switched from argparse to getopt for compatibility with Python 2.6

samples/python2/letter_recog.py

@@ -91,31 +91,34 @@ class SVM(LetterStatModel):
 
 
 if __name__ == '__main__':
-    import argparse
+    import getopt
+    import sys
 
     models = [RTrees, KNearest, Boost, SVM] # MLP, NBayes
     models = dict( [(cls.__name__.lower(), cls) for cls in models] )
-    
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-model', default='rtrees', choices=models.keys())
-    parser.add_argument('-data', nargs=1, default='../cpp/letter-recognition.data')
-    parser.add_argument('-load', nargs=1)
-    parser.add_argument('-save', nargs=1)
-    args = parser.parse_args()
 
-    print 'loading data %s ...' % args.data
-    samples, responses = load_base(args.data)
-    Model = models[args.model]
+    print 'USAGE: letter_recog.py [--model <model>] [--data <data fn>] [--load <model fn>] [--save <model fn>]'
+    print 'Models: ', ', '.join(models)
+    print
+    
+    args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
+    args = dict(args)
+    args.setdefault('--model', 'rtrees')
+    args.setdefault('--data', '../cpp/letter-recognition.data')
+
+    print 'loading data %s ...' % args['--data']
+    samples, responses = load_base(args['--data'])
+    Model = models[args['--model']]
     model = Model()
 
     train_n = int(len(samples)*model.train_ratio)
-    if args.load is None:
-        print 'training %s ...' % Model.__name__
-        model.train(samples[:train_n], responses[:train_n])
-    else:
-        fn = args.load[0]
+    if '--load' in args:
+        fn = args['--load']
         print 'loading model from %s ...' % fn
         model.load(fn)
+    else:
+        print 'training %s ...' % Model.__name__
+        model.train(samples[:train_n], responses[:train_n])
 
     print 'testing...'
     train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
@@ -123,7 +126,7 @@ if __name__ == '__main__':
 
     print 'train rate: %f  test rate: %f' % (train_rate*100, test_rate*100)
 
-    if args.save is not None:
-        fn = args.save[0]
+    if '--save' in args:
+        fn = args['--save']
         print 'saving model to %s ...' % fn
         model.save(fn)