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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer.cs @ 8531

Last change on this file since 8531 was 8531, checked in by mkommend, 10 years ago

#1919: Refactored calculation of thresholds for SymbolicDiscriminantFunctionClassficationModels and removed the automatic recalculation of thresholds during solution creation.

File size: 3.9 KB
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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.Classification {
30  /// <summary>
31  /// An operator that collects the validation Pareto-best symbolic classification solutions for single objective symbolic classification problems.
32  /// </summary>
33  [Item("SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer", "An operator that collects the validation Pareto-best symbolic classification solutions for single objective symbolic classification problems.")]
34  [StorableClass]
35  public sealed class SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer<ISymbolicClassificationSolution, ISymbolicClassificationSingleObjectiveEvaluator, IClassificationProblemData> {
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 SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
51    private SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
52    public SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer()
53      : base() {
54      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
55    }
56    public override IDeepCloneable Clone(Cloner cloner) {
57      return new SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer(this, cloner);
58    }
59
60    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree) {
61      var model = new SymbolicDiscriminantFunctionClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
62      if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
63
64      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
65      return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
66    }
67  }
68}
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