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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSquaredExponentialIso.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: 4.9 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.Expressions;
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 = "CovarianceSquaredExponentialIso",
34    Description = "Isotropic squared exponential covariance function for Gaussian processes.")]
35  public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> ScaleParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
38    }
39
40    public IValueParameter<DoubleValue> InverseLengthParameter {
41      get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
42    }
43
44    [StorableConstructor]
45    private CovarianceSquaredExponentialIso(bool deserializing)
46      : base(deserializing) {
47    }
48
49    private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner)
50      : base(original, cloner) {
51    }
52
53    public CovarianceSquaredExponentialIso()
54      : base() {
55      Name = ItemName;
56      Description = ItemDescription;
57
58      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric squared exponential covariance function."));
59      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function."));
60    }
61
62    public override IDeepCloneable Clone(Cloner cloner) {
63      return new CovarianceSquaredExponentialIso(this, cloner);
64    }
65
66    public int GetNumberOfParameters(int numberOfVariables) {
67      return
68        (ScaleParameter.Value != null ? 0 : 1) +
69        (InverseLengthParameter.Value != null ? 0 : 1);
70    }
71
72    public void SetParameter(double[] p) {
73      double scale, inverseLength;
74      GetParameterValues(p, out scale, out inverseLength);
75      ScaleParameter.Value = new DoubleValue(scale);
76      InverseLengthParameter.Value = new DoubleValue(inverseLength);
77    }
78
79
80    private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
81      // gather parameter values
82      int c = 0;
83      if (InverseLengthParameter.Value != null) {
84        inverseLength = InverseLengthParameter.Value.Value;
85      } else {
86        inverseLength = 1.0 / Math.Exp(p[c]);
87        c++;
88      }
89
90      if (ScaleParameter.Value != null) {
91        scale = ScaleParameter.Value.Value;
92      } else {
93        scale = Math.Exp(2 * p[c]);
94        c++;
95      }
96      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialIso", "p");
97    }
98
99    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
100      double inverseLength, scale;
101      GetParameterValues(p, out scale, out inverseLength);
102      // create functions
103      var cov = new ParameterizedCovarianceFunction();
104      cov.Covariance = (x, i, j) => {
105        double d = i == j
106                ? 0.0
107                : Util.SqrDist(x, i, j, inverseLength, columnIndices);
108        return scale * Math.Exp(-d / 2.0);
109      };
110      cov.CrossCovariance = (x, xt, i, j) => {
111        double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
112        return scale * Math.Exp(-d / 2.0);
113      };
114      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, inverseLength, columnIndices);
115      return cov;
116    }
117
118    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double sf2, double inverseLength, IEnumerable<int> columnIndices) {
119      double d = i == j
120                   ? 0.0
121                   : Util.SqrDist(x, i, j, inverseLength, columnIndices);
122      double g = Math.Exp(-d / 2.0);
123      yield return sf2 * g * d;
124      yield return 2.0 * sf2 * g;
125    }
126  }
127}
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