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@tnarihi
Created March 4, 2014 13:07
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日本語によるNumpyのチュートリアル的なもの。IPythonノートブックビューアで見てください。<http://nbviewer.ipython.org/gist/tnarihi/9346213>
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{
"metadata": {
"name": "Numpy&Scipy tutotrial"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "# Numpy\u3001Scipy\u3001Matplotlib\u306b\u3088\u308bPython\u3067\u306e\u79d1\u5b66\u8a08\u7b97\u5165\u9580\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\n"
},
{
"cell_type": "code",
"collapsed": false,
"input": "# \u304a\u307e\u3058\u306a\u3044\u3001\u7121\u8996\u3057\u3066\u304f\u3060\u3055\u3044\n%pylab inline --no-import-all",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Populating the interactive namespace from numpy and matplotlib\n"
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "import numpy as np",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": "numpy\u3092import\u3057\u3066np\u3068\u3044\u3046\u540d\u524d\u306b\u3059\u308b\u3002\u540c\u69d8\u306b\u30b0\u30e9\u30d5\u30d7\u30ed\u30c3\u30c8\u7528\u306epylab\u3092\u30a4\u30f3\u30dd\u30fc\u30c8"
},
{
"cell_type": "code",
"collapsed": false,
"input": "import pylab as pl",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3072\u3064\u307e\u305a\u30b5\u30a4\u30f3\u30ab\u30fc\u30d6\u3067\u3082\u66f8\u3044\u3066\u307f\u308b\u3053\u3068\u306b\u3059\u308b\u3002\u533a\u9593[0,1]\u3092\uff11\uff10\uff10\u523b\u307f\u3067\u30d9\u30af\u30bf\u30fc\u3092\u4f5c\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "x = np.linspace(0,1,100)\nprint x",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[ 0. 0.01010101 0.02020202 0.03030303 0.04040404 0.05050505\n 0.06060606 0.07070707 0.08080808 0.09090909 0.1010101 0.11111111\n 0.12121212 0.13131313 0.14141414 0.15151515 0.16161616 0.17171717\n 0.18181818 0.19191919 0.2020202 0.21212121 0.22222222 0.23232323\n 0.24242424 0.25252525 0.26262626 0.27272727 0.28282828 0.29292929\n 0.3030303 0.31313131 0.32323232 0.33333333 0.34343434 0.35353535\n 0.36363636 0.37373737 0.38383838 0.39393939 0.4040404 0.41414141\n 0.42424242 0.43434343 0.44444444 0.45454545 0.46464646 0.47474747\n 0.48484848 0.49494949 0.50505051 0.51515152 0.52525253 0.53535354\n 0.54545455 0.55555556 0.56565657 0.57575758 0.58585859 0.5959596\n 0.60606061 0.61616162 0.62626263 0.63636364 0.64646465 0.65656566\n 0.66666667 0.67676768 0.68686869 0.6969697 0.70707071 0.71717172\n 0.72727273 0.73737374 0.74747475 0.75757576 0.76767677 0.77777778\n 0.78787879 0.7979798 0.80808081 0.81818182 0.82828283 0.83838384\n 0.84848485 0.85858586 0.86868687 0.87878788 0.88888889 0.8989899\n 0.90909091 0.91919192 0.92929293 0.93939394 0.94949495 0.95959596\n 0.96969697 0.97979798 0.98989899 1. ]\n"
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u7d9a\u3044\u3066sin\u306e\u8a08\u7b97\u3092\u3059\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "freq = 3.0\ny = np.sin(2*np.pi*freq*x)\nprint y",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[ 0.00000000e+00 1.89251244e-01 3.71662456e-01 5.40640817e-01\n 6.90079011e-01 8.14575952e-01 9.09631995e-01 9.71811568e-01\n 9.98867339e-01 9.89821442e-01 9.45000819e-01 8.66025404e-01\n 7.55749574e-01 6.18158986e-01 4.58226522e-01 2.81732557e-01\n 9.50560433e-02 -9.50560433e-02 -2.81732557e-01 -4.58226522e-01\n -6.18158986e-01 -7.55749574e-01 -8.66025404e-01 -9.45000819e-01\n -9.89821442e-01 -9.98867339e-01 -9.71811568e-01 -9.09631995e-01\n -8.14575952e-01 -6.90079011e-01 -5.40640817e-01 -3.71662456e-01\n -1.89251244e-01 6.43249060e-16 1.89251244e-01 3.71662456e-01\n 5.40640817e-01 6.90079011e-01 8.14575952e-01 9.09631995e-01\n 9.71811568e-01 9.98867339e-01 9.89821442e-01 9.45000819e-01\n 8.66025404e-01 7.55749574e-01 6.18158986e-01 4.58226522e-01\n 2.81732557e-01 9.50560433e-02 -9.50560433e-02 -2.81732557e-01\n -4.58226522e-01 -6.18158986e-01 -7.55749574e-01 -8.66025404e-01\n -9.45000819e-01 -9.89821442e-01 -9.98867339e-01 -9.71811568e-01\n -9.09631995e-01 -8.14575952e-01 -6.90079011e-01 -5.40640817e-01\n -3.71662456e-01 -1.89251244e-01 1.28649812e-15 1.89251244e-01\n 3.71662456e-01 5.40640817e-01 6.90079011e-01 8.14575952e-01\n 9.09631995e-01 9.71811568e-01 9.98867339e-01 9.89821442e-01\n 9.45000819e-01 8.66025404e-01 7.55749574e-01 6.18158986e-01\n 4.58226522e-01 2.81732557e-01 9.50560433e-02 -9.50560433e-02\n -2.81732557e-01 -4.58226522e-01 -6.18158986e-01 -7.55749574e-01\n -8.66025404e-01 -9.45000819e-01 -9.89821442e-01 -9.98867339e-01\n -9.71811568e-01 -9.09631995e-01 -8.14575952e-01 -6.90079011e-01\n -5.40640817e-01 -3.71662456e-01 -1.89251244e-01 -7.34788079e-16]\n"
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x, y)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": "[<matplotlib.lines.Line2D at 0x7f9fcc0a0bd0>]"
},
{
"metadata": {},
"output_type": "display_data",
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HUwisrEy2Jf5pbCTbzMifYeGXjOrLynyTS4x/WPj1RuX2O3iQTuvr0sX4sln4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\nRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x2a86d10>"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u30c9\u30c3\u30c8\u3067\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u307f\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x, y, '.')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": "[<matplotlib.lines.Line2D at 0x3d22450>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text": "<matplotlib.figure.Figure at 0x7f9fcc4e4810>"
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": "### \u30b9\u30e9\u30a4\u30b9\n\u30d9\u30af\u30bf\u30fc\u306e\u8981\u7d20\u306e\u3044\u3061\u90e8\u5206\u3092\u53d6\u308a\u51fa\u3059\u30b9\u30e9\u30a4\u30b9\u3092\u3084\u3063\u3066\u307f\u308b\u3002\u30a2\u30af\u30bb\u30b9\u306e\u4ed5\u65b9\u306fbegin:end:step\u3067\u3042\u308b\u3002\u3053\u3053\u3067\u306f\uff11\u3064\u304a\u304d\u306b\u30b5\u30f3\u30d7\u30eb\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u307f\u308b\u3002"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x[::2], y[::2], '.')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": "[<matplotlib.lines.Line2D at 0x4471e10>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x44613d0>"
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u958b\u59cb\u3001\u7d42\u308f\u308a\u306e\u4f4d\u7f6e\u3082\u5909\u3048\u3066\u307f\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x[25:75:2], y[25:75:2], '.')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": "[<matplotlib.lines.Line2D at 0x3eaaa50>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x411b610>"
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3061\u306a\u307f\u306b\u30d9\u30af\u30bf\u30fc\u3092\u30ea\u30d0\u30fc\u30b9\u3057\u305f\u3044\u5834\u5408\u306f::-1\u3067\u3067\u304d\u308b\u3002\u30b0\u30e9\u30d5\u306f\u308f\u304b\u308a\u306b\u304f\u3044\u304b\u3082\u3057\u308c\u306a\u3044\u3051\u3069\u3053\u3093\u306a\u611f\u3058\u3002"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x, y[::-1])",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": "[<matplotlib.lines.Line2D at 0x7f9fcc5e9990>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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uO9mW+If91zw69L1Tp2irU69XtiV6wcJvAiwczePxsP90RgffcRm1MVj4TYCF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\nuIz/B6eGAN4hUEC9AAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x7f9fcc0c1610>"
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3061\u306a\u307f\u306b\u30b9\u30e9\u30a4\u30b9\u306f\u30d9\u30af\u30bf\u30fc\u3092\u4f7f\u3063\u3066\u3082\u3067\u304d\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "indexes = np.arange(10,30)\nprint indexes",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29]\n"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x[indexes], y[indexes])",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": "[<matplotlib.lines.Line2D at 0x7f9fcc905510>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ypdQ13377rbNt2zbn3nvvdRYsWHD637/77jsnISHBKSwsdAoLC52EhARn3759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\nCEREwtz/A/90Mv7yq1avAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x7f9fcc5f0ad0>"
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u30d9\u30af\u30bf\u30fc\u306f\u9023\u7d50\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "indexes2 = np.arange(40, 60)\nprint 'indexes2', indexes2\nindexes3 = np.concatenate((indexes, indexes2)) # \u9023\u7d50\nprint 'indexes3', indexes3",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "indexes2 [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59]\nindexes3 [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 40 41 42 43 44\n 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59]\n"
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3082\u3061\u308d\u3093\u3053\u308c\u3082\u30b9\u30e9\u30a4\u30b9\u306b\u4f7f\u3048\u308b\u3002\uff08\u308f\u304b\u308a\u3084\u3059\u304f\u3059\u308b\u305f\u3081\u306b\u30c9\u30c3\u30c8\u3067\u30d7\u30ed\u30c3\u30c8\uff09"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.plot(x[indexes3], y[indexes3], '.')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": "[<matplotlib.lines.Line2D at 0x410f3d0>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x7f9fccf22e50>"
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": "bool\u306e\u30d9\u30af\u30bf\u30fc\u3067\u3082\u30b9\u30e9\u30a4\u30b9\u3067\u304d\u307e\u3059\u3002\u4e71\u6570\u3067bool\u306e\u30d9\u30af\u30bf\u30fc\u3092\u4f5c\u3063\u3066\u30b9\u30e9\u30a4\u30b9\u3057\u3066\u63cf\u753b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "indexes = np.random.randint(0, 2, x.size)\nprint indexes\nindexes = indexes.astype('bool')\nprint indexes\npl.plot(x[indexes], y[indexes], '.')",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 0\n 0 0 1 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 1 0 1 0 1 1 1\n 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 1 0 1]\n[False True False True True False False True False False False True\n False True False True True True True False True True True True\n True True False True True True False False False True False False\n False False False True True False True False True False False False\n False True False True False False False True False True True True\n False False False False True False False True False True False True\n True True True False False False False False True True False False\n True False False True False True True True False True False True\n True True False True]\n"
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": "[<matplotlib.lines.Line2D at 0x7f9fccec5850>]"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ec7ed6J0nx55JUygUMvv37+/1td27d5s5c+Z0PX/ppZfMiy++2Ou2RUVFRhIPHjx48OjH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\n8293mwAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x4130610>"
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u866b\u98df\u3044\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u304c\u308f\u304b\u308a\u307e\u3059\u306d\u3002"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## k-means\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3088\u308b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u5b9f\u88c5\u3067\u7df4\u7fd2"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u4e71\u6570\u3067\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3057\u3066\u307f\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "n_sample = 100 # Number of sample\nn_feature = 2 # Number of feature\ndata = np.random.rand(n_sample, n_feature)\npl.scatter(data[:, 0], data[:, 1], s=10, marker='o', color='gray', edgecolor='none')",
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": "<matplotlib.collections.PathCollection at 0x4aa2d50>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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arYabmxu2bNnC208SkVFwqMdMabVatLW1Aej4I2Co9vZ2nD17Fi0tLVi4cGGX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\niYhkhoWfiEhmWPiJiGSGhZ+ISGb+H7SkJnu/XbNJAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x4477850>"
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u521d\u671f\u5316\u3068\u3057\u3066\u4e71\u6570\u3092\u4f7f\u3063\u3066\u30af\u30e9\u30b9\u30bf\u4e2d\u5fc3\uff08\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\uff09\u3092\u751f\u6210\u3059\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "n_cluster=5\nplot_colors = ['r','g','b','c','y']\ncent = np.random.rand(n_cluster, n_feature)\npl.scatter(data[:, 0], data[:, 1], s=10, marker='o', color='gray', edgecolor='none')\npl.scatter(cent[:, 0], cent[:, 1], s=200, marker='x', color=plot_colors)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": "<matplotlib.collections.PathCollection at 0x3e79e50>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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otQjRKGX3ndSX/So1hLMWf1JSEvz8/ODt7Y24uLhBjzt27Bh4PB4yMjJUvaRO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\n4SeEEANDhZ8QQgwMFX5CCDEw/x8SqE/h6kwovgAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x3c70610>"
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u66f8\u304f\u306e\u304c\u3081\u3093\u3069\u304f\u3055\u3044\u306e\u3067\u30e1\u30bd\u30c3\u30c9\u306b\u3057\u3066\u304a\u304f"
},
{
"cell_type": "code",
"collapsed": false,
"input": "def plot_centroid(cent):\n pl.scatter(cent[:, 0], cent[:, 1], s=200, marker='x', color=plot_colors)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u53cd\u5fa9\u306b\u3088\u308b\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u306e\u66f4\u65b0\u90e8\u5206\u3092\u66f8\u3044\u3066\u307f\u308b\u3002\n\n\u307e\u305a\u306f\u30b5\u30f3\u30d7\u30eb\u306e\u6240\u5c5e\u3057\u3066\u3044\u308b\u30af\u30e9\u30b9\u30bf\u3092\u6c7a\u3081\u308bE-step\u306e\u8a08\u7b97\u90e8\u5206\u3092\u66f8\u3044\u3066\u3044\u304f\u3002\n\n\u307e\u305a\u306f\u5404\u30b5\u30f3\u30d7\u30eb\u306e\u5404\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u307e\u3067\u306e\u8ddd\u96e2\u3092\u8a08\u7b97\u3059\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "dist = np.zeros((n_sample, n_cluster)) # (i, j)\u6210\u5206\u304c\u30b5\u30f3\u30d7\u30ebi\u306e\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9j\u307e\u3067\u306e\u8ddd\u96e2\u3068\u306a\u308b\u884c\u5217\u3092\u5b9a\u7fa9\nfor i in xrange(n_sample):\n for j in xrange(n_cluster):\n dist[i, j] = np.sum((data[i, :] - cent[j, :])**2)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3053\u308c\u3067\u3082\u826f\u3044\u306e\u3060\u304c\u3001Python\u3067\u30eb\u30fc\u30d7\u3092\u56de\u3059\u3068\u5b9f\u7528\u7684\u306b\u306f\u8a08\u7b97\u901f\u5ea6\u304c\u9045\u304f\u3066\u4f7f\u3048\u306a\u3044\u3002\u30c6\u30af\u30cb\u30c3\u30af\u3068\u3057\u3066\u6b21\u306e\u3088\u3046\u306b\u884c\u5217\u6f14\u7b97\u3067\u304b\u3051\u308b\u3002\u3053\u3063\u3061\u306e\u307b\u3046\u304c\u5727\u5012\u7684\u306b\u65e9\u3044"
},
{
"cell_type": "code",
"collapsed": false,
"input": "dist = (data**2).sum(axis=1)[:, np.newaxis] - 2 * np.dot(data, cent.T) + (cent**2).sum(axis=1)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u8ddd\u96e2\u306e\u884c\u5217\u304c\u6c42\u307e\u3063\u305f\u306e\u3067\u3001\u5404\u30b5\u30f3\u30d7\u30eb\u3054\u3068\u306b\u8ddd\u96e2\u304c\u4e00\u756a\u8fd1\u3044\u30af\u30e9\u30b9\u30bf\u3001\u3064\u307e\u308a\u6240\u5c5e\u3059\u308b\u30af\u30e9\u30b9\u30bf\u3092\u6c42\u3081\u308b\u3002argmin\uff08\u3042\u308b\u8ef8\u65b9\u5411\u306b\u6700\u5c0f\u306e\u5024\u3092\u6301\u3064\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u8fd4\u3059\u30e1\u30bd\u30c3\u30c9\uff09\u3092\u4f7f\u3046\u3068\u4e00\u767a"
},
{
"cell_type": "code",
"collapsed": false,
"input": "belong = dist.argmin(axis=1)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3069\u3046\u306a\u3063\u305f\u304b\u898b\u3066\u307f\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "for k in xrange(n_cluster):\n data_k = data[belong == k] # \u30af\u30e9\u30b9\u30bfk\u306b\u6240\u5c5e\u3059\u308b\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308a\u51fa\u3059\u30b9\u30e9\u30a4\u30b9\n pl.scatter(data_k[:, 0], data_k[:, 1], s=10, marker='o', color=plot_colors[k], edgecolor='none')\nplot_centroid(cent)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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37JOX96VdcwrhVJYtM7/ow9Vhn7/9zba5Oijp8VtZVtbfKCz8HoPhv+jefaJN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\nTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x3e8cad0>"
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3061\u306a\u307f\u306b\u3001\u6bd4\u8f03\u30aa\u30da\u30ec\u30fc\u30bf\u3092\u30d9\u30af\u30bf\u30fc\u306b\u4f7f\u3046\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306bbool\u306e\u30d9\u30af\u30bf\u30fc\u304c\u8fd4\u3063\u3066\u304d\u307e\u3059\u3002"
},
{
"cell_type": "code",
"collapsed": false,
"input": "print belong==k",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[ True False False True False False True False True False False False\n False False False True False False False False True False False False\n False False True False True True False False False False False False\n False False False False False False True False False True False False\n False True False False False False True True False False True False\n False False False True False False False False False False True True\n False False False False False True False False False False False True\n True False False False False False False False False True False False\n False False True False]\n"
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u3042\u3068\u3067\u4f7f\u3046\u306e\u3067\u30e1\u30bd\u30c3\u30c9\u306b\u3057\u3066\u304a\u304f"
},
{
"cell_type": "code",
"collapsed": false,
"input": "def plot_belong_and_centroid(belong, cent):\n for k in xrange(n_cluster):\n data_k = data[belong == k] # \u30af\u30e9\u30b9\u30bfk\u306b\u6240\u5c5e\u3059\u308b\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308a\u51fa\u3059\u30b9\u30e9\u30a4\u30b9\n pl.scatter(data_k[:, 0], data_k[:, 1], s=10, marker='o', color=plot_colors[k], edgecolor='none')\n plot_centroid(cent)\n\ndef e_step(data, cent):\n dist = (data**2).sum(axis=1)[:, np.newaxis] - 2 * np.dot(data, cent.T) + (cent**2).sum(axis=1)\n belong = dist.argmin(axis=1)\n return belong",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u30af\u30e9\u30b9\u30bf\u3078\u306e\u6240\u5c5e\u304c\u6c42\u307e\u3063\u305f\u306e\u3067\u3001\u3053\u308c\u3092\u4f7f\u3063\u3066\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u3092\u66f4\u65b0\u3059\u308bM-step\u3092\u884c\u3046\u3002"
},
{
"cell_type": "code",
"collapsed": false,
"input": "old_cent = cent.copy() # \u4e00\u5fdc\u53e4\u3044\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u3092\u3068\u3063\u3066\u304a\u304f\nfor k in xrange(n_cluster):\n data_k = data[belong == k]\n cent[k] = data_k.mean(axis=0) # \u884c\uff08\u30b5\u30f3\u30d7\u30eb\uff09\u65b9\u5411\u306b\u5e73\u5747\u3092\u3068\u308b\uff1d\u30af\u30e9\u30b9\u30bf\u306e\u4e2d\u5fc3\u3092\u6c42\u3081\u308b ==> shape (2,)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u65b0\u3057\u3044\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u306e\u4f4d\u7f6e\u3092\u898b\u3066\u307f\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "pl.figure()\npl.title('old centroids')\nplot_belong_and_centroid(belong, old_cent)\npl.figure()\npl.title('new centroids')\nplot_belong_and_centroid(belong, cent)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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5427Oqmj5f/SRsdVvE4X/oYdg40bjQgbHjoEdrWvcWkjht0JKlVd5bP4t7Xp9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\nEvWQwt/KuLvfCICDgzOurjW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"text": "<matplotlib.figure.Figure at 0x3ea7610>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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BwXfaLqRwTnv2wLPPalOVHn9c7zROTWb1iEZlZv6d48cXAxAQcAuRkf/UOZEQ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\nAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x3e8e0d0>"
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": "E-step\u3082\u30e1\u30bd\u30c3\u30c9\u306b\u3057\u3066\u304a\u304f"
},
{
"cell_type": "code",
"collapsed": false,
"input": "def m_step(data, cent, belong):\n new_cent = np.array([data[belong==k].mean(axis=0) for k in xrange(n_cluster)]) # \u30ef\u30f3\u30e9\u30a4\u30ca\u30fc\u3067\u3044\u3063\u305f\u308b\n return new_cent",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u9069\u5f53\u306b\u53cd\u5fa9\u3057\u3066\u307f\u308b"
},
{
"cell_type": "code",
"collapsed": false,
"input": "n_iter = 3\nfor t in xrange(n_iter):\n pl.figure()\n plot_belong_and_centroid(belong, cent)\n belong = e_step(data, cent)\n cent = m_step(data, cent, belong)\npl.figure()\nplot_belong_and_centroid(belong, cent)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x7f9fcceb7350>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": "<matplotlib.figure.Figure at 0x7f9fccec5450>"
},
{
"metadata": {},
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PZ/78+QwbNow77riDJ554ghkzZhAbG0vnzp359NNPbZVdCCGEBWQB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\nXwgh3Mz/DxaK8wAeMIVVAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x4106510>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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QQrgYKfxCCOFipPALIYSL6XThLyoq4rrrriM2NpYpU6ZQUlLSpE1G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\nMVL4hRDCxfx/SRZsPXVxs3MAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x7f9fcceb7490>"
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
}
],
"metadata": {}
}
]
}
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