source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinearArd.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: 4.2 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Parameters;
29using HEAL.Attic;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableType("A704FDF2-0566-4AB5-B752-D713831B016C")]
33  [Item(Name = "CovarianceLinearArd",
34    Description = "Linear covariance function with automatic relevance determination for Gaussian processes.")]
35  public sealed class CovarianceLinearArd : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleArray> InverseLengthParameter {
37      get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
38    }
39    private bool HasFixedInverseLengthParameter {
40      get { return InverseLengthParameter.Value != null; }
41    }
42
43    [StorableConstructor]
44    private CovarianceLinearArd(StorableConstructorFlag _) : base(_) { }
45    private CovarianceLinearArd(CovarianceLinearArd original, Cloner cloner)
46      : base(original, cloner) {
47    }
48    public CovarianceLinearArd()
49      : base() {
50      Name = ItemName;
51      Description = ItemDescription;
52
53      Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength",
54                                                             "The inverse length parameter for ARD."));
55    }
56
57    public override IDeepCloneable Clone(Cloner cloner) {
58      return new CovarianceLinearArd(this, cloner);
59    }
60
61    public int GetNumberOfParameters(int numberOfVariables) {
62      if (HasFixedInverseLengthParameter)
63        return 0;
64      else
65        return numberOfVariables;
66    }
67
68    public void SetParameter(double[] p) {
69      double[] inverseLength;
70      GetParameterValues(p, out inverseLength);
71      InverseLengthParameter.Value = new DoubleArray(inverseLength);
72    }
73
74    private void GetParameterValues(double[] p, out double[] inverseLength) {
75      // gather parameter values
76      if (HasFixedInverseLengthParameter) {
77        inverseLength = InverseLengthParameter.Value.ToArray();
78      } else {
79        inverseLength = p.Select(e => 1.0 / Math.Exp(e)).ToArray();
80      }
81    }
82
83    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
84      double[] inverseLength;
85      GetParameterValues(p, out inverseLength);
86      var fixedInverseLength = HasFixedInverseLengthParameter;
87      // create functions
88      var cov = new ParameterizedCovarianceFunction();
89      cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, inverseLength, columnIndices);
90      cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, inverseLength, columnIndices);
91      if (fixedInverseLength)
92        cov.CovarianceGradient = (x, i, j) => new double[0];
93      else
94        cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, inverseLength, columnIndices);
95      return cov;
96    }
97
98    private static IList<double> GetGradient(double[,] x, int i, int j, double[] inverseLength, int[] columnIndices) {
99      int k = 0;
100      var g = new List<double>(columnIndices.Length);
101      for (int c = 0; c < columnIndices.Length; c++) {
102        var columnIndex = columnIndices[c];
103        g.Add(-2.0 * x[i, columnIndex] * x[j, columnIndex] * inverseLength[k] * inverseLength[k]);
104        k++;
105      }
106      return g;
107    }
108  }
109}
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