# # kNN 分类算法

a = np.array([[1,1],[1.2,1.5],[0.3,0.4],[0.2,0.5]])　　　　　　　　#构造样本数据

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

# print(a.shape[0])                 # 行数  shape(1)表示列数

diffMat = np.tile (np.array([[1.5,1.2]]),(a.shape[0],1)) - a        #tile（被重复数据，（重复几行，每行重复几次））

#   diffMat

# [[ 0.5  0.2]

#  [ 0.3 -0.3]

#  [ 1.2  0.8]

#  [ 1.3  0.7]]

a = diffMat ** 2

#       a

# [[0.25 0.04]

#  [0.09 0.09]

#  [1.44 0.64]

#  [1.69 0.49]]

distence = a.sum(axis=1)           # 1表示行  0表示列

#       distence

# [0.29 0.18 2.08 2.18]

dis_sort = distence.argsort()

#  dis_sort

# [1 0 2 3]

k = 3

classcount = {}

for i in range(0,len(dis_sort)):

    if i>=k:

        break

    if dis_sort[i] <= k + 1:

        classcount[labels[i]] = classcount.get(labels[i],0)+1

#                   classcount

#                  {'A': 2, 'B': 1}