#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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item(Name = "CovarianceLinearArd", Description = "Linear covariance function with automatic relevance determination for Gaussian processes.")] public sealed class CovarianceLinearArd : ParameterizedNamedItem, ICovarianceFunction { [Storable] private double[] inverseLength; [Storable] private readonly HyperParameter inverseLengthParameter; public IValueParameter InverseLengthParameter { get { return inverseLengthParameter; } } [StorableConstructor] private CovarianceLinearArd(bool deserializing) : base(deserializing) { } private CovarianceLinearArd(CovarianceLinearArd original, Cloner cloner) : base(original, cloner) { inverseLengthParameter = cloner.Clone(original.inverseLengthParameter); if (original.inverseLength != null) { this.inverseLength = new double[original.inverseLength.Length]; Array.Copy(original.inverseLength, inverseLength, inverseLength.Length); } RegisterEvents(); } public CovarianceLinearArd() : base() { Name = ItemName; Description = ItemDescription; inverseLengthParameter = new HyperParameter("InverseLength", "The inverse length parameter for ARD."); Parameters.Add(inverseLengthParameter); RegisterEvents(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEvents(); } public override IDeepCloneable Clone(Cloner cloner) { return new CovarianceLinearArd(this, cloner); } // caching private void RegisterEvents() { Util.AttachArrayChangeHandler(inverseLengthParameter, () => { inverseLength = inverseLengthParameter.Value.ToArray(); }); } public int GetNumberOfParameters(int numberOfVariables) { if (!inverseLengthParameter.Fixed) return numberOfVariables; else return 0; } public void SetParameter(double[] hyp) { if (!inverseLengthParameter.Fixed && hyp.Length > 0) { inverseLengthParameter.SetValue(new DoubleArray(hyp.Select(e => 1.0 / Math.Exp(e)).ToArray())); } else throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceLinearArd", "hyp"); } public double GetCovariance(double[,] x, int i, int j, IEnumerable columnIndices) { return Util.ScalarProd(x, i, j, inverseLength, columnIndices); } public IEnumerable GetGradient(double[,] x, int i, int j, IEnumerable columnIndices) { if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1)); foreach (int k in columnIndices) { yield return -2.0 * x[i, k] * x[j, k] * inverseLength[k] * inverseLength[k]; } } public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable columnIndices) { return Util.ScalarProd(x, i, xt, j, inverseLength, columnIndices); } } }