剛讀了《machine learning in action》的KNN演算法。

K最近鄰演算法（kNN，k-NearestNeighbo），即計算到每個樣本的距離，選取前k個。從前k個選擇出大多數屬於的class來進行分類，以下特點：

3. 計算到全部樣本的距離，計算量大

書中給出的第一個例項程式碼如下，原書中是python2的，下面改為python3 (僅對一行程式碼進行了修改)：

'''

    first case of KNN classifer

'''
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
    # change itemgetter to item
    sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

if __name__=='__main__':
    print ('dataset - labels')
    print(createDataSet())
    group,labels = createDataSet()
    label = classify0([1,1.3],group,labels,3)
    print (label)