Renamed all test data files to *.dat.

src/.gitignore

@@ -1,8 +1,2 @@
-chars
-learning_set
-test_set
-classifier
-classifier-model
-classifier-characters
-characters
-best_classifier
+*.dat
+results.txt

src/find_svm_params.py

@@ -9,10 +9,10 @@ Y = [float(2 ** p) for p in xrange(-13, 4, 2)]
 best_classifier = None
 
 print 'Loading learning set...'
-learning_set = load(file('learning_set', 'r'))
+learning_set = load(file('learning_set.dat', 'r'))
 print 'Learning set:', [c.value for c in learning_set]
 print 'Loading test set...'
-test_set = load(file('test_set', 'r'))
+test_set = load(file('test_set.dat', 'r'))
 print 'Test set:', [c.value for c in test_set]
 
 # Perform a grid-search on different combinations of soft margin and gamma
@@ -54,4 +54,4 @@ for c in C:
 
 print '\nmax:', maximum
 
-best_classifier.save('best_classifier')
+best_classifier.save('best_classifier.dat')

src/test_chars.py

@@ -3,7 +3,7 @@ from pylab import subplot, show, imshow, axis
 from cPickle import load
 
 x, y = 25, 25
-chars = load(file('chars', 'r'))[:(x * y)]
+chars = load(file('characters.dat', 'r'))[:(x * y)]
 
 for i in range(x):
     for j in range(y):

src/test_classifier.py

@@ -3,7 +3,7 @@ from xml_helper_functions import xml_to_LicensePlate
 from Classifier import Classifier
 from cPickle import dump, load
 
-chars = load(file('characters2', 'r'))
+chars = load(file('characters.dat', 'r'))
 learning_set = []
 test_set = []
 
@@ -31,12 +31,12 @@ for char in chars:
 print 'Learning set:', [c.value for c in learning_set]
 print 'Test set:', [c.value for c in test_set]
 print 'Saving learning set...'
-dump(learning_set, file('learning_set', 'w+'))
+dump(learning_set, file('learning_set.dat', 'w+'))
 print 'Saving test set...'
-dump(test_set, file('test_set', 'w+'))
+dump(test_set, file('test_set.dat', 'w+'))
 #----------------------------------------------------------------
 print 'Loading learning set'
-learning_set = load(file('learning_set', 'r'))
+learning_set = load(file('learning_set.dat', 'r'))
 
 # Train the classifier with the learning set
 classifier = Classifier(c=512, gamma=.125, cell_size=12)
@@ -47,7 +47,7 @@ classifier.train(learning_set)
 #print 'Loading classifier'
 #classifier = Classifier(filename='classifier')
 print 'Loading test set'
-test_set = load(file('test_set', 'r'))
+test_set = load(file('test_set.dat', 'r'))
 l = len(test_set)
 matches = 0
 

src/test_compare.py

@@ -5,7 +5,7 @@ from GrayscaleImage import GrayscaleImage
 from cPickle import load
 from numpy import zeros, resize
 
-chars = load(file('characters', 'r'))[::2]
+chars = load(file('characters.dat', 'r'))[::2]
 left = None
 right = None