机器学习实战-kNN分类手写数字笔记

import numpy as npimport pandas as pd

def classify0(inX, dataSet, labels, k):    #inX代表输入的数据坐标，DataSet:n个m维数据，大小n*m    dataSetSize = dataSet.shape[0]    #求x与各数据坐标的差，形成n*m的差值数组    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet    sqDiffMat = diffMat ** 2     #np.sum(axis = 0/1)；python自带的sum(sequence,[start,])求和再加start    sqDistances = sqDiffMat.sum(axis = 1)    distances = sqDistances ** 0.5    #np.argsort(x),返回排序升序序号。http://blog.csdn.net/maoersong/article/details/21875705    sortedDistIndicies = distances.argsort()    classCount = {}    for i in range(k):        voteLabel = labels[sortedDistIndicies[i]]        classCount[voteLabel] = classCount.get(voteLabel, 0) +1    #http://www.cnblogs.com/zle1992/p/6271105.html    #http://blog.csdn.net/dongtingzhizi/article/details/12068205    #key = operator.itemgetter(1) 提取classCount第2个域作为排序    #python3 中 用dict.items()没有iteritems()方法了    #key = operator.itemgetter(1)    sortedClassCount = sorted(classCount.items(), key = lambda x:x[1], reverse = True)    #sortedClassCount[0]为行最小第一项，第二个[0]提取第一项的列class    return sortedClassCount[0][0]

def img2vector(filename):    returnVect = np.zeros((1,1024))    with open(filename, 'r') as fr:        for i, line in enumerate(fr.readlines()):            #line为str类型，str和list类型均可用len()方法获取长度，            #str可以strip()，list不能strip()。            #将字符串分割成单个字符串没有什么好办法，只有如下办法。            for j in range(len(line.strip())):                returnVect[0,32*i+j] = int(line[j])    return returnVect

filename = 'C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\testDigits\\0_0.txt'c = img2vector(filename)

c

array([[ 0.,  0.,  0., ...,  0.,  0.,  0.]])

c.shape

(1, 1024)

import osos.listdir('C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\trainingDigits')

['0_0.txt', '0_1.txt', '0_10.txt', '0_100.txt', '0_101.txt', '0_102.txt', '0_103.txt', '0_104.txt', '0_105.txt', '0_106.txt', '0_107.txt', '0_108.txt', '0_109.txt', '0_11.txt', '0_110.txt', '0_111.txt', '0_112.txt', '0_113.txt', '0_114.txt', '0_115.txt', '0_116.txt', '0_117.txt', '0_118.txt', '0_119.txt', '0_12.txt', '0_120.txt', '0_121.txt', '0_122.txt', '0_123.txt', '0_124.txt', '0_125.txt', '0_126.txt', '0_127.txt', '0_128.txt', '0_129.txt', '0_13.txt', '0_130.txt', '0_131.txt', '0_132.txt', '0_133.txt', '0_134.txt', '0_135.txt', '0_136.txt', '0_137.txt', '0_138.txt', '0_139.txt', '0_14.txt', '0_140.txt', '0_141.txt', '0_142.txt', '0_143.txt', '0_144.txt', '0_145.txt', '0_146.txt', '0_147.txt', '0_148.txt', '0_149.txt', '0_15.txt', '0_150.txt', '0_151.txt', '0_152.txt', '0_153.txt', '0_154.txt', '0_155.txt', '0_156.txt', '0_157.txt', '0_158.txt', '0_159.txt', '0_16.txt', '0_160.txt', '0_161.txt', '0_162.txt', '0_163.txt', '0_164.txt', '0_165.txt', '0_166.txt', '0_167.txt', '0_168.txt', '0_169.txt', '0_17.txt', '0_170.txt', '0_171.txt', '0_172.txt', '0_173.txt', '0_174.txt', '0_175.txt', '0_176.txt', '0_177.txt', '0_178.txt', '0_179.txt', '0_18.txt', '0_180.txt', '0_181.txt', '0_182.txt', '0_183.txt', '0_184.txt', '0_185.txt', '0_186.txt', '0_187.txt', '0_188.txt', '0_19.txt', '0_2.txt', '0_20.txt', '0_21.txt', '0_22.txt', '0_23.txt', '0_24.txt', '0_25.txt', '0_26.txt', '0_27.txt', '0_28.txt', '0_29.txt', '0_3.txt', '0_30.txt', '0_31.txt', '0_32.txt', '0_33.txt', '0_34.txt', '0_35.txt', '0_36.txt', '0_37.txt', '0_38.txt', '0_39.txt', '0_4.txt', '0_40.txt', '0_41.txt', '0_42.txt', '0_43.txt', '0_44.txt', '0_45.txt', '0_46.txt', '0_47.txt', '0_48.txt', '0_49.txt', '0_5.txt', '0_50.txt', '0_51.txt', '0_52.txt', '0_53.txt', '0_54.txt', '0_55.txt', '0_56.txt', '0_57.txt', '0_58.txt', '0_59.txt', '0_6.txt', '0_60.txt', '0_61.txt', '0_62.txt', '0_63.txt', '0_64.txt', '0_65.txt', '0_66.txt', '0_67.txt', '0_68.txt', '0_69.txt', '0_7.txt', '0_70.txt', '0_71.txt', 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file = 'C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\trainingDigits''\\'.join(file.split('\\')[:-1])+'\\'

'C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\'

def handwritingClassTest():    hwLabels = []    #os.listdir()返回list形式的该目录下的文件列表。    filedir = 'C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\'    trainingFileList = os.listdir(filedir + 'trainingDigits')    m = len(trainingFileList)    trainingMat = np.zeros((m,1024))    for i in range(m):        fileNameStr = trainingFileList[i]        fileStr = fileNameStr.split('.')[0]        #获得label，返回int类型0-9中的一个        classNumStr = int(fileStr.split('_')[0])        #存储label为列表形式        hwLabels.append(classNumStr)        #img2vector返回(1,1024)数组        trainingMat[i,:] = img2vector(filedir + 'trainingDigits\\{}'\                                     .format(fileNameStr))    testFileList = os.listdir(filedir + 'testDigits')    errorCount = 0.0    mTest = len(testFileList)    for i in range(mTest):        fileNameStr = testFileList[i]        fileStr = fileNameStr.split('.')[0]        #获得真实label，返回int类型0-9中的一个        classNumStr = int(fileStr.split('_')[0])        #vectorUnderMat没有[i,:]        vectorUnderTest = img2vector(filedir + 'testDigits\\{}'\                                     .format(fileNameStr))        #输入test的一个数据的(1,1024)数组，进行kNN算法后，与label做对比        #（1,1024）变成（m，1024），想减，求距离，argsort，返回最小的k个邻近，判断        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)        print("the classifier came back with:{0}, the real answer is :{1}".format(classifierResult, classNumStr))        if(classifierResult != classNumStr):            errorCount += 1.0    print("\n total error rate is :{}".format(errorCount/float(mTest)))

handwritingClassTest()

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with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:7, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9the classifier came back with:9, the real answer is :9 total error rate is :0.011627906976744186

filedir = 'C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\'os.listdir(filedir + 'trainingDigits')

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'3_15.txt', '3_150.txt', '3_151.txt', '3_152.txt', '3_153.txt', '3_154.txt', '3_155.txt', '3_156.txt', '3_157.txt', '3_158.txt', '3_159.txt', '3_16.txt', '3_160.txt', '3_161.txt', '3_162.txt', '3_163.txt', '3_164.txt', '3_165.txt', '3_166.txt', '3_167.txt', '3_168.txt', '3_169.txt', '3_17.txt', '3_170.txt', '3_171.txt', '3_172.txt', '3_173.txt', '3_174.txt', '3_175.txt', '3_176.txt', '3_177.txt', '3_178.txt', '3_179.txt', '3_18.txt', '3_180.txt', '3_181.txt', '3_182.txt', '3_183.txt', '3_184.txt', '3_185.txt', '3_186.txt', '3_187.txt', '3_188.txt', '3_189.txt', '3_19.txt', '3_190.txt', '3_191.txt', '3_192.txt', '3_193.txt', '3_194.txt', '3_195.txt', '3_196.txt', '3_197.txt', '3_198.txt', '3_2.txt', '3_20.txt', '3_21.txt', '3_22.txt', '3_23.txt', '3_24.txt', '3_25.txt', '3_26.txt', '3_27.txt', '3_28.txt', '3_29.txt', '3_3.txt', '3_30.txt', '3_31.txt', '3_32.txt', '3_33.txt', '3_34.txt', '3_35.txt', '3_36.txt', '3_37.txt', '3_38.txt', '3_39.txt', '3_4.txt', '3_40.txt', '3_41.txt', '3_42.txt', '3_43.txt', '3_44.txt', '3_45.txt', '3_46.txt', '3_47.txt', '3_48.txt', '3_49.txt', '3_5.txt', '3_50.txt', '3_51.txt', '3_52.txt', '3_53.txt', '3_54.txt', '3_55.txt', '3_56.txt', '3_57.txt', '3_58.txt', '3_59.txt', '3_6.txt', '3_60.txt', '3_61.txt', '3_62.txt', '3_63.txt', '3_64.txt', '3_65.txt', '3_66.txt', '3_67.txt', '3_68.txt', '3_69.txt', '3_7.txt', '3_70.txt', '3_71.txt', '3_72.txt', '3_73.txt', '3_74.txt', '3_75.txt', '3_76.txt', '3_77.txt', '3_78.txt', '3_79.txt', '3_8.txt', '3_80.txt', '3_81.txt', '3_82.txt', '3_83.txt', '3_84.txt', '3_85.txt', '3_86.txt', '3_87.txt', '3_88.txt', '3_89.txt', '3_9.txt', '3_90.txt', '3_91.txt', '3_92.txt', '3_93.txt', '3_94.txt', '3_95.txt', '3_96.txt', '3_97.txt', '3_98.txt', '3_99.txt', '4_0.txt', '4_1.txt', '4_10.txt', '4_100.txt', '4_101.txt', '4_102.txt', '4_103.txt', '4_104.txt', '4_105.txt', '4_106.txt', '4_107.txt', '4_108.txt', '4_109.txt', '4_11.txt', '4_110.txt', '4_111.txt', '4_112.txt', '4_113.txt', '4_114.txt', '4_115.txt', '4_116.txt', '4_117.txt', '4_118.txt', '4_119.txt', '4_12.txt', '4_120.txt', '4_121.txt', '4_122.txt', '4_123.txt', '4_124.txt', '4_125.txt', '4_126.txt', '4_127.txt', '4_128.txt', '4_129.txt', '4_13.txt', '4_130.txt', '4_131.txt', '4_132.txt', '4_133.txt', '4_134.txt', '4_135.txt', '4_136.txt', '4_137.txt', '4_138.txt', '4_139.txt', '4_14.txt', '4_140.txt', '4_141.txt', '4_142.txt', '4_143.txt', '4_144.txt', '4_145.txt', '4_146.txt', '4_147.txt', '4_148.txt', '4_149.txt', '4_15.txt', '4_150.txt', '4_151.txt', '4_152.txt', '4_153.txt', '4_154.txt', '4_155.txt', '4_156.txt', '4_157.txt', '4_158.txt', '4_159.txt', '4_16.txt', '4_160.txt', '4_161.txt', '4_162.txt', '4_163.txt', '4_164.txt', '4_165.txt', '4_166.txt', '4_167.txt', '4_168.txt', '4_169.txt', '4_17.txt', '4_170.txt', '4_171.txt', '4_172.txt', '4_173.txt', '4_174.txt', '4_175.txt', '4_176.txt', '4_177.txt', '4_178.txt', '4_179.txt', '4_18.txt', '4_180.txt', '4_181.txt', '4_182.txt', '4_183.txt', '4_184.txt', '4_185.txt', '4_19.txt', '4_2.txt', '4_20.txt', '4_21.txt', '4_22.txt', '4_23.txt', '4_24.txt', '4_25.txt', '4_26.txt', '4_27.txt', '4_28.txt', '4_29.txt', '4_3.txt', '4_30.txt', '4_31.txt', '4_32.txt', '4_33.txt', '4_34.txt', '4_35.txt', '4_36.txt', '4_37.txt', '4_38.txt', '4_39.txt', '4_4.txt', '4_40.txt', '4_41.txt', '4_42.txt', '4_43.txt', '4_44.txt', '4_45.txt', '4_46.txt', '4_47.txt', '4_48.txt', '4_49.txt', '4_5.txt', '4_50.txt', '4_51.txt', '4_52.txt', '4_53.txt', '4_54.txt', '4_55.txt', '4_56.txt', '4_57.txt', '4_58.txt', '4_59.txt', '4_6.txt', '4_60.txt', '4_61.txt', '4_62.txt', '4_63.txt', '4_64.txt', '4_65.txt', '4_66.txt', '4_67.txt', '4_68.txt', '4_69.txt', '4_7.txt', '4_70.txt', '4_71.txt', '4_72.txt', '4_73.txt', '4_74.txt', '4_75.txt', '4_76.txt', '4_77.txt', '4_78.txt', '4_79.txt', '4_8.txt', '4_80.txt', '4_81.txt', '4_82.txt', '4_83.txt', '4_84.txt', '4_85.txt', '4_86.txt', '4_87.txt', '4_88.txt', '4_89.txt', '4_9.txt', '4_90.txt', '4_91.txt', '4_92.txt', '4_93.txt', '4_94.txt', '4_95.txt', '4_96.txt', '4_97.txt', '4_98.txt', '4_99.txt', '5_0.txt', '5_1.txt', '5_10.txt', '5_100.txt', '5_101.txt', '5_102.txt', '5_103.txt', '5_104.txt', '5_105.txt', '5_106.txt', '5_107.txt', '5_108.txt', '5_109.txt', '5_11.txt', '5_110.txt', '5_111.txt', '5_112.txt', '5_113.txt', '5_114.txt', '5_115.txt', '5_116.txt', '5_117.txt', '5_118.txt', '5_119.txt', '5_12.txt', '5_120.txt', '5_121.txt', '5_122.txt', '5_123.txt', '5_124.txt', '5_125.txt', '5_126.txt', '5_127.txt', ...]

str = '123'list = ['12','23']

len(str)

3

len(list)

2

filename = 'C:\\Users\\Administrator\\Desktop\\data\\machinelearninginaction\\Ch02\\digits\\testDigits\\0_0.txt'with open(filename, 'r') as fr:    #line中含有'\n'，所以print会再加一个'\n'出现隔一行    for i, line in enumerate(fr.readlines()):        print(i,line)    print("********************************************************")with open(filename, 'r') as fr:    for i, line in enumerate(fr.readlines()):        print(i,line.strip())

0 000000000000011000000000000000001 000000000000111111000000000000002 000000000001111111110000000000003 000000000111111111110000000000004 000000011111111111111000000000005 000000001111111000111100000000006 000000011111100000011100000000007 000000011111100000011100000000008 000000111111000000011100000000009 0000001111110000000111100000000010 0000001111110000000001110000000011 0000001111110000000001110000000012 0000001111100000000000111000000013 0000001111100000000000111000000014 0000000111110000000000011100000015 0000000111110000000000011100000016 0000000111110000000000011100000017 0000001111100000000000011100000018 0000001111100000000000011100000019 0000000011110000000000001110000020 0000000011110000000000011110000021 0000000011110000000000011110000022 0000000011110000000000111110000023 0000000001111000000000011111000024 0000000001111100000000111110000025 0000000001111100000001111110000026 0000000001111100000011111100000027 0000000001111110001111111100000028 0000000000011111111111111000000029 0000000000011111111111110000000030 0000000000001111111111000000000031 00000000000000111110000000000000********************************************************0 000000000000011000000000000000001 000000000000111111000000000000002 000000000001111111110000000000003 000000000111111111110000000000004 000000011111111111111000000000005 000000001111111000111100000000006 000000011111100000011100000000007 000000011111100000011100000000008 000000111111000000011100000000009 0000001111110000000111100000000010 0000001111110000000001110000000011 0000001111110000000001110000000012 0000001111100000000000111000000013 0000001111100000000000111000000014 0000000111110000000000011100000015 0000000111110000000000011100000016 0000000111110000000000011100000017 0000001111100000000000011100000018 0000001111100000000000011100000019 0000000011110000000000001110000020 0000000011110000000000011110000021 0000000011110000000000011110000022 0000000011110000000000111110000023 0000000001111000000000011111000024 0000000001111100000000111110000025 0000000001111100000001111110000026 0000000001111100000011111100000027 0000000001111110001111111100000028 0000000000011111111111111000000029 0000000000011111111111110000000030 0000000000001111111111000000000031 00000000000000111110000000000000