#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 . */ #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 GaussianProcessSEIso3 : ArtificialRegressionDataDescriptor { public override string Name { get { return "Gaussian Process SEiso 3"; } } 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 50; } } protected override int TestPartitionStart { get { return 50; } } protected override int TestPartitionEnd { get { return 100; } } protected override List> GenerateValues() { List> data = new List>(); 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, 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 }); var m3 = new CovarianceMask(); m3.SelectedDimensionsParameter.Value = new IntArray(new int[] { 4, 5 }); covFun.Terms.Add(m1); covFun.Terms.Add(m2); covFun.Terms.Add(m3); covFun.Terms.Add(new CovarianceNoise()); covFun.SetParameter(hyp.ToArray()); var mt = new MersenneTwister(); var target = Util.SampleGaussianProcess(mt, covFun, data); data.Add(target); return data; } } }