[4703] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System.Linq;
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[4722] | 23 | using HeuristicLab.Common;
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[4703] | 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Operators;
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| 27 | using HeuristicLab.Optimization;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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| 31 | namespace HeuristicLab.Analysis {
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| 32 | /// <summary>
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| 33 | /// An operator for analyzing the solution diversity in a population.
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| 34 | /// </summary>
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| 35 | [Item("PopulationDiversityAnalyzer", "An operator for analyzing the solution diversity in a population.")]
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| 36 | [StorableClass]
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| 37 | public abstract class PopulationDiversityAnalyzer<T> : SingleSuccessorOperator, IAnalyzer where T : class, IItem {
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| 38 | public LookupParameter<BoolValue> MaximizationParameter {
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| 39 | get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
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| 40 | }
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| 41 | public ScopeTreeLookupParameter<T> SolutionParameter {
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| 42 | get { return (ScopeTreeLookupParameter<T>)Parameters["Solution"]; }
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| 43 | }
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| 44 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 45 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 46 | }
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| 47 | public ValueLookupParameter<ResultCollection> ResultsParameter {
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| 48 | get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
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| 49 | }
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| 50 | public ValueParameter<BoolValue> StoreHistoryParameter {
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| 51 | get { return (ValueParameter<BoolValue>)Parameters["StoreHistory"]; }
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| 52 | }
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| 53 | public ValueParameter<IntValue> UpdateIntervalParameter {
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| 54 | get { return (ValueParameter<IntValue>)Parameters["UpdateInterval"]; }
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| 55 | }
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| 56 | public LookupParameter<IntValue> UpdateCounterParameter {
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| 57 | get { return (LookupParameter<IntValue>)Parameters["UpdateCounter"]; }
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| 58 | }
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| 59 |
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| 60 | [StorableConstructor]
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| 61 | protected PopulationDiversityAnalyzer(bool deserializing) : base(deserializing) { }
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[4722] | 62 | protected PopulationDiversityAnalyzer(PopulationDiversityAnalyzer<T> original, Cloner cloner) : base(original, cloner) { }
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[4703] | 63 | public PopulationDiversityAnalyzer()
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| 64 | : base() {
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| 65 | Parameters.Add(new LookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem."));
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| 66 | Parameters.Add(new ScopeTreeLookupParameter<T>("Solution", "The solutions whose diversity should be analyzed."));
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| 67 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions which should be analyzed."));
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| 68 | Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the population diversity analysis results should be stored."));
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| 69 | Parameters.Add(new ValueParameter<BoolValue>("StoreHistory", "True if the history of the population diversity analysis should be stored.", new BoolValue(false)));
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| 70 | Parameters.Add(new ValueParameter<IntValue>("UpdateInterval", "The interval in which the population diversity analysis should be applied.", new IntValue(1)));
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| 71 | Parameters.Add(new LookupParameter<IntValue>("UpdateCounter", "The value which counts how many times the operator was called since the last update.", "PopulationDiversityAnalyzerUpdateCounter"));
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| 72 | }
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| 73 |
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| 74 | public override IOperation Apply() {
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| 75 | int updateInterval = UpdateIntervalParameter.Value.Value;
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| 76 | IntValue updateCounter = UpdateCounterParameter.ActualValue;
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| 77 | if (updateCounter == null) {
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| 78 | updateCounter = new IntValue(updateInterval);
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| 79 | UpdateCounterParameter.ActualValue = updateCounter;
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| 80 | } else updateCounter.Value++;
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| 81 |
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| 82 | if (updateCounter.Value == updateInterval) {
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| 83 | updateCounter.Value = 0;
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| 84 |
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| 85 | bool max = MaximizationParameter.ActualValue.Value;
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| 86 | ItemArray<T> solutions = SolutionParameter.ActualValue;
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| 87 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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| 88 | bool storeHistory = StoreHistoryParameter.Value.Value;
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| 89 |
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| 90 | // sort solutions by quality
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| 91 | T[] sortedSolutions = null;
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| 92 | if (max)
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| 93 | sortedSolutions = solutions.Select((x, index) => new { Solution = x, Quality = qualities[index] }).OrderByDescending(x => x.Quality).Select(x => x.Solution).ToArray();
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| 94 | else
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| 95 | sortedSolutions = solutions.Select((x, index) => new { Solution = x, Quality = qualities[index] }).OrderBy(x => x.Quality).Select(x => x.Solution).ToArray();
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| 96 |
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| 97 | // calculate solution similarities
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| 98 | double[,] similarities = CalculateSimilarities(sortedSolutions);
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| 99 |
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[4715] | 100 | // calculate minimum, average and maximum similarities
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[4703] | 101 | double similarity;
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| 102 | int count = sortedSolutions.Length;
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[4715] | 103 | double[] minSimilarities = new double[sortedSolutions.Length];
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| 104 | double[] avgSimilarities = new double[sortedSolutions.Length];
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[4703] | 105 | double[] maxSimilarities = new double[sortedSolutions.Length];
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| 106 | double avgSimilarity = 0;
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| 107 | for (int i = 0; i < count; i++) {
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[4715] | 108 | minSimilarities[i] = 1;
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| 109 | avgSimilarities[i] = 0;
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| 110 | maxSimilarities[i] = 0;
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| 111 | for (int j = 0; j < count; j++) {
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| 112 | if (i != j) {
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| 113 | similarity = similarities[i, j];
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| 114 | avgSimilarity += similarity;
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| 115 | if (minSimilarities[i] > similarity) minSimilarities[i] = similarity;
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| 116 | avgSimilarities[i] += similarity;
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| 117 | if (maxSimilarities[i] < similarity) maxSimilarities[i] = similarity;
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| 118 | }
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[4703] | 119 | }
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[4715] | 120 | avgSimilarities[i] = avgSimilarities[i] / (count - 1);
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[4703] | 121 | }
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[4715] | 122 | double avgMinSimilarity = minSimilarities.Average();
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| 123 | double avgAvgSimilarity = avgSimilarities.Average();
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| 124 | double avgMaxSimilarity = maxSimilarities.Average();
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| 125 | avgSimilarity = avgSimilarity / (count * count - count);
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[4703] | 126 |
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| 127 | // fetch results collection
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| 128 | ResultCollection results;
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| 129 | if (!ResultsParameter.ActualValue.ContainsKey("Population Diversity Analysis Results")) {
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| 130 | results = new ResultCollection();
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| 131 | ResultsParameter.ActualValue.Add(new Result("Population Diversity Analysis Results", results));
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| 132 | } else {
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| 133 | results = (ResultCollection)ResultsParameter.ActualValue["Population Diversity Analysis Results"].Value;
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| 134 | }
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| 135 |
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| 136 | // store similarities
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| 137 | HeatMap similaritiesHeatMap = new HeatMap(similarities);
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| 138 | if (!results.ContainsKey("Solution Similarities"))
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| 139 | results.Add(new Result("Solution Similarities", similaritiesHeatMap));
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| 140 | else
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| 141 | results["Solution Similarities"].Value = similaritiesHeatMap;
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| 142 |
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| 143 | // store similarities history
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| 144 | if (storeHistory) {
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| 145 | if (!results.ContainsKey("Solution Similarities History")) {
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| 146 | HeatMapHistory history = new HeatMapHistory();
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| 147 | history.Add(similaritiesHeatMap);
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| 148 | results.Add(new Result("Solution Similarities History", history));
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| 149 | } else {
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| 150 | ((HeatMapHistory)results["Solution Similarities History"].Value).Add(similaritiesHeatMap);
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| 151 | }
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| 152 | }
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| 153 |
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| 154 | // store average similarity
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| 155 | if (!results.ContainsKey("Average Population Similarity"))
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| 156 | results.Add(new Result("Average Population Similarity", new DoubleValue(avgSimilarity)));
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| 157 | else
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| 158 | ((DoubleValue)results["Average Population Similarity"].Value).Value = avgSimilarity;
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| 159 |
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[4715] | 160 | // store average minimum, average and maximum similarity
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| 161 | if (!results.ContainsKey("Average Minimum Solution Similarity"))
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| 162 | results.Add(new Result("Average Minimum Solution Similarity", new DoubleValue(avgMinSimilarity)));
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| 163 | else
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| 164 | ((DoubleValue)results["Average Minimum Solution Similarity"].Value).Value = avgMinSimilarity;
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| 165 |
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| 166 | if (!results.ContainsKey("Average Average Solution Similarity"))
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| 167 | results.Add(new Result("Average Average Solution Similarity", new DoubleValue(avgAvgSimilarity)));
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| 168 | else
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| 169 | ((DoubleValue)results["Average Average Solution Similarity"].Value).Value = avgAvgSimilarity;
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| 170 |
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[4703] | 171 | if (!results.ContainsKey("Average Maximum Solution Similarity"))
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| 172 | results.Add(new Result("Average Maximum Solution Similarity", new DoubleValue(avgMaxSimilarity)));
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| 173 | else
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| 174 | ((DoubleValue)results["Average Maximum Solution Similarity"].Value).Value = avgMaxSimilarity;
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| 175 |
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| 176 | // store population similarity data table
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| 177 | DataTable similarityDataTable;
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[4715] | 178 | if (!results.ContainsKey("Average Solution Similarity")) {
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| 179 | similarityDataTable = new DataTable("Average Solution Similarity");
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| 180 | results.Add(new Result("Average Solution Similarity", similarityDataTable));
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[4703] | 181 | DataRowVisualProperties visualProperties = new DataRowVisualProperties();
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| 182 | visualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Line;
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| 183 | visualProperties.StartIndexZero = true;
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| 184 | similarityDataTable.Rows.Add(new DataRow("Average Population Similarity", null, visualProperties));
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[4715] | 185 | similarityDataTable.Rows.Add(new DataRow("Average Minimum Solution Similarity", null, visualProperties));
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| 186 | similarityDataTable.Rows.Add(new DataRow("Average Average Solution Similarity", null, visualProperties));
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[4703] | 187 | similarityDataTable.Rows.Add(new DataRow("Average Maximum Solution Similarity", null, visualProperties));
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| 188 | } else {
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[4715] | 189 | similarityDataTable = (DataTable)results["Average Solution Similarity"].Value;
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[4703] | 190 | }
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| 191 | similarityDataTable.Rows["Average Population Similarity"].Values.Add(avgSimilarity);
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[4715] | 192 | similarityDataTable.Rows["Average Minimum Solution Similarity"].Values.Add(avgMinSimilarity);
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| 193 | similarityDataTable.Rows["Average Average Solution Similarity"].Values.Add(avgAvgSimilarity);
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[4703] | 194 | similarityDataTable.Rows["Average Maximum Solution Similarity"].Values.Add(avgMaxSimilarity);
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| 195 |
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| 196 | // store maximum similarities
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[4715] | 197 | DataTable minAvgMaxSimilaritiesDataTable = new DataTable("Minimum/Average/Maximum Solution Similarities");
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| 198 | minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Minimum Solution Similarity"));
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| 199 | minAvgMaxSimilaritiesDataTable.Rows["Minimum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Columns;
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| 200 | minAvgMaxSimilaritiesDataTable.Rows["Minimum Solution Similarity"].Values.AddRange(minSimilarities);
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| 201 | minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Average Solution Similarity"));
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| 202 | minAvgMaxSimilaritiesDataTable.Rows["Average Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Columns;
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| 203 | minAvgMaxSimilaritiesDataTable.Rows["Average Solution Similarity"].Values.AddRange(avgSimilarities);
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| 204 | minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Maximum Solution Similarity"));
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| 205 | minAvgMaxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Columns;
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| 206 | minAvgMaxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].Values.AddRange(maxSimilarities);
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| 207 | if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities")) {
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| 208 | results.Add(new Result("Minimum/Average/Maximum Solution Similarities", minAvgMaxSimilaritiesDataTable));
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[4703] | 209 | } else {
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[4715] | 210 | results["Minimum/Average/Maximum Solution Similarities"].Value = minAvgMaxSimilaritiesDataTable;
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[4703] | 211 | }
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| 212 |
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| 213 | // store maximum similarities history
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| 214 | if (storeHistory) {
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[4715] | 215 | if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities History")) {
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[4703] | 216 | DataTableHistory history = new DataTableHistory();
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[4715] | 217 | history.Add(minAvgMaxSimilaritiesDataTable);
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| 218 | results.Add(new Result("Minimum/Average/Maximum Solution Similarities History", history));
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[4703] | 219 | } else {
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[4715] | 220 | ((DataTableHistory)results["Minimum/Average/Maximum Solution Similarities History"].Value).Add(minAvgMaxSimilaritiesDataTable);
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[4703] | 221 | }
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| 222 | }
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| 223 | }
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| 224 | return base.Apply();
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| 225 | }
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| 226 |
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| 227 | protected abstract double[,] CalculateSimilarities(T[] solutions);
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| 228 | }
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| 229 | }
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