https://github.com/0xd34df00d/edit-distance-linear

Levenshtein edit distance in linear memory (also turns out to be faster than C++)
https://github.com/0xd34df00d/edit-distance-linear

edit-distance haskell levenshtein-distance

Last synced: about 1 month ago
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Levenshtein edit distance in linear memory (also turns out to be faster than C++)

• Host: GitHub
• URL: https://github.com/0xd34df00d/edit-distance-linear
• Owner: 0xd34df00d
• License: bsd-3-clause
• Created: 2019-11-30T22:50:55.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-01-19T20:39:36.000Z (almost 2 years ago)
• Last Synced: 2024-11-28T14:09:00.597Z (about 2 months ago)
• Topics: edit-distance, haskell, levenshtein-distance
• Language: Haskell
• Homepage:
• Size: 24.4 KB
• Stars: 3
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog.md
  • License: LICENSE

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README

        
# edit-distance-linear

[![Build Status][travis-badge]][travis]

[![Hackage][hackage-badge]][hackage]

The pure Haskell implementation of the Levenshtein edit distance, with linear space complexity.

## Comparison

There are already several other existing implementations, but the goals and design decisions vary. In particular, this package is intended to be used to:

* compare long strings (think tens of thousands of characters), driving the implementation to live in the `ST` monad and aim at linear space complexity to lower GC pressure;

* not care about Unicode, thus accepting `ByteString`s and comparing them byte-by-byte rather than character-by-character (or glyph-by-glyph, or whatever is the right notion of an edit for Unicode).

Among the alternatives:

* [text-metrics](http://hackage.haskell.org/package/text-metrics) — uses a similar algorithm, but cares about Unicode, making it 4-5 times slower.

* [edit-distance](http://hackage.haskell.org/package/edit-distance) — uses a very different algorithm (which we might implement here one day with huge potential benefits), which tends to consume more memory (I'm not up for estimating its space asymptotics, though).

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