source: branches/2839_HiveProjectManagement/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceProduct.cs @ 16057

Last change on this file since 16057 was 16057, checked in by jkarder, 15 months ago

#2839:

File size: 4.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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]
31  [Item(Name = "CovarianceProduct",
32    Description = "Product covariance function for Gaussian processes.")]
33  public sealed class CovarianceProduct : Item, ICovarianceFunction {
34    [Storable]
35    private ItemList<ICovarianceFunction> factors;
36
37    [Storable]
38    private int numberOfVariables;
39    public ItemList<ICovarianceFunction> Factors {
40      get { return factors; }
41    }
42
43    [StorableConstructor]
44    private CovarianceProduct(bool deserializing)
45      : base(deserializing) {
46    }
47
48    private CovarianceProduct(CovarianceProduct original, Cloner cloner)
49      : base(original, cloner) {
50      this.factors = cloner.Clone(original.factors);
51      this.numberOfVariables = original.numberOfVariables;
52    }
53
54    public CovarianceProduct()
55      : base() {
56      this.factors = new ItemList<ICovarianceFunction>();
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new CovarianceProduct(this, cloner);
61    }
62
63    public int GetNumberOfParameters(int numberOfVariables) {
64      this.numberOfVariables = numberOfVariables;
65      return factors.Select(f => f.GetNumberOfParameters(numberOfVariables)).Sum();
66    }
67
68    public void SetParameter(double[] p) {
69      int offset = 0;
70      foreach (var f in factors) {
71        var numberOfParameters = f.GetNumberOfParameters(numberOfVariables);
72        f.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
73        offset += numberOfParameters;
74      }
75    }
76
77    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
78      if (factors.Count == 0) throw new ArgumentException("at least one factor is necessary for the product covariance function.");
79      var functions = new List<ParameterizedCovarianceFunction>();
80      foreach (var f in factors) {
81        int numberOfParameters = f.GetNumberOfParameters(numberOfVariables);
82        functions.Add(f.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
83        p = p.Skip(numberOfParameters).ToArray();
84      }
85
86
87      var product = new ParameterizedCovarianceFunction();
88      product.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Aggregate((a, b) => a * b);
89      product.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Aggregate((a, b) => a * b);
90      product.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, functions);
91      return product;
92    }
93
94    public static IList<double> GetGradient(double[,] x, int i, int j, List<ParameterizedCovarianceFunction> factorFunctions) {
95      var covariances = factorFunctions.Select(f => f.Covariance(x, i, j)).ToArray();
96      var gr = new List<double>();
97      for (int ii = 0; ii < factorFunctions.Count; ii++) {
98        foreach (var g in factorFunctions[ii].CovarianceGradient(x, i, j)) {
99          double res = g;
100          for (int jj = 0; jj < covariances.Length; jj++)
101            if (ii != jj) res *= covariances[jj];
102          gr.Add(res);
103        }
104      }
105      return gr;
106    }
107  }
108}
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