Initial docs (#39)

* Write documentation structure and initial docs.

Have also included a doctest runner. Once we're happy with this I'll
flick the switch on read the docs.

* Return to the original scaling for GA.

Also include brief background.

.travis.yml

@@ -15,5 +15,6 @@ script:
   - python setup.py develop
   - coverage run --source=src -m unittest discover tests
   - coverage report -m
+  - python doctests.py
 after_success:
   - coveralls

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = python -msphinx
+SPHINXPROJ    = Axelrod-dojo
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/background/genetic_algorithm.rst

@@ -0,0 +1,59 @@
+Genetic Algorithm
+=================
+
+A genetic algorithm aims to mimic evolutionary processes so as to optimise a
+particular function on some space of candidate solutions.
+
+The process can be described by assuming that there is a function
+:math:`f:V\to \mathbb{R}`, where :math:`V` is some vector space. 
+In the case of the Prisoner's dilemma,
+the vector space :math:`V` corresponds to some representation of a
+particular archetype (which might not actually be a numeric vector space) and
+the function :math:`f` corresponds to some measure of performance/fitness of the
+strategy in question.
+
+In this setting a candidate solution :math:`x\in\mathbb{R}^m` corresponds to a
+chromosome with each :math:`x_i` corresponding to a gene.
+
+The genetic algorithm has three essential parameters:
+
+- The population size: the algorithm makes use of a number of candidate
+  solutions at each stage.
+- The bottle neck parameter: at every stage the candidates in the population are
+  ranked according to their fitness, only a certain number are kept (the best
+  performing ones) from one generation to the next. This number is referred to
+  as the bottle neck.
+- The mutation probability: from one stage to the next when new individuals are
+  added to the population (more about this process shortly) there is a
+  probability with which each gene randomly mutates.
+
+New individuals are added to the population (so as to ensure that the population
+size stays constant from one stage to the next) using a process of "crossover".
+Two high performing individuals are paired and according to some predefined
+procedure, genes from both these individuals are combined to create a new
+individual.
+
+For each strategy archetype, this library thus defines a process for mutation as
+well as for crossover.
+
+Finite state machines
+---------------------
+
+A finite state machine is made up of the following:
+
+- a mapping from a state/action pair to another target state/action pair
+- an initial state/action pair.
+
+(See [Harper2017]_ for more details.)
+
+The crossover and mutation are implemented in the following way:
+
+- Crossover: this is done by taking a randomly selected number of target
+  state/actions
+  pairs from one individual and the rest from the other.
+- Mutation: given a mutation probability :math:`delta` each target state/action
+  has a probability :math:`\delta` of being randomly changed to one of the other
+  states or actions. Furthermore the **initial** action has a probability of
+  being swapped of :math:`\delta\times 10^{-1}` and the **initial** state has a
+  probability of being changed to another random state of :math:`\delta \times
+  10^{-1} \times N` (where :math:`N` is the number of states).

docs/background/index.rst

@@ -0,0 +1,20 @@
+Background
+==========
+
+Note that there are currently two algorithms implemented:
+
+- Genetic algorithm
+- Particle swam optimisation
+
+Note that these two algorithms are not equally suited to each archetype. For
+example the Genetic algorithm is believed to be better suited to discrete space
+strategies such as the finite state machines whilst the Particle swarm algorithm
+would be better suited to a continuous space strategy such as the Gambler.
+
+For more information on these algorithms and their implementations see:
+
+
+.. toctree::
+   :maxdepth: 2
+
+   genetic_algorithm.rst

docs/conf.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# Axelrod-dojo documentation build configuration file, created by
+# sphinx-quickstart on Wed Oct 11 09:40:19 2017.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ['sphinx.ext.mathjax']
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = 'Axelrod-dojo'
+copyright = '2017, The Axelrod project developers'
+author = 'The Axelrod project developers'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = ''
+# The full version, including alpha/beta/rc tags.
+release = ''
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This patterns also effect to html_static_path and html_extra_path
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'alabaster'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# This is required for the alabaster theme
+# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
+html_sidebars = {
+    '**': [
+        'about.html',
+        'navigation.html',
+        'relations.html',  # needs 'show_related': True theme option to display
+        'searchbox.html',
+        'donate.html',
+    ]
+}
+
+
+# -- Options for HTMLHelp output ------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'Axelrod-dojodoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (master_doc, 'Axelrod-dojo.tex', 'Axelrod-dojo Documentation',
+     'The Axelrod project developers', 'manual'),
+]
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'axelrod-dojo', 'Axelrod-dojo Documentation',
+     [author], 1)
+]
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (master_doc, 'Axelrod-dojo', 'Axelrod-dojo Documentation',
+     author, 'Axelrod-dojo', 'One line description of project.',
+     'Miscellaneous'),
+]
+
+
+

docs/howtos/index.rst

@@ -0,0 +1,9 @@
+How to
+======
+
+.. toctree::
+   :maxdepth: 1
+
+   use-different-ojective-functions.rst
+   train-using-genetic-algorithm.rst
+   train-using-particle-swarm-algorithm.rst

docs/howtos/train-using-genetic-algorithm.rst

@@ -0,0 +1,4 @@
+Train using the genetic algorithm
+=================================
+
+WIP: include all details for training with genetic algorithm.

docs/howtos/train-using-particle-swarm-algorithm.rst

@@ -0,0 +1,4 @@
+Train using the particle swarm algorithm
+========================================
+
+WIP: include all details for training with PSO

docs/howtos/use-different-ojective-functions.rst

@@ -0,0 +1,16 @@
+Use different objective functions
+=================================
+
+It is currently possible to optimise players for 3 different objectives:
+
+- Score;
+- Score difference;
+- Probability of fixation in a Moran process.
+
+This is done by passing a different objective :code:`name` to the
+:code:`prepare_objective` function::
+
+    >>> import axelrod_dojo as dojo
+    >>> score_objective = dojo.prepare_objective(name="score", turns=10, repetitions=1)
+    >>> diff_objective = dojo.prepare_objective(name="score_diff", turns=10, repetitions=1)
+    >>> moran_objective = dojo.prepare_objective(name="moran", turns=10, repetitions=1)

docs/index.rst

@@ -0,0 +1,36 @@
+.. Axelrod-dojo documentation master file, created by
+   sphinx-quickstart on Wed Oct 11 09:40:19 2017.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to Axelrod-dojo's documentation!
+========================================
+
+This library is a companion library to the `Axelrod
+<http://axelrod.readthedocs.io/en/stable/>`_ library: a research tool for the
+study of the iterated prisoners dilemma. The **Axelrod-dojo** is used to train
+strategies.
+
+This is done using implementations of:
+
+- Strategy archetypes :code:`Parameters`
+- Algorithms
+
+Table of Contents
+-----------------
+
+.. toctree::
+   :maxdepth: 3
+
+   tutorial/index.rst
+   howtos/index.rst
+   background/index.rst
+   reference/index.rst
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/reference/bibliography.rst

@@ -0,0 +1,9 @@
+.. _bibliography:
+
+Bibliography
+============
+
+This is a collection of various bibliographic items referenced in the
+documentation.
+
+.. [Harper2017] Marc Harper, Vincent Knight, Martin Jones, Georgios Koutsovoulo, Nikoleta E. Glynatsi and Owen Campbell (2017) Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma. Arxiv. http://arxiv.org/abs/1707.06307

docs/reference/index.rst

@@ -0,0 +1,9 @@
+Reference
+=========
+
+This section is the reference guide for the various components of the library.
+
+.. toctree::
+   :maxdepth: 1
+
+   bibliography.rst