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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceNoise.cs @ 8464

Last change on this file since 8464 was 8464, checked in by gkronber, 12 years ago

#1902 added const and noise covariance functions.

File size: 2.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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  [Item(Name = "CovarianceNoise",
30    Description = "Noise covariance function for Gaussian processes.")]
31  public class CovarianceNoise : Item, ICovarianceFunction {
32    [Storable]
33    private double sf2;
34
35    [StorableConstructor]
36    protected CovarianceNoise(bool deserializing)
37      : base(deserializing) {
38    }
39
40    protected CovarianceNoise(CovarianceNoise original, Cloner cloner)
41      : base(original, cloner) {
42      this.sf2 = original.sf2;
43    }
44
45    public CovarianceNoise()
46      : base() {
47    }
48
49    public override IDeepCloneable Clone(Cloner cloner) {
50      return new CovarianceNoise(this, cloner);
51    }
52
53    public int GetNumberOfParameters(int numberOfVariables) {
54      return 1;
55    }
56
57    public void SetParameter(double[] hyp) {
58      this.sf2 = Math.Min(1E6, Math.Exp(2 * hyp[0])); // upper limit for scale
59    }
60    public void SetData(double[,] x) {
61      // nothing to do
62    }
63
64
65    public void SetData(double[,] x, double[,] xt) {
66      // nothing to do
67    }
68
69    public double GetCovariance(int i, int j) {
70      if (i == j) return sf2;
71      else return 0.0;
72    }
73
74    public double GetGradient(int i, int j, int k) {
75      if (k != 0) throw new ArgumentException("CovarianceConst has only one hyperparameters", "k");
76      if (i == j)
77        return 2 * sf2;
78      else
79        return 0.0;
80    }
81  }
82}
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