source: branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisMultiObjectiveAnalyzer.cs @ 15802

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
  /// <summary>
  /// Abstract base class for multi objective symbolic data analysis analyzers.
  /// </summary>
  [StorableType("9bbff875-a1ff-417a-9466-a988420578bd")]
  public abstract class SymbolicDataAnalysisMultiObjectiveAnalyzer : SymbolicDataAnalysisAnalyzer, ISymbolicDataAnalysisMultiObjectiveAnalyzer {
    private const string QualitiesParameterName = "Qualities";
    private const string MaximizationParameterName = "Maximization";
    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
    #region parameter properties
    public IScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
      get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters[QualitiesParameterName]; }
    }
    public ILookupParameter<BoolArray> MaximizationParameter {
      get { return (ILookupParameter<BoolArray>)Parameters[MaximizationParameterName]; }
    }
    public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
      get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
    }
    #endregion
    #region properties
    public ItemArray<DoubleArray> Qualities {
      get { return QualitiesParameter.ActualValue; }
    }
    public BoolArray Maximization {
      get { return MaximizationParameter.ActualValue; }
    }
    #endregion
    [StorableConstructor]
    protected SymbolicDataAnalysisMultiObjectiveAnalyzer(StorableConstructorFlag deserializing) : base(deserializing) { }
    protected SymbolicDataAnalysisMultiObjectiveAnalyzer(SymbolicDataAnalysisMultiObjectiveAnalyzer original, Cloner cloner)
      : base(original, cloner) {
    }
    public SymbolicDataAnalysisMultiObjectiveAnalyzer()
      : base() {
      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>(QualitiesParameterName, "The qualities of the trees that should be analyzed."));
      Parameters.Add(new LookupParameter<BoolArray>(MaximizationParameterName, "The directions of optimization for each dimension."));
      Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (Parameters.ContainsKey(ApplyLinearScalingParameterName) && Parameters[ApplyLinearScalingParameterName] is LookupParameter<BoolValue>)
        Parameters.Remove(ApplyLinearScalingParameterName);
      if (!Parameters.ContainsKey(ApplyLinearScalingParameterName))
        Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
    }
  }
}