#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;
}
}
}