机器学习-KNN算法代码详解

雨点打透心脏的1/2处 2022-06-06 13:20 216阅读 0赞

from numpy import *
    import operator
    from os import listdir
    
    def classify0(inX, dataSet, labels, k):  #inx 是输入的数据杭矩阵，dateset是已经知道标签的数据集，lables是该标签，k是距离最精需要比较的个数
        dataSetSize = dataSet.shape[0]          #获取训练数据的个数，在这里也是行数
        diffMat = tile(inX, (dataSetSize,1)) - dataSet          #将inx复制了datesetsize行，与训练矩阵相-
        sqDiffMat = diffMat**2                                      #将diffmat矩阵中的每个数平方
        sqDistances = sqDiffMat.sum(axis=1)                     #axis=1，将各自行中的数据求和，得到多行一列矩阵
        distances = sqDistances**0.5
        sortedDistIndicies = distances.argsort()                #从小到大排序，返回的是他们的索引
        classCount={}                                           #字典，key为v哦忒喇叭了，值为出现的次数
        for i in range(k):                                      #统计前k个距离最近的数据，统计相应标签出现的频次，并返回标签频次最高的训练数据的标签
            voteIlabel = labels[sortedDistIndicies[i]]
            classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1#.get（）函数当voteilable存在时，返回他的值，不存在时返回默认的0
        sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)#key=1,以字典的值降序排列
        return sortedClassCount[0][0]#返回频次最高的标签
    
    
    
    
    
    
    def createDataSet():
        group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
        labels = ['A','A','B','B']
        return group, labels
    
    def file2matrix(filename):
        fr = open(filename)
        numberOfLines = len(fr.readlines())         #get the number of lines in the file
        returnMat = zeros((numberOfLines,3))        #prepare matrix to return
        classLabelVector = []                       #prepare labels return   
        fr = open(filename)
        index = 0
        for line in fr.readlines():
            line = line.strip()  #去除前后空格
            listFromLine = line.split('\t')#以tab将数据分开送给列表listfromline
            returnMat[index,:] = listFromLine[0:3]#将列表中的前3个数据送给returnmat
            classLabelVector.append(int(listFromLine[-1]))#将每行的最后一列追加给标签矩阵
            index += 1
        return returnMat,classLabelVector
        
    def autoNorm(dataSet):                  #dateset 为多为矩阵
        minVals = dataSet.min(0)            #minvals是一行多列，每列的最小值组成
        maxVals = dataSet.max(0)            #maxvals是一行多列，每列的最大值组成
        ranges = maxVals - minVals
        normDataSet = zeros(shape(dataSet))
        m = dataSet.shape[0]                #得到数据的行
        normDataSet = dataSet - tile(minVals, (m,1))
        normDataSet = normDataSet/tile(ranges, (m,1))   #element wise divide
        return normDataSet, ranges, minVals
       
    def datingClassTest():
        hoRatio = 0.50      #hold out 10%
        datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file
        normMat, ranges, minVals = autoNorm(datingDataMat)
        m = normMat.shape[0]
        numTestVecs = int(m*hoRatio)
        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)))
        print (errorCount)
        
    def img2vector(filename):
        returnVect = zeros((1,1024))#将32*32的图像矩阵转换为1*1024的杭矩阵，此处创建他的容器
        fr = open(filename)
        for i in range(32):         #便利每一行
            lineStr = fr.readline()    #仅读取当前的一行
            for j in range(32):         #将该行中每一列的元素强制类型转换为int类型，并伏值给returnvec矩阵
                returnVect[0,32*i+j] = int(lineStr[j])
        return returnVect
    
    
    def handwritingClassTest():
        hwLabels = []
        trainingFileList = listdir('trainingDigits')           #load the training set  trainingDigits这是一个路径
        m = len(trainingFileList)                               #获取文件的各数
        trainingMat = zeros((m,1024))                           #每个文件代表举证中的一行
        for i in range(m):
            fileNameStr = trainingFileList[i]                               #得到文件名
            fileStr = fileNameStr.split('.')[0]     #take off .txt
            classNumStr = int(fileStr.split('_')[0])                #将文件命中_之前的数字作为标签lables
            hwLabels.append(classNumStr)                            #将所有的文件名中得到的标签追加到hwlables中去
            trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)   #调用函数将所有文件中的内容转换为一行数据
        testFileList = listdir('testDigits')        #iterate through the test set得到测试序列目录下的文件名
        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)#括号内依次为测试用的单行数据；上一循环中得到的多行数据矩阵；以及他的标签列，k值等于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 number of errors is: %d" % (errorCount/float(mTest)))

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[Center]: https://img-blog.csdn.net/20171109213010889?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvbWFuamhPSw==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center
[Center 1]: https://img-blog.csdn.net/20171109213110273?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvbWFuamhPSw==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center
[Center 2]: /images/20220606/e10db97746a34ee4bb66f21d44454c36.png
[Center 3]: /images/20220606/24c235dbb0074518852fd36a9f295764.png