.. _glossary:

Glossary of terminology
=======================

.. glossary::

    Apply
        Instances of :class:`Apply` represent the application of an :term:`Op`
        to some input :term:`Variable` (or variables) to produce some output
        :term:`Variable` (or variables).  They are like the application of a [symbolic]
        mathematical function to some [symbolic] inputs.

    Broadcasting
        Broadcasting is a mechanism which allows tensors with
        different numbers of dimensions to be used in element-by-element
        (elementwise) computations.  It works by
        (virtually) replicating the smaller tensor along
        the dimensions that it is lacking.  
        
        For more detail, see :ref:`libdoc_tensor_broadcastable`, and also
        * `SciPy documentation about numpy's broadcasting <http://www.scipy.org/EricsBroadcastingDoc>`_
        * `OnLamp article about numpy's broadcasting <http://www.onlamp.com/pub/a/python/2000/09/27/numerically.html>`_

    Constant
        A variable with an immutable value.
        For example, when you type
        >>> x = tensor.ivector()
        >>> y = x + 3
        Then a `constant` is created to represent the ``3`` in the graph.

        See also: :class:`gof.Constant`


    Elementwise
        An elementwise operation ``f`` on two tensor variables ``M`` and ``N``
        is one such that:

        ``f(M, N)[i, j] == f(M[i, j], N[i, j])``

        In other words, each element of an input matrix is combined
        with the corresponding element of the other(s). There are no
        dependencies between elements whose ``[i, j]`` coordinates do
        not correspond, so an elementwise operation is like a scalar
        operation generalized along several dimensions.  Elementwise
        operations are defined for tensors of different numbers of dimensions by
        :term:`broadcasting` the smaller ones.

    Expression
        See :term:`Apply`

    Expression Graph
        A directed, acyclic set of connected :term:`Variable` and
        :term:`Apply` nodes that express symbolic functional relationship
        between variables.  You use Theano by defining expression graphs, and
        then compiling them with :term:`theano.function`.

        See also :term:`Variable`, :term:`Op`, :term:`Apply`, and
        :term:`Type`, or read more about :ref:`tutorial_graphstructures`.

    Destructive
        An :term:`Op` is destructive (of particular input[s]) if its
        computation requires that one or more inputs be overwritten or
        otherwise invalidated.  For example, :term:`inplace` Ops are
        destructive.  Destructive Ops can sometimes be faster than
        non-destructive alternatives.  Theano encourages users not to put
        destructive Ops into graphs that are given to :term:`theano.function`,
        but instead to trust the optimizations to insert destructive ops
        judiciously.

        Destructive Ops are indicated via a ``destroy_map`` Op attribute. (See
        :class:`gof.Op`.


    Graph 
        see :term:`expression graph`

    Inplace
        Inplace computations are computations that destroy their inputs as a
        side-effect.  For example, if you iterate over a matrix and double
        every element, this is an inplace operation because when you are done,
        the original input has been overwritten.  Ops representing inplace
        computations are :term:`destructive`, and by default these can only be
        inserted by optimizations, not user code.

    Linker
        Part of a function :term:`Mode` -- an object responsible for 'running'
        the compiled function.  Among other things, the linker determines whether computations are carried out with C or Python code.
        
    Mode 
        An object providing an :term:`optimizer` and a :term:`linker` that is
        passed to :term:`theano.function`.  It parametrizes how an expression
        graph is converted to a callable object.

    Op
        The ``.op`` of an :term:`Apply`, together with its symbolic inputs
        fully determines what kind of computation will be carried out for that
        ``Apply`` at run-time.  Mathematical functions such as addition
        (``T.add``) and indexing  ``x[i]`` are Ops in Theano.  Much of the
        library documentation is devoted to describing the various Ops that
        are provided with Theano, but you can add more.

        See also :term:`Variable`, :term:`Type`, and :term:`Apply`, 
        or read more about :ref:`tutorial_graphstructures`.

    Optimizer
        An instance of :class:`Optimizer`, which has the capacity to provide
        an :term:`optimization` (or optimizations).

    Optimization
        A :term:`graph` transformation applied by an :term:`optimizer` during
        the compilation of a :term:`graph` by :term:`theano.function`.

    Pure
        An :term:`Op` is *pure* if it has no :term:`destructive` side-effects.

    Storage
        The memory that is used to store the value of a Variable.  In most
        cases storage is internal to a compiled function, but in some cases
        (such as :term:`constant` and :term:`shared variable <shared variable>` the storage is not internal.

    Shared Variable
        A :term:`Variable` whose value may be shared between multiple functions.  See :func:`shared <shared.shared>` and :func:`theano.function <function.function>`.

    theano.function
        The interface for Theano's compilation from symbolic expression graphs
        to callable objects.  See :func:`function.function`.

    Type
        The ``.type`` of a
        :term:`Variable` indicates what kinds of values might be computed for it in a
        compiled graph.
        An instance that inherits from :class:`Type`, and is used as the
        ``.type`` attribute of a :term:`Variable`.  

        See also :term:`Variable`, :term:`Op`, and :term:`Apply`, 
        or read more about :ref:`tutorial_graphstructures`.

    Variable
        The the main data structure you work with when using Theano.
        For example,

        >>> x = theano.tensor.ivector()
        >>> y = -x**2

        ``x`` and ``y`` are both `Variables`, i.e. instances of the :class:`Variable` class.

        See also :term:`Type`, :term:`Op`, and :term:`Apply`, 
        or read more about :ref:`tutorial_graphstructures`.

    View
        Some Tensor Ops (such as Subtensor and Transpose) can be computed in
        constant time by simply re-indexing their inputs.   The outputs from
        [the Apply instances from] such Ops are called `Views` because their
        storage might be aliased to the storage of other variables (the inputs
        of the Apply).  It is important for Theano to know which Variables are
        views of which other ones in order to introduce :term:`Destructive`
        Ops correctly.

        View Ops are indicated via a ``view_map`` Op attribute. (See
        :class:`gof.Op`.

