Merge pull request #1351 from nouiz/doc_mkl

doc/install.txt

@@ -438,6 +438,14 @@ correctly (for example, for MKL this might be ``-lmkl -lguide -lpthread`` or
     This might be just a problem with the way Theano passes compilation
     arguments to g++, but the problem is not fixed yet.
 
+.. note::
+
+    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
+    <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.
+
 .. _gpu_linux:
 
 Using the GPU

doc/library/tensor/basic.txt

@@ -646,6 +646,35 @@ 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)
+
+    :type tensor_list: an iterable that contain tensors or iterable
+        with at the end tensors.
+    :param tensor_list: tensors to be
+        stackend together.
+
+    Recursivly stack lists of tensors to maintain similar structure.
+
+    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')
+    >>> 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.
+
+    >>> from numpy import ones
+    >>> a,b,c,d, = matrices('abcd')
+    >>> X = stacklists([[a, b], [c, d]])
+    >>> f = function([a, b, c, d], X)
+    >>> x = ones((4, 4), 'float32')
+    >>> f(x, x, x, x).shape
+    >>> # (2, 2, 4, 4)
 
 Reductions
 ==========
@@ -800,7 +829,7 @@ Reductions
         inner summation. This will not necessarily be the dtype of the
         output (in particular if it is a discrete (int/uint) dtype, the
         output will be in a float type).  If None, then we use the same
-        rules as :ref:`sum()`.
+        rules as :func:`sum()`.
     :Returns: mean value of *x* along *axis*
 
     axis can be:

theano/tensor/basic.py

@@ -8234,15 +8234,16 @@ def diag(v, k=0):
     else:
         raise ValueError("Input must be 1- or 2-d.")
 
+
 def stacklists(arg):
     """ Recursivly stack lists of tensors to maintain similar structure
 
     This function can create a tensor from a shaped list of scalars
-    >>> from theano.tensor import stacklists, scalar, matrix
+
+    >>> from theano.tensor import stacklists, scalars, matrices
     >>> from theano import function
-    >>> a,b,c,d = map(scalar, 'abcd')
-    >>> X = stacklists([[a, b],
-    ...                 [c, d]])
+    >>> 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.],
@@ -8250,10 +8251,10 @@ def stacklists(arg):
 
     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, = [tensor.matrix(a) for a in 'abcd']
-    >>> X = stacklists([[a, b],
-    ...                 [c, d]])
+    >>> a,b,c,d, = matrices('abcd')
+    >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> x = ones((4, 4), 'float32')
     >>> f(x, x, x, x).shape