source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceNoise.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: 3.5 KB
Line 
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.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  [StorableClass]
32  [Item(Name = "CovarianceNoise",
33    Description = "Noise covariance function for Gaussian processes.")]
34  public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction {
35    public IValueParameter<DoubleValue> ScaleParameter {
36      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
37    }
38    private bool HasFixedScaleParameter {
39      get { return ScaleParameter.Value != null; }
40    }
41
42    [StorableConstructor]
43    private CovarianceNoise(bool deserializing)
44      : base(deserializing) {
45    }
46
47    private CovarianceNoise(CovarianceNoise original, Cloner cloner)
48      : base(original, cloner) {
49    }
50
51    public CovarianceNoise()
52      : base() {
53      Name = ItemName;
54      Description = ItemDescription;
55
56      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of noise."));
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new CovarianceNoise(this, cloner);
61    }
62
63    public int GetNumberOfParameters(int numberOfVariables) {
64      return HasFixedScaleParameter ? 0 : 1;
65    }
66
67    public void SetParameter(double[] p) {
68      double scale;
69      GetParameterValues(p, out scale);
70      ScaleParameter.Value = new DoubleValue(scale);
71    }
72
73    private void GetParameterValues(double[] p, out double scale) {
74      int c = 0;
75      // gather parameter values
76      if (HasFixedScaleParameter) {
77        scale = ScaleParameter.Value.Value;
78      } else {
79        scale = Math.Exp(2 * p[c]);
80        c++;
81      }
82      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceNoise", "p");
83    }
84
85    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
86      double scale;
87      GetParameterValues(p, out scale);
88      var fixedScale = HasFixedScaleParameter;
89      // create functions
90      var cov = new ParameterizedCovarianceFunction();
91      cov.Covariance = (x, i, j) => i == j ? scale : 0.0;
92      cov.CrossCovariance = (x, xt, i, j) => Util.SqrDist(x, i, xt, j, columnIndices, 1.0) < 1e-9 ? scale : 0.0;
93      if (fixedScale)
94        cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();
95      else
96        cov.CovarianceGradient = (x, i, j) => Enumerable.Repeat(i == j ? 2.0 * scale : 0.0, 1);
97      return cov;
98    }
99  }
100}
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