Changeset 14044 for branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Analyzers/NormalizedHypervolumeAnalyzer.cs

Timestamp:

07/12/16 18:20:50 (8 years ago)

Author:

mkommend

Message:

#1087: Refactored and improved analyzers for multi-objective test functions.

File:

• branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Analyzers/NormalizedHypervolumeAnalyzer.cs (modified) (6 diffs)

branches/HeuristicLab.Problems.MultiObjectiveTestFunctions/HeuristicLab.Problems.MultiObjectiveTestFunctions/3.3/Analyzers/NormalizedHypervolumeAnalyzer.cs

@@ -32 +32 @@
 namespace HeuristicLab.Problems.MultiObjectiveTestFunctions {
   [StorableClass]
-  [Item("GenerationalDistanceAnalyzer", "Computes the enclosed Hypervolume between the current front and a given reference Point")]
+  [Item("NormalizedHypervolumeAnalyzer", "Computes the enclosed Hypervolume between the current front and a given reference Point")]
   public class NormalizedHypervolumeAnalyzer : MOTFAnalyzer {
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-    }
-    [StorableConstructor]
-    protected NormalizedHypervolumeAnalyzer(bool deserializing) : base(deserializing) { }
-    public NormalizedHypervolumeAnalyzer(NormalizedHypervolumeAnalyzer original, Cloner cloner) : base(original, cloner) { }
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new NormalizedHypervolumeAnalyzer(this, cloner);
-    }
-
-
-    #region Names
     private const string bestKnownFront = "BestKnownFront Zitzler";
     private const string resultsHV = "NormalizedHypervolume";
@@ -51 +39 @@
 
     private const string bestknownHV = "NormalizedBestKnownHyperVolume";
-    #endregion
 
-    #region parameters
     public IValueParameter<DoubleMatrix> OptimalFrontParameter {
-      get {
-        return (IValueParameter<DoubleMatrix>)Parameters[bestKnownFront];
-      }
+      get { return (IValueParameter<DoubleMatrix>)Parameters[bestKnownFront]; }
     }
 
     public IValueParameter<DoubleValue> BestKnownHyperVolumeParameter {
-      get {
-        return (IValueParameter<DoubleValue>)Parameters[bestknownHV];
-      }
-      set {
-        Parameters[bestknownHV].ActualValue = value;
-      }
+      get { return (IValueParameter<DoubleValue>)Parameters[bestknownHV]; }
     }
-    #endregion
 
-    public NormalizedHypervolumeAnalyzer() {
-      if (!Parameters.ContainsKey(bestKnownFront)) Parameters.Add(new ValueParameter<DoubleMatrix>(bestKnownFront, "The true / best known pareto front"));
-      if (!Parameters.ContainsKey(bestknownHV)) Parameters.Add(new ValueParameter<DoubleValue>(bestknownHV, "The currently best known hypervolume"));
+
+    public NormalizedHypervolumeAnalyzer()
+      : base() {
+      Parameters.Add(new ValueParameter<DoubleMatrix>(bestKnownFront, "The true / best known pareto front"));
+      Parameters.Add(new ValueParameter<DoubleValue>(bestknownHV, "The currently best known hypervolume"));
     }
+
+    [StorableConstructor]
+    protected NormalizedHypervolumeAnalyzer(bool deserializing) : base(deserializing) { }
+    protected NormalizedHypervolumeAnalyzer(NormalizedHypervolumeAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new NormalizedHypervolumeAnalyzer(this, cloner);
+    }
+
 
     private void RegisterEventHandlers() {
@@ -83 +71 @@
     }
 
-    public override void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results) {
-      if (qualities == null || qualities.Length < 1) return;
+    public override IOperation Apply() {
+      var results = ResultsParameter.ActualValue;
+      var qualities = QualitiesParameter.ActualValue;
+      var testFunction = TestFunctionParameter.ActualValue;
       int objectives = qualities[0].Length;
+
       double best = BestKnownHyperVolumeParameter.Value.Value;
       if (OptimalFrontParameter.Value == null || OptimalFrontParameter.Value.Rows < 1 || OptimalFrontParameter.Value.Columns != qualities[0].Length) {
-        return; // too pareto front nonexistant or with wrong number of dimensions
+        return base.Apply(); // too pareto front nonexistant or with wrong number of dimensions
       }
 
-      IEnumerable<double[]> front = NonDominatedSelect.selectNonDominatedVectors(qualities, TestFunctionParameter.ActualValue.Maximization(objectives), true);
+      IEnumerable<double[]> front = NonDominatedSelect.selectNonDominatedVectors(qualities.Select(q => q.ToArray()), testFunction.Maximization(objectives), true);
 
       if (!results.ContainsKey(resultsHV)) results.Add(new Result(resultsHV, typeof(DoubleValue)));
@@ -101 +92 @@
       }
 
-      bool[] maximization = TestFunctionParameter.ActualValue.Maximization(objectives);
+      bool[] maximization = testFunction.Maximization(objectives);
       double[] invPoint = GetBestPoint(OptimalFrontParameter.Value, maximization);
       double[] refPoint = GetWorstPoint(OptimalFrontParameter.Value, maximization);
@@ -107 +98 @@
       double hv = front.Any() ? Hypervolume.Calculate(front, refPoint, maximization) / normalization : 0;
 
-      if (Double.IsNaN(best)) best = hv; else best = Math.Max(best, hv);
-      double diff;
-      diff = best - hv;
-      if (diff == 0) {
-        BestKnownFrontParameter.ActualValue = new DoubleMatrix(MultiObjectiveTestFunctionProblem.To2D(qualities));
+      if (double.IsNaN(best) || best < hv) {
+        best = hv;
+        BestKnownFrontParameter.ActualValue = new DoubleMatrix(MultiObjectiveTestFunctionProblem.To2D(qualities.Select(q => q.ToArray()).ToArray()));
       }
 
       results[resultsHV].Value = new DoubleValue(hv);
-      results[resultsDist].Value = new DoubleValue(diff);
       results[bestknownHV].Value = new DoubleValue(best);
+      results[resultsDist].Value = new DoubleValue(best - hv);
 
+      return base.Apply();
     }
 
-    private double[] GetWorstPoint(DoubleMatrix value, bool[] maximization) {
+
+    private static double[] GetWorstPoint(DoubleMatrix value, bool[] maximization) {
       bool[] invMax = new bool[maximization.Length];
       int i = 0;
@@ -129 +120 @@
     }
 
-    private double[] GetBestPoint(DoubleMatrix value, bool[] maximization) {
+    private static double[] GetBestPoint(DoubleMatrix value, bool[] maximization) {
       double[] res = new double[maximization.Length];
       for (int i = 0; i < maximization.Length; i++) {