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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39 | public sealed class SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicRegressionSolution>,
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40 | ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
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41 | private const string ProblemDataParameterName = "ProblemData";
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42 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
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43 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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44 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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45 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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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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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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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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63 | #endregion
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64 |
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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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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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78 | public BoolValue ApplyLinearScaling {
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79 | get { return ApplyLinearScalingParameter.Value; }
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80 | }
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81 | #endregion
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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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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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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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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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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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98 | var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreter, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
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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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103 | }
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104 | }
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105 | }
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