def forward(self, inputs, input_z, n_in_node, rel_rec, rel_send, origin_A, adj_A_tilt, Wa):
# # copy adj_A batch size
# adj_A = self.adj_A.unsqueeze(0).repeat(self.batch_size, 1, 1)
adj_A_new = torch.eye(origin_A.size()[0]).double()#preprocess_adj(origin_A)#
adj_A_new1 = preprocess_adj_new1(origin_A)
mat_z = torch.matmul(adj_A_new1, input_z+Wa)-Wa #.unsqueeze(2) #.squeeze(1).unsqueeze(1).repeat(1, self.data_variable_size, 1) # torch.repeat(torch.transpose(input_z), torch.ones(n_in_node), axis=0)
adj_As = adj_A_new
#mat_z_max = torch.matmul(adj_A_new, my_normalize(mat_z))
# mat_z_max = (torch.max(mat_z, torch.matmul(adj_As, mat_z)))
H3 = F.relu(self.out_fc1((mat_z)))
#H3_max = torch.matmul(adj_A_new, my_normalize(H3))
# H3_max = torch.max(H3, torch.matmul(adj_As, H3))
# H4 = F.relu(self.out_fc2(H3))
#H4_max = torch.matmul(adj_A_new, my_normalize(H4))
# H4_max = torch.max(H4, torch.matmul(adj_As, H4))
# H5 = F.relu(self.out_fc4(H4_max)) + H3
#H5_max = torch.max(H5, torch.matmul(adj_As, H5))
# mu and sigma
out = self.softmax(self.out_fc3(H3)) # discretized log
return mat_z, out, adj_A_tilt#, self.adj_A
def forward(self, inputs, input_z, n_in_node, rel_rec, rel_send, origin_A, adj_A_tilt, Wa):
# # copy adj_A batch size
# adj_A = self.adj_A.unsqueeze(0).repeat(self.batch_size, 1, 1)
adj_A_new = torch.eye(origin_A.size()[0]).double()#preprocess_adj(origin_A)#
adj_A_new1 = preprocess_adj_new1(origin_A)
mat_z = torch.matmul(adj_A_new1, input_z+Wa)-Wa
adj_As = adj_A_new
#mat_z_max = torch.matmul(adj_A_new, my_normalize(mat_z))
# mat_z_max = (torch.max(mat_z, torch.matmul(adj_As, mat_z)))
H3 = F.relu(self.out_fc1((mat_z)))
#H3_max = torch.matmul(adj_A_new, my_normalize(H3))
# H3_max = torch.max(H3, torch.matmul(adj_As, H3))
# H4 = F.relu(self.out_fc2(H3))
#H4_max = torch.matmul(adj_A_new, my_normalize(H4))
# H4_max = torch.max(H4, torch.matmul(adj_As, H4))
# H5 = F.relu(self.out_fc4(H4_max)) + H3
#H5_max = torch.max(H5, torch.matmul(adj_As, H5))
# mu and sigma
out = self.out_fc3(H3)
return mat_z, out, adj_A_tilt#, self.adj_A
def forward(self, inputs, input_z, n_in_node, rel_rec, rel_send, origin_A, adj_A_tilt, Wa):
# adj_A_new1 = (I-A^T)^(-1)
adj_A_new1 = preprocess_adj_new1(origin_A)
mat_z = torch.matmul(adj_A_new1, input_z + Wa)
out = mat_z
return mat_z, out-Wa, adj_A_tilt
def forward(self, inputs, input_z, n_in_node, rel_rec, rel_send, origin_A, adj_A_tilt, Wa):
#adj_A_new1 = (I-A^T)^(-1)
adj_A_new1 = preprocess_adj_new1(origin_A)
mat_z = torch.matmul(adj_A_new1, input_z+Wa)-Wa
H3 = F.relu(self.out_fc1((mat_z)))
out = self.out_fc2(H3)
return mat_z, out, adj_A_tilt
def forward(self, inputs, rel_rec, rel_send):
if torch.sum(self.adj_A != self.adj_A):
print('nan error \n')
adj_A1 = torch.sinh(3.*self.adj_A)
# adj_A = I-A^T, adj_A_inv = (I-A^T)^(-1)
adj_A = preprocess_adj_new((adj_A1))
adj_A_inv = preprocess_adj_new1((adj_A1))
meanF = torch.matmul(adj_A_inv, torch.mean(torch.matmul(adj_A, inputs), 0))
logits = torch.matmul(adj_A, inputs-meanF)
return inputs-meanF, logits, adj_A1, adj_A, self.z, self.z_positive, self.adj_A
def forward(self, inputs, input_z, n_in_node, rel_rec, rel_send, origin_A, adj_A_tilt, Wa):
# adj_A_new1 = (I-A^T)^(-1)
adj_A_new1 = preprocess_adj_new1(origin_A)
