#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 = "CovarianceNoise", Description = "Noise covariance function for Gaussian processes.")] public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction { public IValueParameter ScaleParameter { get { return (IValueParameter)Parameters["Scale"]; } } private bool HasFixedScaleParameter { get { return ScaleParameter.Value != null; } } [StorableConstructor] private CovarianceNoise(bool deserializing) : base(deserializing) { } private CovarianceNoise(CovarianceNoise original, Cloner cloner) : base(original, cloner) { } public CovarianceNoise() : base() { Name = ItemName; Description = ItemDescription; Parameters.Add(new OptionalValueParameter("Scale", "The scale of noise.")); } public override IDeepCloneable Clone(Cloner cloner) { return new CovarianceNoise(this, cloner); } public int GetNumberOfParameters(int numberOfVariables) { return HasFixedScaleParameter ? 0 : 1; } public void SetParameter(double[] p) { double scale; GetParameterValues(p, out scale); ScaleParameter.Value = new DoubleValue(scale); } private void GetParameterValues(double[] p, out double scale) { int c = 0; // gather parameter values if (HasFixedScaleParameter) { scale = ScaleParameter.Value.Value; } else { scale = Math.Exp(2 * p[c]); c++; } if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceNoise", "p"); } public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable columnIndices) { double scale; GetParameterValues(p, out scale); var fixedScale = HasFixedScaleParameter; // create functions var cov = new ParameterizedCovarianceFunction(); cov.Covariance = (x, i, j) => i == j ? scale : 0.0; cov.CrossCovariance = (x, xt, i, j) => Util.SqrDist(x, i, xt, j, 1.0, columnIndices) < 1e-9 ? scale : 0.0; if (fixedScale) cov.CovarianceGradient = (x, i, j) => Enumerable.Empty(); else cov.CovarianceGradient = (x, i, j) => Enumerable.Repeat(i == j ? 2.0 * scale : 0.0, 1); return cov; } } }