{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import *    #科学计算包\n",
    "import operator        #运算符模块\n",
    "\n",
    "\n",
    "def creatDataSet():\n",
    "    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])   \n",
    "    labels = ['A','A','B','B']\n",
    "    return group, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  1.1],\n",
       "       [ 1. ,  1. ],\n",
       "       [ 0. ,  0. ],\n",
       "       [ 0. ,  0.1]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group, labels = creatDataSet()\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'A', 'B', 'B']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### k-近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classify0(inX, dataSet, labels, k):\n",
    "    \"\"\"\n",
    "    inx[1,2,3]\n",
    "    DS=[[1,2,3],[1,2,0]]\n",
    "    inX: 用于分类的输入向量\n",
    "    dataSet: 输入的训练样本集\n",
    "    labels: 标签向量\n",
    "    k: 选择最近邻居的数目\n",
    "    注意：labels元素数目和dataSet行数相同；程序使用欧式距离公式.\n",
    "\n",
    "    预测数据所在分类可在输入下列命令\n",
    "    kNN.classify0([0,0], group, labels, 3)\n",
    "    \"\"\"\n",
    "\n",
    "    # -----------实现 classify0() 方法的第一种方式----------------------------------------------------------------------------------------------------------------------------\n",
    "    # 1. 距离计算\n",
    "    dataSetSize = dataSet.shape[0]\n",
    "    # tile生成和训练样本对应的矩阵，并与训练样本求差\n",
    "    \"\"\"\n",
    "    tile: 列-3表示复制的行数， 行-1／2表示对inx的重复的次数\n",
    "\n",
    "    In [8]: tile(inx, (3, 1))\n",
    "    Out[8]:\n",
    "    array([[1, 2, 3],\n",
    "        [1, 2, 3],\n",
    "        [1, 2, 3]])\n",
    "\n",
    "    In [9]: tile(inx, (3, 2))\n",
    "    Out[9]:\n",
    "    array([[1, 2, 3, 1, 2, 3],\n",
    "        [1, 2, 3, 1, 2, 3],\n",
    "        [1, 2, 3, 1, 2, 3]])\n",
    "    \"\"\"\n",
    "    diffMat = tile(inX, (dataSetSize, 1)) - dataSet\n",
    "    \"\"\"\n",
    "    欧氏距离： 点到点之间的距离\n",
    "       第一行： 同一个点 到 dataSet的第一个点的距离。\n",
    "       第二行： 同一个点 到 dataSet的第二个点的距离。\n",
    "       ...\n",
    "       第N行： 同一个点 到 dataSet的第N个点的距离。\n",
    "\n",
    "    [[1,2,3],[1,2,3]]-[[1,2,3],[1,2,0]]\n",
    "    (A1-A2)^2+(B1-B2)^2+(c1-c2)^2\n",
    "    \"\"\"\n",
    "    # 取平方\n",
    "    sqDiffMat = diffMat ** 2\n",
    "    # 将矩阵的每一行相加\n",
    "    sqDistances = sqDiffMat.sum(axis=1)\n",
    "    # 开方\n",
    "    distances = sqDistances ** 0.5\n",
    "    # 根据距离排序从小到大的排序，返回对应的索引位置\n",
    "    # argsort() 是将x中的元素从小到大排列，提取其对应的index（索引），然后输出到y。\n",
    "    # 例如：y=array([3,0,2,1,4,5]) 则，x[3]=-1最小，所以y[0]=3,x[5]=9最大，所以y[5]=5。\n",
    "    # print 'distances=', distances\n",
    "    sortedDistIndicies = distances.argsort()\n",
    "    # print 'distances.argsort()=', sortedDistIndicies\n",
    "\n",
    "    # 2. 选择距离最小的k个点\n",
    "    classCount = {}\n",
    "    for i in range(k):\n",
    "        # 找到该样本的类型\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        # 在字典中将该类型加一\n",
    "        # 字典的get方法\n",
    "        # 如：list.get(k,d) 其中 get相当于一条if...else...语句,参数k在字典中，字典将返回list[k];如果参数k不在字典中则返回参数d,如果K在字典中则返回k对应的value值\n",
    "        # l = {5:2,3:4}\n",
    "        # print l.get(3,0)返回的值是4；\n",
    "        # Print l.get（1,0）返回值是0；\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1\n",
    "    # 3. 排序并返回出现最多的那个类型\n",
    "    # 字典的 items() 方法，以列表返回可遍历的(键，值)元组数组。\n",
    "    # 例如：dict = {'Name': 'Zara', 'Age': 7}   print \"Value : %s\" %  dict.items()   Value : [('Age', 7), ('Name', 'Zara')]\n",
    "    # sorted 中的第2个参数 key=operator.itemgetter(1) 这个参数的意思是先比较第几个元素\n",
    "    # 例如：a=[('b',2),('a',1),('c',0)]  b=sorted(a,key=operator.itemgetter(1)) >>>b=[('c',0),('a',1),('b',2)] 可以看到排序是按照后边的0,1,2进行排序的，而不是a,b,c\n",
    "    # b=sorted(a,key=operator.itemgetter(0)) >>>b=[('a',1),('b',2),('c',0)] 这次比较的是前边的a,b,c而不是0,1,2\n",
    "    # b=sorted(a,key=opertator.itemgetter(1,0)) >>>b=[('c',0),('a',1),('b',2)] 这个是先比较第2个元素，然后对第一个元素进行排序，形成多级排序。\n",
    "    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedClassCount[0][0]\n",
    "    \n",
    "    # ------------------------------------------------------------------------------------------------------------------------------------------\n",
    "    # 实现 classify0() 方法的第二种方式\n",
    "\n",
    "    # \"\"\"\n",
    "    # 1. 计算距离\n",
    "    \n",
    "    # 欧氏距离： 点到点之间的距离\n",
    "    #    第一行： 同一个点 到 dataSet的第一个点的距离。\n",
    "    #    第二行： 同一个点 到 dataSet的第二个点的距离。\n",
    "    #    ...\n",
    "    #    第N行： 同一个点 到 dataSet的第N个点的距离。\n",
    "\n",
    "    # [[1,2,3],[1,2,3]]-[[1,2,3],[1,2,0]]\n",
    "    # (A1-A2)^2+(B1-B2)^2+(c1-c2)^2\n",
    "    \n",
    "    # inx - dataset 使用了numpy broadcasting，见 https://docs.scipy.org/doc/numpy-1.13.0/user/basics.broadcasting.html\n",
    "    # np.sum() 函数的使用见 https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.sum.html\n",
    "    # \"\"\"\n",
    "\t#   dist = np.sum((inx - dataset)**2, axis=1)**0.5\n",
    "    \n",
    "    # \"\"\"\n",
    "    # 2. k个最近的标签\n",
    "    \n",
    "    # 对距离排序使用numpy中的argsort函数， 见 https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.sort.html#numpy.sort\n",
    "    # 函数返回的是索引，因此取前k个索引使用[0 : k]\n",
    "    # 将这k个标签存在列表k_labels中\n",
    "    # \"\"\"\n",
    "    # k_labels = [labels[index] for index in dist.argsort()[0 : k]]\n",
    "\t# \"\"\"\n",
    "    # 3. 出现次数最多的标签即为最终类别\n",
    "    \n",
    "    # 使用collections.Counter可以统计各个标签的出现次数，most_common返回出现次数最多的标签tuple，例如[('lable1', 2)]，因此[0][0]可以取出标签值\n",
    "\t# \"\"\"\n",
    "    # label = Counter(k_labels).most_common(1)[0][0]\n",
    "    # return label\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = classify0([0,0], group, labels, 3)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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4pMDxwAXAjyStTpddHhG3Z9B2JXly1SwxjnHMZnbeZRROFmfFrASUQS3WgHvwZjZaHqwq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\nMg52M7OScbCbmZWMg93MrGQc7GZmJfP/ADWafpnL6AMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce41b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ed4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sklearn实现KNN\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import *\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import neighbors, datasets\n",
    "\n",
    "n_neighbors = 3\n",
    "\n",
    "# 导入一些要玩的数据\n",
    "# iris = datasets.load_iris()\n",
    "# X = iris.data[:, :2]  # 我们只采用前两个feature. 我们可以使用二维数据集避免这个丑陋的切片\n",
    "# y = iris.target\n",
    "\n",
    "# print 'X=', type(X), X\n",
    "# print 'y=', type(y), y\n",
    "\n",
    "X = array([[-1.0, -1.1], [-1.0, -1.0], [0, 0], [1.0, 1.1], [2.0, 2.0], [2.0, 2.1]])\n",
    "y = array([0, 0, 0, 1, 1, 1])\n",
    "\n",
    "# print 'X=', type(X), X\n",
    "# print 'y=', type(y), y\n",
    "\n",
    "h = .02  # 网格中的步长\n",
    "\n",
    "# 创建彩色的地图\n",
    "# cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "# cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00'])\n",
    "\n",
    "for weights in ['uniform', 'distance']:\n",
    "    # 我们创建了一个knn分类器的实例，并适合数据。\n",
    "    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n",
    "    clf.fit(X, y)\n",
    "\n",
    "    # 绘制决策边界。为此，我们将为每个分配一个颜色\n",
    "    # 来绘制网格中的点 [x_min, x_max]x[y_min, y_max].\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # 将结果放入一个彩色图中\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure()\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "    # 绘制训练点\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n",
    "              % (n_neighbors, weights))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 案例1   优化约会网站的配对效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def file2matrix(filename):\n",
    "    \"\"\"\n",
    "    Desc:\n",
    "        导入训练数据\n",
    "    parameters:\n",
    "        filename: 数据文件路径\n",
    "    return: \n",
    "        数据矩阵 returnMat 和对应的类别 classLabelVector\n",
    "    \"\"\"\n",
    "    fr = open(filename)\n",
    "    # 获得文件中的数据行的行数\n",
    "    numberOfLines = len(fr.readlines())\n",
    "    # 生成对应的空矩阵\n",
    "    # 例如：zeros(2，3)就是生成一个 2*3的矩阵，各个位置上全是 0 \n",
    "    returnMat = zeros((numberOfLines, 3))  # prepare matrix to return\n",
    "    classLabelVector = []  # prepare labels return\n",
    "    fr = open(filename)\n",
    "    index = 0\n",
    "    for line in fr.readlines():\n",
    "        # str.strip([chars]) --返回移除字符串头尾指定的字符生成的新字符串\n",
    "        line = line.strip()\n",
    "        # 以 '\\t' 切割字符串\n",
    "        listFromLine = line.split('\\t')\n",
    "        # 每列的属性数据\n",
    "        returnMat[index, :] = listFromLine[0:3]\n",
    "        # 每列的类别数据，就是 label 标签数据\n",
    "        classLabelVector.append(int(listFromLine[-1]))\n",
    "        index += 1\n",
    "    # 返回数据矩阵returnMat和对应的类别classLabelVector\n",
    "    return returnMat, classLabelVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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OnzC215zhvF4x3I7w7c8gQN2PWc6D0c8YMiHZMNDXYyzWcvGYBZtmx/5RLO3I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\nCAgIWGQIhj8gICBgkSEY/oCAgIBFhv8fOTXvzXlXgt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ea8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7mKHFW+0WbE4bZYXlNGhRj8e/vb9yDCUWkTwOqe4FdQeY6oG5Y8j7EeZmiITn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\nFB8rBmbg7ZoMWu16i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cL8rfQ7pRJVtTxu9M3IkjHIwBrLWJbdhZnfC8puIFaWy0G06uTD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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e04908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 15.0*array(datingLabels), 15.0*array(datingLabels))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JURkjkPCsWMhkg7P02P+WWDGNYO8vvhFx5NhKgrKhMUUyJHZ+Nedb8RoiFRCc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f0kScrV4s7PvcdtG9DT7Xa2XTjnFbBVXeePY0dybJM3SBKlQsPpUK48/WrXxSQrKDfR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\n9+PHj5+7hP8D39Wlp0EeIEYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ea8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0*array(datingLabels), 15.0*array(datingLabels))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def autoNorm(dataSet):\n",
    "    \"\"\"\n",
    "    Desc:\n",
    "        归一化特征值，消除特征之间量级不同导致的影响\n",
    "    parameter:\n",
    "        dataSet: 数据集\n",
    "    return:\n",
    "        归一化后的数据集 normDataSet. ranges和minVals即最小值与范围，并没有用到\n",
    "\n",
    "    归一化公式：\n",
    "        Y = (X-Xmin)/(Xmax-Xmin)\n",
    "        其中的 min 和 max 分别是数据集中的最小特征值和最大特征值。该函数可以自动将数字特征值转化为0到1的区间。\n",
    "    \"\"\"\n",
    "    # 计算每种属性的最大值、最小值、范围\n",
    "    minVals = dataSet.min(0)\n",
    "    maxVals = dataSet.max(0)\n",
    "    # 极差\n",
    "    ranges = maxVals - minVals\n",
    "    # -------第一种实现方式---start-------------------------\n",
    "    normDataSet = zeros(shape(dataSet))\n",
    "    m = dataSet.shape[0]\n",
    "    # 生成与最小值之差组成的矩阵\n",
    "    normDataSet = dataSet - tile(minVals, (m, 1))\n",
    "    # 将最小值之差除以范围组成矩阵\n",
    "    normDataSet = normDataSet / tile(ranges, (m, 1))  # element wise divide\n",
    "    # -------第二种实现方式---end---------------------------------------------\n",
    "    \n",
    "    # # -------第二种实现方式---start---------------------------------------\n",
    "    # norm_dataset = (dataset - minvalue) / ranges\n",
    "    # # -------第二种实现方式---end---------------------------------------------\n",
    "    return normDataSet, ranges, minVals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normMat, ranges, minVals = autoNorm(datingDataMat) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  9.12730000e+04,   2.09193490e+01,   1.69436100e+00])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.      ,  0.      ,  0.001156])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minVals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def datingClassTest():\n",
    "    \"\"\"\n",
    "    Desc:\n",
    "        对约会网站的测试方法\n",
    "    parameters:\n",
    "        none\n",
    "    return:\n",
    "        错误数\n",
    "    \"\"\"\n",
    "    # 设置测试数据的的一个比例（训练数据集比例=1-hoRatio）\n",
    "    hoRatio = 0.1  # 测试范围,一部分测试一部分作为样本\n",
    "    # 从文件中加载数据\n",
    "    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')  # load data setfrom file\n",
    "    # 归一化数据\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    # m 表示数据的行数，即矩阵的第一维\n",
    "    m = normMat.shape[0]\n",
    "    # 设置测试的样本数量， numTestVecs:m表示训练样本的数量\n",
    "    numTestVecs = int(m * hoRatio)\n",
    "    print ('numTestVecs=', numTestVecs)\n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        # 对数据测试\n",
    "        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)\n",
    "        print (\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, datingLabels[i]))\n",
    "        if (classifierResult != datingLabels[i]): errorCount += 1.0\n",
    "    print (\"the total error rate is: %f\" % (errorCount / float(numTestVecs)))\n",
    "    print (errorCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numTestVecs= 100\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the total error rate is: 0.050000\n",
      "5.0\n"
     ]
    }
   ],
   "source": [
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classifyPerson():\n",
    "    resultList = ['not at all', 'in small doses', 'in large doses']\n",
    "    percentTats = float(input(\"percentage of time spent playing video games ?\"))\n",
    "    ffMiles = float(input(\"frequent filer miles earned per year?\"))\n",
    "    iceCream = float(input(\"liters of ice cream consumed per year?\"))\n",
    "    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    inArr = array([ffMiles, percentTats, iceCream])\n",
    "    classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels, 3)\n",
    "    print (\"You will probably like this person: \", resultList[classifierResult - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of time spent playing video games ?100\n",
      "frequent filer miles earned per year?10000\n",
      "liters of ice cream consumed per year?22\n",
      "You will probably like this person:  in large doses\n"
     ]
    }
   ],
   "source": [
    "classifyPerson()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  案例2  手写识别系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def img2vector(filename):\n",
    "    \"\"\"\n",
    "    将图像数据转换为向量\n",
    "    :param filename: 图片文件 因为我们的输入数据的图片格式是 32 * 32的\n",
    "    :return: 一维矩阵\n",
    "    该函数将图像转换为向量：该函数创建 1 * 1024 的NumPy数组，然后打开给定的文件，\n",
    "    循环读出文件的前32行，并将每行的头32个字符值存储在NumPy数组中，最后返回数组。\n",
    "    \"\"\"\n",
    "    returnVect = zeros((1, 1024))\n",
    "    fr = open(filename)\n",
    "    for i in range(32):\n",
    "        lineStr = fr.readline()\n",
    "        for j in range(32):\n",
    "            returnVect[0, 32 * i + j] = int(lineStr[j])\n",
    "    return returnVect\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testVector = img2vector('./digits/testDigits/0_13.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testVector[0, 0:31]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testVector[0, 32:63]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import  listdir\n",
    "def handwritingClassTest():\n",
    "    # 1. 导入训练数据\n",
    "    hwLabels = []\n",
    "    trainingFileList = listdir('./digits/trainingDigits')  # load the training set\n",
    "    m = len(trainingFileList)\n",
    "    trainingMat = zeros((m, 1024))\n",
    "    # hwLabels存储0～9对应的index位置， trainingMat存放的每个位置对应的图片向量\n",
    "    for i in range(m):\n",
    "        fileNameStr = trainingFileList[i]\n",
    "        fileStr = fileNameStr.split('.')[0]  # take off .txt\n",
    "        classNumStr = int(fileStr.split('_')[0])\n",
    "        hwLabels.append(classNumStr)\n",
    "        # 将 32*32的矩阵->1*1024的矩阵\n",
    "        trainingMat[i, :] = img2vector('./digits/trainingDigits/%s' % fileNameStr)\n",
    "\n",
    "    # 2. 导入测试数据\n",
    "    testFileList = listdir('./digits/testDigits')  # iterate through the test set\n",
    "    errorCount = 0.0\n",
    "    mTest = len(testFileList)\n",
    "    for i in range(mTest):\n",
    "        fileNameStr = testFileList[i]\n",
    "        fileStr = fileNameStr.split('.')[0]  # take off .txt\n",
    "        classNumStr = int(fileStr.split('_')[0])\n",
    "        vectorUnderTest = img2vector('./digits/testDigits/%s' % fileNameStr)\n",
    "        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)\n",
    "        print (\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, classNumStr))\n",
    "        if (classifierResult != classNumStr): errorCount += 1.0\n",
    "    print (\"\\nthe total number of errors is: %d\" % errorCount)\n",
    "    print (\"\\nthe total error rate is: %f\" % (errorCount / float(mTest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 2, the real answer is: 2\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 4, the real answer is: 4\n",
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      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n"
     ]
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     "output_type": "stream",
     "text": [
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 2, the real answer is: 2\n",
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      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 6, the real answer is: 6\n",
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      "the classifier came back with: 1, the real answer is: 1\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 5, the real answer is: 5\n",
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      "the classifier came back with: 2, the real answer is: 2\n",
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      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
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      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
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      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "\n",
      "the total number of errors is: 11\n",
      "\n",
      "the total error rate is: 0.011628\n"
     ]
    }
   ],
   "source": [
    "handwritingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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