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 compute(matrice_order, loop_number): tmp = numpy.random.rand(matrice_order, 2 * matrice_order) a = numpy.dot(tmp, tmp.T) / 1000 l = [a] covmats = numpy.array(l) for i in range(0, loop_number): mean = mean_riemann(covmats) l += [geodesic_riemann(covmats[j, :, :], mean) for j in range(covmats.shape[0])] covmats = numpy.array(l)
def compute(matrice_order, loop_number): tmp = numpy.random.rand(matrice_order, 2 * matrice_order) a = numpy.dot(tmp, tmp.T) / 1000 l = [a] covmats = numpy.array(l) for i in range(0, loop_number): mean = mean_riemann(covmats) l += [ geodesic_riemann(covmats[j, :, :], mean) for j in range(covmats.shape[0]) ] covmats = numpy.array(l)