2_6最近邻算法kNN(k_nearest_neighbor)——handwritingClassTest_2_6
2017-04-24 21:13
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声明:《机器学习实战》代码详细注释和重构,以及相关的函数、模块和算法的解释;本文为博主原创文章,未经博主允许不得转载。
手写数字识别系统的测试代码(程序清单2-6)
*#代码:Peter Gong_shuai
*#中文注释:Gong_shuai
*#代码重构:Gong_shuai
*#函数注解:Gong_shuai
*#相关的函数、模块和算法的解释:Gong_shuai
*#环境:Python2.7、Sublime Text3、mac
代码
运行结果
手写数字识别系统的测试代码(程序清单2-6)
*#代码:Peter Gong_shuai
*#中文注释:Gong_shuai
*#代码重构:Gong_shuai
*#函数注解:Gong_shuai
*#相关的函数、模块和算法的解释:Gong_shuai
*#环境:Python2.7、Sublime Text3、mac
代码
#coding=utf-8 #手写数字识别系统的测试代码(程序清单2-6) #源代码:Peter Gong_shuai #中文注释:Gong_shuai #代码重构:Gong_shuai #函数注解:Gong_shuai #环境:python2.7 from numpy import * import operator from os import listdir #函数listdir可以列出给定目录的文件名 #处理输入格式问题,输入为文件名字符串,输出为训练样本矩阵和类标签向量 def file2matrix(filename): fr = open(filename) numberOfLines = len(fr.readlines()) #得到文本行数 returnMat = zeros((numberOfLines,3)) #创建以零填充的矩阵,为了简化,另外的一个维度设为3 classLabelVector = [] #返回标签 fr = open(filename) #解析文件数据到列表,循环处理文件中的每一行的数据 index = 0 for line in fr.readlines(): line = line.strip() #截取掉所有的回车字符 listFromLine = line.split('\t') #将整行数据分割成一个元素列表 returnMat[index,:] = listFromLine[0:3] #选取前三个元素,存储到特征矩阵中 classLabelVector.append(int(listFromLine[-1])) index += 1 return returnMat,classLabelVector #归一化特征值 def autoNorm(dataSet): minVals = dataSet.min(0)#每一列的最小值 maxVals = dataSet.max(0)#每一列的最大值 ranges = maxVals - minVals normDataSet = zeros(shape(dataSet)) m = dataSet.shape[0]#数组的大小 normDataSet = dataSet - tile(minVals, (m,1))#注意事项:特征值矩阵有1000*3个值。而minVals和range的值都为1*3.为了解决这个问题使用numpy中tile函数将变量内容复制成输入矩阵同样大小的矩阵 normDataSet = normDataSet/tile(ranges, (m,1)) #element wise divide矩阵除法 return normDataSet, ranges, minVals #k近邻算法 def classify0(inX, dataSet, labels, k):#输入向量,输入的训练样本集,标签向量,选择最近邻的数目 #距离计算 dataSetSize = dataSet.shape[0] diffMat = tile(inX, (dataSetSize,1)) - dataSet sqDiffMat = diffMat**2 sqDistances = sqDiffMat.sum(axis=1) distances = sqDistances**0.5 #选择距离最小的k个点 sortedDistIndicies = distances.argsort() classCount={} for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #排序 sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0] #把一个32x32的二进制图像矩阵转换为1x1024的向量 def img2vector(filename): returnVect = zeros((1,1024))#创建1x1024的numpy数组 fr = open(filename) for i in range(32):#循环读取前32行,并且将每行头32个字符值存储在numpy数组中,最后返回数组 lineStr = fr.readline() for j in range(32): returnVect[0,32*i+j] = int(lineStr[j]) return returnVect # 手写数字识别系统的测试代码 def handwritingClassTest(): hwLabels = [] trainingFileList = listdir('trainingDigits') #load the training set m = len(trainingFileList) #目录中有多少文件 trainingMat = zeros((m,1024)) #创建一个m行1024列的训练矩阵,该矩阵的每一行存储一个图像 #从文件名解析出分类数字,该目录的文件按照规则命名,如文件9_45.txt的分类是9,它是数字9的第45个实例 for i in range(m): fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] #take off .txt classNumStr = int(fileStr.split('_')[0]) hwLabels.append(classNumStr) #类代码存储到变量hwLabels中 trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr) #对testDigits执行相似操作,但是不载入矩阵,而是使用classify()函数测试该目录下的每一个文件 testFileList = listdir('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('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 < 24000 span class="hljs-string">"\nthe total number of errors is: %d" % errorCount print "\nthe total error rate is: %f" % (errorCount/float(mTest)) handwritingClassTest()
运行结果
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classifier came back with: 9, the real answer is: 9 the total number of errors is: 11 the total error rate is: 0.011628 [Finished in 31.4s]
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