source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/CicularKernel.cs @ 17209

Last change on this file since 17209 was 17209, checked in by gkronber, 4 months ago

#2994: merged r17132:17198 from trunk to branch

File size: 2.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HEAL.Attic;
26
27namespace HeuristicLab.Algorithms.DataAnalysis {
28  [StorableType("BB5992FF-783A-490E-91FE-C0782BD1CBB9")]
29  [Item("CircularKernel", "A circular kernel function 2*pi*(acos(-d)-d*(1-d²)^(0.5)) where n = ||x-c|| and d = n/beta \n  As described in http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/")]
30  public class CircularKernel : KernelBase {
31    [StorableConstructor]
32    protected CircularKernel(StorableConstructorFlag _) : base(_) { }
33
34    protected CircularKernel(CircularKernel original, Cloner cloner) : base(original, cloner) { }
35
36    public CircularKernel() { }
37
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new CircularKernel(this, cloner);
40    }
41
42    protected override double Get(double norm) {
43      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance while Beta is null");
44      var beta = Beta.Value;
45      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
46      if (norm >= beta) return 0;
47      var d = norm / beta;
48      return 2 * Math.PI * (Math.Acos(-d) - d * Math.Sqrt(1 - d * d));
49    }
50
51    // 4*pi*n^3 / (beta^4 * sqrt(1-n^2/beta^2)
52    protected override double GetGradient(double norm) {
53      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null");
54      var beta = Beta.Value;
55      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
56      if (beta < norm) return 0;
57      var d = norm / beta;
58      return -4 * Math.PI * d * d * d / beta * Math.Sqrt(1 - d * d);
59    }
60  }
61}
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