source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinear.cs @ 13784

Last change on this file since 13784 was 13784, checked in by pfleck, 5 years ago

#2591 Made the creation of a GaussianProcessModel faster by avoiding additional iterators during calculation of the hyperparameter gradients.
The gradients of the hyperparameters are now calculated in one sweep and returned as IList, instead of returning an iterator (with yield return).
This avoids a large amount of Move-calls of the iterator, especially for covariance functions with a lot of hyperparameters.
Besides, the signature of the CovarianceGradientFunctionDelegate is changed, to return an IList instead of an IEnumerable to avoid unnececary ToList or ToArray calls.

File size: 2.4 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 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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