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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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