Ticket #1040: CrowdingDistanceAssignment.cs

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2008 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.Collections.Generic;
using System.Linq;
using System.Text;
using HeuristicLab.Core;
using HeuristicLab.Common;
using HeuristicLab.Data;
using HeuristicLab.DataAnalysis;
using HeuristicLab.Modeling;
using HeuristicLab.GP;
using HeuristicLab.GP.StructureIdentification;
using HeuristicLab.GP.Interfaces;

namespace HeuristicLab.LinearRegression {
  /// <summary>
  /// CrowdingDistanceAssignment as described in: Deb, Pratap, Agrawal and Meyarivan, "A Fast and Elitist Multiobjective
  /// Genetic Algorithm: NSGA-II", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002
  /// </summary>
  public class CrowdingDistanceAssignment : OperatorBase {
    private static double constant = 1.0;

    public override string Description {
      get {
        return
@"CrowdingDistanceAssignment as described in: Deb, Pratap, Agrawal and Meyarivan, ""A Fast and Elitist Multiobjective
Genetic Algorithm: NSGA-II"", IEEE Transactions On Evolutionary Computation, Vol. 6, No. 2, April 2002";
      }
    }


    public CrowdingDistanceAssignment() {
      AddVariableInfo(new VariableInfo("Quality", "Vector of quality values", typeof(ItemList<DoubleData>), VariableKind.In));
    }

    public override IOperation Apply(IScope scope) {
      IVariableInfo qualityInfo = GetVariableInfo("Quality");
      int l = scope.SubScopes.Count;
      double[] distance = new double[l];
      foreach (var s in scope.SubScopes) {
        SetDistance(s, 0.0);
      }
      ItemList<DoubleData> quality = scope.SubScopes[0].GetVariableValue<ItemList<DoubleData>>("Quality", false);
      for (int m = 0; m < quality.Count; m++) {
        List<IScope> sortedScopes = new List<IScope>(scope.SubScopes);
        Sort(sortedScopes, m);
        SetDistance(sortedScopes[0], double.MaxValue);
        SetDistance(sortedScopes[l - 1], double.MaxValue);
        double minM = ObjectiveValue(sortedScopes[0], m);
        double maxM = ObjectiveValue(sortedScopes[l - 1], m);
        for (int i = 2; i < l - 1; i++) {
          SetDistance(sortedScopes[i], GetDistance(sortedScopes[i]) + (ObjectiveValue(sortedScopes[i + 1], m) - ObjectiveValue(sortedScopes[i - 1], m)) / (maxM - minM));
        }
      }
      return null;
    }

    private static double GetDistance(IScope s) {
      return s.GetVariableValue<DoubleData>("Distance", false).Data;
    }

    private static void SetDistance(IScope s, double p) {
      DoubleData distance = s.GetVariableValue<DoubleData>("Distance", false, false);
      if (distance != null) {
        distance.Data = p;
      } else {
        s.AddVariable(new HeuristicLab.Core.Variable("Distance", new DoubleData(p)));
      }
    }

    private static double ObjectiveValue(IScope s, int m) {
      return s.GetVariableValue<ItemList<DoubleData>>("Quality", false)[m].Data;
    }

    private static void Sort(List<IScope> sortedScopes, int m) {
      sortedScopes.Sort((a, b) => {
        double va = ObjectiveValue(a, m);
        double vb = ObjectiveValue(b, m);
        if (va < vb) return -1;
        else if (va > vb) return +1;
        else return 0;
      });
    }
  }
}