def test_lognormal_distribution(moment):
num_samples = 100000
inputs = OrderedDict(batch=bint(10))
loc = random_tensor(inputs)
scale = random_tensor(inputs).exp()
log_measure = dist.LogNormal(loc, scale)(value='x')
probe = Variable('x', reals())**moment
with monte_carlo_interpretation(particle=bint(num_samples)):
with xfail_if_not_implemented():
actual = Integrate(log_measure, probe, frozenset(['x']))
samples = torch.distributions.LogNormal(loc, scale).sample((num_samples, ))
expected = (samples**moment).mean(0)
assert_close(actual.data, expected, atol=1e-2, rtol=1e-2)
def test_lognormal_density(batch_shape):
batch_dims = ('i', 'j', 'k')[:len(batch_shape)]
inputs = OrderedDict((k, bint(v)) for k, v in zip(batch_dims, batch_shape))
@funsor.torch.function(reals(), reals(), reals(), reals())
def log_normal(loc, scale, value):
return torch.distributions.LogNormal(loc, scale).log_prob(value)
check_funsor(log_normal, {
'loc': reals(),
'scale': reals(),
'value': reals()
}, reals())
loc = Tensor(torch.randn(batch_shape), inputs)
scale = Tensor(torch.randn(batch_shape).exp(), inputs)
value = Tensor(torch.randn(batch_shape).exp(), inputs)
expected = log_normal(loc, scale, value)
check_funsor(expected, inputs, reals())
actual = dist.LogNormal(loc, scale, value)
check_funsor(actual, inputs, reals())
assert_close(actual, expected)