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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanSum.cs @ 8612

Last change on this file since 8612 was 8612, checked in by gkronber, 12 years ago

#1902 implemented all mean and covariance functions with parameters as ParameterizedNamedItems

File size: 2.9 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 sealed 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    [StorableConstructor]
40    private MeanSum(bool deserializing) : base(deserializing) { }
41    private MeanSum(MeanSum original, Cloner cloner)
42      : base(original, cloner) {
43      this.terms = cloner.Clone(original.terms);
44      this.numberOfVariables = original.numberOfVariables;
45    }
46    public MeanSum() {
47      this.terms = new ItemList<IMeanFunction>();
48    }
49
50    public override IDeepCloneable Clone(Cloner cloner) {
51      return new MeanSum(this, cloner);
52    }
53
54    public int GetNumberOfParameters(int numberOfVariables) {
55      this.numberOfVariables = numberOfVariables;
56      return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
57    }
58
59    public void SetParameter(double[] hyp) {
60      int offset = 0;
61      foreach (var t in terms) {
62        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
63        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
64        offset += numberOfParameters;
65      }
66    }
67
68    public double[] GetMean(double[,] x) {
69      var res = terms.First().GetMean(x);
70      foreach (var t in terms.Skip(1)) {
71        var a = t.GetMean(x);
72        for (int i = 0; i < res.Length; i++) res[i] += a[i];
73      }
74      return res;
75    }
76
77    public double[] GetGradients(int k, double[,] x) {
78      int i = 0;
79      while (k >= terms[i].GetNumberOfParameters(numberOfVariables)) {
80        k -= terms[i].GetNumberOfParameters(numberOfVariables);
81        i++;
82      }
83      return terms[i].GetGradients(k, x);
84    }
85  }
86}
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