1 | #region License Information
|
---|
2 | /* HeuristicLab
|
---|
3 | * Copyright (C) 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 |
|
---|
22 | using System;
|
---|
23 | using HeuristicLab.Common;
|
---|
24 | using HeuristicLab.Core;
|
---|
25 | using HEAL.Attic;
|
---|
26 |
|
---|
27 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
28 | [StorableType("5C4AB3C8-48D5-4B6E-830A-89AE2A363C64")]
|
---|
29 | [Item(Name = "CovarianceLinear", Description = "Linear covariance function for Gaussian processes.")]
|
---|
30 | public sealed class CovarianceLinear : Item, ICovarianceFunction {
|
---|
31 | [StorableConstructor]
|
---|
32 | private CovarianceLinear(StorableConstructorFlag _) : base(_) { }
|
---|
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 | }
|
---|