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Several simularity identification algorithm implementations are provided, including:\n\n* Jaccard similarity coefficient\n* Tanimoto coefficient\n* Pearson correlation\n* N-gram edit distance\n* Levenshtein distance\n* Jaro-Winkler distance\n\nFuzzy 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! :) \n\n# Installing\nWuzzy can be installed via [npm](http://npmjs.org/package/wuzzy)  (`npm install wuzzy`).\n\n# Examples\nSome examples of using Wuzzy can be found in the [real-wuzzy](https://github.com/xupit3r/real-wuzzy) repository.\n\n# Methods\nAll bad jokes aside, below is a listing of the available functions. Have fun!\n\n## jarowinkler(a, b, t)\n\nComputes the jaro-winkler distance for two given arrays.\n\nNOTE: this implementation is based on the one found in the\nLucene Java library.\n### Examples:\n\n    wuzzy.jarowinkler(\n            ['D', 'W', 'A', 'Y', 'N', 'E'],\n            ['D', 'U', 'A', 'N', 'E']\n        );\n        // -> 0.840\n\n    wuzzy.jarowinkler(\n            'DWAYNE',\n            'DUANE'\n        );\n        // -> 0.840\n\n### Params: \n\n* **String|Array** *a* - the first string/array to compare\n* **String|Array** *b* - the second string/array to compare\n* **Number** *t* - the threshold for adding\n\n### Return:\n\n* **Number** returns the jaro-winkler distance for\n\n## levenshtein(a, b, w)\n\nCalculates the levenshtein distance for the\ntwo provided arrays and returns the normalized\ndistance.\n### Examples:\n\n    wuzzy.levenshtein(\n            ['D', 'W', 'A', 'Y', 'N', 'E'],\n            ['D', 'U', 'A', 'N', 'E']\n        );\n        // -> 0.66666667\n\n        or\n\n    wuzzy.levenshtein(\n            'DWAYNE',\n            'DUANE'\n        );\n        // -> 0.66666667\n\n### Params: \n\n* **String|Array** *a* - the first string/array to compare\n* **String|Array** *b* - the second string/array to compare\n* **Object** *w* - (optional) a set of key/value pairs\n\n### Return:\n\n* **Number** returns the levenshtein distance for\n\n## ngram(a, b, ng)\n\nComputes the n-gram edit distance for any n (defaults to 2).\n\nNOTE: this implementation is based on the one found in the\nLucene Java library.\n### Examples:\n\n    wuzzy.ngram(\n            ['D', 'W', 'A', 'Y', 'N', 'E'],\n            ['D', 'U', 'A', 'N', 'E']\n        );\n        // -> 0.583\n\n        or\n\n    wuzzy.ngram(\n            'DWAYNE',\n            'DUANE'\n        );\n        // -> 0.583\n\n### Params: \n\n* **String|Array** *a* - the first string/array to compare\n* **String|Array** *b* - the second string/array to compare\n* **Number** *ng* - (optional) the n-gram size to work with (defaults to 2)\n\n### Return:\n\n* **Number** returns the ngram distance for\n\n## pearson(a, b)\n\nCalculates a pearson correlation score for two given\nobjects (compares values of similar keys).\n### Examples:\n\n    wuzzy.pearson(\n            {a: 2.5, b: 3.5, c: 3.0, d: 3.5, e: 2.5, f: 3.0},\n            {a: 3.0, b: 3.5, c: 1.5, d: 5.0, e: 3.5, f: 3.0, g: 5.0}\n        );\n        // -> 0.396\n\n        or\n\n    wuzzy.pearson(\n            {a: 2.5, b: 1},\n            {o: 3.5, e: 6.0}\n        );\n        // -> 1.0\n\n### Params: \n\n* **Object** *a* - the first object to compare\n* **Object** *b* - the second object to compare\n\n### Return:\n\n* **Number** returns the pearson correlation for\n\n## jaccard(a, b)\n\nCalculates the jaccard index for the two\nprovided arrays.\n### Examples:\n\n    wuzzy.jaccard(\n            ['a', 'b', 'c', 'd', 'e', 'f'],\n            ['a', 'e', 'f']\n        );\n        // -> 0.5\n\n        or\n\n    wuzzy.jaccard(\n            'abcdef',\n            'aef'\n        );\n        // -> 0.5\n\n        or \n\n    wuzzy.jaccard(\n            ['abe', 'babe', 'cabe', 'dabe', 'eabe', 'fabe'],\n            ['babe']\n        );\n        // -> 0.16666667\n\n### Params: \n\n* **String|Array** *a* - the first string/array to compare\n* **String|Array** *b* - the second string/array to compare\n\n### Return:\n\n* **Number** returns the jaccard index for\n\n## tanimoto(a, b)\n\nCalculates the tanimoto distance (weighted jaccard index).\n### Examples:\n\n    wuzzy.tanimoto(\n            ['a', 'b', 'c', 'd', 'd', 'e', 'f', 'f'],\n            ['a', 'e', 'f']\n        );\n        // -> 0.375\n\n        or\n\n    wuzzy.tanimoto(\n            'abcddeff',\n            'aef'\n        );\n        // -> 0.375\n\n        or \n\n    wuzzy.tanimoto(\n            ['abe', 'babe', 'cabe', 'dabe', 'eabe', 'fabe', 'fabe'],\n            ['babe']\n        );\n        // -> 0.14285714\n\n### Params: \n\n* **String|Array** *a* - the first string/array to compare\n* **String|Array** *b* - the second string/array to compare\n\n### Return:\n\n* **Number** returns the tanimoto distance for\n\n<!-- End ./index.js -->\n\n\n","maintainers":[{"name":"xupit3r","email":"joe@thejoeshow.net"}],"time":{"modified":"2022-06-29T04:13:50.752Z","created":"2014-03-25T01:36:29.074Z","0.0.0":"2014-03-25T01:36:29.074Z","0.1.0":"2014-04-12T13:18:31.471Z","0.1.1":"2014-06-08T16:54:32.600Z","0.1.2":"2014-08-14T02:12:26.612Z","0.1.3":"2016-11-06T16:01:53.078Z","0.1.4":"2019-07-20T14:45:53.007Z","0.1.5":"2020-04-07T22:07:03.057Z","0.1.6":"2020-09-07T14:15:01.457Z","0.1.7":"2021-04-10T13:00:47.951Z","0.1.8":"2021-06-26T11:48:22.034Z"},"homepage":"https://github.com/xupit3r/wuzzy#readme","keywords":["text","fuzzy","similar","jaccard","jarowinkler","tanimoto","levenshtein","ngram"],"repository":{"type":"git","url":"git+https://github.com/xupit3r/wuzzy.git"},"author":{"name":"Joe D'Alessandro"},"bugs":{"url":"https://github.com/xupit3r/wuzzy/issues"},"license":"MIT","readmeFilename":"README.md","users":{"mastayoda":true,"antixrist":true,"ysilvela":true,"zda":true}}