#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 KornsFunctionFive : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Korns 5 y = 3.0 + (2.13 * log(X4))"; } }
public override string Description {
get {
return "Paper: Accuracy in Symbolic Regression" + Environment.NewLine
+ "Authors: Michael F. Korns" + Environment.NewLine
+ "Function: y = 3.0 + (2.13 * log(X4))" + Environment.NewLine
+ "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine
+ "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine
+ "Constants: random finit 64-bit IEEE double" + Environment.NewLine
+ "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. "
+ "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
+ "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
+ "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
+ "Note: Because of the logarithm only non-negatic values are created for the input variables!";
}
}
protected override string TargetVariable { get { return "Y"; } }
protected override string[] InputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 10000; } }
protected override int TestPartitionStart { get { return 10000; } }
protected override int TestPartitionEnd { get { return 20000; } }
protected override List> GenerateValues() {
List> data = new List>();
data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
double x4;
List results = new List();
for (int i = 0; i < data[0].Count; i++) {
x4 = data[4][i];
results.Add(3.0 + (2.13 * Math.Log(x4)));
}
data.Add(results);
return data;
}
}
}