Merge pull request #972 from delallea/minor2

doc/install.txt

@@ -29,7 +29,7 @@ instructions below for detailed installation steps):
         Not technically required but *highly* recommended, in order to compile
         generated C code. Theano `can` fall back on a NumPy-based Python execution
         model, but a C compiler allows for vastly faster execution.
-	g++ >= 4.2 (for openmp that is currently always used)
+        g++ >= 4.2 (for openmp that is currently always used)
         more recent version recommended!
 
     `NumPy <http://numpy.scipy.org/>`_ >= 1.5.0
@@ -41,7 +41,7 @@ instructions below for detailed installation steps):
         >=0.8 if you are using sparse matrices, because ``scipy.sparse``
         is buggy in 0.6 (the ``scipy.csc_matrix`` version of ``dot()`` has a
         bug with singleton dimensions, there may be more bugs) and we do not
-	tests with 0.7*.
+        run tests with 0.7.
 
     A `BLAS`_ installation (with Level 3 functionality)
         Including the development headers (``-dev``, ``-devel``, depending on

doc/sandbox/sparse.txt

@@ -15,7 +15,7 @@ Note that you want SciPy >= 0.8.0.
     dimensions. There may be more bugs. It also has inconsistent
     implementation of sparse matrices.
 
-    We do not test again SciPy 0.7.*.
+    We do not test against SciPy 0.7.*.
 
 We describe the details of the compressed sparse matrix types.
     ``scipy.sparse.csc_matrix``

doc/tutorial/faq.txt

@@ -49,17 +49,17 @@ Faster Small Theano function
 
    For Theano 0.6 and up.
 
-For Theano function that don't do much work like a regular logistic
+For Theano functions that don't do much work, like a regular logistic
 regression, the overhead of checking the input can be significant. You
-can disable it by setting f.trust_input to True to remove this
-check. Make sure you pass argument as what you said when compiling the
-Theano function.
-
-Also for small Theano function, you can remove more python overhead by
-making a Theano function that don't take any inputs. You can use shared
-variable to help you. Then you can call it like this: ``f.fn()`` or
-``f.fn(n_calls=N)`` to speed up. In the last case, only the last
-function output is returned.
+can disable it by setting ``f.trust_input`` to True.
+Make sure the types of arguments you provide match those defined when
+the function was compiled.
+
+Also, for small Theano functions, you can remove more Python overhead by
+making a Theano function that does not take any input. You can use shared
+variables to achieve this. Then you can call it like this: ``f.fn()`` or
+``f.fn(n_calls=N)`` to speed it up. In the last case, only the last
+function output (out of N calls) is returned.
 
 Related Projects
 ----------------

theano/scan_module/scan_perform_ext.py

-import os, logging, sys
+import logging
+import os
+import sys
 
 import theano
 from theano import config

theano/tensor/extra_ops.py

@@ -96,7 +96,7 @@ class BinCountOp(theano.Op):
             if not bool(numpy_ver >= [1, 6]):
                 raise NotImplementedError(
                     "BinCountOp with minlength attribute"
-                    " need NumPy 1.6 or higher.")
+                    " requires NumPy 1.6 or higher.")
 
     def __eq__(self, other):
         return (type(self) == type(other) and
@@ -152,7 +152,7 @@ class BinCountOp(theano.Op):
         if weights is not None and weights.shape != x.shape:
             raise TypeError("All inputs must have the same shape.")
 
-        #Needed for numpy 1.4.1 compatibility
+        # Needed for numpy 1.4.1 compatibility
         if self.minlength:
             out = np.bincount(x, weights=weights, minlength=self.minlength)
         else: