var clj_fuzzy = require('clj-fuzzy');

// Compute the Dice coefficient of two words
clj_fuzzy.metrics.dice('healed', 'sealed');
0.8

clj_fuzzy.metrics.dice('healed', 'herded');
0.4

// There is also a Sorensen alias
clj_fuzzy.metrics.sorensen('healed', 'herded');
0.4

// Compute the Levenshtein distance between two words
clj_fuzzy.metrics.levenshtein('book', 'back');
2

clj_fuzzy.metrics.levenshtein('hello', 'helo');
1

// Compute the Hamming distance between two words
clj_fuzzy.metrics.hamming('ramer', 'cases');
3

clj_fuzzy.metrics.hamming([0, 1, 0, 1], [1, 1, 0, 1]);
1

// Compute the Jaccard distance between two words
// 0 meaning two identical strings and 1 two totally different ones
clj_fuzzy.metrics.jaccard('abc', 'xyz');
1

clj_fuzzy.metrics.jaccard('night', 'nacht');
0.5714285714285714

// If you are more the Tanimoto kind of guy, an alias exists
clj_fuzzy.metrics.tanimoto('night', 'nacht');
0.5714285714285714

// Compute the Jaro distance between two words
clj_fuzzy.metrics.jaro('Dwayne', 'Duane');
0.8222222222222223

// Compute the Jaro-Winkler distance between two words
clj_fuzzy.metrics.jaro_winkler('Dwayne', 'Duane');
0.8400000000000001

// Compare two string using the Match Rating Approach
clj_fuzzy.metrics.mra_comparison('Byrne', 'Boern');
>>> {
  minimum: 4,
  similarity: 5,
  code: ['BYRN', 'BRN'],
  match: true
}

// Compute the Tversky index of two sequences.
clj_fuzzy.metrics.tversky('night', 'nacht');
0.42857142857142855

// Compute the same index for a precise alpha and beta value
// Default value is alpha = beta = 1 and produces the Jaccard coefficient
// alpha = beta = 0.5 produces the Dice coefficient (without bigrams)
clj_fuzzy.metrics.tversky('healed', 'sealed', {alpha: 0.5, beta: 0.5});
0.8

// You can also specify whether you want to compute the
// symmetric variant of the index
clj_fuzzy.metrics.tversky(
  'healed',
  'sealed',
  {
    alpha: 1,
    beta: 1,
    symmetric: true
  }
);
0.8

// Compute the stem of a word
clj_fuzzy.stemmers.lancaster('worker');
'work'

clj_fuzzy.stemmers.lancaster('presumably');
'presum'

// Compute the stem of a word
clj_fuzzy.stemmers.lovins('nationality');
'nat'

clj_fuzzy.stemmers.lovins('analytic');
'analys'

// Compute the stem of a word
clj_fuzzy.stemmers.porter('adjective');
'adject'

clj_fuzzy.stemmers.porter('building');
'build'

// Compute the stem of a word
clj_fuzzy.stemmers.schinke('aquila');
>>> {noun: 'aquil', verb: 'aquila'}

clj_fuzzy.stemmers.schinke('apparebunt');
>>> {noun: 'apparebu', verb: 'apparebi'}

// Compute the metaphone code for a single word
clj_fuzzy.phonetics.metaphone('hypocrite');
"HPKRT"

clj_fuzzy.phonetics.metaphone('discrimination');
"TSKRMNXN"

// Compute the double metaphone of a word
clj_fuzzy.phonetics.double_metaphone('Smith');
["SM0" "XMT"]

clj_fuzzy.phonetics.double_metaphone('Schmidt');
["XMT" "SMT"]

// Compute the soundex code of a single name
clj_fuzzy.phonetics.soundex('Ashcroft');
"A261"

clj_fuzzy.phonetics.soundex('Andrew');
"A536"

// Compute the NYSIIS code of a single name
clj_fuzzy.phonetics.nysiis('Andrew');
"ANDR"

clj_fuzzy.phonetics.nysiis('Mclaughlin');
"MCLAGLAN"

// Compute the refined NYSIIS code of a single name
clj_fuzzy.phonetics.nysiis('Aegir', 'refined');
"AGAR"

// Compute the caverphone code of a single name
clj_fuzzy.phonetics.caverphone('Henrichsen');
"ANRKSN1111"

clj_fuzzy.phonetics.caverphone('Mclaverty');
"MKLFTA1111"

// Compute the "revisited" caverphone code of a single name
clj_fuzzy.phonetics.caverphone('Stevenson', 'revisited');
"STFNSN1111"

// Compute the cologne phonetic code of a single word
clj_fuzzy.phonetics.cologne('Müller-Lüdenscheidt');
"65752682"

clj_fuzzy.phonetics.cologne('Breschnew');
"17863"

// Compute the MRA codex of a single name
clj_fuzzy.phonetics.mra_codex('Catherine');
"CTHRN"

clj_fuzzy.phonetics.mra_codex('Smith');
"SMTH"