commit_Davide

parent 30487254
{
"cells": [],
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1 À propos du calcul de $\\pi$\n",
"## 1.1 En demandant à la lib maths\n",
"\n",
"Mon ordinateur m'indique que $\\pi$ vaut _approximativement_ : "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.141592653589793\n"
]
}
],
"source": [
"from math import *\n",
"print(pi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.2 En utilisant la méthode des aiguilles de Buffon\n",
"\n",
"Mais calculé avec la **méthode** des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme **approximation** : "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.128911138923655"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.random.seed(seed=42)\n",
"N = 10000\n",
"x = np.random.uniform(size=N, low=0, high=1)\n",
"theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
"2/(sum((x+np.sin(theta))>1)/N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.3 Avec un argument \"fréquentiel\" de surface\n",
"\n",
"Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d’appel à la fonction\n",
"sinus se base sur le fait que si $X ∼ U(0, 1)$ et $Y ∼ U(0, 1)$ alors $P[X^2 + Y^2 ≤ 1] = \\pi/4$ (voir\n",
"[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 :"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"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": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\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",
"accept = (x*x+y*y) <= 1\n",
"reject = np.logical_not(accept)\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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il est alors aisé d’obtenir une approximation (pas terrible) de $\\pi$ en comptant combien de fois,\n",
"en moyenne, $X^2$ + $Y^2$ est inférieur à 1 :\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.1376"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"4*np.mean(accept)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +158,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment