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MachineLearning-KNN算法

def createDataSet():    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    labels = ['A','B','C','D']    return group,labelsdef classify0(inX,dataSet,labels,k):    dataSetSize = dataSet.shape[0]    diffMat = tile(inX,(dataSetSize,1))-dataSet            sqDiffMat = diffMat**2    sqDistance = sqDiffMat.sum(axis=1)    distance = sqDistance**0.5    sortedDistances = distance.argsort()    classCount = {}    for i in range(k):        voteILabel = labels[sortedDistances[i]]        classCount[voteILabel] = classCount.get(voteILabel,0)+1        sortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)        print sortedClassCountreturn sortedClassCount[0][0]

tile(A,reps)：Construct an array by repeating A the number of times given by reps详情参考numpy.tile
sqDiffMat.sum(axis=1)：逐项各自求和参考numpy.sum
#distance.argsort():Array values from small to large index values：按从小到大顺序排序后取排序前索引值，参考numpy.argsort
sortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True):按照classCount中第二个元素排序，reverse=true表示升序。参考sort排序

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