基于python 实现KNN 算法

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2018/11/7 14:50
# @Author  : gylhaut
# @Site    : "http://www.cnblogs.com/gylhaut/"
# @File    : KNNAlgorithm.py
# @Software: PyCharm

# 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):
    '''

    :param input: test集
    :param dataSet: 训练集
    :param label: 训练output
    :param k: k值选择
    :return:
    '''
    dataSize = dataSet.shape[0] # 4
    ####计算欧式距离
    # print(tile(input, (dataSize, 1)))
    diff = tile(input, (dataSize, 1)) - dataSet

    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
    #print(dist)
    ##对距离进行排序
    sortedDistIndex = argsort(dist)  ##argsort()根据元素的值从小到大对元素进行排序，返回下标
    #print(sortedDistIndex)
    classCount = {}
    for i in range(k):
        voteLabel = label[sortedDistIndex[i]]
        #print(voteLabel)
        ###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
    ###选取出现的类别次数最多的类别
    #print(classCount)
    maxCount = 0
    for key, value in classCount.items():
        if value > maxCount:
            maxCount = value
            classes = key

    return classes

from numpy import *
dataSet,labels = createDataSet()
input = array([1.1,0.3])
K = 3
output = classify(input,dataSet,labels,K)
print("测试数据为:",input,"分类结果为：",output)