'''
导入科学计算包numpy和运算符模块operator
'''
from numpy import *
import operator

'''
 创建数据集和标签

 调用方式
 import kNN
 group, labels = kNN.createDataSet()
'''
def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A','A','B','B']
    return group, labels


def classify0(inX, dataSet, labels, k):
    '''
    inX: 用于分类的输入向量
    dataSet: 输入的训练样本集
    labels: 标签向量
    k: 选择最近邻居的数目
    注意：labels元素数目和dataSet行数相同；程序使用欧式距离公式.

    预测数据所在分类可在输入下列命令
    kNN.classify0([0,0], group, labels, 3)
    '''
    # 1. 距离计算
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort()
    # 2. 选择距离最小的k个点
    classCount = {}
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    # 3. 排序
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]