2019-03-03 Riddler (express)

2019-03-03-express.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from itertools import zip_longest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect():\n",
    "    cards_owned = set([])\n",
    "    history = []\n",
    "    while len(cards_owned) != 12:\n",
    "        card = random.choice(range(12))\n",
    "        cards_owned.add(card)\n",
    "        history.append(len(cards_owned))\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = [collect() for _ in range(1000000)]\n",
    "values = [len(h) for h in sims]\n",
    "\n",
    "aggr = zip_longest(*sims, fillvalue=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "y = [np.mean(step) for step in aggr]\n",
    "x = range(1, len(y) + 1)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.vlines(np.mean(values), ymin=0, ymax=12, label=np.mean(values))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "plt.hist(values, bins=range(100))\n",
    "plt.vlines(np.mean(values), ymin=0, ymax=35000, label=np.mean(values))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}