#region License Information /* HeuristicLab * Copyright (C) 2002-2018 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; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item("GaussianKernel", "A kernel function that uses Gaussian function exp(-n²/beta²). As described in http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/")] public class GaussianKernel : KernelBase { [StorableConstructor] protected GaussianKernel(bool deserializing) : base(deserializing) { } protected GaussianKernel(GaussianKernel original, Cloner cloner) : base(original, cloner) { } public GaussianKernel() { } public override IDeepCloneable Clone(Cloner cloner) { return new GaussianKernel(this, cloner); } protected override double Get(double norm) { if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance while Beta is null"); var beta = Beta.Value; if (Math.Abs(beta) < double.Epsilon) return double.NaN; var d = norm / beta; return Math.Exp(-d * d); } //2 * n²/b²* 1/b * exp(-n²/b²) protected override double GetGradient(double norm) { if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null"); var beta = Beta.Value; if (Math.Abs(beta) < double.Epsilon) return double.NaN; var d = norm / beta; return 2 * d * d / beta * Math.Exp(-d * d); } } }