small typo/cleaner code following review comments

NEWS.txt

@@ -32,8 +32,8 @@ New Features
  * If you use Enthought Python Distribution (EPD) now we use its blas
    implementation by default (tested on Linux and Windows)
    (Frederic B., Simon McGregor)
- * MRG random now raise an error with a clear message when the passed shape
-   contain dimensions with bad value like 0. (Frédéric B. reported by Ian G.)
+ * MRG random now raises an error with a clear message when the passed shape
+   contains dimensions with bad value like 0. (Frédéric B. reported by Ian G.)
 
 Sparse Sandbox graduate
  * Remove0 op: it removes stored elements with value 0. (Frederic B.)

theano/sandbox/rng_mrg.py

@@ -730,9 +730,9 @@ class MRG_RandomStreams(object):
             msg = "size must be a tuple of int or a Theano variable"
             assert all([isinstance(i,int) or isinstance(i,Variable)
                 for i in size]), msg
-            if len([i for i in size if isinstance(i, int) and i <= 0]) > 0:
+            if any([isinstance(i, int) and i <= 0 for i in size]):
                 raise ValueError(
-                    "The specified size contain a dimension with value <= 0",
+                    "The specified size contains a dimension with value <= 0",
                     size)
 
         else:
@@ -803,9 +803,9 @@ class MRG_RandomStreams(object):
             raise TypeError("You have to specify pvals")
         pvals = as_tensor_variable(pvals)
         if size is not None:
-            if any([i for i in size if not (isinstance(i, int) and i <= 0)]):
+            if any([isinstance(i, int) and i <= 0 for i in size]):
                 raise ValueError(
-                    "The specified size contain a dimension with value <= 0",
+                    "The specified size contains a dimension with value <= 0",
                     size)
 
         if n == 1 and pvals.ndim == 2:

theano/sandbox/test_rng_mrg.py

@@ -604,10 +604,10 @@ class T_MRG(unittest.TestCase):
 
         R = MRG_RandomStreams(234, use_cuda=False)
 
-        for size, var_input in [
-                ((0, 100), []),
-                ((-1, 100), []),
-                ((1, 0), []),
+        for size in [
+                (0, 100),
+                (-1, 100),
+                (1, 0),
                 ]:
 
             self.assertRaises(ValueError, R.uniform, size)