#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.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item(Name = "MeanProduct", Description = "Product of mean functions for Gaussian processes.")] public sealed class MeanProduct : Item, IMeanFunction { [Storable] private ItemList factors; [Storable] private int numberOfVariables; public ItemList Factors { get { return factors; } } [StorableConstructor] private MeanProduct(bool deserializing) : base(deserializing) { } private MeanProduct(MeanProduct original, Cloner cloner) : base(original, cloner) { this.factors = cloner.Clone(original.factors); this.numberOfVariables = original.numberOfVariables; } public MeanProduct() { this.factors = new ItemList(); } public override IDeepCloneable Clone(Cloner cloner) { return new MeanProduct(this, cloner); } public int GetNumberOfParameters(int numberOfVariables) { this.numberOfVariables = numberOfVariables; return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum(); } public void SetParameter(double[] hyp) { int offset = 0; foreach (var t in factors) { var numberOfParameters = t.GetNumberOfParameters(numberOfVariables); t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray()); offset += numberOfParameters; } } public double[] GetMean(double[,] x) { var res = factors.First().GetMean(x); foreach (var t in factors.Skip(1)) { var a = t.GetMean(x); for (int i = 0; i < res.Length; i++) res[i] *= a[i]; } return res; } public double[] GetGradients(int k, double[,] x) { double[] res = Enumerable.Repeat(1.0, x.GetLength(0)).ToArray(); // find index of factor for the given k int j = 0; while (k >= factors[j].GetNumberOfParameters(numberOfVariables)) { k -= factors[j].GetNumberOfParameters(numberOfVariables); j++; } for (int i = 0; i < factors.Count; i++) { var f = factors[i]; if (i == j) { // multiply gradient var g = f.GetGradients(k, x); for (int ii = 0; ii < res.Length; ii++) res[ii] *= g[ii]; } else { // multiply mean var m = f.GetMean(x); for (int ii = 0; ii < res.Length; ii++) res[ii] *= m[ii]; } } return res; } } }