【读书笔记】机器学习实战-2.3节

机器学习实战

第二章 2.3节 KNN-手写数字识别

#!/usr/bin/python# -*- coding: utf-8 -*-from numpy import *import operatorfrom os import listdirimport matplotlibimport matplotlib.pyplot as pltdef createDataSet():    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    labels = ['A','A','B','B']    return group ,labelsdef classify0( inX,dataSet,labels,k ):                 # 待判定输入，训练样本，样本标签，kNN中k的取值    dataSetSize = dataSet.shape[0]                      # shape 返回矩阵的维度    diffMat = tile(inX, (dataSetSize, 1)) - dataSet     # tile(a,b)：将a重复b次    saDiffMat = diffMat**2                              # 运算符模块：** 平方    sqDistances = saDiffMat.sum(axis=1)                 # axis=1： array按行求和   axis=0：array按列求和    distances = sqDistances**0.5    sortedDistIndicies = distances.argsort()            # 返回数组值从大到小的索引值    classCount = {}    for i in range(k):        voteIlabel  = labels[sortedDistIndicies[i]]        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1   # dic.get()  访问不存在的键时，自动添加并设值为默认值    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)  # dic.iteritems() :迭代器函数  operator.itemgetter()：获取对象的指定域的值  key:进行比较的关键字    return sortedClassCount[0][0]def file2matrix(filename):    fr = open(filename)    arrayOLines = fr.readlines()                        # 将文件全部读入字符串列表,每个字符串为一行    numberOfLines = len(arrayOLines)    returnMat = zeros((numberOfLines,3))                # numpy.zeros() 创建零矩阵    classLabelVector = []    index = 0    for line in arrayOLines:        line = line.strip()                             # 除去两边的空格        listFromLine = line.split('\t')                 # '\t' = Tab  split:拆解字符串        returnMat[index ,:] = listFromLine[0:3]        classLabelVector.append(int(listFromLine[-1]))  # 列表添加元素        index += 1    return returnMat, classLabelVectordef autoNorm(dataSet):    minVals = dataSet.min(0)    # numpy.array.min(0) 返回每列的最小值    maxVals = dataSet.max(0)    # numpy.array.max(0) 返回每列的最大值    ranges = maxVals - minVals    # normDataSet = zeros(dataSet.shape)  # 利用shape属性，建同样大小的矩阵    m = dataSet.shape[0]    normDataSet = dataSet - tile(minVals, (m, 1))   # array全部减去最小值    normDataSet = normDataSet/tile(ranges, (m, 1))  # array全部除去范围值    return normDataSet, ranges, minValsdef datingClassTest():    hoRatio = 0.1                 # 测试样例比例    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')    normMat, ranges, minVals = autoNorm(datingDataMat)    m = normMat.shape[0]    numTestVecs = int(m*hoRatio)  # 测试数据集    errorCount = 0.0    for i in range(numTestVecs):        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))def classifyPerson():    resultList = ['not at all ','in small doses ','in large doses ']    percetTates = float(raw_input('percent of time spent playing video game?'))    ffMiles = float(raw_input('frequent flier 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([ffMiles,percetTates,iceCream]) # 构建分类目标array    inArr = (inArr-minVals)/ranges    classifierResult = classify0(inArr, normMat,datingLabels,3)    print 'you will like this person:',resultList[classifierResult-1]def img2vector(filename):    returnVect = zeros(1024)    fr = open(filename)    for i in range(32):        lineStr = fr.readline()        for j in range(32):            returnVect[32*i+j] = int(lineStr[j])  # 字符转数字    return returnVectdef handwritingClassTest():    hwLabels = []    trainingFileList = listdir('trainingDigits')  # 获取训练目录文件内容    m = len(trainingFileList)    trainingMat = zeros((m,1024))    for i in range(m):        fileNameStr = trainingFileList[i]         # 当前解析文件        fileStr = fileNameStr.split('.')[0]        classNumStr = int(fileStr.split('_')[0])  # 获取真实标签        hwLabels.append(classNumStr)        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)    testFileList = listdir('testDigits')          # 获取测试数据样本    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('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))handwritingClassTest()# classifyPerson()# datingClassTest()# datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')# normMat,ranges,minVals = autoNorm(datingDataMat)# fig = plt.figure()# ax = fig.add_subplot(111)# ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0*array(datingLabels),15.0*array(datingLabels))  # 利用标签属性标记# fig.show() 显示后自动退出# plt.show()  #显示不退出#group,labels = createDataSet()#result = classify0([0,0],group,labels,3)#pass