source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanSum.cs @ 8439

Last change on this file since 8439 was 8439, checked in by gkronber, 8 years ago

#1902 implemented unit tests for mean and covariance functions. implemented sum and product mean function. fixed incorrect gradient calculation in covprod

File size: 3.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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
21using System.Linq;
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
25
26namespace HeuristicLab.Algorithms.DataAnalysis {
27  [StorableClass]
28  [Item(Name = "MeanSum", Description = "Sum of mean functions for Gaussian processes.")]
29  public class MeanSum : Item, IMeanFunction {
30    [Storable]
31    private ItemList<IMeanFunction> terms;
32
33    [Storable]
34    private int numberOfVariables;
35    public ItemList<IMeanFunction> Terms {
36      get { return terms; }
37    }
38
39    public int GetNumberOfParameters(int numberOfVariables) {
40      this.numberOfVariables = numberOfVariables;
41      return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
42    }
43    [StorableConstructor]
44    protected MeanSum(bool deserializing) : base(deserializing) { }
45    protected MeanSum(MeanSum original, Cloner cloner)
46      : base(original, cloner) {
47      this.terms = cloner.Clone(original.terms);
48      this.numberOfVariables = original.numberOfVariables;
49    }
50    public MeanSum() {
51      this.terms = new ItemList<IMeanFunction>();
52    }
53
54    public void SetParameter(double[] hyp) {
55      int offset = 0;
56      foreach (var t in terms) {
57        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
58        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
59        offset += numberOfParameters;
60      }
61    }
62
63    public void SetData(double[,] x) {
64      foreach (var t in terms) t.SetData(x);
65    }
66
67    public double[] GetMean(double[,] x) {
68      var res = terms.First().GetMean(x);
69      foreach (var t in terms.Skip(1)) {
70        var a = t.GetMean(x);
71        for (int i = 0; i < res.Length; i++) res[i] += a[i];
72      }
73      return res;
74    }
75
76    public double[] GetGradients(int k, double[,] x) {
77      int i = 0;
78      while (k >= terms[i].GetNumberOfParameters(numberOfVariables)) {
79        k -= terms[i].GetNumberOfParameters(numberOfVariables);
80        i++;
81      }
82      return terms[i].GetGradients(k, x);
83    }
84
85    public override IDeepCloneable Clone(Cloner cloner) {
86      return new MeanSum(this, cloner);
87    }
88  }
89}
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