simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import connect_four\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking that everything works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joc = connect_four.Table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joc.table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player 1 wins!\n"
     ]
    }
   ],
   "source": [
    "joc.drop(1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "672 ns ± 77.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit joc.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "433 ns ± 54.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit joc.table.fill(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random games!\n",
    "\n",
    "Play `n` games iterating over players 1 and 2 and random integer generator for the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import connect_four\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 45.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "joc = connect_four.Table()\n",
    "simulations = 10000\n",
    "results = []\n",
    "tables = []\n",
    "for _ in range(simulations):\n",
    "    player_generator = (player for player in cycle([1,2]))\n",
    "    player = next(player_generator)\n",
    "    first_game = joc.drop_sim(player)\n",
    "    first_column = first_game[2]\n",
    "    while True:  # play until the game ends \n",
    "        player = next(player_generator)\n",
    "        game_status = joc.drop_sim(player)\n",
    "        if game_status[0] and not game_status[1]:\n",
    "            tables.append(joc.table.copy())\n",
    "            winner_info = (player, first_column)\n",
    "            last_mov_info = game_status[2]\n",
    "            results.append(last_mov_info)\n",
    "            break\n",
    "        elif game_status[1]:\n",
    "            break\n",
    "    joc.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9979"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def last_mov(j, table, mat):\n",
    "    last_col =  table[:,j]\n",
    "    i = np.nonzero(last_col)[0][0]\n",
    "    \n",
    "    mat[i,j] += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.zeros((6,7))\n",
    "\n",
    "for j, table in zip(results, tables):\n",
    "    last_mov(j, table, mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f975a873c8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8EaOqfmqdzdeP2f/dwLunPb4BLGlQsroAjzuekgEsaVj6k78GsKRhmeYGi0VhAEsaFgNY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\nDUp7KximJv4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f975c34898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mat, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "player_wins = [x[0] for x in results]\n",
    "first_columns = [x[1] for x in results]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6JqlzJnAhcHaS+7tf5456qL2Jb1uUpEZ4hS5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQI\ngy5JjfhfsGMrzzTHqR0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f975b9d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "player_id = np.unique(player_wins, return_counts=True)[0]\n",
    "n_wins = np.unique(player_wins, return_counts=True)[1]\n",
    "sns.barplot(player_id, n_wins, )\n",
    "plt.title('Player wins')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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owPHWAGu61aOA+0cqbHEHAY8v0bF3B+ufLOufrOVc/1LX/oqqmhqm4949BpkFjpizfjiw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f975c4cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = np.unique(first_columns, return_counts=True)[1]\n",
    "column_num = np.unique(first_columns, return_counts=True)[0]\n",
    "sns.barplot(column_num, bars, color='C0')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}