#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.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { /// /// Represents a symbolic regression model /// [StorableClass] [Item(Name = "Symbolic Regression Model", Description = "Represents a symbolic regression model.")] public class SymbolicRegressionModel : SymbolicDataAnalysisModel, ISymbolicRegressionModel { [StorableConstructor] protected SymbolicRegressionModel(bool deserializing) : base(deserializing) { } protected SymbolicRegressionModel(SymbolicRegressionModel original, Cloner cloner) : base(original, cloner) { } public SymbolicRegressionModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) : base(tree, interpreter, lowerEstimationLimit, upperEstimationLimit) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionModel(this, cloner); } public IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows) .LimitToRange(LowerEstimationLimit, UpperEstimationLimit); } public ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { return new SymbolicRegressionSolution(this, new RegressionProblemData(problemData)); } IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) { return CreateRegressionSolution(problemData); } public void Scale(IRegressionProblemData problemData) { Scale(problemData, problemData.TargetVariable); } } }