#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 {
[StorableType("61CE9901-EB24-468D-AA62-6C8A889D905A")]
[Item(Name = "CovariancePolynomial",
Description = "Polynomial covariance function for Gaussian processes.")]
public sealed class CovariancePolynomial : ParameterizedNamedItem, ICovarianceFunction {
public IValueParameter ConstParameter {
get { return (IValueParameter)Parameters["Const"]; }
}
public IValueParameter ScaleParameter {
get { return (IValueParameter)Parameters["Scale"]; }
}
public IValueParameter DegreeParameter {
get { return (IValueParameter)Parameters["Degree"]; }
}
private bool HasFixedConstParameter {
get { return ConstParameter.Value != null; }
}
private bool HasFixedScaleParameter {
get { return ScaleParameter.Value != null; }
}
[StorableConstructor]
private CovariancePolynomial(bool deserializing)
: base(deserializing) {
}
private CovariancePolynomial(CovariancePolynomial original, Cloner cloner)
: base(original, cloner) {
}
public CovariancePolynomial()
: base() {
Name = ItemName;
Description = ItemDescription;
Parameters.Add(new OptionalValueParameter("Const", "Additive constant in the polymomial."));
Parameters.Add(new OptionalValueParameter("Scale", "The scale parameter of the polynomial covariance function."));
Parameters.Add(new ValueParameter("Degree", "The degree of the polynomial (only non-zero positive values allowed).", new IntValue(2)));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new CovariancePolynomial(this, cloner);
}
public int GetNumberOfParameters(int numberOfVariables) {
return
(HasFixedConstParameter ? 0 : 1) +
(HasFixedScaleParameter ? 0 : 1);
}
public void SetParameter(double[] p) {
double @const, scale;
GetParameterValues(p, out @const, out scale);
ConstParameter.Value = new DoubleValue(@const);
ScaleParameter.Value = new DoubleValue(scale);
}
private void GetParameterValues(double[] p, out double @const, out double scale) {
// gather parameter values
int n = 0;
if (HasFixedConstParameter) {
@const = ConstParameter.Value.Value;
} else {
@const = Math.Exp(p[n]);
n++;
}
if (HasFixedScaleParameter) {
scale = ScaleParameter.Value.Value;
} else {
scale = Math.Exp(2 * p[n]);
n++;
}
if (p.Length != n) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovariancePolynomial", "p");
}
public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable columnIndices) {
double @const, scale;
int degree = DegreeParameter.Value.Value;
if (degree <= 0) throw new ArgumentException("The degree parameter for CovariancePolynomial must be greater than zero.");
GetParameterValues(p, out @const, out scale);
var fixedConst = HasFixedConstParameter;
var fixedScale = HasFixedScaleParameter;
// create functions
var cov = new ParameterizedCovarianceFunction();
cov.Covariance = (x, i, j) => scale * Math.Pow(@const + Util.ScalarProd(x, i, j, 1.0, columnIndices), degree);
cov.CrossCovariance = (x, xt, i, j) => scale * Math.Pow(@const + Util.ScalarProd(x, i, xt, j, 1.0, columnIndices), degree);
cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, @const, scale, degree, columnIndices, fixedConst, fixedScale);
return cov;
}
private static IEnumerable GetGradient(double[,] x, int i, int j, double c, double scale, int degree, IEnumerable columnIndices,
bool fixedConst, bool fixedScale) {
double s = Util.ScalarProd(x, i, j, 1.0, columnIndices);
if (!fixedConst) yield return c * degree * scale * Math.Pow(c + s, degree - 1);
if (!fixedScale) yield return 2 * scale * Math.Pow(c + s, degree);
}
}
}