def test_dirichlet_samples():
alphas = np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]],
dtype=config.floatX)
res = get_test_value(dirichlet(alphas))
assert np.all(np.diag(res) >= res)
res = get_test_value(dirichlet(alphas, size=2))
assert res.shape == (2, 3, 3)
assert all(np.all(np.diag(r) >= r) for r in res)
for i in range(alphas.shape[0]):
res = get_test_value(dirichlet(alphas[i]))
assert np.all(res[i] > np.delete(res, [i]))
res = get_test_value(dirichlet(alphas[i], size=2))
assert res.shape == (2, 3)
assert all(np.all(r[i] > np.delete(r, [i])) for r in res)
rng_state = np.random.RandomState(
np.random.MT19937(np.random.SeedSequence(1234)))
alphas = np.array([[1000, 1, 1], [1, 1000, 1], [1, 1, 1000]],
dtype=config.floatX)
assert dirichlet.rng_fn(rng_state, alphas, None).shape == alphas.shape
assert dirichlet.rng_fn(rng_state, alphas,
size=10).shape == (10, ) + alphas.shape
assert (dirichlet.rng_fn(rng_state, alphas,
size=(10, 2)).shape == (10, 2) + alphas.shape)
def test_dirichlet_infer_shape():
M_aet = iscalar("M")
M_aet.tag.test_value = 3
test_params = [
([aet.ones((M_aet, ))], None),
([aet.ones((M_aet, ))], (M_aet + 1, )),
([aet.ones((M_aet, ))], (2, M_aet)),
([aet.ones((M_aet, M_aet + 1))], None),
([aet.ones((M_aet, M_aet + 1))], (M_aet + 2, )),
([aet.ones((M_aet, M_aet + 1))], (2, M_aet + 2, M_aet + 3)),
]
for args, size in test_params:
rv = dirichlet(*args, size=size)
rv_shape = tuple(dirichlet._infer_shape(size or (), args, None))
assert tuple(get_test_value(rv_shape)) == tuple(
get_test_value(rv).shape)
def test_dirichlet_infer_shape(M, size):
rv = dirichlet(M, size=size)
rv_shape = list(dirichlet._infer_shape(size or (), [M], None))
all_args = (M, ) + size
fn_inputs = [
i
for i in graph_inputs([a for a in all_args if isinstance(a, Variable)])
if not isinstance(i, (Constant, SharedVariable))
]
aesara_fn = function(fn_inputs, [at.as_tensor(o) for o in rv_shape + [rv]],
mode=py_mode)
*rv_shape_val, rv_val = aesara_fn(*[
i.tag.test_value for i in fn_inputs
if not isinstance(i, (SharedVariable, Constant))
])
assert tuple(rv_shape_val) == tuple(rv_val.shape)
def test_dirichlet_ShapeFeature():
"""Make sure `RandomVariable.infer_shape` works with `ShapeFeature`."""
M_at = iscalar("M")
M_at.tag.test_value = 2
N_at = iscalar("N")
N_at.tag.test_value = 3
d_rv = dirichlet(at.ones((M_at, N_at)), name="Gamma")
fg = FunctionGraph(
outputs=[d_rv],
clone=False,
features=[ShapeFeature()],
)
s1, s2 = fg.shape_feature.shape_of[d_rv]
assert M_at in graph_inputs([s1])
assert N_at in graph_inputs([s2])
def test_dirichlet_ShapeFeature():
"""Make sure `RandomVariable.infer_shape` works with `ShapeFeature`."""
M_tt = iscalar("M")
M_tt.tag.test_value = 2
N_tt = iscalar("N")
N_tt.tag.test_value = 3
d_rv = dirichlet(aet.ones((M_tt, N_tt)), name="Gamma")
fg = FunctionGraph(
[i for i in graph_inputs([d_rv]) if not isinstance(i, Constant)],
[d_rv],
clone=False,
features=[ShapeFeature()],
)
s1, s2 = fg.shape_feature.shape_of[d_rv]
assert M_tt in graph_inputs([s1])
assert N_tt in graph_inputs([s2])
