# WarrenWeckesser/scipy forked from scipy/scipy

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 from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import assert_array_equal, TestCase, run_module_suite, \ assert_array_almost_equal_nulp, assert_raises, assert_almost_equal from scipy.special import gammaln, multigammaln class TestMultiGammaLn(TestCase): 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: yield _check_multigammaln_array_result, a, d if __name__ == '__main__': run_module_suite()
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