org.lightjason.agentspeak.action.buildin.math.linearprogram.CSolve.java Source code

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Here is the source code for org.lightjason.agentspeak.action.buildin.math.linearprogram.CSolve.java

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/*
 * @cond LICENSE
 * ######################################################################################
 * # LGPL License                                                                       #
 * #                                                                                    #
 * # This file is part of the LightJason AgentSpeak(L++)                                #
 * # Copyright (c) 2015-16, LightJason (info@lightjason.org)                            #
 * # This program is free software: you can redistribute it and/or modify               #
 * # it under the terms of the GNU Lesser General Public License as                     #
 * # published by the Free Software Foundation, either version 3 of the                 #
 * # License, or (at your option) any later version.                                    #
 * #                                                                                    #
 * # This program is distributed in the hope that it will be useful,                    #
 * # but WITHOUT ANY WARRANTY; without even the implied warranty of                     #
 * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the                      #
 * # GNU Lesser General Public License for more details.                                #
 * #                                                                                    #
 * # You should have received a copy of the GNU Lesser General Public License           #
 * # along with this program. If not, see http://www.gnu.org/licenses/                  #
 * ######################################################################################
 * @endcond
 */

package org.lightjason.agentspeak.action.buildin.math.linearprogram;

import org.apache.commons.lang3.tuple.Pair;
import org.apache.commons.math3.optim.MaxIter;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.PointValuePair;
import org.apache.commons.math3.optim.linear.LinearConstraint;
import org.apache.commons.math3.optim.linear.LinearConstraintSet;
import org.apache.commons.math3.optim.linear.LinearObjectiveFunction;
import org.apache.commons.math3.optim.linear.NonNegativeConstraint;
import org.apache.commons.math3.optim.linear.SimplexSolver;
import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
import org.lightjason.agentspeak.action.buildin.IBuildinAction;
import org.lightjason.agentspeak.language.CCommon;
import org.lightjason.agentspeak.language.CRawTerm;
import org.lightjason.agentspeak.language.ITerm;
import org.lightjason.agentspeak.language.execution.IContext;
import org.lightjason.agentspeak.language.execution.fuzzy.CFuzzyValue;
import org.lightjason.agentspeak.language.execution.fuzzy.IFuzzyValue;

import java.util.Arrays;
import java.util.Collection;
import java.util.LinkedList;
import java.util.List;
import java.util.Objects;

/**
 * solves the linear program and returns the solution.
 * The action solves the linear program and returns the
 * solution. The first argument is the linear program,
 * all other arguments can be a number or a string with
 * the definition:
 *
 * + maximize / minimize defines the optimization goal
 * + non-negative defines all variables with non-negative values
 * + number is the number of iteration for solving
 *
 * The return arguments are at the first the value, second
 * the number of all referenced \f$ x_i \f$ points and after
 * that all arguments the values of \f$ x_i \f$
 *
 * @code [Value|CountXi|Xi] = math/linearprogram/solve( LP, "maximize", "non-negative" ); @endcode
 * @see https://en.wikipedia.org/wiki/Linear_programming
 * @see http://commons.apache.org/proper/commons-math/userguide/optimization.html
 */
public final class CSolve extends IBuildinAction {

    /**
     * ctor
     */
    public CSolve() {
        super(3);
    }

    @Override
    public final int minimalArgumentNumber() {
        return 1;
    }

    @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;
                }

            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);
    }

}