#region License Information /* HeuristicLab * Copyright (C) 2002-2014 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; using System.Collections.Generic; using System.Linq; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class FriedmanTwo : ArtificialRegressionDataDescriptor { public override string Name { get { return "Friedman - II"; } } public override string Description { get { return "Paper: Multivariate Adaptive Regression Splines" + Environment.NewLine + "Authors: Jerome H. Friedman"; } } 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 5000; } } protected override int TestPartitionStart { get { return 5000; } } protected override int TestPartitionEnd { get { return 10000; } } protected static FastRandom rand = new FastRandom(); protected override List> GenerateValues() { List> data = new List>(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(10000, 0, 1).ToList()); } double x1, x2, x3, x4, x5; double f; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; x3 = data[2][i]; x4 = data[3][i]; x5 = data[4][i]; f = 10 * Math.Sin(Math.PI * x1 * x2) + 20 * Math.Pow(x3 - 0.5, 2) + 10 * x4 + 5 * x5; results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, 1)); } data.Add(results); return data; } } }