Spurious whitespace.

doc/library/compile/function.txt

@@ -46,7 +46,7 @@ Reference
 
     .. method:: __init__(variable, borrow=False)
 
-        Initialize attributes from arguments. 
+        Initialize attributes from arguments.
 
 .. class:: Param
 
@@ -57,21 +57,21 @@ Reference
         A variable in an expression graph to use as a compiled-function parameter
 
     .. attribute:: default
-    
+
         The default value to use at call-time (can also be a Container where
         the function will find a value at call-time.)
 
-    .. attribute:: name 
-    
+    .. attribute:: name
+
         A string to identify an argument for this parameter in keyword arguments.
 
     .. attribute:: mutable
-    
+
         ``True`` means the compiled-function is allowed to modify this
         argument. ``False`` means it is not allowed.
 
     .. attribute:: strict
-    
+
       If ``False``, a function argument may be copied or cast to match the type
       required by the parameter `variable`.  If ``True``, a function argument
       must exactly match the type required by `variable`.

doc/library/tensor/basic.txt

@@ -11,7 +11,7 @@ symbolic matrix expressions.  When you type,
 >>> x = T.fmatrix()
 
 the ``x`` is a :class:`TensorVariable` instance.
-The ``T.fmatrix`` object itself is an instance of :class:`TensorType`. 
+The ``T.fmatrix`` object itself is an instance of :class:`TensorType`.
 Theano knows what type of variable ``x`` is because ``x.type``
 points back to ``T.fmatrix``.
 
@@ -98,71 +98,71 @@ They are all callable, and accept an optional ``name`` argument.  So for example
    xyz = dmatrix('xyz') # creates one Variable with name 'xyz'
 
 .. #COMMENT
-    table generated by 
+    table generated by
     $ python Theano/doc/generate_dtype_tensor_table.py
 
 ============ =========== ==== =========== =================================
 Constructor  dtype       ndim shape       broadcastable
 ============ =========== ==== =========== =================================
-bscalar      int8        0    ()          ()                  
-bvector      int8        1    (?,)        (False,)            
-brow         int8        2    (1,?)       (True, False)       
-bcol         int8        2    (?,1)       (False, True)       
-bmatrix      int8        2    (?,?)       (False, False)      
+bscalar      int8        0    ()          ()
+bvector      int8        1    (?,)        (False,)
+brow         int8        2    (1,?)       (True, False)
+bcol         int8        2    (?,1)       (False, True)
+bmatrix      int8        2    (?,?)       (False, False)
 btensor3     int8        3    (?,?,?)     (False, False, False)
 btensor4     int8        4    (?,?,?,?)   (False, False, False, False)
-wscalar      int16       0    ()          ()                  
-wvector      int16       1    (?,)        (False,)            
-wrow         int16       2    (1,?)       (True, False)       
-wcol         int16       2    (?,1)       (False, True)       
-wmatrix      int16       2    (?,?)       (False, False)      
+wscalar      int16       0    ()          ()
+wvector      int16       1    (?,)        (False,)
+wrow         int16       2    (1,?)       (True, False)
+wcol         int16       2    (?,1)       (False, True)
+wmatrix      int16       2    (?,?)       (False, False)
 wtensor3     int16       3    (?,?,?)     (False, False, False)
 wtensor4     int16       4    (?,?,?,?)   (False, False, False, False)
-iscalar      int32       0    ()          ()                  
-ivector      int32       1    (?,)        (False,)            
-irow         int32       2    (1,?)       (True, False)       
-icol         int32       2    (?,1)       (False, True)       
-imatrix      int32       2    (?,?)       (False, False)      
+iscalar      int32       0    ()          ()
+ivector      int32       1    (?,)        (False,)
+irow         int32       2    (1,?)       (True, False)
+icol         int32       2    (?,1)       (False, True)
+imatrix      int32       2    (?,?)       (False, False)
 itensor3     int32       3    (?,?,?)     (False, False, False)
 itensor4     int32       4    (?,?,?,?)   (False, False, False, False)
-lscalar      int64       0    ()          ()                  
-lvector      int64       1    (?,)        (False,)            
-lrow         int64       2    (1,?)       (True, False)       
-lcol         int64       2    (?,1)       (False, True)       
-lmatrix      int64       2    (?,?)       (False, False)      
+lscalar      int64       0    ()          ()
+lvector      int64       1    (?,)        (False,)
+lrow         int64       2    (1,?)       (True, False)
+lcol         int64       2    (?,1)       (False, True)
+lmatrix      int64       2    (?,?)       (False, False)
 ltensor3     int64       3    (?,?,?)     (False, False, False)
 ltensor4     int64       4    (?,?,?,?)   (False, False, False, False)
-dscalar      float64     0    ()          ()                  
-dvector      float64     1    (?,)        (False,)            
-drow         float64     2    (1,?)       (True, False)       
-dcol         float64     2    (?,1)       (False, True)       
-dmatrix      float64     2    (?,?)       (False, False)      
+dscalar      float64     0    ()          ()
+dvector      float64     1    (?,)        (False,)
+drow         float64     2    (1,?)       (True, False)
+dcol         float64     2    (?,1)       (False, True)
+dmatrix      float64     2    (?,?)       (False, False)
 dtensor3     float64     3    (?,?,?)     (False, False, False)
 dtensor4     float64     4    (?,?,?,?)   (False, False, False, False)
-fscalar      float32     0    ()          ()                  
-fvector      float32     1    (?,)        (False,)            
-frow         float32     2    (1,?)       (True, False)       
-fcol         float32     2    (?,1)       (False, True)       
-fmatrix      float32     2    (?,?)       (False, False)      
+fscalar      float32     0    ()          ()
+fvector      float32     1    (?,)        (False,)
+frow         float32     2    (1,?)       (True, False)
+fcol         float32     2    (?,1)       (False, True)
+fmatrix      float32     2    (?,?)       (False, False)
 ftensor3     float32     3    (?,?,?)     (False, False, False)
 ftensor4     float32     4    (?,?,?,?)   (False, False, False, False)
-cscalar      complex64   0    ()          ()                  
-cvector      complex64   1    (?,)        (False,)            
-crow         complex64   2    (1,?)       (True, False)       
-ccol         complex64   2    (?,1)       (False, True)       
-cmatrix      complex64   2    (?,?)       (False, False)      
+cscalar      complex64   0    ()          ()
+cvector      complex64   1    (?,)        (False,)
+crow         complex64   2    (1,?)       (True, False)
+ccol         complex64   2    (?,1)       (False, True)
+cmatrix      complex64   2    (?,?)       (False, False)
 ctensor3     complex64   3    (?,?,?)     (False, False, False)
 ctensor4     complex64   4    (?,?,?,?)   (False, False, False, False)
-zscalar      complex128  0    ()          ()                  
-zvector      complex128  1    (?,)        (False,)            
-zrow         complex128  2    (1,?)       (True, False)       
-zcol         complex128  2    (?,1)       (False, True)       
-zmatrix      complex128  2    (?,?)       (False, False)      
+zscalar      complex128  0    ()          ()
+zvector      complex128  1    (?,)        (False,)
+zrow         complex128  2    (1,?)       (True, False)
+zcol         complex128  2    (?,1)       (False, True)
+zmatrix      complex128  2    (?,?)       (False, False)
 ztensor3     complex128  3    (?,?,?)     (False, False, False)
 ztensor4     complex128  4    (?,?,?,?)   (False, False, False, False)
 ============ =========== ==== =========== =================================
 
-Plural Constructors 
+Plural Constructors
 --------------------------
 
 There are several constructors that can produce multiple variables at once.
@@ -188,7 +188,7 @@ These are not frequently used in practice, but often used in tutorial examples t
 
     Return one or more matrix variables.
 
-Each of these plural constructors accepts 
+Each of these plural constructors accepts
 an integer or several strings. If an integer is provided, the method
 will return that many Variables and if strings are provided, it will
 create one Variable for each string, using the string as the Variable's
@@ -251,7 +251,7 @@ Finally, when you use a numpy ndarry or a Python number together with
 Theano requires that the inputs to all expressions be Variable instances, so
 Theano automatically wraps them in a :class:`TensorConstant`.
 
-.. note:: 
+.. note::
 
     Theano makes a copy of any ndarray that you use in an expression, so
     subsequent
@@ -288,7 +288,7 @@ them perfectly, but a dscalar otherwise.
     When `x` is a `list` or `tuple` it is passed through numpy.asarray
 
     If the `ndim` argument is not None, it must be an integer and the output
-    will be broadcasted if necessary in order to have this many dimensions.  
+    will be broadcasted if necessary in order to have this many dimensions.
 
     :rtype: :class:`TensorVariable` or :class:`TensorConstant`
 
@@ -304,7 +304,7 @@ TensorType and TensorVariable
     :ref:`the tutorial's graph-structure figure <tutorial-graphfigure>` is an instance of this class.
 
     .. attribute:: broadcastable
-        
+
         A tuple of True/False values, one for each dimension.  True in
         position 'i' indicates that at evaluation-time, the ndarray will have
         size 1 in that 'i'-th dimension.  Such a dimension is called a
@@ -349,7 +349,7 @@ TensorType and TensorVariable
         adding them together, we would define it like this:
 
         >>> middle_broadcaster = TensorType('complex64', [False, True, False])
-        
+
     .. attribute:: ndim
 
         The number of dimensions that a Variable's value will have at
@@ -362,7 +362,7 @@ TensorType and TensorVariable
         the numerical type of the ndarray for which a Variable of this Type
         is standing.
 
-        .. _dtype_list: 
+        .. _dtype_list:
 
         The dtype attribute of a TensorType instance can be any of the
         following strings.
@@ -386,7 +386,7 @@ TensorType and TensorVariable
 
     .. method:: __init__(self, dtype, broadcastable)
 
-        If you wish to use a type of tensor which is not already available 
+        If you wish to use a type of tensor which is not already available
         (for example, a 5D tensor) you can build an appropriate type by instantiating
         :class:`TensorType`.
 
@@ -433,7 +433,7 @@ TensorVariable
 
     .. attribute:: dtype
 
-        The numeric type of this tensor. Aliased to 
+        The numeric type of this tensor. Aliased to
         :attr:`TensorType.dtype`.
 
     .. method:: reshape(shape, ndim=None)
@@ -476,10 +476,10 @@ TensorVariable
         Transpose of this tensor.
 
         >>> x = T.zmatrix()
-        >>> y = 3+.2j * x.T 
+        >>> y = 3+.2j * x.T
 
         .. note::
-            
+
             In numpy and in Theano, the transpose of a vector is exactly the
             same vector!  Use `reshape` or `dimshuffle` to turn your vector
             into a row or column matrix.
@@ -510,20 +510,20 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
     :rtype: variable with x's dtype, but ndim dimensions
 
-    .. note:: 
+    .. note::
 
         This function can infer the length of a symbolic newshape in some
         cases, but if it cannot and you do not provide the `ndim`, then this
         function will raise an Exception.
-    
+
 
 .. function:: shape_padleft(x, n_ones=1)
 
-    Reshape `x` by left padding the shape with `n_ones` 1s. Note that all 
-    this new dimension will be broadcastable. To make them non-broadcastable 
+    Reshape `x` by left padding the shape with `n_ones` 1s. Note that all
+    this new dimension will be broadcastable. To make them non-broadcastable
     see the :func:`unbroadcast`.
 
-    :param x: variable to be reshaped 
+    :param x: variable to be reshaped
     :type x: any TensorVariable (or compatible)
 
     :type n_ones: int
@@ -533,11 +533,11 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
 .. function:: shape_padright(x,n_ones = 1)
 
-    Reshape `x` by right padding the shape with `n_ones` 1s. Note that all 
-    this new dimension will be broadcastable. To make them non-broadcastable 
+    Reshape `x` by right padding the shape with `n_ones` 1s. Note that all
+    this new dimension will be broadcastable. To make them non-broadcastable
     see the :func:`unbroadcast`.
 
-    :param x: variable to be reshaped 
+    :param x: variable to be reshaped
     :type x: any TensorVariable (or compatible)
 
     :type n_ones: int
@@ -557,7 +557,7 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
     along that dimension is not 1, a ``ValueError`` will be raised.
 
 .. function:: flatten(x, outdim=1)
-    
+
     Similar to :func:`reshape`, but the shape is inferred from the shape of `x`.
 
     :param x: variable to be flattened
@@ -591,7 +591,7 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
 .. function:: fill(a,b)
 
-    :param a: tensor that has same shape as output 
+    :param a: tensor that has same shape as output
     :param b: theano scalar or value with which you want to fill the output
 
     Create a matrix by filling the shape of `a` with `b`
@@ -600,9 +600,9 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
     :param n: number of rows in output (value or theano scalar)
     :param m: number of columns in output (value or theano scalar)
-    :param k: Index of the diagonal: 0 refers to the main diagonal, 
-              a positive value refers to an upper diagonal, and a 
-              negative value to a lower diagonal. It can be a theano 
+    :param k: Index of the diagonal: 0 refers to the main diagonal,
+              a positive value refers to an upper diagonal, and a
+              negative value to a lower diagonal. It can be a theano
               scalar.
     :returns: An array where all elements are equal to zero, except for the `k`-th
               diagonal, whose values are equal to one.
@@ -644,7 +644,7 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
     >>> # x.ndim == 2
 
 
-Reductions 
+Reductions
 ==========
 
 
@@ -654,7 +654,7 @@ Reductions
     :Parameter: *axis* - axis along which to compute the maximum
     :Returns: the maximum value along a given axis
     :note: see maximum for elemwise max
-    
+
     if axis=None, Theano 0.5rc1 or later: max over the flattened tensor (like numpy)
                   older: then axis is assumed to be ndim(x)-1
 
@@ -834,7 +834,7 @@ Casting
 
     Cast any tensor `x` to a Tensor of the same shape, but with a different
     numerical type `dtype`.
-    
+
     This is not a reinterpret cast, but a coersion cast, similar to
     ``numpy.asarray(x, dtype=dtype)``.
 
@@ -850,12 +850,12 @@ Casting
 
 .. function:: real(x)
 
-    Return the real (not imaginary) components of Tensor x. 
+    Return the real (not imaginary) components of Tensor x.
     For non-complex `x` this function returns x.
 
 .. function:: imag(x)
 
-    Return the imaginary components of Tensor x. 
+    Return the imaginary components of Tensor x.
     For non-complex `x` this function returns zeros_like(x).
 
 
@@ -875,7 +875,7 @@ The six usual equality and inequality operators share the same interface.
 
   Here is an example with the less-than operator.
 
-  .. code-block:: python 
+  .. code-block:: python
 
     import theano.tensor as T
     x,y = T.dmatrices('x','y')
@@ -927,7 +927,7 @@ Condition
       :Parameter:  *iff* - symbolic Tensor (or compatible)
       :Return type: symbolic Tensor
 
-    .. code-block:: python 
+    .. code-block:: python
 
       import theano.tensor as T
       a,b = T.dmatrices('a','b')
@@ -945,14 +945,14 @@ Bit-wise
 --------
 
 
-The bitwise operators possess this interface: 
+The bitwise operators possess this interface:
     :Parameter:  *a* - symbolic Tensor of integer type.
     :Parameter:  *b* - symbolic Tensor of integer type.
 
-    .. note:: 
+    .. note::
 
         The bitwise operators must have an integer type as input.
-    
+
         The bit-wise not (invert) takes only one parameter.
 
     :Return type: symbolic Tensor with corresponding dtype.
@@ -991,7 +991,7 @@ The bitwise operators possess this interface:
 
 Here is an example using the bit-wise ``and_`` via the ``&`` operator:
 
-.. code-block:: python 
+.. code-block:: python
 
     import theano.tensor as T
     x,y = T.imatrices('x','y')
@@ -1137,7 +1137,7 @@ Linear Algebra
     :param Y: right term
     :type X: symbolic vector
     :type Y: symbolic vector
-    :rtype: symbolic matrix 
+    :rtype: symbolic matrix
 
     :return: vector-vector outer product
 
@@ -1150,7 +1150,7 @@ Linear Algebra
     :param axes: sum out these axes from X and Y.
     :type X: symbolic tensor
     :type Y: symbolic tensor
-    :rtype: symbolic tensor 
+    :rtype: symbolic tensor
     :type axes: see numpy.tensordot
 
     :return: tensor product