def test__compute_logdet_array(self):
""" Test spherical and diagonal on hard-coded results. """
gmm = GaussianMixture(n_components=3,
num_feat_full=5,
num_feat_comp=3,
num_feat_shared=1,
num_samp=4,
transform='dct',
D_indices=self.td.D_indices,
mask=self.td.mask)
logdet_spherical = gmm._compute_logdet_array(
self.td.spherical_covariances, 'spherical')
logdet_diag = gmm._compute_logdet_array(self.td.diagonal_covariances,
'diag')
self.assertArrayEqual(self.td.correct_logdet_spherical,
logdet_spherical)
self.assertArrayEqual(self.td.correct_logdet_diag, logdet_diag)
def test__compute_logdet_array_spherical(self):
""" Test spherical logdet under compression on an example
computed here. Redundant with test__compute_logdet_array below but was
implemented to confirm that test is correct. """
cov_type = 'spherical'
rs = np.random.RandomState(10)
gmm = GaussianMixture(n_components=3,
num_feat_full=5,
num_feat_comp=3,
num_feat_shared=2,
num_samp=4,
transform=None,
mask=None,
D_indices=None,
covariance_type=cov_type,
random_state=rs)
gmm.fit_sparsifier(X=self.td.X)
means = rs.rand(gmm.n_components, gmm.num_feat_full)
covariances = rs.rand(gmm.n_components)
logdet_test = gmm._compute_logdet_array(covariances, 'spherical')
logdet_true = gmm.num_feat_comp * np.log(covariances)
logdet_true = np.tile(logdet_true, (gmm.num_samp, 1))
self.assertArrayEqual(logdet_test, logdet_true)