[8798] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8798] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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| 25 | using HeuristicLab.Optimization;
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| 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
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| 29 | /// <summary>
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| 30 | /// Represents a symbolic time-series prognosis solution (model + data) and attributes of the solution like accuracy and complexity
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| 31 | /// </summary>
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| 32 | [StorableClass]
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| 33 | [Item(Name = "SymbolicTimeSeriesPrognosisSolution", Description = "Represents a symbolic time-series prognosis solution (model + data) and attributes of the solution like accuracy and complexity.")]
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| 34 | public sealed class SymbolicTimeSeriesPrognosisSolution : TimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSolution {
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| 35 | private const string ModelLengthResultName = "Model Length";
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| 36 | private const string ModelDepthResultName = "Model Depth";
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| 37 |
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| 38 | public new ISymbolicTimeSeriesPrognosisModel Model {
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| 39 | get { return (ISymbolicTimeSeriesPrognosisModel)base.Model; }
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| 40 | set { base.Model = value; }
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| 41 | }
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| 42 | ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
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| 43 | get { return (ISymbolicDataAnalysisModel)base.Model; }
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| 44 | }
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| 45 | public int ModelLength {
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| 46 | get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
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| 47 | private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
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| 48 | }
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| 49 |
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| 50 | public int ModelDepth {
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| 51 | get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
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| 52 | private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
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| 53 | }
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| 54 |
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| 55 | [StorableConstructor]
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| 56 | private SymbolicTimeSeriesPrognosisSolution(bool deserializing) : base(deserializing) { }
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| 57 | private SymbolicTimeSeriesPrognosisSolution(SymbolicTimeSeriesPrognosisSolution original, Cloner cloner)
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| 58 | : base(original, cloner) {
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| 59 | }
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| 60 | public SymbolicTimeSeriesPrognosisSolution(ISymbolicTimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData)
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| 61 | : base(model, problemData) {
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| 62 | Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
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| 63 | Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
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| 64 | CalculateResults();
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| 65 | }
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| 66 |
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| 67 | public override IDeepCloneable Clone(Cloner cloner) {
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| 68 | return new SymbolicTimeSeriesPrognosisSolution(this, cloner);
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| 69 | }
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| 70 |
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| 71 | protected override void RecalculateResults() {
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| 72 | base.RecalculateResults();
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| 73 | CalculateResults();
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| 74 | }
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| 75 | private void CalculateResults() {
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| 76 | ModelLength = Model.SymbolicExpressionTree.Length;
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| 77 | ModelDepth = Model.SymbolicExpressionTree.Depth;
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| 78 | }
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| 79 | }
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| 80 | }
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