Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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48 lines
2.0 KiB
48 lines
2.0 KiB
4 years ago
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import sys
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4 years ago
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sys.path.append("../../classes/")
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4 years ago
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import unittest
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import numpy as np
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4 years ago
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import structure_graph.conditional_intensity_matrix as cim
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4 years ago
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class TestConditionalIntensityMatrix(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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cls.state_res_times = np.random.rand(1, 3)[0]
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cls.state_res_times = cls.state_res_times * 1000
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cls.state_transition_matrix = np.random.randint(1, 10000, (3, 3))
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for i in range(0, len(cls.state_res_times)):
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cls.state_transition_matrix[i, i] = 0
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cls.state_transition_matrix[i, i] = np.sum(cls.state_transition_matrix[i])
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def test_init(self):
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c1 = cim.ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
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self.assertTrue(np.array_equal(self.state_res_times, c1.state_residence_times))
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self.assertTrue(np.array_equal(self.state_transition_matrix, c1.state_transition_matrix))
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self.assertEqual(c1.cim.dtype, np.float)
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self.assertEqual(self.state_transition_matrix.shape, c1.cim.shape)
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def test_compute_cim_coefficients(self):
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c1 = cim.ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
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c2 = self.state_transition_matrix.astype(np.float)
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np.fill_diagonal(c2, c2.diagonal() * -1)
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for i in range(0, len(self.state_res_times)):
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for j in range(0, len(self.state_res_times)):
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c2[i, j] = (c2[i, j] + 1) / (self.state_res_times[i] + 1)
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c1.compute_cim_coefficients()
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for i in range(0, len(c1.state_residence_times)):
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self.assertTrue(np.isclose(np.sum(c1.cim[i]), 0.0, 1e-02, 1e-01))
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for i in range(0, len(self.state_res_times)):
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for j in range(0, len(self.state_res_times)):
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self.assertTrue(np.isclose(c1.cim[i, j], c2[i, j], 1e-02, 1e-01))
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4 years ago
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def test_repr(self):
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c1 = cim.ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
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print(c1)
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4 years ago
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if __name__ == '__main__':
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unittest.main()
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