#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 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 covs = new ICovarianceFunction[] {
        new CovarianceSquaredExponentialIso(), 
        new CovarianceSquaredExponentialArd(), 
        new CovarianceLinear(), 
        new CovarianceLinearArd(), 
        new CovarianceMaternIso(), 
        new CovariancePeriodic(), 
        new CovarianceRationalQuadraticArd(), 
        new CovarianceRationalQuadraticIso()
      };
      foreach (var cov in covs) {
        descriptorList.Add(new GaussianProcessRegressionInstance(cov));
      }

      return descriptorList;
    }
  }
}