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| class | TMVA_SOFIE_GNN.MLPGraphNetwork |
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| class | TMVA_SOFIE_GNN.SofieGNN |
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| | TMVA_SOFIE_GNN.CopyData (input_data) |
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| | TMVA_SOFIE_GNN.GenerateData () |
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| | TMVA_SOFIE_GNN.get_graph_data_dict (num_nodes, num_edges, NODE_FEATURE_SIZE=2, EDGE_FEATURE_SIZE=2, GLOBAL_FEATURE_SIZE=1) |
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| | TMVA_SOFIE_GNN.make_mlp_model () |
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| | TMVA_SOFIE_GNN.PrintSofie (output, printShape=False) |
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| | TMVA_SOFIE_GNN.RunGNet (inputGraphData) |
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| | TMVA_SOFIE_GNN.c2 = c0.cd(2) |
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| | TMVA_SOFIE_GNN.core |
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| | TMVA_SOFIE_GNN.CoreGraphData = get_graph_data_dict(num_nodes, num_edges, 2 * LATENT_SIZE, 2 * LATENT_SIZE, 2 * LATENT_SIZE) |
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| | TMVA_SOFIE_GNN.data = GenerateData() |
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| list | TMVA_SOFIE_GNN.dataSet = [] |
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| | TMVA_SOFIE_GNN.DecodeGraphData = get_graph_data_dict(num_nodes, num_edges, LATENT_SIZE, LATENT_SIZE, LATENT_SIZE) |
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| | TMVA_SOFIE_GNN.decoder |
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| | TMVA_SOFIE_GNN.edge_data |
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| | TMVA_SOFIE_GNN.edge_index |
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| int | TMVA_SOFIE_GNN.edge_size = 4 |
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| | TMVA_SOFIE_GNN.edgesG = outGnet[1].edges.numpy() |
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| | TMVA_SOFIE_GNN.edgesS = np.asarray(outSofie[1].edge_data) |
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| | TMVA_SOFIE_GNN.encoder |
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| | TMVA_SOFIE_GNN.end = time.time() |
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| | TMVA_SOFIE_GNN.endSC = time.time() |
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| | TMVA_SOFIE_GNN.ep_model = EncodeProcessDecode() |
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| | TMVA_SOFIE_GNN.g = out[1].globals.numpy() |
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| str | TMVA_SOFIE_GNN.gen_code |
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| | TMVA_SOFIE_GNN.global_data |
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| int | TMVA_SOFIE_GNN.global_size = 1 |
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| | TMVA_SOFIE_GNN.globG = outGnet[1].globals.numpy() |
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| | TMVA_SOFIE_GNN.globS = np.asarray(outSofie[1].global_data) |
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| | TMVA_SOFIE_GNN.gnet_data_i = utils_tf.data_dicts_to_graphs_tuple([graphData]) |
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| list | TMVA_SOFIE_GNN.gnetData = [] |
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| | TMVA_SOFIE_GNN.gnn = SofieGNN() |
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| | TMVA_SOFIE_GNN.GraphData = get_graph_data_dict(num_nodes, num_edges, node_size, edge_size, global_size) |
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| list | TMVA_SOFIE_GNN.graphData = dataSet[i] |
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| | TMVA_SOFIE_GNN.hDe = ROOT.TH1D("hDe", "Difference for edge data", 40, 1, 0) |
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| | TMVA_SOFIE_GNN.hDg = ROOT.TH1D("hDg", "Difference for global data", 40, 1, 0) |
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| | TMVA_SOFIE_GNN.hDn = ROOT.TH1D("hDn", "Difference for node data", 40, 1, 0) |
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| | TMVA_SOFIE_GNN.hG = ROOT.TH1D("hG", "Result from graphnet", 20, 1, 0) |
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| | TMVA_SOFIE_GNN.hS = ROOT.TH1D("hS", "Result from SOFIE", 20, 1, 0) |
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| | TMVA_SOFIE_GNN.input_core_graph_data = utils_tf.data_dicts_to_graphs_tuple([CoreGraphData]) |
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| | TMVA_SOFIE_GNN.input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data() |
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| | TMVA_SOFIE_GNN.input_graph_data = utils_tf.data_dicts_to_graphs_tuple([GraphData]) |
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| int | TMVA_SOFIE_GNN.LATENT_SIZE = 100 |
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| | TMVA_SOFIE_GNN.node_data |
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| int | TMVA_SOFIE_GNN.node_size = 4 |
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| | TMVA_SOFIE_GNN.nodesG = outGnet[1].nodes.numpy() |
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| | TMVA_SOFIE_GNN.nodesS = np.asarray(outSofie[1].node_data) |
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| int | TMVA_SOFIE_GNN.num_edges = 20 |
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| int | TMVA_SOFIE_GNN.NUM_LAYERS = 4 |
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| int | TMVA_SOFIE_GNN.num_nodes = 5 |
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| int | TMVA_SOFIE_GNN.numevts = 40 |
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| | TMVA_SOFIE_GNN.out = RunGNet(gnetData[i]) |
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| | TMVA_SOFIE_GNN.outGnet = RunGNet(gnetData[i]) |
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| | TMVA_SOFIE_GNN.output_gn = ep_model(input_graph_data, processing_steps) |
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| | TMVA_SOFIE_GNN.output_transform |
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| | TMVA_SOFIE_GNN.outSofie = gnn.infer(sofieData[i]) |
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| int | TMVA_SOFIE_GNN.processing_steps = 5 |
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| | TMVA_SOFIE_GNN.rec = np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4], dtype="int32") |
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| | TMVA_SOFIE_GNN.snd = np.array([1, 2, 3, 4, 2, 3, 4, 3, 4, 4, 0, 0, 0, 0, 1, 1, 1, 2, 2, 3], dtype="int32") |
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| list | TMVA_SOFIE_GNN.sofieData = [] |
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| | TMVA_SOFIE_GNN.start = time.time() |
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| | TMVA_SOFIE_GNN.start0 = time.time() |
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Definition in file TMVA_SOFIE_GNN.py.
