ml-levenberg-marquardt/lib/gradient_function.js

Curve fitting method in javascript

import { Matrix } from 'ml-matrix';
/**
 * Difference of the matrix function over the parameters
 * @param data Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
 * @param evaluatedData - Array of previous evaluated function values
 * @param params - Array of previous parameter values
 * @param gradientDifference - The step size to approximate the jacobian matrix
 * @param centralDifference - If true the jacobian matrix is approximated by central differences otherwise by forward differences
 * @param paramFunction - The parameters and returns a function with the independent variable as a parameter
 */
export default function gradientFunction(data, evaluatedData, params, gradientDifference, paramFunction, centralDifference) {
    const nbParams = params.length;
    const nbPoints = data.x.length;
    const ans = Matrix.zeros(nbParams, nbPoints);
    let rowIndex = 0;
    for (let param = 0; param < nbParams; param++) {
        if (gradientDifference[param] === 0)
            continue;
        let delta = gradientDifference[param];
        let auxParams = params.slice();
        auxParams[param] += delta;
        const funcParam = paramFunction(auxParams);
        if (!centralDifference) {
            for (let point = 0; point < nbPoints; point++) {
                ans.set(rowIndex, point, (evaluatedData[point] - funcParam(data.x[point])) / delta);
            }
        }
        else {
            auxParams = params.slice();
            auxParams[param] -= delta;
            delta *= 2;
            const funcParam2 = paramFunction(auxParams);
            for (let point = 0; point < nbPoints; point++) {
                ans.set(rowIndex, point, (funcParam2(data.x[point]) - funcParam(data.x[point])) / delta);
            }
        }
        rowIndex++;
    }
    return ans;
}
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