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.Linq;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Encodings.RealVectorEncoding;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 | using HeuristicLab.Problems.DataAnalysis;
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31 |
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32 | namespace HeuristicLab.Algorithms.DataAnalysis {
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33 | [StorableClass]
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34 | [Item(Name = "GaussianProcessRegressionModelCreator",
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35 | Description = "Creates a Gaussian process model for regression given the data, the hyperparameters, a mean function, and a covariance function.")]
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36 | public sealed class GaussianProcessRegressionModelCreator : GaussianProcessModelCreator, IGaussianProcessRegressionModelCreator {
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37 | private const string ProblemDataParameterName = "ProblemData";
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38 |
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39 | #region Parameter Properties
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40 | public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
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41 | get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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42 | }
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43 | #endregion
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44 |
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45 | #region Properties
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46 | private IRegressionProblemData ProblemData {
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47 | get { return ProblemDataParameter.ActualValue; }
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48 | }
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49 | #endregion
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50 | [StorableConstructor]
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51 | private GaussianProcessRegressionModelCreator(bool deserializing) : base(deserializing) { }
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52 | private GaussianProcessRegressionModelCreator(GaussianProcessRegressionModelCreator original, Cloner cloner) : base(original, cloner) { }
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53 | public GaussianProcessRegressionModelCreator()
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54 | : base() {
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55 | Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The regression problem data for the Gaussian process model."));
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56 | }
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57 |
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58 | public override IDeepCloneable Clone(Cloner cloner) {
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59 | return new GaussianProcessRegressionModelCreator(this, cloner);
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60 | }
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61 |
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62 | public override IOperation Apply() {
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63 | try {
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64 | var model = Create(ProblemData, Hyperparameter.ToArray(), MeanFunction, CovarianceFunction, ScaleInputValues);
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65 | ModelParameter.ActualValue = model;
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66 | NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(model.NegativeLogLikelihood);
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67 | NegativeLogPredictiveProbabilityParameter.ActualValue = new DoubleValue(model.NegativeLooPredictiveProbability);
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68 | HyperparameterGradientsParameter.ActualValue = new RealVector(model.HyperparameterGradients);
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69 | return base.Apply();
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70 | }
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71 | catch (ArgumentException) { }
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72 | catch (alglib.alglibexception) { }
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73 | NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(1E300);
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74 | HyperparameterGradientsParameter.ActualValue = new RealVector(Hyperparameter.Count());
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75 | return base.Apply();
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76 | }
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77 |
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78 | public static IGaussianProcessModel Create(IRegressionProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction, bool scaleInputs = true) {
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79 | return new GaussianProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction, scaleInputs);
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80 | }
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81 | }
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82 | }
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