1 from numpy import * 2 import matplotlib 3 import matplotlib.pyplot as plt 4  5 ‘‘‘ 6 输入文本文件名字符串，输出训练样本矩阵和类标签向量 7 ‘‘‘ 8 def file2matrix(filename): 9     fr = open(filename)10     arrayOLines = fr.readlines()　　#一次读取整个文件，将文件内容分析成一个行的列表11     numberOfLines = len(arrayOLines)12     returnMat = zeros((numberOfLines, 3))13     classLabelVector = []14     index = 015     for line in arrayOLines:16         line = line.strip()        #截取掉所有的回车字符17         listFromLine = line.split(‘\t‘)                #将line分割成一个元素列表18         returnMat[index, :] = listFromLine[0:3]        #选取前3个元素存储到特征矩阵中19         classLabelVector.append(int(listFromLine[-1]))        #选取最后一个元素进行存储20         index += 121     return returnMat, classLabelVector22 23 if __name__ == ‘__main__‘:24     datingDataMat, datingLabels = file2matrix(‘datingTestSet2.txt‘)25     fig = plt.figure()26     ax = fig.add_subplot(111)27     ax.scatter(datingDataMat[:,0],datingDataMat[:,1], 20.0*array(datingLabels), 15.0*array(datingLabels))28     plt.show()

>>> import numpy as np>>> np.zeros((3,2))　　#3行2列的零矩阵array([[ 0.,  0.],       [ 0.,  0.],       [ 0.,  0.]])

ax.scatter(datingDataMat[:,0],datingDataMat[:,1], 20.0*array(datingLabels), 15.0*array(datingLabels))#scatter(x,y,大小,颜色)

‘‘‘归一化特征值newValue = http://www.mamicode.com/(oldValue - min) / (max - min)>‘‘‘def autoNorm(dataSet):    minVals = dataSet.min(0)    maxVals = dataSet.max(0)    ranges = maxVals - minVals    normDataSet = zeros(shape(dataSet))    m = dataSet.shape[0]    normDataSet = dataSet - tile(minVals, (m, 1))    #用tile函数将变量内容复制成输入矩阵同样大小的额矩阵    normDataSet = normDataSet / tile(ranges, (m, 1))    return normDataSet, ranges, minValsif __name__ == ‘__main__‘:    datingDataMat, datingLabels = file2matrix(‘datingTestSet2.txt‘)    normMat, ranges, minVals = autoNorm(datingDataMat)    fig = plt.figure()    ax = fig.add_subplot(111)    ax.scatter(normMat[:,0],normMat[:,1], 30.0*array(datingLabels), 100.0*array(datingLabels))    plt.xlabel(u‘每年获得的飞行常客里程数‘)    plt.ylabel(u‘玩视频游戏所耗时间百分比‘)    plt.show()

 1 ‘‘‘ 2 测试代码。 3 评估算法的正确率：提供已有样本的90%作为训练样本，而使用其余的10%数据去测试分类器。 4 错误率 = 错误结果的次数 / 测试数据的总数 5 ‘‘‘ 6 def datingClassTest(): 7     hoRatio = 0.10 8     datingDataMat, datingLabels = file2matrix(‘datingTestSet2.txt‘) 9     normMat, ranges, minVals = autoNorm(datingDataMat)10     m = normMat.shape[0]11     numTestVecs = int(m * hoRatio)        #10%的样本数用于测试12     errorCount = 0    13     for i in range(numTestVecs):14         classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], 15             datingLabels[numTestVecs:m], 20)16         print "the classifier came back with: %d, the real answer is: %d" 17             % (classifierResult, datingLabels[i])18         if (classifierResult != datingLabels[i]): errorCount += 1.019     print "the total error rate is: %f" % (errorCount / float(numTestVecs))　　#输出错误率20 21 if __name__ == ‘__main__‘:22     datingClassTest()

 1 ‘‘‘ 2 输入某个人的信息，给出对对方喜欢程度的预测值 3 ‘‘‘ 4 def classifyPerson(): 5     resultList = [‘not at all‘, ‘in small doses‘, ‘in large doses‘] 6     percentTats = float(raw_input("percentage of time spend playing video games?")) 7     ffMiles = float(raw_input("frequent flier miles earned per year?")) 8     iceCream = float(raw_input("liters of ice cream consumed per year?")) 9     datingDataMat, datingLabels = file2matrix(‘datingTestSet2.txt‘)10     normMat, ranges, minVals = autoNorm(datingDataMat)11     inArr = array([ffMiles, percentTats, iceCream])12     classifierResult = classify0((inArr - minVals) / ranges, normMat, datingLabels, 3)13     print "You will probably like this person: ", resultList[classifierResult - 1]14 15 if __name__ == ‘__main__‘:16     classifyPerson()

1 percentage of time spend playing video games?102 frequent flier miles earned per year?100003 liters of ice cream consumed per year?0.54 You will probably like this person:  in small doses