1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022011 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 HeuristicLab.Data;


25 


26  namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {


27  public class KeijzerFunctionEight : RegressionBenchmark {


28 


29  public KeijzerFunctionEight() {


30  Name = "Keijzer 8 f(x) = log(x)";


31  Description = "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine


32  + "Authors: Maarten Keijzer" + Environment.NewLine


33  + "Function: f(x) = log(x)" + Environment.NewLine


34  + "range(train): x = [0:1:100]" + Environment.NewLine


35  + "range(test): x = [0:0.1:100]" + Environment.NewLine


36  + "Function Set: x + y, x * y, 1/x, x, sqrt(x)";


37  targetVariable = "F";


38  inputVariables = new List<string>() { "X", "Y", "Z" };


39  trainingPartition = new IntRange(0, 101);


40  testPartition = new IntRange(102, 1103);


41  }


42 


43  protected override List<double> CalculateFunction(List<List<double>> data) {


44  double x;


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


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


47  x = data[0][i];


48  results.Add(Math.Log(x));


49  }


50  return results;


51  }


52 


53  protected override List<List<double>> GenerateInput(List<List<double>> dataList) {


54  dataList.Add(RegressionBenchmark.GenerateSteps(new DoubleRange(0, 100), 1));


55  dataList[0].AddRange(RegressionBenchmark.GenerateSteps(new DoubleRange(0, 100), 0.1));


56 


57  return dataList;


58  }


59  }


60  }

