Corrections_complete pi estimate

parent aed94fca
......@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Module 2"
"# À propos de pi"
]
},
{
......@@ -23,24 +23,75 @@
"pi_estimate = 4 * val / N"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Formule de base pour l’estimation de π :\n",
"\n",
"$P(x^2 + y^2 \\leq 1) = \\frac{\\pi}{4}$\n",
"\n",
"Donc : $\\pi \\approx 4 \\times \\frac{\\text{points dans le cercle}}{\\text{points totaux}}$\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.1672"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
"name": "stdout",
"output_type": "stream",
"text": [
"π = 3.1672\n"
]
}
],
"source": [
"print(\"π =\", pi_estimate)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Buffon's Needle Problem](https://en.wikipedia.org/wiki/Buffon%27s_needle_problem)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimated π: 3.1163326996790177\n"
]
}
],
"source": [
"pi_estimate"
"from random import random\n",
"from math import sin, pi\n",
"\n",
"N = 100000\n",
"L = 1\n",
"D = 2\n",
"hits = 0\n",
"\n",
"for _ in range(N):\n",
" theta = (pi / 2) * random() # random angle\n",
" y = (D / 2) * random() # distance from center of needle to closest line\n",
" if y <= (L / 2) * sin(theta):\n",
" hits += 1\n",
"\n",
"if hits > 0:\n",
" pi_estimate = (2 * L * N) / (hits * D)\n",
" print(\"Estimated π:\", pi_estimate)\n",
"else:\n",
" print(\"No line crossings detected — try again with a larger N.\")\n"
]
},
{
......@@ -65,29 +116,32 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1,2, 3, 4]\n",
"y = [4, 8, 12, 16]\n",
"x = [0,1,2, 3, 4]\n",
"y = [0,1,4,9,16]\n",
"\n",
"plt.plot(x, y)\n",
"plt.title( \"Plot\")\n",
"plt.title( \"Exemple de courbe\")\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y = 4x \")\n",
"plt.ylabel(\"y =xsquared \")\n",
"plt.show()\n"
]
},
......
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