Some minor bug-/comment-fixes.

src/Classifier.py

@@ -57,6 +57,7 @@ class Classifier:
         true_value = 0 if true_value == None else ord(true_value)
         #x = character.get_feature_vector(self.cell_size)
         character.get_single_cell_feature_vector(self.neighbours)
+        #p = svm_predict([true_value], [character.feature], self.model, '-b 1')
         p = svm_predict([true_value], [character.feature], self.model)
         prediction_class = int(p[0][0])
 

src/LocalBinaryPatternizer.py

@@ -57,7 +57,7 @@ class LocalBinaryPatternizer:
              | (self.is_pixel_darker(y - 2, x - 1, value))
 
     def create_features_vector(self):
-        '''Walk around the pixels in clokwise order, shifting 1 bit less at
+        '''Walk around the pixels in clockwise order, shifting 1 bit less at
         each neighbour starting at 7 in the top-left corner. This gives a 8-bit
         feature number of a pixel'''
         self.setup_histograms()

src/test_classifier.py

@@ -1,5 +1,6 @@
 #!/usr/bin/python
 from cPickle import dump, load
+from sys import argv, exit
 
 from Classifier import Classifier
 
@@ -16,7 +17,7 @@ classifier = Classifier(c=float(argv[1]), \
                         neighbours=int(argv[3]))
 classifier.train(learning_set)
 
-print 'Loading test set'
+print 'Loading test set...'
 test_set = load(file('test_set%s.dat' % argv[1], 'r'))
 l = len(test_set)
 matches = 0
@@ -34,5 +35,5 @@ for i, char in enumerate(test_set):
     print '  --  %d of %d (%d%% done)' % (i + 1, l, int(100 * (i + 1) / l))
 
 print '\n%d matches (%d%%), %d fails' % (matches, \
-        int(100 * matches / len(test_set)), \
+        int(100 * matches / l), \
         len(test_set) - matches)