def test_reshape(old_shape, new_shape, dim):
gaussian = random_gaussian(old_shape, dim)
# reshape to new
new = gaussian.reshape(new_shape)
assert new.batch_shape == new_shape
# reshape back to old
g = new.reshape(old_shape)
assert_close_gaussian(g, gaussian)
def test_sequential_gaussian_tensordot(batch_shape, state_dim, num_steps):
g = random_gaussian(batch_shape + (num_steps, ), state_dim + state_dim)
actual = _sequential_gaussian_tensordot(g)
assert actual.dim() == g.dim()
assert actual.batch_shape == batch_shape
# Check against hand computation.
expected = g[..., 0]
for t in range(1, num_steps):
expected = gaussian_tensordot(expected, g[..., t], state_dim)
assert_close_gaussian(actual, expected)
def test_matrix_and_mvn_to_gaussian_2(sample_shape, batch_shape, x_dim, y_dim):
matrix = torch.randn(batch_shape + (x_dim, y_dim))
y_mvn = random_mvn(batch_shape, y_dim)
x_mvn = random_mvn(batch_shape, x_dim)
Mx_cov = matrix.transpose(-2, -1).matmul(
x_mvn.covariance_matrix).matmul(matrix)
Mx_loc = matrix.transpose(-2,
-1).matmul(x_mvn.loc.unsqueeze(-1)).squeeze(-1)
mvn = dist.MultivariateNormal(Mx_loc + y_mvn.loc,
Mx_cov + y_mvn.covariance_matrix)
expected = mvn_to_gaussian(mvn)
actual = gaussian_tensordot(mvn_to_gaussian(x_mvn),
matrix_and_mvn_to_gaussian(matrix, y_mvn),
dims=x_dim)
assert_close_gaussian(expected, actual)
def test_cat(shape, cat_dim, split, dim):
assert sum(split) == shape[cat_dim]
gaussian = random_gaussian(shape, dim)
parts = []
end = 0
for size in split:
beg, end = end, end + size
if cat_dim == -1:
part = gaussian[..., beg:end]
elif cat_dim == -2:
part = gaussian[..., beg:end, :]
elif cat_dim == 1:
part = gaussian[:, beg:end]
else:
raise ValueError
parts.append(part)
actual = Gaussian.cat(parts, cat_dim)
assert_close_gaussian(actual, gaussian)
