#region License Information
/* HeuristicLab
* Copyright (C) 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 HEAL.Attic;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableType("73A3AC0C-849D-409F-8B5A-E5A08292A985")]
[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(StorableConstructorFlag _) : base(_) { }
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();
}
}
}