Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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108 lines
3.5 KiB
108 lines
3.5 KiB
4 years ago
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import pytest
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import numpy as np
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from numpy.testing import assert_allclose
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from numpy.testing import assert_equal
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import scipy.special as sc
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class TestHyperu(object):
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def test_negative_x(self):
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a, b, x = np.meshgrid(
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[-1, -0.5, 0, 0.5, 1],
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[-1, -0.5, 0, 0.5, 1],
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np.linspace(-100, -1, 10),
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)
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assert np.all(np.isnan(sc.hyperu(a, b, x)))
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def test_special_cases(self):
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assert sc.hyperu(0, 1, 1) == 1.0
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@pytest.mark.parametrize('a', [0.5, 1, np.nan])
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@pytest.mark.parametrize('b', [1, 2, np.nan])
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@pytest.mark.parametrize('x', [0.25, 3, np.nan])
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def test_nan_inputs(self, a, b, x):
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assert np.isnan(sc.hyperu(a, b, x)) == np.any(np.isnan([a, b, x]))
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class TestHyp1f1(object):
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@pytest.mark.parametrize('a, b, x', [
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(np.nan, 1, 1),
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(1, np.nan, 1),
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(1, 1, np.nan)
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])
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def test_nan_inputs(self, a, b, x):
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assert np.isnan(sc.hyp1f1(a, b, x))
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def test_poles(self):
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assert_equal(sc.hyp1f1(1, [0, -1, -2, -3, -4], 0.5), np.infty)
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@pytest.mark.parametrize('a, b, x, result', [
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(-1, 1, 0.5, 0.5),
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(1, 1, 0.5, 1.6487212707001281468),
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(2, 1, 0.5, 2.4730819060501922203),
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(1, 2, 0.5, 1.2974425414002562937),
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(-10, 1, 0.5, -0.38937441413785204475)
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])
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def test_special_cases(self, a, b, x, result):
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# Hit all the special case branches at the beginning of the
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# function. Desired answers computed using Mpmath.
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assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=1e-15)
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@pytest.mark.parametrize('a, b, x, result', [
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(1, 1, 0.44, 1.5527072185113360455),
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(-1, 1, 0.44, 0.55999999999999999778),
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(100, 100, 0.89, 2.4351296512898745592),
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(-100, 100, 0.89, 0.40739062490768104667),
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(1.5, 100, 59.99, 3.8073513625965598107),
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(-1.5, 100, 59.99, 0.25099240047125826943)
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])
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def test_geometric_convergence(self, a, b, x, result):
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# Test the region where we are relying on the ratio of
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#
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# (|a| + 1) * |x| / |b|
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#
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# being small. Desired answers computed using Mpmath
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assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=1e-15)
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@pytest.mark.parametrize('a, b, x, result', [
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(-1, 1, 1.5, -0.5),
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(-10, 1, 1.5, 0.41801777430943080357),
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(-25, 1, 1.5, 0.25114491646037839809),
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(-50, 1, 1.5, -0.25683643975194756115),
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(-51, 1, 1.5, -0.19843162753845452972)
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])
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def test_a_negative_integer(self, a, b, x, result):
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# Desired answers computed using Mpmath. After -51 the
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# relative error becomes unsatisfactory and we start returning
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# NaN.
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assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=1e-9)
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def test_gh_3492(self):
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desired = 0.99973683897677527773 # Computed using Mpmath
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assert_allclose(
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sc.hyp1f1(0.01, 150, -4),
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desired,
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atol=0,
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rtol=1e-15
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)
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def test_gh_3593(self):
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desired = 1.0020033381011970966 # Computed using Mpmath
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assert_allclose(
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sc.hyp1f1(1, 5, 0.01),
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desired,
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atol=0,
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rtol=1e-15
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)
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@pytest.mark.parametrize('a, b, x, desired', [
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(-1, -2, 2, 2),
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(-1, -4, 10, 3.5),
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(-2, -2, 1, 2.5)
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])
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def test_gh_11099(self, a, b, x, desired):
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# All desired results computed using Mpmath
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assert sc.hyp1f1(a, b, x) == desired
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