TP3p317_modif.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP3p317 : Faire la distinction entre modèle et réalité"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On souhaite étudier la distance en l'espérance de la v.a $G$ définie dans l'énoncé et la moyenne des gains algébriques obtenu lors d'un échantillon simulé."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici la fonction `jeu()` qui simule une partie de ce jeu. Elle retourne le gain algébrique du joueur à l'issue de la partie.\n",
    "\n",
    "*Remarque : on commence par importer la librairie `random` en la renommant `rd` pour pouvoir utiliser la fonction `randint(a,b)` de cette librairie qui renvoie un nombre choisi au hasard de façon équiprobable parmi les entiers compris entre $a$ et $b$.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rd\n",
    "\n",
    "def jeu():\n",
    "    G=-2\n",
    "    for i in range(1,5) :\n",
    "        if (rd.randint(0,1)==0) and (G==-2) :\n",
    "            G=G+i\n",
    "    return G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le test du `if` a pour rôle de ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va créer une fonction `simul_n_jeux(n)`qui va prendre en argument un nombre $n$ de parties et retourne le gain algébrique moyen par partie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simul_n_jeux(n) :\n",
    "    G_total=0\n",
    "    for i in range(n):\n",
    "        G_total=G_total+jeu()\n",
    "    return G_total/n\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va maintenant tester la fonction sur différentes tailles d'échantillons :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.384"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simul_n_jeux(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va maintenant tester le script de la question 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def evolmoy(nbreexp,nbre_ech) :\n",
    "    n=1\n",
    "    L=[]\n",
    "    while n<=nbre_ech :\n",
    "        s=simul_n_jeux(nbreexp)\n",
    "        L.append(s)\n",
    "        n=n+1\n",
    "    axes = plt.gca()\n",
    "    axes.set_ylim(-0.6, -0.2)\n",
    "    plt.plot(list(range(1,nbre_ech+1)),L, 'b.')\n",
    "    plt.plot([1,nbre_ech],[-3/8,-3/8],'r-')\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evolmoy(10000,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pourquoi l'affichage ne correspond pas à ce qu'on souhaitait ? ..........."
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}