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# npm

## ml-ngmca

1.0.0 • Public • Published

# nGMCA - non-negative Generalized Morphological Component Analysis

A tool for non-negative matrix factorization.

## Instalation

$npm install ml-ngmca  ## Usage import { nGMCA } from 'ml-ngmca'; const result = nGMCA(dataMatrix, options); ### As a CommonJS module const { nGMCA } = require('ml-ngmca'); const result = nGMCA(dataMatrix, options); ## API Documentation In order to get a general idea of the problem you could also check the Wikipedia article. ## Examples You will be able to separate the components of a mixture if you have a series of measurements correlated by a composition profile e.g NMR or mass spectra coming from a chromatographic coupled technique of two or more close retention times. So you will have a matrix with a number of rows equal or greater than the number of pure components of the mixture. import { Matrix } from 'ml-matrix'; import { nGMCA } from 'ml-ngmca'; let pureSpectra = new Matrix([[1, 0, 1, 0]]); let composition = new Matrix([[1, 2, 3, 2, 1]]); // matrix = composition.transpose().mmul(pureSpectra) let matrix = new Matrix([ [1, 0, 1, 0], [2, 0, 2, 0], [3, 0, 3, 0], [2, 0, 2, 0], [1, 0, 1, 0], ]); const options = { maximumIteration: 200, phaseRatio: 0.4, }; const result = nGMCA(matrix, 1, options); const { A, S } = result; console.log(A =${A.to2DArray()} S =${S.to2DArray()}); /** A = [ [ 0.22941573387056177 ], [ 0.45883146774112354 ], [ 0.6882472016116853 ], [ 0.45883146774112354 ], [ 0.22941573387056177 ] ] S = [ [ 4.358898943540674, 0, 4.358898943540674, 0 ] ] if you reescale both S maxS and A with 1/maxS. */ let maxByRow = []; for (let i = 0; i < S.rows; i++) { maxByRow.push(S.maxRow(i)); } S.scale('row', { scale: maxByRow }); A.scale('column', { scale: maxByRow.map((e) => 1 / e), }); /** S = [ [ 1, 0, 1, 0 ] ] A = [ [1.0000000000000002], [2.0000000000000004], [3.0000000000000004], [2.0000000000000004], [1.0000000000000002] ] */ const estimatedMatrix = A.mmul(S); const diff = Matrix.sub(matrix, estimatedMatrix); Here is a second example: let matrix = new Matrix([ [0, 0, 1, 1, 1], [0, 0, 1, 1, 1], [2, 2, 2, 0, 0], [2, 2, 2, 0, 0], ]); const options = { maximumIteration: 200, phaseRatio: 0.4, }; const result = nGMCA(matrix, 1, options); const { A, S } = result; console.log(A =${A} S =${S}); /** A = [ [ 0.707107 0 0.707107 0 2.26e-17 0.707107 2.26e-17 0.707107 ] ] S = [ [ 9.86e-32 9.86e-32 1.41421 1.41421 1.41421 2.82843 2.82843 2.82843 0 0 ] ] note: 9.86e-32 and 2.26e-17 is practically zero so if you reescale both S maxS and A with 1/maxS. */ let maxByRow = []; for (let i = 0; i < S.rows; i++) { maxByRow.push(S.maxRow(i)); } S.scale('row', { scale: maxByRow }); A.scale('column', { scale: maxByRow.map((e) => 1 / e), }); console.log(A =${A} S =\${S});
/**
A = [
[
1 0
1 0
0 1
0 1
]
]
S = [
[
0 0 1 1 1
2 2 2 0 0
]
]
*/

The result has the matrices A and S, the estimated matrices of compositions and pureSpectra respectively. It's possible that the matrices A and S have not the same scale than pureSpectra and composition matrices because of AS has an infinity of combination to get the target matrix.

MIT

## Keywords

none

### Install

npm i ml-ngmca

### Repository

github.com/mljs/nGMCA

27

1.0.0

MIT

38.4 kB

19