#region License Information /* HeuristicLab * Copyright (C) 2002-2019 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.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 { public IValueParameter SelectedDimensionsParameter { get { return (IValueParameter)Parameters["SelectedDimensions"]; } } public IValueParameter CovarianceFunctionParameter { get { return (IValueParameter)Parameters["CovarianceFunction"]; } } [StorableConstructor] private CovarianceMask(bool deserializing) : base(deserializing) { } private CovarianceMask(CovarianceMask original, Cloner cloner) : base(original, cloner) { } public CovarianceMask() : base() { Name = ItemName; Description = ItemDescription; Parameters.Add(new OptionalValueParameter("SelectedDimensions", "The dimensions on which the specified covariance function should be applied to.")); Parameters.Add(new ValueParameter("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso())); } public override IDeepCloneable Clone(Cloner cloner) { return new CovarianceMask(this, cloner); } public int GetNumberOfParameters(int numberOfVariables) { if (SelectedDimensionsParameter.Value == null) return CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables); else return CovarianceFunctionParameter.Value.GetNumberOfParameters(SelectedDimensionsParameter.Value.Length); } public void SetParameter(double[] p) { CovarianceFunctionParameter.Value.SetParameter(p); } public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) { var cov = CovarianceFunctionParameter.Value; var selectedDimensions = SelectedDimensionsParameter.Value; return cov.GetParameterizedCovarianceFunction(p, selectedDimensions.Intersect(columnIndices).ToArray()); } } }