#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.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { /// /// An operator that collects the training Pareto-best symbolic regression solutions for single objective symbolic regression problems. /// [Item("SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer", "An operator that collects the training Pareto-best symbolic regression solutions for single objective symbolic regression problems.")] [StorableClass] public sealed class SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingParetoBestSolutionAnalyzer { private const string ApplyLinearScalingParameterName = "ApplyLinearScaling"; #region parameter properties public IValueParameter ApplyLinearScalingParameter { get { return (IValueParameter)Parameters[ApplyLinearScalingParameterName]; } } #endregion #region properties public BoolValue ApplyLinearScaling { get { return ApplyLinearScalingParameter.Value; } } #endregion [StorableConstructor] private SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer(bool deserializing) : base(deserializing) { } private SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { } public SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer() : base() { Parameters.Add(new ValueParameter(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true))); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer(this, cloner); } protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree) { var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); if (ApplyLinearScaling.Value) SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue); return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone()); } } }