Axelrod 1.7.0

Reproduce the Axelrod iterated prisoners dilemma tournament

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Axelrod
=======

A library with the following principles and goals:

1. Enabling the reproduction of previous Iterated Prisoner's Dilemma research
as easily as possible.
2. Creating the de-facto tool for future Iterated Prisoner's Dilemma
research.
3. Providing as simple a means as possible for anyone to define and contribute
new and original Iterated Prisoner's Dilemma strategies.
4. Emphasizing readability along with an open and welcoming community that
is accommodating for developers and researchers of a variety of skill levels

Currently the library contains well over 100 strategies and can perform a
variety of tournament types (RoundRobin, Noisy, Spatially-distributed, and
probabilistically ending) and population dynamics while taking advantage
of multi-core processors.


**Please contribute via pull request (or just get in touch with us).**

For an overview of how to use and contribute to this repository, see the
documentation: http://axelrod.readthedocs.org/

If you do use this library for your personal research we would love to hear
about it: please do add a link at the bottom of this README file (PR's welcome
or again, just let us know) :) If there is something that is missing in this
library and that you would like implemented so as to be able to carry out a
project please open an issue and let us know!

Installation
============

The simplest way to install is::

$ pip install axelrod

Otherwise::

$ git clone https://github.com/Axelrod-Python/Axelrod.git
$ cd Axelrod
$ python setup.py install

Note that on Ubuntu `some
users <https://github.com/Axelrod-Python/Axelrod/issues/309>`_ have had problems
installing matplotlib. This seems to help with that::

sudo apt-get install libfreetype6-dev
sudo apt-get install libpng12-0-dev

Usage
-----

The full documentation can be found here:
`axelrod.readthedocs.org/ <http://axelrod.readthedocs.org/>`__.

The documentation includes details of how to setup a tournament but here is an
example showing how to create a tournament with all stochastic strategies::

import axelrod
strategies = [s() for s in axelrod.ordinary_strategies if s().classifier['stochastic']]
tournament = axelrod.Tournament(strategies)
results = tournament.play()

The :code:`results` object now contains all the results we could need::

print(results.ranked_names)

gives::

['Meta Hunter', 'Inverse', 'Forgetful Fool Me Once', 'GTFT: 0.33', 'Champion', 'ZD-GTFT-2', 'Eatherley', 'Math Constant Hunter', 'Random Hunter', 'Soft Joss: 0.9', 'Meta Majority', 'Nice Average Copier', 'Feld', 'Meta Minority', 'Grofman', 'Stochastic WSLS', 'ZD-Extort-2', 'Tullock', 'Joss: 0.9', 'Arrogant QLearner', 'Average Copier', 'Cautious QLearner', 'Hesitant QLearner', 'Risky QLearner', 'Random: 0.5', 'Meta Winner']

There is also a `notebooks repository
<https://github.com/Axelrod-Python/Axelrod-notebooks>`_ which shows further
examples of using the library.

Results
=======

A tournament with the full set of strategies from the library can be found at
https://github.com/Axelrod-Python/tournament. These results can be easily viewed
at http://axelrod-tournament.readthedocs.org.


Contributing
============

All contributions are welcome!

You can find helpful instructions about contributing in the
documentation:
http://axelrod.readthedocs.org/en/latest/tutorials/contributing/index.html

.. image:: https://graphs.waffle.io/Axelrod-Python/Axelrod/throughput.svg
:target: https://waffle.io/Axelrod-Python/Axelrod/metrics
:alt: 'Throughput Graph'

Example notebooks
=================

https://github.com/Axelrod-Python/Axelrod-notebooks contains a set of example
Jupyter notebooks.

Projects that use this library
==============================

If you happen to use this library for anything from a blog post to a research
paper please list it here:

- `A 2015 pedagogic paper on active learning
<https://github.com/drvinceknight/Playing-games-a-case-study-in-active-learning>`_
by `drvinceknight <https://twitter.com/drvinceknight>`_ published in `MSOR
Connections <https://journals.gre.ac.uk/index.php/msor/about>`_: the library
is mentioned briefly as a way of demonstrating repeated games.
- `A repository with various example tournaments and visualizations of strategies
<https://github.com/marcharper/AxelrodExamples>`_
by `marcharper <https://github.com/marcharper>`_.
- `Axelrod-Python related blog articles
<http://www.thomascampbell.me.uk/category/axelrod.html>`_
by `Uglyfruitcake <https://github.com/uglyfruitcake>`_.
- `Evolving strategies for an Iterated Prisoner's Dilemma tournament
<http://mojones.net/evolving-strategies-for-an-iterated-prisoners-dilemma-tournament.html>`_
by `mojones <https://github.com/mojones>`_.
- `An Exploratory Data Analysis of the Iterated Prisoner's Dilemma, Part I
<http://marcharper.codes/2015-11-16/ipd.html>`_ and `Part II <http://marcharper.codes/2015-11-17/ipd2.html>`_
by `marcharper <https://github.com/marcharper>`_.
- `Survival of the fittest: Experimenting with a high performing strategy in
other environments
<http://vknight.org/unpeudemath/gametheory/2015/11/28/Experimenting-with-a-high-performing-evolved-strategy-in-other-environments.html>`_
by `drvinceknight <https://twitter.com/drvinceknight>`_
- `An open reproducible framework for the study of the iterated prisoner's
dilemma <https://arxiv.org/abs/1604.00896>_`: a pre print of a paper describing this
library (20 authors).

Contributors
============

The library has had many awesome contributions from many `great
contributors <https://github.com/Axelrod-Python/Axelrod/graphs/contributors>`_.
The Core developers of the project are:

- `drvinceknight`_
- `langner <https://github.com/langner>`_
- `marcharper <https://github.com/marcharper>`_
- `meatballs <https://github.com/meatballs>`_

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