source: branches/PersistenceOverhaul/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinear.cs @ 13368

Last change on this file since 13368 was 13368, checked in by ascheibe, 4 years ago

#2520 added guids to storable classes

File size: 2.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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("DA2F50B2-CCBD-4358-B318-6AF3604370ED")]
31  [Item(Name = "CovarianceLinear", Description = "Linear covariance function for Gaussian processes.")]
32  public sealed class CovarianceLinear : Item, ICovarianceFunction {
33    [StorableConstructor]
34    private CovarianceLinear(bool deserializing) : base(deserializing) { }
35    private CovarianceLinear(CovarianceLinear original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public CovarianceLinear()
39      : base() {
40    }
41
42    public override IDeepCloneable Clone(Cloner cloner) {
43      return new CovarianceLinear(this, cloner);
44    }
45
46    public int GetNumberOfParameters(int numberOfVariables) {
47      return 0;
48    }
49
50    public void SetParameter(double[] p) {
51      if (p.Length > 0) throw new ArgumentException("No parameters are allowed for the linear covariance function.");
52    }
53
54    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
55      if (p.Length > 0) throw new ArgumentException("No parameters are allowed for the linear covariance function.");
56      // create functions
57      var cov = new ParameterizedCovarianceFunction();
58      cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, 1, columnIndices);
59      cov.CrossCovariance = (x, xt, i, j) =>  Util.ScalarProd(x, i, xt, j, 1.0 , columnIndices);
60      cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();
61      return cov;
62    }
63  }
64}
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