- : [23, 24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "--- : [12, 13, 13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "+++ : [9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 10, 15]\n",
      "--- : [12, 11, 10, 15, 10, 15, 20, 20, 21]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [10, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10]\n",
      "--- : [23, 24, 19, 14, 13, 12, 11, 10, 11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23]\n",
      "+++ : [11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "--- : [12, 11, 10, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "--- : [23, 24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "--- : [20, 20, 15, 10, 11, 16]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 2, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "--- : [14, 9, 4, 3, 2, 2, 3, 3, 3, 2, 3, 4, 3, 4, 3, 4, 3, 3, 2, 1, 1, 0, 1, 0, 1]\n",
      "--- : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 10]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "+++ : [4, 3, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "--- : [23, 22, 17]\n",
      "+++ : [14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 10, 15]\n",
      "--- : [4, 3, 2, 2, 3, 4, 9, 14, 14, 9, 9, 4, 3, 2, 1, 1, 0, 5, 0, 1, 0, 0, 1, 2, 1]\n",
      "+++ : [9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "--- : [23, 22, 23, 22, 23, 24, 19, 18]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15]\n",
      "--- : [13, 12, 11, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "--- : [12, 11, 10, 10, 5, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 22, 23, 24, 19, 14, 13, 12, 11]\n",
      "Test wins: 56 / 100\n",
      "--- : [23, 22, 22, 21]\n",
      "--- : [9, 4, 4, 3, 3, 3, 4, 9, 9, 4, 4, 3, 3, 2, 3, 3, 4, 4, 3, 4, 9, 14, 9, 4, 9, 14, 9, 9, 14, 13, 14, 19, 24, 24, 24, 23, 23, 24, 23, 22, 22, 21]\n",
      "--- : [20, 21]\n",
      "--- : [0, 0, 0, 1, 2, 3, 4, 4, 9, 9, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [1, 0, 1, 2, 1, 0, 1, 0, 1, 2, 2, 3, 2, 1, 2, 3, 3, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 5, 5, 5, 5, 5, 5, 5, 0, 1, 2, 7]\n",
      "--- : [24, 24, 23, 22, 17]\n",
      "--- : [11, 11, 10, 11, 10, 10, 15, 16]\n",
      "--- : [19, 18]\n",
      "--- : [10, 10, 15, 15, 15, 20, 15, 16]\n",
      "--- : [4, 4, 4, 3, 2, 3, 2, 7]\n",
      "--- : [19, 14, 9, 9, 9, 14, 9, 4, 3, 2, 3, 2, 3, 4, 4, 9, 14, 14, 19, 19, 19, 19, 18]\n",
      "--- : [0, 0, 1, 0, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 3, 2, 2, 3, 2, 2, 1, 1, 2, 3, 4, 4, 9, 14, 13, 13, 12, 11, 12, 11, 16]\n",
      "--- : [10, 10, 11, 12, 13, 18]\n",
      "--- : [10, 5, 10, 10, 5, 0, 1, 2, 1, 0, 5, 5, 5, 5, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 5, 10, 11, 11, 16]\n",
      "--- : [20, 15, 16]\n",
      "--- : [10, 5, 10, 5, 5, 0, 1, 0, 1, 0, 0, 1, 2, 3, 4, 9, 14, 19, 19, 24, 23, 23, 18]\n",
      "--- : [14, 9, 9, 4, 3, 4, 3, 4, 3, 2, 1, 1, 0, 5, 5, 10, 10, 15, 20, 15, 16]\n",
      "--- : [13, 14, 9, 4, 3, 2, 2, 7]\n",
      "--- : [24, 24, 19, 18]\n",
      "--- : [4, 3, 2, 3, 4, 3, 2, 3, 4, 9, 14, 14, 14, 14, 13, 13, 18]\n",
      "--- : [20, 15, 10, 11, 11, 10, 10, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 15, 20, 15, 20, 15, 20, 15, 15, 20, 21]\n",
      "--- : [13, 18]\n",
      "--- : [4, 4, 3, 2, 3, 4, 9, 9, 14, 19, 24, 24, 23, 22, 23, 22, 21]\n",
      "--- : [10, 5, 0, 1, 2, 2, 3, 2, 7]\n",
      "--- : [11, 10, 15, 16]\n",
      "--- : [19, 19, 14, 14, 9, 9, 9, 4, 3, 4, 3, 2, 3, 4, 9, 14, 19, 19, 14, 13, 13, 13, 12, 11, 11, 11, 11, 10, 10, 10, 5, 5, 5, 10, 15, 20, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15]\n",
      "--- : [3, 2, 1, 1, 1, 0, 0, 0, 5, 10, 11, 16]\n",
      "--- : [9, 4, 3, 2, 3, 3, 2, 3, 3, 3, 4, 3, 3, 3, 2, 7]\n",
      "--- : [19, 19, 14, 9, 4, 3, 3, 2, 3, 4, 4, 9, 14, 14, 19, 24, 24, 19, 19, 24, 23, 23, 23, 22, 21]\n",
      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [12, 11, 11, 12, 13, 14, 9, 4, 3, 2, 2, 3, 3, 4, 9, 14, 19, 19, 19, 18]\n",
      "--- : [19, 14, 13, 14, 9, 9, 4, 3, 2, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 5, 5, 0, 1, 1, 0, 0, 0, 1, 0, 5, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 5, 5]\n",
      "--- : [12, 13, 12, 7]\n",
      "--- : [12, 13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 18]\n",
      "+++ : [9, 9, 4, 3, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 5, 5, 10, 15, 20, 15, 20, 15, 20, 15]\n",
      "+++ : [0, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [19, 14, 9, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 24, 24, 23, 22, 23, 22, 21]\n",
      "--- : [12, 13, 12, 13, 14, 9, 4, 4, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 5, 5, 10, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 16]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 14, 19, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [3, 2, 3, 4, 4, 9, 9, 9, 9, 14, 9, 9, 4, 4, 4, 9, 9, 4, 4, 4, 9, 9, 9, 14, 9, 9, 9, 14, 14, 19, 24, 19, 19, 19, 14, 9, 14, 14, 9, 4, 9, 9, 4, 4, 4, 4, 4, 3, 3, 4]\n",
      "--- : [23, 22, 23, 22, 23, 18]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 24, 23, 24, 24, 23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20]\n",
      "+++ : [19, 14, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [13, 12, 13, 14, 9, 4, 3, 2, 7]\n",
      "+++ : [11, 10, 5, 0, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "Train wins: 11 / 50\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 0, 1, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 20, 15, 16]\n",
      "+++ : [10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 20, 15, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 20, 15, 16]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 16]\n",
      "+++ : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 5, 5, 0, 0, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 16]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 21]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [24, 19, 14, 9, 4, 3, 2, 1, 1, 1, 1, 1, 2, 3, 2, 3, 4, 4, 3, 2, 2, 1, 2, 3, 4, 3, 4, 4, 4, 9, 9, 9, 9, 14, 13, 14, 13, 14, 14, 9, 4, 4, 9, 9, 4, 4, 4, 4, 4, 3]\n",
      "+++ : [1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 15, 20, 15, 20, 15, 20, 15, 20, 15, 15, 20, 15, 15, 20, 15, 20, 15, 20, 15, 10, 15, 20, 21]\n",
      "+++ : [10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 10, 5, 5, 0, 0, 1, 2, 1, 2, 2, 3, 4, 9, 14, 9, 9, 4, 4, 9, 9, 14, 19, 24, 23, 22, 17]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 0, 1, 1, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 10, 10, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "Test wins: 92 / 100\n",
      "--- : [5, 5, 5, 10, 10, 11, 10, 15, 16]\n",
      "--- : [4, 3, 4, 3, 2, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 5, 10, 11, 10, 11, 16]\n",
      "--- : [13, 14, 13, 12, 13, 18]\n",
      "--- : [20, 15, 10, 5, 0, 5, 5, 0, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 3, 4, 3, 4, 3, 4, 4, 4, 4, 3, 4, 3, 3, 4, 3, 2, 2, 7]\n",
      "--- : [10, 15, 20, 20, 20, 20, 20, 15, 15, 16]\n",
      "--- : [4, 3, 2, 1, 1, 0, 0, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 3, 4, 4, 9, 9, 14, 14, 19, 19, 19, 14, 19, 19, 18]\n",
      "--- : [0, 0, 0, 5, 5, 5, 0, 1, 2, 1, 1, 2, 3, 4, 4, 3, 3, 4, 9, 14, 14, 14, 19, 14, 19, 18]\n",
      "--- : [0, 5, 0, 5, 10, 10, 5, 5, 5, 5, 5, 10, 10, 11, 12, 17]\n",
      "--- : [3, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 5, 0, 1, 1, 2, 7]\n",
      "--- : [3, 2, 3, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 10, 10, 5, 5, 10, 15, 16]\n",
      "--- : [4, 4, 4, 3, 2, 3, 2, 2, 2, 3, 4, 4, 4, 4, 9, 14, 13, 13, 14, 19, 19, 14, 14, 13, 14, 19, 19, 19, 24, 23, 22, 21]\n",
      "--- : [9, 9, 9, 4, 3, 4, 9, 9, 4, 3, 2, 3, 3, 2, 2, 2, 2, 1, 0, 1, 1, 2, 7]\n",
      "--- : [10, 15, 15, 10, 11, 10, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 1, 2, 3, 4, 9, 9, 9, 9, 14, 14, 19, 18]\n",
      "--- : [11, 11, 11, 11, 12, 7]\n",
      "--- : [3, 3, 2, 3, 3, 4, 4, 9, 14, 9, 4, 9, 9, 9, 14, 13, 13, 13, 18]\n",
      "--- : [12, 13, 18]\n",
      "--- : [10, 15, 10, 10, 5, 10, 11, 12, 7]\n",
      "--- : [14, 14, 9, 4, 3, 2, 3, 4, 4, 9, 9, 14, 19, 24, 23, 18]\n",
      "--- : [0, 1, 0, 1, 2, 3, 4, 9, 14, 13, 18]\n",
      "--- : [20, 21]\n",
      "--- : [1, 2, 1, 1, 1, 0, 0, 5, 5, 10, 5, 5, 10, 10, 11, 16]\n",
      "--- : [24, 24, 24, 24, 24, 24, 24, 19, 18]\n",
      "--- : [11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 9, 9, 4, 3, 3, 2, 1, 0, 0, 5, 5, 5, 0, 0, 0, 0, 1, 2, 7]\n",
      "--- : [23, 18]\n",
      "--- : [0, 1, 2, 3, 4, 9, 14, 14, 9, 9, 4, 4, 3, 3, 3, 3, 4, 4, 9, 9, 9, 14, 9, 14, 9, 9, 9, 14, 9, 14, 19, 19, 24, 23, 18]\n",
      "--- : [9, 4, 3, 2, 3, 4, 9, 14, 19, 19, 24, 23, 22, 17]\n",
      "--- : [4, 4, 3, 2, 3, 4, 4, 4, 4, 4, 3, 3, 2, 3, 2, 3, 4, 3, 2, 3, 4, 4, 4, 4, 4, 9, 9, 9, 14, 13, 13, 12, 13, 12, 11, 11, 12, 11, 10, 10, 5, 0, 1, 0, 0, 0, 0, 5, 10, 15, 10, 11, 11, 12, 7]\n",
      "+++ : [24, 19, 24, 19, 14, 9, 4, 3, 2, 3, 4, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [1, 0, 1, 2, 1, 1, 2, 7]\n",
      "+++ : [1, 2, 3, 3, 4, 9, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [13, 14, 9, 4, 3, 2, 1, 1, 2, 1, 2, 2, 2, 3, 4, 9, 9, 14, 14, 13, 14, 19, 24, 19, 18]\n",
      "--- : [1, 2, 1, 0, 5, 10, 11, 10, 5, 5, 5, 10, 10, 11, 10, 11, 10, 10, 15, 10, 11, 10, 11, 11, 12, 7]\n",
      "--- : [24, 19, 14, 9, 14, 9, 4, 3, 2, 3, 4, 9, 9, 14, 14, 13, 14, 13, 12, 13, 12, 7]\n",
      "--- : [10, 15, 10, 5, 0, 1, 2, 3, 3, 2, 3, 4, 3, 2, 3, 3, 4, 9, 9, 14, 13, 12, 13, 12, 7]\n",
      "--- : [11, 16]\n",
      "--- : [3, 2, 3, 4, 9, 14, 13, 13, 13, 14, 19, 14, 14, 13, 18]\n",
      "--- : [5, 0, 1, 2, 3, 3, 3, 2, 3, 3, 4, 4, 3, 3, 4, 4, 3, 3, 4, 9, 14, 19, 24, 23, 24, 19, 19, 24, 23, 23, 18]\n",
      "+++ : [10, 10, 10, 10, 5, 10, 11, 12, 13, 14, 9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [0, 5, 10, 15, 16]\n",
      "--- : [19, 14, 9, 4, 9, 4, 3, 3, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 1, 0, 1, 2, 1, 2, 3, 2, 3, 4, 4, 4, 3, 4, 3, 4, 3, 2, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 2, 1, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 4, 9, 9, 14, 13, 14, 19]\n",
      "+++ : [9, 4, 3, 2, 3, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 3, 4, 9, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 3, 4, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 3, 4, 9, 14, 19, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "Train wins: 13 / 50\n",
      "+++ : [12, 11, 12, 13, 14, 9, 4, 3, 3, 2, 3, 4, 9, 14, 19, 24, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
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      "+++ : [10, 11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [11, 10, 11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 23, 23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 11, 10, 11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 20, 20, 15, 10, 11, 11, 11, 10, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 10, 10, 5, 5, 5, 5, 10, 5, 0, 0, 1, 2, 2, 2, 3, 3, 4, 4, 4, 9, 4, 4, 4, 9, 9, 9, 9, 14, 14, 19, 19, 14, 19, 19, 14, 13, 13, 14, 19, 19, 18]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 16]\n",
      "+++ : [13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 11, 12, 13, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 16]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [23, 24, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [10, 11, 12, 13, 14, 9, 4, 3, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 13, 12, 11, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 21]\n",
      "Test wins: 93 / 100\n",
      "--- : [12, 13, 18]\n",
      "--- : [1, 2, 1, 0, 1, 2, 3, 3, 3, 3, 4, 9, 9, 4, 9, 4, 9, 14, 9, 9, 9, 9, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 3, 2, 3, 2, 1, 2, 2, 1, 2, 1, 2, 7]\n",
      "--- : [19, 18]\n",
      "--- : [11, 12, 7]\n",
      "--- : [20, 15, 10, 11, 16]\n",
      "--- : [9, 9, 9, 9, 4, 3, 2, 3, 4, 4, 9, 14, 19, 24, 24, 24, 24, 23, 24, 24, 24, 19, 14, 13, 12, 7]\n",
      "--- : [13, 12, 7]\n",
      "--- : [9, 14, 9, 9, 9, 4, 3, 3, 2, 3, 4, 9, 4, 3, 4, 4, 4, 4, 4, 4, 3, 3, 2, 2, 7]\n",
      "--- : [10, 11, 16]\n",
      "--- : [0, 1, 2, 1, 1, 1, 2, 3, 3, 2, 3, 4, 9, 4, 9, 14, 14, 19, 19, 24, 24, 23, 18]\n",
      "--- : [13, 12, 13, 14, 19, 24, 24, 23, 24, 23, 23, 24, 24, 24, 24, 19, 14, 13, 14, 13, 12, 13, 13, 14, 14, 9, 4, 3, 2, 3, 3, 4, 9, 14, 19, 19, 19, 14, 14, 19, 18]\n",
      "--- : [14, 9, 9, 4, 3, 2, 3, 3, 4, 4, 4, 9, 14, 19, 18]\n",
      "--- : [12, 17]\n",
      "--- : [19, 24, 23, 24, 24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 17]\n",
      "--- : [3, 4, 4, 4, 4, 3, 2, 1, 2, 2, 3, 4, 9, 14, 19, 24, 24, 23, 24, 23, 23, 18]\n",
      "--- : [1, 2, 7]\n",
      "--- : [9, 4, 3, 2, 1, 0, 0, 0, 0, 0, 5, 0, 5, 10, 5, 10, 5, 10, 11, 16]\n",
      "--- : [13, 14, 14, 9, 4, 3, 2, 1, 2, 2, 3, 4, 4, 4, 4, 9, 14, 13, 18]\n",
      "--- : [20, 20, 15, 16]\n",
      "--- : [12, 13, 12, 7]\n",
      "--- : [13, 14, 9, 14, 14, 9, 4, 4, 3, 2, 3, 4, 9, 14, 13, 13, 13, 12, 17]\n",
      "--- : [12, 11, 10, 11, 10, 15, 16]\n",
      "--- : [19, 19, 14, 9, 4, 4, 3, 2, 1, 0, 5, 10, 10, 11, 10, 5, 0, 0, 5, 5, 5, 5, 10, 11, 12, 13, 14, 9, 9, 14, 9, 9, 4, 9, 14, 9, 4, 9, 9, 9, 9, 14, 14, 9, 9, 4, 3, 2, 7]\n",
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      "--- : [12, 11, 10, 11, 10, 5, 0, 1, 2, 3, 4, 9, 9, 9, 9, 9, 9, 9, 14, 9, 4, 3, 2, 2, 7]\n",
      "--- : [14, 9, 4, 3, 2, 1, 2, 3, 4, 9, 14, 19, 19, 19, 18]\n",
      "+++ : [14, 13, 12, 11, 11, 10, 10, 5, 0, 0, 1, 2, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
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      "--- : [20, 20, 15, 15, 10, 11, 10, 5, 0, 0, 0, 5, 0, 5, 5, 10, 11, 16]\n",
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      "+++ : [1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [1, 2, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [5, 0, 1, 2, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "--- : [20, 15, 15, 16]\n",
      "+++ : [24, 19, 14, 9, 4, 3, 2, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "+++ : [4, 3, 2, 2, 3, 4, 9, 14, 19, 24, 23, 22, 23, 24, 19, 14, 9, 4, 3, 2, 1, 0, 5, 10, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15]\n",
      "Test wins: 95 / 100\n"
     ]
    }
   ],
   "source": [
    "search_depth = 50\n",
    "MCTS_samples = 100\n",
    "\n",
    "num_training_epochs =  50\n",
    "num_test_epochs = 100\n",
    "training = True\n",
    "epochs = 20\n",
    "T = [25, 50, 75, 100]\n",
    "K = 1\n",
    "\n",
    "success_rates = []\n",
    "succes_std = []\n",
    "win_hist = []\n",
    "train_history = []\n",
    "\n",
    "for i in T:\n",
    "    # TRAIN ##############################\n",
    "    train_wins = 0\n",
    "    # num_training_epochs = int(200 - 1.9*i)\n",
    "    model = build_model(csrl.ch_states[(0,0,0,0)].shape, csrl.shape[-1])\n",
    "    N, W, Q, P, visited = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), set()\n",
    "    for epoch in range(num_training_epochs):\n",
    "        t1 = time.time()\n",
    "        state_history, channeled_states, trajectory, action_history, reward_history, better_policy = csrl.MC_learning(model, LTL_formula, predicates,\n",
    "                N = N, W = W, Q = Q, P = P, C=3, tow=1, n_samples=MCTS_samples, visited=set(), start=None, search_depth=search_depth,\n",
    "                verbose=0, T=i, K=K)\n",
    "        # print('N',N[(0,0,0,0)])\n",
    "        t2 = time.time()\n",
    "        # print(t2-t1, \" run episode\")\n",
    "\n",
    "        win = check_LTL(LTL_formula, trajectory, predicates)[0]\n",
    "        if win: train_wins+=1\n",
    "\n",
    "        t3= time.time()\n",
    "        if training and len(action_history)>0:\n",
    "            if epoch==0:\n",
    "                x_train = np.array(channeled_states)[:-1]\n",
    "                y1_train = np.array(better_policy)\n",
    "                y2_train = np.array(reward_history) + (check_LTL(LTL_formula, trajectory, predicates)[0]*max_rew)\n",
    "                y2_train = y2_train[:-1]\n",
    "            else:\n",
    "                x_train = np.concatenate((x_train, np.array(channeled_states)[:-1]),0)\n",
    "                y1_train = np.concatenate((y1_train, np.array(better_policy)),0)\n",
    "                y2_train_curr = np.array(reward_history) + (check_LTL(LTL_formula, trajectory, predicates)[0]*max_rew)\n",
    "                y2_train = np.concatenate((y2_train, y2_train_curr[:-1]),0)\n",
    "            tr_hist = model.fit(x_train, [y1_train, y2_train], epochs=epochs, verbose=0)\n",
    "            train_history += tr_hist.history['loss']\n",
    "        win_hist.append(win)\n",
    "        t4 = time.time()\n",
    "        # print(t4-t3, \" fit\")\n",
    "    print(\"Train wins:\",train_wins,\"/\", num_training_epochs)\n",
    "\n",
    "    # TEST ##############################\n",
    "    test_wins = 0\n",
    "    N, W, Q, P, visited = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), set()\n",
    "    for epoch in range(num_test_epochs):\n",
    "        \n",
    "        state_history, channeled_states, trajectory, action_history, reward_history, better_policy = csrl.MC_learning(model, LTL_formula, predicates,\n",
    "                N = N, W = W, Q = Q, P = P, C=3, tow=1, n_samples=100, visited=set(), start=None, search_depth=search_depth, verbose=0, T=i, K=1)\n",
    "\n",
    "        win = check_LTL(LTL_formula, trajectory, predicates)[0]\n",
    "        if win: test_wins+=1\n",
    "        win_hist.append(win)\n",
    "\n",
    "    success_rates.append(100*test_wins/num_test_epochs)\n",
    "    temp = np.zeros(num_test_epochs)\n",
    "    temp[:test_wins]=1\n",
    "    std = np.sqrt(num_test_epochs*np.var(temp))\n",
    "    succes_std.append((success_rates[-1]-std, success_rates[-1]+std))\n",
    "    ###############################################################\n",
    "    print(\"Test wins:\",test_wins,\"/\",num_test_epochs)\n",
    "    # print(\"last reward:\", reward_history[-1], \"  | trajectory:\", trajectory)\n",
    "    # print(\"Actions:\", action_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d50_s100_t50'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'd{search_depth}_s{MCTS_samples}_t{num_training_epochs}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(f'models/ind/p={p}/d{search_depth}_s{MCTS_samples}_t{num_training_epochs}', overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27a2216c310>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(win_hist) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[56.0, 92.0, 93.0, 95.0]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "succes_std = np.array(succes_std)\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.fill_between(T, succes_std[:,0], succes_std[:,1], alpha=0.3, label=\"MCTS area of 1 STD\")\n",
    "\n",
    "plt.plot(T, success_rates)\n",
    "plt.xlabel(\"length of trajectory (T)\")\n",
    "plt.ylabel(f\"succes rate (out of {num_test_epochs} runs)\")\n",
    "plt.title(\"MCTS performance results on the custom gridworld, P=\"+str(p))\n",
    "success_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'training loss')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval = 400\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.plot(train_history)\n",
    "plt.title(\"training loss\")\n",
    "# plt.xticks(range(1,num_training_epochs*epochs,interval), range(0,num_training_epochs,interval//epochs));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = [4, 8, 16, 32, 64, 128, 256]\n",
    "success_rates = [0, 100, 95, 97, 92, 95, 88]\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(T, success_rates)\n",
    "plt.xlabel(\"length of trajectory\")\n",
    "plt.ylabel(\"succes rate (out of 100 runs)\")\n",
    "# plt.title(\"MCTS (search depth=\"+str(search_depth)+\"), P=\"+str(p))\n",
    "plt.title(\"Success rate in the 4x4 grid world\")\n",
    "plt.savefig(\"4x4.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OO7ZBEIrrLSIiokxMLKqEWmPRssaqm/m0bW3Ct7/6KXz3B2/iP/7sN/j9z+6CZWuTXiFW1KXEFE7ELuNk7AreT0xpx/datuCgdQ+ePLAH9+5o0Y5va27ENx/djW8+uhsAkJicwchiojE8lsAbVydwJXULV167hb9+7SoAwLK1CY72tsVeDQsevKfV8L0+RER6Y2JRJdRP3uvpsQCgDYe88+ENPP+zd/Fnf/iwHuFVxM3pOfzk1x/gRPQyfjWa0I5vbWrA7332Hhy07oGj3QJTATUplq1N+OpDd+OrD90NIL18euz9JIZHEzg7lsC591NITM7g1Jsf4dSbH2mvY72vDY8tJhqP7BWxeVPpiR8RUT1gYlElCtkyvRCNDSY8942HcHjwH/FXZ9/Hf/fYvfjMbrMeIW6IhQUF/yBfx4noZfzNGx/i1uKiX4IAfKFzB1y2PfjaQ3cXPXNmpa3Njfjt/Xfgt/ffAQCYnpvHG1fGcXY0ieGxdK/Gjdtz+MWFa/jFhWsAgKYGEz67x4zHOixwdFhgu68NrZs3re8HJiIyGCYWVULbJ2QdQyGqz0k78MQju/Dq+av40x++ieDTv1X1tQOj1yZxInoZr5y7givL1uLo2LkVLtse/MGB3WUttmxubIDtPgts91nwDDoxv6Dg3Q9vLNVpjCXwyY1pjLyXxMh7SeBv4zAJwAN3t+KxjnSPhqPdgju2c+YJEdU3JhZVQt3ZdL2fxFXf+foDiLz1EUbeS+IHr13FHxzYrct59TR+axY/fv0DnIhdRvS9pHZ8++ZGPPHILhy07oH1XrEiSVGDScCDu1rx4K5W/A9faIeiKHjv+pSWZAyPJfDe9Sm89cEE3vpgAv/XfxsDAEg7t6ant3ZY8LkOC/a0ban6pI6ISE9MLKqEtgFZEatu5nOPeQv++Cv78P1T7+LPfvI2nA/ehW3rHGbRw/yCgl9c+AQnYlfwszc/xPTcAgDAJABf/tQdOGjdg+4H76q6WgZBENC+cyvad27FIcdeAMBHE7e1YtCzowm8+9ENyNcmIV+bxMsjlwAAd7duhmOxR+Oxdgv237mtoJoQIqJaVfkrDQEobsv0Qv2PX+rAy8OX8H5iCn9x5iK8v/OAbucu1oWPbiAUu4y/PncFH01Ma8f337lNG+q4q3VzxeIrxV2tm/HEI7vwxCO7AADjU7MYeS/do3F2NIFfXx7HhxO38er5q3j1fHrmidiyCfb70mtpONot+MxuMzZx5gkRGQgTiyqhFW/qUGOh2rypAd/9/QfR85cjeOkXMg7Z96Jj51bdzr+W1NQMfnj+Kk5EL+P85XHtuNiyCd98ZBcO2vbg4d1mwwwVmFs2oevTd6Hr03cBSC/gde5SUuvViL2XQmpqFpG3P0Lk7fTMky2bGmC9T0xPcW234MC9bdii43uAiGijMbGoEje1dSz0/ZU4P30n/smn7sDf/eYT/LsfvYX//K8cup5/pdn5Bfz8N58gFL2Mobc/xsx8eqijwSTg8fvvgMu2B48/cCeaG41/8dzS1IAvdO7EFzp3Aki3zRtXxheHTtKzT8ZvzeKXF6/jlxevAwA2NQj4zG6zNsXVfp8F5hbOPCGi2sHEokroXWOhEgQB/+sTD+Jr/+nnOP3Oxzj9zkf4ygN36foaAPD2BxMIRS/jB69dwbWbM9rxT9/TCpdtD7756C7srPO9OjY1mHDg3jYcuLcNni+np9Ze+PimNnQyPJrAhxO3ce79FM69n0Lg5zIEAbj/ru3arJPHOiw1N2RERPWFiUWV0GNJ71w679iGP/pSBwZ/LuPfvvoWvrhvpy49BtdvTuMHr11FKHoZb30woR3fsbUJf3BgNw5a9+DBXa3rfh2jMpkE3H/3dtx/93b8y8/fB0VRcDl5S1uGfHgsAfnaJN758Abe+fAG/vIf3gMA3LejRRs6cXRY0L6jxTDDSURU+5hYVIn1bkK2lj/5yj68cu4Kxq5P4T///Rie+aedJZ1nZm4Bp9/5GKHoZfztux9jbiG989emBgHOT9+Fg9Y9+Cf338GCxBIIgoC9lhbstbTgoG0PAOCTG9ParJPhsQTe+mAC712fwnvXpxCKXgYA3LG9WRs6cbRbcP/d24veIZeISC9MLKrETW0di/LUHmzfvAnP/s4D+J+D5/Hnpy/gSWvhszAURcEbVyYQil7CD89fRXJqVrvvs3vMcNn24InP7kKbQfYlqSZ3bG/G1x++B19/+B4AwMTtWUTfW1yKfDSB1y+P45Mb0/jxrz/Aj3/9AYD0OiDqdvGPdbTh4d0imhqZ6BHRxmBiUSXUTcjKMRSi+sMDu/H//Oo9nHs/haN/8w7+0+FH8z7+44nb+OvXriAUvYzffHRTO37n9mb8oXU3XNY92H/X9rLFS6u1bt6Ex++/E4/ffycA4PbsPM5fSmkLd8XeS+LG7bnFepqPAQDNjSYcuFfUhk6s97aV9X1GRPWN/7tUiXLWWKhMJgHPfeMhfPMvfolXzl3BP//cvbC3WzIec3t2HpG3P0Ioehk//80nWBzpQFOjCV976G4ctO7Gl/bt5K6fVWLzpgZ8TtqBz0k7AABz8wt464MJbehkeCyJxOQM/lFO4B/l9EZuDSYBn9nVqhWDOtot7G0iIt0IiqIolQ5io0xMTMBsNmN8fBytrdVTVKgoCjq/8xMsKMDZ73ThzjJX/T974nX8f8OX8NCuVvzwj78EkwCcu5TCiehlvHr+KiZuz2mPtd3XhoPWPfi9z94D8xZOe6w1iqIg/slNbXrr2dFExl4sqv13bsvY82RXGfdlIaLaVOg1lIlFFbg9O48HvvtTAMAbz32t7EtvX7s5jcef/1vcuD2H3//sPXjrgwnIn0xq9+8yb8aT1j140rob0h3byhoLbbwrqVsYHk3gV4u9Ghc/vrnqMXvatmhDJ491WCDt3MqZJ0R1rtBrKIdCqoC6ARkAtGzAHhk7tzXjW92fwnOvvoUfvZ4u+NuyqQG/+5m7cdC2B78l7eB+Fga2W9yC3Qd2axvTXb85jeGxpe3i37gyjsvJW7icvIKT564AAHZua1pcijz99el7WjnzhIiyYmJRBdSppi1NDRt2Qf8Xn78PsfdTSExO45uP7sbXH76nKjYpo423Y1szfuczd+N3PnM3gPQMpdh76UTjV6MJvHYphWs3Z/DTNz/ET9/8EACwrbkR1vva8LnFoZPP7jFX3cZxRFQZVXklkWUZkUgEFosFsizD5XJBkiTtvlAoBEmSIMsyPB4PRFGsbMDrdFPnLdMLsanBhD//Zwc27PWodmxrbsSXP3UHvvypOwAA03Pz+PXlcW3oJDqWxI3pOfz8N5/g57/5BEC6uPfRPSIci5ur2e5rw/bNrMkhqkdVmViEQiH09fVpt3t7exEIBAAAbrcb0WgUQDrJ6OnpQTAYrEicetE2INN5OW8iPTQ3NsDebtFmEM0vKHjnw4llW8Ynce3mdHpp8rEEgDhMAvCgOvNksVaj3pd0J6oXVZlYvPzyyxmJhUqW5YzbkiQhEolsVFhlMzld3lU3ifTUYBLw0C4zHtplxr/+YgcURcHotUktyTg7dh2XErfwxpUJvHFlAv/ll2MAAOmOrdrQiaPdgj1tW1gQSmRAVXkls1gssNlsCAaDkGUZ3d3dAKANj6x8bCwWg9VqrUSoumCPBdUyQRAg3bEN0h3bcNhxLwDgw/Hbi5urXcfwaBLvfnQD8ieTkD+ZxF+dvQQAuMe8GY91WPCpu7Zjy6YGbGlqQEtTAzZvatBuZ/y7+H1zo4kJCVEVq8rEIhgMoqurC52dnfB4PNowSCqVyvr4RCKR9fj09DSmp6e12xMTE1kfV2nl2jKdqFLuNm/GNx7ZhW88sgsAkJycwch7S2tpvHFlHB+M38YPXrta9LkFAVqisTlbQqImIysSkuX/Ln9exm0mL0TrVpVXskgkAr/fD1mW0dvbCwBacpFNroSjv78fzz33XDlC1BV7LMjo2rY2ofvBu9D94F0A0u/5c++nlyL/YPwWbs0u4NbMPG7Nzi3+u4Dbs/OYmknfvj27gJn5BQCAoqRnUqmzqcolX6+J+q+azLQ0rb69Odvzlt3evInJCxlT1SUWsixjeHgYfr8fAOB0OmGz2eD1eiGK4qreiUQikXNWiM/nw7e+9S3t9sTEBPbu3Vu22EvFGguqNy1Njfjivp344r6dBT9nbn4Bt+cWMDUzh9szC7g1O49bi8nH7dl53Fp27NbMnHZbS1AWk5fb2vMWv59RnzOvJS8AtHOVU2YvigktTY3p75sasGWTafH+xsV/s9/evKlBe96WJlPG7eZGE9ekoQ1XdVeyWCwGh8Oh3ZYkCT6fD6lUCk6nM2vPhd1uz3qu5uZmNDdXfyW6ukAWN4Yiyq2xwYRtDaayrreiJi9qAjK1LOlYnpDcmp3H7ZkcCcqKZCXjPLPzmJnb2ORl82KC0tLUmP5e63FpXJa8qAlKnqGiHHUwmxs3bv0dqg1VdyWzWq0IBAJwuVzasevXr2ctzpRlGXa7vebXsVA3ICvXlulEVJiNSF7mFxQt2bi9LBFZeftWlmRleZJyO9vzFo9NL0tebs8u4PbsApJTs2X7mdTkRe1tWTMhyTY0tKzGZaknhslLLaq6xEKSJHR3d2NgYEBLGNQ6CyBd2On1euFwODA8PFzza1gAG7NlOhFVhwaTgK3NjWX9e1eTl1xJyu2ZLAnKip6ZXMlN3uQF5UtemhtNWoHt5jUKc1uWHcvoiVn2vGx1MExe9MFNyKrAH//XGH70+gf40ycexL/6YkelwyEiWtPCgoLbc4sJysrel2VDRVpCsjyxmV16XsbtFc+7PbuwdiA6UpOXtQp1lyckeadIZ0l6anmPnQ3ZhGxsbEybkSFJUlVdrGuJWmPRwh4LIqoRJpOAlqbGsk6TV5OXVb0nWRKSXENFGXUwWZOcpeRlem4B03MLSJWx56Wp0aQlJMVMmc42VJRtyvSWKkheinpHTExMIBAIIBAIYHR0FKIooq2tDUC63qGzsxMulws+n49JRhEmZzgrhIhopY1KXqYXZxstJSQLmTOOFo+tvK3NQJpdnCI9M4+p2aUp0suTINXM3AJm5hYwfqv8ycuje0X833/0WNleJ5eCf1snTpyA1+uF2+1GOBxGR8fqLvtz584hEonAZrPB7/fjySef1DVYo+I6FkRElWEyCVrvQLmoyUu26dBLyUfmFGm1MHdlHcyqIaY8yYt6bdloBSUW586dQzgcxsWLF/M+7sCBAzhw4ACOHDmCZ599FpIk4dFHH9UjTkNj8SYRkXFtRPKiKGrPy1KyUakhkYKuZAcOHMCxY8eKOvHRo0dLCqgeLW2bzh4LIiIqniAI2LxYa1FpplKe9Pzzz2NsbAwAcPr0aezbtw/79+/HmTNn9IytbkyxxoKIiAyipMQiEAigvb0dAOB2u/Hss8/i1KlT8Hg8esZWFxRF0RbI4lAIERHVupKuZOrSF0NDQ0gmk3jqqacApFfIpOLcmp2HupIIizeJiKjWlZRYqPt3BINBrZdidHQUkiTpGlw9UDcgEwRgcyMTCyIiqm0lDYUEg0FIkgSv16sVdaZSKfh8Pl2DqwfqdKAWLidLREQGUFKPhdlsRk9PT8YxdaopFWeSU02JiMhASuqxOHnyJPbt24eGhgbty2QyoaGBXfnFYuEmEREZSUlXs56eHgwODsLpdOodT92Z5BoWRERkICUlFjabDQcPHtQ7lrrENSyIiMhISrqaud1uHD58GIcPH4YkSRBFUbtPXd+CCqP2WHCqKRERGUFJiYXf70cikUA4HM44LggC17IoErdMJyIiIynparbWZmRUuKUt09ljQUREta+kWSGknynOCiEiIgMpKbFQp5au/Nq/f7/e8Rmeto4FizeJiMgASrqaJZPJjNuKomBwcBAmEztAirVUY8GhECIiqn0lZQJmsznjSxRF9PX1rSrmpLVxuikRERlJSVezsbGxVcdkWYYsy+uNp+5w5U0iIjKSkq5mVqsVgiBo26cDwI4dO7SdTqlw2joWnBVCREQGUFJikUgk9I6jbqnFm1zHgoiIjKCkGovnn39e7zjqljbdlD0WRERkACUlFmfPnsUrr7yidyx1SVsgiz0WRERkACVdzXw+Hw4dOgRZltHV1cW9QtZhqcaCiQUREdW+krdNVxQFL7zwAl544QXtuCAIuHDhgm7BGd3CgqJNN+U6FkREZAQlJRYjIyN6x1GXbs3Oa99v41AIEREZAJfKrCB1GMQkAM2N/FUQEVHt49WsgiaXrbopCEKFoyEiIlo/JhYVxH1CiIjIaJhYVNAUp5oSEZHBlHRFm5iYgCzLSCQSsFgskCQJra2tesdmeJxqSkRERlP0Fe2rX/0qotEoOjo6AKQ3HxsfH4fT6UQwGGSCUQR1A7IWrrpJREQGUdRQyOHDh+F2u3H9+nWMjIxgZGQEiUQC8/PzOHjwIHp6esoVpyFNTXMohIiIjKWoxCIWi+VMHjweD2KxmC5B1QtumU5EREZTVGLR0dGBM2fOZL3v5MmT2vAIFYZbphMRkdEU9VE5EAigu7sbgiBAkiSIoohUKoV4PA5BEBAOh8sVpyGp61i0sHiTiIgMoqgrWkdHBy5evIhz586tmhVy4MCBcsVoWFNqjwXXsSAiIoMo6aNyZ2cnBEFAW1ubllhQ8W6yeJOIiAym5OmmkiRBURRON12HqRnWWBARkbGUPN10eHiY003XiTUWRERkNJxuWkFLNRZMLIiIyBg43bSCbrJ4k4iIDIbTTStoikMhRERkMFU73TQSiUCWZW3GidPpBJDemyQUCkGSJMiyDI/HA1EUdX3tjaIVb7LHgoiIDKKkj8oHDhwo67oVkUgEwWAQgUAAsiyju7sb8XgcAOB2uxGNRgGkk4yenh4Eg8GyxVJOk+p0U/ZYEBGRQRRVY7GWw4cP63Ke3t5e+P1+AIAkSdoQiyzLGY+TJAmRSESX19xo8wsKbs1yHQsiIjKWoq5oExMTee/XY1aIOsQiiiJisRgkSdKGQyKRCCwWS8bjLRYLYrEYrFbrul97I6nDIAC3TSciIuMoKrFwuVwYGhoCACiKoh0XBAGKokAQhHUHFIvFYLFYEAqF4HQ6MTg4CEmS4HK5kEqlsj4nkUhkPT49PY3p6Wnt9lqJ0UZSCzcbTAKaG3XtOCIiIqqYoq5oR48excGDBzE/P4+FhQXtS72tR91FIpGALMtwOp0QRREejwdutzvvc3IlHP39/TCbzdrX3r171x2fXm4u29lUj4SMiIioGhSVWFit1lVDEcupMzfWQ53Gqs70UP+NxWIQRXFV74Q6bJKNz+fD+Pi49nXp0qV1x6eXKe4TQkREBlR0H/yxY8dy3nf06NF1BQMg74ZmuRIXu92e9XhzczNaW1szvqrF5GKNBesriIjISKru47IkSbDb7UilUhBFUVvLIltxpizLsNvtNbmOxdIaFlX3KyAiIipZVV7VgsEgvF4vbDYbotFoxoqe6n0OhwPDw8M1u4bFTa5hQUREBlSVVzVRFBEIBLLeJ0mStsaFy+XayLB0NcV9QoiIyIA4z7FCuGU6EREZUcGJxde+9rVyxlF3uGU6EREZUcGJhbpXBwA4HI6yBFNPbs4srWNBRERkFAV/XLZarfD5fOjs7EQ8HsdLL72U9XFPPfWUbsEZmbqORQt7LIiIyEAKvqodP34c3//+9zEyMgIA2r/LCYLAxKJAk+yxICIiAyrq4/KRI0cApGdt6LEYVj2bZI0FEREZUElXNTWpOH36tLaA1Ve+8hVdAzM6dRMyTjclIiIjKSmxGB8fh81mQ1tbGzo6OiDLMsbHxxEOh9He3q5ziMak9lhwuikRERlJSVe1Q4cOIRAIoKurSzsWCoXQ29uLU6dO6RackWk9FkwsiIjIQEpaICsej2ckFUB6FcxsBZ2U3U2uvElERAZUUmIhSRLOnz+fcey1115DR0eHLkHVg6UaC/ZYEBGRcZR0VTt27Bjsdju6u7shSRLi8TiGhoYwNDSkd3yGtVRjwR4LIiIyjpJ7LBKJBJxOJxRFQXd3N2RZxqOPPqpzeMY0N7+A6bkFAKyxICIiY1nXVa2np0evOOqKugEZwKEQIiIyFu5uWgFTi6tubmoQ0NTIXwERERkHr2oVMDnNLdOJiMiYmFhUgFq4uY3DIEREZDBMLCpA3YCMM0KIiMhoSkosTp8+verY+Pg4Tp48ue6A6gG3TCciIqMqKbHwer2rjpnNZvT39687oHrALdOJiMioivrI/NJLLwEAZFnWvlddvHgR0WhUv8gMTC3e5FRTIiIymqKubBcvXgQAKIqyal8QURSZWBRoij0WRERkUEUlFkePHgUApFIpHDt2rCwB1YNJ1lgQEZFBlVRjwaRifdhjQURERlVSYmEymdDQ0LDqa//+/XrHZ0hLW6azx4KIiIylpCtbMpnMuK0oCgYHB2EycVmMQmhbpnPlTSIiMpiSMgGz2ZzxJYoi+vr6EA6H9Y7PkLQt05s5FEJERMZS0kfmsbGxVcdkWYYsy+uNpy6wx4KIiIyqpCub1WqFIAhQFEU7tmPHDng8Ht0CMzLWWBARkVGVdGVLJBJ6x1FXOCuEiIiMal0fmU+fPg1ZltHZ2YnHH39cr5gMj+tYEBGRUZV0ZRsfH4fNZkNbWxs6OjogyzLGx8cRDofR3t6uc4jGo+4Vso3Fm0REZDAlJRaHDh1CIBBAV1eXdiwUCqG3txenTp3SLTij0nY3ZfEmEREZTEnTTePxeEZSAQAul2vV/iG02szcAmbmFwBwVggRERlPSYmFJEk4f/58xrHXXnsNHR0dugRlZLcWp5oCXMeCiIiMp6SPzMeOHYPdbkd3dzckSUI8HsfQ0BCGhob0js9wbi7WVzQ1mrCpgSuVEhGRsZTcY5FIJOB0OqEoCrq7uyHLMh599FGdwzOeqWlONSUiIuMqqMdifHwcgiCgtbU143hPT0/e542NjXGWyAqTMyzcJCIi4yq4x8LlcuHMmTMFPXZoaAgOhwMWi6XkwIxK67FgfQURERlQQR+bzWYzjh8/jkOHDsHj8cDlcsHhcECSJIiiCFmWkUqlMDw8jGAwiM7OTkQikVU9HMTlvImIyNgKvrqJooif/exnGB0dRSgUwrFjxyDLMhKJBCRJgsVigdVqRTgc5uyQPLgBGRERGVnRV7eOjg4cOXIER44cKUc8hqeuutnC4k0iIjIgznfcYJMcCiEiIgNjYrHB1A3IWLxJRERGxMRigy1tmc4eCyIiMh4mFhuM61gQEZGRlZxYnD59Gk8//TR8Ph+A9NoVp0+f1i0wldfrRSqV0m7LsoyBgQGEQiEMDAxk3FcLJrmOBRERGVhJiYXP50MgEEBvby8ikQgAwG63w+v16hpcLBbDwMBAxjG3242+vj64XC64XK41V/+sNks1FuyxICIi4ynp6hYKhXDhwgUAgKIoANKLaOndeyDLMiRJyri9nCRJWmJTK6Y43ZSIiAyspB4Ls9msbZsuCAIA4Ny5czCbzboFFgqF4HK5Mo5FIpFVy4RbLBbEYjHdXrfcJrlAFhERGVhJV7fjx4/DbrfD4XAgmUzimWeeQSQSQTAY1CWoVCoFURSzHs8mkUhkPT49PY3p6Wnt9sTEhB7hrQvXsSAiIiNb17bpHo8Hvb29sFqtGBkZ0W3b9OPHj8PpdBb8+FwJR39/P8xms/a1d+9eXeJbD25CRkRERlZSYqHO/jh48CCOHDmiFVCePHly3QFFIhEcOnQo632iKK7qnUgkEll7N4B0ken4+Lj2denSpXXHt16cbkpEREZWUmKRbfaH2WxGf3//ugMC0j0Wg4ODGBwchCzL6O/vRywWy9mLYbfbsx5vbm5Ga2trxlclKYrC6aZERGRoRX1sfumllwCkZ2eo36suXryIaDS67oBWJg+9vb3o7e3NmB2ikmUZdrs9Z49FtZmZX8DcQnoWDWssiIjIiIq6ul28eBFA+pP3yMhIxn2iKOqSWKhSqRQGBwcBAH6/X6vlCAaD8Hq9cDgcGB4e1q1gdCNMLa5hAQAtm9hjQURExiMo6kIURXj66adx7NixcsRTVhMTEzCbzRgfH6/IsMjl5BS+5D+D5kYT3v33v7vhr09ERFSqQq+hJdVY1GJSUQ3UVTe3cRiEiIgMquQr3OnTpxGLxXD9+nXt2I4dO/Dtb39bl8CMaFJddZOFm0REZFAl9Vg8++yzcLlcuHjxIoLBIJLJJMLhMIaHh/WOz1DUGguuuklEREZV0hXuxIkT2noSTz/9NAYGBtDa2orDhw/rGpzRTHKfECIiMriSeiyW13t2dnZiaGgIAGpuQ7CNxuW8iYjI6EpKLFwul7bKpsfjwZEjR+BwONDR0aFrcEbDDciIiMjoSrrCHT16VPvebDYjHA5DlmV0dXXpFpgRqfuEsHiTiIiMqqQei+effz7jdkdHB5OKAmhDIeyxICIigyopsTh79ixeeeUVvWMxPG0ohDUWRERkUCVd4Xw+Hw4dOqQNfyzfq6O9vV2n0IxnakbtseBQCBERGVNJiUVPTw8URcELL7yAF154QTsuCAIuXLigW3BGo6682cIeCyIiMqiSrnArNyCjwizVWLDHgoiIjKmkGgsqjbpAFmssiIjIqJhYbKAprXiTPRZERGRMTCw2kDoU0sLppkREZFBMLDYQt00nIiKjY2KxgbgJGRERGR0Tiw2iKMqyGgv2WBARkTGVvKT32NgYAOD06dPYt28f9u/fjzNnzugZm6FMzy1gfiG9Kyx7LIiIyKhKSiwCgYC2wqbb7cazzz6LU6dOwePx6BmboaiFmwCLN4mIyLhKusIpSvqT99DQEJLJJJ566ikAwPXr1/WLzGDUYZAtmxrQYBIqHA0REVF5lJRYSJIEn8+HYDCo9VKMjo5CkiRdgzOSpcWxOAxCRETGVdJQSDAYhCRJePbZZ3Hs2DEAQCqVgs/n0zU4I+EaFkREVA9KSizMZjMkScLIyAi+853vAAASiQTa2tp0Dc5I1DUsOCOEiIiMrKTEwufzYXBwEL29vQiHwwAAu90Or9era3BGwi3TiYioHpT08TkUCmnbo6uFnGazGalUSrfAjIZbphMRUT0oeSjk/PnzAABBSM9wOHfuHMxms36RGcwkeyyIiKgOlPTx+fjx47Db7XA4HEgmk3jmmWcQiUQQDAb1js8wWGNBRET1oOTppolEAidOnIAsyxBFEUePHmWPRR6ssSAionpQUmJx8uRJSJKEgwcPasdOnDgBQRDw5JNP6hackdxUp5uyx4KIiAyspBoLr9e7ajGsrq4uzgrJY4pbphMRUR0oKbG4fv06WltbM46JosglvfPglulERFQPSkos7HY7XnnllYxjp0+f5pLeeWhbpnPlTSIiMrCSrnLHjh2D3W5HX18fnE4n4vE4otEootGo3vEZxlKNBXssiIjIuHSZFeJ0OjMKOWk1bVYIayyIiMjAOCtkg6jFmxwKISIiI+OskA3C4k0iIqoHnBWyQbjyJhER1QPOCtkAiqIs7RXC4k0iIjIwzgrZALdnF7C4CSxrLIiIyNDWNSskFAphdHSUs0LWoE41BYAtm9hjQURExrWuj88ul0uvOAxtalnhpskkVDgaIiKi8impxsJkMqGhoWHV1/79+/WOzxBYuElERPWipCtdMpnMuK0oCgYHB2EylZSnGB63TCcionpRUiZgNpszvkRRRF9fH8LhsN7xGYK2nDcLN4mIyOBKutKNjY2tOibLMmRZXm88hqRuQMYt04mIyOhKutJZrVYIggBFnUMJwGKxoLe3V7fAjGSSG5AREVGdKCmxSCQSeseRIRaLIRKJAACGh4fx4osvQhRFAOmekVAoBEmSIMsyPB6Pdl+14pbpRERUL0qqsXj++ee14ZDTp09j//792L9/P86cOaNLUJFIBH19fejr64PD4UBXV5d2n9vtRl9fH1wuF1wuF3p6enR5zXJaqrFgjwURERlbSYlFIBBAe3s7gPSF3uv14tSpU/B4POsOKBaLob+/X7vtcrkQi8Wy1nBIkqT1bFQzbplORET1oqQrnVpbMTQ0hGQyiaeeegoAdNmEzGq14sUXX9Rup1IpAOkajuPHj8NisWQ83mKxIBaLwWq1rjrX9PQ0pqentdsTExPrjq8US+tYsMeCiIiMraQeC0mS4PP50Nvbq/VSjI6O6rYJ2fIVPV9++WU4nU6IoqglGSvlqvno7+/PmBa7d+9eXeIr1iSnmxIRUZ0oKbEIBoOQJAlerxfHjh0DkO5Z8Pl8ugaXSqUQCoUQDAbXfFw2Pp8P4+Pj2telS5d0ja9QS8Wb7LEgIiJjK+kjtNlsXlU0eeDAARw4cECXoFRerxfhcFib9SGK4qreiUQikXNWSHNzM5qbm3WNqRSTrLEgIqI6UbVrcA8MDMDr9UKSJKRSKaRSKTidzqyPtdvtGxxdcaa4VwgREdWJqkwsQqEQrFarllQcP34coiiuquGQZRl2u73q17HgdFMiIqoXVfcRWpZluN3ujGOiKGpFosFgEF6vFw6HA8PDw2vWX1QDTjclIqJ6UXVXOkmSMpYKz3a/3+8HkDl7pJpNcuVNIiKqE1U5FGI0U9NqjwWHQoiIyNiYWJTZwoKi9VhwHQsiIjI6JhZldmt2Xvue26YTEZHRMbEoM3UNC0EANm9icxMRkbHxSldm2j4hTY0QBKHC0RAREZUXE4sym+QaFkREVEeYWJSZuk8I6yuIiKgeMLEoM7XGooVTTYmIqA4wsSgzbplORET1hIlFmWkbkLHGgoiI6gATizLjlulERFRPmFiU2RT3CSEiojrCxKLMtC3TWbxJRER1gIlFmWkbkLHHgoiI6gATizLTtkxnjQUREdUBJhZlNskt04mIqI4wsSgzbplORET1hIlFmak1FtvYY0FERHWAiUWZsceCiIjqCROLMmONBRER1RMmFmU2NcO9QoiIqH4wsSizyWlum05ERPWDiUUZzS8ouDWr1lhwKISIiIyPiUUZqcMgABfIIiKi+sDEoozUDchMAtDcyKYmIiLj49WujJZmhDRCEIQKR0NERFR+TCzKSC3c5AZkRERUL5hYlNHkDLdMJyKi+sLEoozU4k1ONSUionrBxKKM1KEQTjUlIqJ6wcSijLTiTdZYEBFRnWBiUUbaBmQcCiEiojrBxKKMuGU6ERHVGyYWZcQt04mIqN4wsSijpRoL9lgQEVF9YGJRRkvrWLDHgoiI6gMTizKaUlfeZGJBRER1golFGak9FhwKISKiesHEoozUGgsWbxIRUb1gYlFG6rbpWzndlIiI6gQTizLShkJYY0FERHWCiUUZcdt0IiKqN0wsymipxoJDIUREVB+YWJTJ3PwCpucWAHDbdCIiqh9MLMpkanZe+76FxZtERFQnmFiUiToM0mgS0NTAZiYiovpQc330siwjFApBkiTIsgyPxwNRFCsd1ipq4WZLUwMEQahwNERERBuj5hILt9uNaDQKIJ1k9PT0IBgMVjiq1aZm1C3Ta66JiYiISlZTffSyLGfcliQJkUikQtHkp/VYMLEgIqI6UlNXvUgkAovFknHMYrEgFovBarWuevz09DSmp6e12xMTE7rGc3t2Hl84ejrrfbOLM0K4TwgREdWTmkosUqlU1uOJRCLr8f7+fjz33HNljAhITM7kvf/hPeayvj4REVE1qanEIpdcCYfP58O3vvUt7fbExAT27t2r2+s2NZgQ/jdfznl/g0lAx86tur0eERFRtaupxEIUxVW9E4lEIueskObmZjQ3N5ctHpNJwP67tpft/ERERLWmpoo3nU5n1uN2u32DIyEiIqJsaiqxkCQp47Ysy7Db7VW5jgUREVE9qqmhEAAIBoPwer1wOBwYHh6uyjUsiIiI6pWgKIpS6SA2ysTEBMxmM8bHx9Ha2lrpcIiIiGpGodfQmhoKISIiourGxIKIiIh0w8SCiIiIdMPEgoiIiHTDxIKIiIh0w8SCiIiIdFNz61ishzqzVu9dTomIiIxOvXautUpFXSUWN27cAABdNyIjIiKqJzdu3IDZnHvn7rpaIGthYQFXr17F9u3bIQhCwc9Td0W9dOkSF9bSAdtTX2xPfbE99cX21Fcl21NRFNy4cQO7du2CyZS7kqKueixMJhP27NlT8vNbW1v5h6Ejtqe+2J76Ynvqi+2pr0q1Z76eChWLN4mIiEg3TCyIiIhIN0wsCtDc3Izvfe97aG5urnQohsD21BfbU19sT32xPfVVC+1ZV8WbREREVF7ssSAiIiLdMLEgIiIi3TCxICIiIt3U1ToWpZBlGaFQCJIkQZZleDweiKJY6bBqRiwWAwBYrVbIsoxUKgWr1QqAbVuoWCyGnp4eRKPRjOP52o9tm1uu9uR7tXixWAyRSAQAMDw8jBdffLGg9yDbM7t87VlT70+F8rJardr38XhccblcFYym9ng8HgWAAkBxOp1KMpnU7mPbri0YDCrRaFTJ9qear/3Yttnla0++V4vn9/szvl/eTnx/Fi9fe9bS+5OJRR7xeDzjF6YoiiKKYoWiqU2BQEBJJpMZfwSKwrYt1soLYb72Y9uuLVtiwfdqcaLRaEY7xONxBYASj8f5/ixBvvZUlNp6f7LGIo9IJAKLxZJxzGKxaF1SVBhRFFd1y7Ft1ydf+7FtS8f3auGsVitefPFF7XYqlQKQbhu+P4uXrz1VtfL+ZI1FHuovdqVEIrGxgdSwVCqFUCgEID1m2NvbC0mS2LbrlK/92Lal4Xu1eC6XS/v+5ZdfhtPphCiKfH+WKFd7ArX1/mRiUYJcv0habXkRkSRJ6O7uRjwez/l4tu365Gs/tm1+fK+WTr3orSyIzfa4Uu6rN9nas5benxwKyUMUxVVZXyKRYPVyEWRZ1r5XK5ZlWWbbrlO+9mPblobv1dJ5vV6Ew2GtTfj+XJ+V7QnU1vuTiUUeTqcz63G73b7BkdSmWCyGrq6uVcctFgvbdp3ytR/btnh8r5ZuYGAAXq9X65ZPpVJ8f65DtvastfcnE4s8JEnKuC3LMux2OzPrAkmSBL/fr92ORCJwuVwQRZFtW4LlXZv52o9tW5iV7cn3avFCoRCsVqt2ETx+/Piabcb2zC1fe9bS+5ObkK1BlmUEAgE4HA4MDw/D5/PxD6AIahW4KIqIx+MZfxxs27VFIhGEw2EMDAygr68PDodDK/DK135s2+zytSffq8WRZRmdnZ0Zx0RRRDKZ1O7n+7Nwa7VnLb0/mVgQERGRbjgUQkRERLphYkFERES6YWJBREREumFiQURERLphYkFERES6YWJBREREumFiQUSGI8sy954gqhAmFkQ1pq2tLWPfgFp7Pbfbjc7OTnR3d+t2zpW8Xi/6+/vLdn4iyo27mxKRxuv1orOzEx6PpyznHxgYgCRJCAaDZY0h3/kLUe52IDIy9lgQ0YaJx+Orli0mIoNRiKimiKKoxONxRVEUJR6PK1arVZEkSenr69MeI0mSEggEFKfTqYiiqASDQe2+cDisSJKkWK1WxePxKE6nU+nr61M8Ho8iiqIiiqJitVqVcDisvZ7f71esVuuqc2UTj8cVp9OpSJKkOJ1OJZlMKoqiKH19fRnnzyZXDFarVYlGo4rL5VL8fr92PkmSVv3siqIofr8/41iudlJjFUVRkSRJCQaDOWPI9XOtjM3j8WgxKoqiJJNJRRTFvG1GZCRMLIhqzPLEQhRFJRqNKoqiKC6XKyMZcLlciqIoSjAYVCRJynh+MpnULnjqBVJR0hf2QCCw6vVynStXfGpMahKT7/wrZXuMmkAsT2qWfy+KovazK8rqxCJfO6nnSSaT2mNytUO2n2tlbNFoNONnDgQCisfjyfszExkJh0KIalQoFILdbofVagUA+Hy+jNqCw4cPAwCcTqdWfJlKpWCxWCCKIkRRhN1uL6gwM9u5shkcHITT6dRicjqdEEURkUiktB9ymd7eXm0nUgAZ3x86dAixWCzr83K1UygUgsVi0c4jiqL2mGJ/ruWxqY9R4wkEAujt7S355yaqNSzeJKpRsixDlmXYbDbtmNPp1L6XJGnVc9StlFOpFERRhCzLWR+3UiGPAdI1FCsfK0mSLrNKlv9sQPpnGBwcRDwe17aTziZXO8myvOqcuaz1c608T29vLwKBAPx+P1KpVM6EhciImFgQ1ShRFOF0OhEIBHLen+t4V1cXUqkU/H5/zscVcq6VOjs7EQ6HM44VmrwUE4Msy+ju7obf74fH44HX6837vGztFAqFVsWay1o/18r28Xg8sNlsOH78eEbPClE94FAIUY06dOgQIpGI1uWeSqUKGnKwWCyIRqOIx+OrLnqiKCIej2vnW29MoVAIqVSq4J6BQmOQZRmiKMLlckEURYyMjBQck9pOLpcLIyMjWpulUimEQqGsMRT7c6nDKhwGoXrExIKoRomiiGAwiJ6eHnR2dqKrq6ug58myDEEQIAgC2traMj7tHz58GAMDA7DZbCXVRYiiiKGhIfT09KCtrQ2BQKDgXoFiYlAv6G1tbXC73ZAkCTt27MgZU652ikaj8Hq9aGtryxgqWRlDKT+XmlDo0VtDVEsERVGUSgdBRBtjYGAA8XhcGxZIpVLo6uqCz+czVJe9miz5/f6KxRAKhSDLMvr6+ioWA1ElsMeCqI6JogiLxVLpMHSjFlPKslzxhbgCgQBX7qS6xMSCqI709fVBFEV0dnbCZrNpe3YYpbfC7/dre5tU6qI+ODgIm82G7u7ugoteiYyEQyFERESkG/ZYEBERkW6YWBAREZFumFgQERGRbphYEBERkW6YWBAREZFumFgQERGRbphYEBERkW6YWBAREZFu/n8LTHGtoAGtZAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = [4, 8, 16, 32, 64, 128, 256]\n",
    "success_rates = [0, 0, 98, 82, 89, 80, 77]\n",
    "\n",
    "succes_std = np.array(succes_std)\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(T, success_rates)\n",
    "plt.xlabel(\"length of trajectory\")\n",
    "plt.ylabel(\"succes rate (out of 100 runs)\")\n",
    "# plt.title(\"MCTS (search depth=\"+str(search_depth)+\"), P=\"+str(p))\n",
    "plt.title(\"Success rate in the 5x5 grid world\")\n",
    "plt.savefig(\"5x5.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "718678c3ccb447058d07396585e3b549",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='t'), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_agent(t)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_agent(t):\n",
    "    csrl.mdp.plot(agent=state_history[t][2:], save=f'test/{t}.png')\n",
    "t=IntSlider(value=0,min=0,max=100)\n",
    "interact(plot_agent,t=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(filepath=f'custom_gw_p{p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(filepath=f'custom_gw_p{p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('ffmpeg -r 3 -i ./test/%d.png -vcodec libx264 -y ./test/test.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from NN import *\n",
    "from LTL import *\n",
    "\n",
    "from csrl.mdp import GridMDP\n",
    "from csrl.oa import OmegaAutomaton\n",
    "from csrl import ControlSynthesis\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ltl = (\"G !d & F a\")\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa = OmegaAutomaton(ltl)\n",
    "print('Number of Omega-automaton states (including the trap state):',oa.shape[1])\n",
    "\n",
    "# MDP Description\n",
    "shape = (2,2)\n",
    "# E: Empty, T: Trap, B: Obstacle\n",
    "structure = np.array([\n",
    "['E',  'E'],\n",
    "['E',  'B'],\n",
    "])\n",
    "\n",
    "# Labels of the states\n",
    "label = np.array([\n",
    "[(),    ('d',)],\n",
    "[('a',),    ()]\n",
    "],dtype=object)\n",
    "# Colors of the labels\n",
    "lcmap={\n",
    "    ('a',):'greenyellow',\n",
    "    ('b',):'greenyellow',\n",
    "    ('c',):'turquoise',\n",
    "    ('d',):'pink'\n",
    "}\n",
    "p = 1\n",
    "grid_mdp = GridMDP(shape=shape,structure=structure,label=label,lcmap=lcmap, p=p, figsize=5)  # Use figsize=4 for smaller figures\n",
    "grid_mdp.plot()\n",
    "\n",
    "# Construct the product MDP\n",
    "csrl = ControlSynthesis(grid_mdp,oa)\n",
    "max_rew = round(csrl.reward.max(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial state: 0\n",
      "Transition function: [\n",
      "  {(): 0, ('a',): 1, ('d',): 2, ('a', 'd'): 2},\n",
      "  {(): 1, ('a',): 1, ('d',): 2, ('a', 'd'): 2},\n",
      "  {(): 2, ('a',): 2, ('d',): 2, ('a', 'd'): 2}\n",
      "]\n",
      "Acceptance: [\n",
      "  {(): [None], ('a',): [None], ('d',): [None], ('a', 'd'): [None]},\n",
      "  {(): [True], ('a',): [True], ('d',): [None], ('a', 'd'): [None]},\n",
      "  {(): [None], ('a',): [None], ('d',): [None], ('a', 'd'): [None]}\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ba = OmegaAutomaton(ltl,'ldba')\n",
    "print('Initial state:',ba.q0)\n",
    "print('Transition function: ['),print(*['  '+str(t) for t in ba.delta],sep=',\\n'),print(']')\n",
    "print('Acceptance: ['),print(*['  '+str(t) for t in ba.acc],sep=',\\n'),print(']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([([(0, 0, 0, 1), (0, 0, 0, 0)], [0.0, 1.0]),\n",
       "       ([(0, 0, 1, 0), (0, 0, 0, 1)], [1, 0.0]),\n",
       "       ([(0, 0, 1, 0), (0, 0, 0, 1)], [0.0, 1]),\n",
       "       ([(0, 0, 1, 0), (0, 0, 0, 0)], [0.0, 1.0]), None, None, None],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csrl.transition_probs[0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ba = OmegaAutomaton(ltl,'ldba')\n",
    "print('Initial state:',ba.q0)\n",
    "print('Transition function: ['),print(*['  '+str(t) for t in ba.delta],sep=',\\n'),print(']')\n",
    "print('Acceptance: ['),print(*['  '+str(t) for t in ba.acc],sep=',\\n'),print(']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 6\n",
      "{'a': [2], 'b': [22], 'c': [15], 'd': [7, 16, 17, 18, 21]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### from the Nursary case ###\n",
    "# LTL Specification\n",
    "# ltl = ('G ('\n",
    "#     '(!d) & '\n",
    "#     '((b & (!(X b)))->(X ((!b) U (a|c)))) & '\n",
    "#     '(((!b) & (X b) & (!(X X b)))->((!a) U c)) & '\n",
    "#     '(a->(X ((!a) U b))) & '\n",
    "#     '(c->((!a) U b)) & '\n",
    "#     '((b & (X b))->(F a))'\n",
    "# ')')\n",
    "\n",
    "ltl = (\"(G !d) & ((!c) U b) & ((!b) U a) & (F G c)\")\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa = OmegaAutomaton(ltl)\n",
    "print('Number of Omega-automaton states (including the trap state):',oa.shape[1])\n",
    "\n",
    "# MDP Description\n",
    "shape = (5,5)\n",
    "# E: Empty, T: Trap, B: Obstacle\n",
    "structure = np.array([\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'B',  'E',  'B',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E']\n",
    "])\n",
    "\n",
    "# Labels of the states\n",
    "label = np.array([\n",
    "[(),    (),    ('a',),    (),    ()],\n",
    "[(),    (),    ('d',),(),    ()],\n",
    "[(),    (),    (),    (),    ()],\n",
    "[('c',),('d',),    ('d',),('d',),()],\n",
    "[(),    ('d',),    ('b',),(),    ()]\n",
    "],dtype=object)\n",
    "# Colors of the labels \n",
    "lcmap={\n",
    "    ('a',):'yellow',\n",
    "    ('b',):'greenyellow',\n",
    "    ('c',):'turquoise',\n",
    "    ('d',):'pink'\n",
    "}\n",
    "\n",
    "p=1\n",
    "grid_mdp = GridMDP(shape=shape,structure=structure,label=label,lcmap=lcmap, p=p, figsize=5)  # Use figsize=4 for smaller figures\n",
    "grid_mdp.plot(save=\"env.pdf\")\n",
    "\n",
    "# Construct the product MDP\n",
    "csrl = ControlSynthesis(grid_mdp,oa)\n",
    "max_rew = round(csrl.reward.max(), 3)\n",
    "\n",
    "t = \"[] ~d /\\ (~c % b) /\\ (~b % a) /\\ (<> [] c)\"\n",
    "\n",
    "LTL_formula = parser.parse(t)\n",
    "predicates=grid_mdp.get_predicates()\n",
    "print(predicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q=csrl.q_learning(T=100,K=1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy=np.argmax(Q,axis=4)\n",
    "value=np.max(Q,axis=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1, (1, 6, 5, 5, 10))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q.max(), (csrl.reward>0).sum(), csrl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<csrl.mdp.GridMDP at 0x1d6f2915630>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csrl.mdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "769dfd22b618409b866695919a443bc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='t', max=99), Output()), _dom_classes=('widget-interact',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "episode=csrl.simulate(policy, LTL_formula, predicates, start=(0,0),T=100)\n",
    "episode[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running 100 simulations with 10 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 11 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 12 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 13 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 14 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 15 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 16 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 17 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 18 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 19 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 20 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 21 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 22 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 23 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 24 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 25 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 26 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 27 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 28 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 29 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 30 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 31 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 32 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 33 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 34 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 35 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 36 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 37 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 38 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 39 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 40 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 41 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 42 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 43 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 44 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 45 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 46 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 47 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 48 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 49 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 50 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 51 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 52 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 53 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 54 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 55 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 56 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 57 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 58 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 59 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 60 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 61 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 62 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 63 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 64 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 65 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 66 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 67 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 68 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 69 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 70 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 71 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 72 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 73 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 74 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 75 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 76 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 77 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 78 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 79 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 80 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 81 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 82 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 83 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 84 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 85 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 86 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 87 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 88 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 89 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 90 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 91 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 92 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 93 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 94 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 95 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 96 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 97 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 98 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 99 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n",
      "Running 100 simulations with 100 time-steps...\n",
      "Test finished with:\n",
      "\tsuccess rate: 100 / 100 = 1.0\n"
     ]
    }
   ],
   "source": [
    "rewards_mean = []\n",
    "rewards_std = []\n",
    "n_runs = 100\n",
    "\n",
    "x = list(range(10,100))+[100]\n",
    "for i in x:\n",
    "    episodes, rew = csrl.run_Q_test(policy, LTL_formula, predicates, T=i, start=(0,0), runs=n_runs, verbose=0)\n",
    "    rewards_mean.append(100*np.mean(rew))\n",
    "    rewards_std.append((rewards_mean[-1]-np.sqrt(n_runs*np.var(rew)), rewards_mean[-1]+np.sqrt(n_runs*np.var(rew))))\n",
    "rewards_std = np.array(rewards_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'T' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m\"\u001b[39m\u001b[39mComparison on the nusery case, P=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m+\u001b[39m\u001b[39mstr\u001b[39m(p))\n\u001b[0;32m      8\u001b[0m plt\u001b[39m.\u001b[39mylim(\u001b[39m-\u001b[39m\u001b[39m10\u001b[39m, \u001b[39m110\u001b[39m)\n\u001b[1;32m---> 10\u001b[0m plt\u001b[39m.\u001b[39mfill_between(T, succes_std[:,\u001b[39m0\u001b[39m], succes_std[:,\u001b[39m1\u001b[39m], alpha\u001b[39m=\u001b[39m\u001b[39m0.3\u001b[39m)\n\u001b[0;32m     11\u001b[0m plt\u001b[39m.\u001b[39mplot(T, success_rates, label\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mMCTS\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     12\u001b[0m plt\u001b[39m.\u001b[39mlegend()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'T' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "\n",
    "plt.fill_between(x, rewards_std[:,0], rewards_std[:,1], alpha=0.3)\n",
    "plt.plot(x, rewards_mean, label=\"CSRL\")\n",
    "plt.xlabel(\"length of trajectory (T)\")\n",
    "plt.ylabel(\"succes rate\")\n",
    "plt.title(\"Comparison on the nusery case, P=\"+str(p))\n",
    "plt.ylim(-10, 110)\n",
    "\n",
    "plt.fill_between(T, succes_std[:,0], succes_std[:,1], alpha=0.3)\n",
    "plt.plot(T, success_rates, label=\"MCTS\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
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   "source": [
    "csrl.plot(value,policy,iq=(0,1))"
   ]
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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