source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer.cs @ 7100

Last change on this file since 7100 was 7100, checked in by gkronber, 9 years ago

#1081 worked on multi-variate time series prognosis

File size: 5.2 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 training best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.
32  /// </summary>
33  [Item("SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.")]
34  [StorableClass]
35  public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution>,
36  ISymbolicTimeSeriesPrognosisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
37    private const string ProblemDataParameterName = "ProblemData";
38    private const string SymbolicTimeSeriesPrognosisInterpreterParameterName = "SymbolicTimeSeriesPrognosisInterpreter";
39    private const string EstimationLimitsParameterName = "EstimationLimits";
40    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
41    #region parameter properties
42    public ILookupParameter<ITimeSeriesPrognosisProblemData> ProblemDataParameter {
43      get { return (ILookupParameter<ITimeSeriesPrognosisProblemData>)Parameters[ProblemDataParameterName]; }
44    }
45    public ILookupParameter<ISymbolicTimeSeriesPrognosisInterpreter> SymbolicTimeSeriesPrognosisInterpreterParameter {
46      get { return (ILookupParameter<ISymbolicTimeSeriesPrognosisInterpreter>)Parameters[SymbolicTimeSeriesPrognosisInterpreterParameterName]; }
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 SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
64    private SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
65    public SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer()
66      : base() {
67      Parameters.Add(new LookupParameter<ITimeSeriesPrognosisProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
68      Parameters.Add(new LookupParameter<ISymbolicTimeSeriesPrognosisInterpreter>(SymbolicTimeSeriesPrognosisInterpreterParameterName, "The symbolic time series prognosis 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    public override IDeepCloneable Clone(Cloner cloner) {
73      return new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
74    }
75
76    protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
77      var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue);
78      if (ApplyLinearScaling.Value)
79        SymbolicTimeSeriesPrognosisModel.Scale(model, ProblemDataParameter.ActualValue);
80      return new SymbolicTimeSeriesPrognosisSolution(model, (ITimeSeriesPrognosisProblemData)ProblemDataParameter.ActualValue.Clone());
81    }
82  }
83}
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