Package org.apache.commons.math3.analysis

Examples of org.apache.commons.math3.analysis.ParametricUnivariateFunction

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        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);
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        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
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        curveFitter.addObservedPoint( 62, 17586);
        curveFitter.addObservedPoint(125, 30582);
        curveFitter.addObservedPoint(250, 45087);
        curveFitter.addObservedPoint(500, 50683);

        ParametricUnivariateFunction f = new ParametricUnivariateFunction() {
            public double value(double x, double ... parameters) {
                double a = parameters[0];
                double b = parameters[1];
                double c = parameters[2];
                double d = parameters[3];
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        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);
View Full Code Here
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        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
View Full Code Here
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        curveFitter.addObservedPoint( 62, 17586);
        curveFitter.addObservedPoint(125, 30582);
        curveFitter.addObservedPoint(250, 45087);
        curveFitter.addObservedPoint(500, 50683);

        ParametricUnivariateFunction f = new ParametricUnivariateFunction() {

            public double value(double x, double ... parameters) {

                double a = parameters[0];
                double b = parameters[1];
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     * @return the parameters of the Gaussian function that best fits the
     * observed points (in the same order as above).
     * @since 3.0
     */
    public double[] fit(double[] initialGuess) {
        final ParametricUnivariateFunction f = new ParametricUnivariateFunction() {
                private final ParametricUnivariateFunction g = new Gaussian.Parametric();

                public double value(double x, double ... p) {
                    double v = Double.POSITIVE_INFINITY;
                    try {
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        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);
View Full Code Here
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        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
View Full Code Here
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        curveFitter.addObservedPoint( 62, 17586);
        curveFitter.addObservedPoint(125, 30582);
        curveFitter.addObservedPoint(250, 45087);
        curveFitter.addObservedPoint(500, 50683);

        ParametricUnivariateFunction f = new ParametricUnivariateFunction() {

            public double value(double x, double ... parameters) {

                double a = parameters[0];
                double b = parameters[1];
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Related Classes of org.apache.commons.math3.analysis.ParametricUnivariateFunction

  • 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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