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| author | Marc Glisse <marc.glisse@inria.fr> | 2020-08-04 12:46:06 +0200 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2020-08-04 12:46:06 +0200 |
| commit | 47f7f50c8cdb40a0a1fd73432498004f21641803 (patch) | |
| tree | c5fdbd3505989b1913aacb4a4320c7fbf53071b4 /src/python/gudhi/wasserstein | |
| parent | e36fa6c9511c387447ef77e062e26671505212a2 (diff) | |
Remove JAX from the documentation
jax.grad does not work with our functions
(I think it used to work...)
Diffstat (limited to 'src/python/gudhi/wasserstein')
| -rw-r--r-- | src/python/gudhi/wasserstein/wasserstein.py | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/src/python/gudhi/wasserstein/wasserstein.py b/src/python/gudhi/wasserstein/wasserstein.py index fe001c37..a9d1cdff 100644 --- a/src/python/gudhi/wasserstein/wasserstein.py +++ b/src/python/gudhi/wasserstein/wasserstein.py @@ -99,7 +99,7 @@ def wasserstein_distance(X, Y, matching=False, order=1., internal_p=np.inf, enab :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 `np.inf`. - :param enable_autodiff: If X and Y are torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation + :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`. |