001package org.cpsolver.studentsct.heuristics;
002
003import java.util.Collections;
004import java.util.Comparator;
005import java.util.HashMap;
006import java.util.Iterator;
007import java.util.List;
008
009import org.cpsolver.ifs.assignment.Assignment;
010import org.cpsolver.ifs.heuristics.BacktrackNeighbourSelection;
011import org.cpsolver.ifs.util.DataProperties;
012import org.cpsolver.studentsct.StudentSectioningModel;
013import org.cpsolver.studentsct.model.CourseRequest;
014import org.cpsolver.studentsct.model.Enrollment;
015import org.cpsolver.studentsct.model.Request;
016
017
018/**
019 * Randomized backtracking-based neighbour selection. This class extends
020 * {@link RandomizedBacktrackNeighbourSelection}, however, only a randomly
021 * selected subset of enrollments of each request is considered (
022 * {@link CourseRequest#computeRandomEnrollments(Assignment, int)} with the given limit is
023 * used).
024 * 
025 * <br>
026 * <br>
027 * Parameters: <br>
028 * <table border='1' summary='Related Solver Parameters'>
029 * <tr>
030 * <th>Parameter</th>
031 * <th>Type</th>
032 * <th>Comment</th>
033 * </tr>
034 * <tr>
035 * <td>Neighbour.MaxValues</td>
036 * <td>{@link Integer}</td>
037 * <td>Limit on the number of enrollments to be visited of each
038 * {@link CourseRequest}.</td>
039 * </tr>
040 * </table>
041 * <br>
042 * <br>
043 * 
044 * @version StudentSct 1.3 (Student Sectioning)<br>
045 *          Copyright (C) 2007 - 2014 Tomas Muller<br>
046 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
047 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
048 * <br>
049 *          This library is free software; you can redistribute it and/or modify
050 *          it under the terms of the GNU Lesser General Public License as
051 *          published by the Free Software Foundation; either version 3 of the
052 *          License, or (at your option) any later version. <br>
053 * <br>
054 *          This library is distributed in the hope that it will be useful, but
055 *          WITHOUT ANY WARRANTY; without even the implied warranty of
056 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
057 *          Lesser General Public License for more details. <br>
058 * <br>
059 *          You should have received a copy of the GNU Lesser General Public
060 *          License along with this library; if not see
061 *          <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>.
062 */
063public class RandomizedBacktrackNeighbourSelection extends BacktrackNeighbourSelection<Request, Enrollment> {
064    private int iMaxValues = 100;
065
066    /**
067     * Constructor
068     * 
069     * @param properties
070     *            configuration
071     * @throws Exception thrown when the initialization fails
072     */
073    public RandomizedBacktrackNeighbourSelection(DataProperties properties) throws Exception {
074        super(properties);
075        iMaxValues = properties.getPropertyInt("Neighbour.MaxValues", iMaxValues);
076    }
077
078    /**
079     * List of values of a variable.
080     * {@link CourseRequest#computeRandomEnrollments(Assignment, int)} with the provided
081     * limit is used for a {@link CourseRequest}.
082     */
083    @Override
084    protected Iterator<Enrollment> values(BacktrackNeighbourSelection<Request, Enrollment>.BacktrackNeighbourSelectionContext context, Request variable) {
085        if (variable instanceof CourseRequest) {
086            final CourseRequest request = (CourseRequest)variable;
087            final StudentSectioningModel model = (StudentSectioningModel)context.getModel();
088            final Assignment<Request, Enrollment> assignment = context.getAssignment();
089            List<Enrollment> values = (iMaxValues > 0 ? request.computeRandomEnrollments(assignment, iMaxValues) : request.computeEnrollments(assignment));
090            Collections.sort(values, new Comparator<Enrollment>() {
091                private HashMap<Enrollment, Double> iValues = new HashMap<Enrollment, Double>();
092                private Double value(Enrollment e) {
093                    Double value = iValues.get(e);
094                    if (value == null) {
095                        value = model.getStudentWeights().getWeight(assignment, e,
096                                (model.getDistanceConflict() == null ? null : model.getDistanceConflict().conflicts(e)),
097                                (model.getTimeOverlaps() == null ? null : model.getTimeOverlaps().freeTimeConflicts(e)));
098                        iValues.put(e, value);
099                    }
100                    return value;
101                }
102                @Override
103                public int compare(Enrollment e1, Enrollment e2) {
104                    if (e1.equals(assignment.getValue(request))) return -1;
105                    if (e2.equals(assignment.getValue(request))) return 1;
106                    Double v1 = value(e1), v2 = value(e2);
107                    return v1.equals(v2) ? e1.compareTo(assignment, e2) : v2.compareTo(v1);
108                }
109            });
110            return values.iterator();
111        } else {
112            return variable.computeEnrollments(context.getAssignment()).iterator();
113        }
114    }
115}