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!
- Lightweight!
- Credits to the great folks at seatgeek for coming up with the algorithm (More here)
In Maven and Gradle examples, remember to replace "VERSION" with the
latest release of this
library.
<dependency>
<groupId>me.xdrop</groupId>
<artifactId>fuzzywuzzy</artifactId>
<version>VERSION</version>
</dependency>repositories {
mavenCentral()
}
dependencies {
implementation 'me.xdrop:fuzzywuzzy:VERSION'
}If you use Java 9 or newer, and use the Java Platform Module System (JPMS), you will need to add
the following declarations to your module-info.java file:
module my.modulename.here {
requires java.base;
requires me.xdrop.fuzzywuzzy;
}Download the latest release here and add to your classpath.
FuzzySearch.ratio("mysmilarstring","myawfullysimilarstirng")
72
FuzzySearch.ratio("mysmilarstring","mysimilarstring")
97
FuzzySearch.partialRatio("similar", "somewhresimlrbetweenthisstring")
71FuzzySearch.tokenSortPartialRatio("order words out of"," words out of order")
100
FuzzySearch.tokenSortRatio("order words out of"," words out of order")
100FuzzySearch.tokenSetRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100
FuzzySearch.tokenSetPartialRatio("fuzzy was a bear", "fuzzy fuzzy fuzzy bear")
100FuzzySearch.weightedRatio("The quick brown fox jimps ofver the small lazy dog", "the quick brown fox jumps over the small lazy dog")
97// groovy
FuzzySearch.extractOne("cowboys", ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"])
(string: Dallas Cowboys, score: 90, index: 3)FuzzySearch.extractTop("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 3)
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index:5), (string: plexoogl, score: 43, index: 7)]FuzzySearch.extractAll("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"]);
[(string: google, score: 83, index: 0), (string: bing, score: 20, index: 1), (string: facebook, score: 29, index: 2), (string: linkedin, score: 29, index: 3), (string: twitter, score: 15, index: 4), (string: googleplus, score: 63, index: 5), (string: bingnews, score: 29, index: 6), (string: plexoogl, score: 43, index: 7)]
// score cutoff
FuzzySearch.extractAll("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 40)
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index: 5), (string: plexoogl, score: 43, index: 7)]FuzzySearch.extractSorted("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"]);
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index: 5), (string: plexoogl, score: 43, index: 7), (string: facebook, score: 29, index: 2), (string: linkedin, score: 29, index: 3), (string: bingnews, score: 29, index: 6), (string: bing, score: 20, index: 1), (string: twitter, score: 15, index: 4)]
// score cutoff
FuzzySearch.extractSorted("goolge", ["google", "bing", "facebook", "linkedin", "twitter", "googleplus", "bingnews", "plexoogl"], 3);
[(string: google, score: 83, index: 0), (string: googleplus, score: 63, index: 5), (string: plexoogl, score: 43, index: 7)]extractOne and related methods can receive Collection<T> and produce BoundExtractedResult<T>
List<Foo> foo = ...;
BoundExtractedResult<Foo> match = FuzzySearch.extractOne("cowboys", foo, x -> x.toString());
Foo matchFoo = match.getReferent();- seatgeek
- Adam Cohen
- David Necas (python-Levenshtein)
- Mikko Ohtamaa (python-Levenshtein)
- Antti Haapala (python-Levenshtein)
- Tobias Burdow (burdoto)