#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.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableClass]
[Item(Name = "CovarianceSEard", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]
public class CovarianceSEard : Item, ICovarianceFunction {
[Storable]
private double sf2;
public double Scale { get { return sf2; } }
[Storable]
private double[] inverseLength;
public double[] InverseLength {
get {
if (inverseLength == null) return new double[0];
var copy = new double[inverseLength.Length];
Array.Copy(inverseLength, copy, copy.Length);
return copy;
}
}
public int GetNumberOfParameters(int numberOfVariables) {
return numberOfVariables + 1;
}
[StorableConstructor]
protected CovarianceSEard(bool deserializing) : base(deserializing) { }
protected CovarianceSEard(CovarianceSEard original, Cloner cloner)
: base(original, cloner) {
this.inverseLength = original.InverseLength; // array is cloned in the getter
this.sf2 = original.sf2;
}
public CovarianceSEard()
: base() {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new CovarianceSEard(this, cloner);
}
public void SetParameter(double[] hyp) {
this.inverseLength = hyp.Take(hyp.Length - 1).Select(p => 1.0 / Math.Exp(p)).ToArray();
this.sf2 = Math.Exp(2 * hyp[hyp.Length - 1]);
}
public double GetCovariance(double[,] x, int i, int j) {
double d = i == j
? 0.0
: Util.SqrDist(x, i, j, inverseLength);
return sf2 * Math.Exp(-d / 2.0);
}
public IEnumerable GetGradient(double[,] x, int i, int j) {
double d = i == j
? 0.0
: Util.SqrDist(x, i, j, inverseLength);
for (int ii = 0; ii < inverseLength.Length; ii++) {
double sqrDist = Util.SqrDist(x[i, ii] * inverseLength[ii], x[j, ii] * inverseLength[ii]);
yield return sf2 * Math.Exp(-d / 2.0) * sqrDist;
}
yield return 2.0 * sf2 * Math.Exp(-d / 2.0);
}
public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j) {
double d = Util.SqrDist(x, i, xt, j, inverseLength);
return sf2 * Math.Exp(-d / 2.0);
}
}
}