#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 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(IDataset dataset, IEnumerable rows) {
return rows.Select(row => Constant);
}
public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
return new ConstantRegressionSolution(this, new RegressionProblemData(problemData));
}
}
}