Example usage for org.apache.commons.math.optimization RealPointValuePair getPoint

List of usage examples for org.apache.commons.math.optimization RealPointValuePair getPoint

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Introduction

In this page you can find the example usage for org.apache.commons.math.optimization RealPointValuePair getPoint.

Prototype

public double[] getPoint()

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Document

Get the point.

Usage

From source file:com.opengamma.analytics.math.util.wrapper.CommonsMathWrapper.java

/**
 * @param x A Commons pair of <i>(x, f(x))</i>, not null
 * @return A matrix of double with the <i>x</i> as the first element and <i>f(x)</i> the second
 *///from   w ww.  j a  v  a2  s  . co  m
public static double[] unwrap(final RealPointValuePair x) {
    Validate.notNull(x);
    return x.getPoint();
}

From source file:ch.algotrader.option.SABR.java

/**
 * Perfors a SABR calibartion based on specified volatilities.
 *
 * @return SABRSmileVO The SABR smile/* www. j  av  a  2 s  .c o m*/
 */
public static SABRSmileVO calibrate(final Double[] strikes, final Double[] volatilities, final double atmVol,
        final double forward, final double years) throws SABRException {

    MultivariateRealFunction estimateRhoAndVol = x -> {

        double r = x[0];
        double v = x[1];
        double alpha = findAlpha(forward, forward, atmVol, years, beta, x[0], x[1]);
        double sumErrors = 0;

        for (int i = 0; i < volatilities.length; i++) {

            double modelVol = vol(forward, strikes[i], years, alpha, beta, r, v);
            sumErrors += Math.pow(modelVol - volatilities[i], 2);
        }

        if (Math.abs(r) > 1) {
            sumErrors = 1e100;
        }

        return sumErrors;
    };

    NelderMead nelderMead = new NelderMead();
    RealPointValuePair result;
    try {
        result = nelderMead.optimize(estimateRhoAndVol, GoalType.MINIMIZE, new double[] { -0.5, 2.6 });
    } catch (MathException ex) {
        throw new SABRException(ex.getMessage(), ex);
    }

    double rho = result.getPoint()[0];
    double volVol = result.getPoint()[1];

    SABRSmileVO params = new SABRSmileVO();
    params.setYears(years);
    params.setRho(rho);
    params.setVolVol(volVol);
    params.setAlpha(findAlpha(forward, forward, atmVol, years, beta, rho, volVol));
    params.setAtmVol(atmVol);

    return params;
}

From source file:com.polytech4A.cuttingstock.core.method.LinearResolutionMethod.java

/**
 * Resolve linear programming problem when minimizing the equation with current constraints. Returns
 *
 * @param solution Solution to minimize the objective the function from.
 * @return Number of printings and cost value.
 *///from w  ww . ja  va 2 s . c o m
public Result minimize(Solution solution) {
    updateFunction(solution);
    updateConstraints(solution);
    try {
        RealPointValuePair result = new SimplexSolver().optimize(function, constraints, GoalType.MINIMIZE,
                true);
        double[] point = result.getPoint();
        if (result.getValue() < 0) {
            return null;
        }
        for (int i = 0; i < point.length; ++i) {
            if (point[i] < 0) {
                return null;
            }
        }
        return new Result(point, context.getSheetCost(), context.getPatternCost());
    } catch (OptimizationException e) {
        logger.debug("LinearResolutionMethod.minimize: " + e.getMessage());
    }
    return null;
}

From source file:edu.valelab.GaussianFit.ZCalibrator.java

/**
 * Use the fitfunction to estimate the z position given width in x and y
 * //from   w ww.  ja va2 s.  c o m
 * minimize the distance D in sqrt wx and sqrt wy space
 * D = sqrt (  square (sqrt wx - sqrt wx, calib) + sqr(sqrt wy - sqrt w, calib) )
 * 
 * 
 */

public double getZ(double wx, double wy) {
    if (!hasFitFunctions())
        return 0.0;

    NelderMead nmx = new NelderMead();
    SimpleScalarValueChecker convergedChecker_ = new SimpleScalarValueChecker(1e-6, -1);

    MultiVariateZFunction mz = new MultiVariateZFunction(fitFunctionWx_, fitFunctionWy_, wx, wy);

    double[] params0_ = new double[1]; // initial estimates:
    params0_[0] = 15; // TODO: Need the middle z value of the stack here!!!

    nmx.setStartConfiguration(params0_);
    nmx.setConvergenceChecker(convergedChecker_);
    nmx.setMaxIterations(maxIterations_);

    double[] paramsOut = { 0.0 };

    try {
        RealPointValuePair result = nmx.optimize(mz, GoalType.MINIMIZE, params0_);
        paramsOut = result.getPoint();
    } catch (java.lang.OutOfMemoryError e) {
        throw (e);
    } catch (Exception e) {
        ij.IJ.log(" " + e.toString());
    }

    return paramsOut[0];
}

From source file:edu.valelab.gaussianfit.fitting.ZCalibrator.java

/**
 * Use the fitfunction to estimate the z position given width in x and y
 * /* w w w  .j ava  2  s .c om*/
 * minimize the distance D in sqrt wx and sqrt wy space
 * D = sqrt (  square (sqrt wx - sqrt wx, calib) + sqr(sqrt wy - sqrt w, calib) )
 * 
 * 
 * @param wx - width in x
 * @param wy - width in y
 * @return - calculated z position
 */

public double getZ(double wx, double wy) {
    if (!hasFitFunctions())
        return 0.0;

    NelderMead nmx = new NelderMead();
    SimpleScalarValueChecker convergedChecker_ = new SimpleScalarValueChecker(1e-6, -1);

    MultiVariateZFunction mz = new MultiVariateZFunction(fitFunctionWx_, fitFunctionWy_, wx, wy);

    double[] params0_ = new double[1]; // initial estimates:
    params0_[0] = 15; // TODO: Need the middle z value of the stack here!!!

    nmx.setStartConfiguration(params0_);
    nmx.setConvergenceChecker(convergedChecker_);
    nmx.setMaxIterations(maxIterations_);

    double[] paramsOut = { 0.0 };

    try {
        RealPointValuePair result = nmx.optimize(mz, GoalType.MINIMIZE, params0_);
        paramsOut = result.getPoint();
    } catch (java.lang.OutOfMemoryError e) {
        throw (e);
    } catch (FunctionEvaluationException e) {
        ij.IJ.log(" " + e.toString());
    } catch (OptimizationException e) {
        ij.IJ.log(" " + e.toString());
    } catch (IllegalArgumentException e) {
        ij.IJ.log(" " + e.toString());
    }

    return paramsOut[0];
}

From source file:edu.valelab.gaussianfit.fitting.ZCalibrator.java

/**
 * Creates fitFunctionWx_ and fitFunctionWy_ based on data in data_
 * /*w ww  .  j av  a2  s.c  o m*/
 * 
 * @throws org.apache.commons.math.FunctionEvaluationException
 * @throws org.apache.commons.math.optimization.OptimizationException
 */
public void fitFunction() throws FunctionEvaluationException, OptimizationException {

    NelderMead nmx = new NelderMead();
    SimpleScalarValueChecker convergedChecker_ = new SimpleScalarValueChecker(1e-6, -1);

    double[][] wxData = getDataAsArray(0);
    MultiVariateZCalibrationFunction mvcx = new MultiVariateZCalibrationFunction(wxData);

    double[] params0_ = new double[5]; // initial estimates:
    params0_[0] = 37; // TODO: better estimate for c
    params0_[1] = 200; // Estimate for w0
    params0_[2] = 10; // TODO: better estimate for d
    params0_[3] = 1; // TODO: better estimate for A
    params0_[4] = 1; // TODO: better estimate for B

    nmx.setStartConfiguration(params0_);
    nmx.setConvergenceChecker(convergedChecker_);
    nmx.setMaxIterations(maxIterations_);

    double[] paramsOut;

    RealPointValuePair result = nmx.optimize(mvcx, GoalType.MINIMIZE, params0_);
    paramsOut = result.getPoint();

    // write fit result to Results Table:
    ResultsTable res = new ResultsTable();
    res.incrementCounter();
    res.addValue("c", paramsOut[0]);
    res.addValue("w0", paramsOut[1]);
    res.addValue("d", paramsOut[2]);
    res.addValue("A", paramsOut[3]);
    res.addValue("B", paramsOut[4]);

    fitFunctionWx_ = paramsOut;

    double[][] yxData = getDataAsArray(1);
    MultiVariateZCalibrationFunction yvcx = new MultiVariateZCalibrationFunction(yxData);

    nmx.setStartConfiguration(params0_);

    result = nmx.optimize(yvcx, GoalType.MINIMIZE, params0_);
    paramsOut = result.getPoint();

    res.incrementCounter();
    res.addValue("c", paramsOut[0]);
    res.addValue("w0", paramsOut[1]);
    res.addValue("d", paramsOut[2]);
    res.addValue("A", paramsOut[3]);
    res.addValue("B", paramsOut[4]);

    res.show("Fit Parameters");

    fitFunctionWy_ = paramsOut;

    plotFitFunctions();

}

From source file:edu.valelab.GaussianFit.ZCalibrator.java

/**
 * Creates fitFunctionWx_ and fitFunctionWy_ based on data in data_
 * /*from   w w w.j  a  va  2s.  c  om*/
 * 
 */
public void fitFunction() throws FunctionEvaluationException, OptimizationException {

    NelderMead nmx = new NelderMead();
    SimpleScalarValueChecker convergedChecker_ = new SimpleScalarValueChecker(1e-6, -1);

    double[][] wxData = getDataAsArray(0);
    MultiVariateZCalibrationFunction mvcx = new MultiVariateZCalibrationFunction(wxData);

    double[] params0_ = new double[5]; // initial estimates:
    params0_[0] = 37; // TODO: better estimate for c
    params0_[1] = 200; // Estimate for w0
    params0_[2] = 10; // TODO: better estimate for d
    params0_[3] = 1; // TODO: better estimate for A
    params0_[4] = 1; // TODO: better estimate for B

    nmx.setStartConfiguration(params0_);
    nmx.setConvergenceChecker(convergedChecker_);
    nmx.setMaxIterations(maxIterations_);

    double[] paramsOut = { 0.0 };

    RealPointValuePair result = nmx.optimize(mvcx, GoalType.MINIMIZE, params0_);
    paramsOut = result.getPoint();

    //for (int i = 0; i < paramsOut.length; i++) {
    //  System.out.println("Result " + i + " value: " + (int) paramsOut[i]);
    //}

    // write fit result to Results Table:
    ResultsTable res = new ResultsTable();
    res.incrementCounter();
    res.addValue("c", paramsOut[0]);
    res.addValue("w0", paramsOut[1]);
    res.addValue("d", paramsOut[2]);
    res.addValue("A", paramsOut[3]);
    res.addValue("B", paramsOut[4]);

    fitFunctionWx_ = paramsOut;

    double[][] yxData = getDataAsArray(1);
    MultiVariateZCalibrationFunction yvcx = new MultiVariateZCalibrationFunction(yxData);

    nmx.setStartConfiguration(params0_);

    result = nmx.optimize(yvcx, GoalType.MINIMIZE, params0_);
    paramsOut = result.getPoint();

    System.out.println("Y:");

    //for (int i = 0; i < paramsOut.length; i++) {
    //  System.out.println("Result " + i + " value: " + (int) paramsOut[i]);
    //}

    res.incrementCounter();
    res.addValue("c", paramsOut[0]);
    res.addValue("w0", paramsOut[1]);
    res.addValue("d", paramsOut[2]);
    res.addValue("A", paramsOut[3]);
    res.addValue("B", paramsOut[4]);

    res.show("Fit Parameters");

    fitFunctionWy_ = paramsOut;

    plotFitFunctions();

}

From source file:circdesigna.DesignSequenceConstraints.java

private void solveSimplex() {
    //Closest-To-Origin objective
    double[] ones = new double[Std.monomer.getNumMonomers()];
    for (int i = 0; i < ones.length; i++) {
        ones[i] = 1;//from   ww  w . ja  v  a 2s.c  om
    }
    LinearObjectiveFunction f = new LinearObjectiveFunction(ones, 0);

    List<LinearConstraint> constraints = new ArrayList();
    for (Constraint d : maxConstituents) {
        if (d.constraintValue == -1) {
            continue;
        }
        double[] ei = new double[Std.monomer.getNumMonomers()];
        for (int i = 0; i < ei.length; i++) {
            if (d.regulates[i]) {
                ei[i] = 1;
            }
        }
        constraints.add(new LinearConstraint(ei, Relationship.LEQ, d.constraintValue));
    }
    for (Constraint d : minConstituents) {
        if (d.constraintValue == -1) {
            continue;
        }
        double[] ei = new double[Std.monomer.getNumMonomers()];
        for (int i = 0; i < ei.length; i++) {
            if (d.regulates[i]) {
                ei[i] = 1;
            }
        }
        constraints.add(new LinearConstraint(ei, Relationship.GEQ, d.constraintValue));
    }
    try {
        RealPointValuePair optimize = new SimplexSolver().optimize(f, constraints, GoalType.MINIMIZE, true);
        simplexSolution = optimize.getPoint();
        //System.out.println(Arrays.toString(simplexSolution));
    } catch (Throwable e) {
        throw new RuntimeException("Constraints are too strict: " + e.getMessage());
    }

}

From source file:Align_Projections.java

public void run() {

    detectorAngle = Double.valueOf(detectorAngleText.getText()).doubleValue();
    centerPixel = Double.valueOf(centerPixelText.getText()).doubleValue();
    horizontalBorder = Integer.valueOf(horizontalBorderText.getText()).intValue();
    topBorder = Integer.valueOf(topBorderText.getText()).intValue();
    bottomBorder = Integer.valueOf(bottomBorderText.getText()).intValue();

    // IJ.log("Starting worker thread");
    int count = 0;
    double[] x = new double[2];
    x[0] = centerPixel * tuningWeights[0];
    x[1] = detectorAngle * tuningWeights[1];
    PowellOptimizer maximizer = new PowellOptimizer(1E-4);
    maximizer.setConvergenceChecker(new ConvergenceCheckerWithManualCancel(this, 1E-4, 1E-4));
    maximizer.setMaxEvaluations(1000000);
    maximizer.setMaxIterations(1000000);
    try {/*from  w  w  w  . j a  v a 2s.  c o  m*/
        // IJ.log("Starting optimization first round");
        RealPointValuePair result = maximizer.optimize(this, GoalType.MAXIMIZE, x);
        centerPixel = result.getPoint()[0] / tuningWeights[0];
        detectorAngle = result.getPoint()[1] / tuningWeights[1];
        centerPixelText.setText(IJ.d2s(centerPixel, 6));
        detectorAngleText.setText(IJ.d2s(detectorAngle, 6));
        crossCorrelation = result.getValue();
        updateCrossCorrelation();
    } catch (GetMeOuttaHereException e) {
    } catch (Exception e) {
        IJ.log("Exception occurred in optimizer.");
        stopTuning = true;
    }
    // Now do the whole thing again, but with narrower tolerances (the defaults, which are roughly machine precision)
    if (!stopTuning) {
        maximizer = new PowellOptimizer();
        maximizer.setConvergenceChecker(new ConvergenceCheckerWithManualCancel(this));
        maximizer.setMaxEvaluations(1000000);
        maximizer.setMaxIterations(1000000);
        try {
            // IJ.log("Starting optimization second round");
            RealPointValuePair result = maximizer.optimize(this, GoalType.MAXIMIZE, x);
            centerPixel = result.getPoint()[0] / tuningWeights[0];
            detectorAngle = result.getPoint()[1] / tuningWeights[1];
            centerPixelText.setText(IJ.d2s(centerPixel, 6));
            detectorAngleText.setText(IJ.d2s(detectorAngle, 6));
            crossCorrelation = result.getValue();
            updateCrossCorrelation();
        } catch (GetMeOuttaHereException e) {
        } catch (Exception e) {
            IJ.log("Exception occurred in optimizer.");
        }
    }

    UpdateOverlayAndControls();
    optimizeButton.setLabel("Optimize");
    optimizeButton.setEnabled(true);
    updateButton.setEnabled(true);
    applyButton.setEnabled(true);
    resetButton.setEnabled(true);
    detectorAngleText.setEnabled(true);
    centerPixelText.setEnabled(true);
    horizontalBorderText.setEnabled(true);
    topBorderText.setEnabled(true);
    bottomBorderText.setEnabled(true);
    // IJ.log("Exiting worker thread");
}

From source file:emlab.role.AbstractEnergyProducerRole.java

/**
 * The fuel mix is calculated with a linear optimization model of the possible fuels and the requirements.
 * //from ww  w .j  a  v a2s.  co  m
 * @param substancePriceMap
 *            contains the possible fuels and their market prices
 * @param minimumFuelMixQuality
 *            is the minimum fuel quality needed for the power plant to work
 * @param efficiency
 *            of the plant determines the need for fuel per MWhe
 * @param co2TaxLevel
 *            is part of the cost for CO2
 * @param co2AuctionPrice
 *            is part of the cost for CO2
 * @return the fuel mix
 */
public Set<SubstanceShareInFuelMix> calculateFuelMix(PowerPlant plant, Map<Substance, Double> substancePriceMap,
        double co2Price) {

    double efficiency = plant.getActualEfficiency();

    Set<SubstanceShareInFuelMix> fuelMix = (plant.getFuelMix() == null) ? new HashSet<SubstanceShareInFuelMix>()
            : plant.getFuelMix();

    int numberOfFuels = substancePriceMap.size();
    if (numberOfFuels == 0) {
        logger.info("No fuels, so no operation mode is set. Empty fuel mix is returned");
        return new HashSet<SubstanceShareInFuelMix>();
    } else if (numberOfFuels == 1) {
        SubstanceShareInFuelMix ssifm = null;
        if (!fuelMix.isEmpty()) {
            ssifm = fuelMix.iterator().next();
        } else {
            ssifm = new SubstanceShareInFuelMix().persist();
            fuelMix.add(ssifm);
        }

        Substance substance = substancePriceMap.keySet().iterator().next();

        ssifm.setShare(calculateFuelConsumptionWhenOnlyOneFuelIsUsed(substance, efficiency));
        ssifm.setSubstance(substance);
        logger.info("Setting fuel consumption for {} to {}", ssifm.getSubstance().getName(), ssifm.getShare());

        return fuelMix;
    } else {

        double minimumFuelMixQuality = plant.getTechnology().getMinimumFuelQuality();

        double[] fuelAndCO2Costs = new double[numberOfFuels];
        double[] fuelDensities = new double[numberOfFuels];
        double[] fuelQuality = new double[numberOfFuels];

        int i = 0;
        for (Substance substance : substancePriceMap.keySet()) {
            fuelAndCO2Costs[i] = substancePriceMap.get(substance) + substance.getCo2Density() * (co2Price);
            fuelDensities[i] = substance.getEnergyDensity();
            fuelQuality[i] = substance.getQuality() - minimumFuelMixQuality;
            i++;
        }

        logger.info("Fuel prices: {}", fuelAndCO2Costs);
        logger.info("Fuel densities: {}", fuelDensities);
        logger.info("Fuel purities: {}", fuelQuality);

        // Objective function = minimize fuel cost (fuel
        // consumption*fuelprices
        // + CO2 intensity*co2 price/tax)
        LinearObjectiveFunction function = new LinearObjectiveFunction(fuelAndCO2Costs, 0d);

        List<LinearConstraint> constraints = new ArrayList<LinearConstraint>();

        // Constraint 1: total fuel density * fuel consumption should match
        // required energy input
        constraints.add(new LinearConstraint(fuelDensities, Relationship.EQ, (1 / efficiency)));

        // Constraint 2&3: minimum fuel quality (times fuel consumption)
        // required
        // The equation is derived from (example for 2 fuels): q1 * x1 / (x1+x2) + q2 * x2 / (x1+x2) >= qmin
        // so that the fuelquality weighted by the mass percentages is greater than the minimum fuel quality.
        constraints.add(new LinearConstraint(fuelQuality, Relationship.GEQ, 0));

        try {
            SimplexSolver solver = new SimplexSolver();
            RealPointValuePair solution = solver.optimize(function, constraints, GoalType.MINIMIZE, true);

            logger.info("Succesfully solved a linear optimization for fuel mix");

            int f = 0;
            Iterator<SubstanceShareInFuelMix> iterator = plant.getFuelMix().iterator();
            for (Substance substance : substancePriceMap.keySet()) {
                double share = solution.getPoint()[f];

                SubstanceShareInFuelMix ssifm;
                if (iterator.hasNext()) {
                    ssifm = iterator.next();
                } else {
                    ssifm = new SubstanceShareInFuelMix().persist();
                    fuelMix.add(ssifm);
                }

                double fuelConsumptionPerMWhElectricityProduced = convertFuelShareToMassVolume(share);
                logger.info("Setting fuel consumption for {} to {}", substance.getName(),
                        fuelConsumptionPerMWhElectricityProduced);
                ssifm.setShare(fuelConsumptionPerMWhElectricityProduced);
                ssifm.setSubstance(substance);
                f++;
            }

            logger.info("If single fired, it would have been: {}",
                    calculateFuelConsumptionWhenOnlyOneFuelIsUsed(substancePriceMap.keySet().iterator().next(),
                            efficiency));
            return fuelMix;
        } catch (OptimizationException e) {
            logger.warn(
                    "Failed to determine the correct fuel mix. Adding only fuel number 1 in fuel mix out of {} substances and minimum quality of {}",
                    substancePriceMap.size(), minimumFuelMixQuality);
            logger.info("The fuel added is: {}", substancePriceMap.keySet().iterator().next().getName());

            // Override the old one
            fuelMix = new HashSet<SubstanceShareInFuelMix>();
            SubstanceShareInFuelMix ssifm = new SubstanceShareInFuelMix().persist();
            Substance substance = substancePriceMap.keySet().iterator().next();

            ssifm.setShare(calculateFuelConsumptionWhenOnlyOneFuelIsUsed(substance, efficiency));
            ssifm.setSubstance(substance);
            logger.info("Setting fuel consumption for {} to {}", ssifm.getSubstance().getName(),
                    ssifm.getShare());
            fuelMix.add(ssifm);
            return fuelMix;
        }
    }
}