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source: branches/histogram/HeuristicLab.Analysis/3.3/PopulationDiversityAnalysis/PopulationDiversityAnalyzer.cs @ 6055

Last change on this file since 6055 was 6055, checked in by abeham, 13 years ago

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Operators;
28using HeuristicLab.Optimization;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Analysis {
33  /// <summary>
34  /// An operator for analyzing the solution diversity in a population.
35  /// </summary>
36  [Item("PopulationDiversityAnalyzer", "An operator for analyzing the solution diversity in a population.")]
37  [StorableClass]
38  public abstract class PopulationDiversityAnalyzer<T> : SingleSuccessorOperator, IAnalyzer where T : class, IItem {
39    public LookupParameter<BoolValue> MaximizationParameter {
40      get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
41    }
42    public ScopeTreeLookupParameter<T> SolutionParameter {
43      get { return (ScopeTreeLookupParameter<T>)Parameters["Solution"]; }
44    }
45    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
46      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
47    }
48    public ValueLookupParameter<ResultCollection> ResultsParameter {
49      get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
50    }
51    public ValueParameter<BoolValue> StoreHistoryParameter {
52      get { return (ValueParameter<BoolValue>)Parameters["StoreHistory"]; }
53    }
54    public ValueParameter<IntValue> UpdateIntervalParameter {
55      get { return (ValueParameter<IntValue>)Parameters["UpdateInterval"]; }
56    }
57    public LookupParameter<IntValue> UpdateCounterParameter {
58      get { return (LookupParameter<IntValue>)Parameters["UpdateCounter"]; }
59    }
60
61    [StorableConstructor]
62    protected PopulationDiversityAnalyzer(bool deserializing) : base(deserializing) { }
63    protected PopulationDiversityAnalyzer(PopulationDiversityAnalyzer<T> original, Cloner cloner) : base(original, cloner) { }
64    public PopulationDiversityAnalyzer()
65      : base() {
66      Parameters.Add(new LookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem."));
67      Parameters.Add(new ScopeTreeLookupParameter<T>("Solution", "The solutions whose diversity should be analyzed."));
68      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions which should be analyzed."));
69      Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the population diversity analysis results should be stored."));
70      Parameters.Add(new ValueParameter<BoolValue>("StoreHistory", "True if the history of the population diversity analysis should be stored.", new BoolValue(false)));
71      Parameters.Add(new ValueParameter<IntValue>("UpdateInterval", "The interval in which the population diversity analysis should be applied.", new IntValue(1)));
72      Parameters.Add(new LookupParameter<IntValue>("UpdateCounter", "The value which counts how many times the operator was called since the last update.", "PopulationDiversityAnalyzerUpdateCounter"));
73
74      MaximizationParameter.Hidden = true;
75      SolutionParameter.Hidden = true;
76      QualityParameter.Hidden = true;
77      ResultsParameter.Hidden = true;
78      UpdateCounterParameter.Hidden = true;
79    }
80
81    public override IOperation Apply() {
82      int updateInterval = UpdateIntervalParameter.Value.Value;
83      IntValue updateCounter = UpdateCounterParameter.ActualValue;
84      // if counter does not yet exist then initialize it with update interval
85      // to make sure the solutions are analyzed on the first application of this operator
86      if (updateCounter == null) {
87        updateCounter = new IntValue(updateInterval);
88        UpdateCounterParameter.ActualValue = updateCounter;
89      } else updateCounter.Value++;
90
91      //analyze solutions only every 'updateInterval' times
92      if (updateCounter.Value == updateInterval) {
93        updateCounter.Value = 0;
94
95        bool max = MaximizationParameter.ActualValue.Value;
96        ItemArray<T> solutions = SolutionParameter.ActualValue;
97        ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
98        bool storeHistory = StoreHistoryParameter.Value.Value;
99        int count = solutions.Length;
100
101        if (count > 1) {
102          // sort solutions by quality
103          T[] sortedSolutions = null;
104          if (max)
105            sortedSolutions = solutions
106              .Select((x, index) => new { Solution = x, Quality = qualities[index] })
107              .OrderByDescending(x => x.Quality)
108              .Select(x => x.Solution)
109              .ToArray();
110          else
111            sortedSolutions = solutions
112              .Select((x, index) => new { Solution = x, Quality = qualities[index] })
113              .OrderBy(x => x.Quality)
114              .Select(x => x.Solution)
115              .ToArray();
116
117          // calculate solution similarities
118          double[,] similarities = CalculateSimilarities(sortedSolutions);
119
120          // calculate minimum, average and maximum similarities
121          double similarity;
122          double[] minSimilarities = new double[count];
123          double[] avgSimilarities = new double[count];
124          double[] maxSimilarities = new double[count];
125          for (int i = 0; i < count; i++) {
126            minSimilarities[i] = 1;
127            avgSimilarities[i] = 0;
128            maxSimilarities[i] = 0;
129            for (int j = 0; j < count; j++) {
130              if (i != j) {
131                similarity = similarities[i, j];
132
133                if ((similarity < 0) || (similarity > 1))
134                  throw new InvalidOperationException("Solution similarities have to be in the interval [0;1].");
135
136                if (minSimilarities[i] > similarity) minSimilarities[i] = similarity;
137                avgSimilarities[i] += similarity;
138                if (maxSimilarities[i] < similarity) maxSimilarities[i] = similarity;
139              }
140            }
141            avgSimilarities[i] = avgSimilarities[i] / (count - 1);
142          }
143          double avgMinSimilarity = minSimilarities.Average();
144          double avgAvgSimilarity = avgSimilarities.Average();
145          double avgMaxSimilarity = maxSimilarities.Average();
146
147          // fetch results collection
148          ResultCollection results;
149          if (!ResultsParameter.ActualValue.ContainsKey(Name + " Results")) {
150            results = new ResultCollection();
151            ResultsParameter.ActualValue.Add(new Result(Name + " Results", results));
152          } else {
153            results = (ResultCollection)ResultsParameter.ActualValue[Name + " Results"].Value;
154          }
155
156          // store similarities
157          HeatMap similaritiesHeatMap = new HeatMap(similarities, "Solution Similarities", 0.0, 1.0);
158          if (!results.ContainsKey("Solution Similarities"))
159            results.Add(new Result("Solution Similarities", similaritiesHeatMap));
160          else
161            results["Solution Similarities"].Value = similaritiesHeatMap;
162
163          // store similarities history
164          if (storeHistory) {
165            if (!results.ContainsKey("Solution Similarities History")) {
166              HeatMapHistory history = new HeatMapHistory();
167              history.Add(similaritiesHeatMap);
168              results.Add(new Result("Solution Similarities History", history));
169            } else {
170              ((HeatMapHistory)results["Solution Similarities History"].Value).Add(similaritiesHeatMap);
171            }
172          }
173
174          // store average minimum, average and maximum similarity
175          if (!results.ContainsKey("Average Minimum Solution Similarity"))
176            results.Add(new Result("Average Minimum Solution Similarity", new DoubleValue(avgMinSimilarity)));
177          else
178            ((DoubleValue)results["Average Minimum Solution Similarity"].Value).Value = avgMinSimilarity;
179
180          if (!results.ContainsKey("Average Average Solution Similarity"))
181            results.Add(new Result("Average Average Solution Similarity", new DoubleValue(avgAvgSimilarity)));
182          else
183            ((DoubleValue)results["Average Average Solution Similarity"].Value).Value = avgAvgSimilarity;
184
185          if (!results.ContainsKey("Average Maximum Solution Similarity"))
186            results.Add(new Result("Average Maximum Solution Similarity", new DoubleValue(avgMaxSimilarity)));
187          else
188            ((DoubleValue)results["Average Maximum Solution Similarity"].Value).Value = avgMaxSimilarity;
189
190          // store average minimum, average and maximum solution similarity data table
191          DataTable minAvgMaxSimilarityDataTable;
192          if (!results.ContainsKey("Average Minimum/Average/Maximum Solution Similarity")) {
193            minAvgMaxSimilarityDataTable = new DataTable("Average Minimum/Average/Maximum Solution Similarity");
194            minAvgMaxSimilarityDataTable.VisualProperties.XAxisTitle = "Iteration";
195            minAvgMaxSimilarityDataTable.VisualProperties.YAxisTitle = "Solution Similarity";
196            minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Minimum Solution Similarity", null));
197            minAvgMaxSimilarityDataTable.Rows["Average Minimum Solution Similarity"].VisualProperties.StartIndexZero = true;
198            minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Average Solution Similarity", null));
199            minAvgMaxSimilarityDataTable.Rows["Average Average Solution Similarity"].VisualProperties.StartIndexZero = true;
200            minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Maximum Solution Similarity", null));
201            minAvgMaxSimilarityDataTable.Rows["Average Maximum Solution Similarity"].VisualProperties.StartIndexZero = true;
202            results.Add(new Result("Average Minimum/Average/Maximum Solution Similarity", minAvgMaxSimilarityDataTable));
203          } else {
204            minAvgMaxSimilarityDataTable = (DataTable)results["Average Minimum/Average/Maximum Solution Similarity"].Value;
205          }
206          minAvgMaxSimilarityDataTable.Rows["Average Minimum Solution Similarity"].Values.Add(avgMinSimilarity);
207          minAvgMaxSimilarityDataTable.Rows["Average Average Solution Similarity"].Values.Add(avgAvgSimilarity);
208          minAvgMaxSimilarityDataTable.Rows["Average Maximum Solution Similarity"].Values.Add(avgMaxSimilarity);
209
210          // store minimum, average, maximum similarities data table
211          DataTable minAvgMaxSimilaritiesDataTable = new DataTable("Minimum/Average/Maximum Solution Similarities");
212          minAvgMaxSimilaritiesDataTable.VisualProperties.XAxisTitle = "Solution Index";
213          minAvgMaxSimilaritiesDataTable.VisualProperties.YAxisTitle = "Solution Similarity";
214          minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Minimum Solution Similarity", null, minSimilarities));
215          minAvgMaxSimilaritiesDataTable.Rows["Minimum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
216          minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Average Solution Similarity", null, avgSimilarities));
217          minAvgMaxSimilaritiesDataTable.Rows["Average Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
218          minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Maximum Solution Similarity", null, maxSimilarities));
219          minAvgMaxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
220          if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities")) {
221            results.Add(new Result("Minimum/Average/Maximum Solution Similarities", minAvgMaxSimilaritiesDataTable));
222          } else {
223            results["Minimum/Average/Maximum Solution Similarities"].Value = minAvgMaxSimilaritiesDataTable;
224          }
225
226          // store minimum, average, maximum similarities history
227          if (storeHistory) {
228            if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities History")) {
229              DataTableHistory history = new DataTableHistory();
230              history.Add(minAvgMaxSimilaritiesDataTable);
231              results.Add(new Result("Minimum/Average/Maximum Solution Similarities History", history));
232            } else {
233              ((DataTableHistory)results["Minimum/Average/Maximum Solution Similarities History"].Value).Add(minAvgMaxSimilaritiesDataTable);
234            }
235          }
236        }
237      }
238      return base.Apply();
239    }
240
241    protected abstract double[,] CalculateSimilarities(T[] solutions);
242  }
243}
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