python学习笔记 python实现k-means聚类

 # -*- coding: utf-8 -*-
 """
 Created on Thu Mar 16 14:52:58 2017

 @author: Jarvis
 """
 import tensorflow as tf
 import numpy as np
 import pandas as pd
 import math
 import random
 from pandas import Series,DataFrame
 def cal_dis(a,b):
     sum = 0
     for x,y in zip(a,b):
         sum = sum+(x-y)*(x-y)
     return math.sqrt(sum)

 def is_same_series(a,b):

     for x,y in zip(a,b):
         if x != y:
             return False
     return True
 def is_constant_vec(a,b):
     if len(a) == 0 or len(b) == 0:
         return False

     for x,y in zip(a,b):
         if not is_same_series(x,y):
             return False 

     return True 

 def init_typeSet(init_set,type_num):
     for i in range(type_num):
         init_set.append([])
     return init_set

 def Mindis_type_no(x,vecs,typ_num = 4):
     mindis = cal_dis(x,vecs[0])
     ans = 0
     for i in range(1,type_num):
         tmp  = cal_dis(x,vecs[i])
         if mindis > tmp:
             ans= i
             mindis = cal_dis(x,vecs[i])
     return ans

 def reduce_mean(vecs):

     reduce_vec = vecs[0].copy()
     print(reduce_vec)
     for i in range(1,len(vecs)):
         print (vecs[i][5])
         print (reduce_vec[5])
         reduce_vec = reduce_vec+vecs[i]

         print (reduce_vec[5])

     reduce_vec = reduce_vec/len(vecs)
 #    print(reduce_vec)
     return reduce_vec
 def get_vecs(sets):
     vecs = []
     for i in sets:
         vecs.append(data.ix[i])
     return vecs

 raw_data_file = pd.read_csv('NDVI_NDWI_all.csv',header = None,encoding = 'gbk')
 data = (raw_data_file)
 del data[0]
 del data[1]
 del data[2]

 type_num = 4
 init_type_vec = []#类质心
 tmp_set = set([])
 data_size = len(data)

 while (len(tmp_set) < type_num):
      tmp_set.add(random.choice(range(data_size)))

 for i in tmp_set:
     tmp = data.ix[i]
     init_type_vec.append(tmp)

 pre_vec = []
 #print (is_constant_vec(pre_vec,init_type_vec))

 while( not is_constant_vec(pre_vec,init_type_vec)):
     type_set = []
     type_set = init_typeSet(type_set,type_num)
     for j in range(len(data)):
         tmp_type = Mindis_type_no(data.ix[j],init_type_vec)
         type_set[tmp_type].append(j)
         #type_set[tmp_type].append(data.ix[j])
     if(len(pre_vec) == 0):
         pre_vec = init_type_vec.copy()
     else:
         for i in range(type_num):
             pre_vec[i] = init_type_vec[i]
             need_cal_vecs = get_ves(type_set[i])
             init_type_vec[i] = reduce_mean(need_cal_vecs).copy()
 for i in range(type_num):
     print('--------------------------------')
     print(type_set[i])

 with open('output.txt',"w"):
     for i in range(type_num):
         print ("type %d"%i)
         for j in type_set[i]:
             print(j)

 #print(reduce_mean(type_set))
 #for i in range(type_num):
      #   pre_vec[i] = tf.reduce_mean()