#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import numpy as np
import operator


def createDataSet():
    group = np.array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
    labels = ['A', 'A', 'B', 'B']
    return group, labels


# K-近邻算法
def classify0(inX, dataSet, labels, k):
    dataSetSize = np.shape(dataSet)[0]
    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat ** 2
    sqDistances = np.sum(sqDiffMat, axis=1)
    distances = sqDistances ** 0.5
    sortedDistIndicies = np.argsort(distances)
    classCount = {}
    for i in range(k):
        voteLabel = labels[sortedDistIndicies[i]]
        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1

    sortedClassCount = np.sort(classCount.iteritems(),
                               key=operator.itemgetter(0),
                               reversed=True)

    return sortedClassCount