#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.Collections.Generic;
using System.Linq;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Data;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class GaussianProcessSEIso2 : ArtificialRegressionDataDescriptor {

    public override string Name {
      get {
        return "Gaussian Process SEiso 2";
      }
    }
    public override string Description {
      get { return ""; }
    }
    protected override string TargetVariable { get { return "Y"; } }
    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
    protected override int TrainingPartitionStart { get { return 0; } }
    protected override int TrainingPartitionEnd { get { return 250; } }
    protected override int TestPartitionStart { get { return 250; } }
    protected override int TestPartitionEnd { get { return 500; } }

    protected override List<List<double>> GenerateValues() {
      List<List<double>> data = new List<List<double>>();
      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
        data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList());
      }


      var hyp = new double[]
        {
          0.0, 0.0, // SEiso
          0.0, 0.0,
          -6.0      // noise
        };


      var covFun = new CovarianceSum();
      var m1 = new CovarianceMask();
      m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0, 1 });

      var m2 = new CovarianceMask();
      m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 2, 3 });

      covFun.Terms.Add(m1);
      covFun.Terms.Add(m2);
      covFun.Terms.Add(new CovarianceNoise());
      var cov = covFun.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 4));

      var mt = new MersenneTwister();
      var target = Util.SampleGaussianProcess(mt, cov, data);
      data.Add(target);

      return data;
    }
  }
}