GenerationAleatoire.ipynb 35 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moyenne: -0.027748, variance: 1.008508\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# -*-coding:utf-8 -*\n",
    "from numpy import *             #import de numpy\n",
    "from pylab import *             #import de matplotlib.pylab\n",
    "from scipy.stats import norm    #import du module norm de scipy.stats\n",
    "\n",
    "r=norm.rvs(size=1000)           #Réalisation aléatoire suivant une loi gaussienne\n",
    "moyenne=mean(r)                 #calcul de la moyenne\n",
    "variance=var(r)                 #calcul de la variance\n",
    "print(\"Moyenne: %f, variance: %f\" %(moyenne,variance))\n",
    "\n",
    "plot(r)                         #Affichage de la réalisation aléatoire via la fonction plot de Matplotlib\n",
    "xlabel('Echantillons')\n",
    "ylabel('Valeur')\n",
    "\n",
    "figure()                        #création d'une nouvelle figure\n",
    "hist(r, histtype='stepfilled')  #affichage de l'histogramme\n",
    "xlabel('Valeur')\n",
    "ylabel('Nb occurences')\n",
    "\n",
    "show()                          #affichage des courbes"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}