#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 . */ #endregion using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic { /// /// Abstract base class for multi objective symbolic data analysis analyzers. /// [StorableClass] 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 QualitiesParameter { get { return (IScopeTreeLookupParameter)Parameters[QualitiesParameterName]; } } public ILookupParameter MaximizationParameter { get { return (ILookupParameter)Parameters[MaximizationParameterName]; } } public ILookupParameter ApplyLinearScalingParameter { get { return (ILookupParameter)Parameters[ApplyLinearScalingParameterName]; } } #endregion #region properties public ItemArray Qualities { get { return QualitiesParameter.ActualValue; } } public BoolArray Maximization { get { return MaximizationParameter.ActualValue; } } #endregion [StorableConstructor] protected SymbolicDataAnalysisMultiObjectiveAnalyzer(bool deserializing) : base(deserializing) { } protected SymbolicDataAnalysisMultiObjectiveAnalyzer(SymbolicDataAnalysisMultiObjectiveAnalyzer original, Cloner cloner) : base(original, cloner) { } public SymbolicDataAnalysisMultiObjectiveAnalyzer() : base() { Parameters.Add(new ScopeTreeLookupParameter(QualitiesParameterName, "The qualities of the trees that should be analyzed.")); Parameters.Add(new LookupParameter(MaximizationParameterName, "The directions of optimization for each dimension.")); Parameters.Add(new LookupParameter(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) Parameters.Remove(ApplyLinearScalingParameterName); if (!Parameters.ContainsKey(ApplyLinearScalingParameterName)) Parameters.Add(new LookupParameter(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating.")); } } }