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rnmf.py
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rnmf.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 08/21/18 9:12 AM
# @Author : gehen
# @Site :
# @File : rnmf.py
# @Software: PyCharm
import numpy as np
from time import time
import scipy.io as sio
np.set_printoptions(precision=4,suppress=True,linewidth=1000)
def factorize(S,view_list,K,item,Max_Iter=500000,check_point=100,thresh=5e-5,alpha=1):
sort_list = sorted(range(len(view_list)), key=lambda k: -view_list[k])
S_indexs = []
S = np.mat(S)
N = S.shape[0]
# init A
A = np.random.rand(N, K)
# start = sum(view_list[0:sort_list[item]])
# end = start + min(K,view_list[sort_list[item]])
# S_index = range(start, end)
# S_indexs.append(S_index)
# A_index = range(min(K,view_list[sort_list[item]]))
# A[S_index, :] = 0
# A[:, A_index] = S[:, S_index]
A = np.mat(A)
# init I1, I2
I1 = np.ones([K,1])
I2 = np.ones([N,1])
# optimize ||S-AA'||^2+alpha*||AI1-I2||^2
check1 = np.linalg.norm(A * A.T, 2)
check2 = np.linalg.norm(S - A*A.T, 2)
exit_flag = 0
for iter in range(1,Max_Iter):
top = 4 * S* A + 2*alpha * I2 * I1.T + 1e-3
bot = 4 * A * A.T* A + 2 * alpha * A * I1 * I1.T + 1e-3
A = np.multiply(A, np.sqrt(top/bot))
if iter % check_point == 0:
if alpha>=3:
alpha = alpha*(1.0/3)
else:
alpha = 1
now_check1 = np.linalg.norm(A * A.T, 2)
now_check2 = np.linalg.norm(S - A*A.T, 2)
if abs(now_check1 - check1) <= thresh and abs(now_check2 - check2) <= thresh:
exit_flag = 1
break
else:
check1 = now_check1
check2 = now_check2
# print('Iteration is : {}, exit flag is : {}'.format(iter, exit_flag))
return A
def assign_H(S,view_list,thresh=0.9):
if sum(view_list)>0:
[eig_value,_] = np.linalg.eig(S)
K = np.sum(eig_value>=thresh)
K = max(K,1)
# print ('\t\t{}'.format(K))
H = assign_kH(S,view_list,K)
return H,K
else:
return np.zeros([0,0]),0
def assign_kH(S,view_list,K,sparsity_thresh=0.6,rank=10):
N = S.shape[0]
sum_view = [sum(view_list[0:i]) for i in range(len(view_list)+1)]
num = len(view_list)
# assert K>=max(view_list),'k({}) is smaller than max view num {}'.format(K,max(view_list))
H_list = []
for item in range(rank):
data_item = int(item%num)
A = factorize(S, view_list, K,data_item)
# assign H row-wise
H = np.zeros([N,K])
temp_A = A.copy()
for i in range(N):
j = np.argmax(temp_A[i,:])
H[i,j] = 1
H_list.append(np.mat(H))
lost_list = [np.linalg.norm(S-H_item*H_item.T) for H_item in H_list]
index = np.argmin(lost_list)
optimal_H = H_list[index]
# print index,lost_list
# print('\n')
# print(index)
# for j in range(K):
# temp = np.where(optimal_H[:,j]==1)[0]
# print (temp+1)
return optimal_H
if __name__ == '__main__':
S14 = np.asarray([[1, 0 ,0 ,0.7, 0.4, 0.3, 0.8, 0.4 ,0.5 ,0.3, 0.6 ,0.5, 0.4 ,0.2],
[0 ,1 ,0, 0.3, 0.4, 0.6, 0.2, 0.5, 0.3, 0.6, 0.1, 0.2, 0.4, 0.],
[0, 0 ,1 ,0.4, 0.6, 0.3, 0.2 ,0.4, 0.1 ,0.3, 0.4, 0.1, 0.3, 0.3],
[0.7, 0.2, 0.3, 1 ,0 ,0, 0.8, 0.3, 0.2, 0.4, 0.6, 0.1, 0.2, 0.5],
[0.3, 0.2, 0.4, 0 ,1, 0, 0.4, 0.8, 0.3, 0.1, 0.2, 0.7, 0.3, 0.5],
[0.3, 0.6, 0.1, 0, 0, 1, 0.3, 0.5, 0.4, 0.2, 0.3, 0.1, 0.4, 0.8],
[0.6, 0.3, 0.2, 0.7, 0.3, 0.1, 1, 0, 0, 0, 0.6, 0.3, 0.2, 0.4],
[0.2, 0.2, 0.4, 0.1, 0.7, 0.3, 0 ,1, 0, 0, 0.4, 0.8, 0.6, 0.4],
[0.1, 0.4 ,0.3, 0.5, 0.6, 0.4, 0, 0, 1, 0, 0.4, 0.5, 0.8, 0.3],
[0.3, 0.2 ,0.5, 0.4 ,0.3, 0.4, 0, 0, 0, 1, 0.4, 0.3, 0.1, 0.2],
[0.6, 0.3, 0.1, 0.6, 0.4, 0.3, 0.8, 0.1, 0.5, 0.3 ,1, 0 ,0, 0],
[0.3, 0.2, 0.1, 0.3, 0.7, 0.4, 0.6, 0.3, 0.2, 0.1, 0, 1, 0 ,0],
[0.3, 0.1, 0.4, 0.3, 0.2, 0.3 ,0.6, 0.3, 0.8, 0.3, 0 ,0 ,1 ,0],
[0.3, 0.7, 0.3, 0.5, 0.3 ,0.9, 0.2, 0.3, 0.1 ,0.6, 0 ,0, 0, 1]])
S = (S14+S14.T)/2
# sigma = 20
# data = sio.loadmat('1741.mat')
# D = data['array']
# D = D[0:18,0:18]
# S = init_S(D,view_list)
view_list = [3,3,4,4]
start = time()
# S = init_S(D, view_list)
# H = assign_kH(S,view_list,k)
# A = factorize(S,view_list,6,2)
H = assign_H(S,view_list)
k = H[0].shape[1]
print (H[0])
for j in range(k):
print (np.where(H[0][:,j]==1)[0]+1)
# H,k = assign_H(D,view_list)
# print ('cost {}s'.format(time()-start))
# print ('k = {}'.format(k))