more changes

theano/tensor/basic.py

 """A `Type` and `Op` classes to work with numpy.ndarrays symbolically."""
+import __builtin__
 import sys
 import warnings
 
@@ -4711,18 +4712,20 @@ def tile(x, reps, ndim=None):
 
     See the docstring of `numpy.tile` for details.
 
-    Currently, 'reps' can be constant integer (e.g. 3), constant vector(e.g. [2 3]),
+    'reps' can be constant integer (e.g. 3), constant vector(e.g. [2 3]),
     symbolic scalar (e.g. tensor.iscalar()), symbolic vector (e.g. tensor.ivector())
     or a list of symbolic scalar (e.g. [tensor.iscalar(), tensor.iscalar()]).
-    If reps is constant vector/list/symbolic vector, the length of such vector(list)
-    should be less or equal to x.ndim, and if specified, 'ndim' should be less or equal
-    to x.ndim.
 
-    TODO: expand this so that it could support cases when the length of reps (or 'ndim')
-    is larger than x.ndim, which is supported by numpy.
+    ndim is the number of the dimensions of the output, if it is provided, ndim
+    should be equal or larger than x.ndim and len(reps), otherwise, we will use
+    max(x.ndim, len(reps)) as ndim. If reps is symbolic vector, the ndim has to
+    be provided.
 
     """
 
+    if ndim is not None and ndim < x.ndim:
+        raise ValueError("ndim should be equal or larger than x.ndim")
+
     # if reps is tensor.scalar, integer or tensor.vector, we convert it to a list.
     if not isinstance(reps, (list, tuple)):
         reps_astensor = as_tensor_variable(reps)
@@ -4740,9 +4743,6 @@ def tile(x, reps, ndim=None):
                 raise ValueError("if reps is tensor.vector, you should specify "
                                  "the ndim")
             else:
-                if ndim > x.ndim:
-                    raise ValueError("ndim > x.ndim not currently supported")
-
                 offset = ndim-reps.shape[0]
 
                 # assert that reps.shape[0] does not exceed ndim
@@ -4757,22 +4757,21 @@ def tile(x, reps, ndim=None):
         else:
             raise ValueError("the dimension of reps should not exceed 1")
     else:
-       if len(reps) > x.ndim:
-           raise ValueError("len(reps) > x.ndim not currently supported")
+       if ndim is not None and len(reps) > ndim:
+           raise ValueError("len(reps) should be equal or less than ndim")
        if not numpy.all([isinstance(r, (int, long)) or
            (isinstance(r, TensorVariable) and
             r.dtype in theano.tensor.discrete_dtypes) for r in reps]):
             raise ValueError("elements of reps must be scalars of integer dtype")
-
+       
     # if reps.ndim is less than x.ndim, we pad the reps with
     # "1" so that reps will have the same ndim as x.
-    reps = list(reps) 
-    if len(reps) < x.ndim:
-        reps = [1]*(x.ndim-len(reps)) + reps
-
-    if ndim is None:
-        ndim = len(reps)
     reps = list(reps)
+    if ndim is None:
+        ndim = __builtin__.max(len(reps), x.ndim) 
+    if len(reps) < ndim:
+        reps = [1]*(ndim-len(reps)) + reps
+
     shape = [x.shape[i] for i in xrange(ndim)]
     alloc_shape = reps + shape
     y = alloc(x, *alloc_shape)