Examples of MultivariateDifferentiableVectorFunction

Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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     * @deprecated this conversion method is temporary in version 3.1, as the {@link
     * DifferentiableMultivariateFunction} interface itself is deprecated
     */
    @Deprecated
    public static MultivariateDifferentiableVectorFunction toMultivariateDifferentiableVectorFunction(final DifferentiableMultivariateVectorFunction f) {
        return new MultivariateDifferentiableVectorFunction() {

            /** {@inheritDoc} */
            public double[] value(final double[] x) {
                return f.value(x);
            }
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Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultivariateDifferentiableVectorMultiStartOptimizer optimizer =
            new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.optimize(100, new MultivariateDifferentiableVectorFunction() {
            public double[] value(double[] point) {
                throw new TestException();
            }
            public DerivativeStructure[] value(DerivativeStructure[] point) {
                return point;
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Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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        if (dummyString == null) {
            throw new IOException("could not find dataset name");
        }
        this.name = dummyString;

        this.problem = new MultivariateDifferentiableVectorFunction() {

            public double[] value(final double[] a) {
                DerivativeStructure[] dsA = new DerivativeStructure[a.length];
                for (int i = 0; i < a.length; ++i) {
                    dsA[i] = new DerivativeStructure(a.length, 0, a[i]);
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Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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        final double[] w = new double[dataset.getNumObservations()];
        Arrays.fill(w, 1.0);

        final double[][] data = dataset.getData();
        final double[] initial = dataset.getStartingPoint(0);
        final MultivariateDifferentiableVectorFunction problem;
        problem = dataset.getLeastSquaresProblem();
        final PointVectorValuePair optimum;
        optimum = optimizer.optimize(100, problem, data[1], w, initial);

        final double[] actual = optimum.getPoint();
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Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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     * @deprecated this conversion method is temporary in version 3.1, as the {@link
     * DifferentiableMultivariateFunction} interface itself is deprecated
     */
    @Deprecated
    public static MultivariateDifferentiableVectorFunction toMultivariateDifferentiableVectorFunction(final DifferentiableMultivariateVectorFunction f) {
        return new MultivariateDifferentiableVectorFunction() {

            /** {@inheritDoc} */
            public double[] value(final double[] x) {
                return f.value(x);
            }
View Full Code Here

Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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     * @deprecated this conversion method is temporary in version 3.1, as the {@link
     * DifferentiableMultivariateFunction} interface itself is deprecated
     */
    @Deprecated
    public static MultivariateDifferentiableVectorFunction toMultivariateDifferentiableVectorFunction(final DifferentiableMultivariateVectorFunction f) {
        return new MultivariateDifferentiableVectorFunction() {

            /** {@inheritDoc} */
            public double[] value(final double[] x) {
                return f.value(x);
            }
View Full Code Here

Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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     * @deprecated this conversion method is temporary in version 3.1, as the {@link
     * DifferentiableMultivariateFunction} interface itself is deprecated
     */
    @Deprecated
    public static MultivariateDifferentiableVectorFunction toMultivariateDifferentiableVectorFunction(final DifferentiableMultivariateVectorFunction f) {
        return new MultivariateDifferentiableVectorFunction() {

            /** {@inheritDoc} */
            public double[] value(final double[] x) {
                return f.value(x);
            }
View Full Code Here

Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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        if (dummyString == null) {
            throw new IOException("could not find dataset name");
        }
        this.name = dummyString;

        this.problem = new MultivariateDifferentiableVectorFunction() {

            public double[] value(final double[] a) {
                DerivativeStructure[] dsA = new DerivativeStructure[a.length];
                for (int i = 0; i < a.length; ++i) {
                    dsA[i] = new DerivativeStructure(a.length, 0, a[i]);
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Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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        final double[] w = new double[dataset.getNumObservations()];
        Arrays.fill(w, 1.0);

        final double[][] data = dataset.getData();
        final double[] initial = dataset.getStartingPoint(0);
        final MultivariateDifferentiableVectorFunction problem;
        problem = dataset.getLeastSquaresProblem();
        final PointVectorValuePair optimum;
        optimum = optimizer.optimize(100, problem, data[1], w, initial);

        final double[] actual = optimum.getPoint();
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Examples of org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultivariateDifferentiableVectorMultiStartOptimizer optimizer =
            new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.optimize(100, new MultivariateDifferentiableVectorFunction() {
            public double[] value(double[] point) {
                throw new TestException();
            }
            public DerivativeStructure[] value(DerivativeStructure[] point) {
                return point;
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