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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer.cs @ 8169

Last change on this file since 8169 was 8169, checked in by gkronber, 12 years ago

#1823 moved parameters up into the base classes of ParetoBestSolutionAnalyzers

File size: 3.7 KB
RevLine 
[7734]1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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 collects the validation Pareto-best symbolic regression solutions for single objective symbolic regression problems.
32  /// </summary>
33  [Item("SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer", "An operator that collects the validation Pareto-best symbolic regression solutions for single objective symbolic regression problems.")]
34  [StorableClass]
[8169]35  public sealed class SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer<ISymbolicRegressionSolution, ISymbolicRegressionSingleObjectiveEvaluator, IRegressionProblemData> {
[7734]36    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
37    #region parameter properties
38    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
39      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
40    }
41    #endregion
42
43    #region properties
44    public BoolValue ApplyLinearScaling {
45      get { return ApplyLinearScalingParameter.Value; }
46    }
47    #endregion
48
49    [StorableConstructor]
50    private SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
51    private SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
52    public SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer()
53      : base() {
54      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
55    }
56    public override IDeepCloneable Clone(Cloner cloner) {
57      return new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer(this, cloner);
58    }
59
60    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree) {
61      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
62      if (ApplyLinearScaling.Value)
63        SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
64      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
65    }
66  }
67}
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