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()