机器学习实战第一节，K近邻算法

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

def classify0(inX, dataset, labels, k):
    '''
        inX：输入向量
        dataset：数据集中X向量
        labels：数据集的标签集合
        k：k-近邻算法中的K

        return：输入向量的标签
    '''
    size = dataset.shape[0]

    '''
        计算欧氏距离
    '''
    d_value = np.tile(inX, (size, 1)) - dataset
    sqrt_value = d_value**2
    sum_value = sqrt_value.sum(axis=1)
    distance = sum_value**0.5
    sort_dis_index = distance.argsort()
    class_count = {}
    for i in range(k):
        label = labels[sort_dis_index[i]]
        class_count[label] = class_count.get(label, 0) + 1

    '''
        根据class_count的第二个字段进行排序
        reverse=true: 降序
        reverse=false: 升序
    '''
    sort_label_index = sorted(class_count.items(), key = operator.itemgetter(1), reverse = True)

    return sort_label_index[0][0]import numpy as np
import operator

def c