fix conflict

1 files changed, 30 insertions, 30 deletions

diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py
index e2c30f8c..59440b1a 100644
--- a/src/python/gudhi/representations/metrics.py
+++ b/src/python/gudhi/representations/metrics.py
@@ -246,27 +246,23 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
         Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric="bottleneck", e=self.epsilon)
         return Xfit
 
-class WassersteinDistance(BaseEstimator, TransformerMixin):
+class PersistenceFisherDistance(BaseEstimator, TransformerMixin):
     """
-    This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. 
+    This is a class for computing the persistence Fisher distance matrix from a list of persistence diagrams. The persistence Fisher distance is obtained by computing the original Fisher distance between the probability distributions associated to the persistence diagrams given by convolving them with a Gaussian kernel. See http://papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details. 
     """
-    def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01):
+    def __init__(self, bandwidth=1., kernel_approx=None):
         """
-        Constructor for the WassersteinDistance class.
+        Constructor for the PersistenceFisherDistance 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".
-            delta (float): relative error 1+delta. Used only if mode == "hera".
+            bandwidth (double): bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions (default 1.).
+            kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance).   
         """
-        self.order, self.internal_p, self.mode = order, internal_p, mode
-        self.metric = "pot_wasserstein" if mode == "pot" else "hera_wasserstein"
-        self.delta = delta
+        self.bandwidth, self.kernel_approx = bandwidth, kernel_approx
 
     def fit(self, X, y=None):
         """
-        Fit the WassersteinDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams**.
+        Fit the PersistenceFisherDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams** and the kernel approximation class (if not None) is applied on them.
 
         Parameters:
             X (list of n x 2 numpy arrays): input persistence diagrams.
@@ -277,37 +273,37 @@ class WassersteinDistance(BaseEstimator, TransformerMixin):
 
     def transform(self, X):
         """
-        Compute all Wasserstein distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams.
+        Compute all persistence Fisher distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams.
 
         Parameters:
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise Wasserstein distances.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher distances.
         """
-        if self.metric == "hera_wasserstein":
-            Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, delta=self.delta)
-        else:
-            Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p)
-        return Xfit
+        return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="persistence_fisher", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx)
 
-class PersistenceFisherDistance(BaseEstimator, TransformerMixin):
+class WassersteinDistance(BaseEstimator, TransformerMixin):
     """
-    This is a class for computing the persistence Fisher distance matrix from a list of persistence diagrams. The persistence Fisher distance is obtained by computing the original Fisher distance between the probability distributions associated to the persistence diagrams given by convolving them with a Gaussian kernel. See http://papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details. 
+    This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. 
     """
-    def __init__(self, bandwidth=1., kernel_approx=None):
+    def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01):
         """
-        Constructor for the PersistenceFisherDistance class.
+        Constructor for the WassersteinDistance class.
 
         Parameters:
-            bandwidth (double): bandwidth of the Gaussian kernel used to turn persistence diagrams into probability distributions (default 1.).
-            kernel_approx (class): kernel approximation class used to speed up computation (default None). Common kernel approximations classes can be found in the scikit-learn library (such as RBFSampler for instance).   
+            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".
+            delta (float): relative error 1+delta. Used only if mode == "hera".
         """
-        self.bandwidth, self.kernel_approx = bandwidth, kernel_approx
+        self.order, self.internal_p, self.mode = order, internal_p, mode
+        self.metric = "pot_wasserstein" if mode == "pot" else "hera_wasserstein"
+        self.delta = delta
 
     def fit(self, X, y=None):
         """
-        Fit the PersistenceFisherDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams** and the kernel approximation class (if not None) is applied on them.
+        Fit the WassersteinDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams**.
 
         Parameters:
             X (list of n x 2 numpy arrays): input persistence diagrams.
@@ -318,12 +314,16 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin):
 
     def transform(self, X):
         """
-        Compute all persistence Fisher distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams.
+        Compute all Wasserstein distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams.
 
         Parameters:
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher distances.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise Wasserstein distances.
         """
-        return pairwise_persistence_diagram_distances(X, self.diagrams_, metric="persistence_fisher", bandwidth=self.bandwidth, kernel_approx=self.kernel_approx)
+        if self.metric == "hera_wasserstein":
+            Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p, delta=self.delta)
+        else:
+            Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric=self.metric, order=self.order, internal_p=self.internal_p)
+        return Xfit