Example usage for org.apache.commons.math3.optim.univariate UnivariatePointValuePair getValue

List of usage examples for org.apache.commons.math3.optim.univariate UnivariatePointValuePair getValue

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Introduction

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

Prototype

public double getValue()

Source Link

Document

Get the value of the objective function.

Usage

From source file:com.wwidesigner.optimization.ObjectiveFunctionOptimizer.java

protected static PointValuePair doSingleStart(BaseObjectiveFunction objective, double[] startPoint,
        int maxEvaluations, double[] nextStart) {
    singleRunEvaluations = 0;/* w w  w. ja v  a2  s  .co  m*/
    PointValuePair result = null;
    try {
        int numVariables = objective.getNrDimensions();
        if (numVariables > 1) // Use BOBYQA
        {
            double trustRegion = objective.getInitialTrustRegionRadius(nextStart);
            double stoppingTrustRegion = objective.getStoppingTrustRegionRadius();
            BOBYQAOptimizer optimizer = new BOBYQAOptimizer(objective.getNrInterpolations(), trustRegion,
                    stoppingTrustRegion);
            result = runBobyqa(optimizer, objective, nextStart, maxEvaluations);
            singleRunEvaluations = optimizer.getEvaluations();
        } else
        // Use Brent
        {
            BrentOptimizer optimizer = new BrentOptimizer(1.e-6, 1.e-14);
            UnivariatePointValuePair outcome = runBrent(optimizer, objective, startPoint);
            result = new PointValuePair(new double[] { outcome.getPoint() }, outcome.getValue());
            singleRunEvaluations = optimizer.getEvaluations();
        }
        double value = result.getValue();
        if (value == Double.POSITIVE_INFINITY) {
            System.out.print("no valid solution found");
        } else {
            System.out.print("optimum " + result.getValue());
        }
    } catch (TooManyEvaluationsException e) {
        System.out.println("Exception: " + e.getMessage());
    }
    // Thrown by BOBYQA for no apparent reason: a bug?
    catch (NoSuchElementException e) {
        System.out.println("no valid solution found");
    } catch (OperationCancelledException e) {
        // Restore starting point.
        objective.setGeometryPoint(startPoint);
        // Re-throw the exception to give up the whole multi-start
        // optimization.
        throw new OperationCancelledException(e.getMessage());
    } catch (Exception e) {
        System.out.println("Exception: " + e.getMessage());
        // e.printStackTrace();
    } finally {
        System.out.println(" at start point " + Arrays.toString(nextStart));
    }

    return result;
}

From source file:eu.crisis_economics.abm.algorithms.optimization.BrentLineSearch.java

/**
  * Perform a line search minimization. This function accepts as input:
  *   (a) a starting point (a vector),/*w w w. j  a va  2s.com*/
  *   (b) a direction in which to travel (a vector),
  *   (c) limits on the total distance to travel along (b).
  *   
  * With these inputs the function attempts to find the minimum of a
  * scalar-valued multivariate function along the line starting at 
  * (a) and pointing in the direction of (b).
  * 
  * @param function
  *        A scalar-valued multivariate function to minimize,
  * @param startingPoint
  *        A vector starting point from which to begin the minimization (P),
  * @param vectorDirection
  *        A vector direction along which to travel from P, (V)
  * @param maximumDistanceToTravel
  *        The maximum distance to travel in the direction of V,
  * @param maximumEvaluations
  *        The maximum number of function evaluations to identify the minimum,
  * @param relativeErrorGoal
  *        The relative error target of the minimization,
  * @param absoluteErrorGoal
  *        The absolute error target of the minimization.
  * @return
  *        A lightweight immutable struct containing the vector solution and
  *        the evaluation of the function at this point.
  */
static public LineSearchResult doLineSearch(final MultivariateFunction function, final double[] startingPoint,
        final double[] vectorDirection, final double maximumDistanceToTravel, final int maximumEvaluations,
        final double relativeErrorGoal, final double absoluteErrorGoal) {
    Preconditions.checkArgument(maximumEvaluations > 0);
    Preconditions.checkArgument(relativeErrorGoal > 0. || absoluteErrorGoal > 0.);
    Preconditions.checkArgument(maximumDistanceToTravel > 0.);
    final LineSearchObjectiveFunction lineSearcher = new LineSearchObjectiveFunction(function, startingPoint,
            vectorDirection);
    final UnivariateOptimizer optimizer = new BrentOptimizer(relativeErrorGoal, absoluteErrorGoal);
    UnivariatePointValuePair result = optimizer.optimize(new MaxEval(maximumEvaluations),
            new UnivariateObjectiveFunction(lineSearcher), GoalType.MINIMIZE,
            new SearchInterval(0, maximumDistanceToTravel, 0));
    final double[] vectorSolution = new double[startingPoint.length];
    for (int i = 0; i < vectorDirection.length; ++i)
        vectorSolution[i] = lineSearcher.startingPoint[i]
                + lineSearcher.normalizedDirection[i] * result.getPoint();
    final LineSearchResult solution = new LineSearchResult(vectorSolution, result.getValue());
    return solution;
}

From source file:edu.uchc.octane.GaussianFitAstigmatism.java

/**
 * calculate the Z coordinate based on astigmatism
 *///from  w w w .  java 2s  .  c o  m
void calculateZ() {

    if (calibration_ == null) {
        z_ = 0;
        return;
    }

    UnivariateFunction func = new UnivariateFunction() {
        @Override
        public double value(double z) {
            double sigmax = FastMath.sqrt(pvp_.getPoint()[3] / 2);
            double sigmay = FastMath.sqrt(pvp_.getPoint()[4] / 2);
            double vx = calibration_[0] + (z - calibration_[1]) * (z - calibration_[1]) * calibration_[2]
                    - sigmax;
            double vy = calibration_[3] + (z - calibration_[4]) * (z - calibration_[4]) * calibration_[5]
                    - sigmay;
            return vx * vx + vy * vy;
        }
    };

    BrentOptimizer optimizer = new BrentOptimizer(1e-4, 1e-4);

    UnivariatePointValuePair upvp = optimizer.optimize(new UnivariateObjectiveFunction(func), GoalType.MINIMIZE,
            MaxEval.unlimited(), new SearchInterval(2 * z0min_ - z0max_, 2 * z0max_ - z0min_));

    if (upvp.getValue() > errTol_) {
        throw (new ConvergenceException());
    }

    z_ = upvp.getPoint();
}

From source file:com.itemanalysis.psychometrics.irt.equating.IrtScaleLinking.java

public void computeCoefficients() {
    raschFamily = checkRaschModel();/*from w w  w.j  a  v a  2  s. com*/

    ms.setPrecision(precision);
    mm.setPrecision(precision);
    hb.setPrecision(precision);
    sl.setPrecision(precision);

    if (raschFamily) {

        double[] sv = { mm.getIntercept() };

        UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
        JDKRandomGenerator g = new JDKRandomGenerator();

        //Haebara method
        MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 5, g);//Five random starts to Brent optimizer.
        UnivariatePointValuePair hbPair = optimizer.optimize(new MaxEval(500),
                new UnivariateObjectiveFunction(hb), GoalType.MINIMIZE, new SearchInterval(-4, 4),
                new InitialGuess(sv));
        hb.setIntercept(hbPair.getPoint());
        hb.setScale(1.0);
        fHB = hbPair.getValue();

        //Stocking-Lord method
        UnivariatePointValuePair slPair = optimizer.optimize(new MaxEval(500),
                new UnivariateObjectiveFunction(sl), GoalType.MINIMIZE, new SearchInterval(-4, 4),
                new InitialGuess(sv));
        sl.setIntercept(slPair.getPoint());
        sl.setScale(1.0);
        fSL = slPair.getValue();

    } else {

        double[] hbStartValues = { mm.getIntercept(), mm.getScale() };
        double[] slStartValues = { mm.getIntercept(), mm.getScale() };

        if (useUncmin) {
            DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer();

            try {

                optimizer.minimize(hb, hbStartValues);
                double[] param = optimizer.getParameters();
                fHB = optimizer.getFunctionValue();
                hb.setIntercept(param[0]);

                if (param.length > 1) {
                    hb.setScale(param[1]);
                } else {
                    hb.setScale(1.0);//Rasch family of models
                }

                optimizer.minimize(sl, slStartValues);
                param = optimizer.getParameters();
                fSL = optimizer.getFunctionValue();
                sl.setIntercept(param[0]);

                if (param.length > 1) {
                    sl.setScale(param[1]);
                } else {
                    sl.setScale(1.0);//Rasch family of models
                }

            } catch (UncminException ex) {
                ex.printStackTrace();
            }
        } else {

            int numIterpolationPoints = 2 * 2;//two dimensions A and B
            BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints);
            RandomGenerator g = new JDKRandomGenerator();
            RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2,
                    new GaussianRandomGenerator(g));
            MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10,
                    generator);
            PointValuePair hbOptimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb),
                    GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(hbStartValues));

            double[] hbCoefficients = hbOptimum.getPoint();
            hb.setIntercept(hbCoefficients[0]);
            hb.setScale(hbCoefficients[1]);
            fHB = hbOptimum.getValue();

            PointValuePair slOptimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl),
                    GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(slStartValues));

            double[] slCoefficients = slOptimum.getPoint();
            sl.setIntercept(slCoefficients[0]);
            sl.setScale(slCoefficients[1]);
            fSL = slOptimum.getValue();

        }

    }

}

From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java

/**
 * This test uses Rasch model items. True results from the sirt package in R.
 *
 *
 *///  w  w  w  . ja v  a  2 s . co m
@Test
public void raschModelTest() {
    System.out.println("Haebara test: Rasch model");

    LinkedHashMap<String, ItemResponseModel> itemFormX = new LinkedHashMap<String, ItemResponseModel>();
    LinkedHashMap<String, ItemResponseModel> itemFormY = new LinkedHashMap<String, ItemResponseModel>();

    itemFormX.put("Item1", new Irm3PL(-3.188047976, 1.0));
    itemFormX.put("Item2", new Irm3PL(1.031760328, 1.0));
    itemFormX.put("Item3", new Irm3PL(0.819040914, 1.0));
    itemFormX.put("Item4", new Irm3PL(-2.706947360, 1.0));
    itemFormX.put("Item5", new Irm3PL(-0.094527077, 1.0));
    itemFormX.put("Item6", new Irm3PL(0.689697135, 1.0));
    itemFormX.put("Item7", new Irm3PL(-0.551837153, 1.0));
    itemFormX.put("Item8", new Irm3PL(-0.359559276, 1.0));

    itemFormY.put("Item1", new Irm3PL(-3.074599226, 1.0));
    itemFormY.put("Item2", new Irm3PL(1.012824350, 1.0));
    itemFormY.put("Item3", new Irm3PL(0.868538408, 1.0));
    itemFormY.put("Item4", new Irm3PL(-2.404483603, 1.0));
    itemFormY.put("Item5", new Irm3PL(0.037402866, 1.0));
    itemFormY.put("Item6", new Irm3PL(0.700747420, 1.0));
    itemFormY.put("Item7", new Irm3PL(-0.602555046, 1.0));
    itemFormY.put("Item8", new Irm3PL(-0.350426446, 1.0));

    double f = 0;
    double[] param1 = new double[2];
    double[] param2 = new double[2];

    UniformDistributionApproximation distX = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default
    UniformDistributionApproximation distY = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default

    HaebaraMethod hb = new HaebaraMethod(itemFormX, itemFormY, distX, distY, EquatingCriterionType.Q1Q2);
    hb.setPrecision(6);
    double[] startValues = { 0 };//Using a start value for the intercept only for the Rasch family of models.

    DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer();

    try {
        optimizer.minimize(hb, startValues);
        double[] r = optimizer.getParameters();
        param1[0] = r[0];
        param1[1] = 1;
        f = optimizer.getFunctionValue();
        hb.setIntercept(param1[0]);
        hb.setScale(param1[1]);

    } catch (UncminException ex) {
        ex.printStackTrace();
    }

    //Check Brent Optimizer values against results from plink.
    System.out.println("  UNCMIN Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + hb.getIntercept() + "  A = " + hb.getScale());

    assertEquals("  Intercept test", 0.065032, hb.getIntercept(), 1e-3);//true result from plink package in R
    assertEquals("  Scale test", 1.0, hb.getScale(), 1e-4);

    BrentOptimizer brentOptimizer = new BrentOptimizer(1e-8, 1e-8);
    UnivariatePointValuePair pair = brentOptimizer.optimize(new MaxEval(200),
            new UnivariateObjectiveFunction(hb),
            org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, new SearchInterval(-3, 3));

    param2 = new double[2];
    hb.setIntercept(pair.getPoint());
    hb.setScale(1.0);
    f = pair.getValue();

    param2[0] = pair.getPoint();
    param2[1] = 1;
    hb.setIntercept(param2[0]);
    hb.setScale(param2[1]);
    f = pair.getValue();

    //Check Brent results against plink results.
    System.out.println();
    System.out.println("  Brent Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + hb.getIntercept() + "  A = " + hb.getScale());

    assertEquals("  Haebara intercept test", 0.06468396, hb.getIntercept(), 1e-4);//True results from sirt package in R
    assertEquals("  Haebara scale test", 1.0, hb.getScale(), 1e-4);

    //Compare UMNCMIN and Brent optimizer results.
    assertEquals("  UNCMIN/Brent intercept test", param1[0], param2[0], 1e-4);
    assertEquals("  UNCMIN/Brent slope test", param1[1], param2[1], 1e-4);

}

From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java

/**
 * This test uses a combination of 3PL and PCM items. Results compared to plink
 *
 * plink code:/*w  w  w  .  j  a  v  a2s .co  m*/
 *
 * library(plink)
 *
 * fX<-matrix(c(
 * 1, -3.188047976, 0,NA,NA,
 * 1,  1.031760328, 0,NA,NA,
 * 1,  0.819040914, 0,NA,NA,
 * 1, -2.706947360, 0,NA,NA,
 * 1, -0.094527077, 0,NA,NA,
 * 1,  0.689697135, 0,NA,NA,
 * 1, -0.551837153, 0,NA,NA,
 * 1, -0.359559276, 0,NA,NA,
 * 1, -1.451470831, -0.146619694, -0.636399040, 0.783018734),
 * nrow=9, byrow=TRUE)
 * fX<-as.data.frame(fX)
 * names(fX)<-c("aparam", "bparam","cparam","s1","s2")
 *
 * fY<-matrix(c(
 * 1,-3.074599226,0,NA,NA,
 * 1,1.01282435,0,NA,NA,
 * 1,0.868538408,0,NA,NA,
 * 1,-2.404483603,0,NA,NA,
 * 1,0.037402866,0,NA,NA,
 * 1,0.70074742,0,NA,NA,
 * 1,-0.602555046,0,NA,NA,
 * 1,-0.350426446,0,NA,NA,
 * 1,-1.267744832,-0.185885988,-0.61535623,0.801242218),
 * nrow=9, byrow=TRUE)
 * fY<-as.data.frame(fY)
 * names(fY)<-c("aparam", "bparam","cparam","s1","s2")
 *
 * common<-cbind(1:9, 1:9)
 * cat<-c(rep(2,8),4)
 *
 * pmX <- as.poly.mod(9,c("drm","gpcm"),list(1:8,9))
 * pmY <- as.poly.mod(9,c("drm","gpcm"),list(1:8,9))
 *
 * pars <- as.irt.pars(list(fx=fX,fy=fY), common, cat=list(fx=cat,fy=cat),
 * poly.mod=list(pmX,pmY), location=c(TRUE,TRUE))
 *
 * plink(pars, startvals=c(1,0), rescale="SL", base.grp=2, D=1.0, symmetric=TRUE)
 *
 *
 */
@Test
public void mixedFormtRaschPCMTest() {
    System.out.println("Mixed format Haebara test: Rasch and PCM");

    LinkedHashMap<String, ItemResponseModel> itemFormX = new LinkedHashMap<String, ItemResponseModel>();
    LinkedHashMap<String, ItemResponseModel> itemFormY = new LinkedHashMap<String, ItemResponseModel>();

    itemFormX.put("Item1", new Irm3PL(-3.188047976, 1.0));
    itemFormX.put("Item2", new Irm3PL(1.031760328, 1.0));
    itemFormX.put("Item3", new Irm3PL(0.819040914, 1.0));
    itemFormX.put("Item4", new Irm3PL(-2.706947360, 1.0));
    itemFormX.put("Item5", new Irm3PL(-0.094527077, 1.0));
    itemFormX.put("Item6", new Irm3PL(0.689697135, 1.0));
    itemFormX.put("Item7", new Irm3PL(-0.551837153, 1.0));
    itemFormX.put("Item8", new Irm3PL(-0.359559276, 1.0));
    double[] step1x = { -0.146619694, -0.636399040, 0.783018734 };
    itemFormX.put("Item9", new IrmPCM(-1.451470831, step1x, 1.0));

    itemFormY.put("Item1", new Irm3PL(-3.074599226, 1.0));
    itemFormY.put("Item2", new Irm3PL(1.012824350, 1.0));
    itemFormY.put("Item3", new Irm3PL(0.868538408, 1.0));
    itemFormY.put("Item4", new Irm3PL(-2.404483603, 1.0));
    itemFormY.put("Item5", new Irm3PL(0.037402866, 1.0));
    itemFormY.put("Item6", new Irm3PL(0.700747420, 1.0));
    itemFormY.put("Item7", new Irm3PL(-0.602555046, 1.0));
    itemFormY.put("Item8", new Irm3PL(-0.350426446, 1.0));
    double[] step1y = { -0.185885988, -0.61535623, 0.801242218 };
    itemFormY.put("Item9", new IrmPCM(-1.267744832, step1y, 1.0));

    double f = 0;
    double[] param1 = new double[2];
    double[] param2 = new double[2];

    UniformDistributionApproximation distX = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default
    UniformDistributionApproximation distY = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default

    HaebaraMethod hb = new HaebaraMethod(itemFormX, itemFormY, distX, distY, EquatingCriterionType.Q1Q2);
    hb.setPrecision(6);
    double[] startValues = { 0.0 };//Using a start value for the intercept only for the Rasch family of models.

    DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer();

    try {
        optimizer.minimize(hb, startValues);
        double[] r = optimizer.getParameters();
        param1[0] = r[0];
        param1[1] = 1.0;
        f = optimizer.getFunctionValue();
        hb.setIntercept(param1[0]);
        hb.setScale(param1[1]);

    } catch (UncminException ex) {
        ex.printStackTrace();
    }

    //Check UNCMIN values against results from plink.
    System.out.println("  UNCMIN Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + hb.getIntercept() + "  A = " + hb.getScale());

    //TODO there's something wrong with plink. I'm getting different results, but sirt agree with mine (at least for the Rasch model).

    //        assertEquals("  Intercept test", 0.105526, hb.getIntercept(), 1e-4);//True results from plink package in R
    //        assertEquals("  Scale test", 1.0, hb.getScale(), 1e-4);

    //Check Brent optimizer values against plink
    BrentOptimizer brentOptimizer = new BrentOptimizer(1e-8, 1e-8);
    UnivariatePointValuePair pair = brentOptimizer.optimize(new MaxEval(200),
            new UnivariateObjectiveFunction(hb),
            org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, new SearchInterval(-3, 3));
    param2[0] = pair.getPoint();
    param2[1] = 1.0;
    hb.setIntercept(param2[0]);
    hb.setScale(param2[1]);
    f = pair.getValue();

    System.out.println();
    System.out.println("  Brent Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + hb.getIntercept() + "  A = " + hb.getScale());

    //Check UNCMIN values against results from plink.
    //        assertEquals("  Intercept test", 0.105526, hb.getIntercept(), 1e-4);
    //        assertEquals("  Scale test", 1.0, hb.getScale(), 1e-4);
    //
    //
    //        //Compare UMNCMIN and Brent optimizer results.
    //        assertEquals("  UNCMIN/Brent intercept test", param1[0], param2[0], 1e-6);
    //        assertEquals("  UNCMIN/Brent slope test", param1[1], param2[1], 1e-6);

}

From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java

/**
 * This test uses Rasch model items.//w  w w  . j ava 2s .c  om
 *
 *
 */
@Test
public void raschModelTest() {
    System.out.println("Stocking-Lord test: Rasch model");

    LinkedHashMap<String, ItemResponseModel> itemFormX = new LinkedHashMap<String, ItemResponseModel>();
    LinkedHashMap<String, ItemResponseModel> itemFormY = new LinkedHashMap<String, ItemResponseModel>();

    itemFormX.put("Item1", new Irm3PL(-3.188047976, 1.0));
    itemFormX.put("Item2", new Irm3PL(1.031760328, 1.0));
    itemFormX.put("Item3", new Irm3PL(0.819040914, 1.0));
    itemFormX.put("Item4", new Irm3PL(-2.706947360, 1.0));
    itemFormX.put("Item5", new Irm3PL(-0.094527077, 1.0));
    itemFormX.put("Item6", new Irm3PL(0.689697135, 1.0));
    itemFormX.put("Item7", new Irm3PL(-0.551837153, 1.0));
    itemFormX.put("Item8", new Irm3PL(-0.359559276, 1.0));

    itemFormY.put("Item1", new Irm3PL(-3.074599226, 1.0));
    itemFormY.put("Item2", new Irm3PL(1.012824350, 1.0));
    itemFormY.put("Item3", new Irm3PL(0.868538408, 1.0));
    itemFormY.put("Item4", new Irm3PL(-2.404483603, 1.0));
    itemFormY.put("Item5", new Irm3PL(0.037402866, 1.0));
    itemFormY.put("Item6", new Irm3PL(0.700747420, 1.0));
    itemFormY.put("Item7", new Irm3PL(-0.602555046, 1.0));
    itemFormY.put("Item8", new Irm3PL(-0.350426446, 1.0));

    double f = 0;
    double[] param1 = new double[2];
    double[] param2 = new double[2];

    UniformDistributionApproximation distX = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default
    UniformDistributionApproximation distY = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default

    StockingLordMethod sl = new StockingLordMethod(itemFormX, itemFormY, distX, distY,
            EquatingCriterionType.Q1Q2);
    sl.setPrecision(6);
    double[] startValues = { 0 };//Using a start value for the intercept only for the Rasch family of models.

    DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer();

    try {
        optimizer.minimize(sl, startValues);
        double[] r = optimizer.getParameters();
        param1[0] = r[0];
        param1[1] = 1;
        f = optimizer.getFunctionValue();
        sl.setIntercept(param1[0]);
        sl.setScale(param1[1]);

    } catch (UncminException ex) {
        ex.printStackTrace();
    }

    //Check Brent Optimizer values against results from plink.
    System.out.println("  UNCMIN Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + sl.getIntercept() + "  A = " + sl.getScale());

    assertEquals("  Intercept test", 0.057566, sl.getIntercept(), 1e-3);//true result from plink package in R
    assertEquals("  Scale test", 1.0, sl.getScale(), 1e-4);

    BrentOptimizer brentOptimizer = new BrentOptimizer(1e-8, 1e-8);
    UnivariatePointValuePair pair = brentOptimizer.optimize(new MaxEval(200),
            new UnivariateObjectiveFunction(sl), GoalType.MINIMIZE, new SearchInterval(-3, 3));

    param2 = new double[2];
    sl.setIntercept(pair.getPoint());
    sl.setScale(1.0);
    f = pair.getValue();

    param2[0] = pair.getPoint();
    param2[1] = 1;
    sl.setIntercept(param2[0]);
    sl.setScale(param2[1]);
    f = pair.getValue();

    //Check Brent results against plink results.
    System.out.println();
    System.out.println("  Brent Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + sl.getIntercept() + "  A = " + sl.getScale());

    assertEquals("  Intercept test", 0.057566, sl.getIntercept(), 1e-3);//true result from plink package in R
    assertEquals("  Scale test", 1.0, sl.getScale(), 1e-4);

    //Compare UMNCMIN and Brent optimizer results.
    assertEquals("  UNCMIN/Brent intercept test", param1[0], param2[0], 1e-6);
    assertEquals("  UNCMIN/Brent slope test", param1[1], param2[1], 1e-6);

}

From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java

/**
 * This test uses a combination of 3PL and PCM items. Results compared to plink
 *
 * plink code://from   ww w. ja  va2  s.  c  o  m
 *
 * library(plink)
 *
 * fX<-matrix(c(
 * 1, -3.188047976, 0,NA,NA,
 * 1,  1.031760328, 0,NA,NA,
 * 1,  0.819040914, 0,NA,NA,
 * 1, -2.706947360, 0,NA,NA,
 * 1, -0.094527077, 0,NA,NA,
 * 1,  0.689697135, 0,NA,NA,
 * 1, -0.551837153, 0,NA,NA,
 * 1, -0.359559276, 0,NA,NA,
 * 1, -1.451470831, -0.146619694, -0.636399040, 0.783018734),
 * nrow=9, byrow=TRUE)
 * fX<-as.data.frame(fX)
 * names(fX)<-c("aparam", "bparam","cparam","s1","s2")
 *
 * fY<-matrix(c(
 * 1,-3.074599226,0,NA,NA,
 * 1,1.01282435,0,NA,NA,
 * 1,0.868538408,0,NA,NA,
 * 1,-2.404483603,0,NA,NA,
 * 1,0.037402866,0,NA,NA,
 * 1,0.70074742,0,NA,NA,
 * 1,-0.602555046,0,NA,NA,
 * 1,-0.350426446,0,NA,NA,
 * 1,-1.267744832,-0.185885988,-0.61535623,0.801242218),
 * nrow=9, byrow=TRUE)
 * fY<-as.data.frame(fY)
 * names(fY)<-c("aparam", "bparam","cparam","s1","s2")
 *
 * common<-cbind(1:9, 1:9)
 * cat<-c(rep(2,8),4)
 *
 * pmX <- as.poly.mod(9,c("drm","gpcm"),list(1:8,9))
 * pmY <- as.poly.mod(9,c("drm","gpcm"),list(1:8,9))
 *
 * pars <- as.irt.pars(list(fx=fX,fy=fY), common, cat=list(fx=cat,fy=cat),
 * poly.mod=list(pmX,pmY), location=c(TRUE,TRUE))
 *
 * plink(pars, startvals=c(1,0), rescale="SL", base.grp=2, D=1.0, symmetric=TRUE)
 *
 *
 */
@Test
public void mixedFormtRaschPCMTest() {
    System.out.println("Mixed format Stocking-Lord test: Rasch and PCM");

    LinkedHashMap<String, ItemResponseModel> itemFormX = new LinkedHashMap<String, ItemResponseModel>();
    LinkedHashMap<String, ItemResponseModel> itemFormY = new LinkedHashMap<String, ItemResponseModel>();

    itemFormX.put("Item1", new Irm3PL(-3.188047976, 1.0));
    itemFormX.put("Item2", new Irm3PL(1.031760328, 1.0));
    itemFormX.put("Item3", new Irm3PL(0.819040914, 1.0));
    itemFormX.put("Item4", new Irm3PL(-2.706947360, 1.0));
    itemFormX.put("Item5", new Irm3PL(-0.094527077, 1.0));
    itemFormX.put("Item6", new Irm3PL(0.689697135, 1.0));
    itemFormX.put("Item7", new Irm3PL(-0.551837153, 1.0));
    itemFormX.put("Item8", new Irm3PL(-0.359559276, 1.0));
    double[] step1x = { -0.146619694, -0.636399040, 0.783018734 };
    itemFormX.put("Item9", new IrmPCM(-1.451470831, step1x, 1.0));

    itemFormY.put("Item1", new Irm3PL(-3.074599226, 1.0));
    itemFormY.put("Item2", new Irm3PL(1.012824350, 1.0));
    itemFormY.put("Item3", new Irm3PL(0.868538408, 1.0));
    itemFormY.put("Item4", new Irm3PL(-2.404483603, 1.0));
    itemFormY.put("Item5", new Irm3PL(0.037402866, 1.0));
    itemFormY.put("Item6", new Irm3PL(0.700747420, 1.0));
    itemFormY.put("Item7", new Irm3PL(-0.602555046, 1.0));
    itemFormY.put("Item8", new Irm3PL(-0.350426446, 1.0));
    double[] step1y = { -0.185885988, -0.61535623, 0.801242218 };
    itemFormY.put("Item9", new IrmPCM(-1.267744832, step1y, 1.0));

    double f = 0;
    double[] param1 = new double[2];
    double[] param2 = new double[2];

    UniformDistributionApproximation distX = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default
    UniformDistributionApproximation distY = new UniformDistributionApproximation(-4.0, 4.0, 161);//plink default

    StockingLordMethod sl = new StockingLordMethod(itemFormX, itemFormY, distX, distY,
            EquatingCriterionType.Q1Q2);
    sl.setPrecision(6);
    double[] startValues = { 0.0 };//Using a start value for the intercept only for the Rasch family of models.

    DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer();

    try {
        optimizer.minimize(sl, startValues);
        double[] r = optimizer.getParameters();
        param1[0] = r[0];
        param1[1] = 1.0;
        f = optimizer.getFunctionValue();
        sl.setIntercept(param1[0]);
        sl.setScale(param1[1]);

    } catch (UncminException ex) {
        ex.printStackTrace();
    }

    //Check UNCMIN values against results from plink.
    System.out.println("  UNCMIN Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + sl.getIntercept() + "  A = " + sl.getScale());

    assertEquals("  Intercept test", 0.101388, sl.getIntercept(), 1e-4);//True results from plink package in R
    assertEquals("  Scale test", 1.0, sl.getScale(), 1e-4);

    //Check Brent optimizer values against plink
    BrentOptimizer brentOptimizer = new BrentOptimizer(1e-8, 1e-8);
    UnivariatePointValuePair pair = brentOptimizer.optimize(new MaxEval(200),
            new UnivariateObjectiveFunction(sl), GoalType.MINIMIZE, new SearchInterval(-3, 3));
    param2[0] = pair.getPoint();
    param2[1] = 1.0;
    sl.setIntercept(param2[0]);
    sl.setScale(param2[1]);
    f = pair.getValue();

    System.out.println();
    System.out.println("  Brent Optimization");
    System.out.println("  Iterations: ");
    System.out.println("  fmin: " + f);
    System.out.println("  B = " + sl.getIntercept() + "  A = " + sl.getScale());

    //Check UNCMIN values against results from plink.
    assertEquals("  Intercept test", 0.101388, sl.getIntercept(), 1e-4);
    assertEquals("  Scale test", 1.0, sl.getScale(), 1e-4);

    //Compare UMNCMIN and Brent optimizer results.
    assertEquals("  UNCMIN/Brent intercept test", param1[0], param2[0], 1e-6);
    assertEquals("  UNCMIN/Brent slope test", param1[1], param2[1], 1e-6);

}