[8371] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17180] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8371] | 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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[8473] | 22 | using System;
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[8371] | 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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[8396] | 27 | using HeuristicLab.Encodings.RealVectorEncoding;
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[8371] | 28 | using HeuristicLab.Parameters;
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[16565] | 29 | using HEAL.Attic;
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[8371] | 30 | using HeuristicLab.Problems.DataAnalysis;
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| 31 |
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| 32 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[16565] | 33 | [StorableType("0C20B1D2-9A58-4E77-9300-F5D76650DC19")]
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[8371] | 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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[9096] | 36 | public sealed class GaussianProcessRegressionModelCreator : GaussianProcessModelCreator, IGaussianProcessRegressionModelCreator {
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[8371] | 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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[8375] | 46 | private IRegressionProblemData ProblemData {
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[8371] | 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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[16565] | 51 | private GaussianProcessRegressionModelCreator(StorableConstructorFlag _) : base(_) { }
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[8371] | 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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[8473] | 63 | try {
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[13118] | 64 | var model = Create(ProblemData, Hyperparameter.ToArray(), MeanFunction, CovarianceFunction, ScaleInputValues);
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[8473] | 65 | ModelParameter.ActualValue = model;
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| 66 | NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(model.NegativeLogLikelihood);
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[15187] | 67 | NegativeLogPseudoLikelihoodParameter.ActualValue = new DoubleValue(model.LooCvNegativeLogPseudoLikelihood);
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[8484] | 68 | HyperparameterGradientsParameter.ActualValue = new RealVector(model.HyperparameterGradients);
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[8473] | 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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[8371] | 75 | return base.Apply();
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| 76 | }
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| 77 |
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[13118] | 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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[8371] | 80 | }
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| 81 | }
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| 82 | }
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