Как считается диапазон в функции countApprox в Spark (Распределение Пуассона)

poisson.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "from scipy.stats import poisson\n",
      "import pandas as pd\n",
      "from matplotlib import pyplot as plt\n",
      "import matplotlib.ticker\n",
      "\n",
      "plt.rc('ytick', labelsize=18)\n",
      "plt.rc('xtick', labelsize=18)\n",
      "plt.rc('figure', figsize=(16,9))\n",
      "plt.rc('axes', titlesize=25)\n",
      "plt.rc('axes', labelsize=25)\n",
      "plt.rc('mathtext', fontset='stix')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mu = 30"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(0, round(1.5*mu))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "proba = pd.DataFrame({\n",
      "    'P(Dtotal <= X)' : poisson.cdf(x, mu)\n",
      "}, index=x)\n",
      "proba.sample(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>P(Dtotal &lt;= X)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>2.083574e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>5.233734e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>2.257349e-08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "    P(Dtotal <= X)\n",
        "25    2.083574e-01\n",
        "7     5.233734e-07\n",
        "5     2.257349e-08"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tau = 0.75\n",
      "prob1 = (1-tau)/2\n",
      "prob2 = (1+tau)/2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def closest(df, val): return (df-val)[df>=val].idxmin()[0]\n",
      "\n",
      "X1 = closest(proba, prob1)\n",
      "X2 = closest(proba, prob2)\n",
      "(X1,X2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "(24, 36)"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = proba.plot.bar(\n",
      "    title=\"\u041a\u0430\u043a \u0441\u0447\u0438\u0442\u0430\u0435\u0442\u0441\u044f \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 countApprox\",\n",
      "    color=np.where(np.in1d(proba.index, [X1,X2]), 'red', 'orange'),\n",
      "    yticks=np.arange(0,1.2,0.1))\n",
      "\n",
      "plt.legend([r'$P(D_{total} \\leq X)$ \u0420\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u041f\u0443\u0430\u0441\u0441\u043e\u043d\u0430 ($\\lambda='+str(mu)+'$)'], fontsize=25)\n",
      "\n",
      "xarrow = X1/4\n",
      "for y in [prob1,.5]:\n",
      "    ax.annotate(s='',\n",
      "                xy=(xarrow,y),\n",
      "                xytext=(xarrow,y+tau/2),\n",
      "                arrowprops=dict(\n",
      "                    arrowstyle='<->',\n",
      "                    lw=4,\n",
      "                    ec='purple'\n",
      "                    ))\n",
      "    ax.text(xarrow+1, y+tau/4, r'$\\frac{\\tau}{2}$',\n",
      "            fontsize=34,\n",
      "            color='purple')\n",
      "\n",
      "plt.text(x=X1/2, y=0.5, s=\"$y = 0,5$\", color='green', fontsize=24)\n",
      "\n",
      "plt.axhline(y=prob1, color='purple')\n",
      "plt.axhline(y=prob2, color='purple')\n",
      "plt.axhline(y=.5, color='green')\n",
      "\n",
      "ax.set_xticks([X1, mu, X2])\n",
      "ax.set_xticklabels([\n",
      "    '$X_1 = %d$' % (X1,),\n",
      "    '$\\lambda = %d$' % (mu,),\n",
      "    '$X_2 = %d$' % (X2,)],\n",
      "    rotation=45)\n",
      "ax.tick_params(axis='x', which='major', labelcolor='red', pad=15)\n",
      "\n",
      "ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1))\n",
      "ax.xaxis.set_minor_formatter(matplotlib.ticker.FormatStrFormatter('%d'))\n",
      "ax.tick_params(axis='x', which='minor', labelcolor=[0,0,0,.25], labelsize=12, zorder=-1)\n",
      "\n",
      "ax.set_yticks([prob1, prob2])\n",
      "ax.set_yticklabels(\n",
      "    [r'$\\frac{1-\\tau}{2}$', r'$\\frac{1+\\tau}{2}$'],\n",
      "    fontsize=34)\n",
      "ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(.1))\n",
      "ax.tick_params(axis='y', which='major', labelcolor='purple', pad=34)\n",
      "ax.tick_params(axis='y', which='minor', labelcolor='grey', labelsize=12)\n",
      "ax.yaxis.set_minor_formatter(matplotlib.ticker.FormatStrFormatter('%.1f'))\n",
      "\n",
      "\n",
      "\n",
      "ax.grid(True, which='minor')\n",
      "ax.grid(False, which='major', axis='x')\n",
      "\n",
      "plt.savefig('poisson.eps')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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me5A110REREREROQSm5QH2erVq6UnNdxERERERER0+9mxY4fLJuWs4SYiIiIi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\nKCJjyWw25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       "text": [
        "<matplotlib.figure.Figure at 0x7f0bf2337748>"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}