def test__estimate_log_prob_resp_spherical_shared_compression(self):
rs = np.random.RandomState(11)
cov_type = 'spherical'
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)
weights = rs.rand(gmm.n_components)
weights /= weights.sum()
log_prob_test, log_resp_test, log_prob_norm_test = gmm._estimate_log_prob_resp(
weights, means, covariances, cov_type)
# find skl's values, pretty ugly to do.
precisions = _compute_precision_cholesky(covariances, cov_type)
gmm_skl = GMSKL(n_components=3, covariance_type=cov_type)
# we need the mask to be shared so that we can use mask[0] on all means
gmm_skl.means_ = means[:, gmm.mask[0]]
gmm_skl.precisions_cholesky_ = precisions
gmm_skl.weights_ = weights
gmm_skl.covariance_type_ = cov_type
log_prob_norm_true, log_resp_true = gmm_skl._estimate_log_prob_resp(
gmm.RHDX)
# if anything is bad later this overwrite with mean seems suspect:
log_prob_norm_true = log_prob_norm_true.mean()
# now get the log_prob from another function
log_prob_true = _estimate_log_gaussian_prob(gmm.RHDX, gmm_skl.means_,
precisions, cov_type)
# run the tests
self.assertArrayEqual(log_prob_test, log_prob_true)
self.assertArrayEqual(log_prob_norm_true, log_prob_norm_test)
self.assertArrayEqual(log_resp_true, log_resp_test)
def test__estimate_log_prob_resp_diagonal_no_compression(self):
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)
weights = np.random.rand(gmm.n_components)
weights /= weights.sum()
log_prob_test, log_resp_test, log_prob_norm_test = gmm._estimate_log_prob_resp(
weights, means, covariances, cov_type)
# find skl's values, pretty ugly to do.
precisions = _compute_precision_cholesky(covariances, cov_type)
gmm_skl = GMSKL(n_components=3, covariance_type=cov_type)
gmm_skl.means_ = means
gmm_skl.precisions_cholesky_ = precisions
gmm_skl.weights_ = weights
gmm_skl.covariance_type_ = cov_type
log_prob_norm_true, log_resp_true = gmm_skl._estimate_log_prob_resp(
self.td.X)
# if anything is bad later this overwrite with mean seems suspect:
log_prob_norm_true = log_prob_norm_true.mean()
# now get the log_prob from another function
log_prob_true = _estimate_log_gaussian_prob(self.td.X, means,
precisions, cov_type)
# run the tests
self.assertArrayEqual(log_prob_test, log_prob_true)
self.assertArrayEqual(log_prob_norm_true, log_prob_norm_test)
self.assertArrayEqual(log_resp_true, log_resp_test)
