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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/PolysplineKernel.cs @ 15568

Last change on this file since 15568 was 15164, checked in by bwerth, 7 years ago

#2699 KRRModel: made helper functions static; made contets of "allowedInputVariables" immutable; made constructor private and added public "Create"-method that does most of the learning now;

Kernels: fixed inconsitency in error messages

File size: 3.1 KB
RevLine 
[14386]1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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
22using System;
23using HeuristicLab.Common;
[14891]24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Parameters;
[14386]27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
[14936]29namespace HeuristicLab.Algorithms.DataAnalysis {
[14386]30  [StorableClass]
[14887]31  // conditionally positive definite. (need to add polynomials) see http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf
[14891]32  [Item("PolysplineKernel", "A kernel function that uses the polyharmonic function (||x-c||/Beta)^Degree as given in http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf with beta as a scaling parameters.")]
[14872]33  public class PolysplineKernel : KernelBase {
[14386]34
[14891]35    private const string DegreeParameterName = "Degree";
[15156]36
[14892]37    public IFixedValueParameter<DoubleValue> DegreeParameter {
[15157]38      get { return (IFixedValueParameter<DoubleValue>)Parameters[DegreeParameterName]; }
[14891]39    }
[15156]40
[14892]41    public DoubleValue Degree {
[14891]42      get { return DegreeParameter.Value; }
43    }
44
[14386]45    [StorableConstructor]
46    protected PolysplineKernel(bool deserializing) : base(deserializing) { }
[15156]47
[14891]48    protected PolysplineKernel(PolysplineKernel original, Cloner cloner) : base(original, cloner) { }
[15156]49
[14386]50    public PolysplineKernel() {
[14891]51      Parameters.Add(new FixedValueParameter<DoubleValue>(DegreeParameterName, "The degree of the kernel. Needs to be greater than zero.", new DoubleValue(1.0)));
[14386]52    }
[15156]53
[14386]54    public override IDeepCloneable Clone(Cloner cloner) {
[14872]55      return new PolysplineKernel(this, cloner);
[14386]56    }
57
58    protected override double Get(double norm) {
[15164]59      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null");
[14891]60      var beta = Beta.Value;
61      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
62      var d = norm / beta;
63      return Math.Pow(d, Degree.Value);
[14386]64    }
65
[14891]66    //-degree/beta * (norm/beta)^degree
[14386]67    protected override double GetGradient(double norm) {
[15158]68      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null");
[14891]69      var beta = Beta.Value;
70      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
71      var d = norm / beta;
72      return -Degree.Value / beta * Math.Pow(d, Degree.Value);
[14386]73    }
74  }
75}
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