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

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

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        final double tol = 1e-100;
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol, maxEval);

        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], 1e-15);
        }
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    }

    @Test
    public void testRedundantSolvable() {
        // Levenberg-Marquardt should handle redundant information gracefully
        checkUnsolvableProblem(new LevenbergMarquardtOptimizer(), true);
    }
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        Random randomizer = new Random(0x5551480dca5b369bl);
        double maxError = 0;
        for (int degree = 0; degree < 10; ++degree) {
            PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

            PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
            for (int i = 0; i < 40000; ++i) {
                double x = -1.0 + i / 20000.0;
                fitter.addObservedPoint(1.0, x,
                                        p.value(x) + 0.1 * randomizer.nextGaussian());
            }
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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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Related Classes of org.apache.commons.math3.optim.nonlinear.vector.jacobian.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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