Index: trunk/sources/HeuristicLab.Analysis/3.3/PopulationDiversityAnalysis/SingleObjectivePopulationDiversityAnalyzer.cs
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--- trunk/sources/HeuristicLab.Analysis/3.3/PopulationDiversityAnalysis/SingleObjectivePopulationDiversityAnalyzer.cs	(revision 8478)
+++ trunk/sources/HeuristicLab.Analysis/3.3/PopulationDiversityAnalysis/SingleObjectivePopulationDiversityAnalyzer.cs	(revision 8478)
@@ -0,0 +1,256 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Operators;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Analysis {
+  /// <summary>
+  /// An operator for analyzing the solution diversity in a population.
+  /// </summary>
+  [Item("SingleObjectivePopulationDiversityAnalyzer", "An operator for analyzing the solution diversity in a population.")]
+  [StorableClass]
+  public class SingleObjectivePopulationDiversityAnalyzer : SingleSuccessorOperator, IAnalyzer, ISimilarityBasedOperator {
+    #region ISimilarityBasedOperator Members
+    public ISolutionSimilarityCalculator SimilarityCalculator { get; set; }
+    #endregion
+
+    public virtual bool EnabledByDefault {
+      get { return false; }
+    }
+
+    public ScopeParameter CurrentScopeParameter {
+      get { return (ScopeParameter)Parameters["CurrentScope"]; }
+    }
+    public LookupParameter<BoolValue> MaximizationParameter {
+      get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
+    }
+    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
+      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
+    }
+    public ValueLookupParameter<ResultCollection> ResultsParameter {
+      get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
+    }
+    public ValueParameter<BoolValue> StoreHistoryParameter {
+      get { return (ValueParameter<BoolValue>)Parameters["StoreHistory"]; }
+    }
+    public ValueParameter<IntValue> UpdateIntervalParameter {
+      get { return (ValueParameter<IntValue>)Parameters["UpdateInterval"]; }
+    }
+    public LookupParameter<IntValue> UpdateCounterParameter {
+      get { return (LookupParameter<IntValue>)Parameters["UpdateCounter"]; }
+    }
+
+    [StorableConstructor]
+    protected SingleObjectivePopulationDiversityAnalyzer(bool deserializing) : base(deserializing) { }
+    protected SingleObjectivePopulationDiversityAnalyzer(SingleObjectivePopulationDiversityAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public SingleObjectivePopulationDiversityAnalyzer()
+      : base() {
+      Parameters.Add(new ScopeParameter("CurrentScope", "The current scope that contains the solutions which should be analyzed."));
+      Parameters.Add(new LookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem."));
+      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions which should be analyzed."));
+      Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the population diversity analysis results should be stored."));
+      Parameters.Add(new ValueParameter<BoolValue>("StoreHistory", "True if the history of the population diversity analysis should be stored.", new BoolValue(false)));
+      Parameters.Add(new ValueParameter<IntValue>("UpdateInterval", "The interval in which the population diversity analysis should be applied.", new IntValue(1)));
+      Parameters.Add(new LookupParameter<IntValue>("UpdateCounter", "The value which counts how many times the operator was called since the last update.", "PopulationDiversityAnalyzerUpdateCounter"));
+
+      MaximizationParameter.Hidden = true;
+      QualityParameter.Hidden = true;
+      ResultsParameter.Hidden = true;
+      UpdateCounterParameter.Hidden = true;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SingleObjectivePopulationDiversityAnalyzer(this, cloner);
+    }
+
+    public override IOperation Apply() {
+      int updateInterval = UpdateIntervalParameter.Value.Value;
+      IntValue updateCounter = UpdateCounterParameter.ActualValue;
+      // if counter does not yet exist then initialize it with update interval
+      // to make sure the solutions are analyzed on the first application of this operator
+      if (updateCounter == null) {
+        updateCounter = new IntValue(updateInterval);
+        UpdateCounterParameter.ActualValue = updateCounter;
+      } else updateCounter.Value++;
+
+      //analyze solutions only every 'updateInterval' times
+      if (updateCounter.Value == updateInterval) {
+        updateCounter.Value = 0;
+
+        bool max = MaximizationParameter.ActualValue.Value;
+        ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
+        bool storeHistory = StoreHistoryParameter.Value.Value;
+        int count = CurrentScopeParameter.ActualValue.SubScopes.Count;
+
+        if (count > 1) {
+          // calculate solution similarities
+          var similarityMatrix = SimilarityCalculator.CalculateSolutionCrowdSimilarity(CurrentScopeParameter.ActualValue);
+
+          // sort similarities by quality
+          double[][] sortedSimilarityMatrix = null;
+          if (max)
+            sortedSimilarityMatrix = similarityMatrix
+              .Select((x, index) => new { Solutions = x, Quality = qualities[index] })
+              .OrderByDescending(x => x.Quality)
+              .Select(x => x.Solutions)
+              .ToArray();
+          else
+            sortedSimilarityMatrix = similarityMatrix
+              .Select((x, index) => new { Solutions = x, Quality = qualities[index] })
+              .OrderBy(x => x.Quality)
+              .Select(x => x.Solutions)
+              .ToArray();
+
+          double[,] similarities = new double[similarityMatrix.Length, similarityMatrix[0].Length];
+          for (int i = 0; i < similarityMatrix.Length; i++)
+            for (int j = 0; j < similarityMatrix[0].Length; j++)
+              similarities[i, j] = similarityMatrix[i][j];
+
+          // calculate minimum, average and maximum similarities
+          double similarity;
+          double[] minSimilarities = new double[count];
+          double[] avgSimilarities = new double[count];
+          double[] maxSimilarities = new double[count];
+          for (int i = 0; i < count; i++) {
+            minSimilarities[i] = 1;
+            avgSimilarities[i] = 0;
+            maxSimilarities[i] = 0;
+            for (int j = 0; j < count; j++) {
+              if (i != j) {
+                similarity = similarities[i, j];
+
+                if ((similarity < 0) || (similarity > 1))
+                  throw new InvalidOperationException("Solution similarities have to be in the interval [0;1].");
+
+                if (minSimilarities[i] > similarity) minSimilarities[i] = similarity;
+                avgSimilarities[i] += similarity;
+                if (maxSimilarities[i] < similarity) maxSimilarities[i] = similarity;
+              }
+            }
+            avgSimilarities[i] = avgSimilarities[i] / (count - 1);
+          }
+          double avgMinSimilarity = minSimilarities.Average();
+          double avgAvgSimilarity = avgSimilarities.Average();
+          double avgMaxSimilarity = maxSimilarities.Average();
+
+          // fetch results collection
+          ResultCollection results;
+          if (!ResultsParameter.ActualValue.ContainsKey(Name + " Results")) {
+            results = new ResultCollection();
+            ResultsParameter.ActualValue.Add(new Result(Name + " Results", results));
+          } else {
+            results = (ResultCollection)ResultsParameter.ActualValue[Name + " Results"].Value;
+          }
+
+          // store similarities
+          HeatMap similaritiesHeatMap = new HeatMap(similarities, "Solution Similarities", 0.0, 1.0);
+          if (!results.ContainsKey("Solution Similarities"))
+            results.Add(new Result("Solution Similarities", similaritiesHeatMap));
+          else
+            results["Solution Similarities"].Value = similaritiesHeatMap;
+
+          // store similarities history
+          if (storeHistory) {
+            if (!results.ContainsKey("Solution Similarities History")) {
+              HeatMapHistory history = new HeatMapHistory();
+              history.Add(similaritiesHeatMap);
+              results.Add(new Result("Solution Similarities History", history));
+            } else {
+              ((HeatMapHistory)results["Solution Similarities History"].Value).Add(similaritiesHeatMap);
+            }
+          }
+
+          // store average minimum, average and maximum similarity
+          if (!results.ContainsKey("Average Minimum Solution Similarity"))
+            results.Add(new Result("Average Minimum Solution Similarity", new DoubleValue(avgMinSimilarity)));
+          else
+            ((DoubleValue)results["Average Minimum Solution Similarity"].Value).Value = avgMinSimilarity;
+
+          if (!results.ContainsKey("Average Average Solution Similarity"))
+            results.Add(new Result("Average Average Solution Similarity", new DoubleValue(avgAvgSimilarity)));
+          else
+            ((DoubleValue)results["Average Average Solution Similarity"].Value).Value = avgAvgSimilarity;
+
+          if (!results.ContainsKey("Average Maximum Solution Similarity"))
+            results.Add(new Result("Average Maximum Solution Similarity", new DoubleValue(avgMaxSimilarity)));
+          else
+            ((DoubleValue)results["Average Maximum Solution Similarity"].Value).Value = avgMaxSimilarity;
+
+          // store average minimum, average and maximum solution similarity data table
+          DataTable minAvgMaxSimilarityDataTable;
+          if (!results.ContainsKey("Average Minimum/Average/Maximum Solution Similarity")) {
+            minAvgMaxSimilarityDataTable = new DataTable("Average Minimum/Average/Maximum Solution Similarity");
+            minAvgMaxSimilarityDataTable.VisualProperties.XAxisTitle = "Iteration";
+            minAvgMaxSimilarityDataTable.VisualProperties.YAxisTitle = "Solution Similarity";
+            minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Minimum Solution Similarity", null));
+            minAvgMaxSimilarityDataTable.Rows["Average Minimum Solution Similarity"].VisualProperties.StartIndexZero = true;
+            minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Average Solution Similarity", null));
+            minAvgMaxSimilarityDataTable.Rows["Average Average Solution Similarity"].VisualProperties.StartIndexZero = true;
+            minAvgMaxSimilarityDataTable.Rows.Add(new DataRow("Average Maximum Solution Similarity", null));
+            minAvgMaxSimilarityDataTable.Rows["Average Maximum Solution Similarity"].VisualProperties.StartIndexZero = true;
+            results.Add(new Result("Average Minimum/Average/Maximum Solution Similarity", minAvgMaxSimilarityDataTable));
+          } else {
+            minAvgMaxSimilarityDataTable = (DataTable)results["Average Minimum/Average/Maximum Solution Similarity"].Value;
+          }
+          minAvgMaxSimilarityDataTable.Rows["Average Minimum Solution Similarity"].Values.Add(avgMinSimilarity);
+          minAvgMaxSimilarityDataTable.Rows["Average Average Solution Similarity"].Values.Add(avgAvgSimilarity);
+          minAvgMaxSimilarityDataTable.Rows["Average Maximum Solution Similarity"].Values.Add(avgMaxSimilarity);
+
+          // store minimum, average, maximum similarities data table
+          DataTable minAvgMaxSimilaritiesDataTable = new DataTable("Minimum/Average/Maximum Solution Similarities");
+          minAvgMaxSimilaritiesDataTable.VisualProperties.XAxisTitle = "Solution Index";
+          minAvgMaxSimilaritiesDataTable.VisualProperties.YAxisTitle = "Solution Similarity";
+          minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Minimum Solution Similarity", null, minSimilarities));
+          minAvgMaxSimilaritiesDataTable.Rows["Minimum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
+          minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Average Solution Similarity", null, avgSimilarities));
+          minAvgMaxSimilaritiesDataTable.Rows["Average Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
+          minAvgMaxSimilaritiesDataTable.Rows.Add(new DataRow("Maximum Solution Similarity", null, maxSimilarities));
+          minAvgMaxSimilaritiesDataTable.Rows["Maximum Solution Similarity"].VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
+          if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities")) {
+            results.Add(new Result("Minimum/Average/Maximum Solution Similarities", minAvgMaxSimilaritiesDataTable));
+          } else {
+            results["Minimum/Average/Maximum Solution Similarities"].Value = minAvgMaxSimilaritiesDataTable;
+          }
+
+          // store minimum, average, maximum similarities history
+          if (storeHistory) {
+            if (!results.ContainsKey("Minimum/Average/Maximum Solution Similarities History")) {
+              DataTableHistory history = new DataTableHistory();
+              history.Add(minAvgMaxSimilaritiesDataTable);
+              results.Add(new Result("Minimum/Average/Maximum Solution Similarities History", history));
+            } else {
+              ((DataTableHistory)results["Minimum/Average/Maximum Solution Similarities History"].Value).Add(minAvgMaxSimilaritiesDataTable);
+            }
+          }
+        }
+      }
+      return base.Apply();
+    }
+  }
+}
