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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceRationalQuadraticArd.cs @ 8982

Last change on this file since 8982 was 8982, checked in by gkronber, 11 years ago

#1902: removed class HyperParameter and changed implementations of covariance and mean functions to remove the parameter value caching and event handlers for parameter caching. Instead it is now possible to create the actual covariance and mean functions as Func from templates and specified parameter values. The instances of mean and covariance functions configured in the GUI are actually templates where the structure and fixed parameters can be specified.

File size: 6.0 KB
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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(2).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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