KNN算法的感受 1

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：

    from numpy import *  
    import operator  
    from os import listdir
  def classify0(inX, dataSet, labels, k):
        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]  
      
  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()  
            listFromLine = line.split('	')  
            returnMat[index,:] = listFromLine[0:3]  
            classLabelVector.append(int(listFromLine[-1]))  
            index += 1  
        return returnMat,classLabelVector  
          
    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))   #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))  
        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')           #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 "
the total number of errors is: %d" % errorCount  
        print "
the 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'