update doc in wasserstein.wasserstein to reflect change in default param

1 files changed, 4 insertions, 4 deletions

diff --git a/src/python/gudhi/wasserstein/wasserstein.py b/src/python/gudhi/wasserstein/wasserstein.py
index 6d7ec65f..b37d30bb 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
@@ -96,9 +96,9 @@ def wasserstein_distance(X, Y, matching=False, order=1., internal_p=np.inf, enab
     :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).
+                       Default value is `np.inf`.
     :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 and is currently incompatible
         with `matching=True`.