#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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.Common; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class KeijzerFunctionThree : ArtificialRegressionDataDescriptor { public override string Name { get { return "Keijzer 3 f(x) = 0.3 * x *sin(2 * PI * x); Interval [-3, 3]"; } } 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) = 0.3 * x *sin(2 * PI * x)" + Environment.NewLine + "range(train): x = [-3:0.1:3]" + Environment.NewLine + "range(test): x = [-3:0.001:3]" + Environment.NewLine + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)"; } } protected override string TargetVariable { get { return "F"; } } protected override string[] VariableNames { get { return new string[] { "X", "F" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } } protected override int TrainingPartitionStart { get { return 0; } } protected override int TrainingPartitionEnd { get { return 61; } } protected override int TestPartitionStart { get { return 61; } } protected override int TestPartitionEnd { get { return 6062; } } protected override List> GenerateValues() { List> data = new List>(); data.Add(SequenceGenerator.GenerateSteps(-3, 3, 0.1m).Select(v => (double)v).ToList()); data[0].AddRange(SequenceGenerator.GenerateSteps(-3, 3, 0.001m).Select(v => (double)v)); double x; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; results.Add(0.3 * x * Math.Sin(2 * Math.PI * x)); } data.Add(results); return data; } } }