source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/VariousInstanceProvider.cs @ 9175

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Algorithms.DataAnalysis;

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class InstanceProvider : ArtificialRegressionInstanceProvider {
    public override string Name {
      get { return "GPR Benchmark Problems"; }
    }
    public override string Description {
      get { return ""; }
    }
    public override Uri WebLink {
      get { return new Uri("http://dev.heuristiclab.com/trac/hl/core/wiki/AdditionalMaterial"); }
    }
    public override string ReferencePublication {
      get { return ""; }
    }

    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
      List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
      descriptorList.Add(new GaussianProcessSEIso());
      descriptorList.Add(new GaussianProcessSEIso1());
      descriptorList.Add(new GaussianProcessSEIso2());
      descriptorList.Add(new GaussianProcessSEIso3());
      descriptorList.Add(new GaussianProcessSEIso4());
      descriptorList.Add(new GaussianProcessSEIso5());
      descriptorList.Add(new GaussianProcessSEIso6());
      descriptorList.Add(new GaussianProcessPolyTen());
      descriptorList.Add(new GaussianProcessSEIsoDependentNoise());

      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovarianceSquaredExponentialIso());
        cov.Terms.Add(new CovarianceSquaredExponentialIso());
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { -2.8, -0.1, 0.5, 0.3, -1.5 };
        descriptorList.Add(new GaussianProcessRegressionInstance("SE+SE", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovarianceRationalQuadraticIso());
        cov.Terms.Add(new CovarianceRationalQuadraticIso());
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { -3, 0, 0, -1.5, 0, 2.5, -1.5 };
        descriptorList.Add(new GaussianProcessRegressionInstance("RQ+RQ", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovariancePeriodic());
        cov.Terms.Add(new CovariancePeriodic());
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { 0, -1.8, -1.5, 0, -0.5, -1, -2.1 };
        descriptorList.Add(new GaussianProcessRegressionInstance("Periodic+Periodic", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovarianceMaternIso());
        cov.Terms.Add(new CovarianceMaternIso());
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { 0, 0, -1, 1, -4 };
        descriptorList.Add(new GaussianProcessRegressionInstance("Matern1+Matern1", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        var m1 = new CovarianceMaternIso();
        m1.DParameter.Value = m1.DParameter.ValidValues.First(v => v.Value == 3);
        var m2 = new CovarianceMaternIso();
        m2.DParameter.Value = m2.DParameter.ValidValues.First(v => v.Value == 3);
        cov.Terms.Add(m1);
        cov.Terms.Add(m2);
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { -2.7, 0, -1, 1, -1.5 };
        descriptorList.Add(new GaussianProcessRegressionInstance("Matern3+Matern3", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovarianceRationalQuadraticIso());
        cov.Terms.Add(new CovarianceSquaredExponentialIso());
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { -1.5, -0.5, -3, -1, -1, -3 };
        descriptorList.Add(new GaussianProcessRegressionInstance("RQ+SE", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovarianceSquaredExponentialIso());
        var prod = new CovarianceProduct();
        prod.Factors.Add(new CovarianceLinear());
        prod.Factors.Add(new CovarianceNoise());
        cov.Terms.Add(prod);
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { -3, 0, 0, -1.5 };
        descriptorList.Add(new GaussianProcessRegressionInstance("SE+Linear*Noise", cov, hyp));
      }
      {
        var cov = new CovarianceSum();
        cov.Terms.Add(new CovarianceSquaredExponentialIso());
        cov.Terms.Add(new CovariancePeriodic());
        cov.Terms.Add(new CovarianceNoise());
        var hyp = new double[] { -1, 0, 0, -1.5, 0, -2 };
        descriptorList.Add(new GaussianProcessRegressionInstance("SE+Periodic", cov, hyp));
      }


      return descriptorList;
    }
  }
}