# wuzzy > library for simularity identification Latest version **0.1.8** (published 2021-06-26) · MIT license · 0 weekly downloads ## Install ```sh npm install wuzzy pnpm add wuzzy yarn add wuzzy bun add wuzzy ``` ## Health **Score 23/100 (F)** — status: abandoned. Positive: has types package; no vulnerabilities; high quality score. Warnings: low downloads; no esm support; pre 1.0. Negative: abandoned; low maintenance score. ## Facts | | | |---|---| | Version | 0.1.8 | | Published | 2021-06-26 | | First published | 2014-03-25 | | Weekly downloads | 0 | | License | MIT | | TypeScript types | separate (@types/wuzzy) | | Module format | CommonJS | | Dependencies | 1 | | Unpacked size | 25.2 KB | | Known vulnerabilities | 0 | | Install scripts | no | | GitHub stars | 37 | | Author | Joe D'Alessandro | | Maintainers | xupit3r | | Keywords | text, fuzzy, similar, jaccard, jarowinkler, tanimoto, levenshtein, ngram | ## Links - npm: https://www.npmjs.com/package/wuzzy - Repository: https://github.com/xupit3r/wuzzy - Homepage: https://github.com/xupit3r/wuzzy#readme - Issues: https://github.com/xupit3r/wuzzy/issues - npm.io page: https://npm.io/package/wuzzy ## Dependencies (1) - [lodash](https://npm.io/package/lodash.md) ^4.17.15 ## Alternatives - [ext-list](https://npm.io/package/ext-list.md) — 6.3M weekly downloads - [@lexical/selection](https://npm.io/package/@lexical/selection.md) — 3.8M weekly downloads - [@lexical/text](https://npm.io/package/@lexical/text.md) — 3.6M weekly downloads - [@lexical/clipboard](https://npm.io/package/@lexical/clipboard.md) — 3.0M weekly downloads - [@tiptap/extension-mention](https://npm.io/package/@tiptap/extension-mention.md) — 3.0M weekly downloads ## Recent versions - 0.1.8 (latest) — 2021-06-26 - 0.1.7 — 2021-04-10 - 0.1.6 — 2020-09-07 - 0.1.5 — 2020-04-07 - 0.1.4 — 2019-07-20 - 0.1.3 — 2016-11-06 - 0.1.2 — 2014-08-14 - 0.1.1 — 2014-06-08 - 0.1.0 — 2014-04-12 - 0.0.0 — 2014-03-25 ## README # Overview Wuzzy was created to provide a smattering of some similarity identification stuff. Several simularity identification algorithm implementations are provided, including: * Jaccard similarity coefficient * Tanimoto coefficient * Pearson correlation * N-gram edit distance * Levenshtein distance * Jaro-Winkler distance Fuzzy wuzzy was a bear, fuzzy wuzzy had no hair, fuzzy wuzzy wasn't very fuzzy, was he? Well, if you aren't sure maybe this library can help! :) # Installing Wuzzy can be installed via [npm](http://npmjs.org/package/wuzzy) (`npm install wuzzy`). # Examples Some examples of using Wuzzy can be found in the [real-wuzzy](https://github.com/xupit3r/real-wuzzy) repository. # Methods All bad jokes aside, below is a listing of the available functions. Have fun! ## jarowinkler(a, b, t) Computes the jaro-winkler distance for two given arrays. NOTE: this implementation is based on the one found in the Lucene Java library. ### Examples: wuzzy.jarowinkler( ['D', 'W', 'A', 'Y', 'N', 'E'], ['D', 'U', 'A', 'N', 'E'] ); // -> 0.840 wuzzy.jarowinkler( 'DWAYNE', 'DUANE' ); // -> 0.840 ### Params: * **String|Array** *a* - the first string/array to compare * **String|Array** *b* - the second string/array to compare * **Number** *t* - the threshold for adding ### Return: * **Number** returns the jaro-winkler distance for ## levenshtein(a, b, w) Calculates the levenshtein distance for the two provided arrays and returns the normalized distance. ### Examples: wuzzy.levenshtein( ['D', 'W', 'A', 'Y', 'N', 'E'], ['D', 'U', 'A', 'N', 'E'] ); // -> 0.66666667 or wuzzy.levenshtein( 'DWAYNE', 'DUANE' ); // -> 0.66666667 ### Params: * **String|Array** *a* - the first string/array to compare * **String|Array** *b* - the second string/array to compare * **Object** *w* - (optional) a set of key/value pairs ### Return: * **Number** returns the levenshtein distance for ## ngram(a, b, ng) Computes the n-gram edit distance for any n (defaults to 2). NOTE: this implementation is based on the one found in the Lucene Java library. ### Examples: wuzzy.ngram( ['D', 'W', 'A', 'Y', 'N', 'E'], ['D', 'U', 'A', 'N', 'E'] ); // -> 0.583 or wuzzy.ngram( 'DWAYNE', 'DUANE' ); // -> 0.583 ### Params: * **String|Array** *a* - the first string/array to compare * **String|Array** *b* - the second string/array to compare * **Number** *ng* - (optional) the n-gram size to work with (defaults to 2) ### Return: * **Number** returns the ngram distance for ## pearson(a, b) Calculates a pearson correlation score for two given objects (compares values of similar keys). ### Examples: wuzzy.pearson( {a: 2.5, b: 3.5, c: 3.0, d: 3.5, e: 2.5, f: 3.0}, {a: 3.0, b: 3.5, c: 1.5, d: 5.0, e: 3.5, f: 3.0, g: 5.0} ); // -> 0.396 or wuzzy.pearson( {a: 2.5, b: 1}, {o: 3.5, e: 6.0} ); // -> 1.0 ### Params: * **Object** *a* - the first object to compare * **Object** *b* - the second object to compare ### Return: * **Number** returns the pearson correlation for ## jaccard(a, b) Calculates the jaccard index for the two provided arrays. ### Examples: wuzzy.jaccard( ['a', 'b', 'c', 'd', 'e', 'f'], ['a', 'e', 'f'] ); // -> 0.5 or wuzzy.jaccard( 'abcdef', 'aef' ); // -> 0.5 or wuzzy.jaccard( ['abe', 'babe', 'cabe', 'dabe', 'eabe', 'fabe'], ['babe'] ); // -> 0.16666667 ### Params: * **String|Array** *a* - the first string/array to compare * **String|Array** *b* - the second string/array to compare ### Return: * **Number** returns the jaccard index for ## tanimoto(a, b) Calculates the tanimoto distance (weighted jaccard index). ### Examples: wuzzy.tanimoto( ['a', 'b', 'c', 'd', 'd', 'e', 'f', 'f'], ['a', 'e', 'f'] ); // -> 0.375 or wuzzy.tanimoto( 'abcddeff', 'aef' ); // -> 0.375 or wuzzy.tanimoto( ['abe', 'babe', 'cabe', 'dabe', 'eabe', 'fabe', 'fabe'], ['babe'] ); // -> 0.14285714 ### Params: * **String|Array** *a* - the first string/array to compare * **String|Array** *b* - the second string/array to compare ### Return: * **Number** returns the tanimoto distance for --- _Source: https://npm.io/package/wuzzy · Machine-readable twin of the npm.io package page. 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