机器学习实战 第2章 k-近邻算法

原理：
选取k个最近的样本，样本中比例最大的种类即是新数据的分类。

简单的分类器：

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,labelsg,la = createDataSet()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;    sortedDis = distances.argsort()    classCount = {}    for i in range (k):        votelabel = labels[sortedDis[i]]        classCount[votelabel] = classCount.get(votelabel,0) + 1    sortedClassCount = sorted(classCount.iteritems(),                              key = operator.itemgetter(1),reverse = True)    return sortedClassCount[0][0]print classify0([0,0],g,la,3)