|
class | TMVA_SOFIE_GNN.MLPGraphNetwork |
|
class | TMVA_SOFIE_GNN.SofieGNN |
|
|
| input_data) |
|
| TMVA_SOFIE_GNN.GenerateData () |
|
| GLOBAL_FEATURE_SIZE=1) |
|
| TMVA_SOFIE_GNN.make_mlp_model () |
|
| printShape=False) |
|
| inputGraphData) |
|
|
| TMVA_SOFIE_GNN.c2 = c0.cd(2) |
|
| filename = "gnn_core") |
|
| LATENT_SIZE) |
|
| GenerateData() |
|
list | TMVA_SOFIE_GNN.dataSet = [] |
|
| LATENT_SIZE) |
|
| filename = "gnn_decoder") |
|
| TMVA_SOFIE_GNN.edge_data |
|
| TMVA_SOFIE_GNN.edge_index |
|
TMVA_SOFIE_GNN.edge_size = 4 |
|
| outGnet[1].edges.numpy() |
|
| edge_data) |
|
| filename = "gnn_encoder") |
|
| TMVA_SOFIE_GNN.end = time.time() |
|
| TMVA_SOFIE_GNN.endSC = time.time() |
|
| EncodeProcessDecode() |
|
| out[1].globals.numpy() |
|
str | TMVA_SOFIE_GNN.gen_code |
|
| TMVA_SOFIE_GNN.global_data |
|
TMVA_SOFIE_GNN.global_size = 1 |
|
| outGnet[1].globals.numpy() |
|
| global_data) |
|
| graphData]) |
|
list | TMVA_SOFIE_GNN.gnetData = [] |
|
| SofieGNN() |
|
| global_size) |
|
list | dataSet[i] |
|
| data",40,1,0) |
|
| data",40,1,0) |
|
| data",40,1,0) |
|
| graphnet",20,1,0) |
|
| SOFIE",20,1,0) |
|
| CoreGraphData]) |
|
| TMVA_SOFIE_GNN.input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data() |
|
| GraphData]) |
|
TMVA_SOFIE_GNN.LATENT_SIZE = 100 |
|
| TMVA_SOFIE_GNN.node_data |
|
TMVA_SOFIE_GNN.node_size = 4 |
|
| outGnet[1].nodes.numpy() |
|
| node_data) |
|
TMVA_SOFIE_GNN.num_edges = 20 |
|
TMVA_SOFIE_GNN.NUM_LAYERS = 4 |
|
TMVA_SOFIE_GNN.num_nodes = 5 |
|
TMVA_SOFIE_GNN.numevts = 40 |
|
| gnetData[i]) |
|
| gnetData[i]) |
|
| processing_steps) |
|
| filename = "gnn_output_transform") |
|
| sofieData[i]) |
|
TMVA_SOFIE_GNN.processing_steps = 5 |
|
| int32') |
|
| int32') |
|
list | TMVA_SOFIE_GNN.sofieData = [] |
|
| TMVA_SOFIE_GNN.start = time.time() |
|
| TMVA_SOFIE_GNN.start0 = time.time() |
|
Definition in file TMVA_SOFIE_GNN.py.