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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.cs @ 5809

Last change on this file since 5809 was 5809, checked in by mkommend, 13 years ago

#1418: Reintegrated branch into trunk.

File size: 4.0 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Data;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Parameters;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
30  /// <summary>
31  /// An operator that analyzes the validation best symbolic regression solution for single objective symbolic regression problems.
32  /// </summary>
33  [Item("SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic regression solution for single objective symbolic regression problems.")]
34  [StorableClass]
35  public sealed class SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicRegressionSolution, ISymbolicRegressionSingleObjectiveEvaluator, IRegressionProblemData>,
36    ISymbolicDataAnalysisBoundedOperator {
37    private const string EstimationLimitsParameterName = "EstimationLimits";
38    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
39
40    #region parameter properties
41    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
42      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
43    }
44    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
45      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
46    }
47    #endregion
48
49    #region properties
50    public BoolValue ApplyLinearScaling {
51      get { return ApplyLinearScalingParameter.Value; }
52    }
53    #endregion
54
55    [StorableConstructor]
56    private SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
57    private SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
58    public SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer()
59      : base() {
60      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
61      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
62    }
63
64    public override IDeepCloneable Clone(Cloner cloner) {
65      return new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
66    }
67
68    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
69      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
70      var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
71      if (ApplyLinearScaling.Value)
72        solution.ScaleModel();
73      return solution;
74    }
75  }
76}
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