Test new cases for local_subtensor_merge, and fix one of them.

theano/tensor/opt.py

@@ -1152,11 +1152,13 @@ def local_subtensor_merge(node):
     1) var[int:][-1] -> var[-1] # a little different for when the first subtensor is empty.
     2) var[::-1][int] -> var[-int-1]
     3) var[::-1][:int] -> var[:-int-1:-1]
-    4) var[int1::][:int2] -> var[int1:switch(idx1>=0,
-                                             idx1,
-                                             maximum(u.owner.inputs[0].shape[0]+idx1, 0)
-                                             ) + idx2]
-
+    4) var[int1::][:int2] ->
+        var[int1:int2 + switch(int2<0,
+                               0,
+                               switch(int1>=0,
+                                      int1,
+                                      maximum(u.owner.inputs[0].shape[0]+int1,
+                                              0))]
     """
     if (isinstance(node.op, T.Subtensor) and
         len(node.op.idx_list)==1):
@@ -1270,11 +1272,17 @@ def local_subtensor_merge(node):
             elif isinstance(idx2, int):
                 idx2 = T.as_tensor_variable(idx2)
 
-            # The maximum is needed to don't have shape[0] - idx1 < 0
-            idx2_neg = T.maximum(u.owner.inputs[0].shape[0]+idx1, 0)
-            new_idx2 = T.switch(idx1>=0, idx1, idx2_neg)+idx2
+            # Get positive version of idx1
+            # TODO: use Razvan's code for that
+            # The maximum is needed so that shape[0] + idx1 >= 0
+            neg_idx1 = T.maximum(u.owner.inputs[0].shape[0]+idx1, 0)
+            new_idx1 = T.switch((idx1 >= 0), idx1, neg_idx1)
+
+            # If idx2<0, we are indexing from the end, so idx2 is OK
+            # If we are indexing from the beginning, we need to add pos_idx1
+            new_idx2 = idx2 + T.switch((idx2 < 0), 0, new_idx1)
 
-            return [u.owner.inputs[0][idx1:new_idx2]]
+            return [u.owner.inputs[0][new_idx1:new_idx2]]
 
 @register_canonicalize
 @gof.local_optimizer([None])