black_jack_21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NEURAL EVOLUTION FROM SCRATCH\n",
    "\n",
    "This is a neural network evolved by genetic algorithm using only python and numpy library.  \n",
    "\n",
    "This implementation is slow and take some hours to converge for good results.  \n",
    "\n",
    " I used a minimalist version of blackjack (A game known as 21 or vinte-um in Brazil). \n",
    " \n",
    "\n",
    " The neural network use as inputs itself pontuation, the player with higher pontuation less than 21 and the cummulative sum of all cads played until that particular turn.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://brasilcasinos.com.br/images/maos-do-blackjack.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short Game explanation:\n",
    "    \n",
    "    - The players start with 2 cards\n",
    "    - The sum of those cards should be exactly 21 in order to win the game.\n",
    "    - Each turn, the players CAN DECIDE to draw a new card to try approximate the current value to 21.  \n",
    "    - Case the sum of all cards in the player's hand is higher than 21,  that player lost the game.\n",
    "    - The Player with score closest to 21 is the winner.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import logistic\n",
    "import pandas as pd\n",
    "import copy\n",
    "from random import shuffle, sample\n",
    "import random\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a simple blackjack game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Game:\n",
    "    def __init__(self):\n",
    "        self.deck =  4* range(1,11)\n",
    "        shuffle(self.deck)\n",
    "        self.game_running = False\n",
    "        self.cumulative_draw = 0 \n",
    "        self.best_player_score = 0\n",
    "    def start_game(self, player_list):\n",
    "        while  len([p for p in player_list if p.burn == False and p.stop == False]) > 1:\n",
    "            for player in [p_t for p_t in player_list if p_t.burn == False and p_t.stop == False]:\n",
    "                #print player.name,player.points\n",
    "                if player.decision(self)[0] == 0:\n",
    "                    player.stop_game()\n",
    "                else:\n",
    "                    self.cumulative_draw += player.draw(self.deck)\n",
    "                if self.best_player_score < player.points:\n",
    "                    self.best_player_score = player.points\n",
    "        #print [[p_e.name, p_e.points] for p_e in player_list]\n",
    "        winner_table =  [p_e for p_e in player_list if p_e.burn == False]\n",
    "        winner_table = sorted(winner_table, key = lambda x : 21 - x.points)\n",
    "        #print [p_e.points for p_e in winner_table] \n",
    "        #print 'Game ended...'\n",
    "        \n",
    "         \n",
    "        if set([p_end.points for p_end in winner_table]) == 1 or not winner_table:\n",
    "            #print 'ko'\n",
    "            return 'ko'\n",
    "        else:\n",
    "            #print 'ganhador',   winner_table[0].name \n",
    "            return winner_table[0].name\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Player class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Player:\n",
    "    def __init__(self, name):\n",
    "        self.stop = False\n",
    "        self.burn = False\n",
    "        self.points = 0\n",
    "        self.name = name\n",
    "        self.neural_network = None\n",
    "    def decision(self, game_obj):\n",
    "        \n",
    "        out = sample([0,1], 1) # the output shoud be [0] or [1]\n",
    "        #print out \n",
    "        return out  # random behavior\n",
    "    \n",
    "    def stop_game(self):\n",
    "        #print self.name, 'Stop'\n",
    "        self.stop = True\n",
    "    def draw(self, deck_game):\n",
    "        \n",
    "        card = deck_game.pop()\n",
    "        #print self.name, 'comprou card', card\n",
    "        self.points += card\n",
    "        if self.points > 21:\n",
    "            self.burn = True\n",
    "            #print self.name, 'BURNEDD!'\n",
    "        return card\n",
    "            \n",
    "    \n",
    "    \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gerating simple Players usig Player class inheritance and  overwriting the decision method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class First_strategy(Player):\n",
    "    def decision(self, game_obj):\n",
    "        if self.points < 15:\n",
    "            return [1]\n",
    "        else:\n",
    "            return [0]\n",
    "    \n",
    "class Second_strategy(Player):\n",
    "    def decision(self, game_obj):\n",
    "        return [1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating a simple championship with 3 different players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "['Miura', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Miura', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Miura', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Miura', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Sergio', 'Lucas', 'Lucas', 'Miura', 'Lucas', 'Sergio', 'Sergio', 'Lucas', 'Lucas', 'Miura', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Miura', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Lucas', 'Lucas', 'Miura', 'Lucas', 'Lucas', 'Lucas', 'Sergio', 'Lucas', 'Sergio', 'Lucas']\n"
     ]
    }
   ],
   "source": [
    "matchs = []\n",
    "\n",
    "for x in range(100):\n",
    "    jogo = Game()\n",
    "    lucas = First_strategy('Lucas')\n",
    "    miura = Player('Miura')\n",
    "    sergio = Second_strategy('Sergio')\n",
    "    \n",
    "    matchs.append(jogo.start_game([lucas, miura, sergio]))\n",
    "\n",
    "print len([m for m in matchs if m == 'Miura'])\n",
    "print matchs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Neural network class implementation with forward propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network:\n",
    "    def __init__(self, input_number = 2):\n",
    "        self.l_1 = np.random.uniform(-1,1, [10,input_number])\n",
    "\n",
    "        self.l_2 = np.random.uniform(-1,1,[10,10])\n",
    "        self.l_end = np.random.uniform(-1,1,[1,10])\n",
    "        self.layers_stack = [self.l_1 , self.l_2 , self.l_end]\n",
    "    \n",
    "    \n",
    "    def sigmoid (self, x): return 1/(1 + np.exp(-x))\n",
    "    \n",
    "    def foward(self, input_value):\n",
    "        #print input_value\n",
    "        res_l1 =  np.array([self.sigmoid(l1_s) for l1_s in      input_value.dot(self.l_1.T)])\n",
    "        res_l2 = np.array([ self.sigmoid(l2_s)  for l2_s in     res_l1.dot(self.l_2.T)])\n",
    "        res_end = np.array([self.sigmoid (l_r) for l_r in       res_l2.dot(self.l_end.T)])\n",
    "        #print res_end\n",
    "        return res_end\n",
    "    \n",
    "    def mutate(self, layer):\n",
    "        index_to_mutate = [np.random.randint(layer.shape[0]), np.random.randint(layer.shape[1])]\n",
    "        #print 'mutate',  np.random.uniform(-1, 1)\n",
    "        layer[index_to_mutate[0], index_to_mutate[1]] += np.random.uniform(-0.01, 0.01)\n",
    "        \n",
    "        \n",
    "    def mutate_all_layers(self):\n",
    "        for l in self.layers_stack:\n",
    "            self.mutate(l)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Neural Network Player\n",
    "\n",
    "The decision method returns 0 (stop),1 (draw a card)\n",
    "The inputs  are: \n",
    "\n",
    "- Agent score.\n",
    "- Highest opponent pontuation.\n",
    "- Sum of all cards played until that moment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Neural_strategy(Player):\n",
    "    def decision(self, game_obj):\n",
    "        best_rank_player = game_obj.best_player_score\n",
    "        #print best_rank_player\n",
    "        #print  [self.neural_network.foward(np.array([ self.points, best_rank_player ,game_obj.cumulative_draw]))]\n",
    "\n",
    "        return [int(round(self.neural_network.foward(np.array([ self.points, best_rank_player ,game_obj.cumulative_draw]))))]\n",
    "\n",
    "    def init_neural_activity(self):\n",
    "        self.neural_network = Network(input_number = 3)\n",
    "    def reset_status(self):\n",
    "        self.stop = False\n",
    "        self.burn = False\n",
    "        self.points = 0\n",
    "        \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training/Evolving the neural networks\n",
    "\n",
    "using the follow parameters. \n",
    "-  population = 500  \n",
    "-  iterations = 100  \n",
    "- matchs per championship = 300  \n",
    "- The two best AI neural networks are choosed to ploriferate and mutate.  \n",
    "- Fitness fuction is given as the mean of championship won games. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 12.49\n",
      "Iteration: 0 Best value: 61.6666666667\n"
     ]
    },
    {
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      "Iteration MEAN: 29.53333333333333\n",
      "Iteration: 1 Best value: 61.6666666667\n"
     ]
    },
    {
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      "Iteration MEAN: 47.336666666666666\n",
      "Iteration: 2 Best value: 66.6666666667\n"
     ]
    },
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      "Iteration MEAN: 49.33333333333334\n",
      "Iteration: 3 Best value: 66.6666666667\n"
     ]
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      "Iteration MEAN: 49.36000000000001\n",
      "Iteration: 4 Best value: 68.3333333333\n"
     ]
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      "Iteration MEAN: 49.170000000000016\n",
      "Iteration: 5 Best value: 68.3333333333\n"
     ]
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      "Iteration MEAN: 49.83666666666667\n",
      "Iteration: 6 Best value: 75.0\n"
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      "Iteration MEAN: 49.87333333333334\n",
      "Iteration: 7 Best value: 66.6666666667\n"
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      "Iteration MEAN: 49.64000000000001\n",
      "Iteration: 8 Best value: 70.0\n"
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      "Iteration MEAN: 49.83333333333334\n",
      "Iteration: 9 Best value: 73.3333333333\n"
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      "Iteration: 10 Best value: 70.0\n"
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      "Iteration MEAN: 49.58666666666667\n",
      "Iteration: 11 Best value: 66.6666666667\n"
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      "Iteration: 12 Best value: 70.0\n"
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      "Iteration: 13 Best value: 66.6666666667\n"
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      "Iteration: 14 Best value: 70.0\n"
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      "Iteration: 15 Best value: 71.6666666667\n"
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      "Iteration MEAN: 49.82000000000001\n",
      "Iteration: 16 Best value: 73.3333333333\n"
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      "Iteration: 17 Best value: 70.0\n"
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      "Iteration: 18 Best value: 73.3333333333\n"
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      "Iteration: 19 Best value: 70.0\n"
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      "Iteration: 20 Best value: 65.0\n"
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      "Iteration: 21 Best value: 65.0\n"
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      "Iteration: 22 Best value: 68.3333333333\n"
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    },
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     "text": [
      "Iteration MEAN: 49.59333333333334\n",
      "Iteration: 23 Best value: 68.3333333333\n"
     ]
    },
    {
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     "text": [
      "Iteration MEAN: 49.65333333333334\n",
      "Iteration: 24 Best value: 70.0\n"
     ]
    },
    {
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     "text": [
      "Iteration MEAN: 49.180000000000014\n",
      "Iteration: 25 Best value: 66.6666666667\n"
     ]
    },
    {
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     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00,  8.56it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.780000000000015\n",
      "Iteration: 26 Best value: 71.6666666667\n"
     ]
    },
    {
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     "output_type": "stream",
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      "100%|██████████| 500/500 [00:53<00:00,  9.82it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.360000000000014\n",
      "Iteration: 27 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:52<00:00,  9.78it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.193333333333335\n",
      "Iteration: 28 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00, 10.52it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.46000000000001\n",
      "Iteration: 29 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:50<00:00,  8.84it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 48.966666666666676\n",
      "Iteration: 30 Best value: 65.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00,  9.93it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.27666666666667\n",
      "Iteration: 31 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00, 10.33it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.1\n",
      "Iteration: 32 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.72it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.26666666666667\n",
      "Iteration: 33 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00,  9.49it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.673333333333346\n",
      "Iteration: 34 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:46<00:00, 10.69it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.15333333333334\n",
      "Iteration: 35 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:46<00:00, 10.86it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.89\n",
      "Iteration: 36 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.27it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.31\n",
      "Iteration: 37 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:46<00:00, 10.86it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.81\n",
      "Iteration: 38 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:46<00:00, 10.45it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.05666666666667\n",
      "Iteration: 39 Best value: 65.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.70it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.74333333333333\n",
      "Iteration: 40 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.59it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.39333333333334\n",
      "Iteration: 41 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.61it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.10333333333334\n",
      "Iteration: 42 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:46<00:00, 10.81it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.34\n",
      "Iteration: 43 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:46<00:00, 10.67it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.720000000000006\n",
      "Iteration: 44 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.62it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.64\n",
      "Iteration: 45 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.11it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.68\n",
      "Iteration: 46 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00, 10.10it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.39666666666667\n",
      "Iteration: 47 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 49.863333333333344\n",
      "Iteration: 48 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00,  9.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.73\n",
      "Iteration: 49 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.37it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.01666666666667\n",
      "Iteration: 50 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.883333333333326\n",
      "Iteration: 51 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.92333333333334\n",
      "Iteration: 52 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:46<00:00, 10.65it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.13666666666667\n",
      "Iteration: 53 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.43it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.24\n",
      "Iteration: 54 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.75it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.09666666666667\n",
      "Iteration: 55 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.41it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.77333333333333\n",
      "Iteration: 56 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.69333333333333\n",
      "Iteration: 57 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:51<00:00,  9.21it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.15\n",
      "Iteration: 58 Best value: 65.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:53<00:00,  9.28it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.943333333333335\n",
      "Iteration: 59 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00,  9.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.773333333333326\n",
      "Iteration: 60 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.72it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.83666666666667\n",
      "Iteration: 61 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.63it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.22\n",
      "Iteration: 62 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:46<00:00, 10.60it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.843333333333334\n",
      "Iteration: 63 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00, 10.36it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.08333333333333\n",
      "Iteration: 64 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:50<00:00,  9.88it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.26\n",
      "Iteration: 65 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00, 10.32it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.206666666666656\n",
      "Iteration: 66 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:52<00:00,  9.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.06666666666666\n",
      "Iteration: 67 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.08it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.06333333333333\n",
      "Iteration: 68 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:52<00:00,  7.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.683333333333344\n",
      "Iteration: 69 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:52<00:00,  9.60it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.33666666666666\n",
      "Iteration: 70 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
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      "100%|██████████| 500/500 [00:47<00:00, 10.61it/s]\n",
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    },
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     "text": [
      "Iteration MEAN: 50.71333333333334\n",
      "Iteration: 71 Best value: 68.3333333333\n"
     ]
    },
    {
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.14999999999999\n",
      "Iteration: 72 Best value: 68.3333333333\n"
     ]
    },
    {
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     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.59it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.58666666666667\n",
      "Iteration: 73 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.76it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.87\n",
      "Iteration: 74 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.52it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.796666666666674\n",
      "Iteration: 75 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.63it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.71333333333334\n",
      "Iteration: 76 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.60it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.06666666666667\n",
      "Iteration: 77 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.41it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.09\n",
      "Iteration: 78 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:50<00:00, 10.60it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.703333333333326\n",
      "Iteration: 79 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.06it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.193333333333335\n",
      "Iteration: 80 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00,  9.56it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.81\n",
      "Iteration: 81 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.68it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.67000000000001\n",
      "Iteration: 82 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.97666666666666\n",
      "Iteration: 83 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00,  9.95it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.13\n",
      "Iteration: 84 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.51it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.87\n",
      "Iteration: 85 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00,  9.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.526666666666664\n",
      "Iteration: 86 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:51<00:00,  9.43it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.55666666666667\n",
      "Iteration: 87 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.04it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.81666666666667\n",
      "Iteration: 88 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.56it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.186666666666675\n",
      "Iteration: 89 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.61it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.02\n",
      "Iteration: 90 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.59it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.32666666666666\n",
      "Iteration: 91 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00, 10.51it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.33666666666666\n",
      "Iteration: 92 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.48it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.25\n",
      "Iteration: 93 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.43it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.48333333333333\n",
      "Iteration: 94 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.44it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.426666666666655\n",
      "Iteration: 95 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.54it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.23\n",
      "Iteration: 96 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.80it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.95666666666667\n",
      "Iteration: 97 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.31it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.44\n",
      "Iteration: 98 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.67it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.10333333333334\n",
      "Iteration: 99 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.47it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 50.86333333333334\n",
      "Iteration: 100 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00, 10.27it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.42999999999999\n",
      "Iteration: 101 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.36it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.53333333333333\n",
      "Iteration: 102 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.51it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.383333333333326\n",
      "Iteration: 103 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00, 10.42it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.53999999999999\n",
      "Iteration: 104 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:49<00:00, 10.14it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.849999999999994\n",
      "Iteration: 105 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.49it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.986666666666665\n",
      "Iteration: 106 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.50it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.84666666666666\n",
      "Iteration: 107 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.64it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.48333333333333\n",
      "Iteration: 108 Best value: 66.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:47<00:00, 10.79it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.32333333333333\n",
      "Iteration: 109 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:47<00:00, 10.49it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.69333333333333\n",
      "Iteration: 110 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.40it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.78333333333333\n",
      "Iteration: 111 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:48<00:00, 10.04it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.96666666666666\n",
      "Iteration: 112 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:50<00:00,  8.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.79333333333333\n",
      "Iteration: 113 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:53<00:00,  9.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.55333333333333\n",
      "Iteration: 114 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:53<00:00,  8.66it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.65\n",
      "Iteration: 115 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:52<00:00,  9.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.986666666666665\n",
      "Iteration: 116 Best value: 78.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:52<00:00,  9.60it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.71333333333333\n",
      "Iteration: 117 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:51<00:00,  8.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.23333333333333\n",
      "Iteration: 118 Best value: 71.6666666667\n"
     ]
    },
    {
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      "Iteration MEAN: 52.529999999999994\n",
      "Iteration: 119 Best value: 71.6666666667\n"
     ]
    },
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      "Iteration MEAN: 52.633333333333326\n",
      "Iteration: 120 Best value: 71.6666666667\n"
     ]
    },
    {
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     "text": [
      "Iteration MEAN: 51.94333333333333\n",
      "Iteration: 121 Best value: 71.6666666667\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.5\n",
      "Iteration: 122 Best value: 76.6666666667\n"
     ]
    },
    {
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      "Iteration MEAN: 52.383333333333326\n",
      "Iteration: 123 Best value: 71.6666666667\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.78333333333333\n",
      "Iteration: 124 Best value: 70.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.31999999999999\n",
      "Iteration: 125 Best value: 68.3333333333\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.58333333333333\n",
      "Iteration: 126 Best value: 70.0\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.14999999999999\n",
      "Iteration: 127 Best value: 70.0\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.75666666666667\n",
      "Iteration: 128 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00, 10.31it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.459999999999994\n",
      "Iteration: 129 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:49<00:00, 10.25it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 51.97666666666666\n",
      "Iteration: 130 Best value: 76.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 500/500 [00:48<00:00, 10.69it/s]\n",
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.62666666666667\n",
      "Iteration: 131 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.279999999999994\n",
      "Iteration: 132 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.756666666666675\n",
      "Iteration: 133 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.93333333333333\n",
      "Iteration: 134 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.50333333333333\n",
      "Iteration: 135 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.28666666666667\n",
      "Iteration: 136 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.213333333333345\n",
      "Iteration: 137 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.703333333333326\n",
      "Iteration: 138 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.64666666666666\n",
      "Iteration: 139 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.883333333333326\n",
      "Iteration: 140 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.05333333333333\n",
      "Iteration: 141 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.176666666666655\n",
      "Iteration: 142 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.10666666666667\n",
      "Iteration: 143 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.88\n",
      "Iteration: 144 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.99666666666667\n",
      "Iteration: 145 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.73333333333333\n",
      "Iteration: 146 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.026666666666664\n",
      "Iteration: 147 Best value: 68.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.31666666666667\n",
      "Iteration: 148 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.98\n",
      "Iteration: 149 Best value: 80.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.25000000000001\n",
      "Iteration: 150 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.12\n",
      "Iteration: 151 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.14666666666667\n",
      "Iteration: 152 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.14\n",
      "Iteration: 153 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.38\n",
      "Iteration: 154 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.31\n",
      "Iteration: 155 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 52.696666666666665\n",
      "Iteration: 156 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.086666666666666\n",
      "Iteration: 157 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.10666666666667\n",
      "Iteration: 158 Best value: 76.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.71333333333334\n",
      "Iteration: 159 Best value: 70.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.49\n",
      "Iteration: 160 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.5\n",
      "Iteration: 161 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.580000000000005\n",
      "Iteration: 162 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.27333333333333\n",
      "Iteration: 163 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.02\n",
      "Iteration: 164 Best value: 70.0\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.120000000000005\n",
      "Iteration: 165 Best value: 70.0\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.26\n",
      "Iteration: 166 Best value: 71.6666666667\n"
     ]
    },
    {
     "name": "stderr",
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      "Iteration MEAN: 53.88333333333334\n",
      "Iteration: 167 Best value: 71.6666666667\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.546666666666674\n",
      "Iteration: 168 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.84333333333334\n",
      "Iteration: 169 Best value: 76.6666666667\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.31000000000002\n",
      "Iteration: 170 Best value: 71.6666666667\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.65000000000001\n",
      "Iteration: 171 Best value: 76.6666666667\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.59333333333334\n",
      "Iteration: 172 Best value: 71.6666666667\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.52333333333334\n",
      "Iteration: 173 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.42666666666667\n",
      "Iteration: 174 Best value: 70.0\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.87333333333334\n",
      "Iteration: 175 Best value: 73.3333333333\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 55.08666666666667\n",
      "Iteration: 176 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.863333333333344\n",
      "Iteration: 177 Best value: 75.0\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.59333333333334\n",
      "Iteration: 178 Best value: 73.3333333333\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.796666666666674\n",
      "Iteration: 179 Best value: 73.3333333333\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.666666666666686\n",
      "Iteration: 180 Best value: 73.3333333333\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.98666666666667\n",
      "Iteration: 181 Best value: 75.0\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 55.01333333333334\n",
      "Iteration: 182 Best value: 75.0\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 53.89666666666667\n",
      "Iteration: 183 Best value: 73.3333333333\n"
     ]
    },
    {
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    {
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      "Iteration MEAN: 54.603333333333346\n",
      "Iteration: 184 Best value: 76.6666666667\n"
     ]
    },
    {
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    {
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     "text": [
      "Iteration MEAN: 54.94333333333334\n",
      "Iteration: 185 Best value: 76.6666666667\n"
     ]
    },
    {
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    },
    {
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     "text": [
      "Iteration MEAN: 54.30666666666668\n",
      "Iteration: 186 Best value: 75.0\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.99333333333335\n",
      "Iteration: 187 Best value: 76.6666666667\n"
     ]
    },
    {
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    {
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     "text": [
      "Iteration MEAN: 54.48666666666668\n",
      "Iteration: 188 Best value: 73.3333333333\n"
     ]
    },
    {
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    },
    {
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     "text": [
      "Iteration MEAN: 54.80333333333334\n",
      "Iteration: 189 Best value: 75.0\n"
     ]
    },
    {
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    {
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     "text": [
      "Iteration MEAN: 54.94666666666667\n",
      "Iteration: 190 Best value: 75.0\n"
     ]
    },
    {
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    },
    {
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     "text": [
      "Iteration MEAN: 54.323333333333345\n",
      "Iteration: 191 Best value: 78.3333333333\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.7\n",
      "Iteration: 192 Best value: 71.6666666667\n"
     ]
    },
    {
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    {
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      "Iteration MEAN: 54.76666666666667\n",
      "Iteration: 193 Best value: 75.0\n"
     ]
    },
    {
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    {
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      "Iteration MEAN: 54.49666666666668\n",
      "Iteration: 194 Best value: 73.3333333333\n"
     ]
    },
    {
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    {
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     "text": [
      "Iteration MEAN: 55.01333333333334\n",
      "Iteration: 195 Best value: 73.3333333333\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.61000000000001\n",
      "Iteration: 196 Best value: 73.3333333333\n"
     ]
    },
    {
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    {
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     "text": [
      "Iteration MEAN: 55.13\n",
      "Iteration: 197 Best value: 73.3333333333\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 54.94333333333334\n",
      "Iteration: 198 Best value: 76.6666666667\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Iteration MEAN: 55.173333333333346\n",
      "Iteration: 199 Best value: 76.6666666667\n"
     ]
    }
   ],
   "source": [
    "best_and_mean_scores = []\n",
    "p_size= 500\n",
    "population = [Neural_strategy('AI') for n in range(0,p_size)]\n",
    "\n",
    "[p.init_neural_activity() for p in population] #starting network random weights\n",
    "\n",
    "from tqdm import tqdm\n",
    "for i_n in range(200):\n",
    "    fitness = []\n",
    "    for ai in tqdm(population):\n",
    "        matchs = []\n",
    "        f = 0\n",
    "        for x in range(60):\n",
    "            jogo = Game()\n",
    "            lucas = First_strategy('Lucas')\n",
    "            matchs.append(jogo.start_game([ ai, lucas]))\n",
    "            ai.reset_status()\n",
    "        #print matchs\n",
    "        f =  100 * (len([m for m in matchs if m == 'AI'])/float(len(matchs)))\n",
    "        #print f \n",
    "        fitness.append(f)\n",
    "    higher_fitness = sorted( [[f, p_f] for f , p_f in zip(fitness, population)], key=lambda s: s[0])\n",
    "    print 'Iteration MEAN:', np.mean([v[0] for v in higher_fitness])\n",
    "        \n",
    "    loss_str= 'Best value: {}'.format(str(higher_fitness[-1][0]))\n",
    "    best_and_mean_scores.append([higher_fitness[-1][0], np.mean([v[0] for v in higher_fitness])])\n",
    "    itr_str = 'Iteration: {}'.format( str(i_n))\n",
    "    print itr_str, loss_str\n",
    "    #print [ h[0] for h in  higher_fitness[-5:]]\n",
    "    best_creatures = higher_fitness[-10:]\n",
    "    #print [float(b_c) for b_c in best_creatures]\n",
    "    population = [copy.deepcopy(random.choice(best_creatures)[1]) for i in range(0, p_size)]\n",
    "    [p_m.neural_network.mutate_all_layers() for p_m in population]\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Ploting the mean game values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_training = pd.DataFrame(best_and_mean_scores, columns=['best_result', 'mean_result'])\n",
    "\n",
    "\n",
    "plt.plot(df_training['mean_result'], color= 'red', lw=2)\n",
    "plt.title('Mean % of won games by iteraction')\n",
    "plt.xlabel('Iteraction number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_training['best_result'], color= 'blue', lw=2)\n",
    "plt.title('% of won games BEST RESULTS only')\n",
    "plt.xlabel('Iteraction number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "502"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_teste = 0\n",
    "for g in range(1000):\n",
    "    jogo = Game()\n",
    "    lucas = First_strategy('Lucas')\n",
    "    ai = copy.deepcopy(best_creatures[-1][1])\n",
    "\n",
    "    r =  jogo.start_game([lucas, ai])\n",
    "    if r =='AI':\n",
    "        score_teste +=1\n",
    "    ai.reset_status()\n",
    "score_teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_teste = 0\n",
    "for g in range(100):\n",
    "    if   == \"ai\":\n",
    "        score_teste +=1\n",
    "    ai.reset_status()\n",
    "    score_teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lucas\n"
     ]
    }
   ],
   "source": [
    "print jogo.start_game([lucas, ai])\n",
    "ai.reset_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lucas\n"
     ]
    }
   ],
   "source": [
    "print jogo.start_game([lucas, ai])\n",
    "ai.reset_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}