def __init__(self, *args, **kwargs):
self.num_nodes = num_node
self.edges = neighbor
self.self_loops = [(i, i) for i in range(self.num_nodes)]
self.A_binary = tools.get_adjacency_matrix(self.edges, self.num_nodes)
self.A_binary_with_I = tools.get_adjacency_matrix(
self.edges + self.self_loops, self.num_nodes)
def __init__(self, in_channels, drop_out, adj_filename, id_filename, num_of_vertices, num_for_predict,
num_of_hours,num_of_days,num_of_weeks,):
super(Model, self).__init__()
A = get_adjacency_matrix(adj_filename, num_of_vertices, id_filename)
# num_pre = bool(num_of_hours) + bool(num_of_days) + bool(num_of_weeks)
#self.data_bn = nn.BatchNorm1d(num_pre * num_of_vertices * 3)
self.l1 = STMultiAdaptiveGCN(in_channels, 64, A, residual=False)
self.l2 = STMultiAdaptiveGCN(64, 64, A,)
self.l3 = STMultiAdaptiveGCN(64, 64, A,)
self.final_conv = nn.Conv2d(int(num_for_predict/num_of_hours), num_for_predict, kernel_size=(1, 64))
if drop_out:
self.drop_out = nn.Dropout(drop_out)
else:
self.drop_out = lambda x: x
