https://github.com/taggon/kled-js

KLED: Fuzzy Matching Library with Levenshtein Edit Distance, Tailored for Korean Language Support
https://github.com/taggon/kled-js

Last synced: about 2 months ago
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KLED: Fuzzy Matching Library with Levenshtein Edit Distance, Tailored for Korean Language Support

• Host: GitHub
• URL: https://github.com/taggon/kled-js
• Owner: taggon
• License: mit
• Created: 2023-11-18T09:04:47.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-29T13:03:58.000Z (about 1 year ago)
• Last Synced: 2024-10-30T10:16:45.335Z (about 2 months ago)
• Language: TypeScript
• Size: 26.4 KB
• Stars: 13
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# kled.js

Fuzzy Matching Library with Levenshtein Edit Distance, Tailored for Korean Language Support

Also available in: [한국어](https://github.com/taggon/kled-js/blob/main/docs/README-ko.md)

## APIs

### `distance(a: string, b: string, caseSensitive: bool): number`

Calculate the Levenshtein distance between two strings.

**Parameters**

- `a`: a string

- `b`: another string

- `caseSensitive`: optional parameter (default: false), determines whether to consider case sensitivity.

**Returns**

The Levenshtein distance between the input strings.

### `matches(needle: string, haystack: string, caseSensitive: bool): number`

Calculate the similarity score between two strings, providing a numerical value between 0 and 1. If the "haystack" does not contain the "needle," the function returns 0.

It also supports partial Korean letter matching. For example, "ㅇㄴ" and "아녀" matches "안녕" with a slightly lower score than "안녕", which exactly matches the haystack.

**Parameters**

- `needle`: a string to search for

- `haystack`: a string to search in

- `caseSensitive`: optional parameter (default: false), determines whether to consider case sensitivity.

**Returns**

A similarity score between the input strings, where 0 indicates no similarity, and 1 indicates a perfect match based on the number of matched letters and their positions.

## Usage

```ts

import { distance, matches } from 'kled';

const levenshteinDistance = distance('hello', 'hola');

console.log(`Levenshtein Distance: ${levenshteinDistance}`);

const similarityScore = matches('abc', 'abCde');

console.log(`Similarity Score: ${similarityScore}`);

```

## Reporting Issues

Please report issues [here](https://github.com/taggon/kled-js) if you find any.

## License

MIT