1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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 System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence;
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30 |
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31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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32 | [StorableType("6e4de939-ae8c-485f-a522-579afc57c1af")]
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33 | [Item(Name = "CovarianceLinearArd",
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34 | Description = "Linear covariance function with automatic relevance determination for Gaussian processes.")]
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35 | public sealed class CovarianceLinearArd : ParameterizedNamedItem, ICovarianceFunction {
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36 | public IValueParameter<DoubleArray> InverseLengthParameter {
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37 | get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
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38 | }
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39 | private bool HasFixedInverseLengthParameter {
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40 | get { return InverseLengthParameter.Value != null; }
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41 | }
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42 |
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43 | [StorableConstructor]
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44 | private CovarianceLinearArd(bool deserializing) : base(deserializing) { }
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45 | private CovarianceLinearArd(CovarianceLinearArd original, Cloner cloner)
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46 | : base(original, cloner) {
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47 | }
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48 | public CovarianceLinearArd()
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49 | : base() {
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50 | Name = ItemName;
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51 | Description = ItemDescription;
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52 |
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53 | Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength",
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54 | "The inverse length parameter for ARD."));
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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 CovarianceLinearArd(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 | if (HasFixedInverseLengthParameter)
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63 | return 0;
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64 | else
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65 | return numberOfVariables;
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66 | }
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67 |
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68 | public void SetParameter(double[] p) {
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69 | double[] inverseLength;
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70 | GetParameterValues(p, out inverseLength);
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71 | InverseLengthParameter.Value = new DoubleArray(inverseLength);
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72 | }
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73 |
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74 | private void GetParameterValues(double[] p, out double[] inverseLength) {
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75 | // gather parameter values
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76 | if (HasFixedInverseLengthParameter) {
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77 | inverseLength = InverseLengthParameter.Value.ToArray();
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78 | } else {
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79 | inverseLength = p.Select(e => 1.0 / Math.Exp(e)).ToArray();
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80 | }
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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[] inverseLength;
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85 | GetParameterValues(p, out inverseLength);
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86 | var fixedInverseLength = HasFixedInverseLengthParameter;
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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) => Util.ScalarProd(x, i, j, inverseLength, columnIndices);
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90 | cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, inverseLength, columnIndices);
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91 | if (fixedInverseLength)
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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) => GetGradient(x, i, j, inverseLength, columnIndices);
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95 | return cov;
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96 | }
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97 |
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98 | private static IList<double> GetGradient(double[,] x, int i, int j, double[] inverseLength, int[] columnIndices) {
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99 | int k = 0;
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100 | var g = new List<double>(columnIndices.Length);
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101 | for (int c = 0; c < columnIndices.Length; c++) {
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102 | var columnIndex = columnIndices[c];
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103 | g.Add(-2.0 * x[i, columnIndex] * x[j, columnIndex] * inverseLength[k] * inverseLength[k]);
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104 | k++;
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105 | }
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106 | return g;
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107 | }
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108 | }
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109 | }
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