Examples of org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer

Package org.apache.commons.math3.optim.nonlinear.vector.jacobian

Examples of org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer

org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer

etlib.org/minpack/lmder.f">lmder routine with minor changes. The changes include the over-determined resolution, the use of inherited convergence checker 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 authors of the original fortran version are:

Argonne National Laboratory. MINPACK project. March 1980
Burton S. Garbow
Kenneth E. Hillstrom
Jorge J. More

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:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
The end-user documentation included with the redistribution, if any, must include the following acknowledgment: This product includes software developed by the University of Chicago, as Operator of Argonne National Laboratory. Alternately, this acknowledgment may appear in the software itself, if and wherever such third-party acknowledgments normally appear.
WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL BE CORRECTED.
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@since 2.0 @deprecated All classes and interfaces in this package are deprecated.The optimizers that were provided here were moved to the {@link org.apache.commons.math3.fitting.leastsquares} package(cf. MATH-1008).

    /**
     * Poor data: right of peak not symmetric with left of peak.
     */
    @Test
    public void testFit04() {
        GaussianFitter fitter = new GaussianFitter(new LevenbergMarquardtOptimizer());
        addDatasetToGaussianFitter(DATASET2, fitter);
        double[] parameters = fitter.fit();


        Assert.assertEquals(233003.2967252038, parameters[0], 1e-4);
        Assert.assertEquals(-10.654887521095983, parameters[1], 1e-4);

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    /**
     * Poor data: long tails.
     */
    @Test
    public void testFit05() {
        GaussianFitter fitter = new GaussianFitter(new LevenbergMarquardtOptimizer());
        addDatasetToGaussianFitter(DATASET3, fitter);
        double[] parameters = fitter.fit();


        Assert.assertEquals(283863.81929180305, parameters[0], 1e-4);
        Assert.assertEquals(-13.29641995105174, parameters[1], 1e-4);

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    /**
     * Poor data: right of peak is missing.
     */
    @Test
    public void testFit06() {
        GaussianFitter fitter = new GaussianFitter(new LevenbergMarquardtOptimizer());
        addDatasetToGaussianFitter(DATASET4, fitter);
        double[] parameters = fitter.fit();


        Assert.assertEquals(285250.66754309234, parameters[0], 1e-4);
        Assert.assertEquals(-13.528375695228455, parameters[1], 1e-4);

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    /**
     * Basic with smaller dataset.
     */
    @Test
    public void testFit07() {
        GaussianFitter fitter = new GaussianFitter(new LevenbergMarquardtOptimizer());
        addDatasetToGaussianFitter(DATASET5, fitter);
        double[] parameters = fitter.fit();


        Assert.assertEquals(3514384.729342235, parameters[0], 1e-4);
        Assert.assertEquals(4.054970307455625, parameters[1], 1e-4);

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            2.267636001476696E-12,
            5.3880460095836855E-12,
            1.2431632654852931E-11
        };


        GaussianFitter fitter = new GaussianFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i < data.length; i++) {
            fitter.addObservedPoint(i, data[i]);
        }
        final double[] p = fitter.fit();

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        Assert.assertEquals(5.75214622, p[2], 1e-8);
    }


    @Test
    public void testMath798() {
        final GaussianFitter fitter = new GaussianFitter(new LevenbergMarquardtOptimizer());


        // When the data points are not commented out below, the fit stalls.
        // This is expected however, since the whole dataset hardly looks like
        // a Gaussian.
        // When commented out, the fit proceeds fine.

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    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);


        final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
        final PolynomialFitter fitter = new PolynomialFitter(optim);
        final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
        final PolynomialFunction f = new PolynomialFunction(coeff);


        // Collect data from a known polynomial.

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    public void testNoError() {
        Random randomizer = new Random(64925784252l);
        for (int degree = 1; degree < 10; ++degree) {
            PolynomialFunction p = buildRandomPolynomial(degree, randomizer);


            PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
            for (int i = 0; i <= degree; ++i) {
                fitter.addObservedPoint(1.0, i, p.value(i));
            }


            final double[] init = new double[degree + 1];

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        Random randomizer = new Random(53882150042l);
        double maxError = 0;
        for (int degree = 0; degree < 10; ++degree) {
            PolynomialFunction p = buildRandomPolynomial(degree, randomizer);


            PolynomialFitter fitter = new PolynomialFitter(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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        final double tol = 1e-14;
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 3;


        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);


        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], tol);
        }

View Full Code Here

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