Tried to speed-up the scoring process; fixed small bug

xbob/boosting/core/boosting.py

@@ -27,6 +27,7 @@ import numpy
 import trainers
 import losses
 import scipy.optimize
+import itertools
 
 
 
@@ -261,6 +262,7 @@ class BoostMachine():
       """Returns the indices of the features that are selected by the weak classifiers."""
       return sorted(list(self.selected_indices))
 
+
     def __call__(self, feature):
       """Returns the predicted score for the given single feature, assuming only single output.
 
@@ -268,7 +270,7 @@ class BoostMachine():
 
       Output: A single floating point number
       """
-      return sum([a * weak.get_weak_score(feature) for (a, weak) in zip(self.alpha, self.weak_trainer)])
+      return sum([a * weak.get_weak_score(feature) for (a, weak) in itertools.izip(self.alpha, self.weak_trainer)])
 
 
     def classify(self, test_features):

xbob/boosting/core/trainers.py

@@ -23,6 +23,8 @@ class StumpMachine():
         self.threshold = threshold
         self.polarity = polarity
         self.selected_indices = selected_indices
+        if isinstance(self.selected_indices, int):
+            self.selected_indices = numpy.array([self.selected_indices], dtype=numpy.int)
 
 
     def get_weak_scores(self,test_features):