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2 | #region License Information
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3 | /* HeuristicLab
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4 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 | #endregion
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22 |
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23 | using System;
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24 | using System.Collections.Generic;
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25 | using System.Linq;
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26 | using HeuristicLab.Algorithms.GradientDescent;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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29 | using HeuristicLab.Data;
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30 | using HeuristicLab.Operators;
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31 | using HeuristicLab.Optimization;
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32 | using HeuristicLab.Parameters;
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33 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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34 | using HeuristicLab.PluginInfrastructure;
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35 | using HeuristicLab.Problems.DataAnalysis;
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36 |
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37 | namespace HeuristicLab.Algorithms.DataAnalysis {
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38 | /// <summary>
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39 | ///Gaussian process regression data analysis algorithm.
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40 | /// </summary>
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41 | [Item("Gaussian Process Regression", "Gaussian process regression data analysis algorithm.")]
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42 | [Creatable("Data Analysis")]
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43 | [StorableClass]
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44 | public sealed class GaussianProcessRegression : EngineAlgorithm, IStorableContent {
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45 | public string Filename { get; set; }
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46 |
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47 | public override Type ProblemType { get { return typeof(IRegressionProblem); } }
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48 | public new IRegressionProblem Problem {
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49 | get { return (IRegressionProblem)base.Problem; }
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50 | set { base.Problem = value; }
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51 | }
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52 |
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53 | private const string MeanFunctionParameterName = "MeanFunction";
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54 | private const string CovarianceFunctionParameterName = "CovarianceFunction";
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55 | private const string MinimizationIterationsParameterName = "Iterations";
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56 | private const string ApproximateGradientsParameterName = "ApproximateGradients";
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57 |
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58 | #region parameter properties
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59 | public IConstrainedValueParameter<IMeanFunction> MeanFunctionParameter {
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60 | get { return (IConstrainedValueParameter<IMeanFunction>)Parameters[MeanFunctionParameterName]; }
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61 | }
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62 | public IConstrainedValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
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63 | get { return (IConstrainedValueParameter<ICovarianceFunction>)Parameters[CovarianceFunctionParameterName]; }
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64 | }
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65 | public IValueParameter<IntValue> MinimizationIterationsParameter {
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66 | get { return (IValueParameter<IntValue>)Parameters[MinimizationIterationsParameterName]; }
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67 | }
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68 | #endregion
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69 | #region properties
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70 | public IMeanFunction MeanFunction {
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71 | set { MeanFunctionParameter.Value = value; }
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72 | get { return MeanFunctionParameter.Value; }
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73 | }
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74 | public ICovarianceFunction CovarianceFunction {
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75 | set { CovarianceFunctionParameter.Value = value; }
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76 | get { return CovarianceFunctionParameter.Value; }
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77 | }
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78 | public int MinimizationIterations {
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79 | set { MinimizationIterationsParameter.Value.Value = value; }
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80 | get { return MinimizationIterationsParameter.Value.Value; }
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81 | }
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82 | #endregion
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83 | [StorableConstructor]
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84 | private GaussianProcessRegression(bool deserializing) : base(deserializing) { }
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85 | private GaussianProcessRegression(GaussianProcessRegression original, Cloner cloner)
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86 | : base(original, cloner) {
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87 | }
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88 | public GaussianProcessRegression()
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89 | : base() {
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90 | this.name = ItemName;
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91 | this.description = ItemDescription;
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92 |
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93 | Problem = new RegressionProblem();
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94 |
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95 | List<IMeanFunction> meanFunctions = ApplicationManager.Manager.GetInstances<IMeanFunction>().ToList();
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96 | List<ICovarianceFunction> covFunctions = ApplicationManager.Manager.GetInstances<ICovarianceFunction>().ToList();
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97 |
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98 | Parameters.Add(new ConstrainedValueParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function to use.",
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99 | new ItemSet<IMeanFunction>(meanFunctions), meanFunctions.First()));
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100 | Parameters.Add(new ConstrainedValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.",
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101 | new ItemSet<ICovarianceFunction>(covFunctions), covFunctions.First()));
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102 | Parameters.Add(new ValueParameter<IntValue>(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20)));
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103 | Parameters.Add(new ValueParameter<BoolValue>(ApproximateGradientsParameterName, "Indicates that gradients should not be approximated (necessary for LM-BFGS).", new BoolValue(false)));
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104 | Parameters[ApproximateGradientsParameterName].Hidden = true; // should not be changed
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105 |
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106 | var gpInitializer = new GaussianProcessHyperparameterInitializer();
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107 | var bfgsInitializer = new LbfgsInitializer();
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108 | var makeStep = new LbfgsMakeStep();
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109 | var branch = new ConditionalBranch();
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110 | var modelCreator = new GaussianProcessRegressionModelCreator();
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111 | var updateResults = new LbfgsUpdateResults();
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112 | var analyzer = new LbfgsAnalyzer();
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113 | var finalModelCreator = new GaussianProcessRegressionModelCreator();
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114 | var finalAnalyzer = new LbfgsAnalyzer();
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115 | var solutionCreator = new GaussianProcessRegressionSolutionCreator();
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116 |
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117 | OperatorGraph.InitialOperator = gpInitializer;
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118 |
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119 | gpInitializer.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
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120 | gpInitializer.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
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121 | gpInitializer.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
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122 | gpInitializer.HyperparameterParameter.ActualName = modelCreator.HyperparameterParameter.Name;
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123 | gpInitializer.Successor = bfgsInitializer;
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124 |
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125 | bfgsInitializer.IterationsParameter.ActualName = MinimizationIterationsParameterName;
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126 | bfgsInitializer.PointParameter.ActualName = modelCreator.HyperparameterParameter.Name;
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127 | bfgsInitializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
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128 | bfgsInitializer.Successor = makeStep;
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129 |
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130 | makeStep.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
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131 | makeStep.PointParameter.ActualName = modelCreator.HyperparameterParameter.Name;
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132 | makeStep.Successor = branch;
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133 |
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134 | branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
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135 | branch.FalseBranch = modelCreator;
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136 | branch.TrueBranch = finalModelCreator;
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137 |
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138 | modelCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
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139 | modelCreator.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
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140 | modelCreator.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
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141 | modelCreator.Successor = updateResults;
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142 |
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143 | updateResults.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
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144 | updateResults.QualityParameter.ActualName = modelCreator.NegativeLogLikelihoodParameter.Name;
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145 | updateResults.QualityGradientsParameter.ActualName = modelCreator.HyperparameterGradientsParameter.Name;
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146 | updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
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147 | updateResults.Successor = analyzer;
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148 |
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149 | analyzer.QualityParameter.ActualName = modelCreator.NegativeLogLikelihoodParameter.Name;
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150 | analyzer.PointParameter.ActualName = modelCreator.HyperparameterParameter.Name;
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151 | analyzer.QualityGradientsParameter.ActualName = modelCreator.HyperparameterGradientsParameter.Name;
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152 | analyzer.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
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153 | analyzer.PointsTableParameter.ActualName = "Hyperparameter table";
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154 | analyzer.QualityGradientsTableParameter.ActualName = "Gradients table";
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155 | analyzer.QualitiesTableParameter.ActualName = "Negative log likelihood table";
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156 | analyzer.Successor = makeStep;
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157 |
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158 | finalModelCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
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159 | finalModelCreator.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
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160 | finalModelCreator.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
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161 | finalModelCreator.HyperparameterParameter.ActualName = bfgsInitializer.PointParameter.ActualName;
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162 | finalModelCreator.Successor = finalAnalyzer;
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163 |
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164 | finalAnalyzer.QualityParameter.ActualName = modelCreator.NegativeLogLikelihoodParameter.Name;
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165 | finalAnalyzer.PointParameter.ActualName = modelCreator.HyperparameterParameter.Name;
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166 | finalAnalyzer.QualityGradientsParameter.ActualName = modelCreator.HyperparameterGradientsParameter.Name;
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167 | finalAnalyzer.PointsTableParameter.ActualName = analyzer.PointsTableParameter.ActualName;
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168 | finalAnalyzer.QualityGradientsTableParameter.ActualName = analyzer.QualityGradientsTableParameter.ActualName;
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169 | finalAnalyzer.QualitiesTableParameter.ActualName = analyzer.QualitiesTableParameter.ActualName;
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170 | finalAnalyzer.Successor = solutionCreator;
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171 |
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172 | solutionCreator.ModelParameter.ActualName = finalModelCreator.ModelParameter.Name;
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173 | solutionCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
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174 | }
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175 |
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176 | [StorableHook(HookType.AfterDeserialization)]
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177 | private void AfterDeserialization() { }
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178 |
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179 | public override IDeepCloneable Clone(Cloner cloner) {
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180 | return new GaussianProcessRegression(this, cloner);
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181 | }
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182 | }
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183 | }
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