Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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34 lines
1.3 KiB
34 lines
1.3 KiB
#!/usr/bin/env python3
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# License: MIT License
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import unittest
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import numpy as np
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import glob
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import os
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from pyctbn.legacy.structure_graph.trajectory import Trajectory
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from pyctbn.legacy.utility.json_importer import JsonImporter
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class TestTrajectory(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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cls.read_files = glob.glob(os.path.join('./tests/data', "*.json"))
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cls.importer = JsonImporter(cls.read_files[2], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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cls.importer.import_data(0)
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def test_init(self):
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t1 = Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples),
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len(self.importer.sorter) + 1)
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self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:, 0].to_numpy(), t1.times))
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self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:,1:].to_numpy(), t1.complete_trajectory))
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self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:, 1: len(self.importer.sorter) + 1], t1.trajectory))
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self.assertEqual(len(self.importer.sorter) + 1, t1._original_cols_number)
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self.assertEqual(self.importer.concatenated_samples.iloc[:,1:].to_numpy().shape[0], t1.size())
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print(t1)
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if __name__ == '__main__':
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unittest.main()
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