source: branches/2943_MOBasicProblem_MOCMAES/HeuristicLab.Analysis/3.3/MultiObjective/GenerationalDistanceAnalyzer.cs

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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.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;

namespace HeuristicLab.Analysis {
  [StorableClass]
  [Item("GenerationalDistanceAnalyzer", "The generational distance between the current and the optimal front (if known)(see Multi-Objective Performance Metrics - Shodhganga for more information). The calculation of generational distance requires a known optimal pareto front")]
  public class GenerationalDistanceAnalyzer : MultiObjectiveSuccessAnalyzer {
    public override string ResultName => "Generational Distance";

    public IFixedValueParameter<DoubleValue> DampeningParameter => (IFixedValueParameter<DoubleValue>)Parameters["Dampening"];

    public double Dampening {
      get => DampeningParameter.Value.Value;
      set => DampeningParameter.Value.Value = value;
    }

    public ILookupParameter<DoubleMatrix> OptimalParetoFrontParameter => (ILookupParameter<DoubleMatrix>)Parameters["BestKnownFront"];


    [StorableConstructor]
    protected GenerationalDistanceAnalyzer(bool deserializing) : base(deserializing) { }
    protected GenerationalDistanceAnalyzer(GenerationalDistanceAnalyzer original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new GenerationalDistanceAnalyzer(this, cloner);
    }

    public GenerationalDistanceAnalyzer() {
      Parameters.Add(new FixedValueParameter<DoubleValue>("Dampening", "", new DoubleValue(1)));
      Parameters.Add(new LookupParameter<ItemArray<DoubleArray>>("OptimalParetoFront", "The analytically best known Pareto front"));
      Parameters.Add(new ResultParameter<DoubleValue>(ResultName, "The generational distance between the current front and the optimal front", "Results", new DoubleValue(double.NaN)));
    }

    public override IOperation Apply() {
      var qualities = QualitiesParameter.ActualValue;
      var optimalFront = OptimalParetoFrontParameter.ActualValue;
      if (optimalFront == null) return base.Apply();
      var front = Enumerable.Range(0, optimalFront.Rows).Select(r => Enumerable.Range(0, optimalFront.Columns).Select(c => optimalFront[r, c]).ToList()).ToList();
      ResultParameter.ActualValue.Value = CalculateDistance(qualities,front);
      return base.Apply();
    }

    protected virtual double CalculateDistance(ItemArray<DoubleArray> qualities, IList<List<double>> optimalFront) {
      return GenerationalDistanceCalculator.CalculateGenerationalDistance(qualities, optimalFront, Dampening);
    }
  }
}