#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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [StorableClass] [Item("SymbolicRegressionPruningOperator", "An operator which prunes symbolic regression trees.")] public class SymbolicRegressionPruningOperator : SymbolicDataAnalysisExpressionPruningOperator { private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator"; protected SymbolicRegressionPruningOperator(SymbolicRegressionPruningOperator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionPruningOperator(this, cloner); } [StorableConstructor] protected SymbolicRegressionPruningOperator(bool deserializing) : base(deserializing) { } public SymbolicRegressionPruningOperator() { var impactValuesCalculator = new SymbolicRegressionSolutionImpactValuesCalculator(); Parameters.Add(new ValueParameter(ImpactValuesCalculatorParameterName, "The impact values calculator to be used for figuring out the node impacts.", impactValuesCalculator)); } protected override ISymbolicDataAnalysisModel CreateModel() { return new SymbolicRegressionModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper); } protected override double Evaluate(IDataAnalysisModel model) { var regressionModel = (IRegressionModel)model; var regressionProblemData = (IRegressionProblemData)ProblemData; var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size); var estimatedValues = regressionModel.GetEstimatedValues(ProblemData.Dataset, trainingIndices); // also bounds the values var targetValues = ProblemData.Dataset.GetDoubleValues(regressionProblemData.TargetVariable, trainingIndices); OnlineCalculatorError errorState; var quality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState); if (errorState != OnlineCalculatorError.None) return double.NaN; return quality; } } }