#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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; 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 { public IValueParameter ScaleParameter { get { return (IValueParameter)Parameters["Scale"]; } } public IValueParameter InverseLengthParameter { get { return (IValueParameter)Parameters["InverseLength"]; } } private bool HasFixedInverseLengthParameter { get { return InverseLengthParameter.Value != null; } } private bool HasFixedScaleParameter { get { return ScaleParameter.Value != null; } } [StorableConstructor] private CovarianceSquaredExponentialIso(bool deserializing) : base(deserializing) { } private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner) : base(original, cloner) { } public CovarianceSquaredExponentialIso() : base() { Name = ItemName; Description = ItemDescription; Parameters.Add(new OptionalValueParameter("Scale", "The scale parameter of the isometric squared exponential covariance function.")); Parameters.Add(new OptionalValueParameter("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function.")); } public override IDeepCloneable Clone(Cloner cloner) { return new CovarianceSquaredExponentialIso(this, cloner); } public int GetNumberOfParameters(int numberOfVariables) { return (HasFixedScaleParameter ? 0 : 1) + (HasFixedInverseLengthParameter ? 0 : 1); } public void SetParameter(double[] p) { double scale, inverseLength; GetParameterValues(p, out scale, out inverseLength); ScaleParameter.Value = new DoubleValue(scale); InverseLengthParameter.Value = new DoubleValue(inverseLength); } private void GetParameterValues(double[] p, out double scale, out double inverseLength) { // gather parameter values int c = 0; if (HasFixedInverseLengthParameter) { inverseLength = InverseLengthParameter.Value.Value; } else { inverseLength = 1.0 / Math.Exp(p[c]); c++; } 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 CovarianceSquaredExponentialIso", "p"); } public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) { double inverseLength, scale; GetParameterValues(p, out scale, out inverseLength); var fixedInverseLength = HasFixedInverseLengthParameter; var fixedScale = HasFixedScaleParameter; // create functions var cov = new ParameterizedCovarianceFunction(); cov.Covariance = (x, i, j) => { double d = i == j ? 0.0 : Util.SqrDist(x, i, j, columnIndices, inverseLength); return scale * Math.Exp(-d / 2.0); }; cov.CrossCovariance = (x, xt, i, j) => { double d = Util.SqrDist(x, i, xt, j, columnIndices, inverseLength); return scale * Math.Exp(-d / 2.0); }; cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, inverseLength, columnIndices, fixedInverseLength, fixedScale); return cov; } // order of returned gradients must match the order in GetParameterValues! private static IList GetGradient(double[,] x, int i, int j, double sf2, double inverseLength, int[] columnIndices, bool fixedInverseLength, bool fixedScale) { double d = i == j ? 0.0 : Util.SqrDist(x, i, j, columnIndices, inverseLength); double g = Math.Exp(-d / 2.0); var gr = new List(2); if (!fixedInverseLength) gr.Add(sf2 * g * d); if (!fixedScale) gr.Add(2.0 * sf2 * g); return gr; } } }