```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
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
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];
091            return true;
092        } else if (values[0] <= iBestValueZero * (iBestValueZero < 0.0 ? 1.0 - iThreshKoef[0] : 1.0 + iThreshKoef[0])) {
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);
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}

```