https://github.com/slott56/fuzzycomp

Automatically exported from code.google.com/p/fuzzycomp
https://github.com/slott56/fuzzycomp

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Automatically exported from code.google.com/p/fuzzycomp

• Host: GitHub
• URL: https://github.com/slott56/fuzzycomp
• Owner: slott56
• License: other
• Created: 2015-07-28T13:19:55.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-07-28T13:22:27.000Z (over 9 years ago)
• Last Synced: 2023-03-22T16:36:36.104Z (over 1 year ago)
• Language: Python
• Size: 215 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.txt
  • Changelog: CHANGES.txt
  • License: COPYING.txt

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README

        

About

=====

*Fuzzycomp* is a package purely implemented in Python for comparing

sequences or strings. Some algorithms work equally well on strings as on any

iterable and some algorithms are only for string comparison.

Platforms

=========

*Fuzzycomp* has been tested to work with the following versions of python.

  * Python 2.4

  * Python 2.5

  * Python 2.6

  * Python 2.7

Algorithms

==========

*Fuzzycomp* implements the following algorithms.

Comparison

----------

  * `Levenshtein Distance `__

  * `JaccardDistance `__

  * `Hamming Distance `__

  * `Jaro Distance `__

  * `Jaro Winkler Distance `__

  * `Dice Coefficient `__

  * `Longest common subsequence `__

Phonetic

--------

  * `American Soundex `__

  * `New York State Identification and Intelligence System ( NYSIIS ) `__

  * `Metaphone `__

  * `Cologne Phonetic (Kölner Phonetik) `__

Background

==========

There several major reasons for developing and publishing *fuzzycomp*

although there are other packages available, implementing most of the

algorithms present in *fuzzycomp*.

  #. There are an astonishing amount of different ways available for

     classifying how similar two strings are, once you leave the domain of

     perfect matching. It started with me, needing a way to tell how well a

     search phrase was matching the results provided by an external API. Once I

     started searching, I was really amazed by the subject and by the many

     alternatives there were so I wanted to learn more by developing the

     algorithms in Python.

  #. This is my first Python packages developed with the intent to be

     distributed and used by others. I wanted to learn how to structure and

     develop a Python package that could be released to the public and that

     could be useful in some way.

  #. The last couple of years I have read more and more about unit-testing

     and test driven development (TDD), almost always positive things. I have

     not had the opportunity to use unit-tests in any previous projects,

     so this is my learning ground for TDD and writing unit-tests.

Install

=======

The package is not yet represented on PyPI so the best ways to install

*fuzzycomp* at the moment are:

Using *pip* replacing X Y Z for the version number that you want to install::

 pip install http://fuzzycomp.googlecode.com/files/fuzzycomp-X.Y.Z.tar.gz

Or by downloading and unpacking the desired version and from the console

executing::

 python setup.py install

Usage

=====

Some example usage of *fuzzycomp*::

 >>> from fuzzycomp import fuzzycomp

 >>> fuzzycomp.levenshtein_distance( "Hello", "world" )

 3

 >>> fuzzycomp.soundex("Alfred")

 'A416'

Alternatives

============

If speed is of utmost importance to you or you find yourself comparing very

long sequences, you should probably consider some of the available

alternatives out there. They implement most of the algorithms in C so should

be considerably faster.

Some, but by no means all, alternatives are:

  * `python-Levenshtein `__

  * `Fuzzy `__

  * `jellyfish `__

Contact

=======

For bugs or feature requests, please use the issue tracker on the project page.

To get in contact with me regarding the project,

please email [email protected] or follow me on twitter

`@fuzzycode `__.