#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 SineCosineFunction : ArtificialRegressionDataDescriptor { public override string Name { get { return "Vladislavleva SineCosine"; } } 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: F6(X1, X2) = 6 * sin(X1) * cos(X2)" + Environment.NewLine + "Training Data: 30 points X1, X2 = Rand(0.1, 5.9)" + Environment.NewLine + "Test Data: 961 points X1, X2 = (-0.05:0.02:6.05)" + Environment.NewLine + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x"; } } protected override string TargetVariable { get { return "Y"; } } protected override string[] InputVariables { get { return new string[] { "X1", "X2", "Y" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } } protected override int TrainingPartitionStart { get { return 0; } } protected override int TrainingPartitionEnd { get { return 30; } } protected override int TestPartitionStart { get { return 500; } } protected override int TestPartitionEnd { get { return 1461; } } protected override List> GenerateValues() { List> data = new List>(); List oneVariableTestData = ValueGenerator.GenerateSteps(-0.05, 6.05, 0.02).ToList(); List> testData = new List>() { oneVariableTestData, oneVariableTestData }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList>(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(500, 0.1, 5.9).ToList()); data[i].AddRange(combinations[i]); } double x1, x2; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; results.Add(6 * Math.Sin(x1) * Math.Cos(x2)); } data.Add(results); return data; } } }