def test_gamma_gaussian_hmm_shape(scale_shape, init_shape, trans_mat_shape,
trans_mvn_shape, obs_mat_shape,
obs_mvn_shape, hidden_dim, obs_dim):
init_dist = random_mvn(init_shape, hidden_dim)
trans_mat = torch.randn(trans_mat_shape + (hidden_dim, hidden_dim))
trans_dist = random_mvn(trans_mvn_shape, hidden_dim)
obs_mat = torch.randn(obs_mat_shape + (hidden_dim, obs_dim))
obs_dist = random_mvn(obs_mvn_shape, obs_dim)
scale_dist = random_gamma(scale_shape)
d = dist.GammaGaussianHMM(scale_dist, init_dist, trans_mat, trans_dist,
obs_mat, obs_dist)
shape = broadcast_shape(scale_shape + (1, ), init_shape + (1, ),
trans_mat_shape, trans_mvn_shape, obs_mat_shape,
obs_mvn_shape)
expected_batch_shape, time_shape = shape[:-1], shape[-1:]
expected_event_shape = time_shape + (obs_dim, )
assert d.batch_shape == expected_batch_shape
assert d.event_shape == expected_event_shape
assert d.support.event_dim == d.event_dim
data = obs_dist.expand(shape).sample()
assert data.shape == d.shape()
actual = d.log_prob(data)
assert actual.shape == expected_batch_shape
check_expand(d, data)
mixing, final = d.filter(data)
assert isinstance(mixing, dist.Gamma)
assert mixing.batch_shape == d.batch_shape
assert mixing.event_shape == ()
assert isinstance(final, dist.MultivariateNormal)
assert final.batch_shape == d.batch_shape
assert final.event_shape == (hidden_dim, )
def test_gamma_gaussian_hmm_log_prob(sample_shape, batch_shape, num_steps,
hidden_dim, obs_dim):
init_dist = random_mvn(batch_shape, hidden_dim)
trans_mat = torch.randn(batch_shape + (num_steps, hidden_dim, hidden_dim))
trans_dist = random_mvn(batch_shape + (num_steps, ), hidden_dim)
obs_mat = torch.randn(batch_shape + (num_steps, hidden_dim, obs_dim))
obs_dist = random_mvn(batch_shape + (num_steps, ), obs_dim)
scale_dist = random_gamma(batch_shape)
d = dist.GammaGaussianHMM(scale_dist, init_dist, trans_mat, trans_dist,
obs_mat, obs_dist)
obs_mvn = obs_dist
data = obs_dist.sample(sample_shape)
assert data.shape == sample_shape + d.shape()
actual_log_prob = d.log_prob(data)
# Compare against hand-computed density.
# We will construct enormous unrolled joint gaussian-gammas with shapes:
# t | 0 1 2 3 1 2 3 T = 3 in this example
# ------+-----------------------------------------
# init | H
# trans | H H H H H = hidden
# obs | H H H O O O O = observed
# and then combine these using gamma_gaussian_tensordot().
T = num_steps
init = gamma_and_mvn_to_gamma_gaussian(scale_dist, init_dist)
trans = matrix_and_mvn_to_gamma_gaussian(trans_mat, trans_dist)
obs = matrix_and_mvn_to_gamma_gaussian(obs_mat, obs_mvn)
unrolled_trans = reduce(
operator.add,
[
trans[..., t].event_pad(left=t * hidden_dim,
right=(T - t - 1) * hidden_dim)
for t in range(T)
],
)
unrolled_obs = reduce(
operator.add,
[
obs[..., t].event_pad(left=t * obs.dim(),
right=(T - t - 1) * obs.dim())
for t in range(T)
],
)
# Permute obs from HOHOHO to HHHOOO.
perm = torch.cat(
[torch.arange(hidden_dim) + t * obs.dim() for t in range(T)] +
[torch.arange(obs_dim) + hidden_dim + t * obs.dim() for t in range(T)])
unrolled_obs = unrolled_obs.event_permute(perm)
unrolled_data = data.reshape(data.shape[:-2] + (T * obs_dim, ))
assert init.dim() == hidden_dim
assert unrolled_trans.dim() == (1 + T) * hidden_dim
assert unrolled_obs.dim() == T * (hidden_dim + obs_dim)
logp = gamma_gaussian_tensordot(init, unrolled_trans, hidden_dim)
logp = gamma_gaussian_tensordot(logp, unrolled_obs, T * hidden_dim)
# compute log_prob of the joint student-t distribution
expected_log_prob = logp.compound().log_prob(unrolled_data)
assert_close(actual_log_prob, expected_log_prob)