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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceMaternIso.cs @ 14186

Last change on this file since 14186 was 14186, checked in by swagner, 8 years ago

#2526: Updated year of copyrights in license headers

File size: 6.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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    private bool HasFixedScaleParameter {
48      get { return ScaleParameter.Value != null; }
49    }
50    private bool HasFixedInverseLengthParameter {
51      get { return InverseLengthParameter.Value != null; }
52    }
53
54    [StorableConstructor]
55    private CovarianceMaternIso(bool deserializing)
56      : base(deserializing) {
57    }
58
59    private CovarianceMaternIso(CovarianceMaternIso original, Cloner cloner)
60      : base(original, cloner) {
61    }
62
63    public CovarianceMaternIso()
64      : base() {
65      Name = ItemName;
66      Description = ItemDescription;
67
68      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric Matern covariance function."));
69      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric Matern covariance function."));
70      var validDValues = new ItemSet<IntValue>();
71      validDValues.Add((IntValue)new IntValue(1).AsReadOnly());
72      validDValues.Add((IntValue)new IntValue(3).AsReadOnly());
73      validDValues.Add((IntValue)new IntValue(5).AsReadOnly());
74      Parameters.Add(new ConstrainedValueParameter<IntValue>("D", "The d parameter (allowed values: 1, 3, or 5) of the isometric Matern covariance function.", validDValues, validDValues.First()));
75    }
76
77    public override IDeepCloneable Clone(Cloner cloner) {
78      return new CovarianceMaternIso(this, cloner);
79    }
80
81    public int GetNumberOfParameters(int numberOfVariables) {
82      return
83        (HasFixedInverseLengthParameter ? 0 : 1) +
84        (HasFixedScaleParameter ? 0 : 1);
85    }
86
87    public void SetParameter(double[] p) {
88      double inverseLength, scale;
89      GetParameterValues(p, out scale, out inverseLength);
90      InverseLengthParameter.Value = new DoubleValue(inverseLength);
91      ScaleParameter.Value = new DoubleValue(scale);
92    }
93
94    private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
95      // gather parameter values
96      int c = 0;
97      if (HasFixedInverseLengthParameter) {
98        inverseLength = InverseLengthParameter.Value.Value;
99      } else {
100        inverseLength = 1.0 / Math.Exp(p[c]);
101        c++;
102      }
103
104      if (HasFixedScaleParameter) {
105        scale = ScaleParameter.Value.Value;
106      } else {
107        scale = Math.Exp(2 * p[c]);
108        c++;
109      }
110      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceMaternIso", "p");
111    }
112
113    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
114      double inverseLength, scale;
115      int d = DParameter.Value.Value;
116      GetParameterValues(p, out scale, out inverseLength);
117      var fixedInverseLength = HasFixedInverseLengthParameter;
118      var fixedScale = HasFixedScaleParameter;
119      // create functions
120      var cov = new ParameterizedCovarianceFunction();
121      cov.Covariance = (x, i, j) => {
122        double dist = i == j
123                       ? 0.0
124                       : Math.Sqrt(Util.SqrDist(x, i, j, columnIndices, Math.Sqrt(d) * inverseLength));
125        return scale * m(d, dist);
126      };
127      cov.CrossCovariance = (x, xt, i, j) => {
128        double dist = Math.Sqrt(Util.SqrDist(x, i, xt, j, columnIndices, Math.Sqrt(d) * inverseLength));
129        return scale * m(d, dist);
130      };
131      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, d, scale, inverseLength, columnIndices, fixedInverseLength, fixedScale);
132      return cov;
133    }
134
135    private static double m(int d, double t) {
136      double f;
137      switch (d) {
138        case 1: { f = 1; break; }
139        case 3: { f = 1 + t; break; }
140        case 5: { f = 1 + t * (1 + t / 3.0); break; }
141        default: throw new InvalidOperationException();
142      }
143      return f * Math.Exp(-t);
144    }
145
146    private static double dm(int d, double t) {
147      double df;
148      switch (d) {
149        case 1: { df = 1; break; }
150        case 3: { df = t; break; }
151        case 5: { df = t * (1 + t) / 3.0; break; }
152        default: throw new InvalidOperationException();
153      }
154      return df * t * Math.Exp(-t);
155    }
156
157    private static IList<double> GetGradient(double[,] x, int i, int j, int d, double scale, double inverseLength, int[] columnIndices,
158      bool fixedInverseLength, bool fixedScale) {
159      double dist = i == j
160                   ? 0.0
161                   : Math.Sqrt(Util.SqrDist(x, i, j, columnIndices, Math.Sqrt(d) * inverseLength));
162
163      var g = new List<double>(2);
164      if (!fixedInverseLength) g.Add(scale * dm(d, dist));
165      if (!fixedScale) g.Add(2 * scale * m(d, dist));
166      return g;
167    }
168  }
169}
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