Metadata-Version: 2.1
Name: poisson-approval
Version: 0.3.0
Summary: Poisson Approval studies the Poisson Game of Approval Voting.
Home-page: https://github.com/francois-durand/poisson_approval
Author: François Durand
Author-email: fradurand@gmail.com
License: GNU General Public License v3
Keywords: poisson_approval
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib

================
Poisson Approval
================


.. image:: https://img.shields.io/pypi/v/poisson_approval.svg
        :target: https://pypi.python.org/pypi/poisson_approval

.. image:: https://img.shields.io/travis/francois-durand/poisson_approval.svg
        :target: https://travis-ci.org/francois-durand/poisson_approval

.. image:: https://readthedocs.org/projects/poisson-approval/badge/?version=latest
        :target: https://poisson-approval.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status




Poisson Approval studies the Poisson Game of Approval Voting.


* Free software: GNU General Public License v3
* Documentation: https://poisson-approval.readthedocs.io.

--------
Features
--------

* Implement only the case of 3 candidates.
* Deal with ordinal or cardinal profiles.
* Compute the asymptotic developments of the probability of pivot events when the number of players tends to infinity.
* Compute the best response to a given tau-vector.
* Explore automatically a grid of ordinal profiles or a grid of tau-vectors.
* Perform Monte-Carlo experiments on profiles or tau-vectors.

-------
Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

------------------
0.3.0 (2020-01-08)
------------------

* Add new random generators:

  * ``GeneratorExamples``: run another generator until the generated object meets a given test.
  * ``GeneratorStrategyOrdinalUniform``: draw a StrategyOrdinal uniformly.
  * ``GeneratorProfileOrdinalGridUniform``: draw a ProfileOrdinal uniformly on a grid of rational numbers.
  * ``GeneratorTauVectorGridUniform``: draw a TauVector uniformly on a grid of rational numbers.

* Utilities:

  * Add ``rand_integers_fixed_sum``: draw an array of integers with a given sum.
  * Add ``rand_simplex_grid``: draw a random point in the simplex, with rational coordinates of a given denominator.
  * Update ``probability``: allow for a tuple of generators.

* Tutorials:

  * Add a tutorial on asymptotic developments.
  * Update the tutorial on mass simulations with the new features.

------------------
0.2.1 (2020-01-05)
------------------

* Relaunch deployment.

------------------
0.2.0 (2020-01-05)
------------------

* Add ``GeneratorProfileStrategyThreshold``.
* Add ``ProfileHistogram.plot_cdf``.
* Modify ``masks_distribution``: remove the trailing zeros. This has the same impact on
  ``ProfileOrdinal.distribution_equilibria``.
* Modify ``NiceStatsProfileOrdinal.plot_cutoff``: center the textual indications.
* Replace all notations ``r`` with ``profile`` and ``sigma`` with ``strategy``.
* Add tutorials.

------------------
0.1.1 (2019-12-24)
------------------

* Convert all the documentation to NumPy format, making it more readable in plain text.

------------------
0.1.0 (2019-12-20)
------------------

* First release on PyPI.
* Implement only the case of 3 candidates.
* Deal with ordinal or cardinal profiles.
* Compute the asymptotic developments of the probability of pivot events when the number of players tends to infinity.
* Compute the best response to a given tau-vector.
* Explore automatically a grid of ordinal profiles or a grid of tau-vectors.
* Perform Monte-Carlo experiments on profiles or tau-vectors.


