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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSEard.cs @ 8491

Last change on this file since 8491 was 8491, checked in by gkronber, 12 years ago

#1902 fixed test cases, improved performance

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 = "CovarianceSEard", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]
32  public class CovarianceSEard : Item, ICovarianceFunction {
33    [Storable]
34    private double sf2;
35    public double Scale { get { return sf2; } }
36
37    [Storable]
38    private double[] inverseLength;
39    public double[] Length {
40      get {
41        if (inverseLength == null) return new double[0];
42        var copy = new double[inverseLength.Length];
43        Array.Copy(inverseLength, copy, copy.Length);
44        return copy;
45      }
46    }
47
48    public int GetNumberOfParameters(int numberOfVariables) {
49      return numberOfVariables + 1;
50    }
51    [StorableConstructor]
52    protected CovarianceSEard(bool deserializing) : base(deserializing) { }
53    protected CovarianceSEard(CovarianceSEard original, Cloner cloner)
54      : base(original, cloner) {
55      if (original.inverseLength != null) {
56        this.inverseLength = new double[original.inverseLength.Length];
57        Array.Copy(original.inverseLength, this.inverseLength, inverseLength.Length);
58      }
59      this.sf2 = original.sf2;
60    }
61    public CovarianceSEard()
62      : base() {
63    }
64
65    public override IDeepCloneable Clone(Cloner cloner) {
66      return new CovarianceSEard(this, cloner);
67    }
68
69    public void SetParameter(double[] hyp) {
70      this.inverseLength = hyp.Take(hyp.Length - 1).Select(p => 1.0 / Math.Exp(p)).ToArray();
71      this.sf2 = Math.Exp(2 * hyp[hyp.Length - 1]);
72    }
73
74    public double GetCovariance(double[,] x, int i, int j) {
75      double d = i == j
76                   ? 0.0
77                   : Util.SqrDist(x, i, j, inverseLength);
78      return sf2 * Math.Exp(-d / 2.0);
79    }
80
81    public IEnumerable<double> GetGradient(double[,] x, int i, int j) {
82      double d = i == j
83                   ? 0.0
84                   : Util.SqrDist(x, i, j, inverseLength);
85
86      for (int ii = 0; ii < inverseLength.Length; ii++) {
87        double sqrDist = Util.SqrDist(x[i, ii] * inverseLength[ii], x[j, ii] * inverseLength[ii]);
88        yield return sf2 * Math.Exp(-d / 2.0) * sqrDist;
89      }
90      yield return 2.0 * sf2 * Math.Exp(-d / 2.0);
91    }
92
93    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j) {
94      double d = Util.SqrDist(x, i, xt, j, inverseLength);
95      return sf2 * Math.Exp(-d / 2.0);
96    }
97  }
98}
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