| Name | pylev JSON |
| Version |
1.4.0
JSON |
| download |
| home_page | http://github.com/toastdriven/pylev |
| Summary | A pure Python Levenshtein implementation that's not freaking GPL'd. |
| upload_time | 2021-05-30 20:07:59 |
| maintainer | |
| docs_url | None |
| author | Daniel Lindsley |
| requires_python | |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
|
| coveralls test coverage |
No coveralls.
|
pylev
=====
A pure Python Levenshtein implementation that's not freaking GPL'd.
Based off the Wikipedia code samples at
http://en.wikipedia.org/wiki/Levenshtein_distance.
Requirements
------------
* Python 2.7.X, Python 3.3+ or PyPy 1.6.0+
Usage
-----
Usage is fairly straightforward:
.. code-block:: python
import pylev
distance = pylev.levenshtein('kitten', 'sitting')
assert distance == 3
License
-------
New BSD.
Tests
-----
Setup::
$ git clone https://github.com/toastdriven/pylev.git
$ cd pylev
Running::
$ python -m unittest tests
.. image:: https://travis-ci.com/toastdriven/pylev.svg?branch=main
:target: http://travis-ci.com/toastdriven/pylev
Version History
---------------
* v1.4.0
* Updated for current versions of Python
* Integrated a better Travis matrix. Thanks to @grainert!
* Fixed mistaken docs about the `assert`. Thanks to @adamchainz!
* Reorganized the package.
* Blacked all the source code.
* v1.3.0
* Implemented a considerably faster variants (orders of magnitude).
* Tested & working on Python 2.7.4, Python 3.3.1 & PyPy 1.9.0.
* v1.2.0
* Fixed all incorrect spellings of "Levenshtein" (there's no "c" in it).
* Old methods are aliased for backward-compatibility.
* v1.1.0
* Implemented a much faster variant (several orders of magnitude).
* The older variant was renamed to ``classic_levenschtein``.
* Tested & working on Python 3.3 & PyPy 1.6.0 as well.
* v1.0.2
* Python packaging is **REALLY** hard. Including the README *this time*.
* v1.0.1
* Python packaging is hard. Including the README this time.
* v1.0.0
* Initial release, just the naive implementation of Levenshtein.
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