diff options
| -rw-r--r-- | src/python/gudhi/wasserstein/wasserstein.py | 3 |
1 files changed, 2 insertions, 1 deletions
diff --git a/src/python/gudhi/wasserstein/wasserstein.py b/src/python/gudhi/wasserstein/wasserstein.py index 0d164eda..89ecab1c 100644 --- a/src/python/gudhi/wasserstein/wasserstein.py +++ b/src/python/gudhi/wasserstein/wasserstein.py @@ -100,7 +100,8 @@ def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2., enable_a :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 - transparent to automatic differentiation. This requires the package EagerPy. + transparent to automatic differentiation. This requires the package EagerPy and is currently incompatible + with `matching=True`. .. note:: This considers the function defined on the coordinates of the off-diagonal points of X and Y and lets the various frameworks compute its gradient. It never pulls new points from the diagonal. |