#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 System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HEAL.Fossil; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [StorableType("927C21BD-8913-406F-ADEA-DA4FED3FE4A2")] [Item("SymbolicRegressionSolutionImpactValuesCalculator", "Calculate symbolic expression tree node impact values for regression problems.")] public class SymbolicRegressionSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator { public SymbolicRegressionSolutionImpactValuesCalculator() { } protected SymbolicRegressionSolutionImpactValuesCalculator(SymbolicRegressionSolutionImpactValuesCalculator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSolutionImpactValuesCalculator(this, cloner); } [StorableConstructor] protected SymbolicRegressionSolutionImpactValuesCalculator(StorableConstructorFlag _) : base(_) { } protected override double CalculateQualityForImpacts(ISymbolicDataAnalysisModel model, IDataAnalysisProblemData problemData, IEnumerable rows) { var regressionModel = (ISymbolicRegressionModel)model; var regressionProblemData = (IRegressionProblemData)problemData; var estimatedValues = regressionModel.GetEstimatedValues(problemData.Dataset, rows); // also bounds the values var targetValues = problemData.Dataset.GetDoubleValues(regressionProblemData.TargetVariable, rows); OnlineCalculatorError errorState; var r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState); var quality = r * r; if (errorState != OnlineCalculatorError.None) return double.NaN; return quality; } } }