    001package org.cpsolver.coursett.heuristics;
    002
    003import java.util.ArrayList;
    004import java.util.List;
    005
    006
    007/**
    008 * General hierarchical selection. <br>
    009 * <br>
    010 * We have implemented a hierarchical handling of the value selection criteria.
    011 * There are three levels of comparison. At each level a weighted sum of the
    012 * criteria described below is computed. Only solutions with the smallest sum
    013 * are considered in the next level. The weights express how quickly a complete
    014 * solution should be found. Only hard constraints are satisfied in the first
    015 * level sum. Distance from the initial solution (MPP), and a weighting of major
    016 * preferences (including time, classroom requirements and student conflicts),
    017 * are considered in the next level. In the third level, other minor criteria
    018 * are considered. In general, a criterion can be used in more than one level,
    019 * e.g., with different weights. <br>
    020 * <br>
    021 * The above sums order the values lexicographically: the best value having the
    022 * smallest first level sum, the smallest second level sum among values with the
    023 * smallest first level sum, and the smallest third level sum among these
    024 * values. As mentioned above, this allows diversification between the
    025 * importance of individual criteria. <br>
    026 * <br>
    027 * Furthermore, the value selection heuristics also support some limits (e.g.,
    028 * that all values with a first level sum smaller than a given percentage Pth
    029 * above the best value [typically 10%] will go to the second level comparison
    030 * and so on). This allows for the continued feasibility of a value near to the
    031 * best that may yet be much better in the next level of comparison. If there is
    032 * more than one solution after these three levels of comparison, one is
    033 * selected randomly. This approach helped us to significantly improve the
    034 * quality of the resultant solutions. <br>
    035 * <br>
    036 * In general, there can be more than three levels of these weighted sums,
    037 * however three of them seem to be sufficient for spreading weights of various
    038 * criteria for our problem.
    039 *
    040 * @see PlacementSelection
    041 * @version CourseTT 1.3 (University Course Timetabling)<br>
    042 *          Copyright (C) 2006 - 2014 Tomáš Müller<br>
    043 *          <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
    044 *          <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
    045 * <br>
    046 *          This library is free software; you can redistribute it and/or modify
    047 *          it under the terms of the GNU Lesser General Public License as
    048 *          published by the Free Software Foundation; either version 3 of the
    049 *          License, or (at your option) any later version. <br>
    050 * <br>
    051 *          This library is distributed in the hope that it will be useful, but
    052 *          WITHOUT ANY WARRANTY; without even the implied warranty of
    053 *          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
    054 *          Lesser General Public License for more details. <br>
    055 * <br>
    056 *          You should have received a copy of the GNU Lesser General Public
    057 *          License along with this library; if not see
    058 *          <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>.
    059 * @param <E> A class of the objects that are being selected from
    060 */
    061public class HeuristicSelector<E> {
    062    private double[] iThreshKoef;
    063    private List<Element> iElements = new ArrayList<Element>();
    064    private double iBestValueZero = 0.0;
    065
    066    /**
    067     * Constructor
    068     *
    069     * @param threshKoef
    070     *            limit for each level, e.g., new double[] {0.1, 0.1, 0.1} for
    071     *            three level selection with 10% limit on each level
    072     */
    073    public HeuristicSelector(double[] threshKoef) {
    074        iThreshKoef = threshKoef;
    075    }
    076
    077    /**
    078     * Adds an object to selection
    079     *
    080     * @param values
    081     *            weighted sum for each level
    082     * @param object
    083     *            object to be returned if selected
    084     * @return true if added (it is not added if it is obvious that it cannot be
    085     *         selected)
    086     */
    087    public boolean add(double[] values, E object) {
    088        if (iElements.isEmpty() || values[0] < iBestValueZero) {
    089            iBestValueZero = values[0];
    090            iElements.add(new Element(values, object));
    091            return true;
    092        } else if (values[0] <= iBestValueZero * (iBestValueZero < 0.0 ? 1.0 - iThreshKoef[0] : 1.0 + iThreshKoef[0])) {
    093            iElements.add(new Element(values, object));
    094            return true;
    095        }
    096        return false;
    097    }
    098
    099    public Double firstLevelThreshold() {
    100        return (iElements.isEmpty() ? null : Double.valueOf(iBestValueZero
    101                * (iBestValueZero < 0.0 ? 1.0 - iThreshKoef[0] : 1.0 + iThreshKoef[0])));
    102    }
    103
    104    /**
    105     * Do the selection.
    106     *
    107     * @return inserted objects which met the criteria
    108     */
    109    public List<Element> selection() {
    110        List<Element> selection = iElements;
    111        double bestValue = iBestValueZero;
    112        for (int level = 0; level < iThreshKoef.length; level++) {
    113            List<Element> x = new ArrayList<Element>(selection.size());
    114            double threshold = (bestValue < 0.0 ? 1.0 - iThreshKoef[level] : 1.0 + iThreshKoef[level]) * bestValue;
    115            // System.out.println("B"+(level+1)+": "+bestValue);
    116            // System.out.println("T"+(level+1)+": "+threshold);
    117            double nextBestValue = 0.0;
    118            boolean lastLevel = (level + 1 == iThreshKoef.length);
    119            for (Element element : selection) {
    120                if (element.getValue(level) <= threshold) {
    121                    if (!lastLevel && (x.isEmpty() || element.getValue(level + 1) < nextBestValue))
    122                        nextBestValue = element.getValue(level + 1);
    123                    x.add(element);
    124                }
    125            }
    126            selection = x;
    127            bestValue = nextBestValue;
    128        }
    129        return selection;
    130    }
    131
    132    /** An element in heuristical selection */
    133    public class Element {
    134        private double[] iValues;
    135        private E iObject;
    136
    137        private Element(double[] values, E object) {
    138            iValues = values;
    139            iObject = object;
    140        }
    141
    142        /** weighted sum in each level
    143         * @return weighted sum for each level
    144         **/
    145        public double[] getValues() {
    146            return iValues;
    147        }
    148
    149        /** weighted sum in the given level
    150         * @param level a level
    151         * @return weighted sum for the given level
    152         **/
    153        public double getValue(int level) {
    154            return iValues[level];
    155        }
    156
    157        /** given object
    158         * @return the object in the selection
    159         **/
    160        public E getObject() {
    161            return iObject;
    162        }
    163
    164        @Override
    165        public String toString() {
    166            StringBuffer sb = new StringBuffer();
    167            for (int i = 0; i < iValues.length; i++)
    168                sb.append(i == 0 ? "" : ",").append(iValues[i]);
    169            return "[" + sb + "]:" + iObject;
    170        }
    171    }
    172}