#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Parameters; 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 { public IValueParameter InverseLengthParameter { get { return (IValueParameter)Parameters["InverseLength"]; } } private bool HasFixedInverseLengthParameter { get { return InverseLengthParameter.Value != null; } } [StorableConstructor] private CovarianceLinearArd(bool deserializing) : base(deserializing) { } private CovarianceLinearArd(CovarianceLinearArd original, Cloner cloner) : base(original, cloner) { } public CovarianceLinearArd() : base() { Name = ItemName; Description = ItemDescription; Parameters.Add(new OptionalValueParameter("InverseLength", "The inverse length parameter for ARD.")); } public override IDeepCloneable Clone(Cloner cloner) { return new CovarianceLinearArd(this, cloner); } public int GetNumberOfParameters(int numberOfVariables) { if (HasFixedInverseLengthParameter) return 0; else return numberOfVariables; } public void SetParameter(double[] p) { double[] inverseLength; GetParameterValues(p, out inverseLength); InverseLengthParameter.Value = new DoubleArray(inverseLength); } private void GetParameterValues(double[] p, out double[] inverseLength) { // gather parameter values if (HasFixedInverseLengthParameter) { inverseLength = InverseLengthParameter.Value.ToArray(); } else { inverseLength = p.Select(e => 1.0 / Math.Exp(e)).ToArray(); } } public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) { double[] inverseLength; GetParameterValues(p, out inverseLength); var fixedInverseLength = HasFixedInverseLengthParameter; // create functions var cov = new ParameterizedCovarianceFunction(); cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, inverseLength, columnIndices); cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, inverseLength, columnIndices); if (fixedInverseLength) cov.CovarianceGradient = (x, i, j) => new double[0]; else cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, inverseLength, columnIndices); return cov; } private static IList GetGradient(double[,] x, int i, int j, double[] inverseLength, int[] columnIndices) { int k = 0; var g = new List(columnIndices.Length); for (int c = 0; c < columnIndices.Length; c++) { var columnIndex = columnIndices[c]; g.Add(-2.0 * x[i, columnIndex] * x[j, columnIndex] * inverseLength[k] * inverseLength[k]); k++; } return g; } } }