1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System;


23  using System.Collections.Generic;


24  using System.Linq;


25 


26  namespace HeuristicLab.Problems.Instances.DataAnalysis {


27  public class SalutowiczFunctionTwoDimensional : ArtificialRegressionDataDescriptor {


28 


29  public override string Name { get { return "Vladislavleva3 F3(X1, X2) = exp(X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)²  1)(X2  5)"; } }


30  public override string Description {


31  get {


32  return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine


33  + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine


34  + "Function: F3(X1, X2) = exp(X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)²  1)(X2  5)" + Environment.NewLine


35  + "Training Data: 601 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine


36  + "Test Data: 4840 points X1 = (0.5:0.05:10.5), X2 = (0.5:0.5:10.5)" + Environment.NewLine


37  + "Function Set: +, , *, /, square, e^x, e^x, sin(x), cos(x), x^eps, x + eps, x + eps";


38  }


39  }


40  protected override string TargetVariable { get { return "Y"; } }


41  protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }


42  protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }


43  protected override int TrainingPartitionStart { get { return 0; } }


44  protected override int TrainingPartitionEnd { get { return 601; } }


45  protected override int TestPartitionStart { get { return 601; } }


46  protected override int TestPartitionEnd { get { return 5441; } }


47 


48  protected override List<List<double>> GenerateValues() {


49  List<List<double>> data = new List<List<double>>();


50  List<List<double>> trainingData = new List<List<double>>() {


51  ValueGenerator.GenerateSteps(0.05, 10, 0.1).ToList(),


52  ValueGenerator.GenerateSteps(0.05, 10.05, 2).ToList()


53  };


54 


55  List<List<double>> testData = new List<List<double>>() {


56  ValueGenerator.GenerateSteps(0.5, 10.5, 0.05).ToList(),


57  ValueGenerator.GenerateSteps(0.5, 10.5, 0.5).ToList()


58  };


59 


60  var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList<IEnumerable<double>>();


61  var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();


62 


63  for (int i = 0; i < AllowedInputVariables.Count(); i++) {


64  data.Add(trainingComb[i].ToList());


65  data[i].AddRange(testComb[i]);


66  }


67 


68  double x1, x2;


69  List<double> results = new List<double>();


70  for (int i = 0; i < data[0].Count; i++) {


71  x1 = data[0][i];


72  x2 = data[1][i];


73  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));


74  }


75  data.Add(results);


76 


77  return data;


78  }


79  }


80  }

