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source: branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceMaternIso.cs @ 16462

Last change on this file since 16462 was 16462, checked in by jkarder, 5 years ago

#2520: worked on reintegration of new persistence

  • added nuget references to HEAL.Fossil
  • added StorableType attributes to many classes
  • changed signature of StorableConstructors
  • removed some classes in old persistence
  • removed some unnecessary usings
File size: 6.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 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 HEAL.Fossil;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableType("D251400A-4DCA-4500-9738-CE3B7BF96B0D")]
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(StorableConstructorFlag _) : base(_) {
56    }
57
58    private CovarianceMaternIso(CovarianceMaternIso original, Cloner cloner)
59      : base(original, cloner) {
60    }
61
62    public CovarianceMaternIso()
63      : base() {
64      Name = ItemName;
65      Description = ItemDescription;
66
67      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric Matern covariance function."));
68      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric Matern covariance function."));
69      var validDValues = new ItemSet<IntValue>();
70      validDValues.Add((IntValue)new IntValue(1).AsReadOnly());
71      validDValues.Add((IntValue)new IntValue(3).AsReadOnly());
72      validDValues.Add((IntValue)new IntValue(5).AsReadOnly());
73      Parameters.Add(new ConstrainedValueParameter<IntValue>("D", "The d parameter (allowed values: 1, 3, or 5) of the isometric Matern covariance function.", validDValues, validDValues.First()));
74    }
75
76    public override IDeepCloneable Clone(Cloner cloner) {
77      return new CovarianceMaternIso(this, cloner);
78    }
79
80    public int GetNumberOfParameters(int numberOfVariables) {
81      return
82        (HasFixedInverseLengthParameter ? 0 : 1) +
83        (HasFixedScaleParameter ? 0 : 1);
84    }
85
86    public void SetParameter(double[] p) {
87      double inverseLength, scale;
88      GetParameterValues(p, out scale, out inverseLength);
89      InverseLengthParameter.Value = new DoubleValue(inverseLength);
90      ScaleParameter.Value = new DoubleValue(scale);
91    }
92
93    private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
94      // gather parameter values
95      int c = 0;
96      if (HasFixedInverseLengthParameter) {
97        inverseLength = InverseLengthParameter.Value.Value;
98      } else {
99        inverseLength = 1.0 / Math.Exp(p[c]);
100        c++;
101      }
102
103      if (HasFixedScaleParameter) {
104        scale = ScaleParameter.Value.Value;
105      } else {
106        scale = Math.Exp(2 * p[c]);
107        c++;
108      }
109      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceMaternIso", "p");
110    }
111
112    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
113      double inverseLength, scale;
114      int d = DParameter.Value.Value;
115      GetParameterValues(p, out scale, out inverseLength);
116      var fixedInverseLength = HasFixedInverseLengthParameter;
117      var fixedScale = HasFixedScaleParameter;
118      // create functions
119      var cov = new ParameterizedCovarianceFunction();
120      cov.Covariance = (x, i, j) => {
121        double dist = i == j
122                       ? 0.0
123                       : Math.Sqrt(Util.SqrDist(x, i, j, columnIndices, Math.Sqrt(d) * inverseLength));
124        return scale * m(d, dist);
125      };
126      cov.CrossCovariance = (x, xt, i, j) => {
127        double dist = Math.Sqrt(Util.SqrDist(x, i, xt, j, columnIndices, Math.Sqrt(d) * inverseLength));
128        return scale * m(d, dist);
129      };
130      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, d, scale, inverseLength, columnIndices, fixedInverseLength, fixedScale);
131      return cov;
132    }
133
134    private static double m(int d, double t) {
135      double f;
136      switch (d) {
137        case 1: { f = 1; break; }
138        case 3: { f = 1 + t; break; }
139        case 5: { f = 1 + t * (1 + t / 3.0); break; }
140        default: throw new InvalidOperationException();
141      }
142      return f * Math.Exp(-t);
143    }
144
145    private static double dm(int d, double t) {
146      double df;
147      switch (d) {
148        case 1: { df = 1; break; }
149        case 3: { df = t; break; }
150        case 5: { df = t * (1 + t) / 3.0; break; }
151        default: throw new InvalidOperationException();
152      }
153      return df * t * Math.Exp(-t);
154    }
155
156    private static IList<double> GetGradient(double[,] x, int i, int j, int d, double scale, double inverseLength, int[] columnIndices,
157      bool fixedInverseLength, bool fixedScale) {
158      double dist = i == j
159                   ? 0.0
160                   : Math.Sqrt(Util.SqrDist(x, i, j, columnIndices, Math.Sqrt(d) * inverseLength));
161
162      var g = new List<double>(2);
163      if (!fixedInverseLength) g.Add(scale * dm(d, dist));
164      if (!fixedScale) g.Add(2 * scale * m(d, dist));
165      return g;
166    }
167  }
168}
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