#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.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item(Name = "MeanConst", Description = "Constant mean function for Gaussian processes.")] public sealed class MeanConst : ParameterizedNamedItem, IMeanFunction { [Storable] private double c; [Storable] private readonly HyperParameter valueParameter; public IValueParameter ValueParameter { get { return valueParameter; } } [StorableConstructor] private MeanConst(bool deserializing) : base(deserializing) { } private MeanConst(MeanConst original, Cloner cloner) : base(original, cloner) { this.c = original.c; this.valueParameter = cloner.Clone(original.valueParameter); RegisterEvents(); } public MeanConst() : base() { this.name = ItemName; this.description = ItemDescription; this.valueParameter = new HyperParameter("Value", "The constant value for the constant mean function."); Parameters.Add(valueParameter); RegisterEvents(); } public override IDeepCloneable Clone(Cloner cloner) { return new MeanConst(this, cloner); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEvents(); } private void RegisterEvents() { Util.AttachValueChangeHandler(valueParameter, () => { c = valueParameter.Value.Value; }); } public int GetNumberOfParameters(int numberOfVariables) { return valueParameter.Fixed ? 0 : 1; } public void SetParameter(double[] hyp) { if (!valueParameter.Fixed) { valueParameter.SetValue(new DoubleValue(hyp[0])); } else if (hyp.Length > 0) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for the constant mean function.", "hyp"); } public double[] GetMean(double[,] x) { return Enumerable.Repeat(c, x.GetLength(0)).ToArray(); } public double[] GetGradients(int k, double[,] x) { if (k > 0) throw new ArgumentException(); return Enumerable.Repeat(1.0, x.GetLength(0)).ToArray(); } } }