def file2matrix(filename):

   """

   Desc:

       导入训练数据

   parameters:

       filename: 数据文件路径

   return:

       数据矩阵 returnMat 和对应的类别 classLabelVector

   """

   fr = open(filename)

   # 获得文件中的数据行的行数

   numberOfLines = len(fr.readlines())

   # 生成对应的空矩阵

   # 例如：zeros(2，3)就是生成一个 2*3的矩阵，各个位置上全是 0

   returnMat = zeros((numberOfLines, 3))  # prepare matrix to return

   classLabelVector = []  # prepare labels return

   fr = open(filename)

   index = 0

   for line in fr.readlines():

       # str.strip([chars]) --返回移除字符串头尾指定的字符生成的新字符串

       line = line.strip()

       # 以 '\t' 切割字符串

       listFromLine = line.split('\t')

       # 每列的属性数据

       returnMat[index, :] = listFromLine[0:3]

       # 每列的类别数据，就是 label 标签数据

       classLabelVector.append(int(listFromLine[-1]))

       index += 1

   # 返回数据矩阵returnMat和对应的类别classLabelVector

   return returnMat, classLabelVector

import matplotlib

import matplotlib.pyplot as plt

fig = plt.figure()

ax = fig.add_subplot(111)

ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 15.0*array(datingLabels), 15.0*array(datingLabels))

plt.show()

def autoNorm(dataSet):

    """

    Desc:

        归一化特征值，消除特征之间量级不同导致的影响

    parameter:

        dataSet: 数据集

    return:

        归一化后的数据集 normDataSet. ranges和minVals即最小值与范围，并没有用到

    归一化公式：

        Y = (X-Xmin)/(Xmax-Xmin)

        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。

    """

    # 计算每种属性的最大值、最小值、范围

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    # 极差

    ranges = maxVals - minVals

    normDataSet = zeros(shape(dataSet))

    m = dataSet.shape[0]

    # 生成与最小值之差组成的矩阵

    normDataSet = dataSet - tile(minVals, (m, 1))

    # 将最小值之差除以范围组成矩阵

    normDataSet = normDataSet / tile(ranges, (m, 1))  # element wise divide

    return normDataSet, ranges, minVals

def datingClassTest():

    """

    Desc:

        对约会网站的测试方法

    parameters:

        none

    return:

        错误数

    """

    # 设置测试数据的的一个比例（训练数据集比例=1-hoRatio）

    hoRatio = 0.1  # 测试范围,一部分测试一部分作为样本

    # 从文件中加载数据

    datingDataMat, datingLabels = file2matrix('input/2.KNN/datingTestSet2.txt')  # load data setfrom file

    # 归一化数据

    normMat, ranges, minVals = autoNorm(datingDataMat)

    # m 表示数据的行数，即矩阵的第一维

    m = normMat.shape[0]

    # 设置测试的样本数量， numTestVecs:m表示训练样本的数量

    numTestVecs = int(m * hoRatio)

    print 'numTestVecs=', numTestVecs

    errorCount = 0.0

    for i in range(numTestVecs):

        # 对数据测试

        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)

        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])

        if (classifierResult != datingLabels[i]): errorCount += 1.0

    print "the total error rate is: %f" % (errorCount / float(numTestVecs))

    print errorCount

def clasdifyPerson():

    resultList = ['not at all', 'in small doses', 'in large doses']

    percentTats = float(raw_input("percentage of time spent playing video games ?"))

    ffMiles = float(raw_input("frequent filer miles earned per year?"))

    iceCream = float(raw_input("liters of ice cream consumed per year?"))

    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')

    normMat, ranges, minVals = autoNorm(datingDataMat)

    inArr = array([ffMils, percentTats, iceCream])

    classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels, 3)

    print "You will probably like this person: ", resultList[classifierResult - 1]

>>> kNN.classifyPerson()

percentage of time spent playing video games?10

frequent flier miles earned per year?10000

liters of ice cream consumed per year?0.5

You will probably like this person: in small doses

def img2vector(filename):

    returnVect = zeros((1,1024))

    fr = open(filename)

    for i in range(32):

        lineStr = fr.readLine()

        for j in range(32):

            returnVect[0,32*i+j] = int(lineStr[j])

    return returnVect

>>> testVector = kNN.img2vector('testDigits/0_13.txt')

>>> testVector[0,0:31]

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

>>> testVector[0,31:63]

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

def handwritingClassTest():

    # 1. 导入训练数据

    hwLabels = []

    trainingFileList = listdir('input/2.KNN/trainingDigits')  # load the training set

    m = len(trainingFileList)

    trainingMat = zeros((m, 1024))

    # 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

    for i in range(m):

        fileNameStr = trainingFileList[i]

        fileStr = fileNameStr.split('.')[0]  # take off .txt

        classNumStr = int(fileStr.split('_')[0])

        hwLabels.append(classNumStr)

        # 将 32*32的矩阵->1*1024的矩阵

        trainingMat[i, :] = img2vector('input/2.KNN/trainingDigits/%s' % fileNameStr)

    # 2. 导入测试数据

    testFileList = listdir('input/2.KNN/testDigits')  # iterate through the test set

    errorCount = 0.0

    mTest = len(testFileList)

    for i in range(mTest):

        fileNameStr = testFileList[i]

        fileStr = fileNameStr.split('.')[0]  # take off .txt

        classNumStr = int(fileStr.split('_')[0])

        vectorUnderTest = img2vector('input/2.KNN/testDigits/%s' % fileNameStr)

        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)

        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)

        if (classifierResult != classNumStr): errorCount += 1.0

    print "\nthe total number of errors is: %d" % errorCount

    print "\nthe total error rate is: %f" % (errorCount / float(mTest))