def test__compute_log_prob_spherical_shared_compression(self):
""" Compare the log_prob computation to that of sklearn with
shared compression. Spherical covariances. """
cov_type = 'spherical'
rs = np.random.RandomState(10)
gmm = GaussianMixture(n_components=3,
num_feat_full=5,
num_feat_comp=3,
num_feat_shared=3,
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)
log_prob_test = gmm._compute_log_prob(means, covariances, cov_type)
log_prob_true = np.zeros((gmm.num_samp, gmm.n_components))
for data_ind in range(gmm.num_samp):
for comp_ind in range(gmm.n_components):
true_const = gmm.num_feat_comp * np.log(2 * np.pi)
true_logdet = gmm.num_feat_comp * np.log(covariances[comp_ind])
true_mahadist = 1/covariances[comp_ind] * \
np.linalg.norm(gmm.RHDX[data_ind] -
means[comp_ind][gmm.mask[data_ind]])**2
log_prob_true[data_ind, comp_ind] = -.5*(true_const + \
true_logdet + true_mahadist)
self.assertArrayEqual(log_prob_test, log_prob_true)
def test__compute_log_prob_diagonal_no_compression(self):
""" Compare the log_prob computation to that of sklearn with no
compression. Implemented as a precursor to testing it with
compression, to follow. Diagonal covariances. """
cov_type = 'diag'
gmm = GaussianMixture(n_components=3,
num_feat_full=5,
num_feat_comp=5,
num_feat_shared=5,
num_samp=4,
transform=None,
mask=None,
D_indices=None,
covariance_type=cov_type)
gmm.fit_sparsifier(X=self.td.X)
means = np.random.rand(gmm.n_components, gmm.num_feat_comp)
covariances = np.random.rand(gmm.n_components, gmm.num_feat_comp)
log_prob_test = gmm._compute_log_prob(means, covariances, cov_type)
precisions = _compute_precision_cholesky(covariances, cov_type)
log_prob_true = _estimate_log_gaussian_prob(self.td.X, means,
precisions, cov_type)
self.assertArrayEqual(log_prob_test, log_prob_true)