#region License Information
/* HeuristicLab
* Copyright (C) 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.Parameters;
using HEAL.Attic;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[Item("SymbolicRegressionPruningAnalyzer", "An analyzer that prunes introns from the population.")]
[StorableType("F1180389-1393-4102-9EEC-E4F183A017F2")]
public sealed class SymbolicRegressionPruningAnalyzer : SymbolicDataAnalysisSingleObjectivePruningAnalyzer {
private const string PruningOperatorParameterName = "PruningOperator";
public IValueParameter PruningOperatorParameter {
get { return (IValueParameter)Parameters[PruningOperatorParameterName]; }
}
protected override SymbolicDataAnalysisExpressionPruningOperator PruningOperator {
get { return PruningOperatorParameter.Value; }
}
private SymbolicRegressionPruningAnalyzer(SymbolicRegressionPruningAnalyzer original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionPruningAnalyzer(this, cloner); }
[StorableConstructor]
private SymbolicRegressionPruningAnalyzer(StorableConstructorFlag _) : base(_) { }
public SymbolicRegressionPruningAnalyzer() {
Parameters.Add(new ValueParameter(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicRegressionPruningOperator(new SymbolicRegressionSolutionImpactValuesCalculator())));
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
// BackwardsCompatibility3.3
#region Backwards compatible code, remove with 3.4
if (Parameters.ContainsKey(PruningOperatorParameterName)) {
var oldParam = Parameters[PruningOperatorParameterName] as ValueParameter;
if (oldParam != null) {
Parameters.Remove(oldParam);
Parameters.Add(new ValueParameter(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicRegressionPruningOperator(new SymbolicRegressionSolutionImpactValuesCalculator())));
}
} else {
// not yet contained
Parameters.Add(new ValueParameter(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicRegressionPruningOperator(new SymbolicRegressionSolutionImpactValuesCalculator())));
}
if (Parameters.ContainsKey("PruneOnlyZeroImpactNodes")) {
PruningOperator.PruneOnlyZeroImpactNodes = ((IFixedValueParameter)Parameters["PruneOnlyZeroImpactNodes"]).Value.Value;
Parameters.Remove(Parameters["PruneOnlyZeroImpactNodes"]);
}
if (Parameters.ContainsKey("ImpactThreshold")) {
PruningOperator.NodeImpactThreshold = ((IFixedValueParameter)Parameters["ImpactThreshold"]).Value.Value;
Parameters.Remove(Parameters["ImpactThreshold"]);
}
if (Parameters.ContainsKey("ImpactValuesCalculator")) {
PruningOperator.ImpactValuesCalculator = ((ValueParameter)Parameters["ImpactValuesCalculator"]).Value;
Parameters.Remove(Parameters["ImpactValuesCalculator"]);
}
#endregion
}
}
}