FuzzyWuzzy

This is a Dart port of the popular FuzzyWuzzy python package. This algorithm uses Levenshtein distance to calculate similarity between strings.

I personally needed to use this for my own search algorithms, and there weren't any packages as good as my experience with FuzzyWuzzy was, so here we are. Enjoy!

Just one dependency (collection).
Pure Dart implementation of the superfast python-Levenshtein.
Simple to use.
Lightweight.
Massive props to the folks over at seatgeek for coming up with the algorithm.

Get started

Add dependency

dependencies:
  fuzzywuzzy: <latest version>

Usage

First, import the package

import 'package:fuzzywuzzy/fuzzywuzzy.dart'

Simple ratio

ratio("mysmilarstring", "myawfullysimilarstirng") // 72
ratio("mysmilarstring", "mysimilarstring")        // 97

Partial ratio

partialRatio("similar", "somewhresimlrbetweenthisstring") // 71

Token sort ratio

tokenSortPartialRatio("order words out of", "words out of order") // 100
tokenSortRatio("order words out of","  words out of order")       // 100

Token set ratio

tokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")         // 100
tokenSetPartialRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")  // 100

Weighted ratio

weightedRatio("The quick brown fox jimps ofver the small lazy dog", "the quick brown fox jumps over the small lazy dog") // 97

Extract

extractOne(
        query: 'cowboys',
        choices: [
          'Atlanta Falcons',
          'New York Jets',
          'New York Giants',
          'Dallas Cowboys'
        ],
        cutoff: 10,
      ) // (string Dallas Cowboys, score: 90, index: 3)

extractTop(
        query: 'goolge',
        choices: [
          'google',
          'bing',
          'facebook',
          'linkedin',
          'twitter',
          'googleplus',
          'bingnews',
          'plexoogl'
        ],
        limit: 4,
        cutoff: 50,
      ) // [(string google, score: 83, index: 0), (string googleplus, score: 75, index: 5)]

extractAllSorted(
        query: 'goolge',
        choices: [
          'google',
          'bing',
          'facebook',
          'linkedin',
          'twitter',
          'googleplus',
          'bingnews',
          'plexoogl'
        ],
        cutoff: 10,
      ) // [(string google, score: 83, index: 0), (string googleplus, score: 75, index: 5), (string plexoogl, score: 43, index: 7), (string bingnews, score: 29, index: 6), (string linkedin, score: 29, index: 3), (string facebook, score: 29, index: 2), (string bing, score: 23, index: 1), (string twitter, score: 15, index: 4)]

extractAll(
        query: 'goolge',
        choices: [
          'google',
          'bing',
          'facebook',
          'linkedin',
          'twitter',
          'googleplus',
          'bingnews',
          'plexoogl'
        ],
        cutoff: 10,
      ) // [(string google, score: 83, index: 0), (string bing, score: 23, index: 1), (string facebook, score: 29, index: 2), (string linkedin, score: 29, index: 3), (string twitter, score: 15, index: 4), (string googleplus, score: 75, index: 5), (string bingnews, score: 29, index: 6), (string plexoogl, score: 43, index: 7)]

Extract using any a list of any type

All extract methods can receive List<T> and return List<ExtractedResult<T>>

class TestContainer {
  final String innerVal;
  TestContainer(this.innerVal);
}

extractOne<TestContainer>(
        query: 'cowboys',
        choices: [
          'Atlanta Falcons',
          'New York Jets',
          'New York Giants',
          'Dallas Cowboys'
        ].map((e) => TestContainer(e)).toList(),
        cutoff: 10,
        getter: (x) => x.innerVal
      ).toString(); // (string Dallas Cowboys, score: 90, index: 3)