def test_basic_all_scipy_dists(self, distname, shapes):
slow_dists = {'ksone', 'kstwo', 'levy_stable', 'skewnorm'}
fail_dists = {
'beta', 'gausshyper', 'geninvgauss', 'ncf', 'nct', 'norminvgauss',
'genhyperbolic', 'studentized_range', 'vonmises', 'kappa4',
'invgauss', 'wald'
}
if distname in slow_dists:
pytest.skip("Distribution is too slow")
if distname in fail_dists:
# specific reasons documented in gh-13319
# https://github.com/scipy/scipy/pull/13319#discussion_r626188955
pytest.xfail("Fails - usually due to inaccurate CDF/PDF")
np.random.seed(0)
dist = getattr(stats, distname)(*shapes)
with np.testing.suppress_warnings() as sup:
sup.filter(RuntimeWarning, "overflow encountered")
sup.filter(RuntimeWarning, "divide by zero")
sup.filter(RuntimeWarning, "invalid value encountered")
fni = NumericalInverseHermite(dist)
x = np.random.rand(10)
p_tol = np.max(np.abs(dist.ppf(x) - fni.ppf(x)) / np.abs(dist.ppf(x)))
u_tol = np.max(np.abs(dist.cdf(fni.ppf(x)) - x))
assert p_tol < 1e-8
assert u_tol < 1e-12
def test_with_scipy_distribution():
# test if the setup works with SciPy's rv_frozen distributions
dist = stats.norm()
urng = np.random.default_rng(0)
rng = NumericalInverseHermite(dist, random_state=urng)
u = np.linspace(0, 1, num=100)
check_cont_samples(rng, dist, dist.stats())
assert_allclose(dist.ppf(u), rng.ppf(u))
# test if it works with `loc` and `scale`
dist = stats.norm(loc=10., scale=5.)
rng = NumericalInverseHermite(dist, random_state=urng)
check_cont_samples(rng, dist, dist.stats())
assert_allclose(dist.ppf(u), rng.ppf(u))
# check for discrete distributions
dist = stats.binom(10, 0.2)
rng = DiscreteAliasUrn(dist, random_state=urng)
domain = dist.support()
pv = dist.pmf(np.arange(domain[0], domain[1] + 1))
check_discr_samples(rng, pv, dist.stats())
def test_ppf(self, u):
dist = StandardNormal()
rng = NumericalInverseHermite(dist, u_resolution=1e-12)
# Older versions of NumPy throw RuntimeWarnings for comparisons
# with nan.
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in greater")
sup.filter(RuntimeWarning, "invalid value encountered in "
"greater_equal")
sup.filter(RuntimeWarning, "invalid value encountered in less")
sup.filter(RuntimeWarning, "invalid value encountered in "
"less_equal")
res = rng.ppf(u)
expected = stats.norm.ppf(u)
assert_allclose(res, expected, rtol=1e-9, atol=3e-10)
assert res.shape == expected.shape