Merge pull request #147 from mglisse/sktda-tweaks-glisse

Minor tweaks to representations

1 files changed, 5 insertions, 5 deletions

diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py
index 83227ca1..a39b00e4 100644
--- a/src/python/gudhi/representations/preprocessing.py
+++ b/src/python/gudhi/representations/preprocessing.py
@@ -30,7 +30,7 @@ class BirthPersistenceTransform(BaseEstimator, TransformerMixin):
         Fit the BirthPersistenceTransform class on a list of persistence diagrams (this function actually does nothing but is useful when BirthPersistenceTransform is included in a scikit-learn Pipeline).
 
         Parameters:
-            X (n x 2 numpy array): input persistence diagrams.
+            X (list of n x 2 numpy array): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
         return self
@@ -58,14 +58,15 @@ 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.
     """
-    def __init__(self, limit=np.inf):
+    def __init__(self, minimum=-np.inf, maximum=np.inf):
         """
         Constructor for the Clamping class.
 
         Parameters:
             limit (double): clamping value (default np.inf).
         """
-        self.limit = limit
+        self.minimum = minimum
+        self.maximum = maximum
 
     def fit(self, X, y=None):
         """
@@ -87,8 +88,7 @@ class Clamping(BaseEstimator, TransformerMixin):
         Returns:
             numpy array of size n: output list of values.
         """
-        Xfit = np.minimum(X, self.limit)
-        #Xfit = np.where(X >= self.limit, self.limit * np.ones(X.shape), X)
+        Xfit = np.clip(X, self.minimum, self.maximum)
         return Xfit
 
 class DiagramScaler(BaseEstimator, TransformerMixin):