Java tutorial
/* * @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.linear.LinearConstraint; import org.apache.commons.math3.optim.linear.LinearObjectiveFunction; import org.lightjason.agentspeak.error.CIllegalArgumentException; import org.lightjason.agentspeak.language.CCommon; 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.Collection; import java.util.List; import java.util.stream.Collectors; import java.util.stream.IntStream; /** * add a linear equation constraint to the LP. * The arguments of the action contains the left and right side of the equation: * * + \f$ \left( \sum_{i=1} c_i \cdot x_i \right) + c_{const} = \left( \sum_{i=1} r_i \cdot x_i \right) + r_{const} \f$ * + \f$ \left( \sum_{i=1} c_i \cdot x_i \right) + c_{const} \geq \left( \sum_{i=1} r_i \cdot x_i \right) + r_{const} \f$ * + \f$ \left( \sum_{i=1} c_i \cdot x_i \right) + c_{const} \leq \left( \sum_{i=1} r_i \cdot x_i \right) + r_{const} \f$ * * The first arguments is the LP object, the following arguments are the \f$ c_i \f$ values, after that the \f$ c_{const} \f$ value must be added, in the middle * of the arguments the relation symbol (\f$ = \f$, \f$ \geq \f$ or \f$ \leq \f$) must be set as string, after that all \f$ r_i \f$ * elements must be set and the last argument is the \f$ r_{const} \f$, the action never fails * * @code math/linearprogram/equationconstraint( LP, [2,7,[7,12,[19]]], "<", [1,2],3,5 ) @endcode * @warning the action throws an exception if the relation symbol is not found * @see https://en.wikipedia.org/wiki/Linear_programming * @see http://commons.apache.org/proper/commons-math/userguide/optimization.html */ public final class CEquationConstraint extends IConstraint { /** * ctor */ public CEquationConstraint() { super(); } @Override public final int minimalArgumentNumber() { return 6; } @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) { final List<ITerm> l_arguments = CCommon.flatcollection(p_argument).collect(Collectors.toList()); // first search the relation symbol and create splitting lists final int l_index = IntStream.range(1, l_arguments.size()).boxed() .mapToInt(i -> CCommon.rawvalueAssignableTo(l_arguments.get(i), String.class) ? i : -1) .filter(i -> i > -1).findFirst().orElseThrow(() -> new CIllegalArgumentException( org.lightjason.agentspeak.common.CCommon.languagestring(this, "relation"))); // create linear constraint based on an equation l_arguments.get(0).<Pair<LinearObjectiveFunction, Collection<LinearConstraint>>>raw().getRight() .add(new LinearConstraint( // c_i values l_arguments.stream().limit(l_index - 2).skip(1) .mapToDouble(i -> i.<Number>raw().doubleValue()).toArray(), // c_const value l_arguments.get(l_index - 1).<Number>raw().doubleValue(), // relation symbol this.getRelation(p_argument.get(l_index).<String>raw()), // r_i values l_arguments.stream().skip(l_index + 2).mapToDouble(i -> i.<Number>raw().doubleValue()) .toArray(), // r_const value l_arguments.get(p_argument.size() - 1).<Number>raw().doubleValue())); return CFuzzyValue.from(true); } }