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source: branches/GP-MoveOperators/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceConst.cs @ 9579

Last change on this file since 9579 was 8612, checked in by gkronber, 12 years ago

#1902 implemented all mean and covariance functions with parameters as ParameterizedNamedItems

File size: 3.4 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 System.Collections.Generic;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [StorableClass]
31  [Item(Name = "CovarianceConst",
32    Description = "Constant covariance function for Gaussian processes.")]
33  public sealed class CovarianceConst : ParameterizedNamedItem, ICovarianceFunction {
34
35    [Storable]
36    private double scale;
37    [Storable]
38    private readonly HyperParameter<DoubleValue> scaleParameter;
39    public IValueParameter<DoubleValue> ScaleParameter {
40      get { return scaleParameter; }
41    }
42
43    [StorableConstructor]
44    private CovarianceConst(bool deserializing)
45      : base(deserializing) {
46    }
47
48    private CovarianceConst(CovarianceConst original, Cloner cloner)
49      : base(original, cloner) {
50      this.scaleParameter = cloner.Clone(original.scaleParameter);
51      this.scale = original.scale;
52
53      RegisterEvents();
54    }
55
56    public CovarianceConst()
57      : base() {
58      Name = ItemName;
59      Description = ItemDescription;
60
61      scaleParameter = new HyperParameter<DoubleValue>("Scale", "The scale of the constant covariance function.");
62      Parameters.Add(scaleParameter);
63      RegisterEvents();
64    }
65
66    [StorableHook(HookType.AfterDeserialization)]
67    private void AfterDeserialization() {
68      RegisterEvents();
69    }
70
71    // caching
72    private void RegisterEvents() {
73      Util.AttachValueChangeHandler<DoubleValue, double>(scaleParameter, () => { scale = scaleParameter.Value.Value; });
74    }
75
76
77    public override IDeepCloneable Clone(Cloner cloner) {
78      return new CovarianceConst(this, cloner);
79    }
80
81    public int GetNumberOfParameters(int numberOfVariables) {
82      return scaleParameter.Fixed ? 0 : 1;
83    }
84
85    public void SetParameter(double[] hyp) {
86      if (!scaleParameter.Fixed && hyp.Length == 1) {
87        scaleParameter.SetValue(new DoubleValue(Math.Exp(2 * hyp[0])));
88      } else {
89        throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceConst", "hyp");
90      }
91    }
92
93    public double GetCovariance(double[,] x, int i, int j) {
94      return scale;
95    }
96
97    public IEnumerable<double> GetGradient(double[,] x, int i, int j) {
98      yield return 2.0 * scale;
99    }
100
101    public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j) {
102      return scale;
103    }
104  }
105}
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