#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;
namespace HeuristicLab.Problems.Instances.Regression {
public class KeijzerFunctionFour : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Keijzer 4 f(x) = 0.3 * x *sin(2 * PI * 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) = 0.3 * x *sin(2 * PI * x)" + Environment.NewLine
+ "range(train): x = [-1:0.1:1]" + Environment.NewLine
+ "range(test): x = [-1:0.001:1]" + Environment.NewLine
+ "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
}
}
protected override string TargetVariable { get { return "F"; } }
protected override IEnumerable InputVariables { get { return new List() { "X", "F" }; } }
protected override IEnumerable AllowedInputVariables { get { return new List() { "X" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 21; } }
protected override int TestPartitionStart { get { return 21; } }
protected override int TestPartitionEnd { get { return 2022; } }
protected override double[,] GenerateValues() {
List> data = new List>();
data.Add(ValueGenerator.GenerateSteps(-1, 1, 0.1));
data[0].AddRange(ValueGenerator.GenerateSteps(-1, 1, 0.001));
double x;
List results = new List();
for (int i = 0; i < data[0].Count; i++) {
x = data[0][i];
results.Add(0.3 * x * Math.Sin(2 * Math.PI * x));
}
data.Add(results);
return ValueGenerator.Transformation(data);
}
}
}