1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 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.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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27 |
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28 | namespace HeuristicLab.Algorithms.DataAnalysis {
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29 | [StorableClass]
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30 | [Item(Name = "MeanSum", Description = "Sum of mean functions for Gaussian processes.")]
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31 | public sealed class MeanSum : Item, IMeanFunction {
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32 | [Storable]
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33 | private ItemList<IMeanFunction> terms;
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34 |
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35 | [Storable]
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36 | private int numberOfVariables;
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37 | public ItemList<IMeanFunction> Terms {
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38 | get { return terms; }
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39 | }
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40 |
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41 | [StorableConstructor]
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42 | private MeanSum(bool deserializing) : base(deserializing) { }
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43 | private MeanSum(MeanSum original, Cloner cloner)
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44 | : base(original, cloner) {
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45 | this.terms = cloner.Clone(original.terms);
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46 | this.numberOfVariables = original.numberOfVariables;
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47 | }
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48 | public MeanSum() {
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49 | this.terms = new ItemList<IMeanFunction>();
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50 | }
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51 |
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52 | public override IDeepCloneable Clone(Cloner cloner) {
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53 | return new MeanSum(this, cloner);
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54 | }
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55 |
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56 | public int GetNumberOfParameters(int numberOfVariables) {
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57 | this.numberOfVariables = numberOfVariables;
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58 | return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
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59 | }
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60 |
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61 | public void SetParameter(double[] p) {
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62 | int offset = 0;
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63 | foreach (var t in terms) {
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64 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
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65 | t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
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66 | offset += numberOfParameters;
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67 | }
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68 | }
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69 |
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70 | public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable<int> columnIndices) {
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71 | var termMf = new List<ParameterizedMeanFunction>();
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72 | int totalNumberOfParameters = GetNumberOfParameters(numberOfVariables);
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73 | int[] termIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the correct mean-term
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74 | int[] hyperParameterIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the l-th parameter of the correct mean-term
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75 | int c = 0;
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76 | // get the parameterized mean function for each term
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77 | for (int termIndex = 0; termIndex < terms.Count; termIndex++) {
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78 | var numberOfParameters = terms[termIndex].GetNumberOfParameters(numberOfVariables);
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79 | termMf.Add(terms[termIndex].GetParameterizedMeanFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
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80 | p = p.Skip(numberOfParameters).ToArray();
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81 |
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82 | for (int hyperParameterIndex = 0; hyperParameterIndex < numberOfParameters; hyperParameterIndex++) {
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83 | termIndexMap[c] = termIndex;
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84 | hyperParameterIndexMap[c] = hyperParameterIndex;
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85 | c++;
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86 | }
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87 | }
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88 |
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89 | var mf = new ParameterizedMeanFunction();
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90 | mf.Mean = (x, i) => termMf.Select(t => t.Mean(x, i)).Sum();
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91 | mf.Gradient = (x, i, k) => {
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92 | return termMf[termIndexMap[k]].Gradient(x, i, hyperParameterIndexMap[k]);
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93 | };
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94 | return mf;
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95 | }
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96 | }
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97 | }
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