Package Exports
- ml-fcnnls
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Readme
fcnnls
Fast Combinatorial Non-negative Least Squares.
Fast algorithm for the solution of large‐scale non‐negativity‐constrained least squares problems from Van Benthem and Keenan (10.1002/cem.889), based on the active-set method algorithm published by Lawson and Hanson.
It solves the following optimisation problem.
Given 
an 
matrix and 
an 
matrix, find 
subject to 
, where 
is an 
matrix and 
is the Frobenius norm. In fact, is the best solution to the equation: 
, where . Note that if is a column vector, it performs the regular Non-negative Least Squares algorithm and finds a vector as a solution to the problem. Also, performing this algorithm when is a matrix is like running the algorithm on each column of , it will give the same result but in a much more efficient way.
https://en.wikipedia.org/wiki/Non-negative_least_squares
Installation
$ npm i ml-fcnnls
API Documentation
Usage
import { Matrix } from 'ml-matrix';
import { fcnnls } from 'ml-fcnnls';
// Example with multiple RHS
let X = new Matrix([
[1, 1, 2],
[10, 11, -9],
[-1, 0, 0],
[-5, 6, -7],
]);
// Y can either be a Matrix of an array of array
let Y = new Matrix([
[-1, 0, 0, 9],
[11, -20, 103, 5],
[0, 0, 0, 0],
[1, 2, 3, 4],
]);
let K = fcnnls(X, Y);
/*
K = Matrix([
[0.461, 0, 4.9714, 0],
[0.5611, 0, 4.7362, 2.2404],
[0, 1.2388, 0, 1.9136],
])
*/
import { fcnnlsVector } from 'ml-fcnnls';
// Example with single RHS and same X
// Should be giving a vector with the element of the first column of K in the previous example, since y is the first column of Y
let X = new Matrix([
[1, 1, 2],
[10, 11, -9],
[-1, 0, 0],
[-5, 6, -7],
]);
let y = [-1, 11, 0, 1];
let k = fcnnlsVector(X, y);
/*
k = [0.461, 0.5611, 0]
*/