source: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceProd.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: 4.3 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
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 = "CovarianceProd",
32    Description = "Product covariance function for Gaussian processes.")]
33  public class CovarianceProd : 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    protected CovarianceProd(bool deserializing)
45      : base(deserializing) {
46    }
47
48    protected CovarianceProd(CovarianceProd original, Cloner cloner)
49      : base(original, cloner) {
50      this.factors = cloner.Clone(original.factors);
51      this.numberOfVariables = original.numberOfVariables;
52      AttachEventHandlers();
53    }
54
55    public CovarianceProd()
56      : base() {
57      this.factors = new ItemList<ICovarianceFunction>();
58      AttachEventHandlers();
59    }
60
61    private void AttachEventHandlers() {
62      this.factors.CollectionReset += (sender, args) => ClearCache();
63      this.factors.ItemsAdded += (sender, args) => ClearCache();
64      this.factors.ItemsRemoved += (sender, args) => ClearCache();
65      this.factors.ItemsReplaced += (sender, args) => ClearCache();
66      this.factors.ItemsMoved += (sender, args) => ClearCache();
67    }
68
69    public override IDeepCloneable Clone(Cloner cloner) {
70      return new CovarianceProd(this, cloner);
71    }
72
73    public int GetNumberOfParameters(int numberOfVariables) {
74      this.numberOfVariables = numberOfVariables;
75      return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
76    }
77
78    public void SetParameter(double[] hyp) {
79      int offset = 0;
80      foreach (var t in factors) {
81        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
82        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
83        offset += numberOfParameters;
84      }
85    }
86    public void SetData(double[,] x) {
87      SetData(x, x);
88    }
89
90    public void SetData(double[,] x, double[,] xt) {
91      foreach (var t in factors) {
92        t.SetData(x, xt);
93      }
94    }
95
96    public double GetCovariance(int i, int j) {
97      return factors.Select(t => t.GetCovariance(i, j)).Aggregate((a, b) => a * b);
98    }
99
100    private Dictionary<int, Tuple<int, int>> cachedParameterMap;
101    public double GetGradient(int i, int j, int k) {
102      if (cachedParameterMap == null) {
103        CalculateParameterMap();
104      }
105      int ti = cachedParameterMap[k].Item1;
106      k = cachedParameterMap[k].Item2;
107      double res = 1.0;
108      for (int ii = 0; ii < factors.Count; ii++) {
109        var f = factors[ii];
110        if (ii == ti) {
111          res *= f.GetGradient(i, j, k);
112        } else {
113          res *= f.GetCovariance(i, j);
114        }
115      }
116      return res;
117    }
118
119    private void ClearCache() {
120      cachedParameterMap = null;
121    }
122
123    private void CalculateParameterMap() {
124      cachedParameterMap = new Dictionary<int, Tuple<int, int>>();
125      int k = 0;
126      for (int ti = 0; ti < factors.Count; ti++) {
127        for (int ti_k = 0; ti_k < factors[ti].GetNumberOfParameters(numberOfVariables); ti_k++) {
128          cachedParameterMap[k++] = Tuple.Create(ti, ti_k);
129        }
130      }
131    }
132  }
133}
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