Package org.apache.commons.math3.analysis.differentiation

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


                return false;
            }
            final int    n = FastMath.max(1, (int) FastMath.ceil(FastMath.abs(dt) / maxCheckInterval));
            final double h = dt / n;

            final UnivariateFunction f = new UnivariateFunction() {
                public double value(final double t) throws LocalMaxCountExceededException {
                    try {
                        interpolator.setInterpolatedTime(t);
                        return handler.g(t, getCompleteState(interpolator));
                    } catch (MaxCountExceededException mcee) {
                        throw new LocalMaxCountExceededException(mcee);
                    }
                }
            };

            double ta = t0;
            double ga = g0;
            for (int i = 0; i < n; ++i) {

                // evaluate handler value at the end of the substep
                final double tb = t0 + (i + 1) * h;
                interpolator.setInterpolatedTime(tb);
                final double gb = handler.g(tb, getCompleteState(interpolator));

                // check events occurrence
                if (g0Positive ^ (gb >= 0)) {
                    // there is a sign change: an event is expected during this step

                    // variation direction, with respect to the integration direction
                    increasing = gb >= ga;

                    // find the event time making sure we select a solution just at or past the exact root
                    final double root;
                    if (solver instanceof BracketedUnivariateSolver<?>) {
                        @SuppressWarnings("unchecked")
                        BracketedUnivariateSolver<UnivariateFunction> bracketing =
                                (BracketedUnivariateSolver<UnivariateFunction>) solver;
                        root = forward ?
                               bracketing.solve(maxIterationCount, f, ta, tb, AllowedSolution.RIGHT_SIDE) :
                               bracketing.solve(maxIterationCount, f, tb, ta, AllowedSolution.LEFT_SIDE);
                    } else {
                        final double baseRoot = forward ?
                                                solver.solve(maxIterationCount, f, ta, tb) :
                                                solver.solve(maxIterationCount, f, tb, ta);
                        final int remainingEval = maxIterationCount - solver.getEvaluations();
                        BracketedUnivariateSolver<UnivariateFunction> bracketing =
                                new PegasusSolver(solver.getRelativeAccuracy(), solver.getAbsoluteAccuracy());
                        root = forward ?
                               UnivariateSolverUtils.forceSide(remainingEval, f, bracketing,
                                                                   baseRoot, ta, tb, AllowedSolution.RIGHT_SIDE) :
                               UnivariateSolverUtils.forceSide(remainingEval, f, bracketing,
                                                                   baseRoot, tb, ta, AllowedSolution.LEFT_SIDE);
                    }

                    if ((!Double.isNaN(previousEventTime)) &&
                        (FastMath.abs(root - ta) <= convergence) &&
                        (FastMath.abs(root - previousEventTime) <= convergence)) {
                        // we have either found nothing or found (again ?) a past event,
                        // retry the substep excluding this value, and taking care to have the
                        // required sign in case the g function is noisy around its zero and
                        // crosses the axis several times
                        do {
                            ta = forward ? ta + convergence : ta - convergence;
                            ga = f.value(ta);
                        } while ((g0Positive ^ (ga >= 0)) && (forward ^ (ta >= tb)));
                        --i;
                    } else if (Double.isNaN(previousEventTime) ||
                               (FastMath.abs(previousEventTime - root) > convergence)) {
                        pendingEventTime = root;
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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

        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;
View Full Code Here

        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]);
View Full Code Here

        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();
View Full Code Here

     * @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

     * @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

     * @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

        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]);
View Full Code Here

        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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