Examples of org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer

Package org.apache.commons.math.optimization.general

Examples of org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer

org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer

etlib.org/minpack/lmder.f">lmder routine with minor changes. The changes include the over-determined resolution and the Q.R. decomposition which has been rewritten following the algorithm described in the P. Lascaux and R. Theodor book Analyse numérique matricielle appliquée à l'art de l'ingénieur, Masson 1986. The redistribution policy for MINPACK is available here, for convenience, it is reproduced below.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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@author Argonne National Laboratory. MINPACK project. March 1980 (original fortran) @author Burton S. Garbow (original fortran) @author Kenneth E. Hillstrom (original fortran) @author Jorge J. More (original fortran) @version $Revision: 795978 $ $Date: 2009-07-20 15:57:08 -0400 (Mon, 20 Jul 2009) $ @since 2.0

        double maxError = 0;
        for (int degree = 0; degree < 10; ++degree) {
            PolynomialFunction p = buildRandomPolynomial(degree, randomizer);


            PolynomialFitter fitter =
                new PolynomialFitter(degree, new LevenbergMarquardtOptimizer());
            for (double x = -1.0; x < 1.0; x += 0.01) {
                fitter.addObservedPoint(1.0, x,
                                        p.value(x) + 0.1 * randomizer.nextGaussian());
            }

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    }


    @Test
    public void testRedundantSolvable() {
        // Levenberg-Marquardt should handle redundant information gracefully
        checkUnsolvableProblem(new LevenbergMarquardtOptimizer(), true);
    }

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    @Test
    public void testNoError() throws OptimizationException {
        HarmonicFunction f = new HarmonicFunction(0.2, 3.4, 4.1);


        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 1.3; x += 0.01) {
            fitter.addObservedPoint(1.0, x, f.value(x));
        }


        HarmonicFunction fitted = fitter.fit();

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    public void test1PercentError() throws OptimizationException {
        Random randomizer = new Random(64925784252l);
        HarmonicFunction f = new HarmonicFunction(0.2, 3.4, 4.1);


        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1.0, x,
                                   f.value(x) + 0.01 * randomizer.nextGaussian());
        }

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    public void testInitialGuess() throws OptimizationException {
        Random randomizer = new Random(45314242l);
        HarmonicFunction f = new HarmonicFunction(0.2, 3.4, 4.1);


        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer(), new double[] { 0.15, 3.6, 4.5 });
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1.0, x,
                                   f.value(x) + 0.01 * randomizer.nextGaussian());
        }

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    public void testUnsorted() throws OptimizationException {
        Random randomizer = new Random(64925784252l);
        HarmonicFunction f = new HarmonicFunction(0.2, 3.4, 4.1);


        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());


        // build a regularly spaced array of measurements
        int size = 100;
        double[] xTab = new double[size];
        double[] yTab = new double[size];

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        // Number of transits
        logger.debug("Fit: Gaia will perform "+transits.size()+" transits of the system.");
        
        // Optimizer set-up
        logger.debug("Fit: Start fit.");
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        optimizer.setMaxIterations(MAX_ITERATIONS);
        optimizer.setCostRelativeTolerance(CONVERGENCE);
        optimizer.setParRelativeTolerance(CONVERGENCE);
        optimizer.setOrthoTolerance(CONVERGENCE);
        
        // Function optimization
        VectorialPointValuePair optimum;
        
        optimum = optimizer.optimize(function, targets, weights, FIRST_GUESS);
        
        // Covariances and errors
        logger.debug("Fit: Calculate errors and covariances.");
        double[]   fitErrors     = optimizer.guessParametersErrors();
        double[][] fitCovariance = optimizer.getCovariances();
        
        // Calculate chi square
        double[] bestFit = optimum.getValue();
        double chi2 = 0.0;
        for ( int i = 0; i < targets.length; i++ ) {

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    nTransits = function.getNTransits();


    System.out.printf("Number of effective transits: %4d\n", nTransits);
      
    // Optimizer set-up
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    optimizer.setMaxIterations(         MAX_ITERATIONS );
    optimizer.setCostRelativeTolerance( CONVERGENCE    );
    optimizer.setParRelativeTolerance(  CONVERGENCE    );
    optimizer.setOrthoTolerance(        CONVERGENCE    );
    
    // BamFunction optimization
    VectorialPointValuePair optimum = optimizer.optimize(function, target, weight, FIRST_GUESS);
    
    // Covariances and errors
    double[]   fitErrors     = optimizer.guessParametersErrors();
    double[][] fitCovariance = optimizer.getCovariances();
    
    // Output values
    FitResults fitResults = new FitResults(
        optimum.getValue(), optimum.getPoint(), fitErrors, fitCovariance);

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org.apache.commons.math.optimization.VectorialPointValuePair

org.apache.commons.math.optimization.OptimizationException

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