# set-distance

1.0.2 • Public • Published

# set-distance

Finds similarity/distance between two input sets.
Algorithms implemented:
1). Sorensen-Dice Coefficient.
2). Jaccard Index.
3). Ochiai Coefficient.
4). Overlap Coefficient.
5). Levenshtein/Edit Distance.
6). Euclidean Distance.

## Installation

```npm install set-distance --save
bower install set-distance --save```

## Usage

### Javascript

```var Distance = require('set-distance');
//SorensenDice Coefficient
var sc = new Distance.SorensenDice(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(sc);
//Output: 0.7142857142857143

//Jaccard Index
var jc = new Distance.Jaccard(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(jc);
//Output: 0.5555555555555556

//Ochiai Coefficient
var oc = new Distance.Ochiai(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(oc);
//Output: 0.7216878364870323

//Overlap Coefficient
var ov = new Distance.Overlap(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(ov);
//Output: 0.8333333333333334

//Levenshtein/Edit Distance
var ld = new Distance.Levenshtein(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(ld);
//Output: 3

//Euclidean Distance
var ed = new Distance.Euclidean([50, 60], [20, 25]).getDistance(); // Here cartesian co-ordinates given in both arrays. Eg.: cartestian co-ordinates p1=50, p2=60 given in first list. And cartesian co-ordinates q1=20, q2=25 given in second list. Thus array1 in this case holds caretsian co-ordinates of "p" and array2 holds caretsian co-ordinates of "q".
console.log(ed);
//Output: 46.0977```

### TypeScript

```import * as Distance from 'set-distance';
//SorensenDice Coefficient
var sc = new Distance.SorensenDice(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(sc);
//Output: 0.7142857142857143

//Jaccard Index
var jc = new Distance.Jaccard(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(jc);
//Output: 0.5555555555555556

//Ochiai Coefficient
var oc = new Distance.Ochiai(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(oc);
//Output: 0.7216878364870323

//Overlap Coefficient
var ov = new Distance.Overlap(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(ov);
//Output: 0.8333333333333334

//Levenshtein/Edit Distance
var ld = new Distance.Levenshtein(['S', 'A', 'T', 'U', 'R', 'D', 'A', 'Y'], ['S', 'U', 'N', 'D', 'A', 'Y']).getCoefficient();
console.log(ld);
//Output: 3

//Euclidean Distance
var ed = new Distance.Euclidean([50, 60], [20, 25]).getDistance(); // Here cartesian co-ordinates given in both arrays. Eg.: cartestian co-ordinates p1=50, p2=60 given in first list. And cartesian co-ordinates q1=20, q2=25 given in second list. Thus array1 in this case holds caretsian co-ordinates of "p" and array2 holds caretsian co-ordinates of "q".
console.log(ed);
//Output: 46.0977```

## Package Sidebar

### Install

`npm i set-distance`

30

1.0.2

MIT

26.1 kB

8