1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022015 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 HeuristicLab.Common;


25  using HeuristicLab.Core;


26  using HeuristicLab.Data;


27  using HeuristicLab.Parameters;


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


29 


30  namespace HeuristicLab.Algorithms.DataAnalysis {


31  [StorableClass]


32  [Item(Name = "CovarianceSquaredExponentialIso",


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


34  public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction {


35  public IValueParameter<DoubleValue> ScaleParameter {


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


37  }


38 


39  public IValueParameter<DoubleValue> InverseLengthParameter {


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


41  }


42 


43  private bool HasFixedInverseLengthParameter {


44  get { return InverseLengthParameter.Value != null; }


45  }


46  private bool HasFixedScaleParameter {


47  get { return ScaleParameter.Value != null; }


48  }


49 


50  [StorableConstructor]


51  private CovarianceSquaredExponentialIso(bool deserializing)


52  : base(deserializing) {


53  }


54 


55  private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner)


56  : base(original, cloner) {


57  }


58 


59  public CovarianceSquaredExponentialIso()


60  : base() {


61  Name = ItemName;


62  Description = ItemDescription;


63 


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


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


66  }


67 


68  public override IDeepCloneable Clone(Cloner cloner) {


69  return new CovarianceSquaredExponentialIso(this, cloner);


70  }


71 


72  public int GetNumberOfParameters(int numberOfVariables) {


73  return


74  (HasFixedScaleParameter ? 0 : 1) +


75  (HasFixedInverseLengthParameter ? 0 : 1);


76  }


77 


78  public void SetParameter(double[] p) {


79  double scale, inverseLength;


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


81  ScaleParameter.Value = new DoubleValue(scale);


82  InverseLengthParameter.Value = new DoubleValue(inverseLength);


83  }


84 


85 


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


87  // gather parameter values


88  int c = 0;


89  if (HasFixedInverseLengthParameter) {


90  inverseLength = InverseLengthParameter.Value.Value;


91  } else {


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


93  c++;


94  }


95 


96  if (HasFixedScaleParameter) {


97  scale = ScaleParameter.Value.Value;


98  } else {


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


100  c++;


101  }


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


103  }


104 


105  public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {


106  double inverseLength, scale;


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


108  var fixedInverseLength = HasFixedInverseLengthParameter;


109  var fixedScale = HasFixedScaleParameter;


110  // create functions


111  var cov = new ParameterizedCovarianceFunction();


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


113  double d = i == j


114  ? 0.0


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


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


117  };


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


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


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


121  };


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


123  fixedInverseLength, fixedScale);


124  return cov;


125  }


126 


127  // order of returned gradients must match the order in GetParameterValues!


128  private static IList<double> GetGradient(double[,] x, int i, int j, double sf2, double inverseLength, int[] columnIndices,


129  bool fixedInverseLength, bool fixedScale) {


130  double d = i == j


131  ? 0.0


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


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


134  var gr = new List<double>(2);


135  if (!fixedInverseLength) gr.Add(sf2 * g * d);


136  if (!fixedScale) gr.Add(2.0 * sf2 * g);


137  return gr;


138  }


139  }


140  }

