#region License Information /* HeuristicLab * Copyright (C) 2002-2019 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 HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { /// /// An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems. /// [Item("SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.")] [StorableType("85786F8E-F84D-4909-9A66-620668B0C7FB")] public sealed class SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer, ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator { private const string ProblemDataParameterName = "ProblemData"; private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter"; private const string EstimationLimitsParameterName = "EstimationLimits"; #region parameter properties public ILookupParameter ProblemDataParameter { get { return (ILookupParameter)Parameters[ProblemDataParameterName]; } } public ILookupParameter SymbolicDataAnalysisTreeInterpreterParameter { get { return (ILookupParameter)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; } } public IValueLookupParameter EstimationLimitsParameter { get { return (IValueLookupParameter)Parameters[EstimationLimitsParameterName]; } } #endregion [StorableConstructor] private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(StorableConstructorFlag _) : base(_) { } private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { } public SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer() : base() { Parameters.Add(new LookupParameter(ProblemDataParameterName, "The problem data for the symbolic regression solution.")); Parameters.Add(new LookupParameter(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree.")); Parameters.Add(new ValueLookupParameter(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model.")); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner); } protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) { var model = new SymbolicRegressionModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue); return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone()); } } }