Machine Learning in Action_CH2_2_使用kNN改进约会网站的配对效果

from numpy import *import operator# 创建数据def createDataBase():    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]]) # numpy向量    labels = ['A', 'A', 'B', 'B'] # 列表    return group, labels# kNN算法def classify0(inX, dataSet, labels, k):    dataSetSize = dataSet.shape[0] # 获得向量第一维长度    diffMat = tile(inX, (dataSetSize, 1)) - dataSet # 纵向扩大dataSetSize倍    sqDiffMat = diffMat ** 2    sqDistances = sqDiffMat.sum(axis = 1) # 按行求和    distances = sqDistances ** 0.5    sortedDistIndicies = distances.argsort() # 从小到大排序，返回的是索引值的列表    classCount = {} # python字典    for i in range(k):        voteIlabel = labels[sortedDistIndicies[i]]        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 # 数频度，每次加1    # 对字典进行排序    # Python 2 才能使用classCount.iteritems()    sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True)    return sortedClassCount[0][0]# 准备数据：处理读入的数据，只取前三个特征def file2matrix(filename):    fr = open(filename)    arrayOLines = fr.readlines() # 将文件每一行，变成列表的每个元素    numberOfLines = len(arrayOLines)    returnMat = zeros((numberOfLines, 3)) # 3列，注意不能少括号    classLabelVector = []    index = 0    for line in arrayOLines:        line = line.strip() # 截取所有的回车字符        listFromLine = line.split('\t') # 返回一个列表        returnMat[index, :] = listFromLine[0:3] # 列表赋值        # 把datingTestSet.txt文件里的largeDoses变成3，smallDoses变成2，didntLike变成1        classLabelVector.append(int(listFromLine[-1])) # 取最后一个        index += 1    return returnMat, classLabelVector# 归一化特征值def autoNorm(dataSet):    minVals = dataSet.min(0) # 每一列最小值    maxVals = dataSet.max(0) # 每一列最大值    ranges = maxVals - minVals    normDataSet = zeros(shape(dataSet))    m = dataSet.shape[0] # 行数（样本数）    # 归一化公式，处理到0-1    normDataSet = dataSet - tile(minVals, (m, 1))    normDataSet = normDataSet / tile(ranges, (m, 1))    # 也可以只返回矩阵    return normDataSet, ranges, minVals# 分类器针对约会代码的测试代码def datingClassTest():    hoRadio = 0.10    # 获取数据    datingDataMat, datingLabels = file2matrix("datingTestSet.txt")    # 均值归一化    normMat, ranges, minVals = autoNorm(datingDataMat)    m = normMat.shape[0]    numTestVecs = int(m * hoRadio) # 测试向量的数量    errorCount = 0.0    for i in range(numTestVecs):        # 前numTestVecs个作为测试数据，后面作为样本        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)))# 构建完整可用系统def classifyPerson():    resultList = ['完全不喜欢', '有点喜欢', '很喜欢']    # 注意Python 3不能用raw_input    ffMiles = float(input("frequent flier miles earned per year?  "))    percentTats = float(input("percentage of time spent playing video games?  "))    iceCream = float(input("liters of ice cream consumed per year?  "))    datingDataMat, datingLabels = file2matrix("datingTestSet.txt")    normMat, ranges, minVals = autoNorm(datingDataMat)    inArr = array([ffMiles, percentTats, iceCream])    # 注意输入的测试向量也要均值归一化    classifierResult = classify0((inArr - minVals) / ranges, normMat, datingLabels, 3)    print("你大概对这个男人" + resultList[classifierResult - 1])if __name__ == '__main__':    # 从文本文件中解析数据    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')    print(datingDataMat)    print(datingLabels[0:20])    # 用Matplotlib画散点图    import matplotlib    import matplotlib.pyplot as plt    fig = plt.figure()    ax = fig.add_subplot(111)    # ax.scatter(datingDataMat[ : , 1], datingDataMat[ : , 2])    # 不同的颜色，使用第2列和第3列数据    # ax.scatter(datingDataMat[ : , 1], datingDataMat[ : , 2], 15.0 * array(datingLabels), 15.0 * array(datingLabels))    # 使用第1列和第2列数据    ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0 * array(datingLabels), 15.0 * array(datingLabels))    plt.show()    # 关闭图像，否则下面跑不出来！！！！    # 归一化数值    normMat, ranges, minVals = autoNorm(datingDataMat)    print("-------------------归一化数值-----------------------")    print(normMat)    print(ranges)    print(minVals)    print("-------------------测试算法-----------------------")    datingClassTest()    print("-------------------构建完整可用系统-----------------------")    classifyPerson()    # arr = array([[1, 2, 3, 4], [5, 6, 7, 8]])    # print(arr.shape)    # matrix = mat(arr)    # print(matrix.shape)    # print(array([[1, 2],[3, 4]]))    # print(array([(1, 2), (3, 4)]))    # a = array([1, 2])    # print(a.dtype)    # a = [1, 2, 3, 4]    # print(tile(a, 2))    # group, labels = createDataBase()    # print(classify0([0, 0], group, labels, 3)) # 输出B