#coding:utf-8 from numpy import * import operator 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 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 file2matrix(filename): fr = open(filename) numberOfLines = len(fr.readlines()) #get the number of lines in the file returnMat = zeros((numberOfLines,37)) #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[0:37] classLabelVector.append(int(listFromLine[-1])) index += 1 fr.close() return returnMat,classLabelVector def genderClassTest(): hoRatio = 0.10 #hold out 10% datingDataMat,datingLabels = file2matrix('doubanMovieDataSet.txt') #load data setfrom file normMat,ranges,minVals=autoNorm(datingDataMat) m = normMat.shape[0] numTestVecs = int(m*hoRatio) testMat=normMat[0:numTestVecs,:] trainMat=normMat[numTestVecs:m,:] trainLabels=datingLabels[numTestVecs:m] k=3 errorCount = 0.0 for i in range(numTestVecs): classifierResult = classify0(testMat[i,:],trainMat,trainLabels,k) print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i]) if (classifierResult != datingLabels[i]): errorCount += 1.0 print "Total errors:%d" %errorCount print "The total accuracy rate is %f" %(1.0-errorCount/float(numTestVecs))
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