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const levenbergMarquardt = require('ml-levenberg-marquardt'); function sinFunction([a, b]) { return (t) => a * Math.sin(b * t); } var len = 20; var data = { x: new Array(len), y: new Array(len) }; var sampleFunction = sinFunction([2, 2]); for (var i = 0; i < len; i++) { data.x[i] = i; data.y[i] = sampleFunction(i); } const options = { damping: 0.001, initialValues: [3, 3] }; var ans = levenbergMarquardt(data, sinFunction, options); console.log(ans);

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ml-levenberg-marquardt v1.0.3

Curve fitting method in javascript


NPM version build status Test coverage David deps npm download

Curve fitting method in javascript


$ npm install ml-levenberg-marquardt

API Documentation

This algorithm is based on the article Brown, Kenneth M., and J. E. Dennis. "Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation." Numerische Mathematik 18.4 (1971): 289-297.

In order to get a general idea of the problem you could also check the Wikipedia article.


// import library
import LM from 'ml-levenberg-marquardt';
// const LM = require('ml-levenberg-marquardt').default;

// function that receives the parameters and returns
// a function with the independent variable as a parameter
function sinFunction([a, b]) {
  return (t) => a * Math.sin(b * t);

// array of points to fit
let data = {
  x: [
    /* x1, x2, ... */
  y: [
    /* y1, y2, ... */

// array of initial parameter values
let initialValues = [
  /* a, b, c, ... */

const options = {
  damping: 1.5,
  initialValues: initialValues,
  gradientDifference: 10e-2,
  maxIterations: 100,
  errorTolerance: 10e-3

let fittedParams = LM(data, sinFunction, options);

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