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.Persistence;
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28 |
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29 | namespace HeuristicLab.Algorithms.DataAnalysis {
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30 | [StorableType("8db41291-bc6e-40d7-92cc-5690853edb8c")]
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31 | [Item(Name = "CovarianceSum",
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32 | Description = "Sum covariance function for Gaussian processes.")]
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33 | public sealed class CovarianceSum : Item, ICovarianceFunction {
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34 | [Storable]
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35 | private ItemList<ICovarianceFunction> terms;
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36 |
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37 | [Storable]
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38 | private int numberOfVariables;
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39 | public ItemList<ICovarianceFunction> Terms {
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40 | get { return terms; }
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41 | }
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42 |
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43 | [StorableConstructor]
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44 | private CovarianceSum(bool deserializing)
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45 | : base(deserializing) {
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46 | }
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47 |
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48 | private CovarianceSum(CovarianceSum original, Cloner cloner)
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49 | : base(original, cloner) {
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50 | this.terms = cloner.Clone(original.terms);
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51 | this.numberOfVariables = original.numberOfVariables;
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52 | }
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53 |
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54 | public CovarianceSum()
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55 | : base() {
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56 | this.terms = new ItemList<ICovarianceFunction>();
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57 | }
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58 |
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59 | public override IDeepCloneable Clone(Cloner cloner) {
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60 | return new CovarianceSum(this, cloner);
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61 | }
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62 |
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63 | public int GetNumberOfParameters(int numberOfVariables) {
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64 | this.numberOfVariables = numberOfVariables;
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65 | return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
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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 | int offset = 0;
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70 | foreach (var t in terms) {
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71 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
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72 | t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
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73 | offset += numberOfParameters;
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74 | }
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75 | }
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76 |
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77 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
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78 | if (terms.Count == 0) throw new ArgumentException("at least one term is necessary for the product covariance function.");
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79 | var functions = new List<ParameterizedCovarianceFunction>();
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80 | foreach (var t in terms) {
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81 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
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82 | functions.Add(t.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
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83 | p = p.Skip(numberOfParameters).ToArray();
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84 | }
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85 |
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86 | var sum = new ParameterizedCovarianceFunction();
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87 | sum.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Sum();
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88 | sum.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Sum();
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89 | sum.CovarianceGradient = (x, i, j) => {
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90 | var g = new List<double>();
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91 | foreach (var e in functions)
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92 | g.AddRange(e.CovarianceGradient(x, i, j));
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93 | return g;
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94 | };
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95 | return sum;
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96 | }
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97 | }
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98 | }
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