def test_normal_independent():
loc = random_tensor(OrderedDict(), reals(2))
scale = random_tensor(OrderedDict(), reals(2)).exp()
fn = dist.Normal(loc['i'], scale['i'], value='z_i')
assert fn.inputs['z_i'] == reals()
d = Independent(fn, 'z', 'i', 'z_i')
assert d.inputs['z'] == reals(2)
sample = d.sample(frozenset(['z']))
assert isinstance(sample, Contraction)
assert sample.inputs['z'] == reals(2)
def test_normal_independent():
loc = random_tensor(OrderedDict(), Reals[2])
scale = ops.exp(random_tensor(OrderedDict(), Reals[2]))
fn = dist.Normal(loc['i'], scale['i'], value='z_i')
assert fn.inputs['z_i'] == Real
d = Independent(fn, 'z', 'i', 'z_i')
assert d.inputs['z'] == Reals[2]
rng_key = None if get_backend() == "torch" else np.array([0, 0], dtype=np.uint32)
sample = d.sample(frozenset(['z']), rng_key=rng_key)
assert isinstance(sample, Contraction)
assert sample.inputs['z'] == Reals[2]
def test_sample_independent():
f = Variable('x_i', reals(4, 5)) + random_tensor(OrderedDict(i=bint(3)))
actual = Independent(f, 'x', 'i', 'x_i')
assert actual.sample('i')
assert actual.sample('j', {'i': 2})
def test_sample_independent():
f = Variable('x_i', Reals[4, 5]) + random_tensor(OrderedDict(i=Bint[3]))
actual = Independent(f, 'x', 'i', 'x_i')
assert actual.sample('i')
assert actual.sample('j', {'i': 2})
