ml-levenberg-marquardt/lib/step.js

Curve fitting method in javascript

import { inverse, Matrix } from 'ml-matrix';
import gradientFunction from "./gradient_function.js";
/**
 * Matrix function over the samples
 *
 * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
 * @param evaluatedData - Array of previous evaluated function values
 */
function matrixFunction(data, evaluatedData) {
    const m = data.x.length;
    const ans = new Matrix(m, 1);
    for (let point = 0; point < m; point++) {
        ans.set(point, 0, data.y[point] - evaluatedData[point]);
    }
    return ans;
}
/**
 * Iteration for Levenberg-Marquardt
 *
 * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
 * @param params - Array of previous parameter values
 * @param damping - Levenberg-Marquardt parameter
 * @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 parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
 * @param weights - scale the gradient and residual error by weights
 */
export default function step(data, params, damping, gradientDifference, parameterizedFunction, centralDifference, weights) {
    const identity = Matrix.eye(params.length, params.length, damping);
    const func = parameterizedFunction(params);
    const evaluatedData = new Float64Array(data.x.length);
    for (let i = 0; i < data.x.length; i++) {
        evaluatedData[i] = func(data.x[i]);
    }
    const gradientFunc = gradientFunction(data, evaluatedData, params, gradientDifference, parameterizedFunction, centralDifference);
    const residualError = matrixFunction(data, evaluatedData);
    const inverseMatrix = inverse(identity.add(gradientFunc.mmul(gradientFunc.transpose().scale('row', { scale: weights }))));
    const jacobianWeightResidualError = gradientFunc.mmul(residualError.scale('row', { scale: weights }));
    const perturbations = inverseMatrix.mmul(jacobianWeightResidualError);
    return {
        perturbations,
        jacobianWeightResidualError,
    };
}
//# sourceMappingURL=step.js.map