Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
41 lines
1.6 KiB
41 lines
1.6 KiB
4 years ago
|
from PyCTBN.PyCTBN.structure_graph.trajectory_generator import TrajectoryGenerator
|
||
|
|
from PyCTBN.PyCTBN.structure_graph.network_generator import NetworkGenerator
|
||
|
|
from PyCTBN.PyCTBN.utility.json_importer import JsonImporter
|
||
|
|
from PyCTBN.PyCTBN.utility.json_exporter import JsonExporter
|
||
|
|
from PyCTBN.PyCTBN.structure_graph.structure import Structure
|
||
|
|
from PyCTBN.PyCTBN.structure_graph.sample_path import SamplePath
|
||
|
|
from PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator import StructureConstraintBasedEstimator
|
||
|
|
|
||
|
|
# Network Generation
|
||
|
|
labels = ["X", "Y", "Z"]
|
||
|
|
card = 3
|
||
|
|
vals = [card for l in labels]
|
||
|
|
cim_min = 1
|
||
|
|
cim_max = 3
|
||
|
|
|
||
|
|
ng = NetworkGenerator(labels, vals)
|
||
|
|
ng.generate_graph(0.3)
|
||
|
|
ng.generate_cims(cim_min, cim_max)
|
||
|
|
|
||
|
|
# Trajectory Generation
|
||
|
|
print(ng.dyn_str)
|
||
|
|
e1 = JsonExporter(ng.variables, ng.dyn_str, ng.dyn_cims)
|
||
|
|
tg = TrajectoryGenerator(variables = ng.variables, dyn_str = ng.dyn_str, dyn_cims = ng.dyn_cims)
|
||
|
|
sigma = tg.CTBN_Sample(max_tr = 10)
|
||
|
|
e1.add_trajectory(tg.to_json())
|
||
|
|
e1.out_json("example.json")
|
||
|
|
|
||
|
|
# Network Estimation (Constraint Based)
|
||
|
|
importer = JsonImporter(file_path="example.json", samples_label='samples',
|
||
|
|
structure_label='dyn.str', variables_label='variables',
|
||
|
|
time_key='Time', variables_key='Name')
|
||
|
|
importer.import_data(0)
|
||
|
|
s1 = SamplePath(importer=importer)
|
||
|
|
s1.build_trajectories()
|
||
|
|
s1.build_structure()
|
||
|
|
se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1,
|
||
|
|
known_edges=[], thumb_threshold=25)
|
||
|
|
edges = se1.estimate_structure(True)
|
||
|
|
se1.save_plot_estimated_structure_graph('./result.png')
|
||
|
|
print(se1.adjacency_matrix())
|
||
|
|
print(edges)
|