A collection of edit distance algorithms in Crystal.

Includes Levenshtein, Restricted Edit (Optimal Alignment) and Damerau-Levenshtein distances, and Jaro and Jaro-Winkler similarity.

Add this to your application's shard.yml:

dependencies:
  edits:
    github: tcrouch/edits.cr

require "edits"

Calculate the edit distance between two sequences with variants of the Levenshtein distance algorithm.

Edits::Levenshtein.distance "raked", "bakers"
# => 3
Edits::RestrictedEdit.distance "iota", "atom"
# => 3
Edits::DamerauLevenshtein.distance "acer", "earn"
# => 3

• Levenshtein edit distance, counting insertion, deletion and substitution.
• Restricted Damerau-Levenshtein edit distance (aka Optimal Alignment), counting insertion, deletion, substitution and transposition (adjacent symbols swapped). Restricted by the condition that no substring is edited more than once.
• Damerau-Levenshtein edit distance, counting insertion, deletion, substitution and transposition (adjacent symbols swapped).

Levenshtein and Restricted Edit distances accept an optional maximum bound.

Edits::Levenshtein.distance "fghijk", "abcde", 3
# => 3

The convenience method most_similar searches for the best match to a given sequence from a collection. It is similar to using min_by, but leverages a maximum bound.

Edits::RestrictedEdit.most_similar "atom", ["iota", "tome", "mown", "tame"]
# => "tome"

Calculate the Jaro and Jaro-Winkler similarity/distance of two sequences.

Edits::Jaro.similarity "information", "informant"
# => 0.90235690235690236
Edits::Jaro.distance "information", "informant"
# => 0.097643097643097643

Edits::JaroWinkler.similarity "information", "informant"
# => 0.94141414141414137
Edits::JaroWinkler.distance "information", "informant"
# => 0.05858585858585863

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

• [tcrouch] Tom Crouch - creator, maintainer