    001package org.cpsolver.ifs.heuristics;
    002
    003import java.util.ArrayList;
    004import java.util.Iterator;
    005import java.util.List;
    006import java.util.Set;
    007
    008import org.cpsolver.ifs.extension.Extension;
    009import org.cpsolver.ifs.extension.MacPropagation;
    010import org.cpsolver.ifs.model.Value;
    011import org.cpsolver.ifs.model.Variable;
    012import org.cpsolver.ifs.solution.Solution;
    013import org.cpsolver.ifs.solver.Solver;
    014import org.cpsolver.ifs.util.DataProperties;
    015import org.cpsolver.ifs.util.ToolBox;
    016
    017
    018/**
    019 * General implementation of variable selection criterion. <br>
    020 * <br>
    021 * In case that all variables are assigned, one of the variables is selected
    022 * randomly. In case of MPP, the random selection is made among the variables
    023 * which have not assigned initial values. <br>
    024 * <br>
    025 * When there are unassigned variables, a variable is selected randomly among
    026 * all unassigned variables (when Variable.RandomSelection is true) or the
    027 * following roulette wheel selection takes place (MPP):
    028 * <ul>
    029 * <li>one point for a variable with no initial assignment
    030 * <li>3 * ( 1 + number of conflicts with the initial assignment) for a variable
    031 * with an initial assignment
    032 * </ul>
    033 * <br>
    034 * If {@link MacPropagation} is used and Variable.UnassignWhenNoGood parameter
    035 * is true, while there is a variable with an empty domain:
    036 * <ul>
    037 * <li>with Variable.UnassignWhenNoGoodRandomWalk probabilty an arbitrary
    038 * assigned variable is selected
    039 * <li>otherwise, one variable with empty domain is picked, one of its original
    040 * values is picked and one of the variables from the explanation of that value
    041 * is then returned. If the explanation is empty, another variable and value is
    042 * tried (up to ten times).
    043 * </ul>
    044 * <br>
    045 * Parameters: <br>
    046 * <table border='1'><caption>Related Solver Parameters</caption>
    047 * <tr>
    048 * <th>Parameter</th>
    049 * <th>Type</th>
    050 * <th>Comment</th>
    051 * </tr>
    052 * <tr>
    053 * <td>Variable.RandomSelection</td>
    054 * <td>{@link Boolean}</td>
    055 * <td>if true, an unassigned variable is picked randomly</td>
    056 * </tr>
    057 * <tr>
    058 * <td>Variable.UnassignWhenNoGood</td>
    059 * <td>{@link Boolean}</td>
    060 * <td>if true and if {@link MacPropagation} is used: if there is a variable
    061 * with empty domain, assigned variable (which is present in some explanation
    062 * for a vairable with empty domain) is selected (for reassignment)</td>
    063 * </tr>
    064 * <tr>
    065 * <td>Variable.UnassignWhenNoGoodRandomWalk</td>
    066 * <td>{@link Double}</td>
    067 * <td>if Variable.UnassignWhenNoGood is true and if {@link MacPropagation} is
    068 * used: if there is a variable with empty domain, with the given probability an
    069 * arbitrary assigned variable is selected</td>
    070 * </tr>
    071 * </table>
    072 *
    073 * @see VariableSelection
    074 * @see Solver
    075 *
    076 * @author  Tomáš Müller
    077 * @version IFS 1.3 (Iterative Forward Search)<br>
    078 *          Copyright (C) 2006 - 2014 Tomáš Müller<br>
    079 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
    080 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
    081 * <br>
    082 *          This library is free software; you can redistribute it and/or modify
    083 *          it under the terms of the GNU Lesser General Public License as
    084 *          published by the Free Software Foundation; either version 3 of the
    085 *          License, or (at your option) any later version. <br>
    086 * <br>
    087 *          This library is distributed in the hope that it will be useful, but
    088 *          WITHOUT ANY WARRANTY; without even the implied warranty of
    089 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
    090 *          Lesser General Public License for more details. <br>
    091 * <br>
    092 *          You should have received a copy of the GNU Lesser General Public
    093 *          License along with this library; if not see <a href='http://www.gnu.org/licenses'>http://www.gnu.org/licenses</a>.
    094 *
    095 * @param <V> Variable
    096 * @param <T> Value
    097 **/
    098public class GeneralVariableSelection<V extends Variable<V, T>, T extends Value<V, T>> implements VariableSelection<V, T> {
    099    private boolean iUnassignWhenNotGood = false;
    100    private double iUnassignWhenNotGoodRandWalk = 0.02;
    101    private boolean iRandomSelection = true;
    102
    103    private MacPropagation<V, T> iProp = null;
    104
    105    /**
    106     * Constructor
    107     *
    108     * @param properties
    109     *            input configuration
    110     */
    111    public GeneralVariableSelection(DataProperties properties) {
    112        iUnassignWhenNotGood = properties.getPropertyBoolean("Variable.UnassignWhenNoGood", iUnassignWhenNotGood);
    113        iUnassignWhenNotGoodRandWalk = properties.getPropertyDouble("Variable.UnassignWhenNoGoodRandomWalk",
    114                iUnassignWhenNotGoodRandWalk);
    115        iRandomSelection = properties.getPropertyBoolean("Variable.RandomSelection", iRandomSelection);
    116    }
    117
    118    public GeneralVariableSelection() {
    119    }
    120
    121    /** Initialization */
    122    @Override
    123    public void init(Solver<V, T> solver) {
    124        for (Extension<V, T> extension : solver.getExtensions()) {
    125            if (MacPropagation.class.isInstance(extension))
    126                iProp = (MacPropagation<V, T>) extension;
    127        }
    128    }
    129
    130    /** Variable selection */
    131    @Override
    132    public V selectVariable(Solution<V, T> solution) {
    133        if (solution.getModel().variables().size() == solution.getAssignment().nrAssignedVariables()) {
    134            if (!solution.getModel().perturbVariables(solution.getAssignment()).isEmpty())
    135                return ToolBox.random(solution.getModel().perturbVariables(solution.getAssignment()));
    136            else
    137                return ToolBox.random(solution.getAssignment().assignedVariables());
    138        } else {
    139            if (iProp != null && iUnassignWhenNotGood) {
    140                List<V> noGoodVariables = new ArrayList<V>();
    141                for (V variable : solution.getAssignment().unassignedVariables(solution.getModel())) {
    142                    if (iProp.goodValues(solution.getAssignment(), variable).isEmpty())
    143                        noGoodVariables.add(variable);
    144                }
    145                if (!noGoodVariables.isEmpty()) {
    146                    if (ToolBox.random() < iUnassignWhenNotGoodRandWalk)
    147                        return ToolBox.random(solution.getAssignment().assignedVariables());
    148                    for (int attempt = 0; attempt < 10; attempt++) {
    149                        V noGoodVariable = ToolBox.random(noGoodVariables);
    150                        T noGoodValue = ToolBox.random(noGoodVariable.values(solution.getAssignment()));
    151                        Set<T> noGood = iProp.noGood(solution.getAssignment(), noGoodValue);
    152                        if (noGood != null && !noGood.isEmpty())
    153                            return ToolBox.random(noGood).variable();
    154                    }
    155                }
    156            }
    157            if (iRandomSelection)
    158                return ToolBox.random(solution.getAssignment().unassignedVariables(solution.getModel()));
    159            List<Integer> points = new ArrayList<Integer>();
    160            int totalPoints = 0;
    161            for (V variable : solution.getAssignment().unassignedVariables(solution.getModel())) {
    162                int pointsThisVariable = (variable.getInitialAssignment() != null ? 3 * (1 + solution.getModel().conflictValues(solution.getAssignment(), variable.getInitialAssignment()).size()) : 1);
    163                totalPoints += pointsThisVariable;
    164                points.add(totalPoints);
    165            }
    166            int rndPoints = ToolBox.random(totalPoints);
    167            Iterator<V> x = solution.getAssignment().unassignedVariables(solution.getModel()).iterator();
    168            for (int i = 0; x.hasNext() && i < points.size(); i++) {
    169                V variable = x.next();
    170                int tp = points.get(i);
    171                if (tp > rndPoints)
    172                    return variable;
    173            }
    174            return ToolBox.random(solution.getAssignment().unassignedVariables(solution.getModel()));
    175        }
    176    }
    177
    178}