# ml-distance-euclidean

> Compute the euclidean distance between two vectors

Latest version **3.0.1** (published 2025-11-14) · MIT license · 0 weekly downloads

## Install

```sh
npm install ml-distance-euclidean
pnpm add ml-distance-euclidean
yarn add ml-distance-euclidean
bun add ml-distance-euclidean
```

## Health

**Score 50/100 (C)** — status: stable.

Positive: esm support; no vulnerabilities.

Warnings: low downloads; no types.

## Facts

| | |
|---|---|
| Version | 3.0.1 |
| Published | 2025-11-14 |
| First published | 2016-08-03 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | ESM |
| Dependencies | 0 |
| Unpacked size | 6.9 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 6 |
| Author | Michaël Zasso |
| Maintainers | stropitek, targos, lpatiny, mljs-bot |
| Keywords | euclidean, squared, distance, metric, vector, data, mining, datamining, machine, learning |

## Links

- npm: https://www.npmjs.com/package/ml-distance-euclidean
- Repository: https://github.com/mljs/distance-euclidean
- Homepage: https://github.com/mljs/distance-euclidean#readme
- Issues: https://github.com/mljs/distance-euclidean/issues
- npm.io page: https://npm.io/package/ml-distance-euclidean

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## Recent versions

- 3.0.1 (latest) — 2025-11-14
- 3.0.0 — 2025-11-13
- 2.0.0 — 2018-08-12
- 1.0.0 — 2016-08-03

## README

# ml-distance-euclidean

<h3 align="center">
  
  <a href="https://www.zakodium.com">
   <img src="https://www.zakodium.com/brand/zakodium-logo-white.svg" width="50" alt="Zakodium logo" />
  </a>
  
  <p>
    Maintained by <a href="https://www.zakodium.com">Zakodium</a>
  </p>

[![NPM version](https://img.shields.io/npm/v/ml-distance-euclidean.svg)](https://www.npmjs.com/package/ml-distance-euclidean)
[![npm download](https://img.shields.io/npm/dm/ml-distance-euclidean.svg)](https://www.npmjs.com/package/ml-distance-euclidean)
[![test coverage](https://img.shields.io/codecov/c/github/mljs/spectra-processing.svg)](https://codecov.io/gh/mljs/spectra-processing)
[![license](https://img.shields.io/npm/l/ml-distance-euclidean.svg)](https://github.com/mljs/spectra-processing/blob/main/LICENSE)

</h3>

Compute the euclidean distance between two vectors

## Installation

`$ npm install ml-distance-euclidean`

## API

```js
const { euclidean, squaredEuclidean } = require('ml-distance-euclidean');

euclidean([0, 1, 4, 6, 2], [3, 6, 9, 4, 3]); // 8
```

### euclidean(p, q)

Returns the [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance#n_dimensions) between vectors p and q.

### squaredEuclidean(p, q)

Returns the [squared Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance#Squared_Euclidean_distance) between vectors p and q.

## License

[MIT](./LICENSE)

---
_Source: https://npm.io/package/ml-distance-euclidean · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._