Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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40 lines
1.3 KiB
40 lines
1.3 KiB
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import os
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import glob
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from PyCTBN.json_importer import JsonImporter
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from PyCTBN.sample_path import SamplePath
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from PyCTBN.network_graph import NetworkGraph
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from PyCTBN.parameters_estimator import ParametersEstimator
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def main():
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read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
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#import data
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importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 1)
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#Create a SamplePath Obj
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s1 = SamplePath(importer)
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#Build The trajectries and the structural infos
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s1.build_trajectories()
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s1.build_structure()
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print(s1.structure.edges)
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print(s1.structure.nodes_values)
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#From The Structure Object build the Graph
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g = NetworkGraph(s1.structure)
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#Select a node you want to estimate the parameters
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node = g.nodes[2]
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print("Node", node)
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#Init the _graph specifically for THIS node
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g.fast_init(node)
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#Use SamplePath and Grpah to create a ParametersEstimator Object
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p1 = ParametersEstimator(s1.trajectories, g)
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#Init the peEst specifically for THIS node
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p1.fast_init(node)
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#Compute the parameters
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sofc1 = p1.compute_parameters_for_node(node)
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#The est CIMS are inside the resultant SetOfCIms Obj
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print(sofc1.actual_cims)
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if __name__ == "__main__":
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main()
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