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.Linq;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Encodings.RealVectorEncoding;
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26 | using HeuristicLab.Operators;
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27 | using HeuristicLab.Parameters;
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28 | using HEAL.Attic;
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29 | using HeuristicLab.Problems.DataAnalysis;
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30 |
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31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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32 | [StorableType("73A3AC0C-849D-409F-8B5A-E5A08292A985")]
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33 | [Item(Name = "GaussianProcessHyperparameterInitializer",
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34 | Description = "Initializers the hyperparameter vector based on the mean function, covariance function, and number of allowed input variables.")]
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35 | public sealed class GaussianProcessHyperparameterInitializer : SingleSuccessorOperator {
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36 | private const string MeanFunctionParameterName = "MeanFunction";
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37 | private const string CovarianceFunctionParameterName = "CovarianceFunction";
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38 | private const string ProblemDataParameterName = "ProblemData";
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39 | private const string HyperparameterParameterName = "Hyperparameter";
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40 | private const string RandomParameterName = "Random";
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41 |
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42 | #region Parameter Properties
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43 | // in
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44 | public ILookupParameter<IMeanFunction> MeanFunctionParameter {
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45 | get { return (ILookupParameter<IMeanFunction>)Parameters[MeanFunctionParameterName]; }
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46 | }
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47 | public ILookupParameter<ICovarianceFunction> CovarianceFunctionParameter {
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48 | get { return (ILookupParameter<ICovarianceFunction>)Parameters[CovarianceFunctionParameterName]; }
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49 | }
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50 | public ILookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
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51 | get { return (ILookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
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52 | }
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53 | public ILookupParameter<IRandom> RandomParameter {
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54 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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55 | }
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56 | // out
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57 | public ILookupParameter<RealVector> HyperparameterParameter {
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58 | get { return (ILookupParameter<RealVector>)Parameters[HyperparameterParameterName]; }
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59 | }
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60 | #endregion
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61 |
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62 | #region Properties
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63 | private IMeanFunction MeanFunction { get { return MeanFunctionParameter.ActualValue; } }
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64 | private ICovarianceFunction CovarianceFunction { get { return CovarianceFunctionParameter.ActualValue; } }
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65 | private IDataAnalysisProblemData ProblemData { get { return ProblemDataParameter.ActualValue; } }
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66 | #endregion
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67 |
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68 | [StorableConstructor]
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69 | private GaussianProcessHyperparameterInitializer(StorableConstructorFlag _) : base(_) { }
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70 | private GaussianProcessHyperparameterInitializer(GaussianProcessHyperparameterInitializer original, Cloner cloner) : base(original, cloner) { }
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71 | public GaussianProcessHyperparameterInitializer()
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72 | : base() {
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73 | // in
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74 | Parameters.Add(new LookupParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function for the Gaussian process model."));
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75 | Parameters.Add(new LookupParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function for the Gaussian process model."));
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76 | Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName, "The input data for the Gaussian process."));
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77 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The pseudo random number generator to use for initializing the hyperparameter vector."));
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78 | // out
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79 | Parameters.Add(new LookupParameter<RealVector>(HyperparameterParameterName, "The initial hyperparameter vector for the Gaussian process model."));
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80 | }
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81 |
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82 | public override IDeepCloneable Clone(Cloner cloner) {
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83 | return new GaussianProcessHyperparameterInitializer(this, cloner);
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84 | }
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85 |
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86 | public override IOperation Apply() {
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87 | var inputVariablesCount = ProblemData.AllowedInputVariables.Count();
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88 | int l = 1 + MeanFunction.GetNumberOfParameters(inputVariablesCount) +
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89 | CovarianceFunction.GetNumberOfParameters(inputVariablesCount);
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90 | var r = new RealVector(l);
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91 | var rand = RandomParameter.ActualValue;
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92 | for (int i = 0; i < r.Length; i++)
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93 | r[i] = rand.NextDouble() * 10 - 5;
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94 |
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95 | HyperparameterParameter.ActualValue = r;
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96 | return base.Apply();
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97 | }
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98 | }
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99 | }
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