[5557] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 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 System.Collections.Generic;
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| 23 | using System.Linq;
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| 24 | using HeuristicLab.Common;
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| 25 | using HeuristicLab.Core;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 |
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| 33 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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| 34 | /// <summary>
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| 35 | /// An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.
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| 36 | /// </summary>
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| 37 | [Item("SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.")]
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| 38 | [StorableClass]
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[5685] | 39 | public sealed class SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicRegressionSolution>,
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[5747] | 40 | ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
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[5685] | 41 | private const string ProblemDataParameterName = "ProblemData";
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| 42 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
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[5720] | 43 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 44 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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[5722] | 45 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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[5685] | 46 | #region parameter properties
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| 47 | public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
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| 48 | get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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| 49 | }
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| 50 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 51 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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| 52 | }
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[5720] | 53 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 54 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 55 | }
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| 56 |
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| 57 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 58 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 59 | }
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[5722] | 60 | public IValueParameter<BoolValue> ApplyLinearScalingParameter {
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| 61 | get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
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| 62 | }
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[5685] | 63 | #endregion
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[5720] | 64 |
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[5685] | 65 | #region properties
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| 66 | public IRegressionProblemData ProblemData {
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| 67 | get { return ProblemDataParameter.ActualValue; }
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| 68 | }
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| 69 | public ISymbolicDataAnalysisExpressionTreeInterpreter SymbolicDataAnalysisTreeInterpreter {
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| 70 | get { return SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; }
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| 71 | }
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[5720] | 72 | public DoubleValue UpperEstimationLimit {
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| 73 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 74 | }
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| 75 | public DoubleValue LowerEstimationLimit {
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| 76 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 77 | }
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[5722] | 78 | public BoolValue ApplyLinearScaling {
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| 79 | get { return ApplyLinearScalingParameter.Value; }
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| 80 | }
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[5685] | 81 | #endregion
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[5557] | 82 | [StorableConstructor]
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| 83 | private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
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| 84 | private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
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| 85 | public SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer()
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| 86 | : base() {
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[5685] | 87 | Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
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| 88 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
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[5720] | 89 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
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| 90 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
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[5729] | 91 | Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
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[5557] | 92 | }
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| 93 | public override IDeepCloneable Clone(Cloner cloner) {
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| 94 | return new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
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| 95 | }
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| 96 |
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| 97 | protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
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[5720] | 98 | var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreter, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
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[5729] | 99 | var solution = new SymbolicRegressionSolution(model, ProblemData);
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| 100 | if (ApplyLinearScaling.Value)
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| 101 | solution.ScaleModel();
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| 102 | return solution;
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[5557] | 103 | }
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| 104 | }
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| 105 | }
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