-
Notifications
You must be signed in to change notification settings - Fork 0
/
gl_and_sl.py
130 lines (101 loc) · 3.92 KB
/
gl_and_sl.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
## Graph learning and signal learning
import numpy as np
from matplotlib.pylab import *
import matplotlib.pyplot as plt
import os
os.chdir('C:/Kaige_Research/Graph Learning/graph_learning_code/')
from sklearn.metrics.pairwise import rbf_kernel, euclidean_distances
import seaborn as sns
from synthetic_data import *
from primal_dual_gl import Primal_dual_gl
from utils import *
from pygsp import graphs, plotting, filters
import pyunlocbox
import networkx as nx
from gl_sigrep import Gl_sigrep
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
path='C:/Kaige_Research/Graph Learning/graph_learning_code/results/test_results2/'
timeRun = datetime.datetime.now().strftime('_%m_%d_%H_%M_%S')
node_num=20
signal_num=100
error_sigma=0.1
adj_matrix, knn_lap, knn_pos=rbf_graph(node_num)
X, X_noise, item_features=generate_signal(signal_num, node_num, knn_pos, error_sigma)
#original_signal=original_signal(node_num)
#X=Heat_diffusion_signal(original_signal, adj_matrix)
#X=Tikhonov_signal(original_signal, adj_matrix)
#X=Generative_model_signal(original_signal, adj_matrix)
newpath=path+'error_%s'%(int(error_sigma*100))+str(timeRun)+'/'
if not os.path.exists(newpath):
os.makedirs(newpath)
signals=X_noise
Z=euclidean_distances(signals.T, squared=True)
np.fill_diagonal(Z, 0)
Z=norm_W(Z, node_num)
alpha=1
beta=0.2
theta=0.01
#primal_gl=Primal_dual_gl(node_num, Z, alpha=alpha, beta=beta)
primal_gl=Gl_sigrep(node_num, Z, alpha=alpha, beta=beta)
primal_adj, error=primal_gl.run(adj_matrix)
laplacian=csgraph.laplacian(primal_adj, normed=False)
learned_signals=np.dot(signals, np.linalg.inv((np.identity(node_num)+theta*laplacian)))
print('adj_matrix\n', adj_matrix)
print('primal_adj\n', primal_adj)
print('X\n', X[0,:])
print('learned_signals\n', learned_signals[0,:])
signal_error=np.linalg.norm(learned_signals-X)
graph_error=np.linalg.norm(primal_adj-adj_matrix)
############################################ Results
## Real Graph and real signal
j=0
real_signal=X[j,:]
learned_signal=learned_signals[j,:]
real_graph=create_networkx_graph(node_num, adj_matrix)
edge_num=real_graph.number_of_edges()
edge_weights=adj_matrix[np.triu_indices(node_num,0)]
edge_color=edge_weights[edge_weights>0]
edge_alpha=edge_color
nodes=nx.draw_networkx_nodes(real_graph, knn_pos, node_color=real_signal,node_size=100, cmap=plt.cm.Reds)
edges=nx.draw_networkx_edges(real_graph, knn_pos, width=1.0, alpha=1.0, edge_color=edge_color, edge_cmap=plt.cm.Blues)
plt.axis('off')
plt.show()
learned_graph=create_networkx_graph(node_num, primal_adj)
edge_num=learned_graph.number_of_edges()
edge_weights=primal_adj[np.triu_indices(node_num,1)]
edge_weights[edge_weights<0]=0
edge_color=edge_weights
edge_alpha=edge_color
nodes=nx.draw_networkx_nodes(learned_graph, knn_pos, node_color=learned_signal,node_size=100, cmap=plt.cm.Reds)
edges=nx.draw_networkx_edges(learned_graph, knn_pos, width=1.0, alpha=1.0, edge_color=edge_color, edge_cmap=plt.cm.Blues)
plt.axis('off')
plt.show()
##Plot graph
fig,(ax1, ax2)=plt.subplots(1,2, figsize=(4,2))
ax1.pcolor(adj_matrix, cmap='RdBu')
ax1.set_title('real W')
ax2.pcolor(primal_adj, cmap='RdBu')
ax2.set_title('learned w')
plt.show()
### plot graph learning error
plt.plot(error)
plt.ylabel('Learning Error', fontsize=12)
plt.show()
# fig, axes=plt.subplots(3,2, figsize=(6,6))
# axes[1,0].pcolor(Z,cmap='RdBu')
# axes[1,0].axis('off')
# axes[1,1].pcolor(rbf_z,cmap='RdBu')
# axes[1,1].axis('off')
# axes[0,0].pcolor(adj_matrix,cmap='RdBu')
# axes[0,0].axis('off')
# axes[2,0].pcolor(primal_adj,cmap='RdBu')
# axes[2,0].axis('off')
# axes[2,1].pcolor(primal_adj_z,cmap='RdBu')
# axes[2,1].axis('off')
# axes[1,0].set_title('Z')
# axes[1,1].set_title('rbf_z')
# axes[0,0].set_title('adj_matrix')
# axes[2,0].set_title('primal_adj')
# axes[2,1].set_title('primal_adj_z')
# axes[0,1].axis('off')
# plt.show()