[](https://coveralls.io/github/drvinceknight/Nashpy?branch=master)
[](https://travis-ci.org/drvinceknight/Nashpy)
[](https://ci.appveyor.com/project/drvinceknight/nashpy)
[](https://doi.org/10.5281/zenodo.596758)
[](https://gitter.im/Nashpy/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://doi.org/10.21105/joss.00904)
# Nash: a python library for the computation of equilibria of 2 player strategic games.
This library implements the following algorithms for Nash equilibria on 2 player
games:
- Support enumeration
- Best response polytope vertex enumeration
- Lemke Howson algorithm
**Nashpy** has a simple set of Python dependencies: it only requires `numpy`
and `scipy` so is straightforward to install on all operating systems.
## Installation
**By design Nashpy** is easy to install: the easiest way to install is from
pypi:
```bash
$ pip install nashpy
```
## Usage
Create bi matrix games by passing two 2 dimensional arrays/lists:
```python
>>> import nashpy as nash
>>> A = [[1, 2], [3, 0]]
>>> B = [[0, 2], [3, 1]]
>>> game = nash.Game(A, B)
>>> for eq in game.support_enumeration():
... print(eq)
(array([1., 0.]), array([0., 1.]))
(array([0., 1.]), array([1., 0.]))
(array([0.5, 0.5]), array([0.5, 0.5]))
>>> game[[0, 1], [1, 0]]
array([3, 3])
```
## Documentation
Full documentation is available here: http://nashpy.readthedocs.io/
## Other game theoretic software
- [Gambit](http://www.gambit-project.org/) is a library with a python api and
support for more algorithms and more than 2 player games.
- [Game theory explorer](http://gte.csc.liv.ac.uk/index/) a web interface to
gambit useful for teaching.
- [Axelrod](http://axelrod.readthedocs.io/en/stable/) a research library aimed
at the study of the Iterated Prisoners dilemma
## Development
To install a development version of this library:
```
$ python setup.py develop
```
To run the full test suite (install `pytest`: `pip install pytest`) and run:
```
$ pytest .
```
All contributions are welcome, although this is meant to be a simple library,
for more detailed game theoretic contribution please see
[gambit](https://github.com/gambitproject/gambit).
## Code of conduct
In the interest of fostering an open and welcoming environment, all
contributors, maintainers and users are expected to abide by the Python code of
conduct: https://www.python.org/psf/codeofconduct/
Python-Nash一个用于计算2人策略游戏的平衡的python库
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Python-Nash一个用于计算2人策略游戏的平衡的python库.zip （66个子文件） 
drvinceknight-Nashpy-cdb482e 
readthedocs.yml 33B paper.bib 2KB CITATION.md 637B CHANGES.md 1KB doctests.py 591B src nashpy integer_pivoting __init__.py 32B integer_pivoting.py 2KB algorithms support_enumeration.py 5KB __init__.py 0B lemke_howson.py 3KB vertex_enumeration.py 1KB learning __init__.py 0B fictitious_play.py 1KB __init__.py 53B game.py 5KB polytope polytope.py 2KB __init__.py 24B version.py 23B
requirements.txt 28B .travis.yml 1KB LICENSE 1KB environment.yml 356B paper.md 5KB setup.py 587B README.md 3KB docs reference source nashpy.learning.rst 262B nashpy.rst 373B nashpy.algorithms.rst 676B lemke-howson.rst 7KB bibliography.rst 2KB degenerate-games.rst 3KB index.rst 212B vertex-enumeration.rst 4KB fictitious-play.rst 3KB support-enumeration.rst 2KB how-to create-a-game.rst 1017B install-nashpy.rst 343B calculate-utilities.rst 839B use-fictitious-play.rst 2KB solve-with-vertex-enumeration.rst 588B index.rst 257B solve-with-support-enumeration.rst 593B solve-with-lemke-howson.rst 1KB discussion other-python-game-theory-libraries.rst 975B john-nash.rst 613B gambit.rst 2KB index.rst 126B conf.py 5KB index.rst 727B Makefile 606B tutorial index.rst 7KB _static learning divergent_example 
main.svg 34KB main.py 843B convergent_example main.svg 38KB main.py 867B appveyor.yml 656B tests unit test_support_enumeration.py 12KB test_lemke_howson.py 3KB test_integer_pivoting.py 4KB __init__.py 0B test_vertex_enumeration.py 2KB test_polytope.py 7KB test_fictitious_play.py 3KB test_version.py 201B test_game.py 16KB .gitignore 83B资源评论
