Merge pull request #205 from MathieuCarriere/wasserstein_representations

Integration of Wasserstein distances in representations module

1 files changed, 60 insertions, 0 deletions

diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py
index a39b00e4..a8545349 100644
--- a/src/python/gudhi/representations/preprocessing.py
+++ b/src/python/gudhi/representations/preprocessing.py
@@ -54,6 +54,18 @@ class BirthPersistenceTransform(BaseEstimator, TransformerMixin):
             Xfit.append(new_diag)
         return Xfit
 
+    def __call__(self, diag):
+        """
+        Apply BirthPersistenceTransform on a single persistence diagram and outputs the result.
+
+        Parameters:
+            diag (n x 2 numpy array): input persistence diagram.
+
+        Returns:
+            n x 2 numpy array: transformed persistence diagram.
+        """
+        return self.fit_transform([diag])[0]
+
 class Clamping(BaseEstimator, TransformerMixin):
     """
     This is a class for clamping values. It can be used as a parameter for the DiagramScaler class, for instance if you want to clamp abscissae or ordinates of persistence diagrams.
@@ -142,6 +154,18 @@ class DiagramScaler(BaseEstimator, TransformerMixin):
                             Xfit[i][:,I] = np.squeeze(scaler.transform(np.reshape(Xfit[i][:,I], [-1,1])))
         return Xfit
 
+    def __call__(self, diag):
+        """
+        Apply DiagramScaler on a single persistence diagram and outputs the result.
+
+        Parameters:
+            diag (n x 2 numpy array): input persistence diagram.
+
+        Returns:
+            n x 2 numpy array: transformed persistence diagram.
+        """
+        return self.fit_transform([diag])[0]
+
 class Padding(BaseEstimator, TransformerMixin):
     """
     This is a class for padding a list of persistence diagrams with dummy points, so that all persistence diagrams end up with the same number of points.
@@ -186,6 +210,18 @@ class Padding(BaseEstimator, TransformerMixin):
             Xfit = X
         return Xfit
 
+    def __call__(self, diag):
+        """
+        Apply Padding on a single persistence diagram and outputs the result.
+
+        Parameters:
+            diag (n x 2 numpy array): input persistence diagram.
+
+        Returns:
+            n x 2 numpy array: padded persistence diagram.
+        """
+        return self.fit_transform([diag])[0]
+
 class ProminentPoints(BaseEstimator, TransformerMixin):
     """
     This is a class for removing points that are close or far from the diagonal in persistence diagrams.  If persistence diagrams are n x 2 numpy arrays (i.e. persistence diagrams with ordinary features), points are ordered and thresholded by distance-to-diagonal. If persistence diagrams are n x 1 numpy arrays (i.e. persistence diagrams with essential features), points are not ordered and thresholded by first coordinate.
@@ -259,6 +295,18 @@ class ProminentPoints(BaseEstimator, TransformerMixin):
             Xfit = X
         return Xfit
 
+    def __call__(self, diag):
+        """
+        Apply ProminentPoints on a single persistence diagram and outputs the result.
+
+        Parameters:
+            diag (n x 2 numpy array): input persistence diagram.
+
+        Returns:
+            n x 2 numpy array: thresholded persistence diagram.
+        """
+        return self.fit_transform([diag])[0]
+
 class DiagramSelector(BaseEstimator, TransformerMixin):
     """
     This is a class for extracting finite or essential points in persistence diagrams.
@@ -303,3 +351,15 @@ class DiagramSelector(BaseEstimator, TransformerMixin):
         else:
             Xfit = X
         return Xfit
+
+    def __call__(self, diag):
+        """
+        Apply DiagramSelector on a single persistence diagram and outputs the result.
+
+        Parameters:
+            diag (n x 2 numpy array): input persistence diagram.
+
+        Returns:
+            n x 2 numpy array: extracted persistence diagram.
+        """
+        return self.fit_transform([diag])[0]