#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; using System.Collections.Generic; using System.Linq; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class KeijzerFunctionFourteen : ArtificialRegressionDataDescriptor { public override string Name { get { return "Keijzer 14 f(x, y) = 8 / (2 + x² + y²)"; } } public override string Description { get { return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine + "Authors: Maarten Keijzer" + Environment.NewLine + "Function: f(x, y) = 8 / (2 + x² + y²)" + Environment.NewLine + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) " + ", but 5000 cases are created"; } } protected override string TargetVariable { get { return "F"; } } protected override string[] InputVariables { get { return new string[] { "X", "Y", "F" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } } protected override int TrainingPartitionStart { get { return 0; } } protected override int TrainingPartitionEnd { get { return 20; } } protected override int TestPartitionStart { get { return 2500; } } protected override int TestPartitionEnd { get { return 2600; } } protected override List> GenerateValues() { List> data = new List>(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList()); } double x, y; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(8 / (2 + Math.Pow(x, 2) + Math.Pow(y, 2))); } data.Add(results); return data; } } }