#region License Information
/* HeuristicLab
* Copyright (C) 2002-2013 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.Algorithms.GradientDescent;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Gaussian process least-squares classification data analysis algorithm.
///
[Item("Gaussian Process Least-Squares Classification", "Gaussian process least-squares classification data analysis algorithm.")]
[Creatable("Data Analysis")]
[StorableClass]
public sealed class GaussianProcessClassification : GaussianProcessBase, IStorableContent {
public string Filename { get; set; }
public override Type ProblemType { get { return typeof(IClassificationProblem); } }
public new IClassificationProblem Problem {
get { return (IClassificationProblem)base.Problem; }
set { base.Problem = value; }
}
private const string ModelParameterName = "Model";
#region parameter properties
public IConstrainedValueParameter GaussianProcessModelCreatorParameter {
get { return (IConstrainedValueParameter)Parameters[ModelCreatorParameterName]; }
}
public IFixedValueParameter GaussianProcessSolutionCreatorParameter {
get { return (IFixedValueParameter)Parameters[SolutionCreatorParameterName]; }
}
#endregion
[StorableConstructor]
private GaussianProcessClassification(bool deserializing) : base(deserializing) { }
private GaussianProcessClassification(GaussianProcessClassification original, Cloner cloner)
: base(original, cloner) {
RegisterEventHandlers();
}
public GaussianProcessClassification()
: base(new ClassificationProblem()) {
this.name = ItemName;
this.description = ItemDescription;
var modelCreators = ApplicationManager.Manager.GetInstances();
var defaultModelCreator = modelCreators.First(c => c is GaussianProcessClassificationModelCreator);
// GP regression and classification algorithms only differ in the model and solution creators,
// thus we use a common base class and use operator parameters to implement the specific versions.
// Different model creators can be implemented,
// but the solution creator is implemented in a generic fashion already and we don't allow derived solution creators
Parameters.Add(new ConstrainedValueParameter(ModelCreatorParameterName, "The operator to create the Gaussian process model.",
new ItemSet(modelCreators), defaultModelCreator));
// this parameter is not intended to be changed,
Parameters.Add(new FixedValueParameter(SolutionCreatorParameterName, "The solution creator for the algorithm",
new GaussianProcessClassificationSolutionCreator()));
Parameters[SolutionCreatorParameterName].Hidden = true;
ParameterizedModelCreators();
ParameterizeSolutionCreator(GaussianProcessSolutionCreatorParameter.Value);
RegisterEventHandlers();
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
RegisterEventHandlers();
}
public override IDeepCloneable Clone(Cloner cloner) {
return new GaussianProcessClassification(this, cloner);
}
#region events
private void RegisterEventHandlers() {
GaussianProcessModelCreatorParameter.ValueChanged += ModelCreatorParameter_ValueChanged;
}
private void ModelCreatorParameter_ValueChanged(object sender, EventArgs e) {
ParameterizedModelCreator(GaussianProcessModelCreatorParameter.Value);
}
#endregion
private void ParameterizedModelCreators() {
foreach (var creator in GaussianProcessModelCreatorParameter.ValidValues) {
ParameterizedModelCreator(creator);
}
}
private void ParameterizedModelCreator(IGaussianProcessClassificationModelCreator modelCreator) {
modelCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
modelCreator.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
modelCreator.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
// parameter names fixed by the algorithm
modelCreator.ModelParameter.ActualName = ModelParameterName;
modelCreator.HyperparameterParameter.ActualName = HyperparameterParameterName;
modelCreator.HyperparameterGradientsParameter.ActualName = HyperparameterGradientsParameterName;
modelCreator.NegativeLogLikelihoodParameter.ActualName = NegativeLogLikelihoodParameterName;
}
private void ParameterizeSolutionCreator(GaussianProcessClassificationSolutionCreator solutionCreator) {
solutionCreator.ModelParameter.ActualName = ModelParameterName;
solutionCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
}
}
}