
.. _introduction:

==================
Aesara at a Glance
==================

Aesara is a Python library that lets you define, optimize, and evaluate
mathematical expressions, especially ones with multi-dimensional arrays
(numpy.ndarray).  Using Aesara it is
possible to attain speeds rivaling hand-crafted C implementations for problems
involving large amounts of data.

Aesara combines aspects of a computer algebra system (CAS) with aspects of an
optimizing compiler. It can also generate customized C code for many
mathematical operations.  This combination of CAS with optimizing compilation
is particularly useful for tasks in which complicated mathematical expressions
are evaluated repeatedly and evaluation speed is critical.  For situations
where many different expressions are each evaluated once Aesara can minimize
the amount of compilation/analysis overhead, but still provide symbolic
features such as automatic differentiation.

Aesara's compiler applies many optimizations of varying complexity to
these symbolic expressions. These optimizations include, but are not
limited to:

* constant folding
* merging of similar subgraphs, to avoid redundant calculation
* arithmetic simplification (e.g. ``x*y/x -> y``, ``--x -> x``)
* inserting efficient BLAS_ operations (e.g. ``GEMM``) in a variety of
  contexts
* using memory aliasing to avoid calculation
* using inplace operations wherever it does not interfere with aliasing
* loop fusion for elementwise sub-expressions
* improvements to numerical stability (e.g.  :math:`\log(1+\exp(x))` and :math:`\log(\sum_i \exp(x[i]))`)
* for a complete list, see :ref:`optimizations`

The library that Aesara is based on, Theano, was written at the LISA_ lab to
support rapid development of efficient machine learning algorithms. Theano was
named after the `Greek mathematician`_, who may have been Pythagoras' wife.
Aesara is an alleged daughter of Pythagoras and Theano.

Aesara is released under a BSD license (:ref:`link <license>`).


Sneak peek
==========

Here is an example of how to use Aesara. It doesn't show off many of
Aesara's features, but it illustrates concretely what Aesara is.


.. If you modify this code, also change :
.. tests/test_tutorial.py:T_introduction.test_introduction_1

.. code-block:: python

    import aesara
    from aesara import tensor as aet

    # declare two symbolic floating-point scalars
    a = aet.dscalar()
    b = aet.dscalar()

    # create a simple expression
    c = a + b

    # convert the expression into a callable object that takes `(a, b)`
    # values as input and computes a value for `c`
    f = aesara.function([a, b], c)

    # bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c'
    assert 4.0 == f(1.5, 2.5)


Aesara is not a programming language in the normal sense because you
write a program in Python that builds expressions for Aesara. Still it
is like a programming language in the sense that you have to

- declare variables (``a, b``) and give their types

- build expressions for how to put those variables together

- compile expression graphs to functions in order to use them for computation.

It is good to think of ``aesara.function`` as the interface to a
compiler which builds a callable object from a purely symbolic graph.
One of Aesara's most important features is that ``aesara.function``
can optimize a graph and even compile some or all of it into native
machine instructions.


What does it do that they don't?
================================

Aesara is a Python library and optimizing compiler for manipulating
and evaluating expressions, especially matrix-valued
ones. Manipulation of matrices is typically done using the numpy
package, so what does Aesara do that Python and numpy do not?

- *execution speed optimizations*: Aesara can use `g++` or `nvcc` to compile
  parts your expression graph into CPU or GPU instructions, which run
  much faster than pure Python.

- *symbolic differentiation*: Aesara can automatically build symbolic graphs
  for computing gradients.

- *stability optimizations*: Aesara can recognize [some] numerically unstable
  expressions and compute them with more stable algorithms.

The closest Python package to Aesara is sympy_.
Aesara focuses more on tensor expressions than Sympy, and has more machinery
for compilation.  Sympy has more sophisticated algebra rules and can
handle a wider variety of mathematical operations (such as series, limits, and integrals).

If numpy_ is to be compared to MATLAB_ and sympy_ to Mathematica_,
Aesara is a sort of hybrid of the two which tries to combine the best of
both worlds.


Getting started
===============

:ref:`install`
  Instructions to download and install Aesara on your system.

:ref:`tutorial`
  Getting started with Aesara's basic features. Go here if you are
  new!

:ref:`libdoc`
  Details of what Aesara provides. It is recommended to go through
  the :ref:`tutorial` first though.


Contact us
==========

Questions and bug reports should be submitted in the form of an issue at
pymc-aesara-dev_

We welcome all kinds of contributions. If you have any questions regarding how
to extend Aesara, please feel free to ask.


.. _LISA:  https://mila.umontreal.ca/
.. _Greek mathematician: http://en.wikipedia.org/wiki/Theano_(mathematician)
.. _numpy: http://numpy.scipy.org/
.. _BLAS: http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms

.. _sympy: http://www.sympy.org/
.. _MATLAB: http://www.mathworks.com/products/matlab/
.. _Mathematica: http://www.wolfram.com/mathematica/

.. _pymc-aesara-dev: https://github.com/pymc-devs/aesara/issues
