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('\t')
             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 "\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'