#region License Information
/* HeuristicLab
* Copyright (C) 2002-2012 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableClass]
[Item(Name = "CovarianceMask",
Description = "Masking covariance function for dimension selection can be used to apply a covariance function only on certain input dimensions.")]
public sealed class CovarianceMask : ParameterizedNamedItem, ICovarianceFunction {
[Storable]
private int[] selectedDimensions;
[Storable]
private readonly ValueParameter selectedDimensionsParameter;
public IValueParameter SelectedDimensionsParameter {
get { return selectedDimensionsParameter; }
}
[Storable]
private ICovarianceFunction cov;
[Storable]
private readonly ValueParameter covParameter;
public IValueParameter CovarianceFunctionParameter {
get { return covParameter; }
}
[StorableConstructor]
private CovarianceMask(bool deserializing)
: base(deserializing) {
}
private CovarianceMask(CovarianceMask original, Cloner cloner)
: base(original, cloner) {
this.selectedDimensionsParameter = cloner.Clone(original.selectedDimensionsParameter);
this.selectedDimensions = (int[])original.selectedDimensions.Clone();
this.covParameter = cloner.Clone(original.covParameter);
this.cov = cloner.Clone(original.cov);
RegisterEvents();
}
public CovarianceMask()
: base() {
Name = ItemName;
Description = ItemDescription;
this.selectedDimensionsParameter = new ValueParameter("SelectedDimensions", "The dimensions on which the specified covariance function should be applied to.");
this.covParameter = new ValueParameter("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso());
cov = covParameter.Value;
Parameters.Add(selectedDimensionsParameter);
Parameters.Add(covParameter);
RegisterEvents();
}
public override IDeepCloneable Clone(Cloner cloner) {
return new CovarianceMask(this, cloner);
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
RegisterEvents();
}
private void RegisterEvents() {
Util.AttachArrayChangeHandler(selectedDimensionsParameter, () => {
selectedDimensions = selectedDimensionsParameter.Value
.OrderBy(x => x)
.Distinct()
.ToArray();
if (selectedDimensions.Length == 0) selectedDimensions = null;
});
covParameter.ValueChanged += (sender, args) => { cov = covParameter.Value; };
}
public int GetNumberOfParameters(int numberOfVariables) {
if (selectedDimensions == null) return cov.GetNumberOfParameters(numberOfVariables);
else return cov.GetNumberOfParameters(selectedDimensions.Length);
}
public void SetParameter(double[] hyp) {
cov.SetParameter(hyp);
}
public double GetCovariance(double[,] x, int i, int j, IEnumerable columnIndices) {
return cov.GetCovariance(x, i, j, selectedDimensions);
}
public IEnumerable GetGradient(double[,] x, int i, int j, IEnumerable columnIndices) {
return cov.GetGradient(x, i, j, selectedDimensions);
}
public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable columnIndices) {
return cov.GetCrossCovariance(x, xt, i, j, selectedDimensions);
}
}
}