2 Red Neuronal aprendiendo a jugar piedra papel o tijera.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Código de red neuronal aprendiendo a jugar a piedra papel o tijera.\n",
    "\n",
    "- difunde\n",
    "- coopera\n",
    "- aprende\n",
    "- vuelve\n",
    "\n",
    "-> simpre busca la solución mas sencilla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos las tres opciones del juego\n",
    "options = [\"piedra\", \"tijeras\", \"papel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# esta funsión busca quien es el ganador y da como resultado un número aludiendo al player ganador.\n",
    "def search_winner(p1, p2):\n",
    "    if p1 == p2:\n",
    "        result = 0\n",
    "        \n",
    "    elif p1 == \"piedra\" and p2 == \"tijeras\":\n",
    "        result = 1\n",
    "    elif p1 == \"piedra\" and p2 == \"papel\":\n",
    "        result = 2\n",
    "    elif p1 == \"tijeras\" and p2 == \"piedra\":\n",
    "        result = 2\n",
    "    elif p1 == \"tijeras\" and p2 == \"papel\":\n",
    "        result = 1\n",
    "    elif p1 == \"papel\" and p2 == \"piedra\":\n",
    "        result = 1\n",
    "    elif p1 == \"papel\" and p2 == \"tijeras\":\n",
    "        result = 2\n",
    "        \n",
    "    return result    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# es solo un ejemplo que funsiona el buscar al ganador\n",
    "search_winner(\"papel\", \"tijeras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['piedra', 'piedra', 0]\n",
      "Player1 piedra Player2: piedra Winner: 0 Validation 0\n",
      "['piedra', 'tijeras', 1]\n",
      "Player1 piedra Player2: tijeras Winner: 1 Validation 1\n",
      "['piedra', 'papel', 2]\n",
      "Player1 piedra Player2: papel Winner: 2 Validation 2\n"
     ]
    }
   ],
   "source": [
    "# se crea un test para la busqueda dle ganador lo que se hace es saber si al buscar el ganador la funsión entrega el resultado \n",
    "# correcto\n",
    "test = [\n",
    "    [\"piedra\", \"piedra\", 0 ],\n",
    "    [\"piedra\", \"tijeras\", 1],\n",
    "    [\"piedra\", \"papel\", 2]\n",
    "]\n",
    "\n",
    "for partida in test:\n",
    "    print(partida)\n",
    "    print(\"Player1 %s Player2: %s Winner: %s Validation %s\" % (\n",
    "        partida[0], partida[1], search_winner(partida[0], partida[1]), partida[2]\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para elegir de forma aleatoria las tres tipos de opciones, piedra , pepel o tijeras.\n",
    "from random import choice\n",
    "def get_choice():\n",
    "    return choice(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player1 tijeras Player2: tijeras Winner: 0\n",
      "Player1 papel Player2: piedra Winner: 1\n",
      "Player1 papel Player2: piedra Winner: 1\n",
      "Player1 tijeras Player2: papel Winner: 1\n",
      "Player1 papel Player2: tijeras Winner: 2\n",
      "Player1 piedra Player2: tijeras Winner: 1\n",
      "Player1 piedra Player2: papel Winner: 2\n",
      "Player1 papel Player2: piedra Winner: 1\n",
      "Player1 piedra Player2: papel Winner: 2\n",
      "Player1 tijeras Player2: tijeras Winner: 0\n"
     ]
    }
   ],
   "source": [
    "# este ciclo for lo que hace es saber si al selecionar al asar piedra papel o tijera el player1 y el player2\n",
    "# es posible buscar quien es ele ganador. Se utiliza solo como validador.\n",
    "for i in range(10):\n",
    "    player1 = get_choice()\n",
    "    player2 = get_choice()\n",
    "    print(\"Player1 %s Player2: %s Winner: %s\" % (\n",
    "        player1, player2, search_winner(player1, player2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vamos a convertir los datos de papel, piedra o tijera de datos 0 y 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 0, 0], [0, 1, 0], [0, 0, 1]]\n",
      "[[0, 0, 1], [1, 0, 0], [0, 1, 0]]\n"
     ]
    }
   ],
   "source": [
    "# convertimos la opción en una matriz de 1x3 con valores de 0 y 1\n",
    "def str_to_list(option):\n",
    "    if option==\"piedra\":\n",
    "        res = [1, 0, 0]\n",
    "    elif option==\"tijeras\":\n",
    "        res = [0, 1, 0]\n",
    "    elif option==\"papel\":\n",
    "        res = [0, 0, 1]\n",
    "    \n",
    "    return res\n",
    "\n",
    "#asiganamos la lista a una variable\n",
    "data_X = list(map(str_to_list, [\"piedra\", \"tijeras\", \"papel\"]))\n",
    "data_y = list(map(str_to_list, [\"papel\", \"piedra\", \"tijeras\"]))\n",
    "\n",
    "print(data_X) # la mostramos para corroborar.\n",
    "print(data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ocupamos la libreria de sklearn\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no tengo puta idea que es esta mierda, pero funsiona., es el tipo de clasificador. \n",
    "# seguro es la mágia para que la neurona aprenda.\n",
    "clf = MLPClassifier(verbose=False, warm_start=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
      "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
      "       verbose=False, warm_start=True)\n"
     ]
    }
   ],
   "source": [
    "# Entrenamos al modelo, parte muy importante para entrenar el modelo.\n",
    "model = clf.fit([data_X[0]], [data_y[0]])\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aquí la neurona aprende a jugar \n",
    "def play_and_learn(iters=10, debug=False):\n",
    "    #iniciamos en 0 los win y los loss\n",
    "    score = {\"win\": 0, \"loose\": 0}\n",
    "    \n",
    "    data_X = [] # sin valores\n",
    "    data_y = [] # sin valores\n",
    "    # iters sera el número de vueltas en el ciclo for para aprender\n",
    "    for i in range(iters):\n",
    "        player1 = get_choice()\n",
    "        # aquí predecimos la probabilidad en porcentaje de ganarle a player1\n",
    "        predict = model.predict_proba([str_to_list(player1)])[0]\n",
    "        # aquí seleccionamos cual es la mejor alternativa para ganar a player1 según la predicción en porcentaje de acierto\n",
    "        if predict[0] >= 0.95:\n",
    "            player2 = options[0]\n",
    "        elif predict[1] >= 0.95:\n",
    "            player2 = options[1]\n",
    "        elif predict[2] >= 0.95:\n",
    "            player2 = options[2]\n",
    "        else:\n",
    "            player2 = get_choice()\n",
    "        \n",
    "        if debug==True:\n",
    "            print(\"Player1: %s  Modelo %s Player2: %s -->\" % (player1, predict, player2))\n",
    "        \n",
    "        winner = search_winner(player1, player2)\n",
    "\n",
    "        if debug==True:\n",
    "            print(\"Comprobamos: p1 VS p2: %s\" % winner)\n",
    "            \n",
    "        if winner==2:\n",
    "            data_X.append(str_to_list(player1))\n",
    "            data_y.append(str_to_list(player2))\n",
    "            score[\"win\"]+= 1\n",
    "        else:\n",
    "            score[\"loose\"]+=1\n",
    "        \n",
    "    return score, data_X, data_y\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player1: tijeras Player2: [0.97996714 0.03019889 0.03392681] Modelo: piedra -->\n",
      "Comprobamos: p1 VS p2: 2\n",
      "[[0, 1, 0]]\n",
      "[[1, 0, 0]]\n",
      "score: {'win': 1, 'loose': 0} el porciento 100.0 %\n"
     ]
    }
   ],
   "source": [
    "score, data_X, data_y = play_and_learn(1, debug=True)\n",
    "print(data_X)\n",
    "print(data_y)\n",
    "print(\"score: %s el porciento %s %%\" % (score, (score[\"win\"]*100/(score[\"win\"]+score[\"loose\"]))))\n",
    "if len(data_X):\n",
    "    model = model.partial_fit(data_X, data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 1 - score: {'win': 522, 'loose': 478} 52.2 %\n",
      "Iter: 2 - score: {'win': 567, 'loose': 433} 56.7 %\n",
      "Iter: 3 - score: {'win': 551, 'loose': 449} 55.1 %\n",
      "Iter: 4 - score: {'win': 551, 'loose': 449} 55.1 %\n",
      "Iter: 5 - score: {'win': 548, 'loose': 452} 54.8 %\n",
      "Iter: 6 - score: {'win': 551, 'loose': 449} 55.1 %\n",
      "Iter: 7 - score: {'win': 531, 'loose': 469} 53.1 %\n",
      "Iter: 8 - score: {'win': 565, 'loose': 435} 56.5 %\n",
      "Iter: 9 - score: {'win': 562, 'loose': 438} 56.2 %\n",
      "Iter: 10 - score: {'win': 557, 'loose': 443} 55.7 %\n",
      "Iter: 11 - score: {'win': 555, 'loose': 445} 55.5 %\n",
      "Iter: 12 - score: {'win': 559, 'loose': 441} 55.9 %\n",
      "Iter: 13 - score: {'win': 354, 'loose': 646} 35.4 %\n",
      "Iter: 14 - score: {'win': 319, 'loose': 681} 31.9 %\n",
      "Iter: 15 - score: {'win': 346, 'loose': 654} 34.6 %\n",
      "Iter: 16 - score: {'win': 340, 'loose': 660} 34.0 %\n",
      "Iter: 17 - score: {'win': 322, 'loose': 678} 32.2 %\n",
      "Iter: 18 - score: {'win': 358, 'loose': 642} 35.8 %\n",
      "Iter: 19 - score: {'win': 305, 'loose': 695} 30.5 %\n",
      "Iter: 20 - score: {'win': 328, 'loose': 672} 32.8 %\n",
      "Iter: 21 - score: {'win': 324, 'loose': 676} 32.4 %\n",
      "Iter: 22 - score: {'win': 335, 'loose': 665} 33.5 %\n",
      "Iter: 23 - score: {'win': 318, 'loose': 682} 31.8 %\n",
      "Iter: 24 - score: {'win': 340, 'loose': 660} 34.0 %\n",
      "Iter: 25 - score: {'win': 353, 'loose': 647} 35.3 %\n",
      "Iter: 26 - score: {'win': 334, 'loose': 666} 33.4 %\n",
      "Iter: 27 - score: {'win': 344, 'loose': 656} 34.4 %\n",
      "Iter: 28 - score: {'win': 342, 'loose': 658} 34.2 %\n",
      "Iter: 29 - score: {'win': 326, 'loose': 674} 32.6 %\n",
      "Iter: 30 - score: {'win': 325, 'loose': 675} 32.5 %\n",
      "Iter: 31 - score: {'win': 314, 'loose': 686} 31.4 %\n",
      "Iter: 32 - score: {'win': 319, 'loose': 681} 31.9 %\n",
      "Iter: 33 - score: {'win': 313, 'loose': 687} 31.3 %\n",
      "Iter: 34 - score: {'win': 342, 'loose': 658} 34.2 %\n",
      "Iter: 35 - score: {'win': 326, 'loose': 674} 32.6 %\n",
      "Iter: 36 - score: {'win': 361, 'loose': 639} 36.1 %\n",
      "Iter: 37 - score: {'win': 337, 'loose': 663} 33.7 %\n",
      "Iter: 38 - score: {'win': 346, 'loose': 654} 34.6 %\n",
      "Iter: 39 - score: {'win': 338, 'loose': 662} 33.8 %\n",
      "Iter: 40 - score: {'win': 348, 'loose': 652} 34.8 %\n",
      "Iter: 41 - score: {'win': 333, 'loose': 667} 33.3 %\n",
      "Iter: 42 - score: {'win': 322, 'loose': 678} 32.2 %\n",
      "Iter: 43 - score: {'win': 324, 'loose': 676} 32.4 %\n",
      "Iter: 44 - score: {'win': 342, 'loose': 658} 34.2 %\n",
      "Iter: 45 - score: {'win': 310, 'loose': 690} 31.0 %\n",
      "Iter: 46 - score: {'win': 331, 'loose': 669} 33.1 %\n",
      "Iter: 47 - score: {'win': 340, 'loose': 660} 34.0 %\n",
      "Iter: 48 - score: {'win': 336, 'loose': 664} 33.6 %\n",
      "Iter: 49 - score: {'win': 312, 'loose': 688} 31.2 %\n",
      "Iter: 50 - score: {'win': 358, 'loose': 642} 35.8 %\n",
      "Iter: 51 - score: {'win': 341, 'loose': 659} 34.1 %\n",
      "Iter: 52 - score: {'win': 326, 'loose': 674} 32.6 %\n",
      "Iter: 53 - score: {'win': 308, 'loose': 692} 30.8 %\n",
      "Iter: 54 - score: {'win': 345, 'loose': 655} 34.5 %\n",
      "Iter: 55 - score: {'win': 335, 'loose': 665} 33.5 %\n",
      "Iter: 56 - score: {'win': 345, 'loose': 655} 34.5 %\n",
      "Iter: 57 - score: {'win': 369, 'loose': 631} 36.9 %\n",
      "Iter: 58 - score: {'win': 327, 'loose': 673} 32.7 %\n",
      "Iter: 59 - score: {'win': 332, 'loose': 668} 33.2 %\n",
      "Iter: 60 - score: {'win': 347, 'loose': 653} 34.7 %\n",
      "Iter: 61 - score: {'win': 325, 'loose': 675} 32.5 %\n",
      "Iter: 62 - score: {'win': 324, 'loose': 676} 32.4 %\n",
      "Iter: 63 - score: {'win': 329, 'loose': 671} 32.9 %\n",
      "Iter: 64 - score: {'win': 331, 'loose': 669} 33.1 %\n",
      "Iter: 65 - score: {'win': 350, 'loose': 650} 35.0 %\n",
      "Iter: 66 - score: {'win': 349, 'loose': 651} 34.9 %\n",
      "Iter: 67 - score: {'win': 325, 'loose': 675} 32.5 %\n",
      "Iter: 68 - score: {'win': 331, 'loose': 669} 33.1 %\n",
      "Iter: 69 - score: {'win': 330, 'loose': 670} 33.0 %\n",
      "Iter: 70 - score: {'win': 349, 'loose': 651} 34.9 %\n",
      "Iter: 71 - score: {'win': 320, 'loose': 680} 32.0 %\n",
      "Iter: 72 - score: {'win': 321, 'loose': 679} 32.1 %\n",
      "Iter: 73 - score: {'win': 316, 'loose': 684} 31.6 %\n",
      "Iter: 74 - score: {'win': 322, 'loose': 678} 32.2 %\n",
      "Iter: 75 - score: {'win': 350, 'loose': 650} 35.0 %\n",
      "Iter: 76 - score: {'win': 328, 'loose': 672} 32.8 %\n",
      "Iter: 77 - score: {'win': 315, 'loose': 685} 31.5 %\n",
      "Iter: 78 - score: {'win': 312, 'loose': 688} 31.2 %\n",
      "Iter: 79 - score: {'win': 347, 'loose': 653} 34.7 %\n",
      "Iter: 80 - score: {'win': 346, 'loose': 654} 34.6 %\n",
      "Iter: 81 - score: {'win': 333, 'loose': 667} 33.3 %\n",
      "Iter: 82 - score: {'win': 325, 'loose': 675} 32.5 %\n",
      "Iter: 83 - score: {'win': 346, 'loose': 654} 34.6 %\n",
      "Iter: 84 - score: {'win': 311, 'loose': 689} 31.1 %\n",
      "Iter: 85 - score: {'win': 344, 'loose': 656} 34.4 %\n",
      "Iter: 86 - score: {'win': 328, 'loose': 672} 32.8 %\n",
      "Iter: 87 - score: {'win': 347, 'loose': 653} 34.7 %\n",
      "Iter: 88 - score: {'win': 337, 'loose': 663} 33.7 %\n",
      "Iter: 89 - score: {'win': 384, 'loose': 616} 38.4 %\n",
      "Iter: 90 - score: {'win': 319, 'loose': 681} 31.9 %\n",
      "Iter: 91 - score: {'win': 348, 'loose': 652} 34.8 %\n",
      "Iter: 92 - score: {'win': 355, 'loose': 645} 35.5 %\n",
      "Iter: 93 - score: {'win': 344, 'loose': 656} 34.4 %\n",
      "Iter: 94 - score: {'win': 330, 'loose': 670} 33.0 %\n",
      "Iter: 95 - score: {'win': 330, 'loose': 670} 33.0 %\n",
      "Iter: 96 - score: {'win': 360, 'loose': 640} 36.0 %\n",
      "Iter: 97 - score: {'win': 311, 'loose': 689} 31.1 %\n",
      "Iter: 98 - score: {'win': 330, 'loose': 670} 33.0 %\n",
      "Iter: 99 - score: {'win': 317, 'loose': 683} 31.7 %\n",
      "Iter: 100 - score: {'win': 346, 'loose': 654} 34.6 %\n",
      "Iter: 101 - score: {'win': 330, 'loose': 670} 33.0 %\n",
      "Iter: 102 - score: {'win': 320, 'loose': 680} 32.0 %\n",
      "Iter: 103 - score: {'win': 338, 'loose': 662} 33.8 %\n",
      "Iter: 104 - score: {'win': 314, 'loose': 686} 31.4 %\n",
      "Iter: 105 - score: {'win': 299, 'loose': 701} 29.9 %\n",
      "Iter: 106 - score: {'win': 350, 'loose': 650} 35.0 %\n",
      "Iter: 107 - score: {'win': 347, 'loose': 653} 34.7 %\n",
      "Iter: 108 - score: {'win': 323, 'loose': 677} 32.3 %\n",
      "Iter: 109 - score: {'win': 366, 'loose': 634} 36.6 %\n",
      "Iter: 110 - score: {'win': 325, 'loose': 675} 32.5 %\n",
      "Iter: 111 - score: {'win': 352, 'loose': 648} 35.2 %\n",
      "Iter: 112 - score: {'win': 348, 'loose': 652} 34.8 %\n",
      "Iter: 113 - score: {'win': 294, 'loose': 706} 29.4 %\n",
      "Iter: 114 - score: {'win': 571, 'loose': 429} 57.1 %\n",
      "Iter: 115 - score: {'win': 534, 'loose': 466} 53.4 %\n",
      "Iter: 116 - score: {'win': 544, 'loose': 456} 54.4 %\n",
      "Iter: 117 - score: {'win': 573, 'loose': 427} 57.3 %\n",
      "Iter: 118 - score: {'win': 554, 'loose': 446} 55.4 %\n",
      "Iter: 119 - score: {'win': 553, 'loose': 447} 55.3 %\n",
      "Iter: 120 - score: {'win': 550, 'loose': 450} 55.0 %\n",
      "Iter: 121 - score: {'win': 560, 'loose': 440} 56.0 %\n",
      "Iter: 122 - score: {'win': 544, 'loose': 456} 54.4 %\n",
      "Iter: 123 - score: {'win': 538, 'loose': 462} 53.8 %\n",
      "Iter: 124 - score: {'win': 544, 'loose': 456} 54.4 %\n",
      "Iter: 125 - score: {'win': 576, 'loose': 424} 57.6 %\n",
      "Iter: 126 - score: {'win': 555, 'loose': 445} 55.5 %\n",
      "Iter: 127 - score: {'win': 585, 'loose': 415} 58.5 %\n",
      "Iter: 128 - score: {'win': 552, 'loose': 448} 55.2 %\n",
      "Iter: 129 - score: {'win': 559, 'loose': 441} 55.9 %\n",
      "Iter: 130 - score: {'win': 566, 'loose': 434} 56.6 %\n",
      "Iter: 131 - score: {'win': 533, 'loose': 467} 53.3 %\n",
      "Iter: 132 - score: {'win': 550, 'loose': 450} 55.0 %\n",
      "Iter: 133 - score: {'win': 541, 'loose': 459} 54.1 %\n",
      "Iter: 134 - score: {'win': 553, 'loose': 447} 55.3 %\n",
      "Iter: 135 - score: {'win': 563, 'loose': 437} 56.3 %\n",
      "Iter: 136 - score: {'win': 561, 'loose': 439} 56.1 %\n",
      "Iter: 137 - score: {'win': 567, 'loose': 433} 56.7 %\n",
      "Iter: 138 - score: {'win': 557, 'loose': 443} 55.7 %\n",
      "Iter: 139 - score: {'win': 561, 'loose': 439} 56.1 %\n",
      "Iter: 140 - score: {'win': 541, 'loose': 459} 54.1 %\n",
      "Iter: 141 - score: {'win': 562, 'loose': 438} 56.2 %\n",
      "Iter: 142 - score: {'win': 570, 'loose': 430} 57.0 %\n",
      "Iter: 143 - score: {'win': 555, 'loose': 445} 55.5 %\n",
      "Iter: 144 - score: {'win': 549, 'loose': 451} 54.9 %\n",
      "Iter: 145 - score: {'win': 544, 'loose': 456} 54.4 %\n",
      "Iter: 146 - score: {'win': 546, 'loose': 454} 54.6 %\n",
      "Iter: 147 - score: {'win': 570, 'loose': 430} 57.0 %\n",
      "Iter: 148 - score: {'win': 571, 'loose': 429} 57.1 %\n",
      "Iter: 149 - score: {'win': 535, 'loose': 465} 53.5 %\n",
      "Iter: 150 - score: {'win': 576, 'loose': 424} 57.6 %\n",
      "Iter: 151 - score: {'win': 578, 'loose': 422} 57.8 %\n",
      "Iter: 152 - score: {'win': 591, 'loose': 409} 59.1 %\n",
      "Iter: 153 - score: {'win': 578, 'loose': 422} 57.8 %\n",
      "Iter: 154 - score: {'win': 565, 'loose': 435} 56.5 %\n",
      "Iter: 155 - score: {'win': 547, 'loose': 453} 54.7 %\n",
      "Iter: 156 - score: {'win': 541, 'loose': 459} 54.1 %\n",
      "Iter: 157 - score: {'win': 550, 'loose': 450} 55.0 %\n",
      "Iter: 158 - score: {'win': 549, 'loose': 451} 54.9 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 159 - score: {'win': 523, 'loose': 477} 52.3 %\n",
      "Iter: 160 - score: {'win': 568, 'loose': 432} 56.8 %\n",
      "Iter: 161 - score: {'win': 549, 'loose': 451} 54.9 %\n",
      "Iter: 162 - score: {'win': 555, 'loose': 445} 55.5 %\n",
      "Iter: 163 - score: {'win': 586, 'loose': 414} 58.6 %\n",
      "Iter: 164 - score: {'win': 578, 'loose': 422} 57.8 %\n",
      "Iter: 165 - score: {'win': 557, 'loose': 443} 55.7 %\n",
      "Iter: 166 - score: {'win': 556, 'loose': 444} 55.6 %\n",
      "Iter: 167 - score: {'win': 552, 'loose': 448} 55.2 %\n",
      "Iter: 168 - score: {'win': 544, 'loose': 456} 54.4 %\n",
      "Iter: 169 - score: {'win': 565, 'loose': 435} 56.5 %\n",
      "Iter: 170 - score: {'win': 555, 'loose': 445} 55.5 %\n",
      "Iter: 171 - score: {'win': 554, 'loose': 446} 55.4 %\n",
      "Iter: 172 - score: {'win': 556, 'loose': 444} 55.6 %\n",
      "Iter: 173 - score: {'win': 544, 'loose': 456} 54.4 %\n",
      "Iter: 174 - score: {'win': 558, 'loose': 442} 55.8 %\n",
      "Iter: 175 - score: {'win': 553, 'loose': 447} 55.3 %\n",
      "Iter: 176 - score: {'win': 752, 'loose': 248} 75.2 %\n",
      "Iter: 177 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 178 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 179 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 180 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 181 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 182 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 183 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 184 - score: {'win': 1000, 'loose': 0} 100.0 %\n",
      "Iter: 185 - score: {'win': 1000, 'loose': 0} 100.0 %\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "historico_pct = []\n",
    "while True:\n",
    "    i+=1\n",
    "    score, data_X, data_y = play_and_learn(iters=1000, debug=False)\n",
    "    pct = (score[\"win\"]*100/(score[\"win\"]+score[\"loose\"]))\n",
    "    historico_pct.append(pct)\n",
    "    print(\"Iter: %s - score: %s %s %%\" % (i, score, pct))\n",
    "    \n",
    "    if len(data_X):\n",
    "        model = model.partial_fit(data_X, data_y)\n",
    "    \n",
    "    if sum(historico_pct[-9:])==900:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"73a06715-eabf-48aa-8746-bbb5bea6c030\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"73a06715-eabf-48aa-8746-bbb5bea6c030\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"73a06715-eabf-48aa-8746-bbb5bea6c030\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '73a06715-eabf-48aa-8746-bbb5bea6c030' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"73a06715-eabf-48aa-8746-bbb5bea6c030\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"73a06715-eabf-48aa-8746-bbb5bea6c030\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"73a06715-eabf-48aa-8746-bbb5bea6c030\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '73a06715-eabf-48aa-8746-bbb5bea6c030' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"73a06715-eabf-48aa-8746-bbb5bea6c030\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Librerias para pintar un gráfico\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"4f2f74e2-39b2-472d-af61-a65505f1d2c2\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"fa894e5d-cf22-4b81-a60b-e36279a0313b\":{\"roots\":{\"references\":[{\"attributes\":{\"source\":{\"id\":\"d76cffc8-1e72-4ecb-9086-83a51cd05156\",\"type\":\"ColumnDataSource\"}},\"id\":\"ee8e7058-01c6-40d2-8a87-7360d2b5d882\",\"type\":\"CDSView\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"b32c3b9e-e091-4697-9e88-e2cd0d59a1e3\",\"type\":\"PanTool\"},{\"id\":\"8e7f45f4-57ee-4433-9f90-4ab9bbe82337\",\"type\":\"WheelZoomTool\"},{\"id\":\"00a16d04-57e2-4819-be4b-3ea2caa8e1e4\",\"type\":\"BoxZoomTool\"},{\"id\":\"d700993d-5117-4010-871e-d630186ce318\",\"type\":\"SaveTool\"},{\"id\":\"2edb1d7a-a838-4c0f-a66e-32cc546254e2\",\"type\":\"ResetTool\"},{\"id\":\"30359c03-ada2-49bf-825b-6383fe2de8a2\",\"type\":\"HelpTool\"}]},\"id\":\"aee817c4-833d-4173-8fed-5f862ed67c55\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"48a897fa-02c0-431d-a9fe-77a4585c34c3\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null},\"id\":\"105a02e5-4d6e-4d34-a39a-d1ef1ea63c79\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":{\"id\":\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"4db738a3-289a-4f94-ab32-6eccede0211b\",\"type\":\"BasicTicker\"}},\"id\":\"00d704c4-29de-4720-bb17-cd5223085f38\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"aff03d68-b5e2-48e2-9b59-b688474a05b6\",\"type\":\"DataRange1d\"},{\"attributes\":{\"axis_label\":\"% porcentaje de aprendisaje\",\"formatter\":{\"id\":\"48a897fa-02c0-431d-a9fe-77a4585c34c3\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"ff9b0817-d081-4080-9bca-e66eb04c22ac\",\"type\":\"BasicTicker\"}},\"id\":\"8ca133c8-4588-463d-9390-a07311700e09\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis_label\":\"Iteraciones\",\"formatter\":{\"id\":\"bc6fa74f-b825-4239-9d85-ddb2940d833c\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"4db738a3-289a-4f94-ab32-6eccede0211b\",\"type\":\"BasicTicker\"}},\"id\":\"c2eb8071-9924-4419-b0db-ba13c99450c5\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1a3f518e-7d04-45c1-8bcb-b690ea2ab7af\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"ff9b0817-d081-4080-9bca-e66eb04c22ac\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"ff9b0817-d081-4080-9bca-e66eb04c22ac\",\"type\":\"BasicTicker\"}},\"id\":\"6b57b962-710e-4455-b636-950745581935\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"4db738a3-289a-4f94-ab32-6eccede0211b\",\"type\":\"BasicTicker\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"d1345af6-892c-49bd-b16b-def3de5c73cc\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184],\"y\":[52.2,56.7,55.1,55.1,54.8,55.1,53.1,56.5,56.2,55.7,55.5,55.9,35.4,31.9,34.6,34.0,32.2,35.8,30.5,32.8,32.4,33.5,31.8,34.0,35.3,33.4,34.4,34.2,32.6,32.5,31.4,31.9,31.3,34.2,32.6,36.1,33.7,34.6,33.8,34.8,33.3,32.2,32.4,34.2,31.0,33.1,34.0,33.6,31.2,35.8,34.1,32.6,30.8,34.5,33.5,34.5,36.9,32.7,33.2,34.7,32.5,32.4,32.9,33.1,35.0,34.9,32.5,33.1,33.0,34.9,32.0,32.1,31.6,32.2,35.0,32.8,31.5,31.2,34.7,34.6,33.3,32.5,34.6,31.1,34.4,32.8,34.7,33.7,38.4,31.9,34.8,35.5,34.4,33.0,33.0,36.0,31.1,33.0,31.7,34.6,33.0,32.0,33.8,31.4,29.9,35.0,34.7,32.3,36.6,32.5,35.2,34.8,29.4,57.1,53.4,54.4,57.3,55.4,55.3,55.0,56.0,54.4,53.8,54.4,57.6,55.5,58.5,55.2,55.9,56.6,53.3,55.0,54.1,55.3,56.3,56.1,56.7,55.7,56.1,54.1,56.2,57.0,55.5,54.9,54.4,54.6,57.0,57.1,53.5,57.6,57.8,59.1,57.8,56.5,54.7,54.1,55.0,54.9,52.3,56.8,54.9,55.5,58.6,57.8,55.7,55.6,55.2,54.4,56.5,55.5,55.4,55.6,54.4,55.8,55.3,75.2,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0]}},\"id\":\"d76cffc8-1e72-4ecb-9086-83a51cd05156\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"e04e6996-b55b-4c75-af4f-5deb09414acc\",\"type\":\"Line\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"5c42e081-7b65-4da0-b343-408db606081e\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"below\":[{\"id\":\"c2eb8071-9924-4419-b0db-ba13c99450c5\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"8ca133c8-4588-463d-9390-a07311700e09\",\"type\":\"LinearAxis\"}],\"plot_width\":900,\"renderers\":[{\"id\":\"c2eb8071-9924-4419-b0db-ba13c99450c5\",\"type\":\"LinearAxis\"},{\"id\":\"00d704c4-29de-4720-bb17-cd5223085f38\",\"type\":\"Grid\"},{\"id\":\"8ca133c8-4588-463d-9390-a07311700e09\",\"type\":\"LinearAxis\"},{\"id\":\"6b57b962-710e-4455-b636-950745581935\",\"type\":\"Grid\"},{\"id\":\"5c42e081-7b65-4da0-b343-408db606081e\",\"type\":\"BoxAnnotation\"},{\"id\":\"4b60b005-c318-4198-9d86-53a534cc4fbd\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"c9299883-fcff-4d3d-b51f-be3df9c22293\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"aee817c4-833d-4173-8fed-5f862ed67c55\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"105a02e5-4d6e-4d34-a39a-d1ef1ea63c79\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"5400e73e-c80c-40a6-971c-87095d8105ce\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"aff03d68-b5e2-48e2-9b59-b688474a05b6\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1a3f518e-7d04-45c1-8bcb-b690ea2ab7af\",\"type\":\"LinearScale\"}},\"id\":\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"b32c3b9e-e091-4697-9e88-e2cd0d59a1e3\",\"type\":\"PanTool\"},{\"attributes\":{\"plot\":null,\"text\":\"porcentaje de aprendisaje en cada iteraci\\u00f3n\"},\"id\":\"c9299883-fcff-4d3d-b51f-be3df9c22293\",\"type\":\"Title\"},{\"attributes\":{\"data_source\":{\"id\":\"d76cffc8-1e72-4ecb-9086-83a51cd05156\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"e04e6996-b55b-4c75-af4f-5deb09414acc\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"d1345af6-892c-49bd-b16b-def3de5c73cc\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"ee8e7058-01c6-40d2-8a87-7360d2b5d882\",\"type\":\"CDSView\"}},\"id\":\"4b60b005-c318-4198-9d86-53a534cc4fbd\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"8e7f45f4-57ee-4433-9f90-4ab9bbe82337\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"5c42e081-7b65-4da0-b343-408db606081e\",\"type\":\"BoxAnnotation\"}},\"id\":\"00a16d04-57e2-4819-be4b-3ea2caa8e1e4\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"5400e73e-c80c-40a6-971c-87095d8105ce\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"d700993d-5117-4010-871e-d630186ce318\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"2edb1d7a-a838-4c0f-a66e-32cc546254e2\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"30359c03-ada2-49bf-825b-6383fe2de8a2\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"bc6fa74f-b825-4239-9d85-ddb2940d833c\",\"type\":\"BasicTickFormatter\"}],\"root_ids\":[\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"fa894e5d-cf22-4b81-a60b-e36279a0313b\",\"elementid\":\"4f2f74e2-39b2-472d-af61-a65505f1d2c2\",\"modelid\":\"26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "26fa4fd4-1485-4c3b-a9f7-dcc71366f8e2"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(len(historico_pct))\n",
    "y = historico_pct\n",
    "\n",
    "p = figure(\n",
    "    title=\"porcentaje de aprendisaje en cada iteración\",\n",
    "    x_axis_label=\"Iteraciones\", y_axis_label=\"% porcentaje de aprendisaje\", width=900)\n",
    "p.line(x, y, legend=None, line_width=2)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}