philiprehberger-fuzzy_match

Fuzzy string matching with Levenshtein, Jaro-Winkler, and ranked search

Requirements

• Ruby >= 3.1

Installation

Add to your Gemfile:

gem "philiprehberger-fuzzy_match"

Or install directly:

gem install philiprehberger-fuzzy_match

Usage

require 'philiprehberger/fuzzy_match'

# Individual algorithms
Philiprehberger::FuzzyMatch.levenshtein('kitten', 'sitting')   # => 3
Philiprehberger::FuzzyMatch.jaro_winkler('martha', 'marhta')   # => ~0.96
Philiprehberger::FuzzyMatch.dice_coefficient('night', 'nacht') # => 0.25

# Normalized ratio (0.0 to 1.0)
Philiprehberger::FuzzyMatch.ratio('kitten', 'sitting')  # => ~0.57

Best Match

candidates = %w[Ruby Python Rust JavaScript]
result = Philiprehberger::FuzzyMatch.best('rubyy', candidates)
result[:match]  # => "Ruby"
result[:score]  # => 0.8

Ranked Search

candidates = %w[commit comment command compare]
results = Philiprehberger::FuzzyMatch.search('comit', candidates, threshold: 0.5)
# => [{ match: "commit", score: 0.8333 }, { match: "comment", score: 0.7143 }, ...]

Did-You-Mean Suggestions

Philiprehberger::FuzzyMatch.suggest('comit', %w[commit comment zebra], threshold: 0.6, max: 3)
# => ["commit", "comment"]

API

Philiprehberger::FuzzyMatch

Method	Description
.levenshtein(a, b)	Levenshtein edit distance (integer)
.jaro_winkler(a, b)	Jaro-Winkler similarity (0.0 to 1.0)
.dice_coefficient(a, b)	Dice coefficient from bigram overlap (0.0 to 1.0)
.ratio(a, b)	Normalized Levenshtein ratio (0.0 to 1.0)
.best(query, candidates, threshold: 0.0)	Best match as { match:, score: }
.search(query, candidates, threshold: 0.3)	Ranked array of { match:, score: }
.suggest(query, candidates, threshold: 0.6, max: 5)	Array of match strings

All methods are case-insensitive by default.

Development

bundle install
bundle exec rspec
bundle exec rubocop

License

MIT