Example usage for org.apache.commons.math3.analysis MultivariateVectorFunction MultivariateVectorFunction

Introduction

In this page you can find the example usage for org.apache.commons.math3.analysis MultivariateVectorFunction MultivariateVectorFunction.

Prototype

MultivariateVectorFunction

Source Link

Usage

From source file:com.itemanalysis.psychometrics.analysis.AbstractDiffFunction.java

public MultivariateVectorFunction gradient() {
    return new MultivariateVectorFunction() {
        public double[] value(double[] point) {
            return gradient(point);
        }/*from w  w w.  java 2  s  .  c  om*/
    };
}

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From source file:com.itemanalysis.psychometrics.analysis.AbstractMultivariateFunction.java

public MultivariateVectorFunction gradient() {
    return new MultivariateVectorFunction() {
        public double[] value(double[] point) {
            return gradientAt(point);
        }/*from   w  w w .  j ava  2  s.  c  o  m*/
    };
}

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From source file:net.sf.tweety.math.opt.solver.ApacheCommonsNonLinearConjugateGradientOptimizer.java

@Override
public Map<Variable, Term> solve(ConstraintSatisfactionProblem problem) throws GeneralMathException {
    // only optimization problems
    if (!(problem instanceof OptimizationProblem))
        throw new IllegalArgumentException("Only optimization problems allowed for this solver.");
    OptimizationProblem p = (OptimizationProblem) problem;
    // no constraints allowed
    if (!p.isEmpty())
        throw new IllegalArgumentException(
                "Only optimization problems without constraints allowed for this solver.");
    final Term target = p.getTargetFunction();
    final List<Variable> vars = new ArrayList<Variable>(target.getVariables());
    MultivariateFunction acTarget = new MultivariateFunction() {
        @Override//  w  w w  .  j av  a  2  s . c om
        public double value(double[] arg0) {
            return target.replaceAllTerms(arg0, vars).doubleValue();
        }
    };
    final Term[] targetGradient = new Term[vars.size()];
    for (int i = 0; i < vars.size(); i++)
        targetGradient[i] = target.derive(vars.get(i));
    MultivariateVectorFunction acTargetGradient = new MultivariateVectorFunction() {
        @Override
        public double[] value(double[] arg0) throws IllegalArgumentException {
            double[] result = new double[arg0.length];
            for (int i = 0; i < arg0.length; i++)
                result[i] = targetGradient[i].replaceAllTerms(arg0, vars).doubleValue();
            return result;
        }
    };
    // create solver
    NonLinearConjugateGradientOptimizer optimizer = new NonLinearConjugateGradientOptimizer(
            NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
            new SimplePointChecker<PointValuePair>(this.precision, this.precision));
    double[] s = new double[vars.size()];
    for (int i = 0; i < vars.size(); i++)
        s[i] = 0.5;
    PointValuePair val = optimizer.optimize(new ObjectiveFunction(acTarget),
            new ObjectiveFunctionGradient(acTargetGradient), new InitialGuess(s),
            p.getType() == OptimizationProblem.MAXIMIZE ? GoalType.MAXIMIZE : GoalType.MINIMIZE,
            new MaxEval(this.maxEval));
    Map<Variable, Term> result = new HashMap<Variable, Term>();
    for (int i = 0; i < vars.size(); i++)
        result.put(vars.get(i), new FloatConstant(val.getPoint()[i]));
    return result;
}

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From source file:com.itemanalysis.psychometrics.cfa.MaximumLikelihoodEstimation.java

@Override
public MultivariateVectorFunction gradient() {
    return new MultivariateVectorFunction() {
        public double[] value(double[] point) {
            return gradientAt(point);
        }/* w  ww  .  j  av  a 2  s .  c o  m*/
    };
}

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From source file:uk.ac.diamond.scisoft.analysis.optimize.ApacheOptimizer.java

public MultivariateVectorFunction createGradientFunction() {
    MultivariateVectorFunction f = new MultivariateVectorFunction() {

        @Override//from   w  w  w .  ja  va 2  s.  com
        public double[] value(double[] parameters) throws IllegalArgumentException {
            double[] result = new double[n];

            for (int i = 0; i < n; i++) {
                result[i] = calculateResidualDerivative(params.get(i), parameters);
            }
            return result;
        }
    };

    return f;
}

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