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...@@ -21,19 +21,83 @@ ...@@ -21,19 +21,83 @@
"#À propos du calcul de π" "#À propos du calcul de π"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##En demandant à la lib maths"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mon ordinateur m’indique que π vaut approximativement"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"name": "stdout",
"output_type": "stream",
"text": [
"3.141592653589793\n"
]
}
],
"source": [
"In [1]: from math import *\n",
"print(pi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##En utilisant la méthode des aiguilles de Buffon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mais calculé avec la méthode des aiguilles de Buffon, on obtiendrait comme approximation :"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"data": {
"text/plain": [
"3.128911138923655"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"In [2]: 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": [
"## Avec un argument \"fréquentiel\" de surface"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
......
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