source: addons/HeuristicLab.FitnessLandscapeAnalysis/HeuristicLab.Analysis.FitnessLandscape/BestWorstSelector.cs @ 15781

using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Selection;

namespace HeuristicLab.Analysis.FitnessLandscape {
  [Item("BestOrWorstSelector", "A selection operator that moves towards the next local optimum, when found reverses direction and so on.")]
  [StorableClass]
  public class BestWorstSelector : SingleObjectiveSelector, ISingleObjectiveSelector {

    #region Parameters
    public ScopePathLookupParameter<BoolValue> MoveTowardsOptimumParameter {
      get { return (ScopePathLookupParameter<BoolValue>)Parameters["MoveTowardsOptimum"]; }
    }
    public ScopePathLookupParameter<DoubleValue> BaseQualityParameter {
      get { return (ScopePathLookupParameter<DoubleValue>)Parameters["BaseQuality"]; }
    }
    public LookupParameter<IRandom> RandomParameter {
      get { return (LookupParameter<IRandom>)Parameters["Random"]; }
    }
    #endregion

    #region Construction & Cloning

    [StorableConstructor]
    protected BestWorstSelector(bool deserializing) : base(deserializing) { }
    protected BestWorstSelector(BestWorstSelector original, Cloner cloner) : base(original, cloner) { }
    public BestWorstSelector() {
      Parameters.Add(new ScopePathLookupParameter<BoolValue>("MoveTowardsOptimum", "Specifies whether the selector should optimize towards or away from the optimum."));
      Parameters.Add(new ScopePathLookupParameter<DoubleValue>("BaseQuality", "The base quality value to compare to. This is required to determine wheter a local optimum has been found and the direction should be reversed."));
      Parameters.Add(new LookupParameter<IRandom>("Random", "Random number generator"));
      MoveTowardsOptimumParameter.Hidden = false;
      BaseQualityParameter.Hidden = false;
      MoveTowardsOptimumParameter.Path = "..";
      BaseQualityParameter.Path = "../Remaining/{0}";
      BaseQualityParameter.ActualName = "Quality";
      QualityParameter.Hidden = false;
      MaximizationParameter.Hidden = true;
      NumberOfSelectedSubScopesParameter.Value = new IntValue(1);
      NumberOfSelectedSubScopesParameter.Hidden = true;
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new BestWorstSelector(this, cloner);
    }
    #endregion

    protected override IScope[] Select(List<IScope> scopes) {
      bool optimize = true;
      if (MoveTowardsOptimumParameter.ActualValue == null) {
        MoveTowardsOptimumParameter.ActualValue = new BoolValue(true);
      } else {
        optimize = MoveTowardsOptimumParameter.ActualValue.Value;
      }
      double baseQuality = BaseQualityParameter.ActualValue.Value;
      int count = NumberOfSelectedSubScopesParameter.ActualValue.Value;
      bool copy = CopySelectedParameter.Value.Value;
      bool maximization = MaximizationParameter.ActualValue.Value;
      ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
      IScope[] selected = new IScope[count];
      IRandom random = RandomParameter.ActualValue;

      var list = qualities.Select((x, i) => new { Index=i, Quality=x.Value }).ToList();
      if (random != null)
        list.Shuffle(random);
      if (maximization && optimize || !maximization && !optimize) {
        list = list.OrderByDescending(x => x.Quality).ToList();
        if (list.Count > 0 && list[0].Quality <= baseQuality)
          MoveTowardsOptimumParameter.ActualValue = new BoolValue(!optimize);
      } else {
        list = list.OrderBy(x => x.Quality).ToList();
        if (list.Count > 0 && list[0].Quality >= baseQuality)
          MoveTowardsOptimumParameter.ActualValue = new BoolValue(!optimize);
      }

      if (copy) {
        int j = 0;
        for (int i = 0; i < count; i++) {
          selected[i] = (IScope)scopes[list[j].Index].Clone();
          j++;
          if (j >= list.Count) j = 0;
        }
      } else {
        for (int i = 0; i < count; i++) {
          selected[i] = scopes[list[0].Index];
          scopes[list[0].Index] = null;
          list.RemoveAt(0);
        }
        scopes.RemoveAll(x => x == null);
      }

      return selected;
    }
  }
}