#region License Information
/* HeuristicLab
* Copyright (C) 2002-2012 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Represents a Gaussian process model.
///
[StorableClass]
[Item("GaussianProcessDiscriminantFunctionClassificationSolution",
"Represents a Gaussian process discriminant function classification solution.")]
public sealed class GaussianProcessDiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolution {
[StorableConstructor]
private GaussianProcessDiscriminantFunctionClassificationSolution(bool deserializing)
: base(deserializing) {
}
private GaussianProcessDiscriminantFunctionClassificationSolution(
GaussianProcessDiscriminantFunctionClassificationSolution original, Cloner cloner)
: base(original, cloner) {
}
public GaussianProcessDiscriminantFunctionClassificationSolution(GaussianProcessDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
: base(model, problemData) {
RecalculateResults();
}
public override IDeepCloneable Clone(Cloner cloner) {
return new GaussianProcessDiscriminantFunctionClassificationSolution(this, cloner);
}
protected override void RecalculateResults() {
CalculateResults();
CalculateRegressionResults();
}
}
}