#region License Information /* HeuristicLab * Copyright (C) 2002-2014 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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis { [StorableClass] [Item("Constant Regression Model", "A model that always returns the same constant value regardless of the presented input data.")] public class ConstantRegressionModel : NamedItem, IRegressionModel { [Storable] protected double constant; public double Constant { get { return constant; } } [StorableConstructor] protected ConstantRegressionModel(bool deserializing) : base(deserializing) { } protected ConstantRegressionModel(ConstantRegressionModel original, Cloner cloner) : base(original, cloner) { this.constant = original.constant; } public override IDeepCloneable Clone(Cloner cloner) { return new ConstantRegressionModel(this, cloner); } public ConstantRegressionModel(double constant) : base() { this.name = ItemName; this.description = ItemDescription; this.constant = constant; } public IEnumerable GetEstimatedValues(Dataset dataset, IEnumerable rows) { return rows.Select(row => Constant); } public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { return new ConstantRegressionSolution(this, new RegressionProblemData(problemData)); } } }