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

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

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

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

            PolynomialFunction fitted = fitter.fit();
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        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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     * @throws FunctionEvaluationException in the event of a test case error
     */
    @Test
    public void testFit01()
    throws OptimizationException, FunctionEvaluationException {
        CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer());
        addDatasetToCurveFitter(DATASET1, fitter);
        double[] parameters = fitter.fit(new ParametricGaussianFunction(),
                                         new double[] {8.64753e3, 3.483323e6, 4.06322, 1.946857e-2});
        assertEquals(99200.94715858076, parameters[0], 1e-4);
        assertEquals(3410515.221897707, parameters[1], 1e-4);
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     * @throws FunctionEvaluationException in the event of a test case error
     */
    @Test
    public void testFit02()
    throws OptimizationException, FunctionEvaluationException {
        CurveFitter fitter = new CurveFitter(new LevenbergMarquardtOptimizer());
        addDatasetToCurveFitter(DATASET1, fitter);
        double[] parameters = fitter.fit(new ParametricGaussianFunction(),
                                         new double[] {500000.0, 3500000.0, 4.055, 0.025479654});
        assertEquals(99200.81836264656, parameters[0], 1e-4);
        assertEquals(3410515.327151986, parameters[1], 1e-4);
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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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    @Test
    public void testMath303()
        throws OptimizationException, FunctionEvaluationException {

        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        CurveFitter fitter = new CurveFitter(optimizer);
        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);
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Related Classes of org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer

  • newExamples.archive.Exoplanet
  • newExamples.FittingRun
  • org.aavso.tools.vstar.plugin.model.impl.ApacheCommonsPolynomialFitCreatorPlugin$PolynomialFitCreator
  • org.apache.commons.math.optimization.fitting.CurveFitterTest
  • org.apache.commons.math.optimization.fitting.GaussianFitterTest
  • org.apache.commons.math.optimization.fitting.HarmonicFitterTest
  • org.apache.commons.math.optimization.fitting.ParametricGaussianFunctionTest
  • org.apache.commons.math.optimization.fitting.PolynomialFitterTest
  • org.apache.commons.math.optimization.VectorialPointValuePair
  • org.apache.commons.math.optimization.OptimizationException
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