机器学习实战_KNN

# -*- encoding:utf-8 -*-'''KNN 计算过程1.对计算一致类别数据集中的点与当前点的距离2.安装距离递增顺序排序3.选取与当前点距离最小的k个点4.确定k个点所在类别出现的概率5.返回前k个点出现频率最高的类别作为当前点的预测分类'''from numpy import *import operatorfrom os import listdirimport matplotlib.pyplot as pltimport matplotlibdef classify0(inX, dataSet, labels, k):    # 4    dataSetSize = dataSet.shape[0]    # numpy.tile(A,B)函数:    # 重复A，B次，这里的B可以时int类型也可以是元组类型。    diffMat = tile(inX, (dataSetSize,1)) - dataSet    # 正数 --> 负数    sqDiffMat = diffMat**2    sqDistances = sqDiffMat.sum(axis=1)    distances = sqDistances**0.5    sortedDistIndicies = distances.argsort()    classCount={}    for i in range(k):        voteIlabel = labels[sortedDistIndicies[i]]        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1    sortedClassCount = sorted(iter(list(classCount.items())), key=operator.itemgetter(1), reverse=True)    return sortedClassCount[0][0]def createDataSet():    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    labels = ['A','A','B','B']    return group, labelsdef 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()        listFromLine = line.split('\t')        returnMat[index,:] = listFromLine[0:3]        classLabelVector.append(int(listFromLine[-1]))        index += 1    return returnMat,classLabelVectordef autoNorm(dataSet):    '''    newValue=(oldValue-min)/(max-min)    min为最小值    max为最大值    :param dataSet:    :return:    '''    minVals = dataSet.min(0)    maxVals = dataSet.max(0)    ranges = maxVals - minVals    normDataSet = zeros(shape(dataSet))    m = dataSet.shape[0]    normDataSet = dataSet - tile(minVals, (m,1))    normDataSet = normDataSet/tile(ranges, (m,1))   #element wise divide    return normDataSet, ranges, minValsdef datingClassTest():    hoRatio = 0.50      #hold out 10%    datingDataMat,datingLabels = file2matrix('data/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))    fr = open(filename)    for i in range(32):        lineStr = fr.readline()        for j in range(32):            returnVect[0,32*i+j] = int(lineStr[j])    return returnVectdef handwritingClassTest():    hwLabels = []    trainingFileList = listdir('trainingDigits')           #load the training set    m = len(trainingFileList)    trainingMat = zeros((m,1024))    for i in range(m):        fileNameStr = trainingFileList[i]        fileStr = fileNameStr.split('.')[0]     #take off .txt        classNumStr = int(fileStr.split('_')[0])        hwLabels.append(classNumStr)        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)    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(("\nthe total number of errors is: %d" % errorCount))    print(("\nthe total error rate is: %f" % (errorCount/float(mTest))))if __name__=='__main__':    # value,labels=createDataSet()    # print((classify0([0,0], value,labels,3)))    datingDataMat,datingLabel=file2matrix('data/datingTestSet2.txt')    norMat,ranges,minVals=autoNorm(datingDataMat)    print(norMat)    fig=plt.figure()    ax=fig.add_subplot(111)    ax.scatter(datingDataMat[:,1],datingDataMat[:,2])    plt.show()    datingClassTest()    handwritingClassTest()