#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Operators; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item(Name = "GaussianProcessHyperparameterInitializer", Description = "Initializers the hyperparameter vector based on the mean function, covariance function, and number of allowed input variables.")] public sealed class GaussianProcessHyperparameterInitializer : SingleSuccessorOperator { private const string MeanFunctionParameterName = "MeanFunction"; private const string CovarianceFunctionParameterName = "CovarianceFunction"; private const string ProblemDataParameterName = "ProblemData"; private const string HyperparameterParameterName = "Hyperparameter"; private const string RandomParameterName = "Random"; #region Parameter Properties // in public ILookupParameter MeanFunctionParameter { get { return (ILookupParameter)Parameters[MeanFunctionParameterName]; } } public ILookupParameter CovarianceFunctionParameter { get { return (ILookupParameter)Parameters[CovarianceFunctionParameterName]; } } public ILookupParameter ProblemDataParameter { get { return (ILookupParameter)Parameters[ProblemDataParameterName]; } } public ILookupParameter RandomParameter { get { return (ILookupParameter)Parameters[RandomParameterName]; } } // out public ILookupParameter HyperparameterParameter { get { return (ILookupParameter)Parameters[HyperparameterParameterName]; } } #endregion #region Properties private IMeanFunction MeanFunction { get { return MeanFunctionParameter.ActualValue; } } private ICovarianceFunction CovarianceFunction { get { return CovarianceFunctionParameter.ActualValue; } } private IDataAnalysisProblemData ProblemData { get { return ProblemDataParameter.ActualValue; } } #endregion [StorableConstructor] private GaussianProcessHyperparameterInitializer(bool deserializing) : base(deserializing) { } private GaussianProcessHyperparameterInitializer(GaussianProcessHyperparameterInitializer original, Cloner cloner) : base(original, cloner) { } public GaussianProcessHyperparameterInitializer() : base() { // in Parameters.Add(new LookupParameter(MeanFunctionParameterName, "The mean function for the Gaussian process model.")); Parameters.Add(new LookupParameter(CovarianceFunctionParameterName, "The covariance function for the Gaussian process model.")); Parameters.Add(new LookupParameter(ProblemDataParameterName, "The input data for the Gaussian process.")); Parameters.Add(new LookupParameter(RandomParameterName, "The pseudo random number generator to use for initializing the hyperparameter vector.")); // out Parameters.Add(new LookupParameter(HyperparameterParameterName, "The initial hyperparameter vector for the Gaussian process model.")); } public override IDeepCloneable Clone(Cloner cloner) { return new GaussianProcessHyperparameterInitializer(this, cloner); } public override IOperation Apply() { var inputVariablesCount = ProblemData.AllowedInputVariables.Count(); int l = 1 + MeanFunction.GetNumberOfParameters(inputVariablesCount) + CovarianceFunction.GetNumberOfParameters(inputVariablesCount); var r = new RealVector(l); var rand = RandomParameter.ActualValue; for (int i = 0; i < r.Length; i++) r[i] = rand.NextDouble() * 10 - 5; HyperparameterParameter.ActualValue = r; return base.Apply(); } } }