source: branches/FitnessLandscapeAnalysis/HeuristicLab.Problems.NK/NKBitFlipMoveEvaluator.cs @ 10879

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

namespace HeuristicLab.Problems.NK {

  [Item("NK BitFlip Move Evaluator", "Evaluates a single bit flip on an NK landscape.")]
  [StorableClass]
  public class NKBitFlipMoveEvaluator : NKMoveEvaluator, IOneBitflipMoveOperator {

    public ILookupParameter<OneBitflipMove> OneBitflipMoveParameter {
      get { return (ILookupParameter<OneBitflipMove>)Parameters["OneBitflipMove"]; }
    }
    public LookupParameter<BinaryVector> MovedBinaryVectorParameter {
      get { return (LookupParameter<BinaryVector>)Parameters["MovedBinaryVector"]; }
    }    

    private NKEvaluator NKEvaluator = new NKEvaluator();

    [StorableConstructor]
    protected NKBitFlipMoveEvaluator(bool deserializing) : base(deserializing) { }
    protected NKBitFlipMoveEvaluator(NKBitFlipMoveEvaluator original, Cloner cloner) : base(original, cloner) { }
    public NKBitFlipMoveEvaluator()
      : base() {
      Parameters.Add(new LookupParameter<OneBitflipMove>("OneBitflipMove", "The move to evaluate."));
      Parameters.Add(new LookupParameter<BinaryVector>("MovedBinaryVector", "The resulting binary vector after the move."));
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new NKBitFlipMoveEvaluator(this, cloner);
    }

    public override IOperation Apply() {
      BinaryVector binaryVector = BinaryVectorParameter.ActualValue;
      OneBitflipMove move = OneBitflipMoveParameter.ActualValue;
      BoolMatrix interactions = GeneInteractionsParameter.ActualValue;
      DoubleArray weights = WeightsParameter.ActualValue;
      int seed = InteractionSeedParameter.ActualValue.Value;
      double moveQuality = QualityParameter.ActualValue.Value;

      List<int> affectedFitnessComponents = new List<int>();
      for (int c = 0; c<interactions.Columns; c++)
        if (interactions[move.Index, c])
          affectedFitnessComponents.Add(c);
      BinaryVector moved = new BinaryVector(binaryVector);
      MovedBinaryVectorParameter.ActualValue = moved;
      moved[move.Index] = !moved[move.Index];
      if (affectedFitnessComponents.Count*2 > interactions.Columns) {
        double[] f_i;
        moveQuality = NKEvaluator.Evaluate(moved, interactions, weights, seed, out f_i);
      } else {
        long x = NKEvaluator.Encode(binaryVector);
        long y = NKEvaluator.Encode(moved);
        long[] g = NKEvaluator.Encode(interactions);
        double[] w = NKEvaluator.Normalize(weights);
        foreach (var c in affectedFitnessComponents) {
          moveQuality -= w[c%w.Length] * NKEvaluator.F_i(x, c, g[c], seed);
          moveQuality += w[c%w.Length] * NKEvaluator.F_i(y, c, g[c], seed);
        }
      }

      if (MoveQualityParameter.ActualValue == null) MoveQualityParameter.ActualValue = new DoubleValue(moveQuality);
      else MoveQualityParameter.ActualValue.Value = moveQuality;
      return base.Apply();
    }
  }
}