Proba-Roulette/roulette.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gain ( somme à addition en plus de la mise de départ si victoire ): \n",
    "\n",
    "    - Rouge, Noir, Pair, Impair, 1 à 18, 19 à 36 : mise * 1\n",
    "    - à cheval sur 2 colonnes ( moitié des nombres ), douze nombres à cheval ( 1/3 des nombres, 24 ) : mise * 0.5\n",
    "    - colonne, douzaine : mise * 2\n",
    "    - sixain ( six nombres ) : mise * 5\n",
    "    - à cheval sur deux cases : mise * 17\n",
    "    - un seul nombre : mise * 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2892, 208, 6900), (2913, 218, 6869), (2921, 215, 6864), (2923, 224, 6853), (2980, 199, 6821), (2879, 206, 6915), (2896, 210, 6894), (2917, 217, 6866), (2961, 215, 6824), (2968, 209, 6823), (2938, 246, 6816), (2899, 223, 6878), (3037, 190, 6773), (2967, 225, 6808), (3060, 220, 6720), (2873, 218, 6909), (2927, 248, 6825), (2958, 210, 6832), (3044, 211, 6745), (2931, 200, 6869)]\n",
      "moyenne séances gagnée ( stop à 14€ ) : 29.441999999999997\n",
      "moyenne séances perdue ( stop à 14€ ) : 2.156\n",
      "moyenne séances finies à + de 0 mais moins de 14 ( stop à 14€ ) : 68.402\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import numpy.random as rd\n",
    "\n",
    "\n",
    "def tirage_num(n):\n",
    "    return rd.randint(0, 36, n)\n",
    "\n",
    "def rouge_noir(nb):\n",
    "    rouge = [1, 3, 5, 7, 9, 12, 14, 16, 18, 19, 21, 23, 25, 27, 30, 32, 34, 36]\n",
    "    noir = [2, 4, 6, 8, 10, 11, 13, 15, 17, 20, 22, 24, 26, 28, 29, 31, 33, 35]\n",
    "    if nb in rouge:\n",
    "        return 1\n",
    "    elif nb in noir:\n",
    "        return 2\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def pair_impair(nb):\n",
    "    if nb % 2 == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "def victoire(nb, argent): # on considère que l'on joue à chaque partie 1€ sur rouge et 1€ sur pair\n",
    "    argent -= 2\n",
    "    if(rouge_noir(nb) == 1 and pair_impair(nb) == 0):\n",
    "        argent += 4\n",
    "    elif(rouge_noir(nb) == 1):\n",
    "        argent += 2\n",
    "    elif(pair_impair(nb) == 0):\n",
    "        argent += 2\n",
    "    if(pair_impair(nb) == 1 and rouge_noir(nb) == 2 ):\n",
    "        argent += 0\n",
    "        \n",
    "    return argent\n",
    "\n",
    "def partie(cashin,tirage): # cashin = prix auquel on veut stopper la partie, tirage = nombre de tirage par partie\n",
    "    argent_base = 10\n",
    "    max = argent_base\n",
    "    for i in tirage_num(tirage):\n",
    "        if(argent_base > 0 and argent_base < cashin):\n",
    "            argent_base = victoire(i, argent_base)\n",
    "            if(argent_base > max):\n",
    "                max = argent_base\n",
    "        else:\n",
    "            break\n",
    "    return argent_base\n",
    "\n",
    "def npartie_return_argent(n,cashin, tirage): # comme au dessus mais le boucle n fois\n",
    "    list = [] \n",
    "    for i in range(n):\n",
    "        argent_base = 10\n",
    "        for i in tirage_num(tirage):\n",
    "            if(argent_base > 0 and argent_base < cashin):\n",
    "                argent_base = victoire(i, argent_base)\n",
    "            else:\n",
    "                break\n",
    "        list.append(argent_base)\n",
    "    return list\n",
    "    \n",
    "def npartie(n,cashin,tirage): # n = nombre de partie que l'on veut faire, cashin = prix auquel on veut stopper la partie, tirage = nombre de tirage par partie\n",
    "    nb = 0\n",
    "    gagnée = 0\n",
    "    perdue = 0\n",
    "    autre = 0\n",
    "    for i in range(n):\n",
    "        nb = partie(cashin,tirage)\n",
    "        if(nb == cashin):\n",
    "            gagnée += 1\n",
    "        elif(nb > 0 and nb < cashin):\n",
    "            autre += 1\n",
    "        elif(nb == 0):\n",
    "            perdue += 1\n",
    "    return gagnée, perdue, autre\n",
    "        \n",
    "list = []\n",
    "count = 20\n",
    "for i in range(count):\n",
    "    list.append(npartie(10000,14,10))\n",
    "\n",
    "print(list)\n",
    "\n",
    "gagnee_moy = 0\n",
    "perdue_moy = 0\n",
    "autre_moy = 0\n",
    "\n",
    "for i in list:\n",
    "    gagnee_moy += i[0]\n",
    "    perdue_moy += i[1]\n",
    "    autre_moy += i[2]\n",
    "\n",
    "\n",
    "print(\"moyenne séances gagnée ( stop à 14€ ) :\", (gagnee_moy/count)/100)\n",
    "print(\"moyenne séances perdue ( stop à 14€ ) :\", (perdue_moy/count)/100)\n",
    "print(\"moyenne séances finies à + de 0 mais moins de 14 ( stop à 14€ ) :\", (autre_moy/count)/100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Tab = []\n",
    "Tab.append(npartie_return_argent(1000000,20,10))\n",
    "\n",
    "plt.hist(Tab)\n",
    "plt.ylabel('Nb de séances de 10 tours de roulette')\n",
    "plt.xlabel('Argent à la fin des 10 tours (ou après avoir tout perdu / cashout ) ')\n",
    "plt.title('Histogramme des séances de 10 tours de roulette avec stop à 20€ et argent initial de 10€')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7a9ac839c22f4ef1a703331818f49c39c32798a52e733cd7e7abe47abde9bc75"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "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": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}