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source: branches/GeneralizedQAP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs @ 6650

Last change on this file since 6650 was 5914, checked in by mkommend, 14 years ago

#1418: Changed DataAnalysisSolutions and -Models and updated GenerateRowsToEvaluate method in SymbolicDataAnalysisEvaluator.

File size: 5.0 KB
Line 
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 training best symbolic regression solution for multi objective symbolic regression problems.
32  /// </summary>
33  [Item("SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic regression solution for multi objective symbolic regression problems.")]
34  [StorableClass]
35  public sealed class SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer<ISymbolicRegressionSolution>,
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    #region parameter properties
42    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
43      get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
44    }
45    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
46      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
47    }
48    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
49      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
50    }
51    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
52      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
53    }
54    #endregion
55
56    #region properties
57    public BoolValue ApplyLinearScaling {
58      get { return ApplyLinearScalingParameter.Value; }
59    }
60    #endregion
61
62    [StorableConstructor]
63    private SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
64    private SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer(SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
65    public SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer()
66      : base() {
67      Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
68      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
69      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
70      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
71    }
72
73    public override IDeepCloneable Clone(Cloner cloner) {
74      return new SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer(this, cloner);
75    }
76
77    protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
78      var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
79      if (ApplyLinearScaling.Value)
80        SymbolicRegressionModel.Scale(model, ProblemDataParameter.ActualValue);
81      return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
82    }
83  }
84}
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