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))