knn

转载https://www.cnblogs.com/ybjourney/p/4702562.html

#coding:utf-8

from numpy import *
import operator

##给出训练数据以及对应的类别
def createDataSet():
    group = array([[1.0,2.0],[1.2,0.1],[0.1,1.4],[0.3,3.5]])
    labels = ['A','A','B','B']
    return group,labels

###通过KNN进行分类
def classify(input,dataSet,label,k):
    dataSize = dataSet.shape[0] #列数
    ####计算欧式距离
    diff = tile(input,(dataSize,1)) - dataSet #input 4行1列
    sqdiff = diff ** 2
    squareDist = sum(sqdiff,axis = 1)###行向量分别相加，从而得到新的一个行向量
    dist = squareDist ** 0.5
    
    ##对距离进行排序
    sortedDistIndex = argsort(dist)##argsort()根据元素的值从大到小对元素进行排序，返回下标

    classCount={} #空字典
    for i in range(k):
        voteLabel = label[sortedDistIndex[i]]
        ###对选取的K个样本所属的类别个数进行统计
        classCount[voteLabel] = classCount.get(voteLabel,0) + 1 #空字典值默认为0
    ###选取出现的类别次数最多的类别
    maxCount = 0
    for key,value in classCount.items():
        if value > maxCount:
            maxCount = value
            classes = key

    return classes