Machine Learning in Action_CH2_3_使用kNN手写数字识别

from numpy import *import operatorfrom os import listdir# kNNdef 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]# 将32*32二进制图像矩阵转换为1*1024的向量def img2vector(filename):    fr = open(filename)    returnVect = zeros((1, 1024))    for i in range(32):        # 每次读一行        lineStr = fr.readline()        # 错误        # returnVect[32 * i, 32 * (i + 1)] = lineStr[0 : 32]        for j in range(32):            returnVect[0, 32 * i + j] = int(lineStr[j]) # 要强转为int类型，所以不能列表整体赋值    return returnVect# 手写数字识别系统def handwritingClassTest():    hwLabels = []    trainingFileList = listdir('trainingDigits') # 得到一个列表    m = len(trainingFileList)    trainingMat = zeros((m, 1024))    for i in range(m):        fileNameStr = trainingFileList[i]        fileStr = fileNameStr.split('.')[0] # 将文件名截断        classNameStr = int(fileStr.split('_')[0])        hwLabels.append(classNameStr)        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)    errorCount = 0.0    testFileList = listdir('testDigits')    mTest = len(testFileList)    for i in range(mTest):        fileNameStr = testFileList[i]        fileStr = fileNameStr.split('.')[0]        classNameStr = int(fileStr.split('_')[0])        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)        # k=4时，errorrate=0.014799        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)        print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNameStr))        if(classifierResult != classNameStr):            errorCount += 1.0    print("the total number of errors is: %d" % errorCount)    print("the total error rate is: %f" % (errorCount / float(mTest)))if __name__ == "__main__":    # 测试img2vector函数    testVector = img2vector('testDigits/0_0.txt')    print(testVector[0, 0 : 32])    print(testVector[0, 32 : 64])    # 测试    print("-------------测试开始--------------")    handwritingClassTest()