Examples with TooManyEvaluationsException org.apache.commons.math3.exception.TooManyEvaluationsException used on opensource projects

Example 16 with TooManyEvaluationsException

use of org.apache.commons.math3.exception.TooManyEvaluationsException in project GDSC-SMLM by aherbert.

the class PCPALMFitting method runBoundedOptimiser.

private PointValuePair runBoundedOptimiser(double[][] gr, double[] initialSolution, double[] lB, double[] uB, SumOfSquaresModelFunction function) {
    // Create the functions to optimise
    ObjectiveFunction objective = new ObjectiveFunction(new SumOfSquaresMultivariateFunction(function));
    ObjectiveFunctionGradient gradient = new ObjectiveFunctionGradient(new SumOfSquaresMultivariateVectorFunction(function));
    final boolean debug = false;
    // Try a BFGS optimiser since this will produce a deterministic solution and can respect bounds.
    PointValuePair optimum = null;
    boundedEvaluations = 0;
    final MaxEval maxEvaluations = new MaxEval(2000);
    MultivariateOptimizer opt = null;
    for (int iteration = 0; iteration <= fitRestarts; iteration++) {
        try {
            opt = new BFGSOptimizer();
            final double relativeThreshold = 1e-6;
            // Configure maximum step length for each dimension using the bounds
            double[] stepLength = new double[lB.length];
            for (int i = 0; i < stepLength.length; i++) stepLength[i] = (uB[i] - lB[i]) * 0.3333333;
            // The GoalType is always minimise so no need to pass this in
            optimum = opt.optimize(maxEvaluations, gradient, objective, new InitialGuess((optimum == null) ? initialSolution : optimum.getPointRef()), new SimpleBounds(lB, uB), new BFGSOptimizer.GradientTolerance(relativeThreshold), new BFGSOptimizer.StepLength(stepLength));
            if (debug)
                System.out.printf("BFGS Iter %d = %g (%d)\n", iteration, optimum.getValue(), opt.getEvaluations());
        } catch (TooManyEvaluationsException e) {
            // No need to restart
            break;
        } catch (RuntimeException e) {
            // No need to restart
            break;
        } finally {
            boundedEvaluations += opt.getEvaluations();
        }
    }
    // Try a CMAES optimiser which is non-deterministic. To overcome this we perform restarts.
    // CMAESOptimiser based on Matlab code:
    // https://www.lri.fr/~hansen/cmaes.m
    // Take the defaults from the Matlab documentation
    //Double.NEGATIVE_INFINITY;
    double stopFitness = 0;
    boolean isActiveCMA = true;
    int diagonalOnly = 0;
    int checkFeasableCount = 1;
    //Well19937c();
    RandomGenerator random = new Well44497b();
    boolean generateStatistics = false;
    ConvergenceChecker<PointValuePair> checker = new SimpleValueChecker(1e-6, 1e-10);
    // The sigma determines the search range for the variables. It should be 1/3 of the initial search region.
    double[] range = new double[lB.length];
    for (int i = 0; i < lB.length; i++) range[i] = (uB[i] - lB[i]) / 3;
    OptimizationData sigma = new CMAESOptimizer.Sigma(range);
    OptimizationData popSize = new CMAESOptimizer.PopulationSize((int) (4 + Math.floor(3 * Math.log(initialSolution.length))));
    SimpleBounds bounds = new SimpleBounds(lB, uB);
    opt = new CMAESOptimizer(maxEvaluations.getMaxEval(), stopFitness, isActiveCMA, diagonalOnly, checkFeasableCount, random, generateStatistics, checker);
    // Restart the optimiser several times and take the best answer.
    for (int iteration = 0; iteration <= fitRestarts; iteration++) {
        try {
            // Start from the initial solution
            PointValuePair constrainedSolution = opt.optimize(new InitialGuess(initialSolution), objective, GoalType.MINIMIZE, bounds, sigma, popSize, maxEvaluations);
            if (debug)
                System.out.printf("CMAES Iter %d initial = %g (%d)\n", iteration, constrainedSolution.getValue(), opt.getEvaluations());
            boundedEvaluations += opt.getEvaluations();
            if (optimum == null || constrainedSolution.getValue() < optimum.getValue()) {
                optimum = constrainedSolution;
            }
        } catch (TooManyEvaluationsException e) {
        } catch (TooManyIterationsException e) {
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
        if (optimum == null)
            continue;
        try {
            // Also restart from the current optimum
            PointValuePair constrainedSolution = opt.optimize(new InitialGuess(optimum.getPointRef()), objective, GoalType.MINIMIZE, bounds, sigma, popSize, maxEvaluations);
            if (debug)
                System.out.printf("CMAES Iter %d restart = %g (%d)\n", iteration, constrainedSolution.getValue(), opt.getEvaluations());
            if (constrainedSolution.getValue() < optimum.getValue()) {
                optimum = constrainedSolution;
            }
        } catch (TooManyEvaluationsException e) {
        } catch (TooManyIterationsException e) {
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
    }
    return optimum;
}

Example 17 with TooManyEvaluationsException

use of org.apache.commons.math3.exception.TooManyEvaluationsException in project GDSC-SMLM by aherbert.

the class PoissonFunctionTest method cumulativeProbabilityIsOneWithRealAbove4.

private void cumulativeProbabilityIsOneWithRealAbove4(final double gain, final double mu, int min, int max) {
    final double o = mu * gain;
    final PoissonFunction f = new PoissonFunction(1.0 / gain, true);
    double p = 0;
    SimpsonIntegrator in = new SimpsonIntegrator(3, 30);
    try {
        p = in.integrate(Integer.MAX_VALUE, new UnivariateFunction() {

            public double value(double x) {
                return f.likelihood(x, o);
            }
        }, min, max);
        System.out.printf("g=%f, mu=%f, o=%f, p=%f\n", gain, mu, o, p);
        Assert.assertEquals(String.format("g=%f, mu=%f", gain, mu), 1, p, 0.02);
    } catch (TooManyEvaluationsException e) {
        //double inc = max / 20000.0;
        //for (double x = 0; x <= max; x += inc)
        //{
        //	final double pp = f.likelihood(x, o);
        //	//System.out.printf("g=%f, mu=%f, o=%f, p=%f\n", gain, mu, o, pp);
        //	p += pp;
        //}
        //System.out.printf("g=%f, mu=%f, o=%f, p=%f\n", gain, mu, o, p);
        Assert.assertFalse(e.getMessage(), true);
    }
}

Example 18 with TooManyEvaluationsException

use of org.apache.commons.math3.exception.TooManyEvaluationsException in project GDSC-SMLM by aherbert.

the class PCPALMMolecules method fitGaussian.

private double[] fitGaussian(float[] x, float[] y) {
    WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = 0; i < x.length; i++) obs.add(x[i], y[i]);
    Collection<WeightedObservedPoint> observations = obs.toList();
    GaussianCurveFitter fitter = GaussianCurveFitter.create().withMaxIterations(2000);
    GaussianCurveFitter.ParameterGuesser guess = new GaussianCurveFitter.ParameterGuesser(observations);
    double[] initialGuess = null;
    try {
        initialGuess = guess.guess();
        return fitter.withStartPoint(initialGuess).fit(observations);
    } catch (TooManyEvaluationsException e) {
        // Use the initial estimate
        return initialGuess;
    } catch (Exception e) {
        // Just in case there is another exception type, or the initial estimate failed
        return null;
    }
}