Package Exports

string-metric

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (string-metric) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

String Metric

A library implementing different string similarity and distance measures, and Implement by TypeScript. Also, you can use in JavaScript.

Algorithm reference java-string-similarity

Install

npm install string-metric

Progress

Algorithm	Complete?
Jaro-Winkler	Yes
Levenshtein	Yes
Normalized-Levenshtein	Yes
Weighted-Levenshtein	Yes
Damerau-Levenshtein	Yes
Optimal String Alignment	No
Longest Common Subsequence	No
Metric Longest Common Subsequence	No
N-Gram	No
Q-Gram	No
Shingle (n-gram) based algorithms	No
Cosine similarity	No
Jaccard index	No
Sorensen-Dice coefficient	No
Ratcliff-Obershelp	No

Jaro-Winkler

For more specs, please go to tests/JaroWinkler.spec.ts in the repository.

const instance = new JaroWinkler();

const s1 = 'My string';
const s2 = 'My string';
instance.similarity(s1, s2); // 1

const s1 = 'My string';
const s2 = 'My tsring';
instance.similarity(s1, s2); // 0.974074

const s1 = 'My string';
const s2 = 'My ntrisg';
instance.similarity(s1, s2); // 0.896296

Levenshtein

For more specs, please go to tests/Levenshtein.spec.ts in the repository.

const instance = new Levenshtein();

const s1 = 'My string';
const s2 = 'My string';
instance.distance(s1, s2); // 0

const s1 = 'My string';
const s2 = 'My tring';
instance.distance(s1, s2); // 1

const s1 = 'My string';
const s2 = 'M string2';
instance.distance(s1, s2); // 2

Normalized-Levenshtein

For more specs, please go to tests/NormalizedLevenshtein.spec.ts in the repository.

const instance = new NormalizedLevenshtein();

Weighted-Levenshtein

For more specs, please go to tests/WeightedLevenshtein.spec.ts in the repository.

const instance = new WeightedLevenshtein();