2021-11-18T18-18 MontyHall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ad2cfa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from random import randint, seed\n",
    "#hop commentaire\n",
    "from enum import Enum\n",
    "import matplotlib.pyplot as plt\n",
    "class Strategie(Enum):\n",
    "    CHANGER = 1\n",
    "    GARDER = 2\n",
    "seed()\n",
    "\n",
    "def play_game(strategie):\n",
    "    '''Simule une partie du jeu Monty Hall\n",
    "    \n",
    "    Cette fonction simule le choix de la porte par le participant\n",
    "    \n",
    "    Args:\n",
    "        strategie (Strategie): la stratégie du joueur\n",
    "        \n",
    "    Returns:\n",
    "        bool: le joueur a-t-il gagné ?\n",
    "    '''\n",
    "    portes = [0,1,2]\n",
    "    bonne_porte = randint(0,2)\n",
    "    premier_choix = randint(0,2)\n",
    "    portes.remove(premier_choix)\n",
    "    \n",
    "    if premier_choix == bonne_porte:\n",
    "        portes.remove(portes[randint(0,1)])\n",
    "    else:\n",
    "        portes = [bonne_porte]\n",
    "        \n",
    "    deuxieme_choix = 0\n",
    "    if strategie == Strategie.CHANGER:\n",
    "        deuxieme_choix = portes[0]\n",
    "    elif strategie == Strategie.GARDER:\n",
    "        deuxieme_choix = premier_choix\n",
    "    else:\n",
    "        raise ValueError(\"Stratégie non reconnue !\")\n",
    "        \n",
    "    return deuxieme_choix == bonne_porte\n",
    "    \n",
    "play_game(Strategie.CHANGER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "502e478c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_game(Strategie.CHANGER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16ec1081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_game(Strategie.CHANGER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "159cad7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_game(Strategie.CHANGER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44406ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_game(Strategie.CHANGER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "465a135c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En changeant de porte, le joueur a gagné 6632 sur 10000 parties.\n",
      "En gardant son choix initial, le joueur a gagné 3357 sur 10000 parties.\n"
     ]
    }
   ],
   "source": [
    "def play(strategie, nb_tours):\n",
    "    '''\n",
    "    Simule une suite de tours de jeu\n",
    "    \n",
    "    Renvoie une liste de résultats de jeu\n",
    "    \n",
    "    Args:\n",
    "        strategie (Strategie): la stratégie du joueur\n",
    "        nb_tours (int): nombre de tours\n",
    "        \n",
    "    Returns:\n",
    "        list: listes des gains du joueur\n",
    "    '''\n",
    "    \n",
    "    return[1 if play_game(strategie) else 0 for i in range(nb_tours)]\n",
    "\n",
    "print(\"En changeant de porte, le joueur a gagné {} sur 10000 parties.\"\n",
    "     .format(sum(play(Strategie.CHANGER, 10000))))\n",
    "\n",
    "print(\"En gardant son choix initial, le joueur a gagné {} sur 10000 parties.\"\n",
    "     .format(sum(play(Strategie.GARDER, 10000))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4795313",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.plot(play(Strategie.CHANGER, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "357cd665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o9AnikT5TdFJU1YUke4HDXPzA6uMdj9WldwAfAI4l+Vp/3x/1PwNW+iDwmf7Fzyng9o7n6URVfTXJ54BHmX9l2GM0+iMAfOu/JDVirT3kIkm6DIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUiP8HCI4T97IuX0YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.scatter(range(10), play(Strategie.CHANGER, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d984a26a",
   "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": [
    "nb_test = 10000\n",
    "plot = plt.bar([1,2], [sum(play(Strategie.CHANGER, nb_test)),\n",
    "                      sum(play(Strategie.GARDER, nb_test))],\n",
    "               tick_label=[\"Changer\",\"Garder\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6706cca1",
   "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": [
    "gains_changer = []\n",
    "gains_garder = []\n",
    "samples = [1000, 10000, 20000, 50000, 80000, 100000]\n",
    "for tours in samples:\n",
    "    gains_changer.append(play(Strategie.CHANGER, tours))\n",
    "    gains_garder.append(play(Strategie.GARDER, tours))\n",
    "figure = plt.figure()\n",
    "plot = plt.scatter(samples, [sum(x) for x in gains_changer])\n",
    "plot = plt.scatter(samples, [sum(x) for x in gains_garder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f248288f",
   "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": [
    "figure = plt.figure()\n",
    "plot = plt.scatter(samples, [sum(x)/samples[gains_changer.index(x)] for x in gains_changer])\n",
    "plot = plt.scatter(samples, [sum(x)/samples[gains_garder.index(x)] for x in gains_garder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca69df1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}