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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/ThinPlatePolysplineKernel.cs @ 15298

Last change on this file since 15298 was 15249, checked in by gkronber, 7 years ago

#2699,#2700
merged r14862, r14863, r14911, r14936, r15156, r15157, r15158, r15164, r15169, r15207:15209, r15225, r15227, r15234, r15248 from trunk to stable

File size: 3.3 KB
Line 
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;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Parameters;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [StorableClass]
31  // conditionally positive definite. (need to add polynomials) see http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf
32  [Item("ThinPlatePolysplineKernel", "A kernel function that uses the ThinPlatePolyspline function (||x-c||/Beta)^(Degree)*log(||x-c||/Beta) as described in \"Thin-Plate Spline Radial Basis Function Scheme for Advection-Diffusion Problems\" with beta as a scaling parameter.")]
33  public class ThinPlatePolysplineKernel : KernelBase {
34
35    private const string DegreeParameterName = "Degree";
36
37    public IFixedValueParameter<DoubleValue> DegreeParameter {
38      get { return (IFixedValueParameter<DoubleValue>)Parameters[DegreeParameterName]; }
39    }
40    public DoubleValue Degree {
41      get { return DegreeParameter.Value; }
42    }
43
44    [StorableConstructor]
45    protected ThinPlatePolysplineKernel(bool deserializing) : base(deserializing) { }
46
47    protected ThinPlatePolysplineKernel(ThinPlatePolysplineKernel original, Cloner cloner) : base(original, cloner) { }
48
49    public ThinPlatePolysplineKernel() {
50      Parameters.Add(new FixedValueParameter<DoubleValue>(DegreeParameterName, "The degree of the kernel. Needs to be greater than zero.", new DoubleValue(2.0)));
51    }
52
53    public override IDeepCloneable Clone(Cloner cloner) {
54      return new ThinPlatePolysplineKernel(this, cloner);
55    }
56
57    protected override double Get(double norm) {
58      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance while Beta is null");
59      var beta = Beta.Value;
60      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
61      var d = norm / beta;
62      if (Math.Abs(d) < double.Epsilon) return 0;
63      return Math.Pow(d, Degree.Value) * Math.Log(d);
64    }
65
66    // (Degree/beta) * (norm/beta)^Degree * log(norm/beta)
67    protected override double GetGradient(double norm) {
68      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null");
69      var beta = Beta.Value;
70      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
71      var d = norm / beta;
72      if (Math.Abs(d) < double.Epsilon) return 0;
73      return Degree.Value / beta * Math.Pow(d, Degree.Value) * Math.Log(d);
74    }
75  }
76}
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