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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceRationalQuadraticArd.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: 6.0 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 = "CovarianceRationalQuadraticArd",
34    Description = "Rational quadratic covariance function with automatic relevance determination for Gaussian processes.")]
35  public sealed class CovarianceRationalQuadraticArd : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> ScaleParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
38    }
39
40    public IValueParameter<DoubleArray> InverseLengthParameter {
41      get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
42    }
43
44    public IValueParameter<DoubleValue> ShapeParameter {
45      get { return (IValueParameter<DoubleValue>)Parameters["Shape"]; }
46    }
47
48    [StorableConstructor]
49    private CovarianceRationalQuadraticArd(bool deserializing)
50      : base(deserializing) {
51    }
52
53    private CovarianceRationalQuadraticArd(CovarianceRationalQuadraticArd original, Cloner cloner)
54      : base(original, cloner) {
55    }
56
57    public CovarianceRationalQuadraticArd()
58      : base() {
59      Name = ItemName;
60      Description = ItemDescription;
61
62      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the rational quadratic covariance function with ARD."));
63      Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength", "The inverse length parameter for automatic relevance determination."));
64      Parameters.Add(new OptionalValueParameter<DoubleValue>("Shape", "The shape parameter (alpha) of the rational quadratic covariance function with ARD."));
65    }
66
67    public override IDeepCloneable Clone(Cloner cloner) {
68      return new CovarianceRationalQuadraticArd(this, cloner);
69    }
70
71    public int GetNumberOfParameters(int numberOfVariables) {
72      return
73        (ScaleParameter.Value != null ? 0 : 1) +
74        (ShapeParameter.Value != null ? 0 : 1) +
75        (InverseLengthParameter.Value != null ? 0 : numberOfVariables);
76    }
77
78    public void SetParameter(double[] p) {
79      double scale, shape;
80      double[] inverseLength;
81      GetParameterValues(p, out scale, out shape, out inverseLength);
82      ScaleParameter.Value = new DoubleValue(scale);
83      ShapeParameter.Value = new DoubleValue(shape);
84      InverseLengthParameter.Value = new DoubleArray(inverseLength);
85    }
86
87    private void GetParameterValues(double[] p, out double scale, out double shape, out double[] inverseLength) {
88      int c = 0;
89      // gather parameter values
90      if (ScaleParameter.Value != null) {
91        scale = ScaleParameter.Value.Value;
92      } else {
93        scale = Math.Exp(2 * p[c]);
94        c++;
95      }
96      if (ShapeParameter.Value != null) {
97        shape = ShapeParameter.Value.Value;
98      } else {
99        shape = Math.Exp(p[c]);
100        c++;
101      }
102      if (InverseLengthParameter.Value != null) {
103        inverseLength = InverseLengthParameter.Value.ToArray();
104      } else {
105        inverseLength = p.Skip(c).Select(e => 1.0 / Math.Exp(e)).ToArray();
106        c += inverseLength.Length;
107      }
108      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceRationalQuadraticArd", "p");
109    }
110
111    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
112      double scale, shape;
113      double[] inverseLength;
114      GetParameterValues(p, out scale, out shape, out inverseLength);
115      // create functions
116      var cov = new ParameterizedCovarianceFunction();
117      cov.Covariance = (x, i, j) => {
118        double d = i == j
119                    ? 0.0
120                    : Util.SqrDist(x, i, j, inverseLength, columnIndices);
121        return scale * Math.Pow(1 + 0.5 * d / shape, -shape);
122      };
123      cov.CrossCovariance = (x, xt, i, j) => {
124        double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
125        return scale * Math.Pow(1 + 0.5 * d / shape, -shape);
126      };
127      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, shape, inverseLength);
128      return cov;
129    }
130
131    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices, double scale, double shape, double[] inverseLength) {
132      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
133      double d = i == j
134                   ? 0.0
135                   : Util.SqrDist(x, i, j, inverseLength, columnIndices);
136      double b = 1 + 0.5 * d / shape;
137      int k = 0;
138      foreach (var columnIndex in columnIndices) {
139        yield return scale * Math.Pow(b, -shape - 1) * Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
140        k++;
141      }
142      yield return 2 * scale * Math.Pow(b, -shape);
143      yield return scale * Math.Pow(b, -shape) * (0.5 * d / b - shape * Math.Log(b));
144    }
145  }
146}
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