Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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48 lines
1.3 KiB
48 lines
1.3 KiB
import numpy as np
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import scipy.special as sc
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from numpy.testing import assert_almost_equal, assert_array_equal
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class TestPdtr(object):
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def test(self):
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val = sc.pdtr(0, 1)
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assert_almost_equal(val, np.exp(-1))
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def test_m_zero(self):
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val = sc.pdtr([0, 1, 2], 0)
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assert_array_equal(val, [1, 1, 1])
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def test_rounding(self):
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double_val = sc.pdtr([0.1, 1.1, 2.1], 1.0)
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int_val = sc.pdtr([0, 1, 2], 1.0)
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assert_array_equal(double_val, int_val)
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def test_inf(self):
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val = sc.pdtr(np.inf, 1.0)
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assert_almost_equal(val, 1.0)
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def test_domain(self):
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val = sc.pdtr(-1.1, 1.0)
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assert np.isnan(val)
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class TestPdtrc(object):
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def test_value(self):
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val = sc.pdtrc(0, 1)
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assert_almost_equal(val, 1 - np.exp(-1))
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def test_m_zero(self):
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val = sc.pdtrc([0, 1, 2], 0.0)
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assert_array_equal(val, [0, 0, 0])
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def test_rounding(self):
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double_val = sc.pdtrc([0.1, 1.1, 2.1], 1.0)
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int_val = sc.pdtrc([0, 1, 2], 1.0)
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assert_array_equal(double_val, int_val)
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def test_inf(self):
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val = sc.pdtrc(np.inf, 1.0)
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assert_almost_equal(val, 0.0)
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def test_domain(self):
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val = sc.pdtrc(-1.1, 1.0)
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assert np.isnan(val)
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