{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 階層的クラスタリング\n", "* 本実習では教師なし学習の一種である階層的クラスタリングを行ないます。\n", " * 階層的クラスタリング とは何か、知らない人は下記リンク参照↓\n", " * [階層的クラスタリングとは](http://image.slidesharecdn.com/140914-intel-l-141004040412-conversion-gate02/95/-12-638.jpg) \n", " * [クラスタリング (クラスター分析)](http://www.kamishima.net/jp/clustering/)\n", "* 本実習は[総合実験(1〜4日目)](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E7%B7%8F%E5%90%88%E5%AE%9F%E9%A8%93%EF%BC%91%E6%97%A5%E7%9B%AE.ipynb)の内容を全て理解していることを前提としています。\n", "\n", "### まずはサンプルデータの取得から" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('iris.txt', )" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# URL によるリソースへのアクセスを提供するライブラリをインポートする。\n", "import urllib\n", "# ウェブ上のリソースを指定する\n", "url = 'https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/iris.txt'\n", "# 指定したURLからリソースをダウンロードし、名前をつける。\n", "urllib.urlretrieve(url, 'iris.txt')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd # データフレームワーク処理のライブラリをインポート\n", "df = pd.read_csv(\"iris.txt\", sep='\\t', na_values=\".\") # データの読み込み" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 得られたデータを確認します。全体像も眺めます。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies
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" ], "text/plain": [ " Unnamed: 0 Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n", "0 1 5.1 3.5 1.4 0.2 setosa\n", "1 2 4.9 3.0 1.4 0.2 setosa\n", "2 3 4.7 3.2 1.3 0.2 setosa\n", "3 4 4.6 3.1 1.5 0.2 setosa\n", "4 5 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head() #データの確認" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Sepal.LengthSepal.WidthPetal.LengthPetal.Width
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" ], "text/plain": [ " Sepal.Length Sepal.Width Petal.Length Petal.Width\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:, 1:5].head() #特徴量のデータは2列目〜5列目" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MZDAspBtvvFNSgxdSRAiPud1uH+1Uq4EfLhUBka8s1ahPbfCafHJyARkM+ZSc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\nktj5cCH2iWrhG4SIv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 図やグラフを図示するためのライブラリをインポートする。\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from pandas.tools import plotting # 高度なプロットを行うツールのインポート\n", "plotting.scatter_matrix(df[df.columns[1:5]], figsize=(6,6), alpha=0.8, diagonal='kde') #全体像を眺める\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### さていよいよ階層的クラスタリングの実行です。\n", "* 階層的クラスタリングを実行するにあたって検討すべき事項は3つ。\n", " * 「特徴量の定義」\n", " * 「距離の定義」(metric)\n", " * 「リンケージ手法」(method)\n", "\n", "ここでは、特徴量の定義は既に済んでいる( df[df.columns[1:5] )ものとし、距離の定義とリンケージ手法の選択を行って階層的クラスタリングを実行します。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# metric は色々あるので、ケースバイケースでどれかひとつ好きなものを選ぶ。\n", "# method も色々あるので、ケースバイケースでどれかひとつ好きなものを選ぶ。\n", "from scipy.cluster.hierarchy import linkage, dendrogram\n", "result1 = linkage(df.iloc[:, 1:5], \n", " metric = 'braycurtis', \n", " #metric = 'canberra', \n", " #metric = 'chebyshev', \n", " #metric = 'cityblock', \n", " #metric = 'correlation', \n", " #metric = 'cosine', \n", " #metric = 'euclidean', \n", " #metric = 'hamming', \n", " #metric = 'jaccard', \n", " #method= 'single')\n", " method = 'average')\n", " #method= 'complete')\n", " #method='weighted')\n", "dendrogram(result1)\n", "plt.title(\"Dedrogram\")\n", "plt.ylabel(\"Threshold\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "とりあえずこれで、デンドログラム(樹形図)は描けました。\n", "\n", "### クラスタを得る\n", "\n", "Threshold (閾値)を変えることでクラスタの数は変わります。どのくらいの閾値が適切か、どのくらいのクラスタ数が適切か、データの性質を理解した上でケースバイケースで検討しなければいけません。適切な閾値、あるいは適切なクラスタ数でクラスタを得る方法を作りましょう。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sXAitW6/njDP2c+XhOE6TJpMgh1MaoNy9gbGSDgGKgXaS7gKWS+oWM1V9ku4G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\nb5jZZ1GQw+nAnkRBDoGUQQ7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Threshold を変えるとクラスタ数や平均クラスタサイズがどう変わるか調べる\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "n_clusters = len(df)\n", "n_samples = len(df)\n", "df1 = pd.DataFrame(result1)\n", "x1 = []\n", "y1 = []\n", "x2 = []\n", "y2 = []\n", "for i in df1.index:\n", " n1 = int(df1.ix[i][0])\n", " n2 = int(df1.ix[i][1])\n", " val = df1.ix[i][2]\n", " n_clusters -= 1\n", " x1.append(val)\n", " x2.append(val)\n", " y1.append(n_clusters)\n", " y2.append(float(n_samples) / float(n_clusters))\n", " \n", "plt.subplot(2, 1, 1)\n", "plt.plot(x1, y1, 'yo-')\n", "plt.title('Threshold dependency of hierarchical clustering')\n", "plt.ylabel('Num of clusters')\n", "plt.subplot(2, 1, 2)\n", "plt.plot(x2, y2, 'ro-')\n", "plt.xlabel('Threshold')\n", "plt.ylabel('Ave cluster size')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 指定した thoreshold でクラスタを得る関数を作る\n", "def get_cluster_by_threshold(result, threshold):\n", " output_clusters = []\n", " output_cluster_ids = []\n", " x_result, y_result = result.shape\n", " n_clusters = x_result + 1\n", " cluster_id = x_result + 1\n", " father_of = {}\n", " df1 = pd.DataFrame(result)\n", " x1 = []\n", " y1 = []\n", " x2 = []\n", " y2 = []\n", " for i in df1.index:\n", " n1 = int(df1.ix[i][0])\n", " n2 = int(df1.ix[i][1])\n", " val = df1.ix[i][2]\n", " n_clusters -= 1\n", " if val < threshold:\n", " father_of[n1] = cluster_id\n", " father_of[n2] = cluster_id\n", " \n", " cluster_id += 1\n", " \n", " cluster_dict = {}\n", " for n in range(x_result + 1):\n", " if not father_of.has_key(n):\n", " output_clusters.append([n])\n", " continue\n", " \n", " n2 = n\n", " m = False\n", " while father_of.has_key(n2):\n", " m = father_of[n2]\n", " #print [n2, m]\n", " n2 = m\n", " \n", " if not cluster_dict.has_key(m):\n", " cluster_dict.update({m:[]})\n", " cluster_dict[m].append(n)\n", " \n", " output_clusters += cluster_dict.values()\n", " \n", " output_cluster_id = 0\n", " output_cluster_ids = [0] * (x_result + 1)\n", " for cluster in sorted(output_clusters):\n", " for i in cluster:\n", " output_cluster_ids[i] = output_cluster_id\n", " output_cluster_id += 1\n", " \n", " return output_cluster_ids" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 1, 1, 0, 2, 1, 0, 1, 1, 0, 0, 1, 3, 2, 2, 2, 0, 2, 0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 1, 1, 0, 2, 2, 1, 1, 0, 0, 1, 0, 0, 5, 1, 0, 2, 1, 0, 1, 0, 0, 6, 6, 6, 7, 6, 7, 6, 8, 6, 7, 8, 7, 9, 6, 7, 6, 7, 7, 9, 7, 10, 6, 10, 6, 6, 6, 6, 11, 6, 7, 7, 7, 7, 10, 7, 6, 6, 9, 7, 7, 7, 6, 7, 8, 7, 7, 7, 6, 8, 7, 11, 10, 12, 11, 11, 13, 14, 12, 12, 12, 11, 11, 11, 10, 10, 11, 11, 13, 13, 9, 11, 10, 13, 10, 11, 12, 10, 10, 11, 12, 12, 13, 11, 10, 10, 13, 11, 11, 10, 11, 11, 11, 10, 11, 11, 11, 10, 11, 11, 10]\n" ] } ], "source": [ "# 指定した thoreshold でクラスタを得る関数を使う。\n", "print get_cluster_by_threshold(result1, 0.05)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 指定したクラスタ数でクラスタを得る関数を作る。\n", "def get_cluster_by_number(result, number):\n", " output_clusters = []\n", " x_result, y_result = result.shape\n", " n_clusters = x_result + 1\n", " cluster_id = x_result + 1\n", " father_of = {}\n", " df1 = pd.DataFrame(result)\n", " x1 = []\n", " y1 = []\n", " x2 = []\n", " y2 = []\n", " for i in df1.index:\n", " n1 = int(df1.ix[i][0])\n", " n2 = int(df1.ix[i][1])\n", " val = df1.ix[i][2]\n", " n_clusters -= 1\n", " if n_clusters >= number:\n", " father_of[n1] = cluster_id\n", " father_of[n2] = cluster_id\n", " \n", " cluster_id += 1\n", " \n", " cluster_dict = {}\n", " for n in range(x_result + 1):\n", " if not father_of.has_key(n):\n", " output_clusters.append([n])\n", " continue\n", " \n", " n2 = n\n", " m = False\n", " while father_of.has_key(n2):\n", " m = father_of[n2]\n", " #print [n2, m]\n", " n2 = m\n", " \n", " if not cluster_dict.has_key(m):\n", " cluster_dict.update({m:[]})\n", " cluster_dict[m].append(n)\n", " \n", " output_clusters += cluster_dict.values()\n", " \n", " output_cluster_id = 0\n", " output_cluster_ids = [0] * (x_result + 1)\n", " for cluster in sorted(output_clusters):\n", " for i in cluster:\n", " output_cluster_ids[i] = output_cluster_id\n", " output_cluster_id += 1\n", " \n", " return output_cluster_ids" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 4, 2, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 2, 4, 2, 4, 2, 4, 4, 2, 2, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 2, 4, 4, 4, 2, 4, 4, 4, 2, 4, 4, 2]\n" ] } ], "source": [ "# 指定したクラスタ数でクラスタを得る関数を使う。\n", "print get_cluster_by_number(result1, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "以上で、階層的クラスタリングができるようになりました。でもこれだけでは、しっくり来る結果が得られたのかどうかよく分かりませんので、図示してみましょう。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### クラスタリング結果のマッピング\n", "* クラスタリング結果を Scatter Matrix 上にマッピングします。\n", "* 色は自分で決めることもできます([総合実験4日目](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E7%B7%8F%E5%90%88%E5%AE%9F%E9%A8%93%EF%BC%94%E6%97%A5%E7%9B%AE.ipynb) 参照)が、色の数が増えると面倒なので次のように自動で決めることもできます。 [cmapのオプション](http://fififactory.com/wiki/index.php?plugin=attach&refer=FrontPage%2FPython%2FMatplotlib&openfile=mpl_cmap.png) を設定すればいろんな色の組み合わせで表現できます。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Mqt7tl1/2J9JLSpKKaoMHB+0JFKzwvwX4JzBGKZWPGG6vCPLcsNCT/fub4vzz\nRbXXFcI/OuA2iMZCAtGMbUZETySbiUaF3QOUs5siBpDKSPoyCvFb9iYJc+Mhnigqs9MonD+NaCxU\nUccXbCOOKM5lks+e0BhLTDwDzjVq82454M9iGJn5B00NdtZxwCeUoo3AJCvmgECvetaQQy0OtPFZ\nItGMIotYAgK3kpN9wr9oygi2XHwSQ0j3TwGsVkkA14vIo5xdFJFNCmZMuHCjjNXQi3xHNXZOZDgx\nWInCggJiiWJAE8I8CjPuKAvOqFhMWIgliqzGSQ9bIzDlSXKyDLzewRdkMG4DQQl/rfVe4DQji6dJ\na12tlFoAtOrc20J6h37Aa0A08Pu2RvouX96ip1OPY948+L//Ezfq6A4UAmoPw+lDHQ7KqGU8/VC0\nHm5ch4Mv2IobzW6KSSGWPkjFoTzKceBCI37QcQHL5E/Z6ps5/Y/NnM/RrTfwqqtEp1laKkauCEHx\nNTt9KQkUipMZwVoOYMVMf5L4gh1UUUexYQB24CaJGAqo4lt2cxoB6pu//hVefZXa7AwWXzgKTTF7\nKOYipjbt197DqcPB5wH3dxLReONT1pNHKZJ08HO2chXT2Gukd0gnvsnrNbZmNOVJ1yre1NmVlSHJ\nYtkmF0uttS3g7RNAi6kWW0nvcC9wH7AR+BgIWvg7HOIXP31668f2FPr2FbXd119LEsvORiPl6SxB\nmoFceHAH3MB1ONlBIVGYqcfly2vuQWM2QuBNmHDg8BkUGwQWtYTVSs6NC6jDyQj6tM8v+AikHhd2\nnJhQ1OMiliimIzPFYqpx4GwkguS3cuHmIJWsJZcskulPsuScue8+bFSj2WAcLbUAejIePOyiGCtm\nhpLum/g0vr+deHx2rsAavhqNQmHHgQmFAxfL2U0C0Q3qWDhxY8Lkew7a1W9RUSF1CezIcxRMNpqW\n0jtM9BZ2UUpVKaUSgi3l6C3W3o1LvLYLr+qns4X/eg7wCVsA2EoBvwqixk4iMUxjCDuNZfFuithj\n2A2mMJAiqrHh5DgGs44D2A3/gCySyKUMhfIFyrTGdg6xnN2AqJpOp/cZFMNBFGbqDOEfF5Bt0oGL\nV/ieely4DbdOF5poLNTiBDRV2CmgklTiOJMJPr10HxI5mgHkUMZQ0g83VvYwvmUv2xHX1WMZzNFI\n/vzA+7s/yQynD9+xl2jMHMUA3mQN9biYQH9e5wfcaNaRRxLRPlfaKuo4nXEAxBOFGzd2XEb2z05e\n3jdBR4RQ7tbCAAAgAElEQVR/MOuWltI7BE4xq4AU4DDh31R6h2XLepe+38t558n/9eyz/mytHSHY\n9A7eot0gM3gXLixB3BqTGMAkBgDi6umlhnpuQH6gEmrYQoFPt+/GQzKxmFC+AcGLEzcV1JJCXINc\nMyUBt0Xp4bdIhGaw4/QZ7EW9U00ysVRhx2Hory0ojmUIeyjGhQeXUcm3xpjduvBQhq2BUfJYhnAs\nQ7riXwo5gfdTCbYG+wLvb4ALAlSUd3I6IK6emzkIyCqgOiAA8hD+AKwq7FgxE4sMylXUNRiQu4IW\nn3Cl1CaaFvIKaDmhu1AJfGNsLwGm4hf+gSFwSTRTESxQ+HtZtkyym/Y2RoyQOJpQ1SNunAvpwQcf\nbPK4YxnMdg5Rj8tQq7R9TnAUA1jGbqIwMy7AWJxOPINII5cy+pHEIap8Ra51g2W1mw9YTwV1pBDL\n+RztGwDGksU+SqnHyVEBD2OEljmKgSxnF1FYKKCSnRSRQDTnczTZpJBPBXFEcQxDyKWMEmxYMJFN\nCrU4cOHxBYL1ViYxgK/ZgRkT44PyQ2vIQFKxYPIFeU0kmw3kY8bEiQFqn3iiKKMWF24sRn6grqa1\np7yj5vvvgJ8a2w3SOwAblVLHA5uAxGBVPm63CMfemkDwssskrUpnrmz6kMivOAUHLmLaOBvx6jxH\nkclgUrEa7nCB++YyDidurJj5K1/48snnUIb33yylhgrqAKigjlJsZPkKYyfwE44z7AchWBIdIYyi\nL8PJoBo7C1mLQlFDPYVUcRXHU4uDOKJw4saJx2esPIoB2HCg8aBQlFITvDtiCNE6/KnOh5HBYNJQ\nRlxDYzxGcGJzVFFPKvG+4yYxgNMY69Pte8k1Umt47999lDKefh2u+et9xtpDi8Jfa92hVH3NpHf4\nm9b6VuAx4BUgBgg6HHDzZsmE2kohqR7LZZdJ/qennvLHeHQGJkxtFvzL2MVOCskmhVJs7DV0/jMY\nTgk2qrFzAsMYRz/fLL4fyeQYuVFG4s/amUo8iURTTT2JRJMWWBQDfC52oaB4G3z2K3DWwSkPweAO\nDrRFm+GzO8DtgFMfhoEntn5OOPGm6NhKAfW4AY0LD7FYSSOevoZXllftYMVMP5IpoNLwBEqhBjvV\nhurnsHTPYcZZC5/cCoUbYcJlcMLtrZ/TXnIpYwk7sGBiLuN8HmsAr/A9+VSQQizXM71Jt+RkYkkm\nlkrqiCeKdBKaPG4QqURhwYGLKMzkUcHHbMKKhUuZSnYbB1c3Hr5gG3mUM5JMZtL2albt1vkrpf6p\ntb6xteO01nc1+uhW4/N8CMKy2Ijequ/3MmSIBOh9/nn3dpuuoJYdFAKQRwWHAuwG37PP9xCtZn8D\nNdClHMNWCkgihkH4s3ZGY+ECjqYEGxnEN+v/Hwo2vgq1pbK99p8dF/7rX4Y6I/5p7b+6XviXUMMe\nSoxkw6Lbj8FKNFYWMJnYJgb5MxnPIapIIZYYLD7BD7CaHMbTTOWpMLB/KRxaL9ub/gtHXw2xaS2e\n0m7WkmusfNxsJJ9TkejYA5Qb0c+yEl3HgQbeO16smDmfoyimhnTiffETjUkghhuYzl5KGEYG/2A5\nGnzeQZdybJvanU+FL1HfTgqZRHaDgMxg6MgT9o8OnNtuli8Xn/jezBVXSHbk7ib83XhYwR4qqWUC\n2bhwU0M90ViMIBgx48RgpZQa3GiySWEzB33FXAaQwn5KiSWKTJJ8gUcA0VgbpJDoKGueh/xVMGoe\njJ3v/zxlaNPbLaE98P2TMssff0nDfalDYf/X/uvVlcHyR8BRDcPPgF0fQWJ/mHEvWNv2fLaKHSfL\n2U09Lo5nKBkkkGBUlrUYgUluPNQa6Rz+x2YWMIVdFLGVAjJJYhpDMBu6fo3mB/ajUIZKATIDZsOd\nQcoQUCbp87gMOLgGNr8JGWNkJfDdY3Lc9HsgKbtj3xWNlUqjnnGgATaVWGTFJH1QhZ1H+QwzJi7n\nOIl6NojCEtR9m0Ssz5sonmgqDTVnKnF8xXbqcDCNoQ1WH81fKwYTCo/hpRXXzKDTEkEldusqGid2\n0xr69ZPUDkOGdF27wo23TkFentRxDhUdTZ61mXxWGmYbMwoHbupxYcXMLEbxDTuJwUoKMWylENBY\nMDcwGMZipc7w759EdpOzqVBwcA189DPZVgou+xASjLrW2gM7PhT1wtj5YAnC9rZvCXxxt2ybzHDD\nD/6+1B7Y/gG4nTD2Alj5OGx9R46tOeT/3mNuhinXh/CfRFI2eL1NMojnAiYDYkPZRyn7KWE/ZdTi\nQAEWzJzGGDaQ7zO4n85YhhirsBzK+JytOHBRhZ0J9GeeLyt7+Gh8b+avloF2yEx47yeiUgNIHgyV\nhjJ68Mkw9/GOfe+HbGQfJSgUk8hmppGwrYZ6XmYltTiIxkIN9b40zanE8wtmdeh7vbWtk42V1gby\njWvHcSHBpWcopIoCKhlM+mGz/mDy+bfm7fMhLbh0aq2DK+gaIrZulfxSvVnwgxRIOvVUMfx2pypl\nZqPikwsPScRgwewzYCUQzVQj948337lG/Hldhn9zFGZisFCOrZE5LAxtDdBsKBOYLA3fj2lj3RFT\nwMTK1OipUaaGK4vA7w481hwGG47XSOnARSk2CqikH8nUG6qMDBLJM9QXGgm6i8KMGze1OLFi9tlS\nXLjZTRE26o2VWSJzuiimIvtY+fO4pA+9wj+wD80hcJixYPKpwcyY2M4hyqllKOlEG2kbQAK7mhOE\nBVSy34jwHUhw+qkkYnyD6hr2U2cEP7YlWjqTpIYFeNpIa2qfv7T7yoBSajCwCtgKOLTWZwTsux+4\nACgDFmutn2ztel98IWVDjwRuvlnSPdx4Y/cp7m7ChBsPHjxYMTODEeyiiAGksJUCnw4yk0SsmHCj\nSScBO07jLBOl1ODADbg5SPiStGVOgul3Q973MPJsUR90hMEnwXG/gMJNMG4B0EJ0+dSbwFUvap8R\nZ8LODyGhH0y4pPlz2ssUBlGNnY2Ge+EnbGYek/iUzbiNbJ2BhUViMJNJEgqFBw9uFBbDoPsD+9lL\nia8o+amMaaCW6wpMFpndb3pD1D5j58MPfxctwLS21StvkpMYwQ/kYMFEOgm+YMIcyjiV0b7cPh/i\nr6trCXA8qMXBJ0Zfb+EgC5jSZt272XhWPHhC5tQQDK15+3zT0v4g+VxrfVUz++5oS06fL7+UwkFH\nAqeeKllbV67018voLOw4yaOCPiSQSDSrySEaCwpFNBbceHDiph9JeND0JdEXJQnyQKQaqh4NWLFg\nNW61Cpy+1UJgmHxbqC4QT5B+UyC+hSDh8RfLX1tw1kLuCtE7p49quO/oa1o+tzIXDm0Aj6H+yTAy\n63bUoNwSVsyMpz/7KEWjqcXJJvKNWb1koDRjwmqIlSTiOEglDlwkEoMKyFJpM169s/7BdF0ZzdwV\nsP8bMfb2P0b+vMx6IHTfE080w8nAgpkSanw2EhMyiXGj6UuCLyUJYHhQCTK4Guo/vPd+24S/HZfP\n79/ZuAJYGAm2ktdI4BFgHOKaCYDWelizJ/k5xSgG834Ts/tHlVJlwF1a6w0tXcThEGPvyy8H0+Ke\nj8kEP/uZRPt2pvB342ERG6jCjsXIReJVGwwnA5uRrTOZOD5gAzXUY8XMdIbxI7lEY2E6I/iBfVRh\nZxpDKaLaZ/CNxco37CIas8+zoi3UlsB7V0B9lQj+ixZCVAgzDPzvFpndm8ww7wVZQQRD5QFpV8l2\ncLsgbTic8w/oPzV0bWuOLJIYQyZrOYATN6vYjwUTlbjRaBy4icFCMrH0I4kfycWNxoaDCfT36fun\nMohK6vDg6dII3v1L4Y1zQbthzXNw616whMnt+Xv2+WwmI+nru7+TiGUxG6k27u84rFQZk5VAY28a\n8Uwkm90UMZA0yYPURiaSTSFV1OHkRIIRqaEhWG+flxBf/CeA2cC1BFcI5iAwEqgHFimlvtRabzb2\nPaW1flApNQJ4EWgyCbg3wjcnB7KyZpGePivIJvd8rr0WHnoI9u9vn50j2PQOgdTioMoIUfeG9nsp\npMqnk6yl3qe1d+ImCksDd7VzAoyEw+nToFLROPphwdwglXRrVB0Eezk4akTwA9iKoSrPP8MG2eeq\nb3pFULAWkgc1rwLyuETwA3jcsh2s8C/ZLqsGh03UdA4b7P5EhH/hRojPgoS+rV+nPSgUJzGSHMoo\npganEUXqwOVbZWWSxFUcz0byKKCKOKPewmxG+Y5JIz5oY2M42bdUfgu0/OaVOZAe4MZ+0MgkEoqB\n9VCA6vEgFQH3t5N6nIDCiRsX2hfvEJjPH+A4hjCOfsQT1a6AK2/UdWcTbBnHH7XWU5VSm7TWEwM/\nC/qLlPoZUKG1frOJfd9orQ8rxhjo7XP//ZLu+E9/CvYbewe/+Q2Ul8Nzz3X8WsF4+2g0n7ONXMqM\nAJYY1nIABUxnBDmUUoeT8fSjgjryqSCVuBZz8weyngOsJgcrZs5gvC+KtyW2vA0f3iACYdxFolYp\n2SFqn7OeAbOhls5fLcFbbgccfztMvNx/jf+eBTnfiJHwssUwqJnaz8sfhm3vyeBx7r/FRbMpGvdl\n5QH41/FQWyzvPW6ZrSYOgJqDYqic/zqMPLPVf7fdrCWXVcaKK5EY4oniIJWYUJzNBCaQTRV2PmQD\nh6jChKIfyZzHUU36/ncmgf25dwm8ejrgEdfYuyvBYtxai66HDf+R7YlXwgX/6dj37jCSBppQnMBw\nfiSHOpyMI4sq7OQZ93ccVjaQj0JiIo4yXDbdePgfmzlEFWnEcy6TOj0orik67O0TQL1SygTsUkr9\nAsiH1tP5NcrUOR34W8C+RKMuQEYw7fjiCykmdKRx++0wejTcdx8M6IS0NgrFHMb6qhGZMXEcQzFj\nIokYTmAo9biIJxqNxkY9cUQF7buzzbANOA3PkmCE/9p/GzNBYPencEe+qH/i+4injZddH8usH2D7\n+w2Ff84yeXXXw7p/Ny/8T/oNTPkpRCcH5wLqpWS7uHTGZUDpLqlyqExQvgessTIgrft3eIX/FAYx\nhizf+ziiKMNGFGYSDG1tEjGcwyTeYDVmTFRTTx4VDaKtu5qcZX6vHo8HKvf5Z/7bP/AftzMEpZxH\nk8Ug0jEZ9qyR9Gl0fzuIw4oJE8czrEFfApRT61s9lGGjkKomi7l0R4L1trsNiEOic6cC3gItrXGS\nUmqNUmoFcEBrvVop9ZSx7zHj80VIbv9mKS2FLVt6V/7+YOnTR9w977uv875ToUgg2qcOSCWOJOOG\nt2D2paOV42La5LQZqC/tF6R+dMgs/3bGGNHHJ2Q2FPwASYOgfC+U7YLYRqqdlCHGhoJhrXiMxfdt\nm+AH6DMWouJlFZKQJasTlx0sXs89BUPbHM/eduKI8v2BqHIChRVIpS5vKmYLJvp0s7TMQ2aKvt/j\nkIEzOaDsc58Az9P0tpuMmiQWq08Fefj9He27v5vqyyRjhQUSpd6TkuC1KchLKZUEaK11dasHhwCv\n2ufVV+G99+D99zvjW7sf1dWS8uGdd6RcZ3vpaJBXKNBocikjBmubfJS3vgvVB+HYmw/3s/ey7I/i\nEqjdkHUUXPiWf5+9Cta+AH0nwIi5HfwnaLovq/JlBbDvK9j8lgwA2dNg4AmQNgpGd6OI7Xqc5FNJ\nOvHdohJXYH/u/gw+/ZW4ysamwzVLITZgMr3kdxJYd+pDXdPWxtThoIAq+pJw2ODQVYRM7aOUOgYx\n+iYa7yuB67TWP7Z4YohYvBjO7dRwsu5FYiI8+qh4//zwQ+eXeQyGSupYzX6isTCNoc3q/xWqzS6E\nzlqZzdeViTtlwVpxBRw+B0ae5T8uPlNmiiB+9YHEJMGJdzZ9/Q2vyDVHnwdDZ7epaQ1Iyoa6Ushd\nDtX5skIxWeCELig3upNC9lHCINKarMccjZVhdDD4IcQsfUDUY0NPFduJs1ZWU41TYpzSxepfNx5+\nYD9V2JnCQPqQ2O36MhiCNfhuBG7RWi833s8AntVahzXuWyml6+s1ffvCzp1S6vBIRWtYsACGDpVy\nqu0hnDP/xWyg0CheMZH+HB9Cl7XVz8K6F2U7oR/UFMi2MsEl7/vzu3jckgOmvgomXQHRQSws8laJ\neyeIoP7JZxAThDaqub58dY5c01Et7UsZCrfuCuKfDCGV1LGQH32e6fM5mvRuptppjFKKf0yVFkcl\nQt5Kmd2brXDrvvB5SrWHTeTzvZHmJIFoLmtjUrbOIJiZf7DKWrdX8ANorVdAozJMYeKbb2Ds2CNb\n8IO4D77wAixcKOqfLqNkK+QuE+kWQGAQjKfZQPjgqa+CdS/Brv9Jzhx7hWTidNvF/a9iv7zqgFKo\nSslMMSpeBPmyP8K/ToT1rzT/PYHna0/D9+1tt7MW0HKtulJJ8rb7UyjeCj++IK9tpnwP5H4jF2wF\n3ag8eCh+j87E4zJ0/i4Z0A+tgw+ugR+eBkctfHKbpHx21EDut+IQUH0wRF/uvb/rm48+D/W93lUE\nO/N/EogF3kAC2S4B7MBrAFrrtc2c11J6h37G+dHA75uK9FVK6bvu0qSkiMtjBFi/HubMkQHg5CYj\nI5qnwzP/kq2wbaFsx/eFKf5i0mXY+J59RpDX8GZT2wbLhzeKKgZgwAmw7R0RBP2nwr6vRTCYo+Dm\nTRJQBZJa+Ye/y3ZClvjZg8zAb94CGaMO/x6QlUXBOonKDVQjtURzffmXTLAVBR4og1HSYHyui5Zo\nWbHEBzuhqcqDDUb1oqh4OPZXzRs+DLZwkL2G2qcnVD9TSvHZnRq3A2JSYdkfjM9NEJMGLin+RtpI\nUQECZE0WozqIA8BlHx7uBNAmSrbBtrdlO64PTP15k4e5cPMde6mijqkMDtpxoTMJpavnUcZr46Ir\nk5HB4JQWzm0uvcO9wH3ARuBjpMzjYTzyiPj3RxCOPloSvi1YIK+nn97GC5TvkRu7JZ2Iux6qDkBc\n34bHBc46G81A04jnLCa0+vVai1CPSfELbS/2SpkV9xkHFTkSKGWySJBPdJKsAKoPAcpI+auhbKc0\nJSHLn+0RoHRnwHd6oLgF4X9s0894myjeBrZCqGs8YdQS8euulz9rnLij1hxqg/C3l/m3HTa/xLMd\ngsSBUF8BrjpIHuI7bDz9OzUHfyiYY2QSe+9K/2faI8Z6k/GbVx+Uz0DsP94+rCmEunIZGFKGymAQ\nNPmrwBLTcDVbV9psKTELZk5uR/GU7kZQwl9r3QEzWLPpHSZqrW8DUEpVNYoJ8GE2SybPCH5OO008\nn+bPh7/9DS69tA0nb35NIp0m3yCuFI3xuGHDSyLJLDEw+SaR1ACZk0WK1pXAkPZl2Pv2z5LuWJng\n9MfErQ+gvlrSI3hTIMckwYEDMrvvOx7K9gBa3Ps8TlELmIAd/4P8lTJIzPg1HFwtK/aZD8Dia8VI\nnDJMZvXhYtt7sPRBqNgn7omNcdvF2ydjBOT/AENmi9dR0KSPEX/HqjxxH9IeWPcP0S+Zo2Q0UQr6\nT5MCAj2cxjUWkgdA5X75VwedBDsWyefD50hXlGyHSVfC53dA0RZZHF3wqkRzt8ra56FwnWwPmiVe\nA3UlMOTU7pNRMUwE6+2TCTwM9Ndan6mUGgecoLVurZJuS+kdAhdoVUAKcJjwDyzg3rgg+ZHMjBmS\n6O6ccyT9wz33HH6vNpvewTuzb0r4O6pE8IPMMKvzwBpvKNQT8Ey5CZeRzqE9HPjWmLlpOPAdDJgm\nutuqPBH8IK9mqz9tQ8E6v9991YGGPvg5X4tQcDvFw+ayD/377ipuVxPbTO63MvHWbmgqL5cywbj5\nHcjlb46CSdf435dsA6dNZqa2IklupCxQvgsIXvjXG2m221sDNtQ4akSdt/29w/dlTfYfk2VkQjBZ\n4Rwj8r2uXILoQBZHh9YHKfzL9/iT1pfvghm/78i/cBiSOVV3i6jfxgT7BP8HcfX0hhrtBN4CWhT+\nWmsnSOUOpdTHwATAK/wDH5MkMLKHNSJQ+EdoyKRJkvXznHNg3z545hl/GDwcPlg++OCDshEV33wJ\nq+hkf8WMqESxsK38E5gsVE+4kA+TyrHhYDIDOYbBTV+jBeIzpTCKySIT1sezZYyZcDmkDpMgrdRh\nkrph6zsi6EfNg5V/kUFj8CwoWAP1laK9Sh4KOUtkMdM4C2dnMWKuVPIyWZApTaMBwBIX4pVHQqZh\nWbZBVLJRbEBDn4lBX2IpO9lFEanEMY+JXZ66GeRe0B4YPFsWmF5GnS0ThZhkmHAprHlWPg+M14hJ\nEdtQ3kop+Zh9XJBfmpjtV2GmDG/52DZSTi0fswk7To5jCJO6me0lWOGfobV+Wyn1awCttUsp1apf\nREvpHYCNSqnjgU1AYlMqnwitk50tdY0vuUTKW779divVvybfJE+RpZnAHmWCCVeKo3VMKmz6jzyR\nbge7bZuxJUnGtA3ktUv424pEDaNMsPVtv/p6+/twR57o+lMGi8Zp3EXyUMelS2pmW6FE+9aWiXpn\n0HR4/WwZLJRZ0iq0Fr0bDobPETVOfRU8P1nUPAAoaf+tu0Ncg7amSAZmS5yMOJOuktVZXAv5rQOo\nxcEuxCpdTi25lHeL9A7eFB5534IpSlI7mMxw3C+llkJClth+hs8BNCQFyFKl4IwnZSKf0A+ig62A\nZ42XGQkq5NV2dlLoq1q3kfxuJ/yDtY3blFLpGAskQ2hXtnwK0HR6B+8A8BjwEPA5olKK0E4SEyUQ\nbvBgKW6fn9/CwQlZzQt+LyazcVw0JPiLpGaY030Kgox2+o17PFC6Qwy1yUMkqMdVL8LREgMZo+UV\nxCAcly76/oUXwftXwYbXIC5NZn1RCZAxVmb9JkvD0P/OJrG/qKmiAqP7tagpPjOCvH54Bt6aLy6L\nHSK+jwgqs1WkYUK/oAU/SBqCRCOFgQnVbVISuOrlz5seAxcoLTUSvvo1rPiz3C9J2Q0FvxeTWVZ/\nQQt+gMR+csNZopvP4tdOAp+R9j4v4SRYV88pwN/xq236ABdqrTe2eGJHG9eohm+EltEaHnsMnn4a\nPvpI1EKBtMvVU3ugaJMIm4yxFFBJJXUMI6Ndev+/9hcjLEiyrvpqeeAzJ8GVnzV9zhvnwb4vZTsm\nRRK7eXHWwp7PZfWe3YmxNs315SNJov/3GHYNS4zMSs95Ab4PqDd7wSvi1dRuag6JPSZ1ZHBRaY2o\nxUEOpfQhsVsIJqUUjw/QeNxgTTDMFwZ9J/on5Sf/ru0lOFuk0f0davIox0Y9w+njq5jWGYSihu+x\nyIx9rVJqJnATsACZreeFrKURQoJScPfdsgI47TRYtKhjuYDkoibIPMr3th/JHfJrjkoQfT2IIPfW\nYY3PgO/+Kp4co89rmIohMK+LtdEk1RoHY85vd3NCyvZFhheSRWasHpe/Bm1CluGe6pHXqLbMTpsi\nIctfGb4dxBHVZNqHriTecM80WRsK/6hE8VGAdo1zLdPo/g413TnDZ4szf6XUWuA0rXWZUupk4E3g\nl8DRwFit9YVhbVxk5t9uPvkErrkGliyB8ePls+6Q2K1wI3x+l2guznpO9P62QugzAd67HFEsKrjy\nC0mIBmJz/uhGKd4y57HgC6yEk8Z9WbBOag7UV4v7/dDTIe87UfvEpcNPV0ksQM43MPjkhplKI0h/\nfv+UBHmZo6Uug9YyIb/qK0mWlzGm7WU5j1RCEeRl1lp7I0wuAf6ptX4XeFcptT4UjYwQHs48E554\nAs44QzyCOqMWQDA0Vu/0GSsze6/vvndmXBsQQxYVB1NvFHVR+uiG13PYYM9novYZMK1z/gcvhZvE\nx3zobH98kDlaVD0mi6xKEvsBSgzbQ2b64xoiHI63IPu3jxqRuh5AiVkjY2zXeXP1VloV/kopi9ba\nBZwK3NiGc30opW4H5mutTwr47H7gAqAMWNxEfd8IHeTyyyE1VWoCdEd2fQJf/062sybL8t5eIQa7\ngQHqqu0fSJ4eEJe/0x/17/vsdn8aiNMfhaEtxZqHmMXXy2C16b9SS3jchdJOd71kwYhKkpnr9LuD\n9DmPAIg60Ov543HAkt9AdYEMCPNekHTdETpOa94+bwDfKKUWAXWAN6vnCILz9kEpFYWkh2hK33CH\n1vqUiOAPH2ee2fYU0E6PZmu1i2pXExFLIaRku3+7aLMIyMxJ8lqV1/RxgdsgnkPN7Qs33jQDVXli\n5D3q/zw4rRqt5FY3mcUF9OhrOrddPZ3934ig96bwKN6tKchwYYvyNEjbEaFjtCj8tdYPAXciQV4z\nAhTwJkT3HwzXG+c3xaNKqc+VUpGxvJvg1pqfba7hqg01XLyumhJH+AaAMeeLPlyZxI97wPHy+YDj\nJaWDl7HzRTWkTIcL0snXi6E7vq8Eg3Um3txE4y6E6hgPF6+rZvVldVRkeFAWTUJWRPC3h5n3+919\nU4bCd/fZeHFBDf+6phrrjA6mXY3go02VvNp8caUswGta60uVUssbqX1StNYVxiriRa31YTkqIwbf\n0BKMwfeg3c25a/wJrh4ZHcfpfToe/BKYI6vBtkdSM3hTNjhsjXzl8R/nzeTZGJddPu9QRsc2opTC\n49G47FJA5otiB7/eUQsaVD28e2wi2bHmUMcN9VqaujfLciEmW3PyykrfPXPP8Fgu6tcNqxl1M0KZ\n1bO9XAm83tQOrXWF8bpbKdWsRIrk9mk/zeb2aYG+USZGJ5jZUeMmxaqYlNSxW0Rr+Pr3YpQdNEN0\n8isekaCuM582onmjRbh/+WtJkzBkNpz2SENhrkzNB2BauqhynlL+ymGjsTDhd7Ek7DNTPMLNygwT\n856RlU2EtlGZC89NFE+pjHGKY163sKbSRZwZpiaHW2QdOYR75v8n/OmgpwG/01o/Y+xL1FpXK6Uy\ngEVa68PKs0dm/qElWFfPOrdmc7WL4XFm0qI6Np0u3QnvXu5/b40zip0g6Rtm3CPbhRth0XX+4857\nsXu4dDZH47789jFY8gdNvVtjdSjSRymm3wWTr+3CRvYgAvvz3Stgy5v+fVet0hSPcDEo1kxmdCcu\n75EsH/wAACAASURBVHowXT7z11rfG9CYZVrrZ5RSTxmpnB9TSk0AFJLbP0I3IdasODYlNIm+4jJE\njeOwycw9ro8USlKWhjnXE7JkFu2sk9cOxC91KvuXSg1gALNHEeWQ580SBanN5M6L0DSf3i7pG7zZ\nXEFyNmUOUwwJ0f0YwU9YZ/4dJTLzDy1dFeRVuksKrmcfB0t/DwfXih/8tF/6fbtBvHUOfAcDT2wo\nALojSilcDs1/ZorAAhg8UwK8kofB8NMiPv1tIbCGb/Y0mTTsWyIrw1HndHHjeiBdPvOPEAEkh0+6\nUfjIGufXg5saTeYyxnR/oR+IMskg5hX+w+c0TDMcoX2Yo2D2g/IXIXxEZv5HEN0hvUPFfljzvBh8\np93adcbajuLty4K1sOl1iT6dckOvL/4UNpRSLH1Q43bKijDoEpcRmiSYmX+3F/5d3YYIESJE6In0\neLVPVw1OH/0MDq6R7ZFn944laHeY+T/0EPzhDzBsGLz3Hoztwhz8HaGlvlz+CGx7V7b7jofzX+7E\nhvVQgr03P79L3IFBEuTNfbzl449UVBBL0IjfVDOYA+JIeqpqorvx3//CK69ATg7ccYeUn7TZurpV\noSewxrA5cu+EFEvkuQwZ3V7t01XtsxVJ5SVLjKQeaFN1oG5KV878a2pg9Gh4/304zqiveumlMvO/\n//4uaVKHaKkvnbVSrau+Go69OeQFonolQceglBuV0LQ8lyEtj9mL6BU6/+7cvp5GVwr/Z56Br74S\nVY+XPXtg2jRZCcR3j0qCQdMdVGi9iUh/hpZghH9E7RMh7GgNzz0Hv/hFw8+HD4cZM+DVV7umXREi\nHMlEhL+B2yk5RdzOrm5J7+P778HphNmzD993883w0kud36ZwUnlAIpojtB+XXZ5HHd6s4kc0EeGP\nFBBffD28NR8+uFpSDEQIHQsXSmGZphwQTj1V1D67d3d+u8LB8ofhrQvgzXOhfF9Xt6ZnUlsKb18o\nz+Nnd8rKMULoiQh/JPlY8Vb/dsm2rm1Pb0Jr0fPPn9/0fosFLroI3nijc9sVLnYskld7pd8lMULb\nyPseag7Jdu5yKd8ZIfREhD+SVtgbURibBqnDurY9vYl168BqhQkTmj/m0kvh7bc7r03hpN8x8moy\nS2nKCG2n7wR/quy0EVLWMULoOaK8fQ5tgJV/gZhUmPVAQzex2lJJK5w5UZJK9Ua6wqPit78Vff+f\n/9z8MW439OsHq1bB0B6SCbO5vnQ7YNf/YMs7YDLBjHuhz7guaGAPQynF4hs1Hiec9BtxsS7bDf2P\ngaiErm5dzyPi7dOIFQ9D8TbJHLnuxYb74tJh6OzeK/i7ik8/hbPOavkYs1kCvj78sHPaFE7MUaJC\nLN0uryv+1NUt6jkU/CgTsO/+CkkDYMisiOAPJ50i/JVStyulljf6rJ9S6iul1Aql1Cmd0Q5rwI0U\nuanCT2kp7NwJJ5zQ+rHz5sHixeFvU2cQFbnPOkSkzzqHsOf2UUpFIdW8Gq+R7wXuAzYCHwNLwt2W\nU/4Ia/8lxcAnX9f68SAGy50fQm2JFOqOTgpvG3sTX38NJ50EUUHUsT39dLj6aqishOTk8LctlHhc\nsPVdUfmMvwim3iif11f5tyO0zoTLpA+PuUlW54WbYMQZYpOLEHrCrvNXSt0MbAP+EFikXSm1RGt9\nirG9CLhCa13T6Nwuj/Dd8jZ8+6hsZx8HZz/bpc3pEJ2t87/pJhgzBm6/Pbjjzz4brroKLrkkvO0K\nBYF9uervsMFI3jZqHszqgekquprA/izcBIuvk4lXbBpc/lHz9ZsjNE1Ii7kopU4EhgSeo7V+pZVz\nLMBMrfVz6vA0c4EqpyogBahpdEyXF3CvPBCwndupX91h2lPAPZR8+eXhUb0tMW+e6P17gvAPpKoH\n3yPdkao8v29/XZkUco/k8Ak9Qc38lVKvAsOB9YDb+FhrrW9t/ixQSl0LlGqtFyullmutTwrY1y1m\n/lV5YgQeOluqMh22Px8++xXYisULYficsDYnrHTmzH/vXjjxRCgoCL7ASV4eHHUUFBaK/393JrAv\ni7fBF/8HTrskchtzfsN7qXyvuCtGBFjzBPanyw6f3SHG3wmXwXG3NDy2YK289pvSyY3sQYQssZtS\nahswrq2SWCn1J0TfDzAN+J3W+hlj35PAm8Am4EPvQNDo/LAK/wMr4fWzwV0PfSbADavC9lXdgs4U\n/v/8JyxbBq+91rbzpk6Fxx+Hmd28/m3jvqwrg/evhJpCEUrnPC9lHr99DLa8JX7rZz8nPuwRDuf/\ns3fe8VFV6f9/n5lJJj0hCQkJvXdEEEFUBBQUEQvWtburrmVXXcva1r72Xvb3VXdXF3vHgg0VBZEq\noHRCDyQhlfRJMuX8/njutNRJMpMC+eQ1r7kzd+6dkzPnPvec5/k8nyfQsfnjvbD8GUDDpJtg+sOh\nb1tnRDCpnhuBHs1tgNb6Dq31LK31LGCj1vpfSqkXjN1PAg8DC4FHmnvuYGDDO2L4AfI3yvKyC8HB\n99/DSSc1/zi366ezIXuNGH6Qmak7Q3XHV/Jst8HurozfVmPLfDzUkS2ftmtTOj0aNf5KqS+UUp8D\nycBmpdS3SqnP3Y/mfJE72Ot2FWmts7TWJ2qtj9Vaf9/Sf6A1GHIaKLNsx/XpopgFCy4XLFrUMuN/\n+umd0/injPIywZIGQ1R32e41WZ5NZug1sX3adiih3xTvdp9j268dhwIadfsopRpdfGutFwe9Rf7f\nHxS3z7tzYOd3ksB17SaI9KESZv8qM7XRF/ob//zNsOQhqeg17SGI793qZoQW5eVw992wb59EWadP\nh48+Er/LyJFw//2o8PA2cfusWQMXXQRbtzb/WK2hd2/R/h86NPhtCxbqc1NU5EHRTohJgSWPQM6v\ngAkcldBzInQfCfuXQb/pMOnG9ml3m6K6Gu65B7ZvhyuukGy/Bx6ADRtE6e+88zwf9e1PlwMWP+j1\n+ef+Dr/9D+L7wJx/w1d/ATSc8ZrIP3R6FBbKtVtYCLfd5q12BOI7fe456NlTaqDGBcY1D6bP/3Gt\n9e1NvRdsBMP4H9wNL/oMkJEXwNlvN33c51fCgd9ke9ApkiPQofH661IxBWSAfPGFaCi7DE3cBx9E\nzZ7dJsb/iScgMxNeeqllx19zDQwaBLfeGtx2BRON+aiX/BPWvw2lhkS4OQxi0sFV410RnPXGYSD7\n8Mkn8Ijh0TWb4d57vWXblJI7vGHMfPtz50L44S75mNZQsBXcVqz7SK/Mc+/JMMvtRO7MeOYZeOcd\n2e7Tx7/i0cyZUGQo2119tTwCQDB9/jPqeW9WgMe2KyLiJfDmRnyvAI/rVv92h0VCgv92WJh/eaxu\nbfdPLFokUs0tRWf1+7sR0U3cPGCMPSU3AJPBVTeZD5NkQd8xGRcHiT50p+hosFrrHoP/9RYe7c/x\n95Vf6RTXZSDwvTYTaqnY+b4O8jXclNvnWuA6YACw02dXLPCL1vrioLam7vcHxe3z432w+l+QPBz+\n+LMsKze+D/YKmYmV7hN6Xnwf7zFVxaL/Y4mQbOAOXyza5ZLZQ2YmXHyxzCA2bYJPPxW3z5lntgnb\np6YGkpNhzx7/a705sNmgRw+hiyYlBbV5QUNjfWkrgdcmQUWBUIOt8eLvT+gnEsV9p8jjsMCHH4rb\n55xzYMgQ0fDYsAHOOMNP6rV2f372J8haARNvhLjewvDpMRaOuxO+Ntw+s148RLS4HA6YNw8KCuDy\nyyE11btv/34pddezp1zXpsDm6612+yil4oFuwKOIHIMbZVrrkKtsB8P4O+3wzqlS+BmEgpe1Gtb9\nV9g9VcUiIhWdAhd+GTgnvTOiLYz/kiVw883w66+tO8+ZZ4q9uDik04uWo7G+/M9EiSUBRCbDbblt\n2LBOCt/+3Pi+ZPiCxNxu3AMRxkrp50dhy8eyPewsmHJ327e1MyAYbh8zkn17PVDm80Ap1SlSVpzV\nXsMPQsErz5Ftl91btrGyQFYEXWgdWuvycaMzu34q8rzb9i76cLNRtN277awGW6H3tfvarb3dheaj\nKeO/BvjVeM4HMoDtxvaa0DatcTiqRQOkurTuvoo8YetUl0nAd/yfxfffezIMmCFunG4DhCnQb6pk\nXx5zs/hlOxxyclpGm2kn/PCDEI1ai9NOg4ULoaqq9ecKFWwHIW+j/6TB5RBSgSkMlAUmNpoDfwgi\nPx82b/YSDZqBkkzJhp50EyT0BxQMPQu6+dR4OOoaWanH9YSjrg1eszs9XC7p9/z8gA8JlO3zb2C+\n1vor4/Us4Eyt9Z9b2taAGteA28dpl5q7+ZvFXXPWm6LHD5C3CRb8Wfz5NRUSWEsaAmf+rxOKQ61a\nBTfeKNVQLrxQ/CmtQKjdPuXl4qvPzfWPNbcU06fDX/8KZ53V+nMFG0op5k3XVJWI4N+p/xKX4X8n\nCzXRZIGz34PBnYIWESRs3QpXXSVBm1NOgX8GTpFTSvHqURqtRT132ZMi8xDXB67bUL/0Shd8cM89\n8PXXEBkJr76KGjEiaGyfSW7DD6C1/hqY3NRBSqmRSqlflFKLlVL/rbXvPqXUb0qpRUqpmwJsByDL\nPXfN3Yo84QO7kblUBo3dBmXZ8l5hRicV3PrpJzH8ICmzHRxLl4o8QzAMP0h5x/feC865QoGqEnnO\nWuVdgbrHossBmw+R0pQB45dfxPBDi8are16y8X25hkHoskU7Gz6mCwa++06ebTb5HQJAoMY/Wyn1\nD6VUP+NxN5AdwHFbjQzeEwCllBpfa//NWuvpWuvnAmwHADFpMpsHEctKHe3d1/sYmeFbIiDGCJp3\nGyCMgU6H444TfjRAG6uZtgTBcvm4cfbZ8O23sqLoiLDGynOPI73UTTd3X5klIHlY4ZhjvPTNVogz\nDT/Tu0qP7QmJA4PQtkMdbvsQHg6TJgV0SKBun0TgPsBNUFsCPNAcxo9S6v+A+7TWecbr+4A5QBFw\nm9b693qOaZDtY7dBwRYx7LULPJdly+Obv0HeBrlB1JRBZJJ8PioZTnocYtO8xyx7GnZ9JzGAY2/v\nQKyfffvg4EEYM6bVpwq122fcOHjhBblnBQuzZ0u28IUXBu+cwYBSiooCTclekXbQwLxpcGCdkdgV\nLuMrurtkqR42vv8DB+QxZkzAtESQ/izcITV8o1PgpaESU+l9DKQdBRvekgncZYsaLui+/i3Y8LaI\nNJ74cCd087YGLhesXy9+1x49gpfh2xoopeYgwm0ZwHlaa6fxfoLWulgpNQh4zbfQi8+xLaZ6rntd\n+MBag8MmlDGXQwZWbDqMOBeOM/KTC7bCJz6UwjNeg9TW29oOh1Aa/9xckWPIz5f8smDhzTeFKt7R\nSjzW7ssVz8Giu8Fe6f2MyQLhcRKwPPdD/8BlF/zh25+f/hHWG8VxtAsskd7J2IS/wIzH6x5fXQpv\nnOh1HU17EAY3UTv6UEari7kopZ7TWt+klPqCumUY0Vqf3lQjtNZfAF8Yap6nAZ8Z7xcbzzuUUg1a\npJYWc4nvg+SEu8+sZAC5ZwPR3b2ftcYL08dpl+zLQyVzsC2LuXz7rVA8g2n4QXKB/vIXyXBvadJY\nWyC+vlKDCswWsFgPk4zeICFxgM8Ls/Sfs0ZeNlTS0RIB4bHe2EtU9/o/1wUvmkryGq+1XtOQwFtT\nwm5KqXCtdY2x/U9gidZ6ofE6VmtdppRKBj7TWtfR6Gts5p+7HnZ8A+lHQf/pUFlkzPRdcMoLIq71\n/jmQuUT0QCpyIXGwGPeoZMkOtFhltpbzmyh82iug97Gy1GwMuZSyg3zSiac/yRJkmTcPnE6IjZXp\n7wUXSFZeB0IoZ/4XXCB1eP/0p9Cce8oUuO664J+7paivL5c9Db+/AUW7QNcI1TMyQVaRYy4Vw5W5\nVLJ7ewXmlg0Z8ikjgzxSiWMQAVjK7GyJvqenS/b40qXi158YHKnS2v35SKzkSKSNFxG8nx+FlDEw\n+yX4+gaZ4Z9aK8O3YBtkLICUkaLH1R6opIb1ZBFJGKPpiYkg+o/tdnj7bSl0fcklkim9cqVceEce\n6ffRYGT4ngksc/vpmwul1OnAzcj8e7vW+mql1PNa6xuVUi8Do5D5+R1a65/rOb5e419dKkVY7DaZ\nzZ/5BnxzI+wzgtzpR8OUf8B7Z8jNwFkD3YeL3npkkuiFTLhOzrPIyBC0RMKteU27Kaux8w6rceBC\nAWdwBN0feVHEmIqLobJSLpD+/cVf0YEQKuPvcEhG+vr1obnfffONaIKtWhX8c7cUDfXl02mGrr/P\nLmWCboPkPWucuIPO+1i46u0BO07eYRU1RlG+0xhNGvGNH3TBBbBjh+h32GwQHy/l1j75RMZ7K+Hb\nn/NOgj0/ePelTwTtk0tRsEWee06UGEBHwgI2kIPQwCbQl7EEkWny6qvyACmOnZEhvn6rFRYs8NP+\nCUaG78XAOqXUdqXUPKXU1UqpgGsRaa0/11pP1VpP01pfbbx3o/F8jdb6OIMNVMfwN4aacjH8ch7J\nAKz0yQK0FRrUTvcFqMWl47J7E3Iq8v1rrzqqvPSyRr8bJw5c7tNiwy6aHCBW0GF8QTOSLTo7Vq2C\nXr1Ct9CZMQOysiSHpaOjpqL+953V3sJBLofIirQXHLg8hh9kttok3GPc6ZQbAMhYLw7+P1Ky1/91\nuc/U01ZY/3ZHgW9fVgTSr82B+zcACaq7E+mqq6GsrNmna9T4a63P0Vr3RFQ9vwXGAPOUUvlKqa8a\nOzaUiE2H8VdL8HbwbMncPekxmdVHJsr2EZdAj/ES6O1xpCSB9T9JVgCpo2HsZXDc3ZA0FMKMlUB4\nVADfTQTj6EM04Qwmhd50g2uvFQ3iI4/EPnki1ZFh6DsaUbv+6SeYPz9o/dHe+PprmBXCZCazWVa5\n8+aF7juChUl/gzD3OFIy/iISZYweeaWM2ZHni2uivRBJGEfTj2jCGUAy3YkhmxKcNJKVe+edkJYG\nxx8vvr3u3WHECHFBuLFhg6jxtRIX/4BHwzk8Hk5+Qvqt7xRx6bqv8xMfk0lc9hpvTKC9MZkBhGMm\ngUiOIEAJ4dooLRVxrNoc58svF5HGXr1E2//00yElBf74R1l9vf02rF0b8NcEzPZRSg0DjkWSuyYB\neVrraQF/UwvQGraPrQg+uUgGh61I6GHWONFR91XvDCYK1y/Dcc2fMdXYqZh5Av0eeaXuhx5+2Ktx\nfvrp8O67oWlMPQiV22fcOHj22dDW3d2yRQLKmZkdo7h7Q3258DbY86PEk+b8GzZ9IH5ocxjMegnS\na2e6tDOKqeRTfseOkzTimM1oVCB+6lmzRMVPKXj6afFH/+9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MpuvOYvcFJ6GUWe7kKUbW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\nF2Ad8HdVbfMg6SzHbqcKyeSMrLvjtmV8iUd7XnWVsYa98cbo8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Scatter Matrix に、クラスタリング結果をマッピング\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "%matplotlib inline\n", "from pandas.tools import plotting # 高度なプロットを行うツールのインポート\n", "plotting.scatter_matrix(df[df.columns[1:5]], figsize=(6,6), alpha=0.8, diagonal='kde', \n", " c=get_cluster_by_number(result1, 5), cmap=cm.rainbow, color=[], s=50)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* クラスタリング結果を、主成分分析のプロット上にマッピングします。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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v5CFauzrQ2KpIi0tm8fxF2NuPvWavcH0SC6Bcoe7ubrZ8voPcQ+XYyvaYpF7i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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 主成分分析のプロットに、クラスタリング結果をマッピング\n", "from sklearn.decomposition import PCA\n", "pca = PCA()\n", "pca.fit(df[df.columns[1:5]])\n", "feature = pca.transform(df[df.columns[1:5]])\n", "plt.figure(figsize=(6, 6))\n", "plt.scatter(feature[:, 0], feature[:, 1], c=get_cluster_by_number(result1, 5), s=60, alpha=0.5,\n", " cmap=plt.cm.rainbow)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### もうちょっと詳しく、流れを理解したい\n", "以上で、階層的クラスタリングと、クラスタリング結果のマッピングまでできるようになりました。ここから先は、やらなくても良いと言えばやらなくても良いところです。階層的クラスタリングをするときに、何が起こっているのか少しだけ詳しく見ながら進めると次のようになります。\n", "\n", "__距離行列生成__" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 距離といっても色んな距離があるので、どれか選ぶ。\n", "import scipy.spatial.distance as distance\n", "n_samples = len(df)\n", "dMatrix = np.zeros([n_samples, n_samples])\n", "for i in range(n_samples):\n", " for j in range(n_samples):\n", " dMatrix[i, j] = distance.braycurtis(df.ix[i, 1:5], df.ix[j, 1:5]) # braycurtis 距離\n", " #dMatrix[i, j] = distance.canberra(df.ix[i, 1:5], df.ix[j, 1:5]) # canberra 距離\n", " #dMatrix[i, j] = distance.chebyshev(df.ix[i, 1:5], df.ix[j, 1:5]) # chebyshev 距離\n", " #dMatrix[i, j] = distance.cityblock(df.ix[i, 1:5], df.ix[j, 1:5]) # cityblock 距離\n", " #dMatrix[i, j] = distance.correlation(df.ix[i, 1:5], df.ix[j, 1:5]) # correlation 距離\n", " #dMatrix[i, j] = distance.cosine(df.ix[i, 1:5], df.ix[j, 1:5]) # cosine 距離\n", " #dMatrix[i, j] = distance.euclidean(df.ix[i, 1:5], df.ix[j, 1:5]) # euclidean 距離\n", " #dMatrix[i, j] = distance.hamming(df.ix[i, 1:5], df.ix[j, 1:5]) # hamming 距離\n", " #dMatrix[i, j] = distance.jaccard(df.ix[i, 1:5], df.ix[j, 1:5]) # jaccard 距離" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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1 rows × 11175 columns

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" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "0 0.035533 0.040816 0.05102 0.009804 0.055556 0.035176 0.014778 \n", "\n", " 7 8 9 ... 11165 11166 11167 \\\n", "0 0.068063 0.040404 0.028571 ... 0.045593 0.014749 0.031884 \n", "\n", " 11168 11169 11170 11171 11172 11173 11174 \n", "0 0.042424 0.030864 0.054545 0.034921 0.035294 0.027692 0.045317 \n", "\n", "[1 rows x 11175 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 距離ベクトルを確認する\n", "pd.DataFrame(dArray).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__階層的クラスタリング__" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#クラスタリングする。リンケージ手法にもいろいろあるので、どれか選ぶ。\n", "result = linkage(dArray, method = 'average')\n", "#result = linkage(dArray, method = 'complete')\n", "#result = linkage(dArray, method = 'single')\n", "#result = linkage(dArray, method = 'weighted')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__デンドログラムの描画__" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# デンドログラムの描画\n", "dend = dendrogram(result)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "----------\n", "## 課題\n", "以下の課題を解いてください。\n", "* __課題1__: 上記のクラスタリングで、「距離の定義」(metric)と「リンケージ手法」(method)を変えるとデンドログラムがどのように変わるか確認してください。\n", "* __課題2__: 同様に、クラスタ数を2〜6に変えるとマッピング結果がどのように変わるか確認してください。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "お疲れ様でした。他の教材は随時下記に追加予定です。→ [IPython Notebook](https://sites.google.com/site/masaakikotera/8-python/8-2-ipython-notebook)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }