1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022013 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 = "CovarianceNoise",


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


35  public sealed class CovarianceNoise : ParameterizedNamedItem, ICovarianceFunction {


36  public IValueParameter<DoubleValue> ScaleParameter {


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


38  }


39  private bool HasFixedScaleParameter {


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


41  }


42 


43  [StorableConstructor]


44  private CovarianceNoise(bool deserializing)


45  : base(deserializing) {


46  }


47 


48  private CovarianceNoise(CovarianceNoise original, Cloner cloner)


49  : base(original, cloner) {


50  }


51 


52  public CovarianceNoise()


53  : base() {


54  Name = ItemName;


55  Description = ItemDescription;


56 


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


58  }


59 


60  public override IDeepCloneable Clone(Cloner cloner) {


61  return new CovarianceNoise(this, cloner);


62  }


63 


64  public int GetNumberOfParameters(int numberOfVariables) {


65  return HasFixedScaleParameter ? 0 : 1;


66  }


67 


68  public void SetParameter(double[] p) {


69  double scale;


70  GetParameterValues(p, out scale);


71  ScaleParameter.Value = new DoubleValue(scale);


72  }


73 


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


75  int c = 0;


76  // gather parameter values


77  if (HasFixedScaleParameter) {


78  scale = ScaleParameter.Value.Value;


79  } else {


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


81  c++;


82  }


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


84  }


85 


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


87  double scale;


88  GetParameterValues(p, out scale);


89  var fixedScale = HasFixedScaleParameter;


90  // create functions


91  var cov = new ParameterizedCovarianceFunction();


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


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


94  if (fixedScale)


95  cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();


96  else


97  cov.CovarianceGradient = (x, i, j) => Enumerable.Repeat(i == j ? 2.0 * scale : 0.0, 1);


98  return cov;


99  }


100  }


101  }

