#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
///
/// Represents a symbolic time-series prognosis solution (model + data) and attributes of the solution like accuracy and complexity
///
[StorableClass]
[Item(Name = "SymbolicTimeSeriesPrognosisSolution", Description = "Represents a symbolic time-series prognosis solution (model + data) and attributes of the solution like accuracy and complexity.")]
public sealed class SymbolicTimeSeriesPrognosisSolution : TimeSeriesPrognosisSolution, ISymbolicTimeSeriesPrognosisSolution {
private const string ModelLengthResultName = "Model Length";
private const string ModelDepthResultName = "Model Depth";
public new ISymbolicTimeSeriesPrognosisModel Model {
get { return (ISymbolicTimeSeriesPrognosisModel)base.Model; }
set { base.Model = value; }
}
ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
get { return (ISymbolicDataAnalysisModel)base.Model; }
}
public int ModelLength {
get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
}
public int ModelDepth {
get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
}
[StorableConstructor]
private SymbolicTimeSeriesPrognosisSolution(bool deserializing) : base(deserializing) { }
private SymbolicTimeSeriesPrognosisSolution(SymbolicTimeSeriesPrognosisSolution original, Cloner cloner)
: base(original, cloner) {
}
public SymbolicTimeSeriesPrognosisSolution(ISymbolicTimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData)
: base(model, problemData) {
Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
CalculateResults();
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicTimeSeriesPrognosisSolution(this, cloner);
}
protected override void RecalculateResults() {
base.RecalculateResults();
CalculateResults();
}
private void CalculateResults() {
ModelLength = Model.SymbolicExpressionTree.Length;
ModelDepth = Model.SymbolicExpressionTree.Depth;
}
}
}