K-Nearest Neighbors algorithm
2015-07-07 19:03
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from numpy import * import operator import matplotlib import matplotlib.pyplot as plt from os import listdir 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 group, labels = createDataSet() def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] diffMat = tile(inX, (dataSetSize, 1))-dataSet sqDiffMat = diffMat ** 2 sqlDistances = sqDiffMat.sum(axis=1) distances = sqlDistances**0.5 sortedDistIndicies = distances.argsort() classCount = {} for i in range(k): voteLabel = labels[sortedDistIndicies[i]] classCount[voteLabel] = classCount.get(voteLabel, 0)+1 sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) # print sortedClassCount return sortedClassCount[0][0] def file2matrix(filename): fr = open(filename) numberOfLines = len(fr.readlines()) returnMat = zeros((numberOfLines, 3)) classLabelVector = [] fr = open(filename) index = 0 for line in fr.readlines(): line = line.strip() listFromLine = line.split('\t') returnMat[index,:]=listFromLine[0:3] classLabelVector.append(listFromLine[-1]) index += 1 return returnMat, classLabelVector datingDataMat, datingLabels = file2matrix('datingTestSet.txt') 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)) return normDataSet, ranges, minVals def datingClassTest(): hoRatio = 0.10; datingDataMat, datingLabels = file2matrix('datingTestSet.txt') normMat, ranges, minVals = autoNorm(datingDataMat) m = normMat.shape[0] numTestVecs = int(m*hoRatio) errCount = 0.0 for i in range(numTestVecs): classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 4) print 'the classifier came back with: %s, the real answer is: %s' % (classifierResult, datingLabels[i]) if(classifierResult != datingLabels[i]): errCount += 1.0 print "the total error rate is: %f" % (errCount/float(numTestVecs)) def classifyPerson(): resultList = ['not at all', 'in small doses', 'in large doses'] percentTats = float(raw_input("percent of time spent playing video games?")) ffMiles = float(raw_input("frequent filter miles earned per year?")) iceCream = float(raw_input("liters of ice cream consumed per year?")) datingDataMat, datingLabels = file2matrix('datingTestSet.txt') normMat, ranges, minVals = autoNorm(datingDataMat) inArr = array([percentTats, ffMiles, iceCream]) classifierResult = classify0((inArr-minVals)/ranges, normMat, datingLabels, 3) print "You will probably like this person: ", resultList[classifierResult-1] 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') m=len(trainingFileList) #print "m=",m trainingMat = zeros((m,1024)) for i in range(m): fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] classNumStr = int(fileStr.split('_')[0]) hwLabels.append(classNumStr) trainingMat[i,:]=img2vector('trainingDigits/%s' % fileNameStr) testFileList = listdir('testDigits') errCount = 0.0 mTest = len(testFileList) for i in range(mTest): fileNameStr = testFileList[i] fileStr = fileNameStr.split('.')[0] classNumStr = fileStr.split('_')[0] vectorUnderTest = img2vector('testDigits/%s' % fileNameStr) classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3) print "the classifier came back with: %s, the real answer is: %s" % (classifierResult, classNumStr) #print 'classifierResult = ', classifierResult,"classNumStr = ", classNumStr if (int(classifierResult) != int(classNumStr)): errCount += 1.0 print "\n the total number of error is: %d" % (errCount) print "\n the total error rate is: %f" % (errCount/float(mTest))
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