| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 25 | using HeuristicLab.Parameters;
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| 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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| 29 | /// <summary>
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| 30 | /// An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.
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| 31 | /// </summary>
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| 32 | [Item("SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.")]
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| 33 | [StorableClass]
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| 34 | public sealed class SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicRegressionSolution>,
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| 35 | ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
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| 36 | private const string ProblemDataParameterName = "ProblemData";
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| 37 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
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| 38 | private const string EstimationLimitsParameterName = "EstimationLimits";
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| 39 | #region parameter properties
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| 40 | public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
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| 41 | get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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| 42 | }
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| 43 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 44 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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| 45 | }
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| 46 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
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| 47 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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| 48 | }
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| 49 | #endregion
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| 50 |
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| 51 | [StorableConstructor]
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| 52 | private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
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| 53 | private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
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| 54 | public SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer()
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| 55 | : base() {
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| 56 | Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
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| 57 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
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| 58 | Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
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| 59 | }
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| 60 | public override IDeepCloneable Clone(Cloner cloner) {
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| 61 | return new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
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| 62 | }
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| 63 |
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| 64 | protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
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| 65 | var model = new SymbolicRegressionModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
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| 66 | if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
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| 67 | return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
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| 68 | }
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| 69 | }
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| 70 | }
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