def test_log_normal_spherical(self):
"""
Test the log-normal probabilities for spherical covariance.
"""
N = 100
S = 50
D = 10
means = torch.randn(S, D)
covs = torch.rand(S)
x = torch.randn(N, D)
distributions = [
dist.MultivariateNormal(means[i],
torch.diag(covs[i].clone().expand(D)))
for i in range(S)
]
expected = []
for item in x:
e_item = []
for d in distributions:
e_item.append(d.log_prob(item))
expected.append(e_item)
expected = torch.as_tensor(expected)
predicted = log_normal(x, means, covs, 'spherical')
self.assertTrue(
torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05))
def test_log_responsibilities(self):
"""
Test the log responsibilities with the help of Sklearn.
"""
N = 16384
S = 2048
D = 128
means = torch.randn(S, D)
covs = torch.rand(S)
x = torch.randn(N, D)
prior = torch.rand(S)
prior /= prior.sum()
mixture = GaussianMixture(S, covariance_type='spherical')
mixture.means_ = means.numpy()
mixture.precisions_cholesky_ = np.sqrt(1 / covs.numpy())
mixture.weights_ = prior.numpy()
# pylint: disable=protected-access
_, expected = mixture._estimate_log_prob_resp(x.numpy())
expected = torch.from_numpy(expected)
probs = log_normal(x, means, covs, 'spherical')
predicted = log_responsibilities(probs, prior)
self.assertTrue(
torch.allclose(expected, predicted, atol=1e-03, rtol=1e-05))