source: branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.cs @ 16641

Last change on this file since 16641 was 16641, checked in by gkronber, 4 months ago

#2971: merged r16527:16625 from trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression to branch/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression (resolving all conflicts)

File size: 4.5 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
25using HeuristicLab.Parameters;
26using HEAL.Attic;
27using HEAL.Attic;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
30  /// <summary>
31  /// An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.
32  /// </summary>
33  [Item("SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic regression solution for single objective symbolic regression problems.")]
34  [StorableType("85786F8E-F84D-4909-9A66-620668B0C7FB")]
35  public sealed class SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicRegressionSolution>,
36  ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
37    private const string ProblemDataParameterName = "ProblemData";
38    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
39    private const string EstimationLimitsParameterName = "EstimationLimits";
40    #region parameter properties
41    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
42      get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
43    }
44    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
45      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
46    }
47    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
48      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
49    }
50    #endregion
51
52    [StorableConstructor]
53    private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(StorableConstructorFlag _) : base(_) { }
54    private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
55    public SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer()
56      : base() {
57      Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
58      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
59      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
60    }
61    public override IDeepCloneable Clone(Cloner cloner) {
62      return new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
63    }
64
65    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
66      var model = new SymbolicRegressionModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
67      if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
68      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
69    }
70  }
71}
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