#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;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableType("A28E75C5-E0F9-4FB6-A125-50B77CAC7C7D")]
[Item(Name = "GaussianProcessRegressionModelCreator",
Description = "Creates a Gaussian process model for regression given the data, the hyperparameters, a mean function, and a covariance function.")]
public sealed class GaussianProcessRegressionModelCreator : GaussianProcessModelCreator, IGaussianProcessRegressionModelCreator {
private const string ProblemDataParameterName = "ProblemData";
#region Parameter Properties
public ILookupParameter ProblemDataParameter {
get { return (ILookupParameter)Parameters[ProblemDataParameterName]; }
}
#endregion
#region Properties
private IRegressionProblemData ProblemData {
get { return ProblemDataParameter.ActualValue; }
}
#endregion
[StorableConstructor]
private GaussianProcessRegressionModelCreator(bool deserializing) : base(deserializing) { }
private GaussianProcessRegressionModelCreator(GaussianProcessRegressionModelCreator original, Cloner cloner) : base(original, cloner) { }
public GaussianProcessRegressionModelCreator()
: base() {
Parameters.Add(new LookupParameter(ProblemDataParameterName, "The regression problem data for the Gaussian process model."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new GaussianProcessRegressionModelCreator(this, cloner);
}
public override IOperation Apply() {
try {
var model = Create(ProblemData, Hyperparameter.ToArray(), MeanFunction, CovarianceFunction, ScaleInputValues);
ModelParameter.ActualValue = model;
NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(model.NegativeLogLikelihood);
HyperparameterGradientsParameter.ActualValue = new RealVector(model.HyperparameterGradients);
return base.Apply();
}
catch (ArgumentException) { }
catch (alglib.alglibexception) { }
NegativeLogLikelihoodParameter.ActualValue = new DoubleValue(1E300);
HyperparameterGradientsParameter.ActualValue = new RealVector(Hyperparameter.Count());
return base.Apply();
}
public static IGaussianProcessModel Create(IRegressionProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction, bool scaleInputs = true) {
return new GaussianProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction, scaleInputs);
}
}
}