diff options
Diffstat (limited to 'src/python/gudhi/wasserstein/wasserstein.py')
| -rw-r--r-- | src/python/gudhi/wasserstein/wasserstein.py | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/src/python/gudhi/wasserstein/wasserstein.py b/src/python/gudhi/wasserstein/wasserstein.py index 89ecab1c..a9d1cdff 100644 --- a/src/python/gudhi/wasserstein/wasserstein.py +++ b/src/python/gudhi/wasserstein/wasserstein.py @@ -73,8 +73,8 @@ def _perstot_autodiff(X, order, internal_p): def _perstot(X, order, internal_p, enable_autodiff): ''' :param X: (n x 2) numpy.array (points of a given diagram). - :param order: exponent for Wasserstein. Default value is 2. - :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); Default value is 2 (Euclidean norm). + :param order: exponent for Wasserstein. + :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2). :param enable_autodiff: If X is torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation transparent to automatic differentiation. :type enable_autodiff: bool @@ -88,7 +88,7 @@ def _perstot(X, order, internal_p, enable_autodiff): return np.linalg.norm(_dist_to_diag(X, internal_p), ord=order) -def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2., enable_autodiff=False): +def wasserstein_distance(X, Y, matching=False, order=1., internal_p=np.inf, enable_autodiff=False): ''' :param X: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). @@ -96,10 +96,10 @@ def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2., enable_a :param matching: if True, computes and returns the optimal matching between X and Y, encoded as a (n x 2) np.array [...[i,j]...], meaning the i-th point in X is matched to the j-th point in Y, with the convention (-1) represents the diagonal. - :param order: exponent for Wasserstein; Default value is 2. + :param order: exponent for Wasserstein; Default value is 1. :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); - Default value is 2 (Euclidean norm). - :param enable_autodiff: If X and Y are torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation + Default value is `np.inf`. + :param enable_autodiff: If X and Y are torch.tensor or tensorflow.Tensor, make the computation transparent to automatic differentiation. This requires the package EagerPy and is currently incompatible with `matching=True`. @@ -165,9 +165,9 @@ def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2., enable_a # empty arrays are not handled properly by the helpers, so we avoid calling them if len(pairs_X_Y): dists.append((Y_orig[pairs_X_Y[:, 1]] - X_orig[pairs_X_Y[:, 0]]).norms.lp(internal_p, axis=-1).norms.lp(order)) - if len(pairs_X_diag): + if len(pairs_X_diag[0]): dists.append(_perstot_autodiff(X_orig[pairs_X_diag], order, internal_p)) - if len(pairs_Y_diag): + if len(pairs_Y_diag[0]): dists.append(_perstot_autodiff(Y_orig[pairs_Y_diag], order, internal_p)) dists = [dist.reshape(1) for dist in dists] return ep.concatenate(dists).norms.lp(order).raw |