1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System;


23 


24  using HeuristicLab.Common;


25  using HeuristicLab.Core;


26 


27  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


28 


29  namespace HeuristicLab.Algorithms.DataAnalysis {


30  [StorableClass]


31  [Item("GaussianKernel", "A kernel function that uses Gaussian function exp(n²/beta²). As described in http://crsouza.com/2010/03/17/kernelfunctionsformachinelearningapplications/")]


32  public class GaussianKernel : KernelBase {


33  [StorableConstructor]


34  protected GaussianKernel(bool deserializing) : base(deserializing) { }


35 


36  protected GaussianKernel(GaussianKernel original, Cloner cloner) : base(original, cloner) { }


37 


38  public GaussianKernel() {


39  }


40 


41  public override IDeepCloneable Clone(Cloner cloner) {


42  return new GaussianKernel(this, cloner);


43  }


44 


45  protected override double Get(double norm) {


46  if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance while Beta is null");


47  var beta = Beta.Value;


48  if (Math.Abs(beta) < double.Epsilon) return double.NaN;


49  var d = norm / beta;


50  return Math.Exp(d * d);


51  }


52 


53  //2 * n²/b²* 1/b * exp(n²/b²)


54  protected override double GetGradient(double norm) {


55  if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null");


56  var beta = Beta.Value;


57  if (Math.Abs(beta) < double.Epsilon) return double.NaN;


58  var d = norm / beta;


59  return 2 * d * d / beta * Math.Exp(d * d);


60  }


61  }


62  }

