JavaWuzzy

FuzzyWuzzy Java Implementation

Fuzzy string matching for java based on the FuzzyWuzzy Python algorithm. The algorithm uses Levenshtein distance to calculate similarity between strings.

I've personally needed to use this but all of the other Java implementations out there either had a crazy amount of dependencies, or simply did not output the correct results as the python one, so I've decided to properly re-implement this in Java. Enjoy!

No dependencies!
Includes implementation of the super-fast python-Levenshtein in Java!
Simple to use!
Credits to the great folks at seatgeek for coming up with the algorithm (More here)

Installation

Maven Central

<dependency>
    <groupId>me.xdrop</groupId>
    <artifactId>fuzzywuzzy</artifactId>
    <version>1.1.5</version>
</dependency>

Gradle

compile 'me.xdrop:fuzzywuzzy:1.1.5'

Jar release

Download the latest release here and add to your classpath

Usage

Simple Ratio

FuzzySearch.ratio("mysmilarstring","myawfullysimilarstirng")
72

FuzzySearch.ratio("mysmilarstring","mysimilarstring")
97

Partial Ratio

FuzzySearch.partialRatio("similar", "somewhresimlrbetweenthisstring")
71

Token Sort Ratio

FuzzySearch.tokenSortPartialRatio("order words out of","  words out of order")
100
FuzzySearch.tokenSortRatio("order words out of","  words out of order")
100

Token Set Ratio

FuzzySearch.tokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100
FuzzySearch.tokenSetPartialRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100

Weighted Ratio

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

Extract

// groovy

FuzzySearch.extractOne("cowboys", ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"])
(string: Dallas Cowboys, score: 90)

FuzzySearch.extractTop("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 3)
[(string: google, score:83), (string: googleplus, score:63), (string: plexoogl, score:43)]

FuzzySearch.extractAll("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"]);
[(string: google, score:83), (string: bing, score:20), (string: facebook, score:29), (string: linkedin, score:29), (string: twitter, score:15), (string: googleplus, score:63), (string: bingnews, score:29), (string: plexoogl, score:43)]
// score cutoff
FuzzySearch.extractAll("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 40) 
[(string: google, score:83), (string: googleplus, score:63), (string: plexoogl, score:43)]

FuzzySearch.extractSorted("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"]);
[(string: google, score:83), (string: googleplus, score:63), (string: plexoogl, score:43), (string: facebook, score:29), (string: linkedin, score:29), (string: bingnews, score:29), (string: bing, score:20), (string: twitter, score:15)]
// score cutoff
FuzzySearch.extractSorted("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 3);
[(string: google, score:83), (string: googleplus, score:63), (string: plexoogl, score:43)]

Credits

seatgeek
Adam Cohen
David Necas (python-Levenshtein)
Mikko Ohtamaa (python-Levenshtein)
Antti Haapala (python-Levenshtein)

pengrad / fuzzywuzzy