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source: branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSum.cs @ 17226

Last change on this file since 17226 was 17226, checked in by mkommend, 5 years ago

#2521: Merged trunk changes into problem refactoring branch.

File size: 3.5 KB
Line 
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
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HEAL.Attic;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [StorableType("8F1A684A-98BE-429A-BDA2-E1FB7DDF09F0")]
31  [Item(Name = "CovarianceSum",
32    Description = "Sum covariance function for Gaussian processes.")]
33  public sealed class CovarianceSum : Item, ICovarianceFunction {
34    [Storable]
35    private ItemList<ICovarianceFunction> terms;
36
37    [Storable]
38    private int numberOfVariables;
39    public ItemList<ICovarianceFunction> Terms {
40      get { return terms; }
41    }
42
43    [StorableConstructor]
44    private CovarianceSum(StorableConstructorFlag _) : base(_) {
45    }
46
47    private CovarianceSum(CovarianceSum original, Cloner cloner)
48      : base(original, cloner) {
49      this.terms = cloner.Clone(original.terms);
50      this.numberOfVariables = original.numberOfVariables;
51    }
52
53    public CovarianceSum()
54      : base() {
55      this.terms = new ItemList<ICovarianceFunction>();
56    }
57
58    public override IDeepCloneable Clone(Cloner cloner) {
59      return new CovarianceSum(this, cloner);
60    }
61
62    public int GetNumberOfParameters(int numberOfVariables) {
63      this.numberOfVariables = numberOfVariables;
64      return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
65    }
66
67    public void SetParameter(double[] p) {
68      int offset = 0;
69      foreach (var t in terms) {
70        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
71        t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
72        offset += numberOfParameters;
73      }
74    }
75
76    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
77      if (terms.Count == 0) throw new ArgumentException("at least one term is necessary for the product covariance function.");
78      var functions = new List<ParameterizedCovarianceFunction>();
79      foreach (var t in terms) {
80        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
81        functions.Add(t.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
82        p = p.Skip(numberOfParameters).ToArray();
83      }
84
85      var sum = new ParameterizedCovarianceFunction();
86      sum.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Sum();
87      sum.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Sum();
88      sum.CovarianceGradient = (x, i, j) => {
89        var g = new List<double>();
90        foreach (var e in functions)
91          g.AddRange(e.CovarianceGradient(x, i, j));
92        return g;
93      };
94      return sum;
95    }
96  }
97}
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