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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‘
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原文地址:http://www.cnblogs.com/hellochennan/p/5373123.html