-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathDKFN.py
155 lines (130 loc) · 6.55 KB
/
DKFN.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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from modules import FilterLinear
class DKFN(nn.Module):
def __init__(self, K, A, feature_size, Clamp_A=True):
# GC-LSTM
'''
Args:
K: K-hop graph
A: adjacency matrix
feature_size: the dimension of features
Clamp_A: Boolean value, clamping all elements of A between 0. to 1.
'''
super(DKFN, self).__init__()
self.feature_size = feature_size
self.hidden_size = feature_size
self.K = K
self.A_list = [] # Adjacency Matrix List
# normalization
D_inverse = torch.diag(1 / torch.sum(A, 0))
norm_A = torch.matmul(D_inverse, A)
A = norm_A
A_temp = torch.eye(feature_size, feature_size)
for i in range(K):
A_temp = torch.matmul(A_temp, A)
if Clamp_A:
# confine elements of A
A_temp = torch.clamp(A_temp, max=1.)
self.A_list.append(A_temp)
# a length adjustable Module List for hosting all graph convolutions
self.gc_list = nn.ModuleList([FilterLinear(feature_size, feature_size, self.A_list[i], bias=False) for i in range(K)])
hidden_size = self.feature_size
gc_input_size = self.feature_size * K
self.fl = nn.Linear(gc_input_size + hidden_size, hidden_size)
self.il = nn.Linear(gc_input_size + hidden_size, hidden_size)
self.ol = nn.Linear(gc_input_size + hidden_size, hidden_size)
self.Cl = nn.Linear(gc_input_size + hidden_size, hidden_size)
# initialize the neighbor weight for the cell state
self.Neighbor_weight = Parameter(torch.FloatTensor(feature_size))
stdv = 1. / math.sqrt(feature_size)
self.Neighbor_weight.data.uniform_(-stdv, stdv)
# RNN
input_size = self.feature_size
self.rfl = nn.Linear(input_size + hidden_size, hidden_size)
self.ril = nn.Linear(input_size + hidden_size, hidden_size)
self.rol = nn.Linear(input_size + hidden_size, hidden_size)
self.rCl = nn.Linear(input_size + hidden_size, hidden_size)
# addtional vars
self.c = torch.nn.Parameter(torch.Tensor([1]))
self.fc1 = nn.Linear(64, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, hidden_size)
self.fc4 = nn.Linear(hidden_size, 64)
self.fc5 = nn.Linear(64, hidden_size)
self.fc6 = nn.Linear(hidden_size, hidden_size)
self.fc7 = nn.Linear(hidden_size, hidden_size)
self.fc8 = nn.Linear(hidden_size, 64)
def forward(self, input, Hidden_State, Cell_State, rHidden_State, rCell_State):
# GC-LSTM
x = input
gc = self.gc_list[0](x)
for i in range(1, self.K):
gc = torch.cat((gc, self.gc_list[i](x)), 1)
combined = torch.cat((gc, Hidden_State), 1)
f = torch.sigmoid(self.fl(combined))
i = torch.sigmoid(self.il(combined))
o = torch.sigmoid(self.ol(combined))
C = torch.tanh(self.Cl(combined))
NC = torch.mul(Cell_State,
torch.mv(Variable(self.A_list[-1], requires_grad=False).cuda(), self.Neighbor_weight))
Cell_State = f * NC + i * C
Hidden_State = o * torch.tanh(Cell_State)
# LSTM
rcombined = torch.cat((input, rHidden_State), 1)
rf = torch.sigmoid(self.rfl(rcombined))
ri = torch.sigmoid(self.ril(rcombined))
ro = torch.sigmoid(self.rol(rcombined))
rC = torch.tanh(self.rCl(rcombined))
rCell_State = rf * rCell_State + ri * rC
rHidden_State = ro * torch.tanh(rCell_State)
# Kalman Filtering
var1, var2 = torch.var(input), torch.var(gc)
pred = (Hidden_State * var1 * self.c + rHidden_State * var2) / (var1 + var2 * self.c)
return Hidden_State, Cell_State, gc, rHidden_State, rCell_State, pred
def Bi_torch(self, a):
a[a < 0] = 0
a[a > 0] = 1
return a
def loop(self, inputs):
batch_size = inputs.size(0)
time_step = inputs.size(1)
Hidden_State, Cell_State, rHidden_State, rCell_State = self.initHidden(batch_size)
for i in range(time_step):
Hidden_State, Cell_State, gc, rHidden_State, rCell_State, pred = self.forward(
torch.squeeze(inputs[:, i:i + 1, :]), Hidden_State, Cell_State, rHidden_State, rCell_State)
return pred
# return Hidden_State, Cell_State, rHidden_State, rCell_State, pred
def initHidden(self, batch_size):
use_gpu = torch.cuda.is_available()
if use_gpu:
Hidden_State = Variable(torch.zeros(batch_size, self.hidden_size).cuda())
Cell_State = Variable(torch.zeros(batch_size, self.hidden_size).cuda())
rHidden_State = Variable(torch.zeros(batch_size, self.hidden_size).cuda())
rCell_State = Variable(torch.zeros(batch_size, self.hidden_size).cuda())
return Hidden_State, Cell_State, rHidden_State, rCell_State
else:
Hidden_State = Variable(torch.zeros(batch_size, self.hidden_size))
Cell_State = Variable(torch.zeros(batch_size, self.hidden_size))
rHidden_State = Variable(torch.zeros(batch_size, self.hidden_size))
rCell_State = Variable(torch.zeros(batch_size, self.hidden_size))
return Hidden_State, Cell_State, rHidden_State, rCell_State
def reinitHidden(self, batch_size, Hidden_State_data, Cell_State_data):
use_gpu = torch.cuda.is_available()
if use_gpu:
Hidden_State = Variable(Hidden_State_data.cuda(), requires_grad=True)
Cell_State = Variable(Cell_State_data.cuda(), requires_grad=True)
rHidden_State = Variable(Hidden_State_data.cuda(), requires_grad=True)
rCell_State = Variable(Cell_State_data.cuda(), requires_grad=True)
return Hidden_State, Cell_State, rHidden_State, rCell_State
else:
Hidden_State = Variable(Hidden_State_data, requires_grad=True)
Cell_State = Variable(Cell_State_data, requires_grad=True)
rHidden_State = Variable(Hidden_State_data.cuda(), requires_grad=True)
rCell_State = Variable(Cell_State_data.cuda(), requires_grad=True)
return Hidden_State, Cell_State, rHidden_State, rCell_State