# coding=utf-8

from numpy import *

import operator

def createDataSet():

    group = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]])

    labels = ['A', 'A', 'B', 'B']

    return group, labels

def KNNClassify(newInput, dataSet, labels, k):

    numSamples = dataSet.shape[0]   # shape[0]表示行数

    diff = tile(newInput, (numSamples, 1)) - dataSet  # 按元素求差值

    squaredDiff = diff ** 2  # 将差值平方

    squaredDist = sum(squaredDiff, axis = 1)   # 按行累加

    distance = squaredDist ** 0.5  # 将差值平方和求开方，即得距离

    sortedDistIndices = argsort(distance)

    classCount = {} # define a dictionary (can be append element)

    for i in range(k):

        voteLabel = labels[sortedDistIndices[i]]

        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1

    maxCount = 0

    for key, value in classCount.items():

        if value > maxCount:

            maxCount = value

            maxIndex = key

    return maxIndex

# coding=utf-8

import KNN

from numpy import *

dataSet, labels = KNN.createDataSet()

testX = array([1.2, 1.0])

k = 3

outputLabel = KNN.KNNClassify(testX, dataSet, labels, 3)

print("Your input is:", testX, "and classified to class: ", outputLabel)

testX = array([0.1, 0.3])

outputLabel = KNN.KNNClassify(testX, dataSet, labels, 3)

print("Your input is:", testX, "and classified to class: ", outputLabel)