source: branches/2989_MovingPeaksBenchmark/HeuristicLab.Problems.MovingPeaksBenchmark/3.3/MovingPeaksBenchmarkProblemEvaluator.cs @ 16611

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2019 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.Encodings.RealVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.MovingPeaksBenchmark {
  [Item("MovingPeaksBenchmarkProblemEvaluator", "Evaluation operator for the Moving Peaks Benchmark.")]
  [StorableClass]
  public class MovingPeaksBenchmarkProblemEvaluator : InstrumentedOperator, IMovingPeaksBenchmarkProblemEvaluator {
    [Storable]
    IRandom uniformRandom;
    [Storable]
    long executions;
    [Storable]
    double currentBest;
    [Storable]
    double offlineErrorSum;

    public IValueLookupParameter<DoubleMatrix> PeakLocationsParameter {
      get { return (IValueLookupParameter<DoubleMatrix>)Parameters["PeakLocations"]; }
    }
    public IValueLookupParameter<DoubleArray> PeakWidthsParameter {
      get { return (IValueLookupParameter<DoubleArray>)Parameters["PeakWidths"]; }
    }
    public IValueLookupParameter<DoubleArray> PeakHeightsParameter {
      get { return (IValueLookupParameter<DoubleArray>)Parameters["PeakHeights"]; }
    }
    public ILookupParameter<IntValue> MovingPeaksRandomSeedParameter {
      get { return (ILookupParameter<IntValue>)Parameters["MovingPeaksRandomSeed"]; }
    }
    public ILookupParameter<IntValue> PeakMovementIntervalParameter {
      get { return (ILookupParameter<IntValue>)Parameters["PeakMovementInterval"]; }
    }
    public ILookupParameter<DoubleValue> PeakMovementStrengthParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["PeakMovementStrength"]; }
    }
    public ILookupParameter<RealVector> PointParameter {
      get { return (ILookupParameter<RealVector>)Parameters["Point"]; }
    }
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public ILookupParameter<DoubleValue> BestKnownQualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["BestKnownQuality"]; }
    }
    public ILookupParameter<RealVector> BestKnownSolutionParameter {
      get { return (ILookupParameter<RealVector>)Parameters["BestKnownSolution"]; }
    }
    public IValueLookupParameter<ResultCollection> ResultsParameter {
      get { return (IValueLookupParameter<ResultCollection>)Parameters["Results"]; }
    }

    [StorableConstructor]
    protected MovingPeaksBenchmarkProblemEvaluator(bool deserializing) : base(deserializing) { }
    protected MovingPeaksBenchmarkProblemEvaluator(MovingPeaksBenchmarkProblemEvaluator original, Cloner cloner) : base(original, cloner) { }
    public MovingPeaksBenchmarkProblemEvaluator() : base() {
      Parameters.Add(new ValueLookupParameter<DoubleMatrix>("PeakLocations", "Current position of the peaks."));
      PeakLocationsParameter.ActualName = "InitialPeakLocations";
      Parameters.Add(new ValueLookupParameter<DoubleArray>("PeakWidths", "Current width of the peaks."));
      PeakWidthsParameter.ActualName = "InitialPeakWidths";
      Parameters.Add(new ValueLookupParameter<DoubleArray>("PeakHeights", "Current height of the peaks."));
      PeakHeightsParameter.ActualName = "InitialPeakHeights";
      Parameters.Add(new LookupParameter<IntValue>("MovingPeaksRandomSeed", "The random seed for initializing the PRNG for changing the peaks."));
      Parameters.Add(new LookupParameter<IntValue>("PeakMovementInterval", "The interval in evaluated solutions in which peaks are moved."));
      Parameters.Add(new LookupParameter<DoubleValue>("PeakMovementStrength", "The length of the random vector used for changing peak locations."));
      Parameters.Add(new LookupParameter<RealVector>("Point", "The point which should be evaluated."));
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "Quality value of the evaluated point."));
      Parameters.Add(new LookupParameter<DoubleValue>("BestKnownQuality", "Quality value of the highest peak."));
      Parameters.Add(new LookupParameter<RealVector>("BestKnownSolution", "The location of the highest peak."));
      Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection for storing result values."));

      PeakLocationsParameter.Hidden = true;
      PeakWidthsParameter.Hidden = true;
      PeakHeightsParameter.Hidden = true;
      BestKnownQualityParameter.Hidden = true;
      BestKnownSolutionParameter.Hidden = true;
      ResultsParameter.Hidden = true;
    }

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

    public override IOperation InstrumentedApply() {
      DoubleMatrix peaks = PeakLocationsParameter.ActualValue;
      DoubleArray widths = PeakWidthsParameter.ActualValue;
      DoubleArray heights = PeakHeightsParameter.ActualValue;

      if (PeakLocationsParameter.Value == null) {
        peaks = peaks.Clone() as DoubleMatrix;
        widths = widths.Clone() as DoubleArray;
        heights = heights.Clone() as DoubleArray;
        PeakLocationsParameter.Value = peaks;
        PeakWidthsParameter.Value = widths;
        PeakHeightsParameter.Value = heights;

        ResultCollection results = ResultsParameter.ActualValue;
        results.Add(new Result("Current Peak Locations", peaks));
        results.Add(new Result("Current Peak Widths", widths));
        results.Add(new Result("Current Peak Heights", heights));
      }
      if (uniformRandom == null) {
        uniformRandom = new MersenneTwister();
        uniformRandom.Reset(MovingPeaksRandomSeedParameter.ActualValue.Value);
      }

      // move peaks if peaks movement interval is reached
      lock (this) {
        if ((executions % PeakMovementIntervalParameter.ActualValue.Value) == 0) {
          MovePeaks(uniformRandom, peaks, widths, heights, PeakMovementStrengthParameter.ActualValue.Value);

          // update best known solution & quality according to highest peak
          double maxHeight = heights.Max();
          int peakIndex = Array.IndexOf(heights.CloneAsArray(), maxHeight);
          double[] peak = new double[peaks.Columns];
          for (int i = 0; i < peak.Length; i++) {
            peak[i] = peaks[peakIndex, i];
          }
          BestKnownSolutionParameter.ActualValue = new RealVector(peak);
          BestKnownQualityParameter.ActualValue.Value = heights.Max();
          currentBest = -1;
        }
        executions++;
      }

      RealVector point = PointParameter.ActualValue;
      double quality = Apply(peaks, widths, heights, point);
      QualityParameter.ActualValue = new DoubleValue(quality);

      lock (this) {
        if (quality > currentBest) {
          currentBest = quality;
        }
        offlineErrorSum += heights.Max() - currentBest;
        ResultCollection results = ResultsParameter.ActualValue;
        IResult offlineError;
        if (results.TryGetValue("Offline Error", out offlineError)) {
          (offlineError.Value as DoubleValue).Value = offlineErrorSum / executions;
        } else {
          results.Add(new Result("Offline Error", new DoubleValue(offlineErrorSum / executions)));
        }
      }
      return base.InstrumentedApply();
    }

    public override void InitializeState() {
      base.InitializeState();
      executions = 0;
      currentBest = -1;
      offlineErrorSum = 0;
    }

    public override void ClearState() {
      base.ClearState();
      uniformRandom = null;
      PeakLocationsParameter.Value = null;
      PeakWidthsParameter.Value = null;
      PeakHeightsParameter.Value = null;
    }

    public double Apply(DoubleMatrix peaks, DoubleArray widths, DoubleArray heights, RealVector point) {
      double max = 0;
      double val = 0;

      for (int i = 0; i < widths.Length; i++) {
        val = 0;
        for (int j = 0; j < point.Length; j++) {
          val += (point[j] - peaks[i, j]) * (point[j] - peaks[i, j]);
        }
        val = heights[i] / (1 + widths[i] * val);
        if (val > max) max = val;
      }
      return max;
    }

    private void MovePeaks(IRandom uniformRandom, DoubleMatrix peaks, DoubleArray widths, DoubleArray heights, double strength) {
      IRandom normalRandom = new NormalDistributedRandom(uniformRandom, 0, 1);
      for (int i = 0; i < peaks.Rows; i++) {
        double[] v = RandomVector(uniformRandom, peaks.Columns, strength);
        for (int j = 0; j < v.Length; j++) {
          peaks[i, j] += v[j];
        }
        widths[i] = widths[i] + 0.01 * normalRandom.NextDouble();
        heights[i] = heights[i] + 7 * normalRandom.NextDouble();
      }
    }

    private double[] RandomVector(IRandom uniformRandom, int dimensions, double length) {
      double[] vector = new double[dimensions];

      for (int i = 0; i < vector.Length; i++) {
        vector[i] = uniformRandom.NextDouble() - 0.5;
      }
      double factor = length / Math.Sqrt(vector.Select(x => x * x).Sum());
      for (int i = 0; i < vector.Length; i++) {
        vector[i] *= factor; 
      }
      return vector;
    }
  }
}