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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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Data;
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25 | using HeuristicLab.Operators;
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26 | using HeuristicLab.Optimization;
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27 | using HeuristicLab.Parameters;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 |
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30 | namespace HeuristicLab.Analysis {
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31 | /// <summary>
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32 | /// An operator for analyzing the solution diversity in a population.
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33 | /// </summary>
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34 | [Item("PopulationDiversityAnalyzer", "An operator for analyzing the solution diversity in a population.")]
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35 | [StorableClass]
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36 | public abstract class PopulationDiversityAnalyzer<T> : SingleSuccessorOperator, IAnalyzer where T : class, IItem {
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37 | public LookupParameter<BoolValue> MaximizationParameter {
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38 | get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
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39 | }
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40 | public ScopeTreeLookupParameter<T> SolutionParameter {
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41 | get { return (ScopeTreeLookupParameter<T>)Parameters["Solution"]; }
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42 | }
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43 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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44 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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45 | }
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46 | public ValueLookupParameter<ResultCollection> ResultsParameter {
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47 | get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
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48 | }
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49 | public ValueParameter<BoolValue> StoreHistoryParameter {
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50 | get { return (ValueParameter<BoolValue>)Parameters["StoreHistory"]; }
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51 | }
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52 | public ValueParameter<IntValue> UpdateIntervalParameter {
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53 | get { return (ValueParameter<IntValue>)Parameters["UpdateInterval"]; }
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54 | }
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55 | public LookupParameter<IntValue> UpdateCounterParameter {
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56 | get { return (LookupParameter<IntValue>)Parameters["UpdateCounter"]; }
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57 | }
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58 |
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59 | [StorableConstructor]
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60 | protected PopulationDiversityAnalyzer(bool deserializing) : base(deserializing) { }
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61 | public PopulationDiversityAnalyzer()
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62 | : base() {
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63 | Parameters.Add(new LookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem."));
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64 | Parameters.Add(new ScopeTreeLookupParameter<T>("Solution", "The solutions whose diversity should be analyzed."));
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65 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions which should be analyzed."));
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66 | Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the population diversity analysis results should be stored."));
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67 | 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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68 | Parameters.Add(new ValueParameter<IntValue>("UpdateInterval", "The interval in which the population diversity analysis should be applied.", new IntValue(1)));
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69 | 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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70 | }
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71 |
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72 | public override IOperation Apply() {
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73 | int updateInterval = UpdateIntervalParameter.Value.Value;
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74 | IntValue updateCounter = UpdateCounterParameter.ActualValue;
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75 | if (updateCounter == null) {
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76 | updateCounter = new IntValue(updateInterval);
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77 | UpdateCounterParameter.ActualValue = updateCounter;
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78 | } else updateCounter.Value++;
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79 |
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80 | if (updateCounter.Value == updateInterval) {
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81 | updateCounter.Value = 0;
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82 |
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83 | bool max = MaximizationParameter.ActualValue.Value;
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84 | ItemArray<T> solutions = SolutionParameter.ActualValue;
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85 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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86 | bool storeHistory = StoreHistoryParameter.Value.Value;
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87 |
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88 | // sort solutions by quality
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89 | T[] sortedSolutions = null;
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90 | if (max)
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91 | sortedSolutions = solutions.Select((x, index) => new { Solution = x, Quality = qualities[index] }).OrderByDescending(x => x.Quality).Select(x => x.Solution).ToArray();
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92 | else
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93 | sortedSolutions = solutions.Select((x, index) => new { Solution = x, Quality = qualities[index] }).OrderBy(x => x.Quality).Select(x => x.Solution).ToArray();
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94 |
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95 | // calculate solution similarities
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96 | double[,] similarities = CalculateSimilarities(sortedSolutions);
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97 |
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98 | // calculate maximum similarities, average maximum similarity and average similarity
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99 | double similarity;
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100 | int count = sortedSolutions.Length;
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101 | double[] maxSimilarities = new double[sortedSolutions.Length];
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102 | double avgMaxSimilarity;
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103 | double avgSimilarity = 0;
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104 | maxSimilarities.Initialize();
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105 | for (int i = 0; i < count; i++) {
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106 | for (int j = i + 1; j < count; j++) {
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107 | similarity = similarities[i, j];
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108 | avgSimilarity += similarity;
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109 | if (maxSimilarities[i] < similarity) maxSimilarities[i] = similarity;
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110 | if (maxSimilarities[j] < similarity) maxSimilarities[j] = similarity;
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111 | }
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112 | }
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113 | avgMaxSimilarity = maxSimilarities.Average();
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114 | avgSimilarity = avgSimilarity / ((count - 1) * count / 2);
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115 |
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116 | // fetch results collection
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117 | ResultCollection results;
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118 | if (!ResultsParameter.ActualValue.ContainsKey("Population Diversity Analysis Results")) {
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119 | results = new ResultCollection();
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120 | ResultsParameter.ActualValue.Add(new Result("Population Diversity Analysis Results", results));
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121 | } else {
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122 | results = (ResultCollection)ResultsParameter.ActualValue["Population Diversity Analysis Results"].Value;
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123 | }
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124 |
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125 | // store similarities
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126 | HeatMap similaritiesHeatMap = new HeatMap(similarities);
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127 | if (!results.ContainsKey("Solution Similarities"))
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128 | results.Add(new Result("Solution Similarities", similaritiesHeatMap));
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129 | else
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130 | results["Solution Similarities"].Value = similaritiesHeatMap;
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131 |
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132 | // store similarities history
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133 | if (storeHistory) {
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134 | if (!results.ContainsKey("Solution Similarities History")) {
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135 | HeatMapHistory history = new HeatMapHistory();
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136 | history.Add(similaritiesHeatMap);
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137 | results.Add(new Result("Solution Similarities History", history));
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138 | } else {
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139 | ((HeatMapHistory)results["Solution Similarities History"].Value).Add(similaritiesHeatMap);
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140 | }
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141 | }
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142 |
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143 | // store average similarity
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144 | if (!results.ContainsKey("Average Population Similarity"))
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145 | results.Add(new Result("Average Population Similarity", new DoubleValue(avgSimilarity)));
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146 | else
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147 | ((DoubleValue)results["Average Population Similarity"].Value).Value = avgSimilarity;
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148 |
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149 | // store average maximum similarity
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150 | if (!results.ContainsKey("Average Maximum Solution Similarity"))
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151 | results.Add(new Result("Average Maximum Solution Similarity", new DoubleValue(avgMaxSimilarity)));
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152 | else
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153 | ((DoubleValue)results["Average Maximum Solution Similarity"].Value).Value = avgMaxSimilarity;
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154 |
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155 | // store population similarity data table
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156 | DataTable similarityDataTable;
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157 | if (!results.ContainsKey("Population Similarity")) {
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158 | similarityDataTable = new DataTable("Population Similarity");
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159 | results.Add(new Result("Population Similarity", similarityDataTable));
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160 | DataRowVisualProperties visualProperties = new DataRowVisualProperties();
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161 | visualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Line;
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162 | visualProperties.StartIndexZero = true;
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163 | similarityDataTable.Rows.Add(new DataRow("Average Population Similarity", null, visualProperties));
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164 | similarityDataTable.Rows.Add(new DataRow("Average Maximum Solution Similarity", null, visualProperties));
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165 | } else {
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166 | similarityDataTable = (DataTable)results["Population Similarity"].Value;
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167 | }
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168 | similarityDataTable.Rows["Average Population Similarity"].Values.Add(avgSimilarity);
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169 | similarityDataTable.Rows["Average Maximum Solution Similarity"].Values.Add(avgMaxSimilarity);
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170 |
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171 | // store maximum similarities
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172 | DataTable maxSimilaritiesDataTable = new DataTable("Maximum Solution Similarities");
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173 | maxSimilaritiesDataTable.Rows.Add(new DataRow("Maximum Solution Similarity"));
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174 | maxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Columns;
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175 | maxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].Values.AddRange(maxSimilarities);
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176 | if (!results.ContainsKey("Maximum Solution Similarities")) {
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177 | results.Add(new Result("Maximum Solution Similarities", maxSimilaritiesDataTable));
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178 | } else {
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179 | results["Maximum Solution Similarities"].Value = maxSimilaritiesDataTable;
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180 | }
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181 |
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182 | // store maximum similarities history
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183 | if (storeHistory) {
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184 | if (!results.ContainsKey("Maximum Solution Similarities History")) {
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185 | DataTableHistory history = new DataTableHistory();
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186 | history.Add(maxSimilaritiesDataTable);
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187 | results.Add(new Result("Maximum Solution Similarities History", history));
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188 | } else {
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189 | ((DataTableHistory)results["Maximum Solution Similarities History"].Value).Add(maxSimilaritiesDataTable);
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190 | }
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191 | }
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192 | }
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193 | return base.Apply();
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194 | }
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195 |
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196 | protected abstract double[,] CalculateSimilarities(T[] solutions);
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197 | }
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198 | }
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