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