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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSquaredExponentialArd.cs @ 9106

Last change on this file since 9106 was 9106, checked in by gkronber, 11 years ago

#1902: fixed a bug in ARD covariance functions that would occur when a parameter prior to the inverse length parameter would be fixed.

File size: 5.3 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.Data;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableClass]
33  [Item(Name = "CovarianceSquaredExponentialArd", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]
34  public sealed class CovarianceSquaredExponentialArd : ParameterizedNamedItem, ICovarianceFunction {
35    public IValueParameter<DoubleValue> ScaleParameter {
36      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
37    }
38
39    public IValueParameter<DoubleArray> InverseLengthParameter {
40      get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
41    }
42
43    [StorableConstructor]
44    private CovarianceSquaredExponentialArd(bool deserializing) : base(deserializing) { }
45    private CovarianceSquaredExponentialArd(CovarianceSquaredExponentialArd original, Cloner cloner)
46      : base(original, cloner) {
47    }
48    public CovarianceSquaredExponentialArd()
49      : base() {
50      Name = ItemName;
51      Description = ItemDescription;
52
53      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the squared exponential covariance function with ARD."));
54      Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength", "The inverse length parameter for automatic relevance determination."));
55    }
56
57    public override IDeepCloneable Clone(Cloner cloner) {
58      return new CovarianceSquaredExponentialArd(this, cloner);
59    }
60
61    public int GetNumberOfParameters(int numberOfVariables) {
62      return
63        (ScaleParameter.Value != null ? 0 : 1) +
64        (InverseLengthParameter.Value != null ? 0 : numberOfVariables);
65    }
66
67    public void SetParameter(double[] p) {
68      double scale;
69      double[] inverseLength;
70      GetParameterValues(p, out scale, out inverseLength);
71      ScaleParameter.Value = new DoubleValue(scale);
72      InverseLengthParameter.Value = new DoubleArray(inverseLength);
73    }
74
75    private void GetParameterValues(double[] p, out double scale, out double[] inverseLength) {
76      int c = 0;
77      // gather parameter values
78      if (ScaleParameter.Value != null) {
79        scale = ScaleParameter.Value.Value;
80      } else {
81        scale = Math.Exp(2 * p[c]);
82        c++;
83      }
84      if (InverseLengthParameter.Value != null) {
85        inverseLength = InverseLengthParameter.Value.ToArray();
86      } else {
87        inverseLength = p.Skip(c).Select(e => 1.0 / Math.Exp(e)).ToArray();
88        c += inverseLength.Length;
89      }
90      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialArd", "p");
91    }
92
93    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
94      double scale;
95      double[] inverseLength;
96      GetParameterValues(p, out scale, out inverseLength);
97      // create functions
98      var cov = new ParameterizedCovarianceFunction();
99      cov.Covariance = (x, i, j) => {
100        double d = i == j
101                 ? 0.0
102                 : Util.SqrDist(x, i, j, inverseLength, columnIndices);
103        return scale * Math.Exp(-d / 2.0);
104      };
105      cov.CrossCovariance = (x, xt, i, j) => {
106        double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
107        return scale * Math.Exp(-d / 2.0);
108      };
109      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, inverseLength);
110      return cov;
111    }
112
113
114    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, double[] inverseLength) {
115      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
116      double d = i == j
117                   ? 0.0
118                   : Util.SqrDist(x, i, j, inverseLength, columnIndices);
119      int k = 0;
120      foreach (var columnIndex in columnIndices) {
121        double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
122        yield return scale * Math.Exp(-d / 2.0) * sqrDist;
123        k++;
124      }
125
126      yield return 2.0 * scale * Math.Exp(-d / 2.0);
127    }
128  }
129}
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