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;


25  using HeuristicLab.Algorithms.DataAnalysis;


26  using HeuristicLab.Random;


27 


28  namespace HeuristicLab.Problems.Instances.DataAnalysis {


29  public class GaussianProcessSEIsoDependentNoise : ArtificialRegressionDataDescriptor {


30 


31  public override string Name {


32  get {


33  return "Gaussian Process SE iso with dependent noise";


34  }


35  }


36  public override string Description {


37  get { return ""; }


38  }


39  protected override string TargetVariable { get { return "Y"; } }


40  protected override string[] VariableNames { get { return new string[] { "X1", "Y" }; } }


41  protected override string[] AllowedInputVariables { get { return new string[] { "X1" }; } }


42  protected override int TrainingPartitionStart { get { return 0; } }


43  protected override int TrainingPartitionEnd { get { return 100; } }


44  protected override int TestPartitionStart { get { return 100; } }


45  protected override int TestPartitionEnd { get { return 200; } }


46 


47  protected override List<List<double>> GenerateValues() {


48 


49  List<List<double>> data = new List<List<double>>();


50  for (int i = 0; i < AllowedInputVariables.Count(); i++) {


51  data.Add(ValueGenerator.GenerateSteps(0, 0.99, 0.01).ToList());


52  data[i].AddRange(ValueGenerator.GenerateSteps(0.005, 1, 0.01).ToList());


53  }


54 


55  var covarianceFunction = new CovarianceSum();


56  covarianceFunction.Terms.Add(new CovarianceSquaredExponentialIso());


57  var prod = new CovarianceProduct();


58  prod.Factors.Add(new CovarianceSquaredExponentialIso());


59  prod.Factors.Add(new CovarianceNoise());


60  covarianceFunction.Terms.Add(prod);


61  covarianceFunction.Terms.Add(new CovarianceNoise());


62  covarianceFunction.SetParameter(new double[] { Math.Log(0.1), Math.Log(Math.Sqrt(1)), Math.Log(0.5), Math.Log(Math.Sqrt(1)), Math.Log(Math.Sqrt(0.1)), Math.Log(Math.Sqrt(0.01)) });


63 


64  var mt = new MersenneTwister(31415);


65  var target = Util.SampleGaussianProcess(mt, covarianceFunction, data);


66  data.Add(target);


67 


68  return data;


69  }


70  }


71  }

