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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSum.cs @ 11170

Last change on this file since 11170 was 11170, checked in by ascheibe, 10 years ago

#2115 updated copyright year in stable branch

File size: 3.6 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2014 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 System.Linq.Expressions;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  [StorableClass]
32  [Item(Name = "CovarianceSum",
33    Description = "Sum covariance function for Gaussian processes.")]
34  public sealed class CovarianceSum : Item, ICovarianceFunction {
35    [Storable]
36    private ItemList<ICovarianceFunction> terms;
37
38    [Storable]
39    private int numberOfVariables;
40    public ItemList<ICovarianceFunction> Terms {
41      get { return terms; }
42    }
43
44    [StorableConstructor]
45    private CovarianceSum(bool deserializing)
46      : base(deserializing) {
47    }
48
49    private CovarianceSum(CovarianceSum original, Cloner cloner)
50      : base(original, cloner) {
51      this.terms = cloner.Clone(original.terms);
52      this.numberOfVariables = original.numberOfVariables;
53    }
54
55    public CovarianceSum()
56      : base() {
57      this.terms = new ItemList<ICovarianceFunction>();
58    }
59
60    public override IDeepCloneable Clone(Cloner cloner) {
61      return new CovarianceSum(this, cloner);
62    }
63
64    public int GetNumberOfParameters(int numberOfVariables) {
65      this.numberOfVariables = numberOfVariables;
66      return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
67    }
68
69    public void SetParameter(double[] p) {
70      int offset = 0;
71      foreach (var t in terms) {
72        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
73        t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
74        offset += numberOfParameters;
75      }
76    }
77
78    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
79      if (terms.Count == 0) throw new ArgumentException("at least one term is necessary for the product covariance function.");
80      var functions = new List<ParameterizedCovarianceFunction>();
81      foreach (var t in terms) {
82        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
83        functions.Add(t.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
84        p = p.Skip(numberOfParameters).ToArray();
85      }
86
87      var sum = new ParameterizedCovarianceFunction();
88      sum.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Sum();
89      sum.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Sum();
90      sum.CovarianceGradient = (x, i, j) => functions.Select(e => e.CovarianceGradient(x, i, j)).Aggregate(Enumerable.Concat);
91      return sum;
92    }
93  }
94}
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