https://github.com/mljs/fcnnls
Fast Combinatorial Non-negative Least Squares
https://github.com/mljs/fcnnls
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Last synced: about 1 year ago
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Fast Combinatorial Non-negative Least Squares
- Host: GitHub
- URL: https://github.com/mljs/fcnnls
- Owner: mljs
- License: mit
- Created: 2019-08-08T16:38:06.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2024-10-14T14:29:19.000Z (over 1 year ago)
- Last Synced: 2025-04-11T10:59:35.326Z (about 1 year ago)
- Topics: hacktoberfest
- Language: TypeScript
- Homepage:
- Size: 1.39 MB
- Stars: 6
- Watchers: 8
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
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README
# fcnnls
[![NPM version][npm-image]][npm-url]
[![build status][ci-image]][ci-url]
[![Test coverage][codecov-image]][codecov-url]
[![npm download][download-image]][download-url]
[](https://doi.org/10.5281/zenodo.8189402)
Fast Combinatorial Non-negative Least Squares.
As described in the publication by Van Benthem and Keenan ([10.1002/cem.889](http://doi.org/10.1002/cem.889)), which is in turn based on the active-set method algorithm previously published by Lawson and Hanson. The basic active-set method is implemented in the [nnls repository.](https://github.com/mljs/nnls)
Given the matrices $\mathbf{X}$ and $\mathbf{Y}$, the code finds the matrix $\mathbf{K}$ that minimises the squared Frobenius norm $$\mathrm{argmin}_K ||\mathbf{XK} -\mathbf{Y}||^2_F$$ subject to $\mathbf{K}\geq 0$.
https://en.wikipedia.org/wiki/Non-negative_least_squares
## Installation
```bash
npm i ml-fcnnls
```
## Usage Example
1. Single $y$, using arrays as inputs.
```js
import { fcnnlsVector } from 'ml-fcnnls';
const X = [
[1, 1, 2],
[10, 11, -9],
[-1, 0, 0],
[-5, 6, -7],
];
const y = [-1, 11, 0, 1];
const k = fcnnlsVector(X, y).K.to1DArray();
/* k = [0.4610, 0.5611, 0] */
```
2. Multiple RHS, using `Matrix` instances as inputs.
```js
import { fcnnls } from 'ml-fcnnls';
import { Matrix } from 'ml-matrix'; //npm i ml-matrix
// Example with multiple RHS
const X = new Matrix([
[1, 1, 2],
[10, 11, -9],
[-1, 0, 0],
[-5, 6, -7],
]);
// Y can either be a Matrix or an array of arrays
const Y = new Matrix([
[-1, 0, 0, 9],
[11, -20, 103, 5],
[0, 0, 0, 0],
[1, 2, 3, 4],
]);
const K = fcnnls(X, Y).K;
// `K.to2DArray()` converts the matrix to array.
/*
K = Matrix([
[0.4610, 0, 4.9714, 0],
[0.5611, 0, 4.7362, 2.2404],
[0, 1.2388, 0, 1.9136],
])
*/
```
3. Using the options
```js
const K = fcnnls(X, Y, {
info: true, // returns the error/iteration.
maxIterations: 5,
gradientTolerance: 0,
});
/* same result than 2*/
```
## [API Documentation](https://mljs.github.io/fcnnls/)
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/ml-fcnnls.svg
[npm-url]: https://www.npmjs.com/package/ml-fcnnls
[ci-image]: https://github.com/mljs/fcnnls/workflows/Node.js%20CI/badge.svg?branch=main
[ci-url]: https://github.com/mljs/fcnnls/actions?query=workflow%3A%22Node.js+CI%22
[codecov-image]: https://img.shields.io/codecov/c/github/mljs/fcnnls.svg
[codecov-url]: https://codecov.io/gh/mljs/fcnnls
[download-image]: https://img.shields.io/npm/dm/ml-fcnnls.svg
[download-url]: https://www.npmjs.com/package/ml-fcnnls