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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceScale.cs @ 9456

Last change on this file since 9456 was 9456, checked in by swagner, 11 years ago

Updated copyright year and added some missing license headers (#1889)

File size: 4.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableClass]
33  [Item(Name = "CovarianceScale",
34    Description = "Scale covariance function for Gaussian processes.")]
35  public sealed class CovarianceScale : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> ScaleParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
38    }
39
40    public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
41      get { return (IValueParameter<ICovarianceFunction>)Parameters["CovarianceFunction"]; }
42    }
43
44    [StorableConstructor]
45    private CovarianceScale(bool deserializing)
46      : base(deserializing) {
47    }
48
49    private CovarianceScale(CovarianceScale original, Cloner cloner)
50      : base(original, cloner) {
51    }
52
53    public CovarianceScale()
54      : base() {
55      Name = ItemName;
56      Description = ItemDescription;
57
58      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter."));
59      Parameters.Add(new ValueParameter<ICovarianceFunction>("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso()));
60    }
61
62    public override IDeepCloneable Clone(Cloner cloner) {
63      return new CovarianceScale(this, cloner);
64    }
65
66    public int GetNumberOfParameters(int numberOfVariables) {
67      return (ScaleParameter.Value != null ? 0 : 1) + CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
68    }
69
70    public void SetParameter(double[] p) {
71      double scale;
72      GetParameterValues(p, out scale);
73      ScaleParameter.Value = new DoubleValue(scale);
74      CovarianceFunctionParameter.Value.SetParameter(p.Skip(1).ToArray());
75    }
76
77    private void GetParameterValues(double[] p, out double scale) {
78      // gather parameter values
79      if (ScaleParameter.Value != null) {
80        scale = ScaleParameter.Value.Value;
81      } else {
82        scale = Math.Exp(2 * p[0]);
83      }
84    }
85
86    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
87      double scale;
88      GetParameterValues(p, out scale);
89      var subCov = CovarianceFunctionParameter.Value.GetParameterizedCovarianceFunction(p.Skip(1).ToArray(), columnIndices);
90      // create functions
91      var cov = new ParameterizedCovarianceFunction();
92      cov.Covariance = (x, i, j) => scale * subCov.Covariance(x, i, j);
93      cov.CrossCovariance = (x, xt, i, j) => scale * subCov.CrossCovariance(x, xt, i, j);
94      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, subCov);
95      return cov;
96    }
97
98    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, ParameterizedCovarianceFunction cov) {
99      yield return 2 * scale * cov.Covariance(x, i, j);
100      foreach (var g in cov.CovarianceGradient(x, i, j))
101        yield return scale * g;
102    }
103  }
104}
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