#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; using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item(Name = "CovarianceSquaredExponentialIso", Description = "Isotropic squared exponential covariance function for Gaussian processes.")] public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction { [Storable] private double sf2; [Storable] private readonly HyperParameter scaleParameter; public IValueParameter ScaleParameter { get { return scaleParameter; } } [Storable] private double inverseLength; [Storable] private readonly HyperParameter inverseLengthParameter; public IValueParameter InverseLengthParameter { get { return inverseLengthParameter; } } [StorableConstructor] private CovarianceSquaredExponentialIso(bool deserializing) : base(deserializing) { } private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner) : base(original, cloner) { this.sf2 = original.sf2; this.scaleParameter = cloner.Clone(original.scaleParameter); this.inverseLength = original.inverseLength; this.inverseLengthParameter = cloner.Clone(original.inverseLengthParameter); RegisterEvents(); } public CovarianceSquaredExponentialIso() : base() { Name = ItemName; Description = ItemDescription; this.scaleParameter = new HyperParameter("Scale", "The scale parameter of the isometric squared exponential covariance function."); this.inverseLengthParameter = new HyperParameter("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function."); Parameters.Add(scaleParameter); Parameters.Add(inverseLengthParameter); RegisterEvents(); } public override IDeepCloneable Clone(Cloner cloner) { return new CovarianceSquaredExponentialIso(this, cloner); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEvents(); } private void RegisterEvents() { Util.AttachValueChangeHandler(scaleParameter, () => { sf2 = scaleParameter.Value.Value; }); Util.AttachValueChangeHandler(inverseLengthParameter, () => { inverseLength = inverseLengthParameter.Value.Value; }); } public int GetNumberOfParameters(int numberOfVariables) { return (scaleParameter.Fixed ? 0 : 1) + (inverseLengthParameter.Fixed ? 0 : 1); } public void SetParameter(double[] hyp) { int i = 0; if (!inverseLengthParameter.Fixed) { inverseLengthParameter.SetValue(new DoubleValue(1.0 / Math.Exp(hyp[i]))); i++; } if (!scaleParameter.Fixed) { scaleParameter.SetValue(new DoubleValue(Math.Exp(2 * hyp[i]))); i++; } if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialIso", "hyp"); } public double GetCovariance(double[,] x, int i, int j, IEnumerable columnIndices) { double d = i == j ? 0.0 : Util.SqrDist(x, i, j, inverseLength, columnIndices); return sf2 * Math.Exp(-d / 2.0); } public IEnumerable GetGradient(double[,] x, int i, int j, IEnumerable columnIndices) { double d = i == j ? 0.0 : Util.SqrDist(x, i, j, inverseLength, columnIndices); double g = Math.Exp(-d / 2.0); yield return sf2 * g * d; yield return 2.0 * sf2 * g; } public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable columnIndices) { double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices); return sf2 * Math.Exp(-d / 2.0); } } }