Ticket #2944: CustomCAnalyzer.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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.TestFunctions.MultiObjective;


namespace HeuristicLab.Analysis
{
    [StorableClass]
  [Item("CustomCAnalyzer", "The mean crowding distance for each point of the Front (see Multi-Objective Performance Metrics - Shodhganga for more information)")]
  public class CustomCAnalyzer : SingleSuccessorOperator, IAnalyzer, IMultiObjectiveOperator
    {
        public virtual bool EnabledByDefault { get { return true; } }
    
    //parameters from MOTFAnalyzer
    public IScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
        get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
    }

    public ILookupParameter<ResultCollection> ResultsParameter {
        get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
    }
    
    public ILookupParameter<DoubleMatrix> BestKnownFrontParameter {
        get { return (ILookupParameter<DoubleMatrix>)Parameters["BestKnownFront"]; }
    }
    
    //old ones
    public ILookupParameter<DoubleMatrix> BoundsParameter {
      get { return (ILookupParameter<DoubleMatrix>)Parameters["Bounds"]; }
    }

    public IResultParameter<DoubleValue> CrowdingResultParameter {
      get { return (IResultParameter<DoubleValue>)Parameters["Crowding"]; }
    }

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

    public CustomCAnalyzer() {
      
      //add parameters from MOTFAnalyzer
      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The qualities of the parameter vector."));
      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The results collection to write to."));
      Parameters.Add(new LookupParameter<DoubleMatrix>("BestKnownFront", "The currently best known Pareto front"));

      //old ones
      Parameters.Add(new LookupParameter<DoubleMatrix>("Bounds",
        "The bounds of the solution given as either one line for all variables or a line for each variable. The first column specifies lower bound, the second upper bound."));
      Parameters.Add(new ResultParameter<DoubleValue>("Crowding", "The average corwding value of all points (excluding infinities)"));
      CrowdingResultParameter.DefaultValue = new DoubleValue(double.NaN);

    }

    public override IOperation Apply() {
      var qualities = QualitiesParameter.ActualValue;
      var bounds = BoundsParameter.ActualValue;

      var crowdingDistance = Crowding.Calculate(qualities.Select(x => x.ToArray()), bounds.CloneAsMatrix());
      CrowdingResultParameter.ActualValue.Value = crowdingDistance;

      return base.Apply();
    }
  }
}