svn repository web references are replaced with links to git

samples/python2/digits.py

@@ -1,9 +1,9 @@
 '''
-SVN and KNearest digit recognition.
+SVM and KNearest digit recognition.
 
 Sample loads a dataset of handwritten digits from 'digits.png'.
-Then it trains a SVN and KNearest classifiers on it and evaluates
-their accuracy. 
+Then it trains a SVM and KNearest classifiers on it and evaluates
+their accuracy.
 
 Following preprocessing is applied to the dataset:
  - Moment-based image deskew (see deskew())
@@ -77,7 +77,7 @@ class KNearest(StatModel):
 
 class SVM(StatModel):
     def __init__(self, C = 1, gamma = 0.5):
-        self.params = dict( kernel_type = cv2.SVM_RBF, 
+        self.params = dict( kernel_type = cv2.SVM_RBF,
                             svm_type = cv2.SVM_C_SVC,
                             C = C,
                             gamma = gamma )
@@ -95,7 +95,7 @@ def evaluate_model(model, digits, samples, labels):
     resp = model.predict(samples)
     err = (labels != resp).mean()
     print 'error: %.2f %%' % (err*100)
-    
+
     confusion = np.zeros((10, 10), np.int32)
     for i, j in zip(labels, resp):
         confusion[i, j] += 1
@@ -128,7 +128,7 @@ def preprocess_hog(digits):
         hist = np.hstack(hists)
 
         # transform to Hellinger kernel
-        eps = 1e-7 
+        eps = 1e-7
         hist /= hist.sum() + eps
         hist = np.sqrt(hist)
         hist /= norm(hist) + eps
@@ -141,23 +141,23 @@ if __name__ == '__main__':
     print __doc__
 
     digits, labels = load_digits(DIGITS_FN)
-    
+
     print 'preprocessing...'
     # shuffle digits
     rand = np.random.RandomState(321)
     shuffle = rand.permutation(len(digits))
     digits, labels = digits[shuffle], labels[shuffle]
-    
+
     digits2 = map(deskew, digits)
     samples = preprocess_hog(digits2)
-    
+
     train_n = int(0.9*len(samples))
     cv2.imshow('test set', mosaic(25, digits[train_n:]))
     digits_train, digits_test = np.split(digits2, [train_n])
     samples_train, samples_test = np.split(samples, [train_n])
     labels_train, labels_test = np.split(labels, [train_n])
 
-    
+
     print 'training KNearest...'
     model = KNearest(k=4)
     model.train(samples_train, labels_train)