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fa5d65b9c887dadf0383a678f20d94ad
mooc-rr
Commits
e538db6c
Commit
e538db6c
authored
Aug 25, 2023
by
fa5d65b9c887dadf0383a678f20d94ad
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toy_notebook_fr.ipynb
module2/exo1/toy_notebook_fr.ipynb
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module2/exo1/toy_notebook_fr.ipynb
View file @
e538db6c
...
...
@@ -4,14 +4,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# A propos du calcul de $\\pi$"
"#
1.
A propos du calcul de $\\pi$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## En demandant à la lib maths"
"##
1.1
En demandant à la lib maths"
]
},
{
...
...
@@ -43,7 +43,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## En utilisant la méthode des aiguilles de Buffon"
"##
1.2
En utilisant la méthode des aiguilles de Buffon"
]
},
{
...
...
@@ -82,7 +82,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Avec un argument \"fréquentiel\" de surface"
"##
1.3
Avec un argument \"fréquentiel\" de surface"
]
},
{
...
...
@@ -96,30 +96,6 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"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)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
...
...
@@ -135,6 +111,16 @@
}
],
"source": [
"%matplotlib inline\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",
"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",
...
...
@@ -150,8 +136,10 @@
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
...
...
@@ -159,7 +147,7 @@
"3.112"
]
},
"execution_count":
6
,
"execution_count":
4
,
"metadata": {},
"output_type": "execute_result"
}
...
...
@@ -168,6 +156,18 @@
"4*np.mean(accept)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
...
...
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