https://github.com/nryoung/algorithms

This repository is not maintained
https://github.com/nryoung/algorithms

Last synced: 14 days ago
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This repository is not maintained

• Host: GitHub
• URL: https://github.com/nryoung/algorithms
• Owner: nryoung
• License: other
• Archived: true
• Created: 2012-08-21T18:43:30.000Z (about 12 years ago)
• Default Branch: master
• Last Pushed: 2018-10-30T18:12:00.000Z (about 6 years ago)
• Last Synced: 2024-07-31T15:10:46.046Z (3 months ago)
• Language: Python
• Homepage:
• Size: 1.78 MB
• Stars: 2,850
• Watchers: 189
• Forks: 704
• Open Issues: 7
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.rst

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README

        
**This repository is no longer maintained, but is being kept around for educational purposes. If you want a more complete algorithms repo check out: https://github.com/keon/algorithms**

=====

Algorithms

==========

.. image:: https://travis-ci.org/nryoung/algorithms.svg?branch=master

    :target: https://travis-ci.org/nryoung/algorithms

.. image:: http://codecov.io/github/nryoung/algorithms/coverage.svg?branch=master

    :target: http://codecov.io/github/nryoung/algorithms?branch=master

.. image:: https://readthedocs.org/projects/algorithms/badge/?version=latest

    :target: http://algorithms.readthedocs.org/en/latest/?badge=latest

.. image:: https://badge.fury.io/py/algorithms.svg

    :target: https://badge.fury.io/py/algorithms

Algorithms is a library of algorithms and data structures implemented in Python.

The main purpose of this library is to be an educational tool. You probably

shouldn't use these in production, instead, opting for the optimized versions of

these algorithms that can be found else where.

You should totally check out the `docs`_ for implementation details, complexities

and further info.

Usage

-----

If you want to use the algorithms in your code it is as simple as:

::

    from algorithms.sorting import bubble_sort

    my_list = bubble_sort.sort(my_list)

Features

--------

- Pseudo code, algorithm complexities and futher info with each algorithm.

- Test coverage for each algorithm and data structure.

- Super sweet `documentation`_.

Installation:

-------------

Installation is as easy as:

::

    $ pip install algorithms

Tests:

------

Pytest is used as the main test runner and all Unit Tests can be run with:

::

    $ ./run_tests.py

Contributing:

-------------

Contributions are always welcome. Check out the contributing guidelines to get

started.

.. _`docs`: http://algorithms.readthedocs.org/en/latest/

.. _`documentation`: http://algorithms.readthedocs.org/en/latest/