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62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import numpy as np
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from numpy.testing import (assert_array_equal,
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assert_array_almost_equal_nulp, assert_almost_equal)
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from pytest import raises as assert_raises
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from scipy.special import gammaln, multigammaln
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class TestMultiGammaLn(object):
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def test1(self):
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# A test of the identity
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# Gamma_1(a) = Gamma(a)
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np.random.seed(1234)
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a = np.abs(np.random.randn())
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assert_array_equal(multigammaln(a, 1), gammaln(a))
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def test2(self):
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# A test of the identity
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# Gamma_2(a) = sqrt(pi) * Gamma(a) * Gamma(a - 0.5)
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a = np.array([2.5, 10.0])
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result = multigammaln(a, 2)
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expected = np.log(np.sqrt(np.pi)) + gammaln(a) + gammaln(a - 0.5)
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assert_almost_equal(result, expected)
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def test_bararg(self):
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assert_raises(ValueError, multigammaln, 0.5, 1.2)
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def _check_multigammaln_array_result(a, d):
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# Test that the shape of the array returned by multigammaln
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# matches the input shape, and that all the values match
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# the value computed when multigammaln is called with a scalar.
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result = multigammaln(a, d)
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assert_array_equal(a.shape, result.shape)
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a1 = a.ravel()
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result1 = result.ravel()
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for i in range(a.size):
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assert_array_almost_equal_nulp(result1[i], multigammaln(a1[i], d))
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def test_multigammaln_array_arg():
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# Check that the array returned by multigammaln has the correct
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# shape and contains the correct values. The cases have arrays
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# with several differnent shapes.
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# The cases include a regression test for ticket #1849
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# (a = np.array([2.0]), an array with a single element).
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np.random.seed(1234)
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cases = [
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# a, d
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(np.abs(np.random.randn(3, 2)) + 5, 5),
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(np.abs(np.random.randn(1, 2)) + 5, 5),
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(np.arange(10.0, 18.0).reshape(2, 2, 2), 3),
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(np.array([2.0]), 3),
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(np.float64(2.0), 3),
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]
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for a, d in cases:
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_check_multigammaln_array_result(a, d)
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