| 1 | #region License Information
|
|---|
| 2 | /* HeuristicLab
|
|---|
| 3 | * Copyright (C) 2002-2018 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 |
|
|---|
| 22 | using HeuristicLab.Common;
|
|---|
| 23 | using HeuristicLab.Core;
|
|---|
| 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
|---|
| 25 | using HeuristicLab.Parameters;
|
|---|
| 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
|---|
| 27 |
|
|---|
| 28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
|
|---|
| 29 | /// <summary>
|
|---|
| 30 | /// An operator that analyzes the training best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.
|
|---|
| 31 | /// </summary>
|
|---|
| 32 | [Item("SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic time-series prognosis solution for single objective symbolic time-series prognosis problems.")]
|
|---|
| 33 | [StorableClass]
|
|---|
| 34 | public sealed class SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicTimeSeriesPrognosisSolution>,
|
|---|
| 35 | ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
|
|---|
| 36 | private const string ProblemDataParameterName = "ProblemData";
|
|---|
| 37 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
|
|---|
| 38 | private const string EstimationLimitsParameterName = "EstimationLimits";
|
|---|
| 39 | #region parameter properties
|
|---|
| 40 | public ILookupParameter<ITimeSeriesPrognosisProblemData> ProblemDataParameter {
|
|---|
| 41 | get { return (ILookupParameter<ITimeSeriesPrognosisProblemData>)Parameters[ProblemDataParameterName]; }
|
|---|
| 42 | }
|
|---|
| 43 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
|
|---|
| 44 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
|
|---|
| 45 | }
|
|---|
| 46 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
|
|---|
| 47 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
|
|---|
| 48 | }
|
|---|
| 49 | #endregion
|
|---|
| 50 |
|
|---|
| 51 |
|
|---|
| 52 | [StorableConstructor]
|
|---|
| 53 | private SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
|
|---|
| 54 | private SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
|
|---|
| 55 | public SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer()
|
|---|
| 56 | : base() {
|
|---|
| 57 | Parameters.Add(new LookupParameter<ITimeSeriesPrognosisProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
|
|---|
| 58 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic time series prognosis interpreter for the symbolic expression tree."));
|
|---|
| 59 | Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
|
|---|
| 60 | }
|
|---|
| 61 | public override IDeepCloneable Clone(Cloner cloner) {
|
|---|
| 62 | return new SymbolicTimeSeriesPrognosisSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
|
|---|
| 63 | }
|
|---|
| 64 |
|
|---|
| 65 | protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
|
|---|
| 66 | var model = new SymbolicTimeSeriesPrognosisModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
|
|---|
| 67 | if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
|
|---|
| 68 | return new SymbolicTimeSeriesPrognosisSolution(model, (ITimeSeriesPrognosisProblemData)ProblemDataParameter.ActualValue.Clone());
|
|---|
| 69 | }
|
|---|
| 70 | }
|
|---|
| 71 | }
|
|---|
