Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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45 lines
1.8 KiB
45 lines
1.8 KiB
from PyCTBN.PyCTBN.structure_graph.trajectory_generator import TrajectoryGenerator
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from PyCTBN.PyCTBN.structure_graph.network_generator import NetworkGenerator
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from PyCTBN.PyCTBN.utility.json_importer import JsonImporter
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from PyCTBN.PyCTBN.utility.json_exporter import JsonExporter
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from PyCTBN.PyCTBN.structure_graph.structure import Structure
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from PyCTBN.PyCTBN.structure_graph.sample_path import SamplePath
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from PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator import StructureConstraintBasedEstimator
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"""
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if __name__ == "__main__":
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trajectories = tg.multi_trajectory(t_ends = [100, 100, 100])
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"""
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# Network Generation
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labels = ["X", "Y", "Z"]
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card = 3
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vals = [card for l in labels]
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cim_min = 1
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cim_max = 3
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ng = NetworkGenerator(labels, vals)
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ng.generate_graph(0.3)
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ng.generate_cims(cim_min, cim_max)
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# Trajectory Generation
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print(ng.dyn_str)
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e1 = JsonExporter(ng.variables, ng.dyn_str, ng.dyn_cims)
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tg = TrajectoryGenerator(variables = ng.variables, dyn_str = ng.dyn_str, dyn_cims = ng.dyn_cims)
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sigma = tg.CTBN_Sample(max_tr = 100)
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e1.add_trajectory(tg.to_json())
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e1.out_json("example.json")
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# Network Estimation (Constraint Based)
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importer = JsonImporter(file_path="example.json", samples_label='samples',
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structure_label='dyn.str', variables_label='variables',
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time_key='Time', variables_key='Name')
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importer.import_data(0)
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s1 = SamplePath(importer=importer)
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s1.build_trajectories()
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s1.build_structure()
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se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1,
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known_edges=[], thumb_threshold=25)
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edges = se1.estimate_structure(True)
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# se1.save_plot_estimated_structure_graph('./result1.png')
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print(se1.adjacency_matrix())
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print(edges) |