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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceRQiso.cs @ 8484

Last change on this file since 8484 was 8484, checked in by gkronber, 12 years ago

#1902 changed interface for covariance functions to improve readability, fixed several bugs in the covariance functions and in the line chart for Gaussian process models.

File size: 3.5 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 System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [StorableClass]
31  [Item(Name = "CovarianceRQiso",
32    Description = "Isotropic rational quadratic covariance function for Gaussian processes.")]
33  public class CovarianceRQiso : Item, ICovarianceFunction {
34    [Storable]
35    private double sf2;
36    public double Scale { get { return sf2; } }
37    [Storable]
38    private double l;
39    public double Length { get { return l; } }
40    [Storable]
41    private double alpha;
42    public double Shape { get { return alpha; } }
43
44    [StorableConstructor]
45    protected CovarianceRQiso(bool deserializing)
46      : base(deserializing) {
47    }
48
49    protected CovarianceRQiso(CovarianceRQiso original, Cloner cloner)
50      : base(original, cloner) {
51      this.sf2 = original.sf2;
52      this.l = original.l;
53      this.alpha = original.alpha;
54    }
55
56    public CovarianceRQiso()
57      : base() {
58    }
59
60    public override IDeepCloneable Clone(Cloner cloner) {
61      return new CovarianceRQiso(this, cloner);
62    }
63
64    public int GetNumberOfParameters(int numberOfVariables) {
65      return 3;
66    }
67
68    public void SetParameter(double[] hyp) {
69      if (hyp.Length != 3) throw new ArgumentException("CovarianceRQiso has three hyperparameters", "k");
70      this.l = Math.Exp(hyp[0]);
71      this.sf2 = Math.Exp(2 * hyp[1]);
72      this.alpha = Math.Exp(hyp[2]);
73    }
74
75
76    public double GetCovariance(double[,] x, int i, int j) {
77      double lInv = 1.0 / l;
78      double d = i == j
79                   ? 0.0
80                   : Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(x, j).Select(e => e * lInv));
81      return sf2 * Math.Pow(1 + 0.5 * d / alpha, -alpha);
82    }
83
84    public IEnumerable<double> GetGradient(double[,] x, int i, int j) {
85      double lInv = 1.0 / l;
86      double d = i == j
87                   ? 0.0
88                   : Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(x, j).Select(e => e * lInv));
89
90      double b = 1 + 0.5 * d / alpha;
91      yield return sf2 * Math.Pow(b, -alpha - 1) * d;
92      yield return 2 * sf2 * Math.Pow(b, -alpha);
93      yield return sf2 * Math.Pow(b, -alpha) * (0.5 * d / b - alpha * Math.Log(b));
94    }
95
96    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j) {
97      double lInv = 1.0 / l;
98      double d = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
99      return sf2 * Math.Pow(1 + 0.5 * d / alpha, -alpha);
100    }
101  }
102}
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