学习kNN算法的感受

          本来预计的打算是一天一个十大挖掘算法，然而由于同时要兼顾数据结构面试的事情，所以很难办到，但至少在回家前要把数据挖掘十大算法看完，过个好年，在course上学习老吴的课程还是帮了我很大的忙，虽然浪费了时间，但是也无形中帮助我很多，所以说还是很值得的，今天就总结KNN算法的一部分，这部分老吴的课程中没有太多涉及到，所以我又重新关注了一下，下面是我的总结，希望能对大家有所帮组。

     介绍环镜：python2.7  IDLE  Pycharm5.0.3

     操作系统：windows

    第一步：因为没有numpy，所以要安装numpy，详情见另一篇安装numpy的博客，这里不再多说.

    第二步：贴代码：

<span style="color:#ff0000;background-color: rgb(255, 255, 255);"><strong>from numpy import *import operatorfrom os import listdir</strong></span><span style="background-color: rgb(255, 255, 0);"></span><strong><span style="color:#ff0000;">def classify0(inX, dataSet, labels, k):</span></strong>    dataSetSize = dataSet.shape[0]    diffMat = tile(inX, (dataSetSize,1)) - dataSet    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]<strong><span style="color:#ff0000;">def createDataSet():</span></strong>    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    labels = ['A','A','B','B']    return group, labels<strong><span style="color:#ff0000;">def file2matrix(filename):</span></strong>    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,classLabelVector    <strong><span style="color:#ff0000;">def autoNorm(dataSet):</span></strong>    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, minVals   <strong><span style="color:#ff0000;">def datingClassTest():</span></strong>    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    <strong><span style="color:#ff0000;">def img2vector(filename):</span></strong>    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<strong><span style="color:#ff0000;">def handwritingClassTest():</span></strong>    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))

      第三步：通过命令行交互

      （1）：先将上述代码保存为kNN.py

      （2）：再在IDLE下的run菜单下run一下，将其生成python模块

      （3）： import  kNN（因为上一步已经生成knn模块）
      （4）： kNN.classify0([0,0],group,labels,3) （讨论[0,0]点属于哪一个类）

   注：其中【0,0】可以随意换

即【】内的坐标就是我们要判断的点的坐标：

>>> kNN.classify0([0,0],group,labels,3)
'B'
>>> kNN.classify0([0,1],group,labels,3)
'B'
>>> kNN.classify0([0.6,0.6],group,labels,3)
'A'