Package org.apache.commons.math3.optimization.general

Examples of org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer

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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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public class HarmonicFitterTest {
    @Test(expected=NumberIsTooSmallException.class)
    public void testPreconditions1() {
        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());

        fitter.fit();
    }
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        final double w = 3.4;
        final double p = 4.1;
        HarmonicOscillator f = new HarmonicOscillator(a, w, p);

        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 1.3; x += 0.01) {
            fitter.addObservedPoint(1, x, f.value(x));
        }

        final double[] fitted = fitter.fit();
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        final double w = 3.4;
        final double p = 4.1;
        HarmonicOscillator f = new HarmonicOscillator(a, w, p);

        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1, x,
                                    f.value(x) + 0.01 * randomizer.nextGaussian());
        }

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    @Test
    public void testTinyVariationsData() {
        Random randomizer = new Random(64925784252l);

        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1, x, 1e-7 * randomizer.nextGaussian());
        }

        fitter.fit();
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        final double w = 3.4;
        final double p = 4.1;
        HarmonicOscillator f = new HarmonicOscillator(a, w, p);

        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1, x,
                                    f.value(x) + 0.01 * randomizer.nextGaussian());
        }

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        final double w = 3.4;
        final double p = 4.1;
        HarmonicOscillator f = new HarmonicOscillator(a, w, p);

        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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    @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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Related Classes of org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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