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 Superseded by reCTBN.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.
69 lines
2.6 KiB
69 lines
2.6 KiB
import sys
|
|
sys.path.append("../classes/")
|
|
import unittest
|
|
import numpy as np
|
|
import glob
|
|
import os
|
|
|
|
import network_graph as ng
|
|
import sample_path as sp
|
|
import set_of_cims as sofc
|
|
import parameters_estimator as pe
|
|
import json_importer as ji
|
|
|
|
|
|
class TestParametersEstimatior(unittest.TestCase):
|
|
|
|
@classmethod
|
|
def setUpClass(cls) -> None:
|
|
cls.read_files = glob.glob(os.path.join('../data', "*.json"))
|
|
cls.array_indx = 8
|
|
cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name',
|
|
cls.array_indx)
|
|
cls.s1 = sp.SamplePath(cls.importer)
|
|
cls.s1.build_trajectories()
|
|
cls.s1.build_structure()
|
|
print(cls.s1.structure.edges)
|
|
print(cls.s1.structure.nodes_values)
|
|
cls.g1 = ng.NetworkGraph(cls.s1.structure)
|
|
cls.g1.init_graph()
|
|
|
|
def test_fast_init(self):
|
|
for node in self.g1.nodes:
|
|
g = ng.NetworkGraph(self.s1.structure)
|
|
g.fast_init(node)
|
|
p1 = pe.ParametersEstimator(self.s1.trajectories, g)
|
|
self.assertEqual(p1._trajectories, self.s1.trajectories)
|
|
self.assertEqual(p1._net_graph, g)
|
|
self.assertIsNone(p1._single_set_of_cims)
|
|
p1.fast_init(node)
|
|
self.assertIsInstance(p1._single_set_of_cims, sofc.SetOfCims)
|
|
|
|
def test_compute_parameters_for_node(self):
|
|
for indx, node in enumerate(self.g1.nodes):
|
|
print(node)
|
|
g = ng.NetworkGraph(self.s1.structure)
|
|
g.fast_init(node)
|
|
p1 = pe.ParametersEstimator(self.s1.trajectories, g)
|
|
p1.fast_init(node)
|
|
sofc1 = p1.compute_parameters_for_node(node)
|
|
sampled_cims = self.aux_import_sampled_cims('dyn.cims')
|
|
sc = list(sampled_cims.values())
|
|
self.equality_of_cims_of_node(sc[indx], sofc1._actual_cims)
|
|
|
|
def equality_of_cims_of_node(self, sampled_cims, estimated_cims):
|
|
self.assertEqual(len(sampled_cims), len(estimated_cims))
|
|
for c1, c2 in zip(sampled_cims, estimated_cims):
|
|
self.cim_equality_test(c1, c2.cim)
|
|
|
|
def cim_equality_test(self, cim1, cim2):
|
|
for r1, r2 in zip(cim1, cim2):
|
|
self.assertTrue(np.all(np.isclose(r1, r2, 1e-01, 1e-01) == True))
|
|
|
|
def aux_import_sampled_cims(self, cims_label):
|
|
i1 = ji.JsonImporter(self.read_files[0], '', '', '', '', '', self.array_indx)
|
|
raw_data = i1.read_json_file()
|
|
return i1.import_sampled_cims(raw_data, self.array_indx, cims_label)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|
|
|