1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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 | using System.Linq;
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22 | using HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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25 |
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26 | namespace HeuristicLab.Algorithms.DataAnalysis {
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27 | [StorableClass]
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28 | [Item(Name = "MeanSum", Description = "Sum of mean functions for Gaussian processes.")]
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29 | public sealed class MeanSum : Item, IMeanFunction {
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30 | [Storable]
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31 | private ItemList<IMeanFunction> terms;
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32 |
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33 | [Storable]
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34 | private int numberOfVariables;
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35 | public ItemList<IMeanFunction> Terms {
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36 | get { return terms; }
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37 | }
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38 |
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39 | [StorableConstructor]
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40 | private MeanSum(bool deserializing) : base(deserializing) { }
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41 | private MeanSum(MeanSum original, Cloner cloner)
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42 | : base(original, cloner) {
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43 | this.terms = cloner.Clone(original.terms);
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44 | this.numberOfVariables = original.numberOfVariables;
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45 | }
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46 | public MeanSum() {
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47 | this.terms = new ItemList<IMeanFunction>();
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48 | }
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49 |
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50 | public override IDeepCloneable Clone(Cloner cloner) {
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51 | return new MeanSum(this, cloner);
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52 | }
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53 |
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54 | public int GetNumberOfParameters(int numberOfVariables) {
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55 | this.numberOfVariables = numberOfVariables;
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56 | return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
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57 | }
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58 |
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59 | public void SetParameter(double[] hyp) {
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60 | int offset = 0;
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61 | foreach (var t in terms) {
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62 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
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63 | t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
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64 | offset += numberOfParameters;
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65 | }
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66 | }
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67 |
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68 | public double[] GetMean(double[,] x) {
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69 | var res = terms.First().GetMean(x);
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70 | foreach (var t in terms.Skip(1)) {
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71 | var a = t.GetMean(x);
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72 | for (int i = 0; i < res.Length; i++) res[i] += a[i];
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73 | }
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74 | return res;
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75 | }
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76 |
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77 | public double[] GetGradients(int k, double[,] x) {
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78 | int i = 0;
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79 | while (k >= terms[i].GetNumberOfParameters(numberOfVariables)) {
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80 | k -= terms[i].GetNumberOfParameters(numberOfVariables);
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81 | i++;
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82 | }
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83 | return terms[i].GetGradients(k, x);
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84 | }
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85 | }
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86 | }
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