Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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113 lines
3.2 KiB
113 lines
3.2 KiB
4 years ago
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import numpy as np
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import scipy.special as sc
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import pytest
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from numpy.testing import assert_allclose, assert_array_equal, suppress_warnings
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class TestBdtr(object):
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def test(self):
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val = sc.bdtr(0, 1, 0.5)
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assert_allclose(val, 0.5)
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def test_sum_is_one(self):
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val = sc.bdtr([0, 1, 2], 2, 0.5)
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assert_array_equal(val, [0.25, 0.75, 1.0])
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def test_rounding(self):
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double_val = sc.bdtr([0.1, 1.1, 2.1], 2, 0.5)
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int_val = sc.bdtr([0, 1, 2], 2, 0.5)
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assert_array_equal(double_val, int_val)
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@pytest.mark.parametrize('k, n, p', [
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(np.inf, 2, 0.5),
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(1.0, np.inf, 0.5),
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(1.0, 2, np.inf)
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])
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def test_inf(self, k, n, p):
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with suppress_warnings() as sup:
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sup.filter(DeprecationWarning)
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val = sc.bdtr(k, n, p)
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assert np.isnan(val)
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def test_domain(self):
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val = sc.bdtr(-1.1, 1, 0.5)
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assert np.isnan(val)
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class TestBdtrc(object):
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def test_value(self):
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val = sc.bdtrc(0, 1, 0.5)
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assert_allclose(val, 0.5)
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def test_sum_is_one(self):
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val = sc.bdtrc([0, 1, 2], 2, 0.5)
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assert_array_equal(val, [0.75, 0.25, 0.0])
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def test_rounding(self):
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double_val = sc.bdtrc([0.1, 1.1, 2.1], 2, 0.5)
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int_val = sc.bdtrc([0, 1, 2], 2, 0.5)
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assert_array_equal(double_val, int_val)
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@pytest.mark.parametrize('k, n, p', [
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(np.inf, 2, 0.5),
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(1.0, np.inf, 0.5),
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(1.0, 2, np.inf)
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])
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def test_inf(self, k, n, p):
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with suppress_warnings() as sup:
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sup.filter(DeprecationWarning)
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val = sc.bdtrc(k, n, p)
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assert np.isnan(val)
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def test_domain(self):
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val = sc.bdtrc(-1.1, 1, 0.5)
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val2 = sc.bdtrc(2.1, 1, 0.5)
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assert np.isnan(val2)
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assert_allclose(val, 1.0)
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def test_bdtr_bdtrc_sum_to_one(self):
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bdtr_vals = sc.bdtr([0, 1, 2], 2, 0.5)
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bdtrc_vals = sc.bdtrc([0, 1, 2], 2, 0.5)
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vals = bdtr_vals + bdtrc_vals
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assert_allclose(vals, [1.0, 1.0, 1.0])
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class TestBdtri(object):
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def test_value(self):
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val = sc.bdtri(0, 1, 0.5)
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assert_allclose(val, 0.5)
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def test_sum_is_one(self):
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val = sc.bdtri([0, 1], 2, 0.5)
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actual = np.asarray([1 - 1/np.sqrt(2), 1/np.sqrt(2)])
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assert_allclose(val, actual)
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def test_rounding(self):
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double_val = sc.bdtri([0.1, 1.1], 2, 0.5)
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int_val = sc.bdtri([0, 1], 2, 0.5)
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assert_allclose(double_val, int_val)
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@pytest.mark.parametrize('k, n, p', [
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(np.inf, 2, 0.5),
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(1.0, np.inf, 0.5),
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(1.0, 2, np.inf)
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])
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def test_inf(self, k, n, p):
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with suppress_warnings() as sup:
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sup.filter(DeprecationWarning)
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val = sc.bdtri(k, n, p)
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assert np.isnan(val)
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@pytest.mark.parametrize('k, n, p', [
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(-1.1, 1, 0.5),
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(2.1, 1, 0.5)
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])
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def test_domain(self, k, n, p):
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val = sc.bdtri(k, n, p)
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assert np.isnan(val)
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def test_bdtr_bdtri_roundtrip(self):
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bdtr_vals = sc.bdtr([0, 1, 2], 2, 0.5)
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roundtrip_vals = sc.bdtri([0, 1, 2], 2, bdtr_vals)
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assert_allclose(roundtrip_vals, [0.5, 0.5, np.nan])
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