#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 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 SalutowiczFunctionTwoDimensional : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Vladislavleva-3 F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)"; } }
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: F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)" + Environment.NewLine
+ "Training Data: 600 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine
+ "Test Data: 221 * 23 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
+ "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(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 600; } }
protected override int TestPartitionStart { get { return 600; } }
protected override int TestPartitionEnd { get { return 600 + (221 * 23); } }
protected override List> GenerateValues() {
List> data = new List>();
List> trainingData = new List>() {
ValueGenerator.GenerateSteps(0.05m, 10, 0.1m).Select(v => (double)v).ToList(),
ValueGenerator.GenerateSteps(0.05m, 10.05m, 2).Select(v => (double)v).ToList()
};
List> testData = new List>() {
ValueGenerator.GenerateSteps(-0.5m, 10.5m, 0.05m).Select(v => (double)v).ToList(),
ValueGenerator.GenerateSteps(-0.5m, 10.5m, 0.5m).Select(v => (double)v).ToList()
};
var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList>();
var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList>();
for (int i = 0; i < AllowedInputVariables.Count(); i++) {
data.Add(trainingComb[i].ToList());
data[i].AddRange(testComb[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(-x1) * Math.Pow(x1, 3) * Math.Cos(x1) * Math.Sin(x1) * (Math.Cos(x1) * Math.Pow(Math.Sin(x1), 2) - 1) * (x2 - 5));
}
data.Add(results);
return data;
}
}
}