#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;
using HeuristicLab.Random;

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

    public override string Name {
      get {
        return "Gaussian Process SE iso with dependent noise";
      }
    }
    public override string Description {
      get { return ""; }
    }
    protected override string TargetVariable { get { return "Y"; } }
    protected override string[] VariableNames { get { return new string[] { "X1", "Y" }; } }
    protected override string[] AllowedInputVariables { get { return new string[] { "X1" }; } }
    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.GenerateSteps(0, 0.99, 0.01).ToList());
        data[i].AddRange(ValueGenerator.GenerateSteps(0.005, 1, 0.01).ToList());
      }

      var covarianceFunction = new CovarianceSum();
      covarianceFunction.Terms.Add(new CovarianceSquaredExponentialIso());
      var prod = new CovarianceProduct();
      prod.Factors.Add(new CovarianceLinear());
      prod.Factors.Add(new CovarianceNoise());
      covarianceFunction.Terms.Add(prod);
      covarianceFunction.Terms.Add(new CovarianceNoise());
      var hyp = new double[]
        {
          Math.Log(0.1), Math.Log(Math.Sqrt(1)), // SE iso
          Math.Log(Math.Sqrt(0.5)), // dependent noise
          Math.Log(Math.Sqrt(0.01)) // noise
        };
      var cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, null);

      var mt = new MersenneTwister(31415);
      var target = Util.SampleGaussianProcess(mt, cov, data);
      data.Add(target);

      return data;
    }
  }
}