9

parent 4b3f597a
...@@ -10,8 +10,7 @@ ...@@ -10,8 +10,7 @@
"Mon ordinateur m'indique que $\\pi$ vaut *approximativement*\n", "Mon ordinateur m'indique que $\\pi$ vaut *approximativement*\n",
"\n", "\n",
"\n", "\n",
"In \\[1]:\n", "In \\[1]:>from math import *\n",
">from math import *\n",
">print (pi)\n", ">print (pi)\n",
" \n", " \n",
"3.141592653589793\n", "3.141592653589793\n",
...@@ -23,14 +22,15 @@ ...@@ -23,14 +22,15 @@
"\n", "\n",
"\n", "\n",
"In \\[2]:\n", "In \\[2]:\n",
"import numpy as np\n", ">import numpy as np\n",
"np.random.seed(seed=42)\n", ">np.random.seed(seed=42)\n",
"N = 10000\n", ">N = 10000\n",
"x = np.random.uniform(size=N, low=0, high=1)\n", ">x = np.random.uniform(size=N, low=0, high=1)\n",
"theta = np.random.uniform(size=N, low=0, high=pi/2)\n", ">theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
"2/(sum((x+np.sin(theta))>1)/N)\n", ">2/(sum((x+np.sin(theta))>1)/N)\n",
" \n", " \n",
"3.1289111389236548\n", "Out\\[2]: 3.1289111389236548\n",
"\n",
"\n", "\n",
"## 1.3 Avec un argument \"fréquentiel\" de surface\n", "## 1.3 Avec un argument \"fréquentiel\" de surface\n",
"Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que si $X\\sim U(0,1)$ et $Y\\sim U(0,1)$ alors $P[X^2+Y^2\\leq1] = \\pi/4$ (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80)). Le code suivant illustre ce fait :\n", "Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que si $X\\sim U(0,1)$ et $Y\\sim U(0,1)$ alors $P[X^2+Y^2\\leq1] = \\pi/4$ (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80)). Le code suivant illustre ce fait :\n",
...@@ -38,29 +38,31 @@ ...@@ -38,29 +38,31 @@
"\n", "\n",
"In \\[3]:\n", "In \\[3]:\n",
"\n", "\n",
"%matplotlib inline\n", ">%matplotlib inline\n",
"import matplotlib.pyplot as plt\n", ">import matplotlib.pyplot as plt\n",
"\n",
">np.random.seed(seed=42)\n",
">N = 1000\n",
">x = np.random.uniform(size=N, low=0, high=1)\n",
">y = np.random.uniform(size=N, low=0, high=1)\n",
"\n",
">accept = (x*x+y*y) <=1\n",
">reject = np.logical_not(accept)\n",
"\n",
">fig, ax = plt.subplots(1)\n",
">ax.scatter(x[accept], y [accept], c='b', alpha=0.2, edgecolor=None)\n",
">ax.scatter(x[reject], y [reject], c='r', alpha=0.2, edgecolor=None)\n",
">ax.set_aspect('equal')\n",
"\n", "\n",
"np.random.seed(seed=42)\n",
"N = 1000\n",
"x = np.random.uniform(size=N, low=0, high=1)\n",
"y = np.random.uniform(size=N, low=0, high=1)\n",
"\n", "\n",
"accept = (x*x+y*y) <=1\n",
"reject = np.logical_not(accept)\n",
"\n", "\n",
"fig, ax = plt.subplots(1)\n", ">Il est alors aisé d'obtenir une approximation (pas terrible) de $\\pi$ en comptant combien de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :\n",
"ax.scatter(x[accept], y [accept], c='b', alpha=0.2, edgecolor=None)\n",
"ax.scatter(x[reject], y [reject], c='r', alpha=0.2, edgecolor=None)\n",
"ax.set_aspect('equal')\n",
"\n", "\n",
"Il est alors aisé d'obtenir une approximation (pas terrible) de $\\pi$ en comptant combien de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :\n",
"\n", "\n",
"In \\[4]: 4*np.mean(accept)\n",
"\n", "\n",
"In \\[4]:\n", "Out\\[4]: 3.1120000000000001\n",
"\n", "\n",
"4*np.mean(accept)\n",
"3.1120000000000001\n",
"\n" "\n"
] ]
}, },
......
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