def test_random_state_property():
scale = np.eye(3)
scale[0, 1] = 0.5
scale[1, 0] = 0.5
dists = [[multivariate_normal, ()], [dirichlet, (np.array([1.]), )],
[wishart, (10, scale)], [invwishart, (10, scale)]]
for distfn, args in dists:
check_random_state_property(distfn, args)
def test_random_state_property():
scale = np.eye(3)
scale[0,1] = 0.5
scale[1,0] = 0.5
dists = [
[multivariate_normal, ()],
[dirichlet, (np.array([1.]), )],
[wishart, (10, scale)],
[invwishart, (10, scale)]
]
for distfn, args in dists:
check_random_state_property(distfn, args)
def check(distname, arg):
distfn = getattr(stats, distname)
np.random.seed(765456)
sn = 500
rvs = distfn.rvs(size=sn, *arg)
sm = rvs.mean()
sv = rvs.var()
m, v = distfn.stats(*arg)
check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn,
distname + 'sample mean test')
check_cdf_ppf(distfn, arg, distname)
check_sf_isf(distfn, arg, distname)
check_pdf(distfn, arg, distname)
check_pdf_logpdf(distfn, arg, distname)
check_cdf_logcdf(distfn, arg, distname)
check_sf_logsf(distfn, arg, distname)
# check_oth(distfn, arg # is still missing)
alpha = 0.01
check_distribution_rvs(distname, arg, alpha, rvs)
locscale_defaults = (0, 1)
meths = [
distfn.pdf, distfn.logpdf, distfn.cdf, distfn.logcdf, distfn.logsf
]
# make sure arguments are within support
x = 0.5
if distname == 'invweibull':
arg = (1, )
elif distname == 'ksone':
arg = (3, )
check_named_args(distfn, x, arg, locscale_defaults, meths)
check_random_state_property(distfn, arg)
check_pickling(distfn, arg)
# Entropy
if distname not in ['ksone', 'kstwobign']:
check_entropy(distfn, arg, distname)
if distfn.numargs == 0:
check_vecentropy(distfn, arg)
if (distfn.__class__._entropy != stats.rv_continuous._entropy
and distname != 'vonmises'):
check_private_entropy(distfn, arg, stats.rv_continuous)
check_edge_support(distfn, arg)
check_meth_dtype(distfn, arg, meths)
check_ppf_dtype(distfn, arg)
if distname not in fails_cmplx:
check_cmplx_deriv(distfn, arg)
def check(distname, arg):
try:
distfn = getattr(stats, distname)
except TypeError:
distfn = distname
distname = 'rv_histogram_instance'
np.random.seed(765456)
sn = 500
rvs = distfn.rvs(size=sn, *arg)
sm = rvs.mean()
sv = rvs.var()
m, v = distfn.stats(*arg)
check_sample_meanvar_(distfn, arg, m, v, sm, sv, sn,
distname + 'sample mean test')
check_cdf_ppf(distfn, arg, distname)
check_sf_isf(distfn, arg, distname)
check_pdf(distfn, arg, distname)
check_pdf_logpdf(distfn, arg, distname)
check_cdf_logcdf(distfn, arg, distname)
check_sf_logsf(distfn, arg, distname)
alpha = 0.01
if distname == 'rv_histogram_instance':
check_distribution_rvs(distfn.cdf, arg, alpha, rvs)
else:
check_distribution_rvs(distname, arg, alpha, rvs)
locscale_defaults = (0, 1)
meths = [
distfn.pdf, distfn.logpdf, distfn.cdf, distfn.logcdf, distfn.logsf
]
# make sure arguments are within support
spec_x = {
'frechet_l': -0.5,
'weibull_max': -0.5,
'levy_l': -0.5,
'pareto': 1.5,
'tukeylambda': 0.3,
'rv_histogram_instance': 5.0
}
x = spec_x.get(distname, 0.5)
check_named_args(distfn, x, arg, locscale_defaults, meths)
check_random_state_property(distfn, arg)
check_pickling(distfn, arg)
# Entropy
check_entropy(distfn, arg, distname)
if distfn.numargs == 0:
check_vecentropy(distfn, arg)
if distfn.__class__._entropy != stats.rv_continuous._entropy:
check_private_entropy(distfn, arg, stats.rv_continuous)
check_edge_support(distfn, arg)
check_meth_dtype(distfn, arg, meths)
check_ppf_dtype(distfn, arg)
if distname not in fails_cmplx:
check_cmplx_deriv(distfn, arg)
if distname != 'truncnorm':
check_ppf_private(distfn, arg, distname)
def test_levy_stable_random_state_property():
# levy_stable only implements rvs(), so it is skipped in the
# main loop in test_cont_basic(). Here we apply just the test
# check_random_state_property to levy_stable.
check_random_state_property(stats.levy_stable, (0.5, 0.1))
def test_levy_stable_random_state_property():
# levy_stable only implements rvs(), so it is skipped in the
# main loop in test_cont_basic(). Here we apply just the test
# check_random_state_property to levy_stable.
check_random_state_property(stats.levy_stable, (0.5, 0.1))