1 # -*- coding: utf-8 -*-
2 """
3 Created on Thu Mar 16 14:52:58 2017
4
5 @author: Jarvis
6 """
7 import tensorflow as tf
8 import numpy as np
9 import pandas as pd
10 import math
11 import random
12 from pandas import Series,DataFrame
13 def cal_dis(a,b):
14 sum = 0
15 for x,y in zip(a,b):
16 sum = sum+(x-y)*(x-y)
17 return math.sqrt(sum)
18
19 def is_same_series(a,b):
20
21 for x,y in zip(a,b):
22 if x != y:
23 return False
24 return True
25 def is_constant_vec(a,b):
26 if len(a) == 0 or len(b) == 0:
27 return False
28
29 for x,y in zip(a,b):
30 if not is_same_series(x,y):
31 return False
32
33 return True
34
35 def init_typeSet(init_set,type_num):
36 for i in range(type_num):
37 init_set.append([])
38 return init_set
39
40 def Mindis_type_no(x,vecs,typ_num = 4):
41 mindis = cal_dis(x,vecs[0])
42 ans = 0
43 for i in range(1,type_num):
44 tmp = cal_dis(x,vecs[i])
45 if mindis > tmp:
46 ans= i
47 mindis = cal_dis(x,vecs[i])
48 return ans
49
50 def reduce_mean(vecs):
51
52 reduce_vec = vecs[0].copy()
53 print(reduce_vec)
54 for i in range(1,len(vecs)):
55 print (vecs[i][5])
56 print (reduce_vec[5])
57 reduce_vec = reduce_vec+vecs[i]
58
59 print (reduce_vec[5])
60
61 reduce_vec = reduce_vec/len(vecs)
62 # print(reduce_vec)
63 return reduce_vec
64 def get_vecs(sets):
65 vecs = []
66 for i in sets:
67 vecs.append(data.ix[i])
68 return vecs
69
70 raw_data_file = pd.read_csv('NDVI_NDWI_all.csv',header = None,encoding = 'gbk')
71 data = (raw_data_file)
72 del data[0]
73 del data[1]
74 del data[2]
75
76 type_num = 4
77 init_type_vec = []#类质心
78 tmp_set = set([])
79 data_size = len(data)
80
81 while (len(tmp_set) < type_num):
82 tmp_set.add(random.choice(range(data_size)))
83
84 for i in tmp_set:
85 tmp = data.ix[i]
86 init_type_vec.append(tmp)
87
88 pre_vec = []
89 #print (is_constant_vec(pre_vec,init_type_vec))
90
91 while( not is_constant_vec(pre_vec,init_type_vec)):
92 type_set = []
93 type_set = init_typeSet(type_set,type_num)
94 for j in range(len(data)):
95 tmp_type = Mindis_type_no(data.ix[j],init_type_vec)
96 type_set[tmp_type].append(j)
97 #type_set[tmp_type].append(data.ix[j])
98 if(len(pre_vec) == 0):
99 pre_vec = init_type_vec.copy()
100 else:
101 for i in range(type_num):
102 pre_vec[i] = init_type_vec[i]
103 need_cal_vecs = get_ves(type_set[i])
104 init_type_vec[i] = reduce_mean(need_cal_vecs).copy()
105 for i in range(type_num):
106 print('--------------------------------')
107 print(type_set[i])
108
109
110 with open('output.txt',"w"):
111 for i in range(type_num):
112 print ("type %d"%i)
113 for j in type_set[i]:
114 print(j)
115
116 #print(reduce_mean(type_set))
117 #for i in range(type_num):
118 # pre_vec[i] = tf.reduce_mean()
