def compute(matrice_order, loop_number): covmats = CovMats.random(1, matrice_order) for i in range(0, loop_number): mean = Mean.log_euclidean(covmats) covmats.add_all( [Geodesic.log_euclidean(covmat, mean) for covmat in covmats])
def test_mean_wasserstein(): covmats = CovMats.random(10, 10) old_dist = mean_wasserstein(covmats.numpy_array) covmats.reset_covmats_fields() new_dist = Mean.wasserstein(covmats) return _get_state(old_dist, new_dist, "mean wasserstein")
def test_mean_riemann(): covmats = CovMats.random(10, 10) old_dist = mean_riemann(covmats.numpy_array) covmats.reset_covmats_fields() new_dist = Mean.euclidean(covmats) return _get_state(old_dist, new_dist, "mean riemann")
def test_mean_log_euclidean(): covmats = CovMats.random(10, 10) old_dist = mean_logeuclid(covmats.numpy_array) covmats.reset_covmats_fields() new_dist = Mean.log_euclidean(covmats) return _get_state(old_dist, new_dist, "mean log euclidian")
def test_mean_log_determinant(): covmats = CovMats.random(10, 10) old_dist = mean_logdet(covmats.numpy_array) covmats.reset_covmats_fields() new_dist = Mean.log_determinant(covmats) return _get_state(old_dist, new_dist, "mean log determinant")
def compute(matrice_order, loop_number): covmats = CovMats.random(1, matrice_order) for i in range(0, loop_number): mean = Mean.log_euclidean(covmats) covmats.add_all([Geodesic.log_euclidean(covmat, mean) for covmat in covmats])