#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(); } } }