source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceConst.cs @ 10489

Last change on this file since 10489 was 10489, checked in by gkronber, 7 years ago

#2125 fixed the bug that covariance functions returned the full gradient vector even when parameters are partially fixed.
changed the calculation of NN covariance and gradient to direct calculation (instead of AutoDiff)

File size: 3.7 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 = "CovarianceConst",
34    Description = "Constant covariance function for Gaussian processes.")]
35  public sealed class CovarianceConst : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> ScaleParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
38    }
39    private bool HasFixedScaleParameter {
40      get { return ScaleParameter.Value != null; }
41    }
42    [StorableConstructor]
43    private CovarianceConst(bool deserializing)
44      : base(deserializing) {
45    }
46
47    private CovarianceConst(CovarianceConst original, Cloner cloner)
48      : base(original, cloner) {
49    }
50
51    public CovarianceConst()
52      : base() {
53      Name = ItemName;
54      Description = ItemDescription;
55
56      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of the constant covariance function."));
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new CovarianceConst(this, cloner);
61    }
62
63    public int GetNumberOfParameters(int numberOfVariables) {
64      return HasFixedScaleParameter ? 0 : 1;
65    }
66
67    public void SetParameter(double[] p) {
68      double scale;
69      GetParameterValues(p, out scale);
70      ScaleParameter.Value = new DoubleValue(scale);
71    }
72
73    private void GetParameterValues(double[] p, out double scale) {
74      int c = 0;
75      // gather parameter values
76      if (HasFixedScaleParameter) {
77        scale = ScaleParameter.Value.Value;
78      } else {
79        scale = Math.Exp(2 * p[c]);
80        c++;
81      }
82      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceConst", "p");
83    }
84
85    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
86      double scale;
87      GetParameterValues(p, out scale);
88      // create functions
89      var cov = new ParameterizedCovarianceFunction();
90      cov.Covariance = (x, i, j) => scale;
91      cov.CrossCovariance = (x, xt, i, j) => scale;
92      if (HasFixedScaleParameter) {
93        cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();
94      } else {
95        cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, columnIndices);
96      }
97      return cov;
98    }
99
100    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double scale, IEnumerable<int> columnIndices) {
101      yield return 2.0 * scale;
102    }
103  }
104}
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