#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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 KotanchekFunction : ArtificialRegressionDataDescriptor { public override string Name { get { return "Vladislavleva-1 F1(X1,X2) = exp(- (X1 - 1)²) / (1.2 + (X2 - 2.5)²)"; } } 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: F1(X1, X2) = exp(- (X1 - 1)²) / (1.2 + (X2 - 2.5)²)" + Environment.NewLine + "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine + "Test Data: 45*45 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine + "Function Set: +, -, *, /, square, e^x, e^-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 100; } } protected override int TestPartitionStart { get { return 100; } } protected override int TestPartitionEnd { get { return 100 + (45 * 45); } } protected override List> GenerateValues() { List> data = new List>(); List oneVariableTestData = SequenceGenerator.GenerateSteps(-0.2m, 4.2m, 0.1m).Select(v => (double)v).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(100, 0.3, 4).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(Math.Exp(-Math.Pow(x1 - 1, 2)) / (1.2 + Math.Pow(x2 - 2.5, 2))); } data.Add(results); return data; } } }