def test_log_prob_d2(concentration):
dist = LKJCholesky(2, torch.tensor([concentration]))
test_dist = TransformedDistribution(Beta(concentration, concentration),
AffineTransform(loc=-1., scale=2.0))
samples = dist.sample(torch.Size([100]))
lp = dist.log_prob(samples)
x = samples[..., 1, 0]
tst = test_dist.log_prob(x)
# LKJ prevents inf values in log_prob
lp[tst == math.inf] = math.inf # substitute inf for comparison
assert_tensors_equal(lp, tst, prec=1e-3)
def test_log_prob_conc1(dim):
dist = LKJCholesky(dim, torch.tensor([1.]))
a_sample = dist.sample(torch.Size([100]))
lp = dist.log_prob(a_sample)
if dim == 2:
assert_equal(lp, lp.new_full(lp.size(), -math.log(2)))
else:
ladj = a_sample.diagonal(dim1=-2, dim2=-1).log().mul(
torch.linspace(start=dim - 1,
end=0,
steps=dim,
device=a_sample.device,
dtype=a_sample.dtype)).sum(-1)
lps_less_ladj = lp - ladj
assert (lps_less_ladj - lps_less_ladj.min()).abs().sum() < 1e-4
def test_sample_batch():
# Regression test for https://github.com/pyro-ppl/pyro/issues/2615
dist = LKJCholesky(3, concentration=torch.ones(())).expand([12])
# batch shape and event shape are as you'd expect
assert dist.batch_shape == torch.Size([12])
assert dist.event_shape == torch.Size([3, 3])
# samples have correct shape when sample_shape=()
assert dist.shape(()) == torch.Size([12, 3, 3])
assert dist.sample().shape == torch.Size([12, 3, 3])
# samples had the wrong shape when sample_shape is non-unit
assert dist.shape((4, )) == torch.Size([4, 12, 3, 3])
assert dist.sample((4, )).shape == torch.Size([4, 12, 3, 3])
def __init__(self, dim, concentration=1., validate_args=None):
base_dist = LKJCholesky(dim, concentration)
self.dim, self.concentration = base_dist.dim, base_dist.concentration
super(LKJ, self).__init__(base_dist,
CorrMatrixCholeskyTransform().inv,
validate_args=validate_args)