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source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer.cs @ 8458

Last change on this file since 8458 was 8458, checked in by mkommend, 12 years ago

#1081: Derived time series classes from regression classes to avoid code duplication.

File size: 4.3 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.TimeSeriesPrognosis {
30  /// <summary>
31  /// An operator that analyzes the validation best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.
32  /// </summary>
33  [Item("SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.")]
34  [StorableClass]
35  public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSingleObjectiveEvaluator, ITimeSeriesPrognosisProblemData>, ISymbolicDataAnalysisBoundedOperator {
36    private const string EstimationLimitsParameterName = "EstimationLimits";
37    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
38    #region parameter properties
39    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
40      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
41    }
42    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
43      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
44    }
45    #endregion
46
47    #region properties
48    public BoolValue ApplyLinearScaling {
49      get { return ApplyLinearScalingParameter.Value; }
50    }
51    #endregion
52
53    [StorableConstructor]
54    private SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
55    private SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
56    public SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer()
57      : base() {
58      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
59      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
60    }
61    public override IDeepCloneable Clone(Cloner cloner) {
62      return new SymbolicTimeSeriesPrognosisSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
63    }
64
65    protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
66      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
67      if (ApplyLinearScaling.Value) SymbolicTimeSeriesPrognosisModel.Scale(model, ProblemDataParameter.ActualValue);
68
69      return new SymbolicTimeSeriesPrognosisSolution(model, (ITimeSeriesPrognosisProblemData)ProblemDataParameter.ActualValue.Clone());
70    }
71  }
72}
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