Example usage for org.apache.commons.math3.optim MaxEval MaxEval

Introduction

In this page you can find the example usage for org.apache.commons.math3.optim MaxEval MaxEval.

Prototype

public MaxEval(int max) 

Source Link

Usage

From source file:org.eclipse.dawnsci.analysis.dataset.roi.fitting.CircleCoordinatesFunction.java

@Override
public void geometricFit(IDataset x, IDataset y, double[] init) {
    residual = Double.NaN;//  w  w  w. j  a  v a  2s .  c  o  m
    if (x.getSize() < PARAMETERS || y.getSize() < PARAMETERS) {
        throw new IllegalArgumentException("Need " + PARAMETERS + " or more points");
    }

    if (x.getSize() == PARAMETERS) {
        init = quickfit(x, y);
        for (int i = 0; i < PARAMETERS; i++)
            parameters[i] = init[i];
        residual = 0;
        return;
    }

    if (init == null)
        init = quickfit(x, y);
    else if (init.length < PARAMETERS)
        throw new IllegalArgumentException("Need " + PARAMETERS + " parameters");

    CircleCoordinatesFunction f = (CircleCoordinatesFunction) getFitFunction(x, y);
    LevenbergMarquardtOptimizer opt = new LevenbergMarquardtOptimizer();

    try {
        PointVectorValuePair result = opt.optimize(new ModelFunction(f),
                new ModelFunctionJacobian(f.jacobian()), f.getTarget(), f.getWeight(),
                f.calcAllInitValues(init), new MaxEval(MAX_EVALUATIONS));

        double[] point = result.getPointRef();
        for (int i = 0; i < PARAMETERS; i++)
            parameters[i] = point[i];

        residual = opt.getRMS();
        logger.trace("Circle fit: rms = {}, x^2 = {}", opt.getRMS(), opt.getChiSquare());
    } catch (DimensionMismatchException e) {
        // cannot happen
    } catch (IllegalArgumentException e) {
        // should not happen!
    } catch (TooManyEvaluationsException e) {
        throw new IllegalArgumentException("Problem with optimizer converging");
    }
}

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From source file:org.eclipse.dawnsci.analysis.dataset.roi.fitting.EllipseCoordinatesFunction.java

@Override
public void geometricFit(IDataset x, IDataset y, double[] init) {
    residual = Double.NaN;/*from   w  w  w . j a  v  a2 s  .  c o m*/
    if (x.getSize() < PARAMETERS || y.getSize() < PARAMETERS) {
        throw new IllegalArgumentException("Need " + PARAMETERS + " or more points");
    }

    if (init == null)
        init = quickfit(x, y);
    else if (init.length < PARAMETERS)
        throw new IllegalArgumentException("Need " + PARAMETERS + " parameters");

    EllipseCoordinatesFunction f = (EllipseCoordinatesFunction) getFitFunction(x, y);
    LevenbergMarquardtOptimizer opt = new LevenbergMarquardtOptimizer();

    try {
        PointVectorValuePair result = opt.optimize(new ModelFunction(f),
                new ModelFunctionJacobian(f.jacobian()), f.getTarget(), f.getWeight(),
                f.calcAllInitValues(init), new MaxEval(MAX_EVALUATIONS));

        double[] point = result.getPointRef();
        for (int i = 0; i < PARAMETERS; i++)
            parameters[i] = point[i];

        residual = opt.getRMS();

        logger.trace("Ellipse fit: rms = {}, x^2 = {}", residual, opt.getChiSquare());
    } catch (DimensionMismatchException e) {
        // cannot happen
    } catch (IllegalArgumentException e) {
        // should not happen!
    } catch (TooManyEvaluationsException e) {
        throw new IllegalArgumentException("Problem with optimizer converging");
    }
}

---

From source file:org.hawkular.datamining.forecast.models.AbstractModelOptimizer.java

protected void optimize(double[] initialGuess, MultivariateFunctionMappingAdapter costFunction) {

    // Nelder-Mead Simplex
    SimplexOptimizer optimizer = new SimplexOptimizer(0.0001, 0.0001);
    PointValuePair unBoundedResult = optimizer.optimize(GoalType.MINIMIZE, new MaxIter(MAX_ITER),
            new MaxEval(MAX_EVAL), new InitialGuess(initialGuess), new ObjectiveFunction(costFunction),
            new NelderMeadSimplex(initialGuess.length));

    result = costFunction.unboundedToBounded(unBoundedResult.getPoint());
}

---

From source file:org.hawkular.datamining.forecast.models.performance.OptimizationAlgorithmsTests.java

private long executeSimplex(ModelData modelData) {
    long start = System.nanoTime();
    SimplexOptimizer optimizer = new SimplexOptimizer(0.00001, 0.00001);
    PointValuePair unbounded = optimizer.optimize(GoalType.MINIMIZE, new MaxIter(MAX_ITER),
            new MaxEval(MAX_EVAL), new InitialGuess(INITIAL_GUESS), new ObjectiveFunction(objectiveFunction),
            new NelderMeadSimplex(2));
    long executionTime = System.nanoTime() - start;

    printOptimizationResult(objectiveFunction, unbounded.getPoint(), modelData);

    return executionTime;
}

---

From source file:org.hawkular.datamining.forecast.models.performance.OptimizationAlgorithmsTests.java

private long executeMultidirectionalSimplex(ModelData modelData) {
    long start = System.nanoTime();
    SimplexOptimizer optimizer = new SimplexOptimizer(0.00001, 0.00001);
    PointValuePair unbounded = optimizer.optimize(GoalType.MINIMIZE, new MaxIter(MAX_ITER),
            new MaxEval(MAX_EVAL), new InitialGuess(INITIAL_GUESS), new ObjectiveFunction(objectiveFunction),
            new MultiDirectionalSimplex(2));
    long executionTime = System.nanoTime() - start;

    printOptimizationResult(objectiveFunction, unbounded.getPoint(), modelData);

    return executionTime;
}

---

From source file:org.hawkular.datamining.forecast.models.performance.OptimizationAlgorithmsTests.java

private long executeBobyQA(ModelData modelData) {
    long start = System.nanoTime();
    BOBYQAOptimizer optimizer = new BOBYQAOptimizer(6);
    PointValuePair bobyQAResult = optimizer.optimize(GoalType.MINIMIZE, new MaxEval(MAX_EVAL),
            new MaxIter(MAX_ITER), new InitialGuess(INITIAL_GUESS), new ObjectiveFunction(objectiveFunction),
            SimpleBounds.unbounded(2));//from w w  w  .  j ava2 s . c om
    long executionTime = System.nanoTime() - start;

    printOptimizationResult(objectiveFunction, bobyQAResult.getPoint(), modelData);

    return executionTime;
}

---

From source file:org.hawkular.datamining.forecast.models.performance.OptimizationAlgorithmsTests.java

private long executePowell(ModelData modelData) {
    long start = System.nanoTime();
    PowellOptimizer optimizer = new PowellOptimizer(0.00001, 0.00001);
    PointValuePair unbounded = optimizer.optimize(GoalType.MINIMIZE, new MaxIter(MAX_ITER),
            new MaxEval(MAX_EVAL), new InitialGuess(INITIAL_GUESS), new ObjectiveFunction(objectiveFunction));
    long executionTime = System.nanoTime() - start;

    printOptimizationResult(objectiveFunction, unbounded.getPoint(), modelData);

    return executionTime;
}

---

From source file:playground.thibautd.initialdemandgeneration.socnetgensimulated.framework.ModelIterator.java

public SocialNetwork iterateModelToTarget(final ModelRunner runner, final Thresholds initialThresholds) {
    final MultivariateOptimizer optimizer = new CMAESOptimizer(maxIterations, 1E-9, true, 3, 50,
            new MersenneTwister(42), false, new Convergence());

    final double x = initialThresholds.getPrimaryThreshold();
    final double y = initialThresholds.getSecondaryReduction();

    final PointValuePair result = optimizer.optimize(GoalType.MINIMIZE, new MaxEval(maxIterations),
            new InitialGuess(new double[] { x, y }), new ObjectiveFunction(new Function(runner)),
            new CMAESOptimizer.Sigma(new double[] { 5, 500 }), new CMAESOptimizer.PopulationSize(7),
            new SimpleBounds(new double[] { Double.NEGATIVE_INFINITY, 0 }, // lower bounds: constrain secondary reduction to be positive
                    new double[] { Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY }) // upper bounds
    );/*from   ww  w.  j  a v a  2s . c  om*/

    final Thresholds bestThresholds = new Thresholds(result.getPoint()[0], result.getPoint()[1]);
    final SocialNetwork bestSn = generate(runner, bestThresholds);

    log.info("best social network found for thresholds: " + bestThresholds);

    return bestSn;
}

---

From source file:smlm.fitting.FittingClassical.java

@SuppressWarnings("unused")
@Override/* ww w  .j a  v  a 2 s. c o  m*/
public boolean FitThis() {

    // Initial estimates (your initial x)
    // Calculating the centroid
    ImageStatistics stat = roi.getStatistics();
    double x0 = stat.xCenterOfMass;
    double y0 = stat.yCenterOfMass;
    double[] start = { x0, y0, param.psfSigma, param.psfSigma, i0Max, backgroundLevel };

    PointValuePair solutionMult = null;
    MultivariateFunctionMappingAdapter fitFunc = null;

    if (param.fitting != Params.Fitting.CentroidFit) {
        // initial step sizes (take a good guess)
        double[] step = { 1, 1, 0.1, 0.1, stdBackground, stdBackground };
        // convergence tolerance
        double ftol = 0.0001;// 0.000001;
        pixelPrecision = 3;// 5;

        if (param.fitting == Params.Fitting.FastGaussianFit) {
            ftol = 0.1;
            pixelPrecision = 3;
        }

        SimplexOptimizer Fit = new SimplexOptimizer(ftol, ftol * ftol);
        double[] low = new double[] { 0, 0, 0, 0, 0, 0 };
        double[] up = new double[] { roi.getWidth(), roi.getHeight(), Double.POSITIVE_INFINITY,
                Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY };
        fitFunc = new MultivariateFunctionMappingAdapter(new LLH(), low, up);

        // maximal number of iterations
        int maxIter = 5000;
        // Nelder and Mead maximisation procedure
        // x0 e [0, xmax]
        // Fit.addConstraint(0, -1, 0);
        // Fit.addConstraint(0, 1, roi.getWidth());
        // y0 e [0, ymax]
        // Fit.addConstraint(1, -1, 0);
        // Fit.addConstraint(1, 1, roi.getHeight());
        /*
         * // sigmax e [PSFSigma/3, 3*PSFSigma] Fit.addConstraint(2, -1,
         * PSFSigmaInt-PSFSigmaInt/2); Fit.addConstraint(2, 1,
         * 2*PSFSigmaInt); // sigmay e [PSFSigma/3, 3*PSFSigma]
         * Fit.addConstraint(3, -1, PSFSigmaInt-PSFSigmaInt/2);
         * Fit.addConstraint(3, 1, 2*PSFSigmaInt); // I0 e [StdBackground,
         * 50*Intensities[i]] Fit.addConstraint(4, -1, StdBackground);
         * Fit.addConstraint(4, 1, 50*XY[3][i]); // PoissonNoise e
         * [BackgroundLevel/3, 3*BackgroundLevel] Fit.addConstraint(5, -1,
         * BackgroundLevel/3); Fit.addConstraint(5, 1, 3*BackgroundLevel);
         */

        solutionMult = Fit.optimize(new MaxEval(maxIter), new ObjectiveFunction(fitFunc), GoalType.MAXIMIZE,
                new InitialGuess(fitFunc.boundedToUnbounded(start)), new MultiDirectionalSimplex(step));
    }

    // Result of minimisation
    // Save the fit results
    this.fit.incrementCounter();
    if (param.fitting == Params.Fitting.CentroidFit) {
        results = start;
    } else {
        results = fitFunc.unboundedToBounded(solutionMult.getPoint());
    }
    for (int i = 0; i < isArgumentFixed.length; i++) {
        if (isArgumentFixed[i]) {
            results[i] = fixedArguments[i];
        }
    }

    if (cycle != -1)
        this.fit.addValue(ResultsTableMt.CYCLE, cycle);
    this.fit.addValue(ResultsTableMt.FRAME, realFrame);
    this.fit.addValue(ResultsTableMt.X0, results[0] + xMax - ((double) roiWidth * param.psfSigmaInt));
    this.fit.addValue(ResultsTableMt.Y0, results[1] + yMax - ((double) roiWidth * param.psfSigmaInt));
    this.fit.addValue(ResultsTableMt.SIGMAX, results[2]);
    this.fit.addValue(ResultsTableMt.SIGMAY, results[3]);
    this.fit.addValue(ResultsTableMt.I0, results[4]);
    this.fit.addValue(ResultsTableMt.NOISE, results[5]);

    if (param.fitting == Params.Fitting.CentroidFit || true) {
        this.fit.addValue(ResultsTableMt.IS_FITTED, 1);
        if (param.fitting != Params.Fitting.CentroidFit) {
            this.fit.addValue("MinFit", solutionMult.getValue());
        }
    } else
        this.fit.addValue(ResultsTableMt.IS_FITTED, 0);

    // Save results
    if (param.debug) {
        DrawInitialRoi(true);
        DrawFit(true);
    }
    return true;
}

---

From source file:uk.ac.diamond.scisoft.analysis.diffraction.FittingUtils.java

/**
 * Optimize given function//  www . j a v  a2  s. co  m
 * @param f
 * @param opt
 * @param min
 * @return residual
 */
public static double optimize(FitFunction f, MultivariateOptimizer opt, double min) {
    double res = Double.NaN;
    try {
        PointValuePair result;

        if (opt instanceof BOBYQAOptimizer) {
            result = opt.optimize(new InitialGuess(f.getInitial()), GoalType.MINIMIZE, new ObjectiveFunction(f),
                    new MaxEval(MAX_EVAL), f.getBounds());
        } else if (opt instanceof CMAESOptimizer) {
            int p = (int) Math.ceil(4 + Math.log(f.getN())) + 1;
            logger.trace("Population size: {}", p);
            result = opt.optimize(new InitialGuess(f.getInitial()), GoalType.MINIMIZE, new ObjectiveFunction(f),
                    new CMAESOptimizer.Sigma(f.getSigma()), new CMAESOptimizer.PopulationSize(p),
                    new MaxEval(MAX_EVAL), f.getBounds());
        } else {
            int n = f.getN();
            double offset = 1e12;
            double[] scale = new double[n];
            for (int i = 0; i < n; i++) {
                scale[i] = offset * 0.25;
            }
            SimpleBounds bnds = f.getBounds();
            MultivariateFunctionPenaltyAdapter of = new MultivariateFunctionPenaltyAdapter(f, bnds.getLower(),
                    bnds.getUpper(), offset, scale);
            result = opt.optimize(new InitialGuess(f.getInitial()), GoalType.MINIMIZE,
                    new ObjectiveFunction(of), new MaxEval(MAX_EVAL), new MultiDirectionalSimplex(n));
            //            new NelderMeadSimplex(n));
        }

        // logger.info("Q-space fit: rms = {}, x^2 = {}", opt.getRMS(), opt.getChiSquare());
        double ires = f.value(opt.getStartPoint());
        logger.trace("Residual: {} from {}", result.getValue(), ires);
        res = result.getValue();
        if (res < min)
            f.setParameters(result.getPoint());
        logger.trace("Used {} evals and {} iters", opt.getEvaluations(), opt.getIterations());
        //         System.err.printf("Used %d evals and %d iters\n", opt.getEvaluations(), opt.getIterations());
        // logger.info("Q-space fit: rms = {}, x^2 = {}", opt.getRMS(), opt.getChiSquare());
    } catch (IllegalArgumentException e) {
        logger.error("Start point has wrong dimension", e);
        // should not happen!
    } catch (TooManyEvaluationsException e) {
        throw new IllegalArgumentException("Could not fit as optimizer did not converge");
        //            logger.error("Convergence problem: max iterations ({}) exceeded", opt.getMaxIterations());
    }

    return res;
}

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