Tsutomu-KKE/指数分布

## 指数分布
{
 "metadata": {
  "name": "",
  "signature": "sha256:9c244e750e37ffaef5599a2183796d1f4afba96c518d3930c37eedbddf192989"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#\u6307\u6570\u5206\u5e03\n",
      "\u7269\u5fd8\u308c\u5206\u5e03"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = random.exponential(30, 100000)\n",
      "hist(data, 50, histtype='step'); None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x6ac7a20>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('\u5e73\u5747 =  %.0f' % mean(data))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u5e73\u5747 =  30\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data20 = data[data > 20]\n",
      "hist(data20, 50, histtype='step'); None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x6b15668>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('\u5e73\u5747 = %.0f' % mean(data20))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u5e73\u5747 = 50\n"
       ]
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "",
	"signature": "sha256:9c244e750e37ffaef5599a2183796d1f4afba96c518d3930c37eedbddf192989"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"#\u6307\u6570\u5206\u5e03\n",
	"\u7269\u5fd8\u308c\u5206\u5e03"
	]
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"data = random.exponential(30, 100000)\n",
	"hist(data, 50, histtype='step'); None"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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33VY9S1KndOxWGxExISIm1tPvAi4GNgD3A9fUi10D3FdP3w/Mi4hjImIGMBNYm5nPA7+O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\nAABJRU5ErkJggg==\n",
	"text": [
	"<matplotlib.figure.Figure at 0x6ac7a20>"
	]
	}
	],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"print('\u5e73\u5747 = %.0f' % mean(data))"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\u5e73\u5747 = 30\n"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"data20 = data[data > 20]\n",
	"hist(data20, 50, histtype='step'); None"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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	"text": [
	"<matplotlib.figure.Figure at 0x6b15668>"
	]
	}
	],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"print('\u5e73\u5747 = %.0f' % mean(data20))"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\u5e73\u5747 = 50\n"
	]
	}
	],
	"prompt_number": 4
	}
	],
	"metadata": {}
	}
	]
	}