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didyoumean2

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Readme

didyoumean2

didyoumean2 is a library for matching human-quality input to a list of potential matches using the Levenshtein distance algorithm. It is inspired by didyoumean.js.

Why reinventing the wheel

Based on leven, the fastest JS implementation of the Levenshtein distance algorithm
~100% faster than didyoumean.js
Well tested with 100% coverage
More control on what kind of matches you want to return
Support matching object's path instead of just key

Installation

npm install didyoumean2

const didYouMean = require('didyoumean2')

Usage

didYouMean(input, matchList[, options])

input {string}: A string that you are not sure and want to match with matchList

matchList {Object[]|string[]}: A List for matching with input

options {Object}(optional): An options that allows you to modify the behavior

@return {Array|null|Object|string}: A list of or single matched result(s), return object if match is {Object[]}

Options

`caseSensitive {boolean}`

default: false
Perform case-sensitive matching

`matchPath {string}`

default: ''
If your matchList is an array of object, you must use matchPath to point to the string that you want to match
Refer to lodash _.get for how to define the path

`returnType {string}`

default: 'first-closest-match'

returnType	Description
`'all-closest-matches'`	Return all matches with the closest value to the `input` in array
`'all-matches'`	Return all matches in array
`'all-sorted-matches'`	Return all matches in array, sorted from closest to furthest
`'first-closest-match'`	Return first match from `'all-closest-matches'`
`'first-match'`	Return first match (FASTEST)
`'random-closest-match'`	Return a randomly pick up from `'all-closest-matches'`

`threshold {integer|number}`

depends on thresholdType
type: {number} (similarity) or {integer} (edit-distance)
default: 0.4 (similarity) or 20 (edit-distance)
If the result is larger (similarity) or smaller (edit-distance) than or equal to the threshold, that result is matched

`thresholdType {string}`

default: 'similarity'

thresholdType	Description
`'edit-distance'`	Refer to Levenshtein distance algorithm, must be `integer`, lower value means more similar
`'similarity'`	`l = max(input.length, matchItem.length), similarity = (l - editDistance) / l`, `number` from `0` to `1`, higher value means more similar

Test

Unit test:

npm install
npm test

Coverage:

npm install
npm run coverage

Linter:

npm install
npm run lint