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 HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Data;


26  using HeuristicLab.Parameters;


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


28 


29  namespace HeuristicLab.Algorithms.DataAnalysis {


30  [StorableClass]


31  [Item(Name = "CovarianceNoise",


32  Description = "Noise covariance function for Gaussian processes.")]


33  public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction {


34  public IValueParameter<DoubleValue> ScaleParameter {


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


36  }


37  private bool HasFixedScaleParameter {


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


39  }


40 


41  [StorableConstructor]


42  private CovarianceNoise(bool deserializing)


43  : base(deserializing) {


44  }


45 


46  private CovarianceNoise(CovarianceNoise original, Cloner cloner)


47  : base(original, cloner) {


48  }


49 


50  public CovarianceNoise()


51  : base() {


52  Name = ItemName;


53  Description = ItemDescription;


54 


55  Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of noise."));


56  }


57 


58  public override IDeepCloneable Clone(Cloner cloner) {


59  return new CovarianceNoise(this, cloner);


60  }


61 


62  public int GetNumberOfParameters(int numberOfVariables) {


63  return HasFixedScaleParameter ? 0 : 1;


64  }


65 


66  public void SetParameter(double[] p) {


67  double scale;


68  GetParameterValues(p, out scale);


69  ScaleParameter.Value = new DoubleValue(scale);


70  }


71 


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


73  int c = 0;


74  // gather parameter values


75  if (HasFixedScaleParameter) {


76  scale = ScaleParameter.Value.Value;


77  } else {


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


79  c++;


80  }


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


82  }


83 


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


85  double scale;


86  GetParameterValues(p, out scale);


87  var fixedScale = HasFixedScaleParameter;


88  // create functions


89  var cov = new ParameterizedCovarianceFunction();


90  cov.Covariance = (x, i, j) => i == j ? scale : 0.0;


91  cov.CrossCovariance = (x, xt, i, j) => Util.SqrDist(x, i, xt, j, columnIndices, 1.0) < 1e9 ? scale : 0.0;


92  if (fixedScale)


93  cov.CovarianceGradient = (x, i, j) => new double[0];


94  else


95  cov.CovarianceGradient = (x, i, j) => new double[1] { i == j ? 2.0 * scale : 0.0 };


96  return cov;


97  }


98  }


99  }

