Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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67 lines
2.0 KiB
67 lines
2.0 KiB
4 years ago
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import numpy as np
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from numpy.testing import assert_equal, assert_allclose, suppress_warnings
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from scipy.special._ufuncs import _sinpi as sinpi
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from scipy.special._ufuncs import _cospi as cospi
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def test_integer_real_part():
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x = np.arange(-100, 101)
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y = np.hstack((-np.linspace(310, -30, 10), np.linspace(-30, 310, 10)))
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x, y = np.meshgrid(x, y)
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z = x + 1j*y
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# In the following we should be *exactly* right
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res = sinpi(z)
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assert_equal(res.real, 0.0)
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res = cospi(z)
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assert_equal(res.imag, 0.0)
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def test_half_integer_real_part():
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x = np.arange(-100, 101) + 0.5
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y = np.hstack((-np.linspace(310, -30, 10), np.linspace(-30, 310, 10)))
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x, y = np.meshgrid(x, y)
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z = x + 1j*y
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# In the following we should be *exactly* right
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res = sinpi(z)
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assert_equal(res.imag, 0.0)
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res = cospi(z)
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assert_equal(res.real, 0.0)
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def test_intermediate_overlow():
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# Make sure we avoid overflow in situations where cosh/sinh would
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# overflow but the product with sin/cos would not
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sinpi_pts = [complex(1 + 1e-14, 227),
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complex(1e-35, 250),
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complex(1e-301, 445)]
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# Data generated with mpmath
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sinpi_std = [complex(-8.113438309924894e+295, -np.inf),
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complex(1.9507801934611995e+306, np.inf),
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complex(2.205958493464539e+306, np.inf)]
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with suppress_warnings() as sup:
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sup.filter(RuntimeWarning, "invalid value encountered in multiply")
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for p, std in zip(sinpi_pts, sinpi_std):
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assert_allclose(sinpi(p), std)
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# Test for cosine, less interesting because cos(0) = 1.
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p = complex(0.5 + 1e-14, 227)
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std = complex(-8.113438309924894e+295, -np.inf)
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with suppress_warnings() as sup:
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sup.filter(RuntimeWarning, "invalid value encountered in multiply")
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assert_allclose(cospi(p), std)
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def test_zero_sign():
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y = sinpi(-0.0)
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assert y == 0.0
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assert np.signbit(y)
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y = sinpi(0.0)
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assert y == 0.0
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assert not np.signbit(y)
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y = cospi(0.5)
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assert y == 0.0
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assert not np.signbit(y)
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