Fix import loop problem.

theano/__init__.py

@@ -69,6 +69,8 @@ FancyModule = Module
 
 from theano.printing import pprint, pp
 
+from theano import tensor
+
 from theano.scan_module import scan, map, reduce, foldl, foldr, clone
 
 from theano.updates import Updates, OrderedUpdates
@@ -197,7 +199,6 @@ else:
 # This cannot be done in tensor/__init__.py due to a circular dependency -- randomstreams
 # depends on raw_random which depends on tensor.  As a work-around, we import RandomStreams
 # here and inject an instance in tensor.
-from theano import tensor
 from theano.tensor.randomstreams import RandomStreams
 # Imitate the numpy.random symbol with a tensor.random one
 tensor.random = RandomStreams(seed=0xBAD5EED, no_warn=True)

theano/tensor/__init__.py

@@ -37,6 +37,8 @@ from theano.tensor import sharedvar  # adds shared-variable constructors
 from theano.tensor.sharedvar import tensor_constructor as _shared
 
 from theano.tensor.io import *
+from theano.tensor import nlinalg
+
 
 def shared(*args, **kw):
     """

theano/tensor/nlinalg.py

@@ -5,10 +5,9 @@ import numpy
 
 from theano.gof import Op, Apply
 
+import theano.tensor
 from theano.tensor import as_tensor_variable, dot, DimShuffle, Dot
 from theano.tensor.blas import Dot22
-from theano import tensor
-import theano.tensor
 from theano.tensor.opt import (register_stabilize,
         register_specialize, register_canonicalize)
 from theano.gof import local_optimizer
@@ -170,7 +169,7 @@ class AllocDiag(Op):
         x = as_tensor_variable(_x)
         if x.type.ndim != 1:
             raise TypeError('AllocDiag only works on vectors', _x)
-        return Apply(self, [x], [tensor.matrix(dtype=x.type.dtype)])
+        return Apply(self, [x], [theano.tensor.matrix(dtype=x.type.dtype)])
 
     def grad(self, inputs, g_outputs):
         return [extract_diag(g_outputs[0])]
@@ -239,15 +238,15 @@ class ExtractDiag(Op):
         return 'ExtractDiag{view=%s}' % self.view
 
     def grad(self, inputs, g_outputs):
-        x = tensor.zeros_like(inputs[0])
+        x = theano.tensor.zeros_like(inputs[0])
         xdiag = alloc_diag(g_outputs[0])
-        return [tensor.set_subtensor(
+        return [theano.tensor.set_subtensor(
             x[:xdiag.shape[0], :xdiag.shape[1]],
             xdiag)]
 
     def infer_shape(self, node, shapes):
         x_s, = shapes
-        shp = tensor.min(node.inputs[0].shape)
+        shp = theano.tensor.min(node.inputs[0].shape)
         return [(shp,)]
 
 extract_diag = ExtractDiag()

theano/tensor/var.py

@@ -9,7 +9,6 @@ from theano.gof import Constant, Variable
 from theano.gof.utils import hashtype
 from theano.tensor.utils import hash_from_ndarray
 from theano.tensor.type import TensorType
-from theano.tensor import nlinalg
 
 
 class AsTensorError(TypeError):
@@ -536,7 +535,7 @@ class _tensor_py_operators:
         return theano.tensor.basic.round(self, mode)
 
     def trace(self):
-        return theano.sandbox.linalg.ops.trace(self)
+        return theano.tensor.nlinalg.ops.trace(self)
 
     # TO TRUMP NUMPY OPERATORS
     __array_priority__ = 1000