#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 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.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
///
/// An operator that analyzes the training best symbolic regression solution for multi objective symbolic regression problems.
///
[Item("SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic regression solution for multi objective symbolic regression problems.")]
[StorableClass]
public sealed class SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer,
ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
private const string ProblemDataParameterName = "ProblemData";
private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
private const string EstimationLimitsParameterName = "EstimationLimits";
#region parameter properties
public ILookupParameter ProblemDataParameter {
get { return (ILookupParameter)Parameters[ProblemDataParameterName]; }
}
public ILookupParameter SymbolicDataAnalysisTreeInterpreterParameter {
get { return (ILookupParameter)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
}
public IValueLookupParameter EstimationLimitsParameter {
get { return (IValueLookupParameter)Parameters[EstimationLimitsParameterName]; }
}
#endregion
[StorableConstructor]
private SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
private SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer(SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
public SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer()
: base() {
Parameters.Add(new LookupParameter(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
Parameters.Add(new LookupParameter(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
Parameters.Add(new ValueLookupParameter(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer(this, cloner);
}
protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
var model = new SymbolicRegressionModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
}
}
}