source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovariancePeriodic.cs @ 13721

Last change on this file since 13721 was 13721, checked in by mkommend, 5 years ago

#2591: Changed all GP covariance and mean functions to use int[] for column indices instead of IEnumerable<int>. Changed GP utils, GPModel and StudentTProcessModell as well to use fewer iterators and adapted unit tests to new interface.

File size: 6.5 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 = "CovariancePeriodic", Description = "Periodic covariance function for Gaussian processes.")]
34  public sealed class CovariancePeriodic : ParameterizedNamedItem, ICovarianceFunction {
35
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    public IValueParameter<DoubleValue> PeriodParameter {
45      get { return (IValueParameter<DoubleValue>)Parameters["Period"]; }
46    }
47
48    private bool HasFixedScaleParameter {
49      get { return ScaleParameter.Value != null; }
50    }
51    private bool HasFixedInverseLengthParameter {
52      get { return InverseLengthParameter.Value != null; }
53    }
54    private bool HasFixedPeriodParameter {
55      get { return PeriodParameter.Value != null; }
56    }
57
58
59    [StorableConstructor]
60    private CovariancePeriodic(bool deserializing) : base(deserializing) { }
61    private CovariancePeriodic(CovariancePeriodic original, Cloner cloner)
62      : base(original, cloner) {
63    }
64
65    public CovariancePeriodic()
66      : base() {
67      Name = ItemName;
68      Description = ItemDescription;
69
70      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of the periodic covariance function."));
71      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter for the periodic covariance function."));
72      Parameters.Add(new OptionalValueParameter<DoubleValue>("Period", "The period parameter for the periodic covariance function."));
73    }
74
75    public override IDeepCloneable Clone(Cloner cloner) {
76      return new CovariancePeriodic(this, cloner);
77    }
78
79    public int GetNumberOfParameters(int numberOfVariables) {
80      return (HasFixedScaleParameter ? 0 : 1) +
81       (HasFixedPeriodParameter ? 0 : 1) +
82       (HasFixedInverseLengthParameter ? 0 : 1);
83    }
84
85    public void SetParameter(double[] p) {
86      double scale, inverseLength, period;
87      GetParameterValues(p, out scale, out period, out inverseLength);
88      ScaleParameter.Value = new DoubleValue(scale);
89      PeriodParameter.Value = new DoubleValue(period);
90      InverseLengthParameter.Value = new DoubleValue(inverseLength);
91    }
92
93
94    private void GetParameterValues(double[]
95      p, out double scale, out double period, out double inverseLength) {
96      // gather parameter values
97      int c = 0;
98      if (HasFixedInverseLengthParameter) {
99        inverseLength = InverseLengthParameter.Value.Value;
100      } else {
101        inverseLength = 1.0 / Math.Exp(p[c]);
102        c++;
103      }
104      if (HasFixedPeriodParameter) {
105        period = PeriodParameter.Value.Value;
106      } else {
107        period = Math.Exp(p[c]);
108        c++;
109      }
110      if (HasFixedScaleParameter) {
111        scale = ScaleParameter.Value.Value;
112      } else {
113        scale = Math.Exp(2 * p[c]);
114        c++;
115      }
116      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovariancePeriodic", "p");
117    }
118
119    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[]columnIndices) {
120      double inverseLength, period, scale;
121      GetParameterValues(p, out scale, out period, out inverseLength);
122      var fixedInverseLength = HasFixedInverseLengthParameter;
123      var fixedPeriod = HasFixedPeriodParameter;
124      var fixedScale = HasFixedScaleParameter;
125      // create functions
126      var cov = new ParameterizedCovarianceFunction();
127      cov.Covariance = (x, i, j) => {
128        double k = i == j ? 0.0 : GetDistance(x, x, i, j, columnIndices);
129        k = Math.PI * k / period;
130        k = Math.Sin(k) * inverseLength;
131        k = k * k;
132
133        return scale * Math.Exp(-2.0 * k);
134      };
135      cov.CrossCovariance = (x, xt, i, j) => {
136        double k = GetDistance(x, xt, i, j, columnIndices);
137        k = Math.PI * k / period;
138        k = Math.Sin(k) * inverseLength;
139        k = k * k;
140
141        return scale * Math.Exp(-2.0 * k);
142      };
143      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, period, inverseLength, fixedInverseLength, fixedPeriod, fixedScale);
144      return cov;
145    }
146
147
148    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, double period, double inverseLength,
149      bool fixedInverseLength, bool fixedPeriod, bool fixedScale) {
150      double k = i == j ? 0.0 : Math.PI * GetDistance(x, x, i, j, columnIndices) / period;
151      double gradient = Math.Sin(k) * inverseLength;
152      gradient *= gradient;
153      if (!fixedInverseLength) yield return 4.0 * scale * Math.Exp(-2.0 * gradient) * gradient;
154      if (!fixedPeriod) {
155        double r = Math.Sin(k) * inverseLength;
156        yield return 2.0 * k * scale * Math.Exp(-2 * r * r) * Math.Sin(2 * k) * inverseLength * inverseLength;
157      }
158      if (!fixedScale)
159        yield return 2.0 * scale * Math.Exp(-2 * gradient);
160
161    }
162
163    private static double GetDistance(double[,] x, double[,] xt, int i, int j, int[] columnIndices) {
164      return Math.Sqrt(Util.SqrDist(x, i, xt, j, columnIndices, 1));
165    }
166  }
167}
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