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source: branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/MultiquadraticKernel.cs @ 15470

Last change on this file since 15470 was 15158, checked in by bwerth, 7 years ago

#2699 added checks for Beta == null and according exceptions

File size: 2.6 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.Persistence.Default.CompositeSerializers.Storable;
26
27namespace HeuristicLab.Algorithms.DataAnalysis {
28  [StorableClass]
29  // conditionally positive definite. (need to add polynomials) see http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf
30  [Item("MultiquadraticKernel", "A kernel function that uses the multi-quadratic function sqrt(1+||x-c||²/beta²). Similar to http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/ with beta as a scaling factor.")]
31  public class MultiquadraticKernel : KernelBase {
32
33    private const double C = 1.0;
34
35    [StorableConstructor]
36    protected MultiquadraticKernel(bool deserializing) : base(deserializing) { }
37
38    protected MultiquadraticKernel(MultiquadraticKernel original, Cloner cloner) : base(original, cloner) { }
39
40    public MultiquadraticKernel() { }
41
42    public override IDeepCloneable Clone(Cloner cloner) {
43      return new MultiquadraticKernel(this, cloner);
44    }
45
46    protected override double Get(double norm) {
47      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance while Beta is null");
48      var beta = Beta.Value;
49      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
50      var d = norm / beta;
51      return Math.Sqrt(C + d * d);
52    }
53
54    //-n²/(d³*sqrt(C+n²/d²))
55    protected override double GetGradient(double norm) {
56      if (Beta == null) throw new InvalidOperationException("Can not calculate kernel distance gradient while Beta is null");
57      var beta = Beta.Value;
58      if (Math.Abs(beta) < double.Epsilon) return double.NaN;
59      var d = norm / beta;
60      return -d * d / (beta * Math.Sqrt(C + d * d));
61    }
62  }
63}
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