source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionInstance.cs @ 9122

#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 GaussianProcessRegressionInstance : ArtificialRegressionDataDescriptor {

    public override string Name {
      get { return "Gaussian Process " + covarianceFunction.ItemName; }
    }
    public override string Description {
      get { return ""; }
    }
    protected override string TargetVariable { get { return "Y"; } }
    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
    protected override int TrainingPartitionStart { get { return 0; } }
    protected override int TrainingPartitionEnd { get { return 100; } }
    protected override int TestPartitionStart { get { return 100; } }
    protected override int TestPartitionEnd { get { return 200; } }

    private ICovarianceFunction covarianceFunction;

    public GaussianProcessRegressionInstance(ICovarianceFunction covarianceFunction) {
      this.covarianceFunction = covarianceFunction;
    }

    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 mt = new MersenneTwister();

      var noise = new CovarianceNoise();
      noise.SetParameter(new double[] { Math.Log(Math.Sqrt(0.01)) });
      var t = new CovarianceSum();
      t.Terms.Add(covarianceFunction);
      t.Terms.Add(noise);

      var p = Enumerable.Range(0, t.GetNumberOfParameters(data.Count)).Select(i => mt.NextDouble() - 1).ToArray();

      var cov = t.GetParameterizedCovarianceFunction(p, null);

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

      return data;
    }
  }
}