Use super in base class constructor calls

bob/bio/base/algorithm/BIC.py

@@ -73,8 +73,7 @@ class BIC(Algorithm):
     ):
 
         # call base class function and register that this tool requires training for the enrollment
-        Algorithm.__init__(
-            self,
+        super(BIC, self).__init__(
             requires_enroller_training=True,
 
             comparison_function=str(comparison_function),

bob/bio/base/algorithm/Distance.py

@@ -37,8 +37,7 @@ class Distance (Algorithm):
   ):
 
     # call base class constructor and register that the algorithm performs a projection
-    Algorithm.__init__(
-        self,
+    super(Distance, self).__init__(
         distance_function = str(distance_function),
         is_distance_function = is_distance_function,
 

bob/bio/base/algorithm/LDA.py

@@ -65,8 +65,7 @@ class LDA (Algorithm):
   ):
 
     # call base class constructor and register that the LDA tool performs projection and need the training features split by client
-    Algorithm.__init__(
-        self,
+    super(LDA, self).__init__(
         performs_projection = True,
         split_training_features_by_client = True,
 

bob/bio/base/algorithm/PCA.py

@@ -49,8 +49,7 @@ class PCA (Algorithm):
   ):
 
     # call base class constructor and register that the algorithm performs a projection
-    Algorithm.__init__(
-        self,
+    super(PCA, self).__init__(
         performs_projection = True,
 
         subspace_dimension = subspace_dimension,

bob/bio/base/algorithm/PLDA.py

@@ -37,8 +37,7 @@ class PLDA (Algorithm):
 
     """Initializes the local (PCA-)PLDA tool chain with the given file selector object"""
     # call base class constructor and register that this class requires training for enrollment
-    Algorithm.__init__(
-        self,
+    super(PLDA, self).__init__(
         requires_enroller_training = True,
 
         subspace_dimension_of_f = subspace_dimension_of_f, # Size of subspace F
@@ -113,7 +112,7 @@ class PLDA (Algorithm):
     """Generates the PLDA base model from a list of arrays (one per identity),
        and a set of training parameters. If PCA is requested, it is trained on the same data.
        Both the trained PLDABase and the PCA machine are written."""
-    
+
     # arrange PLDA training data
     training_features = self._arrange_data(training_features)
 

bob/bio/base/extractor/Linearize.py

@@ -15,7 +15,7 @@ class Linearize(Extractor):
 
     def __init__(self, dtype=None):
         """If the ``dtype`` parameter is given, it specifies the data type that is enforced for the features."""
-        Extractor.__init__(self, dtype=dtype)
+        super(Linearize, self).__init__(dtype=dtype)
         self.dtype = dtype
 
     def __call__(self, data):

bob/bio/base/preprocessor/Filename.py

@@ -15,7 +15,7 @@ class Filename (Preprocessor):
 
   def __init__(self):
     # call base class constructor, using a custom ``read_original_data`` that does nothing and always returns None
-    Preprocessor.__init__(self, writes_data=False, read_original_data = lambda x,y,z: None)
+    super(Filename, self).__init__(writes_data=False, read_original_data = lambda x,y,z: None)
 
 
   # The call function (i.e. the operator() in C++ terms)