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kunalb/Terrorist Scanner.ipynb

Created May 9, 2021 09:47
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Terrorist Scanner.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "impressive-december",
"metadata": {},
"source": [
"## Terrorist Scanner Simulation"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "centered-scale",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"from functools import partial"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "fuzzy-mountain",
"metadata": {},
"outputs": [],
"source": [
"total_passengers = 100\n",
"terrorists = 1\n",
"\n",
"scan_terrorist = .95\n",
"scan_civilian = 1 - .95\n"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "literary-milan",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(False, (7, 0))]"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def disembark(disembark_all=False):\n",
" passengers = [0] * (total_passengers - terrorists) + [1] * terrorists\n",
" random.shuffle(passengers)\n",
" scanned_positive = []\n",
" \n",
" for (i, x) in enumerate(passengers):\n",
" if x == 1:\n",
" scan_prob = scan_terrorist\n",
" else:\n",
" scan_prob = scan_civilian\n",
" \n",
" if random.random() < scan_prob:\n",
" scanned_positive.append((x == 1, (i, x)))\n",
" if not disembark_all:\n",
" break\n",
" \n",
" return scanned_positive\n",
" \n",
"disembark()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "taken-redhead",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(False, (68, 0)), (True, (76, 1)), (False, (81, 0)), (False, (84, 0))]"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"disembark(disembark_all=True)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "funny-sucking",
"metadata": {},
"outputs": [],
"source": [
"def estimate(iters, fn=disembark):\n",
" results = []\n",
" terrorists = 0\n",
" counter = 0\n",
" \n",
" for j in range(iters):\n",
" for caught, (i, x) in fn():\n",
" counter += 1\n",
" terrorists += caught\n",
" \n",
" if j % 10 == 0:\n",
" results.append((j, terrorists / counter))\n",
" \n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "extreme-border",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"def show_estimates(fn=disembark):\n",
" plt.figure(figsize=(12, 4))\n",
" final_values = []\n",
" for _ in range(10):\n",
" values = estimate(50000, fn)\n",
" final_values.append(values[-1])\n",
" plt.plot([x for (x, y) in values], [y for (x, y) in values])\n",
" plt.show()\n",
" \n",
" mean = np.mean([x[1] for x in final_values])\n",
" stddev = np.std([[x[1]] for x in final_values])\n",
" \n",
" return mean, stddev"
]
},
{
"cell_type": "markdown",
"id": "informed-operator",
"metadata": {},
"source": [
"## Estimate probablity that the first person to trigger the scanner is a terrorist"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "portuguese-zimbabwe",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.18964978157582774, 0.0015616346378492592)"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_estimates()"
]
},
{
"cell_type": "markdown",
"id": "banned-acrylic",
"metadata": {},
"source": [
"## Estimate probablity that any person to trigger the scanner is a terrorist"
]
},
{
"cell_type": "code",
"execution_count": 126,
"id": "documented-consultancy",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.16116702597261534, 0.00022799807533510806)"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"show_estimates(fn=partial(disembark, True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "legislative-bathroom",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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