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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.cs @ 7290

Last change on this file since 7290 was 7290, checked in by gkronber, 12 years ago

#1669 merged r7209:7283 from trunk into regression benchmark branch

File size: 5.2 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 analyzes the training best symbolic classification solution for multi objective symbolic classification problems.
32  /// </summary>
33  [Item("SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic classification solution for multi objective symbolic classification problems.")]
34  [StorableClass]
35  public sealed class SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer<ISymbolicClassificationSolution>,
36    ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
37    private const string ProblemDataParameterName = "ProblemData";
38    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
39    private const string EstimationLimitsParameterName = "EstimationLimits";
40    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
41
42    #region parameter properties
43    public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
44      get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
45    }
46    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
47      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
48    }
49    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
50      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
51    }
52    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
53      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
54    }
55    #endregion
56
57    #region properties
58    public BoolValue ApplyLinearScaling {
59      get { return ApplyLinearScalingParameter.Value; }
60    }
61    #endregion
62
63    [StorableConstructor]
64    private SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
65    private SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer(SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
66    public SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer()
67      : base() {
68      Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data for the symbolic classification solution."));
69      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
70      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic classification model."));
71      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
72    }
73    public override IDeepCloneable Clone(Cloner cloner) {
74      return new SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer(this, cloner);
75    }
76
77    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
78      var model = new SymbolicDiscriminantFunctionClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
79      if (ApplyLinearScaling.Value) {
80        SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
81      }
82      return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
83    }
84  }
85}
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