source: branches/2839_HiveProjectManagement/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinear.cs @ 16057

Last change on this file since 16057 was 16057, checked in by jkarder, 15 months ago

#2839:

File size: 2.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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 = "CovarianceLinear", Description = "Linear covariance function for Gaussian processes.")]
30  public sealed class CovarianceLinear : Item, ICovarianceFunction {
31    [StorableConstructor]
32    private CovarianceLinear(bool deserializing) : base(deserializing) { }
33    private CovarianceLinear(CovarianceLinear original, Cloner cloner)
34      : base(original, cloner) {
35    }
36    public CovarianceLinear()
37      : base() {
38    }
39
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new CovarianceLinear(this, cloner);
42    }
43
44    public int GetNumberOfParameters(int numberOfVariables) {
45      return 0;
46    }
47
48    public void SetParameter(double[] p) {
49      if (p.Length > 0) throw new ArgumentException("No parameters are allowed for the linear covariance function.");
50    }
51
52    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
53      if (p.Length > 0) throw new ArgumentException("No parameters are allowed for the linear covariance function.");
54      // create functions
55      var cov = new ParameterizedCovarianceFunction();
56      cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, columnIndices, 1.0);
57      cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, columnIndices, 1.0);
58      cov.CovarianceGradient = (x, i, j) => new double[0];
59      return cov;
60    }
61  }
62}
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