1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Parameters;
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27 | using HEAL.Fossil;
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28 |
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29 | namespace HeuristicLab.Algorithms.DataAnalysis {
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30 | [StorableType("C6AEEC11-1F8D-40D1-8D8A-DCCCE886E46C")]
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31 | [Item(Name = "CovarianceNoise",
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32 | Description = "Noise covariance function for Gaussian processes.")]
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33 | public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction {
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34 | public IValueParameter<DoubleValue> ScaleParameter {
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35 | get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
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36 | }
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37 | private bool HasFixedScaleParameter {
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38 | get { return ScaleParameter.Value != null; }
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39 | }
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40 |
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41 | [StorableConstructor]
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42 | private CovarianceNoise(StorableConstructorFlag _) : base(_) {
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43 | }
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44 |
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45 | private CovarianceNoise(CovarianceNoise original, Cloner cloner)
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46 | : base(original, cloner) {
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47 | }
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48 |
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49 | public CovarianceNoise()
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50 | : base() {
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51 | Name = ItemName;
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52 | Description = ItemDescription;
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53 |
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54 | Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of noise."));
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55 | }
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56 |
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57 | public override IDeepCloneable Clone(Cloner cloner) {
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58 | return new CovarianceNoise(this, cloner);
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59 | }
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60 |
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61 | public int GetNumberOfParameters(int numberOfVariables) {
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62 | return HasFixedScaleParameter ? 0 : 1;
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63 | }
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64 |
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65 | public void SetParameter(double[] p) {
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66 | double scale;
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67 | GetParameterValues(p, out scale);
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68 | ScaleParameter.Value = new DoubleValue(scale);
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69 | }
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70 |
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71 | private void GetParameterValues(double[] p, out double scale) {
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72 | int c = 0;
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73 | // gather parameter values
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74 | if (HasFixedScaleParameter) {
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75 | scale = ScaleParameter.Value.Value;
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76 | } else {
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77 | scale = Math.Exp(2 * p[c]);
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78 | c++;
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79 | }
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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");
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81 | }
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82 |
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83 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
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84 | double scale;
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85 | GetParameterValues(p, out scale);
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86 | var fixedScale = HasFixedScaleParameter;
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87 | // create functions
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88 | var cov = new ParameterizedCovarianceFunction();
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89 | cov.Covariance = (x, i, j) => i == j ? scale : 0.0;
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90 | cov.CrossCovariance = (x, xt, i, j) => Util.SqrDist(x, i, xt, j, columnIndices, 1.0) < 1e-9 ? scale : 0.0;
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91 | if (fixedScale)
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92 | cov.CovarianceGradient = (x, i, j) => new double[0];
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93 | else
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94 | cov.CovarianceGradient = (x, i, j) => new double[1] { i == j ? 2.0 * scale : 0.0 };
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95 | return cov;
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96 | }
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97 | }
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98 | }
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