#region License Information /* HeuristicLab * Copyright (C) 2002-2013 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 SalutowiczFunctionOneDimensional : ArtificialRegressionDataDescriptor { public override string Name { get { return "Vladislavleva-2 F2(X) = exp(-X) * X³ * cos(X) * sin(X) * (cos(X)sin(X)² - 1)"; } } public override string Description { get { return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine + "Function: F2(X) = exp(-X) * X³ * cos(X) * sin(X) * (cos(X)sin(X)² - 1)" + Environment.NewLine + "Training Data: 100 points X = (0.05:0.1:10)" + Environment.NewLine + "Test Data: 221 points X = (-0.5:0.05:10.5)" + Environment.NewLine + "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(x), x^eps, x + eps, x + eps"; } } protected override string TargetVariable { get { return "Y"; } } protected override string[] VariableNames { get { return new string[] { "X", "Y" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } } 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 321; } } protected override List> GenerateValues() { List> data = new List>(); data.Add(ValueGenerator.GenerateSteps(0.05, 10, 0.1).ToList()); data[0].AddRange(ValueGenerator.GenerateSteps(-0.5, 10.5, 0.05)); double x; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; results.Add(Math.Exp(-x) * Math.Pow(x, 3) * Math.Cos(x) * Math.Sin(x) * (Math.Cos(x) * Math.Pow(Math.Sin(x), 2) - 1)); } data.Add(results); return data; } } }