From 513d1e79516362dfd08b944597c74a681ddebabf Mon Sep 17 00:00:00 2001 From: gablaxy <32848524+gablaxy@users.noreply.github.com> Date: Fri, 18 Nov 2022 01:39:35 +0100 Subject: [PATCH] update code --- essentiel.py | 99 ++++++++++++++++++++++++++++++++++++++++++++++++++ roulette.ipynb | 79 +++++++++++++++++++++++++++++++--------- 2 files changed, 161 insertions(+), 17 deletions(-) create mode 100644 essentiel.py diff --git a/essentiel.py b/essentiel.py new file mode 100644 index 0000000..b272719 --- /dev/null +++ b/essentiel.py @@ -0,0 +1,99 @@ +import numpy as np +import numpy.random as rd + + +def tirage_num(n): # renvoie une liste de n nombres tirés aléatoirement entre 0 et 36 + return rd.randint(0, 36, n) + +def rouge_noir(nb): # detecte si un nb correcpond à une case rouge, noir ou verte + rouge = [1, 3, 5, 7, 9, 12, 14, 16, 18, 19, 21, 23, 25, 27, 30, 32, 34, 36] + noir = [2, 4, 6, 8, 10, 11, 13, 15, 17, 20, 22, 24, 26, 28, 29, 31, 33, 35] + if nb in rouge: + return 1 + elif nb in noir: + return 2 + else: + return 0 + +def tiers(nb): # renvoie le tiers auquel appartient le nombre + tiers_1 = [3,6,9,12,15,18,21,24,27,30,33,36] + tiers_2 = [2,5,8,11,14,17,20,23,26,29,32,35] + tiers_3 = [1,4,7,10,13,16,19,22,25,28,31,34] + + if nb in tiers_1: + return 1 + elif nb in tiers_2: + return 2 + elif nb in tiers_3: + return 3 + else: + return 0 + +def pair_impair(nb): # renvoie 0 si pair et 1 si impair + if nb % 2 == 0: + return 0 + else: + return 1 + +def victoire_pair_rouge(nb, argent): # on considère que l'on joue à chaque partie 1€ sur rouge et 1€ sur pair + argent -= 2 + if(rouge_noir(nb) == 1 and pair_impair(nb) == 0): + argent += 4 + elif(rouge_noir(nb) == 1): + argent += 2 + elif(pair_impair(nb) == 0): + argent += 2 + if(pair_impair(nb) == 1 and rouge_noir(nb) == 2 ): + argent += 0 + + return argent + +def victoire_tiers(nb, argent): # on regarde si on tombe sur un nombre qui appartient à un des tiers sur lesquels on a misé + argent -= 2 + if(tiers(nb) == 1 or tiers(nb) == 2): # on considère que l'on joue à chaque partie 1€ sur le tiers 1 et 1€ sur le tiers 2 + argent += 3 + + return argent + + +def partie(cashin,tirage): # joue un tour de roulette et renvoie l'argent restant + # cashin = prix auquel on veut stopper la partie, tirage = nombre de tirage par partie + argent_base = 10 + maximum = argent_base + + for i in tirage_num(tirage): + if(argent_base > 0 and argent_base < cashin): # on vérifie que l'on a de l'argent et que l'on ne dépasse pas le cashin + argent_base = victoire_tiers(i, argent_base) + if(argent_base > maximum): + maximum = argent_base + + else: # sinon on arrête la partie + break + return argent_base + +def npartie_return_argent(n,cashin, tirage): # comme au dessus mais renvoie la liste de l'argent restant à la fin de chaque partie comprenant x(tirage) tirages + liste = [] + for i in range(n): + argent_base = 10 + for i in tirage_num(tirage): + if(argent_base > 0 and argent_base < cashin): + argent_base = victoire_tiers(i, argent_base) + else: + break + liste.append(argent_base) + return liste + +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 + nb = 0 + gagnee = 0 + perdue = 0 + autre = 0 + for i in range(n): + nb = partie(cashin,tirage) + if(nb == cashin): + gagnee += 1 + elif(nb > 0 and nb < cashin): + autre += 1 + elif(nb == 0): + perdue += 1 + return gagnee, perdue, autre \ No newline at end of file diff --git a/roulette.ipynb b/roulette.ipynb index cefb81d..bdd638f 100644 --- a/roulette.ipynb +++ b/roulette.ipynb @@ -11,22 +11,23 @@ " - colonne, douzaine : mise * 2\n", " - sixain ( six nombres ) : mise * 5\n", " - à cheval sur deux cases : mise * 17\n", - " - un seul nombre : mise * 35" + " - un seul nombre : mise * 35\n", + " - 1/3 : mise * 3 ( pas à additionner à la mise de départ )" ] }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 1, "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" + "[(11, 22, 937), (13, 31, 941), (13, 24, 944), (15, 28, 933), (7, 26, 947), (4, 27, 947), (19, 24, 928), (18, 18, 933), (7, 20, 949), (13, 22, 945), (19, 21, 940), (18, 27, 943), (16, 16, 947), (9, 28, 938), (14, 21, 935), (18, 27, 937), (13, 29, 942), (12, 30, 929), (8, 20, 957), (10, 21, 956)]\n", + "moyenne séances gagnée ( stop à 14€ ) : 0.1285\n", + "moyenne séances perdue ( stop à 14€ ) : 0.24100000000000002\n", + "moyenne séances finies à + de 0 mais moins de 14 ( stop à 14€ ) : 9.414\n" ] } ], @@ -48,13 +49,26 @@ " else:\n", " return 0\n", "\n", + "def tiers(nb):\n", + " tiers_1 = [3,6,9,12,15,18,21,24,27,30,33,36]\n", + " tiers_2 = [2,5,8,11,14,17,20,23,26,29,32,35]\n", + " tiers_3 = [1,4,7,10,13,16,19,22,25,28,31,34]\n", + " if nb in tiers_1:\n", + " return 1\n", + " elif nb in tiers_2:\n", + " return 2\n", + " elif nb in tiers_3:\n", + " return 3\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", + "def victoire_pair_rouge(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", @@ -67,12 +81,20 @@ " \n", " return argent\n", "\n", + "def victoire_tiers(nb, argent): # on considère que l'on joue à chaque partie 1€ sur le tiers 1 et 1€ sur le tiers 2\n", + " argent -= 2\n", + " if(tiers(nb) == 1 or tiers(nb) == 2):\n", + " argent += 3\n", + " \n", + " return argent\n", + "\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", + " argent_base = victoire_tiers(i, argent_base)\n", " if(argent_base > max):\n", " max = argent_base\n", " else:\n", @@ -85,7 +107,7 @@ " 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", + " argent_base = victoire_tiers(i, argent_base)\n", " else:\n", " break\n", " list.append(argent_base)\n", @@ -109,7 +131,7 @@ "list = []\n", "count = 20\n", "for i in range(count):\n", - " list.append(npartie(10000,14,10))\n", + " list.append(npartie(1000,20,10))\n", "\n", "print(list)\n", "\n", @@ -130,12 +152,19 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([-1, 0, 2, 5, 8, 11, 14, 17, 20]), array([ 12, 30, 60, 164, 252, 244, 172, 50, 16], dtype=int64))\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -144,6 +173,16 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -151,20 +190,26 @@ "import matplotlib.pyplot as plt\n", "\n", "Tab = []\n", - "Tab.append(npartie_return_argent(1000000,20,10))\n", + "Tab.append(npartie_return_argent(1000,20,10))\n", + "res = np.unique(Tab, return_counts = True)\n", + "print(res)\n", "\n", - "plt.hist(Tab)\n", + "plt.bar(res[0], res[1])\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" + "\n", + "fig1, ax1 = plt.subplots()\n", + "ax1.pie(res[1], labels=res[0], autopct='%1.1f%%', startangle=90, pctdistance=0.8)\n", + "ax1.axis('equal') \n", + "\n", + "plt.show()\n" ] } ], "metadata": { "interpreter": { - "hash": "7a9ac839c22f4ef1a703331818f49c39c32798a52e733cd7e7abe47abde9bc75" + "hash": "eb33fcf88a50c80f630477f0a4bcd2b0c7dbd10b6c8bd5cc26f5d1fa87076e37" }, "kernelspec": { "display_name": "Python 3.10.4 64-bit",