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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceMaternIso.cs @ 11128

Last change on this file since 11128 was 10489, checked in by gkronber, 11 years ago

#2125 fixed the bug that covariance functions returned the full gradient vector even when parameters are partially fixed.
changed the calculation of NN covariance and gradient to direct calculation (instead of AutoDiff)

File size: 6.5 KB
RevLine 
[8562]1#region License Information
2/* HeuristicLab
[9456]3 * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[8562]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;
[8582]24using System.Linq;
[8562]25using HeuristicLab.Common;
26using HeuristicLab.Core;
[8582]27using HeuristicLab.Data;
28using HeuristicLab.Parameters;
[8562]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.")]
[8612]35  public sealed class CovarianceMaternIso : ParameterizedNamedItem, ICovarianceFunction {
[8582]36    public IValueParameter<DoubleValue> InverseLengthParameter {
[8982]37      get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
[8582]38    }
39
[8612]40    public IValueParameter<DoubleValue> ScaleParameter {
[8982]41      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
[8612]42    }
[8582]43
[8612]44    public IConstrainedValueParameter<IntValue> DParameter {
[8982]45      get { return (IConstrainedValueParameter<IntValue>)Parameters["D"]; }
[8612]46    }
[10489]47    private bool HasFixedScaleParameter {
48      get { return ScaleParameter.Value != null; }
49    }
50    private bool HasFixedInverseLengthParameter {
51      get { return InverseLengthParameter.Value != null; }
52    }
[8562]53
54    [StorableConstructor]
[8612]55    private CovarianceMaternIso(bool deserializing)
[8562]56      : base(deserializing) {
57    }
58
[8612]59    private CovarianceMaternIso(CovarianceMaternIso original, Cloner cloner)
[8562]60      : base(original, cloner) {
61    }
62
63    public CovarianceMaternIso()
64      : base() {
[8612]65      Name = ItemName;
66      Description = ItemDescription;
67
[8982]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."));
[8582]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());
[8982]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()));
[8562]75    }
76
77    public override IDeepCloneable Clone(Cloner cloner) {
78      return new CovarianceMaternIso(this, cloner);
79    }
80
[8612]81    public int GetNumberOfParameters(int numberOfVariables) {
[8582]82      return
[10489]83        (HasFixedInverseLengthParameter ? 0 : 1) +
84        (HasFixedScaleParameter ? 0 : 1);
[8562]85    }
86
[8982]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;
[10489]97      if (HasFixedInverseLengthParameter) {
[8982]98        inverseLength = InverseLengthParameter.Value.Value;
99      } else {
100        inverseLength = 1.0 / Math.Exp(p[c]);
101        c++;
[8582]102      }
[8982]103
[10489]104      if (HasFixedScaleParameter) {
[8982]105        scale = ScaleParameter.Value.Value;
106      } else {
107        scale = Math.Exp(2 * p[c]);
108        c++;
[8582]109      }
[8982]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");
[8582]111    }
[8562]112
[8982]113    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
114      double inverseLength, scale;
115      int d = DParameter.Value.Value;
116      GetParameterValues(p, out scale, out inverseLength);
[10489]117      var fixedInverseLength = HasFixedInverseLengthParameter;
118      var fixedScale = HasFixedScaleParameter;
[8982]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, Math.Sqrt(d) * inverseLength, columnIndices));
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, Math.Sqrt(d) * inverseLength, columnIndices));
129        return scale * m(d, dist);
130      };
[10489]131      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, d, scale, inverseLength, columnIndices, fixedInverseLength, fixedScale);
[8982]132      return cov;
133    }
[8582]134
[8982]135    private static double m(int d, double t) {
[8562]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
[8982]146    private static double dm(int d, double t) {
[8562]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
[10489]158    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, int d, double scale, double inverseLength, IEnumerable<int> columnIndices,
159      bool fixedInverseLength, bool fixedScale) {
[8562]160      double dist = i == j
161                   ? 0.0
[8678]162                   : Math.Sqrt(Util.SqrDist(x, i, j, Math.Sqrt(d) * inverseLength, columnIndices));
[8562]163
[10489]164      if (!fixedInverseLength) yield return scale * dm(d, dist);
165      if (!fixedScale) yield return 2 * scale * m(d, dist);
[8562]166    }
167  }
168}
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