update default param for representation.metrics

1 files changed, 11 insertions, 5 deletions

diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py
index 8a32f7e9..3e8487c4 100644
--- a/src/python/gudhi/representations/metrics.py
+++ b/src/python/gudhi/representations/metrics.py
@@ -328,22 +328,28 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin):
         """
         return _persistence_fisher_distance(diag1, diag2, bandwidth=self.bandwidth, kernel_approx=self.kernel_approx)
 
+
 class WassersteinDistance(BaseEstimator, TransformerMixin):
     """
     This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. 
     """
-    def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01):
+    def __init__(self, order=1, internal_p=np.inf, mode="hera", delta=0.01):
         """
         Constructor for the WassersteinDistance class.
 
         Parameters:
-            order (int): exponent for Wasserstein, default value is 2., see :func:`gudhi.wasserstein.wasserstein_distance`.
-            internal_p (int): ground metric on the (upper-half) plane (i.e. norm l_p in R^2), default value is 2 (euclidean norm), see :func:`gudhi.wasserstein.wasserstein_distance`.
-            mode (str): method for computing Wasserstein distance. Either "pot" or "hera".
+            order (int): exponent for Wasserstein, default value is 1., see :func:`gudhi.wasserstein.wasserstein_distance`.
+            internal_p (int): ground metric on the (upper-half) plane (i.e. norm l_p in R^2), default value is `np.inf`, see :func:`gudhi.wasserstein.wasserstein_distance`.
+            mode (str): method for computing Wasserstein distance. Either "pot" or "hera". Default set to "hera".
             delta (float): relative error 1+delta. Used only if mode == "hera".
         """
         self.order, self.internal_p, self.mode = order, internal_p, mode
-        self.metric = "pot_wasserstein" if mode == "pot" else "hera_wasserstein"
+        if mode == "pot":
+            self.metric = "pot_wasserstein"
+        elif mode == "hera":
+            self.metric = "hera_wasserstein"
+        else:
+            raise NameError("Unknown mode. Current available values for mode are 'hera' and 'pot'")
         self.delta = delta
 
     def fit(self, X, y=None):