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 System.Linq;


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 = "CovarianceSquaredExponentialArd", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]


34  public sealed class CovarianceSquaredExponentialArd : ParameterizedNamedItem, ICovarianceFunction {


35  public IValueParameter<DoubleValue> ScaleParameter {


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


37  }


38 


39  public IValueParameter<DoubleArray> InverseLengthParameter {


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


41  }


42  private bool HasFixedInverseLengthParameter {


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


44  }


45  private bool HasFixedScaleParameter {


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


47  }


48 


49  [StorableConstructor]


50  private CovarianceSquaredExponentialArd(bool deserializing) : base(deserializing) { }


51  private CovarianceSquaredExponentialArd(CovarianceSquaredExponentialArd original, Cloner cloner)


52  : base(original, cloner) {


53  }


54  public CovarianceSquaredExponentialArd()


55  : base() {


56  Name = ItemName;


57  Description = ItemDescription;


58 


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


60  Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength", "The inverse length parameter for automatic relevance determination."));


61  }


62 


63  public override IDeepCloneable Clone(Cloner cloner) {


64  return new CovarianceSquaredExponentialArd(this, cloner);


65  }


66 


67  public int GetNumberOfParameters(int numberOfVariables) {


68  return


69  (HasFixedScaleParameter ? 0 : 1) +


70  (HasFixedInverseLengthParameter ? 0 : numberOfVariables);


71  }


72 


73  public void SetParameter(double[] p) {


74  double scale;


75  double[] inverseLength;


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


77  ScaleParameter.Value = new DoubleValue(scale);


78  InverseLengthParameter.Value = new DoubleArray(inverseLength);


79  }


80 


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


82  int c = 0;


83  // gather parameter values


84  if (HasFixedInverseLengthParameter) {


85  inverseLength = InverseLengthParameter.Value.ToArray();


86  } else {


87  int length = p.Length;


88  if (!HasFixedScaleParameter) length;


89  inverseLength = p.Select(e => 1.0 / Math.Exp(e)).Take(length).ToArray();


90  c += inverseLength.Length;


91  }


92  if (HasFixedScaleParameter) {


93  scale = ScaleParameter.Value.Value;


94  } else {


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


96  c++;


97  }


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


99  }


100 


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


102  double scale;


103  double[] inverseLength;


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


105  var fixedInverseLength = HasFixedInverseLengthParameter;


106  var fixedScale = HasFixedScaleParameter;


107  // create functions


108  var cov = new ParameterizedCovarianceFunction();


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


110  double d = i == j


111  ? 0.0


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


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


114  };


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


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


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


118  };


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


120  return cov;


121  }


122 


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


124  private static IEnumerable<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, double[] inverseLength,


125  bool fixedInverseLength, bool fixedScale) {


126  double d = i == j


127  ? 0.0


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


129 


130  int k = 0;


131  if (!fixedInverseLength) {


132  for (int c = 0; c < columnIndices.Length; c++) {


133  var columnIndex = columnIndices[c];


134  double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);


135  yield return scale * Math.Exp(d / 2.0) * sqrDist;


136  k++;


137  }


138  }


139  if (!fixedScale) yield return 2.0 * scale * Math.Exp(d / 2.0);


140  }


141  }


142  }

