k近邻算法 学习

from numpy import *

import operator


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 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]


groups, labels = createDataSet()

print classify0([0.5, 0.5], groups, labels,3)