Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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70 lines
1.9 KiB
70 lines
1.9 KiB
import numpy as np
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from numpy.testing import assert_allclose, assert_
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from scipy.special._testutils import FuncData
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from scipy.special import gamma, gammaln, loggamma
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def test_identities1():
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# test the identity exp(loggamma(z)) = gamma(z)
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x = np.array([-99.5, -9.5, -0.5, 0.5, 9.5, 99.5])
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y = x.copy()
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x, y = np.meshgrid(x, y)
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z = (x + 1J*y).flatten()
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dataset = np.vstack((z, gamma(z))).T
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def f(z):
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return np.exp(loggamma(z))
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FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
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def test_identities2():
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# test the identity loggamma(z + 1) = log(z) + loggamma(z)
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x = np.array([-99.5, -9.5, -0.5, 0.5, 9.5, 99.5])
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y = x.copy()
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x, y = np.meshgrid(x, y)
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z = (x + 1J*y).flatten()
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dataset = np.vstack((z, np.log(z) + loggamma(z))).T
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def f(z):
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return loggamma(z + 1)
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FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
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def test_complex_dispatch_realpart():
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# Test that the real parts of loggamma and gammaln agree on the
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# real axis.
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x = np.r_[-np.logspace(10, -10), np.logspace(-10, 10)] + 0.5
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dataset = np.vstack((x, gammaln(x))).T
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def f(z):
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z = np.array(z, dtype='complex128')
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return loggamma(z).real
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FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
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def test_real_dispatch():
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x = np.logspace(-10, 10) + 0.5
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dataset = np.vstack((x, gammaln(x))).T
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FuncData(loggamma, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
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assert_(loggamma(0) == np.inf)
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assert_(np.isnan(loggamma(-1)))
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def test_gh_6536():
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z = loggamma(complex(-3.4, +0.0))
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zbar = loggamma(complex(-3.4, -0.0))
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assert_allclose(z, zbar.conjugate(), rtol=1e-15, atol=0)
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def test_branch_cut():
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# Make sure negative zero is treated correctly
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x = -np.logspace(300, -30, 100)
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z = np.asarray([complex(x0, 0.0) for x0 in x])
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zbar = np.asarray([complex(x0, -0.0) for x0 in x])
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assert_allclose(z, zbar.conjugate(), rtol=1e-15, atol=0)
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