#region License Information /* HeuristicLab * Copyright (C) 2002-2019 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 } } }