Fixed norm function and added it to var.py.

theano/sandbox/linalg/ops.py

@@ -1278,7 +1278,7 @@ def norm(x,ord,axis):
 					z = tensor.min(abs(x),axis)
 					return z
 				elif ord == 0:
-					z = tensor.sum(tensor.neq(x,0),1)
+					z = tensor.sum(tensor.neq(x,0),axis)
 					return z
 				else:
 					try:

theano/sandbox/linalg/tests/test_linalg.py

@@ -650,3 +650,7 @@ class T_NormTests(unittest.TestCase):
 		self.assertRaises(TypeError, norm, 3, None, None)
 	def test_no_enough_dimensions(self):
 		self.assertRaises(ValueError, norm, [[2,1],[3,4]], None, 3)
+
+	def test(self):
+            x = theano.tensor.matrix()
+            x.swapaxes()

theano/tensor/var.py

@@ -699,7 +699,7 @@ class TensorConstant(_tensor_py_operators, Constant):
                           copy.deepcopy(self.data, memo),
                           copy.deepcopy(self.name, memo))
 
-def swapaxes(self, axis1, axis2)
+    def swapaxes(self, axis1, axis2)
 	"""Return 'tensor.swapaxes(self, axis1, axis2)
 
 	If a matrix is provided with the right axes, its transpose will be returned.