#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; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class KeijzerFunctionFifteen : ArtificialRegressionDataDescriptor { public override string Name { get { return "Keijzer 15 f(x, y) = x³ / 5 + y³ / 2 - y - x"; } } 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, y) = x³ / 5 + y³ / 2 - y - x" + Environment.NewLine + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine + "range(test): x,y = [-3:0.1:3]" + Environment.NewLine + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + "Comments: Reduced test set compared to original publication!"; } } protected override string TargetVariable { get { return "F"; } } protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } } protected override int TrainingPartitionStart { get { return 0; } } protected override int TrainingPartitionEnd { get { return 20; } } protected override int TestPartitionStart { get { return 20; } } protected override int TestPartitionEnd { get { return 20 + (61 * 61); } } public int Seed { get; private set; } public KeijzerFunctionFifteen() : this((int)System.DateTime.Now.Ticks) { } public KeijzerFunctionFifteen(int seed) : base() { Seed = seed; } protected override List> GenerateValues() { List> data = new List>(); List oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.1m).Select(v => (double)v).ToList(); List> testData = new List>() { oneVariableTestData, oneVariableTestData }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 20, -3, 3).ToList()); data[i].AddRange(combinations[i]); } double x, y; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(x * x * x / 5.0 + y * y * y / 2.0 - y - x); } data.Add(results); return data; } } }