自己动手写KNN分类器

不想用sklearn，想了解一下KNN的原理，动手写了一下

#!usr/bin/python3
# coding:utf-8

from numpy import *


def creatDataSet():
    group = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]])
    label = ['A', 'A', 'B', 'B']
    return group, label


def KNNClassify(newInput, dataSet, label, k):
    # 计算新样本与数据点之间的距离
    numSample = dataSet.shape[0]
    diff = tile(newInput, [numSample, 1])
    squreDiff = diff ** 2
    squreDist = sum(squreDiff, axis=1)
    distance = sqrt(squreDist)
    # 找出与新样本点距离最近的k个样本，用字典存储类别及其出现的次数
    index = argsort(distance)
    classCount = {}
    for i in range(k):
        labelVote = label[index[i]]
        classCount[labelVote] = classCount.get(labelVote, 0) + 1
    # 将出现次数最多的类别进行输出
    count = 0
    for k, v in classCount.items():
        if v > count:
            maxcount = v
            maxvalue = k
    return maxvalue


def main():
    newInput = [1.2, 1.5]
    dataSet, label = creatDataSet()
    k = 3
    newInputLabel = KNNClassify(newInput, dataSet, label, k)
    print(newInputLabel)
    return


if __name__ == '__main__':
    main()