[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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| 22 | using System.Linq;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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[8396] | 25 | using HeuristicLab.Encodings.RealVectorEncoding;
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[8371] | 26 | using HeuristicLab.Operators;
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| 27 | using HeuristicLab.Parameters;
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[16565] | 28 | using HEAL.Attic;
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[8371] | 29 | using HeuristicLab.Problems.DataAnalysis;
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| 30 |
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| 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[16565] | 32 | [StorableType("73A3AC0C-849D-409F-8B5A-E5A08292A985")]
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[8396] | 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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[8371] | 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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[8396] | 39 | private const string HyperparameterParameterName = "Hyperparameter";
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[8419] | 40 | private const string RandomParameterName = "Random";
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[8371] | 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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[8419] | 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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[8371] | 56 | // out
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[8396] | 57 | public ILookupParameter<RealVector> HyperparameterParameter {
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| 58 | get { return (ILookupParameter<RealVector>)Parameters[HyperparameterParameterName]; }
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[8371] | 59 | }
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| 60 | #endregion
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| 61 |
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| 62 | #region Properties
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[8375] | 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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[8371] | 66 | #endregion
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| 67 |
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| 68 | [StorableConstructor]
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[16565] | 69 | private GaussianProcessHyperparameterInitializer(StorableConstructorFlag _) : base(_) { }
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[8396] | 70 | private GaussianProcessHyperparameterInitializer(GaussianProcessHyperparameterInitializer original, Cloner cloner) : base(original, cloner) { }
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| 71 | public GaussianProcessHyperparameterInitializer()
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[8371] | 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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[8419] | 77 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The pseudo random number generator to use for initializing the hyperparameter vector."));
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[8371] | 78 | // out
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[8396] | 79 | Parameters.Add(new LookupParameter<RealVector>(HyperparameterParameterName, "The initial hyperparameter vector for the Gaussian process model."));
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[8371] | 80 | }
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| 81 |
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| 82 | public override IDeepCloneable Clone(Cloner cloner) {
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[8396] | 83 | return new GaussianProcessHyperparameterInitializer(this, cloner);
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[8371] | 84 | }
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| 85 |
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[8396] | 86 | public override IOperation Apply() {
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[8371] | 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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[8419] | 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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[8732] | 93 | r[i] = rand.NextDouble() * 10 - 5;
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[8419] | 94 |
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| 95 | HyperparameterParameter.ActualValue = r;
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[8371] | 96 | return base.Apply();
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| 97 | }
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| 98 | }
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| 99 | }
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