#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; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class SalutowiczFunctionTwoDimensional : ArtificialRegressionDataDescriptor { public override string Name { get { return "Vladislavleva-3 F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)"; } } 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: F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)" + Environment.NewLine + "Training Data: 600 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine + "Test Data: 221 * 23 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + 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[] { "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 600; } } protected override int TestPartitionStart { get { return 600; } } protected override int TestPartitionEnd { get { return 600 + (221 * 23); } } protected override List> GenerateValues() { List> data = new List>(); List> trainingData = new List>() { ValueGenerator.GenerateSteps(0.05m, 10, 0.1m).Select(v => (double)v).ToList(), ValueGenerator.GenerateSteps(0.05m, 10.05m, 2).Select(v => (double)v).ToList() }; List> testData = new List>() { ValueGenerator.GenerateSteps(-0.5m, 10.5m, 0.05m).Select(v => (double)v).ToList(), ValueGenerator.GenerateSteps(-0.5m, 10.5m, 0.5m).Select(v => (double)v).ToList() }; var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList>(); var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList>(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(trainingComb[i].ToList()); data[i].AddRange(testComb[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(Math.Exp(-x1) * Math.Pow(x1, 3) * Math.Cos(x1) * Math.Sin(x1) * (Math.Cos(x1) * Math.Pow(Math.Sin(x1), 2) - 1) * (x2 - 5)); } data.Add(results); return data; } } }