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source: trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceNoise.cs @ 17138

Last change on this file since 17138 was 16565, checked in by gkronber, 6 years ago

#2520: merged changes from PersistenceOverhaul branch (r16451:16564) into trunk

File size: 3.5 KB
RevLine 
[8464]1#region License Information
2/* HeuristicLab
[16565]3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[8464]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 HeuristicLab.Common;
24using HeuristicLab.Core;
[8612]25using HeuristicLab.Data;
[8982]26using HeuristicLab.Parameters;
[16565]27using HEAL.Attic;
[8464]28
29namespace HeuristicLab.Algorithms.DataAnalysis {
[16565]30  [StorableType("C6AEEC11-1F8D-40D1-8D8A-DCCCE886E46C")]
[8464]31  [Item(Name = "CovarianceNoise",
32    Description = "Noise covariance function for Gaussian processes.")]
[8612]33  public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction {
34    public IValueParameter<DoubleValue> ScaleParameter {
[8982]35      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
[8612]36    }
[10489]37    private bool HasFixedScaleParameter {
38      get { return ScaleParameter.Value != null; }
39    }
[8464]40
41    [StorableConstructor]
[16565]42    private CovarianceNoise(StorableConstructorFlag _) : base(_) {
[8464]43    }
44
[8612]45    private CovarianceNoise(CovarianceNoise original, Cloner cloner)
[8464]46      : base(original, cloner) {
47    }
48
49    public CovarianceNoise()
50      : base() {
[8612]51      Name = ItemName;
52      Description = ItemDescription;
53
[8982]54      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of noise."));
[8464]55    }
56
57    public override IDeepCloneable Clone(Cloner cloner) {
58      return new CovarianceNoise(this, cloner);
59    }
60
[8982]61    public int GetNumberOfParameters(int numberOfVariables) {
[10489]62      return HasFixedScaleParameter ? 0 : 1;
[8612]63    }
64
[8982]65    public void SetParameter(double[] p) {
66      double scale;
67      GetParameterValues(p, out scale);
68      ScaleParameter.Value = new DoubleValue(scale);
[8612]69    }
70
[8982]71    private void GetParameterValues(double[] p, out double scale) {
72      int c = 0;
73      // gather parameter values
[10489]74      if (HasFixedScaleParameter) {
[8982]75        scale = ScaleParameter.Value.Value;
[8612]76      } else {
[8982]77        scale = Math.Exp(2 * p[c]);
78        c++;
[8612]79      }
[8982]80      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceNoise", "p");
[8464]81    }
82
[13721]83    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
[8982]84      double scale;
85      GetParameterValues(p, out scale);
[10489]86      var fixedScale = HasFixedScaleParameter;
[8982]87      // create functions
88      var cov = new ParameterizedCovarianceFunction();
89      cov.Covariance = (x, i, j) => i == j ? scale : 0.0;
[13721]90      cov.CrossCovariance = (x, xt, i, j) => Util.SqrDist(x, i, xt, j, columnIndices, 1.0) < 1e-9 ? scale : 0.0;
[10489]91      if (fixedScale)
[13784]92        cov.CovarianceGradient = (x, i, j) => new double[0];
[10489]93      else
[13784]94        cov.CovarianceGradient = (x, i, j) => new double[1] { i == j ? 2.0 * scale : 0.0 };
[8982]95      return cov;
[8464]96    }
97  }
98}
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