Generalize Clamping to min and max

Fix #146.

2 files changed, 5 insertions, 5 deletions

diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py
index a77bbfdd..119072eb 100755
--- a/src/python/example/diagram_vectorizations_distances_kernels.py
+++ b/src/python/example/diagram_vectorizations_distances_kernels.py
@@ -16,7 +16,7 @@ diags = [D]
 
 diags = DiagramSelector(use=True, point_type="finite").fit_transform(diags)
 diags = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())]).fit_transform(diags)
-diags = DiagramScaler(use=True, scalers=[([1], Clamping(limit=.9))]).fit_transform(diags)
+diags = DiagramScaler(use=True, scalers=[([1], Clamping(maximum=.9))]).fit_transform(diags)
 
 D = diags[0]
 plt.scatter(D[:,0],D[:,1])
diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py
index 487b5376..a39b00e4 100644
--- a/src/python/gudhi/representations/preprocessing.py
+++ b/src/python/gudhi/representations/preprocessing.py
@@ -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):