#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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Operators; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] // base class for GaussianProcessModelCreators (specific for classification and regression) public abstract class GaussianProcessModelCreator : SingleSuccessorOperator { private const string HyperparameterParameterName = "Hyperparameter"; private const string MeanFunctionParameterName = "MeanFunction"; private const string CovarianceFunctionParameterName = "CovarianceFunction"; private const string ModelParameterName = "Model"; private const string NegativeLogLikelihoodParameterName = "NegativeLogLikelihood"; private const string HyperparameterGradientsParameterName = "HyperparameterGradients"; protected const string ScaleInputValuesParameterName = "ScaleInputValues"; #region Parameter Properties // in public ILookupParameter HyperparameterParameter { get { return (ILookupParameter)Parameters[HyperparameterParameterName]; } } public ILookupParameter MeanFunctionParameter { get { return (ILookupParameter)Parameters[MeanFunctionParameterName]; } } public ILookupParameter CovarianceFunctionParameter { get { return (ILookupParameter)Parameters[CovarianceFunctionParameterName]; } } // out public ILookupParameter ModelParameter { get { return (ILookupParameter)Parameters[ModelParameterName]; } } public ILookupParameter HyperparameterGradientsParameter { get { return (ILookupParameter)Parameters[HyperparameterGradientsParameterName]; } } public ILookupParameter NegativeLogLikelihoodParameter { get { return (ILookupParameter)Parameters[NegativeLogLikelihoodParameterName]; } } public ILookupParameter ScaleInputValuesParameter { get { return (ILookupParameter)Parameters[ScaleInputValuesParameterName]; } } #endregion #region Properties protected RealVector Hyperparameter { get { return HyperparameterParameter.ActualValue; } } protected IMeanFunction MeanFunction { get { return MeanFunctionParameter.ActualValue; } } protected ICovarianceFunction CovarianceFunction { get { return CovarianceFunctionParameter.ActualValue; } } public bool ScaleInputValues { get { return ScaleInputValuesParameter.ActualValue.Value; } } #endregion [StorableConstructor] protected GaussianProcessModelCreator(bool deserializing) : base(deserializing) { } protected GaussianProcessModelCreator(GaussianProcessModelCreator original, Cloner cloner) : base(original, cloner) { } protected GaussianProcessModelCreator() : base() { // in Parameters.Add(new LookupParameter(HyperparameterParameterName, "The hyperparameters for the Gaussian process model.")); 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.")); // out Parameters.Add(new LookupParameter(ModelParameterName, "The resulting Gaussian process model")); Parameters.Add(new LookupParameter(HyperparameterGradientsParameterName, "The gradients of the hyperparameters for the produced Gaussian process model (necessary for hyperparameter optimization)")); Parameters.Add(new LookupParameter(NegativeLogLikelihoodParameterName, "The negative log-likelihood of the produced Gaussian process model given the data.")); Parameters.Add(new LookupParameter(ScaleInputValuesParameterName, "Determines if the input variable values are scaled to the range [0..1] for training.")); Parameters[ScaleInputValuesParameterName].Hidden = true; } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { if (!Parameters.ContainsKey(ScaleInputValuesParameterName)) { Parameters.Add(new LookupParameter(ScaleInputValuesParameterName, "Determines if the input variable values are scaled to the range [0..1] for training.")); Parameters[ScaleInputValuesParameterName].Hidden = true; } } } }