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 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_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])
