source: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanProd.cs @ 8477

Last change on this file since 8477 was 8477, checked in by mkommend, 9 years ago

#1081:

  • Added autoregressive target variable Symbol
  • Merged trunk changes into the branch.
File size: 3.5 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 = "MeanProd", Description = "Product of mean functions for Gaussian processes.")]
29  public class MeanProd : Item, IMeanFunction {
30    [Storable]
31    private ItemList<IMeanFunction> factors;
32
33    [Storable]
34    private int numberOfVariables;
35
36    public ItemList<IMeanFunction> Factors {
37      get { return factors; }
38    }
39
40    public int GetNumberOfParameters(int numberOfVariables) {
41      this.numberOfVariables = numberOfVariables;
42      return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
43    }
44
45    [StorableConstructor]
46    protected MeanProd(bool deserializing)
47      : base(deserializing) {
48    }
49
50    protected MeanProd(MeanProd original, Cloner cloner)
51      : base(original, cloner) {
52      this.factors = cloner.Clone(original.factors);
53      this.numberOfVariables = original.numberOfVariables;
54    }
55
56    public MeanProd() {
57      this.factors = new ItemList<IMeanFunction>();
58    }
59
60    public void SetParameter(double[] hyp) {
61      int offset = 0;
62      foreach (var t in factors) {
63        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
64        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
65        offset += numberOfParameters;
66      }
67    }
68
69    public void SetData(double[,] x) {
70      foreach (var t in factors) t.SetData(x);
71    }
72
73    public double[] GetMean(double[,] x) {
74      var res = factors.First().GetMean(x);
75      foreach (var t in factors.Skip(1)) {
76        var a = t.GetMean(x);
77        for (int i = 0; i < res.Length; i++) res[i] *= a[i];
78      }
79      return res;
80    }
81
82    public double[] GetGradients(int k, double[,] x) {
83      double[] res = Enumerable.Repeat(1.0, x.GetLength(0)).ToArray();
84      // find index of factor for the given k
85      int j = 0;
86      while (k >= factors[j].GetNumberOfParameters(numberOfVariables)) {
87        k -= factors[j].GetNumberOfParameters(numberOfVariables);
88        j++;
89      }
90      for (int i = 0; i < factors.Count; i++) {
91        var f = factors[i];
92        if (i == j) {
93          // multiply gradient
94          var g = f.GetGradients(k, x);
95          for (int ii = 0; ii < res.Length; ii++) res[ii] *= g[ii];
96        } else {
97          // multiply mean
98          var m = f.GetMean(x);
99          for (int ii = 0; ii < res.Length; ii++) res[ii] *= m[ii];
100        }
101      }
102      return res;
103    }
104
105    public override IDeepCloneable Clone(Cloner cloner) {
106      return new MeanProd(this, cloner);
107    }
108  }
109}
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