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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceMaternIso.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: 6.1 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 = "CovarianceMaternIso",
34    Description = "Matern covariance function for Gaussian processes.")]
35  public sealed class CovarianceMaternIso : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> InverseLengthParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
38    }
39
40    public IValueParameter<DoubleValue> ScaleParameter {
41      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
42    }
43
44    public IConstrainedValueParameter<IntValue> DParameter {
45      get { return (IConstrainedValueParameter<IntValue>)Parameters["D"]; }
46    }
47
48
49    [StorableConstructor]
50    private CovarianceMaternIso(bool deserializing)
51      : base(deserializing) {
52    }
53
54    private CovarianceMaternIso(CovarianceMaternIso original, Cloner cloner)
55      : base(original, cloner) {
56    }
57
58    public CovarianceMaternIso()
59      : base() {
60      Name = ItemName;
61      Description = ItemDescription;
62
63      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric Matern covariance function."));
64      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric Matern covariance function."));
65      var validDValues = new ItemSet<IntValue>();
66      validDValues.Add((IntValue)new IntValue(1).AsReadOnly());
67      validDValues.Add((IntValue)new IntValue(3).AsReadOnly());
68      validDValues.Add((IntValue)new IntValue(5).AsReadOnly());
69      Parameters.Add(new ConstrainedValueParameter<IntValue>("D", "The d parameter (allowed values: 1, 3, or 5) of the isometric Matern covariance function.", validDValues, validDValues.First()));
70    }
71
72    public override IDeepCloneable Clone(Cloner cloner) {
73      return new CovarianceMaternIso(this, cloner);
74    }
75
76    public int GetNumberOfParameters(int numberOfVariables) {
77      return
78        (InverseLengthParameter.Value != null ? 0 : 1) +
79        (ScaleParameter.Value != null ? 0 : 1);
80    }
81
82    public void SetParameter(double[] p) {
83      double inverseLength, scale;
84      GetParameterValues(p, out scale, out inverseLength);
85      InverseLengthParameter.Value = new DoubleValue(inverseLength);
86      ScaleParameter.Value = new DoubleValue(scale);
87    }
88
89    private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
90      // gather parameter values
91      int c = 0;
92      if (InverseLengthParameter.Value != null) {
93        inverseLength = InverseLengthParameter.Value.Value;
94      } else {
95        inverseLength = 1.0 / Math.Exp(p[c]);
96        c++;
97      }
98
99      if (ScaleParameter.Value != null) {
100        scale = ScaleParameter.Value.Value;
101      } else {
102        scale = Math.Exp(2 * p[c]);
103        c++;
104      }
105      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceMaternIso", "p");
106    }
107
108    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
109      double inverseLength, scale;
110      int d = DParameter.Value.Value;
111      GetParameterValues(p, out scale, out inverseLength);
112      // create functions
113      var cov = new ParameterizedCovarianceFunction();
114      cov.Covariance = (x, i, j) => {
115        double dist = i == j
116                       ? 0.0
117                       : Math.Sqrt(Util.SqrDist(x, i, j, Math.Sqrt(d) * inverseLength, columnIndices));
118        return scale * m(d, dist);
119      };
120      cov.CrossCovariance = (x, xt, i, j) => {
121        double dist = Math.Sqrt(Util.SqrDist(x, i, xt, j, Math.Sqrt(d) * inverseLength, columnIndices));
122        return scale * m(d, dist);
123      };
124      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, d, scale, inverseLength, columnIndices);
125      return cov;
126    }
127
128    private static double m(int d, double t) {
129      double f;
130      switch (d) {
131        case 1: { f = 1; break; }
132        case 3: { f = 1 + t; break; }
133        case 5: { f = 1 + t * (1 + t / 3.0); break; }
134        default: throw new InvalidOperationException();
135      }
136      return f * Math.Exp(-t);
137    }
138
139    private static double dm(int d, double t) {
140      double df;
141      switch (d) {
142        case 1: { df = 1; break; }
143        case 3: { df = t; break; }
144        case 5: { df = t * (1 + t) / 3.0; break; }
145        default: throw new InvalidOperationException();
146      }
147      return df * t * Math.Exp(-t);
148    }
149
150
151    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, int d, double scale, double inverseLength, IEnumerable<int> columnIndices) {
152      double dist = i == j
153                   ? 0.0
154                   : Math.Sqrt(Util.SqrDist(x, i, j, Math.Sqrt(d) * inverseLength, columnIndices));
155
156      yield return scale * dm(d, dist);
157      yield return 2 * scale * m(d, dist);
158    }
159  }
160}
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