Fast Combinatorial Non-negative Least Squares.
As described in the publication by Van Benthem and Keenan (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.
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
Usage Example
- Single $y$, using arrays as inputs.
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] */
- Multiple RHS, using
Matrixinstances as inputs.
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], ]) */
- Using the options
const K = fcnnls(X, Y, { info: true, // returns the error/iteration. maxIterations: 5, gradientTolerance: 0, }); /* same result than 2*/