k最近邻(KNN)——实践

# -*- coding: utf-8 -*-"""Created on Tue Sep 15 09:50:33 2015@author: Administrator"""from numpy import *from os import listdirimport operatordef createDataSet():    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])    labels = ['A', 'A', 'B', 'B']    return group, labels#k-近邻算法def classify0(inX, dataSet, labels, k):    dataSetSize = dataSet.shape[0]    diffMat = tile(inX, (dataSetSize, 1)) - dataSet  #注意tile方法的使用。    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(classCount.iteritems(),     key = operator.itemgetter(1), reverse = True)    return sortedClassCount[0][0]#将文本记录转换为NumPy的解析程序def file2matrix(filename):    fr = open(filename)    arrayOLines = fr.readlines()    numberOfLines = len(arrayOLines)    returnMat = zeros((numberOfLines, 3))    classLabelVector = []    index = 0    for line in arrayOLines:        line = line.strip()        listFromLine = line.split('\t')        returnMat[index, :] = listFromLine[0:3]        classLabelVector.append(int(listFromLine[-1]))        index += 1    return returnMat, classLabelVector#归一化特征值def autoNorm(dataSet):    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))    return normDataSet, ranges, minVals#测试算法：分类器针对约会网站的测试代码def datingClassTest():    hoRatio = 0.10    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')    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))    #使用算法，构建完整可用的系统：约会网站预测函数def classifyPerson():    resultList = ['not at all', 'in small doses', 'in large doses']    percentTats = float(raw_input("percentage of time spent playing video games?"))    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, percentTats, iceCream])    classifierResult = classify0((inArr - minVals)/ranges, normMat, datingLabels, 3)    print "You will probably like this person: ",\          resultList[classifierResult - 1]          #将32x32的二进制图像矩阵转换为一个行向量。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 returnVect    def handwritingClassTest():    hwLabels = []    trainingFileList = listdir('trainingDigits')    m = len(trainingFileList)    trainingMat = zeros((m, 1024))    for i in range(m):        fileNameStr = trainFileList[i]        fileStr = fileNameStr.split('.')[0]        classNumStr = int(fileStr.split('_')[0])        hwLabels.append(classNumStr)        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)    testFileList = listdir('testDigits')    errorCount = 0.0    mTest = len(testFileList)    for i in range(mTest):        fileNameStr = testFileList[i]        fileStr = fileNameStr.split('.')[0]        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" % (error/float(mTest))