ml-levenberg-marquardt/lib/levenberg_marquardt.js

Curve fitting method in javascript

import checkOptions from "./check_options.js";
import errorCalculation from "./error_calculation.js";
import step from "./step.js";
/**
 * Curve fitting algorithm
 * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
 * @param parameterizedFunction - Takes an array of parameters and returns a function with the independent variable as its sole argument
 * @param options - Options object
 */
export function levenbergMarquardt(data, parameterizedFunction, options) {
    const checkedOptions = checkOptions(data, options);
    const { checkTimeout, minValues, maxValues, parameters, weightSquare, dampingStepUp, dampingStepDown, maxIterations, errorTolerance, centralDifference, gradientDifference, improvementThreshold, } = checkedOptions;
    let damping = checkedOptions.damping;
    let error = errorCalculation(data, parameters, parameterizedFunction, weightSquare);
    let optimalError = error;
    let optimalParameters = parameters.slice();
    let converged = error <= errorTolerance;
    let iteration = 0;
    for (; iteration < maxIterations && !converged; iteration++) {
        const previousError = error;
        const { perturbations, jacobianWeightResidualError } = step(data, parameters, damping, gradientDifference, parameterizedFunction, centralDifference, weightSquare);
        for (let k = 0; k < parameters.length; k++) {
            parameters[k] = Math.min(Math.max(minValues[k], parameters[k] - perturbations.get(k, 0)), maxValues[k]);
        }
        error = errorCalculation(data, parameters, parameterizedFunction, weightSquare);
        if (isNaN(error))
            break;
        if (error < optimalError - errorTolerance) {
            optimalError = error;
            optimalParameters = parameters.slice();
        }
        const improvementMetric = (previousError - error) /
            perturbations
                .transpose()
                .mmul(perturbations.mul(damping).add(jacobianWeightResidualError))
                .get(0, 0);
        if (improvementMetric > improvementThreshold) {
            damping = Math.max(damping / dampingStepDown, 1e-7);
        }
        else {
            damping = Math.min(damping * dampingStepUp, 1e7);
        }
        if (checkTimeout()) {
            throw new Error(`The execution time is over to ${options.timeout} seconds`);
        }
        converged = error <= errorTolerance;
    }
    return {
        parameterValues: optimalParameters,
        parameterError: optimalError,
        iterations: iteration,
    };
}
//# sourceMappingURL=levenberg_marquardt.js.map