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Functions680 in github.com/benedekrozemberczki/pytorch_geometric_temporal

↓ 27 callersFunctiontemporal_signal_split
r"""Function to split a data iterator according to a fixed ratio. Arg types: * **data_iterator** *(Signal Iterator)* - Node features.
torch_geometric_temporal/signal/train_test_split.py:36
↓ 15 callersMethodget_dataset
Returning the MTM-1 motion data iterator. Args types: * **frames** *(int)* - The number of consecutive frames T, default 16.
torch_geometric_temporal/dataset/mtm.py:66
↓ 12 callersFunctionmasked_mae_loss
(y_pred, y_true)
examples/indexBatching/utils.py:10
↓ 11 callersFunctioncreate_mock_data
Creating a mock feature matrix and edge index.
test/recurrent_test.py:16
↓ 11 callersFunctioncreate_mock_edge_weight
Creating a mock edge weight tensor.
test/recurrent_test.py:62
↓ 8 callersMethod__init__
( self, L: int, K: int, d: int, num_his: int, bn_decay:
torch_geometric_temporal/nn/attention/gman.py:496
↓ 6 callersMethod__init__
( self, gcn_true: bool, build_adj: bool, gcn_depth: int, num_nodes: in
torch_geometric_temporal/nn/attention/mtgnn.py:487
↓ 4 callersMethod__init__
(self, items_total: int, item_embedding_dim: int, n_heads: int)
torch_geometric_temporal/nn/attention/dnntsp.py:158
↓ 4 callersMethod__init__
( self, nb_block: int, in_channels: int, K: int, nb_chev_filter: int,
torch_geometric_temporal/nn/attention/astgcn.py:519
↓ 4 callersFunctioncheck_tennis_data
(event_id, node_count, mode, edge_cnt)
test/dataset_test.py:436
↓ 4 callersFunctioncreate_mock_data
Creating a mock feature matrix and edge index.
test/attention_test.py:26
↓ 4 callersFunctiongenerate_signal
(snapshot_count, n_count, feature_count, additional_features_keys=[])
test/dataset_test.py:38
↓ 4 callersFunctiongenerate_signal
(snapshot_count, node_count, feature_count, graph_count)
test/batch_test.py:24
↓ 4 callersMethodget_index_dataset
Returns torch dataloaders using index batching for PeMS dataset. Args: lags (int, optional): The number of time lags. De
torch_geometric_temporal/dataset/pems.py:71
↓ 3 callersMethod__init__
(self, in_channels, out_channels, K, bias=True)
torch_geometric_temporal/nn/recurrent/dcrnn.py:21
↓ 3 callersFunctioncreate_mock_batch
Creating a mock batch of sequences
test/attention_test.py:77
↓ 3 callersFunctioncreate_mock_target
Creating a mock target vector.
test/attention_test.py:43
↓ 3 callersFunctiongenerate_heterogeneous_signal
(snapshot_count, n_count, feature_count, *additional_features_keys)
test/dataset_test.py:54
↓ 3 callersFunctiongenerate_heterogeneous_signal
(snapshot_count, node_count, feature_count, graph_count)
test/batch_test.py:54
↓ 3 callersFunctiontqdm
(iterable)
examples/recurrent/tgcn_example.py:4
↓ 2 callersMethod__init__
( self, in_channels: int, out_channels: int, edge_index: torch.LongTensor,
torch_geometric_temporal/nn/attention/tsagcn.py:294
↓ 2 callersMethod_bn_init
(self, bn, scale)
torch_geometric_temporal/nn/attention/tsagcn.py:152
↓ 2 callersMethodget_dataset
Returning the Chickenpox Hungary data iterator. Args types: * **lags** *(int)* - The number of time lags. Return types:
torch_geometric_temporal/dataset/chickenpox.py:57
↓ 2 callersMethodget_dataset
Returns data iterator for METR-LA dataset as an instance of the static graph temporal signal class. Return types: * **dat
torch_geometric_temporal/dataset/metr_la.py:102
↓ 2 callersMethodget_dataset
Returns data iterator for PEMS-BAY dataset as an instance of the static graph temporal signal class. Return types: * **da
torch_geometric_temporal/dataset/pems_bay.py:116
↓ 2 callersFunctionget_edge_array
(n_count)
test/dataset_test.py:34
↓ 2 callersFunctionget_edge_array
(node_count, node_start)
test/batch_test.py:17
↓ 2 callersMethodget_index_dataset
Returns torch dataloaders using index batching for Chickenpox Hungary dataset. Args: lags (int, optional): The number of
torch_geometric_temporal/dataset/chickenpox.py:74
↓ 2 callersMethodget_index_dataset
Returns torch dataloaders using index batching for WindmillLarge dataset. Args: lags (int, optional): The number of time
torch_geometric_temporal/dataset/windmilllarge.py:74
↓ 2 callersMethodget_index_dataset
Returns torch dataloaders using index batching for PeMS dataset. Args: lags (int, optional): The number of time lags. De
torch_geometric_temporal/dataset/pemsAllLA.py:72
↓ 2 callersMethodget_index_dataset
Returns torch dataloaders using index batching for PeMSBay dataset. Args: lags (int, optional): The number of time lags.
torch_geometric_temporal/dataset/pems_bay.py:133
↓ 2 callersFunctiononehot_encoding
(x, unique_vals)
torch_geometric_temporal/dataset/twitter_tennis.py:18
↓ 1 callersMethod__init__
( self, num_nodes: int, in_channels: int, hidden_channels: int, out_ch
torch_geometric_temporal/nn/attention/stgcn.py:88
↓ 1 callersMethod__init__
( self, nb_block: int, in_channels: int, K: int, nb_chev_filter: int,
torch_geometric_temporal/nn/attention/mstgcn.py:140
↓ 1 callersMethod__init__
( self, in_channels: int, out_channels: int, improved: bool = False, c
torch_geometric_temporal/nn/recurrent/temporalgcn.py:18
↓ 1 callersMethod__init__
( self, in_channels: int, out_channels: int, periods: int, improved: b
torch_geometric_temporal/nn/recurrent/attentiontemporalgcn.py:21
↓ 1 callersMethod__init__
( self, number_of_nodes: int, in_channels: int, out_channels: int, K:
torch_geometric_temporal/nn/recurrent/agcrn.py:68
↓ 1 callersMethod__init__
( self, in_channels: int, improved: bool = False, cached: bool = False,
torch_geometric_temporal/nn/recurrent/evolvegcno.py:126
↓ 1 callersMethod__norm__
( self, edge_index, num_nodes: Optional[int], edge_weight: OptTensor,
torch_geometric_temporal/nn/attention/astgcn.py:82
↓ 1 callersMethod__repr__
(self)
torch_geometric_temporal/nn/attention/astgcn.py:192
↓ 1 callersMethod__reset_parameters
(self)
torch_geometric_temporal/nn/recurrent/dcrnn.py:35
↓ 1 callersMethod__reset_parameters
(self)
torch_geometric_temporal/nn/recurrent/dcrnn.py:250
↓ 1 callersMethod_adaptive_forward
(self, x, y)
torch_geometric_temporal/nn/attention/tsagcn.py:229
↓ 1 callersMethod_attentive_forward
(self, y)
torch_geometric_temporal/nn/attention/tsagcn.py:210
↓ 1 callersMethod_bn_init
(self, bn, scale)
torch_geometric_temporal/nn/attention/tsagcn.py:73
↓ 1 callersMethod_calculate_candidate_state
(self, X, edge_index, edge_weight, H, R)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:94
↓ 1 callersMethod_calculate_candidate_state
(self, X, edge_index, edge_weight, H, R)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:201
↓ 1 callersMethod_calculate_candidate_state
(self, X, edge_index, edge_weight, H, R)
torch_geometric_temporal/nn/recurrent/dcrnn.py:184
↓ 1 callersMethod_calculate_candidate_state
(self, X, edge_index, edge_weight, H, R)
torch_geometric_temporal/nn/recurrent/dcrnn.py:402
↓ 1 callersMethod_calculate_candidate_state
(self, X, edge_index, edge_weight, H, R, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_gru.py:131
↓ 1 callersMethod_calculate_cell_state
(self, X, edge_index, edge_type, H, C, I, F)
torch_geometric_temporal/nn/recurrent/lrgcn.py:122
↓ 1 callersMethod_calculate_cell_state
(self, X, edge_index, edge_weight, H, C, I, F, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_lstm.py:184
↓ 1 callersMethod_calculate_cell_state
(self, X, edge_index, edge_weight, H, C, I, F, lambda_max)
torch_geometric_temporal/nn/recurrent/gc_lstm.py:152
↓ 1 callersMethod_calculate_cell_state
(self, x_dict, edge_index_dict, h_dict, c_dict, i_dict, f_dict)
torch_geometric_temporal/nn/hetero/heterogclstm.py:126
↓ 1 callersMethod_calculate_forget_gate
(self, X, edge_index, edge_type, H, C)
torch_geometric_temporal/nn/recurrent/lrgcn.py:116
↓ 1 callersMethod_calculate_forget_gate
(self, X, edge_index, edge_weight, H, C, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_lstm.py:176
↓ 1 callersMethod_calculate_forget_gate
(self, X, edge_index, edge_weight, H, C, lambda_max)
torch_geometric_temporal/nn/recurrent/gc_lstm.py:145
↓ 1 callersMethod_calculate_forget_gate
(self, x_dict, edge_index_dict, h_dict, c_dict)
torch_geometric_temporal/nn/hetero/heterogclstm.py:118
↓ 1 callersMethod_calculate_hidden_state
(self, Z, H, H_tilde)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:100
↓ 1 callersMethod_calculate_hidden_state
(self, Z, H, H_tilde)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:208
↓ 1 callersMethod_calculate_hidden_state
(self, O, C)
torch_geometric_temporal/nn/recurrent/lrgcn.py:135
↓ 1 callersMethod_calculate_hidden_state
(self, Z, H, H_tilde)
torch_geometric_temporal/nn/recurrent/dcrnn.py:190
↓ 1 callersMethod_calculate_hidden_state
(self, Z, H, H_tilde)
torch_geometric_temporal/nn/recurrent/dcrnn.py:408
↓ 1 callersMethod_calculate_hidden_state
(self, O, C)
torch_geometric_temporal/nn/recurrent/gconv_lstm.py:200
↓ 1 callersMethod_calculate_hidden_state
(self, Z, H, H_tilde)
torch_geometric_temporal/nn/recurrent/gconv_gru.py:137
↓ 1 callersMethod_calculate_hidden_state
(self, O, C)
torch_geometric_temporal/nn/recurrent/gc_lstm.py:167
↓ 1 callersMethod_calculate_hidden_state
(self, o_dict, c_dict)
torch_geometric_temporal/nn/hetero/heterogclstm.py:143
↓ 1 callersMethod_calculate_input_gate
(self, X, edge_index, edge_type, H, C)
torch_geometric_temporal/nn/recurrent/lrgcn.py:110
↓ 1 callersMethod_calculate_input_gate
(self, X, edge_index, edge_weight, H, C, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_lstm.py:168
↓ 1 callersMethod_calculate_input_gate
(self, X, edge_index, edge_weight, H, C, lambda_max)
torch_geometric_temporal/nn/recurrent/gc_lstm.py:138
↓ 1 callersMethod_calculate_input_gate
(self, x_dict, edge_index_dict, h_dict, c_dict)
torch_geometric_temporal/nn/hetero/heterogclstm.py:110
↓ 1 callersMethod_calculate_output_gate
(self, X, edge_index, edge_type, H, C)
torch_geometric_temporal/nn/recurrent/lrgcn.py:129
↓ 1 callersMethod_calculate_output_gate
(self, X, edge_index, edge_weight, H, C, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_lstm.py:192
↓ 1 callersMethod_calculate_output_gate
(self, X, edge_index, edge_weight, H, C, lambda_max)
torch_geometric_temporal/nn/recurrent/gc_lstm.py:160
↓ 1 callersMethod_calculate_output_gate
(self, x_dict, edge_index_dict, h_dict, c_dict)
torch_geometric_temporal/nn/hetero/heterogclstm.py:135
↓ 1 callersMethod_calculate_reset_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:88
↓ 1 callersMethod_calculate_reset_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:194
↓ 1 callersMethod_calculate_reset_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/dcrnn.py:178
↓ 1 callersMethod_calculate_reset_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/dcrnn.py:396
↓ 1 callersMethod_calculate_reset_gate
(self, X, edge_index, edge_weight, H, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_gru.py:125
↓ 1 callersMethod_calculate_update_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:82
↓ 1 callersMethod_calculate_update_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/temporalgcn.py:187
↓ 1 callersMethod_calculate_update_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/dcrnn.py:172
↓ 1 callersMethod_calculate_update_gate
(self, X, edge_index, edge_weight, H)
torch_geometric_temporal/nn/recurrent/dcrnn.py:389
↓ 1 callersMethod_calculate_update_gate
(self, X, edge_index, edge_weight, H, lambda_max)
torch_geometric_temporal/nn/recurrent/gconv_gru.py:119
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_hetero_graph_static_signal.py:54
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_hetero_graph_temporal_signal.py:54
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/static_hetero_graph_temporal_signal_batch.py:59
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_hetero_graph_static_signal_batch.py:60
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/static_hetero_graph_temporal_signal.py:110
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_hetero_graph_temporal_signal_batch.py:60
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/static_graph_temporal_signal_batch.py:54
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_graph_static_signal_batch.py:54
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_graph_temporal_signal_batch.py:54
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_graph_static_signal.py:49
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/static_graph_temporal_signal.py:50
↓ 1 callersMethod_check_temporal_consistency
(self)
torch_geometric_temporal/signal/dynamic_graph_temporal_signal.py:49
↓ 1 callersMethod_conv_branch_init
(self, conv, branches)
torch_geometric_temporal/nn/attention/tsagcn.py:160
↓ 1 callersMethod_conv_init
(self, conv)
torch_geometric_temporal/nn/attention/tsagcn.py:77
↓ 1 callersMethod_conv_init
(self, conv)
torch_geometric_temporal/nn/attention/tsagcn.py:156
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