source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanSum.cs @ 17209

Last change on this file since 17209 was 17209, checked in by gkronber, 6 weeks ago

#2994: merged r17132:17198 from trunk to branch

File size: 3.8 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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HEAL.Attic;
27
28namespace HeuristicLab.Algorithms.DataAnalysis {
29  [StorableType("E8BB7879-43C7-4B9A-B9AC-C9CC482BC0D7")]
30  [Item(Name = "MeanSum", Description = "Sum of mean functions for Gaussian processes.")]
31  public sealed class MeanSum : Item, IMeanFunction {
32    [Storable]
33    private ItemList<IMeanFunction> terms;
34
35    [Storable]
36    private int numberOfVariables;
37    public ItemList<IMeanFunction> Terms {
38      get { return terms; }
39    }
40
41    [StorableConstructor]
42    private MeanSum(StorableConstructorFlag _) : base(_) { }
43    private MeanSum(MeanSum original, Cloner cloner)
44      : base(original, cloner) {
45      this.terms = cloner.Clone(original.terms);
46      this.numberOfVariables = original.numberOfVariables;
47    }
48    public MeanSum() {
49      this.terms = new ItemList<IMeanFunction>();
50    }
51
52    public override IDeepCloneable Clone(Cloner cloner) {
53      return new MeanSum(this, cloner);
54    }
55
56    public int GetNumberOfParameters(int numberOfVariables) {
57      this.numberOfVariables = numberOfVariables;
58      return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
59    }
60
61    public void SetParameter(double[] p) {
62      int offset = 0;
63      foreach (var t in terms) {
64        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
65        t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
66        offset += numberOfParameters;
67      }
68    }
69
70    public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, int[] columnIndices) {
71      var termMf = new List<ParameterizedMeanFunction>();
72      int totalNumberOfParameters = GetNumberOfParameters(numberOfVariables);
73      int[] termIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the correct mean-term
74      int[] hyperParameterIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the l-th parameter of the correct mean-term
75      int c = 0;
76      // get the parameterized mean function for each term
77      for (int termIndex = 0; termIndex < terms.Count; termIndex++) {
78        var numberOfParameters = terms[termIndex].GetNumberOfParameters(numberOfVariables);
79        termMf.Add(terms[termIndex].GetParameterizedMeanFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
80        p = p.Skip(numberOfParameters).ToArray();
81
82        for (int hyperParameterIndex = 0; hyperParameterIndex < numberOfParameters; hyperParameterIndex++) {
83          termIndexMap[c] = termIndex;
84          hyperParameterIndexMap[c] = hyperParameterIndex;
85          c++;
86        }
87      }
88
89      var mf = new ParameterizedMeanFunction();
90      mf.Mean = (x, i) => termMf.Select(t => t.Mean(x, i)).Sum();
91      mf.Gradient = (x, i, k) => {
92        return termMf[termIndexMap[k]].Gradient(x, i, hyperParameterIndexMap[k]);
93      };
94      return mf;
95    }
96  }
97}
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