#region License Information /* HeuristicLab * Copyright (C) 2002-2012 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.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification { /// /// An operator that collects the validation Pareto-best symbolic classification solutions for single objective symbolic classification problems. /// [Item("SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer", "An operator that collects the validation Pareto-best symbolic classification solutions for single objective symbolic classification problems.")] [StorableClass] public sealed class SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer, ISymbolicClassificationModelCreatorOperator { private const string ModelCreatorParameterName = "ModelCreator"; #region parameter properties public IValueLookupParameter ModelCreatorParameter { get { return (IValueLookupParameter)Parameters[ModelCreatorParameterName]; } } ILookupParameter ISymbolicClassificationModelCreatorOperator.ModelCreatorParameter { get { return ModelCreatorParameter; } } #endregion [StorableConstructor] private SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer(bool deserializing) : base(deserializing) { } private SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { } public SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer() : base() { Parameters.Add(new ValueLookupParameter(ModelCreatorParameterName, "")); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer(this, cloner); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { // BackwardsCompatibility3.4 #region Backwards compatible code, remove with 3.5 if (!Parameters.ContainsKey(ModelCreatorParameterName)) Parameters.Add(new ValueLookupParameter(ModelCreatorParameterName, "")); #endregion } protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree) { var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue); model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices); return model.CreateClassificationSolution((IClassificationProblemData)ProblemDataParameter.ActualValue.Clone()); } } }