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：

1     from numpy import *   2     import operator   3     from os import listdir 5   def classify0(inX, dataSet, labels, k): 6         dataSetSize = dataSet.shape[0]   7         diffMat = tile(inX, (dataSetSize,1)) - dataSet   8         sqDiffMat = diffMat**2   9         sqDistances = sqDiffMat.sum(axis=1)  10         distances = sqDistances**0.5  11         sortedDistIndicies = distances.argsort()       12         classCount={}            13         for i in range(k):  14             voteIlabel = labels[sortedDistIndicies[i]]  15             classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1  16         sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)  17         return sortedClassCount[0][0]  18       19   def createDataSet():20         group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])  21         labels = ['A','A','B','B']  22         return group, labels  23       24    def file2matrix(filename):  25         fr = open(filename)  26         numberOfLines = len(fr.readlines())         #get the number of lines in the file  27         returnMat = zeros((numberOfLines,3))        #prepare matrix to return  28         classLabelVector = []                       #prepare labels return     29         fr = open(filename)  30         index = 0  31         for line in fr.readlines():  32             line = line.strip()  33             listFromLine = line.split('\t')  34             returnMat[index,:] = listFromLine[0:3]  35             classLabelVector.append(int(listFromLine[-1]))  36             index += 1  37         return returnMat,classLabelVector  38           39     def autoNorm(dataSet):40         minVals = dataSet.min(0)  41         maxVals = dataSet.max(0)  42         ranges = maxVals - minVals  43         normDataSet = zeros(shape(dataSet))  44         m = dataSet.shape[0]  45         normDataSet = dataSet - tile(minVals, (m,1))  46         normDataSet = normDataSet/tile(ranges, (m,1))   #element wise divide  47         return normDataSet, ranges, minVals  48          49     def datingClassTest():50         hoRatio = 0.50      #hold out 10%  51         datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file  52         normMat, ranges, minVals = autoNorm(datingDataMat)  53         m = normMat.shape[0]  54         numTestVecs = int(m*hoRatio)  55         errorCount = 0.0  56         for i in range(numTestVecs):  57             classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)  58             print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])  59             if (classifierResult != datingLabels[i]): errorCount += 1.0  60         print "the total error rate is: %f" % (errorCount/float(numTestVecs))  61         print errorCount  62           63     def img2vector(filename): 64         returnVect = zeros((1,1024))  65         fr = open(filename)  66         for i in range(32):  67             lineStr = fr.readline()  68             for j in range(32):  69                 returnVect[0,32*i+j] = int(lineStr[j])  70         return returnVect  71       72     def handwritingClassTest():  73         hwLabels = []  74         trainingFileList = listdir('trainingDigits')           #load the training set  75         m = len(trainingFileList)  76         trainingMat = zeros((m,1024))  77         for i in range(m):  78             fileNameStr = trainingFileList[i]  79             fileStr = fileNameStr.split('.')[0]     #take off .txt  80             classNumStr = int(fileStr.split('_')[0])  81             hwLabels.append(classNumStr)  82             trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)  83         testFileList = listdir('testDigits')        #iterate through the test set  84         errorCount = 0.0  85         mTest = len(testFileList)  86         for i in range(mTest):  87             fileNameStr = testFileList[i]  88             fileStr = fileNameStr.split('.')[0]     #take off .txt  89             classNumStr = int(fileStr.split('_')[0])  90             vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)  91             classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)  92             print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)  93             if (classifierResult != classNumStr): errorCount += 1.0  94         print "\nthe total number of errors is: %d" % errorCount  95         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'