#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 PolyTen : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Poly-10 y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10"; } }
public override string Description {
get {
return "Paper: A Simple but Theoretically-motivated Method to Control Bloat in Genetic Programming" + Environment.NewLine
+ "Authors: Riccardo Poli" + Environment.NewLine
+ "Function: y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10" + Environment.NewLine
+ "Terminal set: x1, x2, x3, x4, x5, x6, x7, x8, x9, x10" + Environment.NewLine
+ "Fitness was minus the sum of the absolute values of the errors made over 50 fitness cases. "
+ "These were generated by randomly assigning values to the variables xiin the range [1, 1].";
}
}
protected override string TargetVariable { get { return "Y"; } }
protected override string[] InputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 250; } }
protected override int TestPartitionStart { get { return 250; } }
protected override int TestPartitionEnd { get { return 500; } }
protected override List> GenerateValues() {
List> data = new List>();
for (int i = 0; i < AllowedInputVariables.Count(); i++) {
data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList());
}
double x1, x2, x3, x4, x5, x6, x7, x8, x9, x10;
List results = new List();
for (int i = 0; i < data[0].Count; i++) {
x1 = data[0][i];
x2 = data[1][i];
x3 = data[2][i];
x4 = data[3][i];
x5 = data[4][i];
x6 = data[5][i];
x7 = data[6][i];
x8 = data[7][i];
x9 = data[8][i];
x10 = data[9][i];
results.Add(x1 * x2 + x3 * x4 + x5 * x6 + x1 * x7 * x9 + x3 * x6 * x10);
}
data.Add(results);
return data;
}
}
}