def test_bad_construction_points_array(self):
# empty array
construction_points = []
with pytest.raises(ValueError, match=r"`construction_points` must "
r"either be a "
r"scalar or a non-empty array."):
TransformedDensityRejection(
StandardNormal(), construction_points=construction_points
)
# construction_points not monotonically increasing
construction_points = [1, 1, 1, 1, 1, 1]
with pytest.warns(RuntimeWarning, match=r"33 : starting points not "
r"strictly monotonically "
r"increasing"):
TransformedDensityRejection(
StandardNormal(), construction_points=construction_points
)
# construction_points containing nans
construction_points = [np.nan, np.nan, np.nan]
with pytest.raises(UNURANError, match=r"50 : bad construction "
r"points."):
TransformedDensityRejection(
StandardNormal(), construction_points=construction_points
)
# construction_points out of domain
construction_points = [-10, 10]
with pytest.warns(RuntimeWarning, match=r"50 : starting point out of "
r"domain"):
TransformedDensityRejection(
StandardNormal(), domain=(-3, 3),
construction_points=construction_points
)
def func2():
dist = Distribution('bar')
rng = TransformedDensityRejection(dist, domain=(10, 100),
random_state=2)
try:
rng.rvs(100000)
except ValueError as e:
errors['err2'] = e.args[0]
def test_rvs_size(size):
# As the `rvs` method is present in the base class and shared between
# all the classes, we can just test with one of the methods.
rng = TransformedDensityRejection(StandardNormal())
if size is None:
assert np.isscalar(rng.rvs(size))
else:
if np.isscalar(size):
size = (size, )
assert rng.rvs(size).shape == size
def test_bad_dist(self):
# Empty distribution
class dist:
...
msg = r"`pdf` required but not found."
with pytest.raises(ValueError, match=msg):
TransformedDensityRejection(dist)
# dPDF not present in dist
class dist:
pdf = lambda x: 1 - x * x # noqa: E731
msg = r"`dpdf` required but not found."
with pytest.raises(ValueError, match=msg):
TransformedDensityRejection(dist)
def test_bad_dpdf(self, dpdf, err, msg):
class dist:
pass
dist.pdf = lambda x: x
dist.dpdf = dpdf
with pytest.raises(err, match=msg):
TransformedDensityRejection(dist, domain=(1, 10))
def test_bad_pdf(self, pdf, err, msg):
class dist:
pass
dist.pdf = pdf
dist.dpdf = lambda x: 1 # an arbitrary dPDF
with pytest.raises(err, match=msg):
TransformedDensityRejection(dist)
def test_ppf_hat(self, u):
# Increase the `max_squeeze_hat_ratio` so the ppf_hat is more
# accurate.
rng = TransformedDensityRejection(StandardNormal(),
max_squeeze_hat_ratio=0.9999)
# 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_hat(u)
expected = stats.norm.ppf(u)
assert_allclose(res, expected, rtol=1e-3, atol=1e-5)
assert res.shape == expected.shape
def test_bad_c(self, c):
msg = r"`c` must either be -0.5 or 0."
with pytest.raises(ValueError, match=msg):
TransformedDensityRejection(StandardNormal(), c=-1.)
def test_bad_construction_points_scalar(self, construction_points):
with pytest.raises(ValueError,
match=r"`construction_points` must be "
r"a positive integer."):
TransformedDensityRejection(
StandardNormal(), construction_points=construction_points)
def test_inf_nan_domains(self, domain, err, msg):
with pytest.raises(err, match=msg):
TransformedDensityRejection(StandardNormal(), domain=domain)
def test_basic(self, dist, mv_ex):
with suppress_warnings() as sup:
# filter the warnings thrown by UNU.RAN
sup.filter(RuntimeWarning)
rng = TransformedDensityRejection(dist, random_state=42)
check_cont_samples(rng, dist, mv_ex)
def test_set_random_state():
rng1 = TransformedDensityRejection(StandardNormal(), random_state=123)
rng2 = TransformedDensityRejection(StandardNormal())
rng2.set_random_state(123)
assert_equal(rng1.rvs(100), rng2.rvs(100))
rng = TransformedDensityRejection(StandardNormal(), random_state=123)
rvs1 = rng.rvs(100)
rng.set_random_state(123)
rvs2 = rng.rvs(100)
assert_equal(rvs1, rvs2)
