added __array_priority__ for Tensor variables.

theano/tensor/basic.py

@@ -263,10 +263,12 @@ class TensorType(Type):
                 return False
             if 'int' in str(a.dtype):
                 return numpy.all(a==b)
-            else:
-                if a.shape == (): #for comparing scalars, use broadcasting.
+            elif a.shape == (): #for comparing scalars, use broadcasting.
                 ones = numpy.ones(2)
                 return numpy.allclose(ones * a, ones*b)
+            #elif str(a.dtype).startswith('complex'):
+            #    print >> sys.stderr, 'WARNING: skipping comparison of complex'
+            #    return True
             else:
                 return numpy.allclose(a,b)
         return False
@@ -663,6 +665,10 @@ class _tensor_py_operators:
         return pow(pow(abs_(self), L).sum(axis=axis), 1.0/L)
 
 
+    #TO TRUMP NUMPY OPERATORS
+    __array_priority__ = 1000
+    
+
 class TensorVariable(Variable, _tensor_py_operators):
     """Subclass to add the tensor operators to the basic `Variable` class."""
 
@@ -898,7 +904,8 @@ class MaxAndArgmax(Op):
 #       g_max has one less dimension than x, so you need to complete g_max to x's shape
 #        when axis=0 the broadcasting mechanism does it automatically
         
-        assert axis.data == 0 or axis.data == x.ndim-1
+        if not ( axis.data == 0 or axis.data == x.ndim-1):
+            raise NotImplementedError('MaxAndArgmax gradient with axis corresponding to internal dimension')
         g_max_pad = shape_padleft(g_max) if axis.data==0 else \
                     shape_padright(g_max)
         xmax = max(x, axis)