Small typos and formatting fixes

doc/install.txt

@@ -442,9 +442,9 @@ correctly (for example, for MKL this might be ``-lmkl -lguide -lpthread`` or
 
     If you have problems linking with MKL, `Intel Line Advisor
     <http://software.intel.com/en-us/articles/intel-mkl-link-line-advisor>`_
-    and `MKL User Guide
+    and the `MKL User Guide
     <http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_userguide_lnx/index.htm>`_
-    can help you find the correct flag to use.
+    can help you find the correct flags to use.
 
 .. _gpu_linux:
 

doc/library/tensor/basic.txt

@@ -646,30 +646,30 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
     >>> x = T.concatenate([x0, x1[0], T.shape_padright(x2)], axis=1)
     >>> # x.ndim == 2
 
-.. function:: stacklist(tensor_list)
+.. function:: stacklists(tensor_list)
 
-    :type tensor_list: an iterable that contain tensors or iterable
-        with at the end tensors.
-    :param tensor_list: tensors to be
-        stackend together.
+    :type tensor_list: an iterable that contains either tensors or other
+        iterables of the same type as `tensor_list` (in other words, this
+        is a tree whose leaves are tensors).
+    :param tensor_list: tensors to be stacked together.
 
-    Recursivly stack lists of tensors to maintain similar structure.
+    Recursively stack lists of tensors to maintain similar structure.
 
-    This function can create a tensor from a shaped list of scalars
+    This function can create a tensor from a shaped list of scalars:
 
     >>> from theano.tensor import stacklists, scalars, matrices
     >>> from theano import function
-    >>> a,b,c,d = scalars('abcd')
+    >>> a, b, c, d = scalars('abcd')
     >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> f(1, 2, 3, 4)
     >>> # array([[ 1.,  2.], [ 3.,  4.]], dtype=float32)
 
-    We can also stack arbitrarily shaped tensors.  Here we stack matrices into
-    a 2 by 2 grid.
+    We can also stack arbitrarily shaped tensors. Here we stack matrices into
+    a 2 by 2 grid:
 
     >>> from numpy import ones
-    >>> a,b,c,d, = matrices('abcd')
+    >>> a, b, c, d = matrices('abcd')
     >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> x = ones((4, 4), 'float32')

theano/tensor/basic.py

@@ -8236,24 +8236,25 @@ def diag(v, k=0):
 
 
 def stacklists(arg):
-    """ Recursivly stack lists of tensors to maintain similar structure
+    """
+    Recursively stack lists of tensors to maintain similar structure.
 
-    This function can create a tensor from a shaped list of scalars
+    This function can create a tensor from a shaped list of scalars:
 
     >>> from theano.tensor import stacklists, scalars, matrices
     >>> from theano import function
-    >>> a,b,c,d = scalars('abcd')
+    >>> a, b, c, d = scalars('abcd')
     >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> f(1, 2, 3, 4)
     array([[ 1.,  2.],
            [ 3.,  4.]], dtype=float32)
 
-    We can also stack arbitrarily shaped tensors.  Here we stack matrices into
-    a 2 by 2 grid.
+    We can also stack arbitrarily shaped tensors. Here we stack matrices into
+    a 2 by 2 grid:
 
     >>> from numpy import ones
-    >>> a,b,c,d, = matrices('abcd')
+    >>> a, b, c, d = matrices('abcd')
     >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> x = ones((4, 4), 'float32')