1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System;


23  using System.Collections.Generic;


24  using System.Linq.Expressions;


25  using HeuristicLab.Common;


26  using HeuristicLab.Core;


27  using HeuristicLab.Data;


28  using HeuristicLab.Parameters;


29  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


30 


31  namespace HeuristicLab.Algorithms.DataAnalysis {


32  [StorableClass]


33  [Item(Name = "CovarianceSquaredExponentialIso",


34  Description = "Isotropic squared exponential covariance function for Gaussian processes.")]


35  public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction {


36  public IValueParameter<DoubleValue> ScaleParameter {


37  get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }


38  }


39 


40  public IValueParameter<DoubleValue> InverseLengthParameter {


41  get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }


42  }


43 


44  [StorableConstructor]


45  private CovarianceSquaredExponentialIso(bool deserializing)


46  : base(deserializing) {


47  }


48 


49  private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner)


50  : base(original, cloner) {


51  }


52 


53  public CovarianceSquaredExponentialIso()


54  : base() {


55  Name = ItemName;


56  Description = ItemDescription;


57 


58  Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric squared exponential covariance function."));


59  Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function."));


60  }


61 


62  public override IDeepCloneable Clone(Cloner cloner) {


63  return new CovarianceSquaredExponentialIso(this, cloner);


64  }


65 


66  public int GetNumberOfParameters(int numberOfVariables) {


67  return


68  (ScaleParameter.Value != null ? 0 : 1) +


69  (InverseLengthParameter.Value != null ? 0 : 1);


70  }


71 


72  public void SetParameter(double[] p) {


73  double scale, inverseLength;


74  GetParameterValues(p, out scale, out inverseLength);


75  ScaleParameter.Value = new DoubleValue(scale);


76  InverseLengthParameter.Value = new DoubleValue(inverseLength);


77  }


78 


79 


80  private void GetParameterValues(double[] p, out double scale, out double inverseLength) {


81  // gather parameter values


82  int c = 0;


83  if (InverseLengthParameter.Value != null) {


84  inverseLength = InverseLengthParameter.Value.Value;


85  } else {


86  inverseLength = 1.0 / Math.Exp(p[c]);


87  c++;


88  }


89 


90  if (ScaleParameter.Value != null) {


91  scale = ScaleParameter.Value.Value;


92  } else {


93  scale = Math.Exp(2 * p[c]);


94  c++;


95  }


96  if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialIso", "p");


97  }


98 


99  public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {


100  double inverseLength, scale;


101  GetParameterValues(p, out scale, out inverseLength);


102  // create functions


103  var cov = new ParameterizedCovarianceFunction();


104  cov.Covariance = (x, i, j) => {


105  double d = i == j


106  ? 0.0


107  : Util.SqrDist(x, i, j, inverseLength, columnIndices);


108  return scale * Math.Exp(d / 2.0);


109  };


110  cov.CrossCovariance = (x, xt, i, j) => {


111  double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);


112  return scale * Math.Exp(d / 2.0);


113  };


114  cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, inverseLength, columnIndices);


115  return cov;


116  }


117 


118  private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double sf2, double inverseLength, IEnumerable<int> columnIndices) {


119  double d = i == j


120  ? 0.0


121  : Util.SqrDist(x, i, j, inverseLength, columnIndices);


122  double g = Math.Exp(d / 2.0);


123  yield return sf2 * g * d;


124  yield return 2.0 * sf2 * g;


125  }


126  }


127  }

