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

Introduction

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

Prototype

public MaxIter(int max) 

Source Link

Usage

From source file:org.lightjason.agentspeak.action.buildin.math.linearprogram.CSolve.java

@Override
public final IFuzzyValue<Boolean> execute(final IContext p_context, final boolean p_parallel,
        final List<ITerm> p_argument, final List<ITerm> p_return, final List<ITerm> p_annotation) {
    // first argument is the LP pair object, second argument is the goal-type (maximize / minimize),
    // third & fourth argument can be the number of iterations or string with "non-negative" variables
    final List<OptimizationData> l_settings = new LinkedList<>();

    final Pair<LinearObjectiveFunction, Collection<LinearConstraint>> l_default = p_argument.get(0).raw();
    l_settings.add(l_default.getLeft());
    l_settings.add(new LinearConstraintSet(l_default.getRight()));

    p_argument.subList(1, p_argument.size()).stream().map(i -> {
        if (CCommon.rawvalueAssignableTo(i, Number.class))
            return new MaxIter(i.raw());

        if (CCommon.rawvalueAssignableTo(i, String.class))
            switch (i.<String>raw().trim().toLowerCase()) {
            case "non-negative":
                return new NonNegativeConstraint(true);
            case "maximize":
                return GoalType.MAXIMIZE;
            case "minimize":
                return GoalType.MINIMIZE;

            default:
                return null;
            }/*from w  w w  . ja  va 2  s .  com*/

        return null;
    }).filter(Objects::nonNull).forEach(l_settings::add);

    // optimze and return
    final SimplexSolver l_lp = new SimplexSolver();
    final PointValuePair l_result = l_lp.optimize(l_settings.toArray(new OptimizationData[l_settings.size()]));

    p_return.add(CRawTerm.from(l_result.getValue()));
    p_return.add(CRawTerm.from(l_result.getPoint().length));
    Arrays.stream(l_result.getPoint()).boxed().map(CRawTerm::from).forEach(p_return::add);

    return CFuzzyValue.from(true);
}

---

From source file:org.lightjason.agentspeak.action.builtin.math.linearprogram.CSolve.java

@Nonnull
@Override//from  w  w  w .j  av  a 2 s  .  c  o  m
public final IFuzzyValue<Boolean> execute(final boolean p_parallel, @Nonnull final IContext p_context,
        @Nonnull final List<ITerm> p_argument, @Nonnull final List<ITerm> p_return) {
    // first argument is the LP pair object, second argument is the goal-type (maximize / minimize),
    // third & fourth argument can be the number of iterations or string with "non-negative" variables
    final List<OptimizationData> l_settings = new LinkedList<>();

    final Pair<LinearObjectiveFunction, Collection<LinearConstraint>> l_default = p_argument.get(0).raw();
    l_settings.add(l_default.getLeft());
    l_settings.add(new LinearConstraintSet(l_default.getRight()));

    p_argument.subList(1, p_argument.size()).stream().map(i -> {
        if (CCommon.rawvalueAssignableTo(i, Number.class))
            return new MaxIter(i.raw());

        if (CCommon.rawvalueAssignableTo(i, String.class))
            switch (i.<String>raw().trim().toLowerCase()) {
            case "non-negative":
                return new NonNegativeConstraint(true);
            case "maximize":
                return GoalType.MAXIMIZE;
            case "minimize":
                return GoalType.MINIMIZE;

            default:
                return null;
            }

        return null;
    }).filter(Objects::nonNull).forEach(l_settings::add);

    // optimze and return
    final SimplexSolver l_lp = new SimplexSolver();
    final PointValuePair l_result = l_lp.optimize(l_settings.toArray(new OptimizationData[l_settings.size()]));

    p_return.add(CRawTerm.from(l_result.getValue()));
    p_return.add(CRawTerm.from(l_result.getPoint().length));
    Arrays.stream(l_result.getPoint()).boxed().map(CRawTerm::from).forEach(p_return::add);

    return CFuzzyValue.from(true);
}

---

From source file:put.ci.cevo.framework.algorithms.ApacheCMAES.java

public void optimize(EvolutionTarget target, int populationSize, double sigma, double[] guess,
        PopulationEvaluator<double[]> populationEvaluator) {
    this.maxIterations = ((GenerationsTarget) target).getGenerations();
    this.populationEvaluator = populationEvaluator;
    optimize(new MaxIter(maxIterations), new PopulationSize(populationSize),
            new Sigma(DoubleArrays.vector(sigma, guess.length)), new InitialGuess(guess), GoalType.MAXIMIZE,
            new SimpleBounds(DoubleArrays.vector(Double.NEGATIVE_INFINITY, guess.length),
                    DoubleArrays.vector(Double.POSITIVE_INFINITY, guess.length)));
}

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