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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer.cs @ 14171

Last change on this file since 14171 was 13941, checked in by mkommend, 8 years ago

#2604:

  • Base classes for data analysis, classification, and regression models
  • Added target variable to classification and regression models
  • Switched parameter order in data analysis solutions (model, problemdata)
File size: 3.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Encodings.SymbolicExpressionTreeEncoding;
25using HeuristicLab.Parameters;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
29  /// <summary>
30  /// An operator that analyzes the validation best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.
31  /// </summary>
32  [Item("SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.")]
33  [StorableClass]
34  public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSingleObjectiveEvaluator, ITimeSeriesPrognosisProblemData>, ISymbolicDataAnalysisBoundedOperator {
35    private const string EstimationLimitsParameterName = "EstimationLimits";
36    #region parameter properties
37    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
38      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
39    }
40    #endregion
41
42    [StorableConstructor]
43    private SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
44    private SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
45    public SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer()
46      : base() {
47      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
48    }
49    public override IDeepCloneable Clone(Cloner cloner) {
50      return new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
51    }
52
53    protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
54      var model = new SymbolicTimeSeriesPrognosisModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
55      if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
56
57      return new SymbolicTimeSeriesPrognosisSolution(model, (ITimeSeriesPrognosisProblemData)ProblemDataParameter.ActualValue.Clone());
58    }
59  }
60}
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