中心極限定理の実験

central_limit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def central_limit_plot(ax, rvs, title=\"\"):\n",
    "    mean = np.mean(rvs, axis=0)\n",
    "    ax.hist(mean, bins=20, normed=True)\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f04f0b38da0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kqWAmWJIkSQUzwZIkSSqYCZYkSVLBTLAkSZIKZoIlSZJUMBMsSZKkgplgSZIk\nFcwES5IkqWAHNbNTRKwBzgAmgXdl5sOz7PPnwBmZeU6xIUqS+l1EvAa4C1iTmZ+YUbYJ2ELlM2QK\nuDwzn+p9lFLvNEywIuJs4FWZeWZEnAz8DXDmjH1OAV4P/LQrUUqS+lZELAFuBL48xy5TwAWZubd3\nUUnlaqYH61wq30rIzO9HxGERMZSZu2v2WQ1cA/xZ8SFKkvrcc8CFwPvnKF9U/W/gTExMMDKycday\nLVs29zga9VIzCdbRQO0twe3VbU8ARMQVwD8AXimSNIAycxJ4PiLq7XZzRJwA3J+Z1/QmsvKNjGzk\nnTfczZJlRx5QtuPJxzni2FNKiEq90NQYrBle/BYSEYcD76DSy/XzDOg3FElSXdcCXwTGgPURcVlm\n3llyTD2zZNmRDB2+4oDte3Y+XUI06pVmEqytVHqspr0SmB6c+KvAK4D7gYOBEyNidWb+UaFRSpLm\nrcy8dfrniNgAnArUTbCGh5cWHkfRdTZT3/j4UKHH7DfLlw91fF7nw3vdjmYSrC9RGVt1S0ScBvwo\nM58FyMw7gDsAIuJ44NMmV5I00Pa7kxERhwLrgIszcx/wBuDzjSoZHd1VaFDDw0sLrbPZ+sbGdjfc\nZz4bG9vd0Xkt+n3pRp3tJmsNE6zM/GZEfDsiHgQmgKur466eycz1bR1VkrRgVL98rwaOB/ZFxFuB\nu4FNmbk+Iu4BHoqIPcAj1S/n0oLW1BisWQYkPjbLPpup3DKUJA2QzPwOMOcciJl5E3BT7yKSyudM\n7pIkSQUzwZIkSSpYO9M0SBLgJIrSIJuanKzbzleuPJHFixf3MKL+YoIlqW1OoigNrr27Rll9+3aW\nLDtwWck9O7ex9n2XsGrVSSVE1h9MsKQ2NFjY9jzgw8ALwL2ZeV0JIfaMkyhKg2uu9i/HYEkta2Jh\n27XApcBZwPnVRdIlSQPEBEtq3fTCtgf0i1fXWtuRmVszcwrYQGUpKUnSADHBklqUmZOZ+fwcxUcD\nozWvtwHHdD8qSVI/McGSussF0CVpADnIXSrWVvbvsVpR3VZXPyxM2k4MC30h22YVseDttH64FiR1\nzgRL6sx+PVSZuTkilkbEcVQSq4uAtzeqpOjFTlvV7uKoC30h22Z1uuDttG4sfNtODJI6Z4IltajR\nwrbAVcDngCngtsx8orRgJUmlMMGSWtTEwrYPAGf2LiKVydmsJc3GBEuSOuBs1pJmY4IlSR1yNmtJ\nMzlNgyS3JAgCAAAOcUlEQVRJUsFMsCRJkgpmgiVJklQwEyxJkqSCmWBJkiQVzARLkiSpYCZYkiRJ\nBXMeLElSxyLiNcBdwJrM/MSMsvOADwMvAPdm5nUlhCj1lD1YkqSORMQS4Ebgy3Pssha4FDgLOD8i\nTu5VbFJZTLAkSZ16DrgQOGC9oIg4AdiRmVszcwrYAJzb4/iknjPBkiR1JDMnM/P5OYqPBkZrXm8D\njul+VFK5TLAkSb20qOwApF5wkLskqZu2sn+P1YrqtrqGh5cWHkjRdTZT3/j4UKHHnE+WLx9q6hzN\nh/e6HSZYkqQi7ddDlZmbI2JpRBxHJbG6CHh7o0pGR3cVGtTw8NJC62y2vrGx3YUdc74ZG9vd8BwV\n/b50o852kzUTLElSRyLiNGA1cDywLyLeCtwNbMrM9cBVwOeAKeC2zHyitGClHjHBkiR1JDO/A5xT\np/wB4MzeRSSVz0HukiRJBTPBkiRJKpi3CCVJ6sDExAQjIxtnLduyZXOPo1G/MMGSJKkDIyMbeecN\nd7Nk2ZEHlO148nGOOPaUEqJS2UywJEnq0JJlRzJ0+IoDtu/Z+XQJ0agfmGBJmlO9Wx/g7Q9JmktT\nCVZErAHOACaBd2XmwzVl5wAfAV4AMjOv7Eagknqv3q0P8PaHJM2l4VOEEXE28KrMPBO4Erhxxi43\nA5dl5uuBQyPiguLDlFSW6Vsfs/13yNLlZYcnSX2pmWkazgXuAsjM7wOHRUTt4kqnZ+ZT1Z9HgSOK\nDVGSJGl+aeYW4dHAwzWvt1e3PQGQmbsBIuIY4E3AnxQcoyRJmkemJifrjtFcufJEFi9e3MOIeq+d\nQe6LZm6IiCOprDt1VWaOdxyVJEmat/buGmX17dtZsuypA8r27NzG2vddwqpVJ5UQWe80k2BtpdJj\nNe2VwItnLCKWAhuAD2TmV4oNT5IkzUdzTV0xKJoZg/Ul4DfgxRXTf5SZz9aUrwHWZOZ9XYhPkiRp\n3mnYg5WZ34yIb0fEg8AEcHVEXAE8QyX5+h1gVUT8HjAF/G1m/nU3g5bK1mDqkk3AlmrZFHB5zYMg\nkqQB0NQYrMy8Zsamx2p+PqS4cKT+Vzt1SUScDPwNcGbNLlPABZm5t5QAJUmlcyZ3qXX7TV0SEYdF\nxND0E7VUHgQ54GEQDR6fpJIGlwmW1Lq6U5dU3RwRJwD3z9IDrAHhk1TS4DLBkjo3s7fqWuCLwBiw\nPiIuy8w7ex+W+sGgP0klDSoTLKl1dacuycxbp3+OiA3AqUDdBGt4eGnBIbZuthjGx4dm2VNFWb58\n6IDz3g/XgqTOmWBJrfsS8GfALTOnLomIQ4F1wMWZuQ94A/D5RhWOju7qXrRNGB5eOmsMY2O7Z9lb\nRRkb273feZ/rfeglEzypGCZYUovqTV2Smesj4h7goYjYAzySmXeUGrAkqedMsKQ21Ju6JDNvAm7q\nbUSSpH5igiVJ6ogT70oHMsGSJLXNiXel2TWzFqEkSXPZb+Jd4LCIqH381Il3NZBMsCRJnTgaGK15\nPT3xbq2bI+L+iPhI78KSymWCJUkq0mwT776HypQlp0bEZb0PSeo9x2BJkjpR+MS70J35uIquc7o+\nJ+RtXe0ku/PhvW6HCZYkqROFT7wLxU++W/QkrrX1OSFv66Yn2e3G5LrdeK/bYYIlSWqbE+9KszPB\nkiR1xIl3pQOZYEkDbmJign/5l3+Z9TbHli2bS4hIkuY/EyxpwI2MbOSdN9zNkmVHHlC248nHOeLY\nU0qISpLmNxMsSSxZdiRDh684YPuenU+XEI3UfyYmJhgZ2fji6/HxoRd7fe3p1WxMsCRJasCeXrXK\nBEuSpCbY06tWOJO7JElSwUywJEmSCuYtQkmS1DNTk5MvPhhQ+7DAtJUrT2Tx4sVlhFYoEyxJKkHt\nh8y02g+bhfIhI820d9coq2/fzpJlTx1QtmfnNta+7xJWrTqphMiKZYIlSSUYlA8ZaTZzPTCwkJhg\nSVJJBuFDRhpUDnKXJEkqmAmWJElSwUywJEmSCmaCJUmSVDATLEmSpIL5FKE0ACYmJhgZ2Thr2cy5\nmCRJnTPBkgbAyMhG3nnD3SxZduQBZTuefJwjjj2lhKgkaX+zTcBbaz5NwGuCJQ2IueZc2rPz6RKi\nkfqPPb3lW0gT8JpgSVKfWUjf4ucTe3r7w0KZgNcES5L6zEL6Fj/f2NOrophgSVIfWijf4qVB1VSC\nFRFrgDOASeBdmflwTdl5wIeBF4B7M/O6bgQq9RPbhPQztgfpQA0TrIg4G3hVZp4ZEScDfwOcWbPL\nWuBNwFPA1yLi7zLz+12JVuoDtgnpZ+Zbe3Ag+/w138YmNtODdS5wF0Bmfj8iDouIoczcHREnADsy\ncytARGyo7u+HiRayvmwTfnAMhkYfMtDzD5q+aw+ztYXx8SHGxnazZctmVt/+qAPZ56H5NjaxmQTr\naODhmtfbq9ueqP5/tKZsG3BiYdFJ/am0NrH6v3+aJ3c8P2vZnrERfvTcsB8cC1y9DxmAZ5/5Me99\n2y9x3HHHz1reheSr7z4jmnka0IHs89NcYxNrv3hMJ9O1yujdameQ+6I2ywCYePYpJn/6wqxlkzu3\n89xLDqv7+3t3jdU9TNnl/RCDMTZXvmfntrrHb0FHbaIVO3ftYnzX1Kxlz+96Fl46POfvzvX31jtP\nnZzjXpcNyjH37hrjkKVHzBnPc7vHue6W+zh4aPksZWN88v+5stvf8nvSHh586Ft84q9umbXsJUzC\ny1495+8W3RZ6WdYvcfRb2djW5Lpb/rnM6/4AzSRYW6l8C5n2Sir30qfLjqkpW1HdNqf1n11b6AeO\nVIJC2wSwaHh4aVMH/svV/7XJEKWeKbo9QBNt4tcvPo9fv/i8FsKUequZxZ6/BPwGQEScBvwoM58F\nyMzNwNKIOC4iDgIuqu4vLWS2CelnbA/SLBZNTc1+u6FWRHwEeAMwAVwNnAY8k5nrI+Is4HpgCvi7\nzPyLLsYr9QXbhPQztgfpQE0lWJIkSWpeM7cIJUmS1AITLEmSpIKZYEmSJBWsq4s9l70+VYPjnwN8\npHr8zMwriz5+oxhq9vlz4IzMPKfXMUTEscBtwEuB72Tm75cQw9XA5VTei4cz8z1diuE1VGacXpOZ\nn5hR1pP10mwTtokmYxiINtHgHLwM+Cvg1Zn5KwXV2fI13qC+3wP+Q7W+RzPzDzqNsWafltpAgzg3\nAVuqZVPA5Zk5+0y1zdXXVhuZq86IeCXw2Wpsi6hMRvtfMvNzHcTYVhtqUOdbgP8KPAfcnpn/vV5d\nXevBql2fCrgSuHHGLmuBS4GzgPOra1j18vg3A5dl5uuBQyPigiKP32QMRMQpwOupXFiFayKG1cAN\nmXkGMFFtOD2LISKWAu8FXpeZZwOvjojXdiGGJdXjfnmOXbp6PVZjsE3YJhrGMChtoon34QbgEVq4\nDoq+xhu8T4cAv0XlfXo9cEpEnFFAjC23gSbqnAIuyMxzMvNXm0iuCm8j9erMzK3TsQHnAZuBu9ut\nr9021KDORcBNwAVUnpi9qJoYzqmbtwj3W58KOCwihqqBvrg+VWZOAdPrU/Xk+FWn11xko8DcUyN3\nLwaoXKjXdOHYDWOoXjBnAX9fLf/DzHyylzEAPwWep/KP3UHAIcBYF2J4DriQn02A+KIeXY9gm2gm\nBrBNDEqbaHQtfGC6vMA6W73G56wvM/dm5psyc7KarB4K/LiAGKH1NtCozkW0Not+N9pIM383wL8H\n7sjMPR3U124bqlfnK4DxzByrtomvUkkG59TNBGvmGlTT61PNVraN/Wf77fbxyczdABFxDJWV3jcU\nfPyGMUTEFcA/UMnWu6VeDMPAbuDjEXF/dS6bnsaQmc8DHwI2ApuAb2XmE0UHkJmT1WM1E183rsfZ\njmObsE0cEMMAtYlG1+OzXaiz1Wu8bn3Vuv4L8K/Auswc6TTGNttAwziBm1u4prvRRpqJESo9R5/q\npL4O2lC9OkepTJq7KiJeCpwDHFWvsl4Ocu/Zem3NHiMijqTSDXlVZo73MoaIOBx4B7CG1r9dFBJD\n9ecVwF9Q6fL8pYi4sJcxVLtyrwFeBZwAnBERp/YghnrKeC/KiME2MSMGbBNzKeV67EadHV7jB9SX\nmf+NypihCyPi33YSY4FtYObvXQu8h8o1fWpEXNZBfUW1kdnemzOAx6cT4XbrK7ANzYzxCuDTwB1U\nkre67083E6xurE9V1PGn34ANwDWZ+ZWCj91MDL9KpcvxfuBOKhfp6h7HsB0YycyRzJwEvgLMvUJq\nd2I4BfhBZo5n5gtUzsfpXYihUXzdvh6nj2ObsE00imFQ2kTd67EbdbZxjc9ZX0QcHhGvhxd7TO4F\nXtdhjO22gbp/d2bempnbq9f0BqBRstGNNtLM+30Rc48JbKW+dttQo/N4f2aenZmXAD8BRupV1s0E\nq+z1qeY8ftUaKk/O3FfwcZuKITPvyMzXVAfTXUrlSYw/6nEME8DGiFhV3fd0IHsZA5UL9JSoPDEE\n8MtUutu7ab9vHT26HsE2UTcG28TAtYlG1+N0XK304hR9jder76XAZ6rjrwBeS3PXSjfawJx1RsSh\nEfHF6m0tqPQ6/VMHMbbbRpp5v38FeLSJuhrVN0J7bahujBGxISKGI+LlNJEMdnWpnCh5faq5jk/l\nJI4B36TSeKeAv83Mv+5VDJm5vmaf44FPZ+UJisI1eB9WAZ+hch4ey8yrSohh+lHnfcA3MvP9XTj+\naVQGjh5fPc6PqNwm2NSr67Eah23CNtFMDAPRJhqcg3XAzwO/AHwb+GQ2eGy/Xp20eY03iPHfAX9A\n5fw9ms1PV1B4G2gQ5x9SGTy+B3gkM/9zh/W11UYa/d0R8ShwXnW8U6d/c1ttqEGdlwJ/SmUKhxsa\nXY+uRShJklQwZ3KXJEkqmAmWJElSwUywJEmSCmaCJUmSVDATLEmSpIKZYEmSJBXMBEuSJKlgJliS\nJEkF+9+cYaz6ynTJAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04f04a84e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# uniform\n",
    "rvs_1 = stats.uniform.rvs(size=(1, M))\n",
    "rvs_5 = stats.uniform.rvs(size=(5, M))\n",
    "rvs_10 = stats.uniform.rvs(size=(10, M))\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(10, 5))\n",
    "central_limit_plot(axes[0], rvs_1, title=\"$N=1$\")\n",
    "central_limit_plot(axes[1], rvs_5, title=\"$N=5$\")\n",
    "central_limit_plot(axes[2], rvs_10, title=\"$N=10$\")\n",
    "fig.suptitle(\"uniform\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f04f06b8940>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XEfFcYE+zZAt6O4NwGTPO9rrNeu2VNWNws3Zr1Rvm9dhtm5Ik9UrTU4qZeRswN4vwn1GZ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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04f08314e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# binomial\n",
    "n = 100\n",
    "p = 0.3\n",
    "rvs_1 = stats.binom.rvs(n, p, size=(1, M))\n",
    "rvs_5 = stats.binom.rvs(n, p, size=(5, M))\n",
    "rvs_10 = stats.binom.rvs(n, p, size=(10, M))\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(10, 5))\n",
    "central_limit_plot(axes[0], rvs_1, title=\"$N=1$\")\n",
    "central_limit_plot(axes[1], rvs_5, title=\"$N=5$\")\n",
    "central_limit_plot(axes[2], rvs_10, title=\"$N=10$\")\n",
    "fig.suptitle(\"binomial\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f04eaf57208>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bYvl1rmKYD52tWrOBsXWnHTftyMHHKopGZempwJKkuSYmdnPZNbewas2GeW3+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\nZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04eafc0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# poisson\n",
    "mu = 2.5\n",
    "rvs_1 = stats.poisson.rvs(mu, size=(1, M))\n",
    "rvs_5 = stats.poisson.rvs(mu, size=(5, M))\n",
    "rvs_10 = stats.poisson.rvs(mu, size=(10, M))\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(10, 5))\n",
    "central_limit_plot(axes[0], rvs_1, title=\"$N=1$\")\n",
    "central_limit_plot(axes[1], rvs_5, title=\"$N=5$\")\n",
    "central_limit_plot(axes[2], rvs_10, title=\"$N=10$\")\n",
    "fig.suptitle(\"poisson\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}