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 KeijzerFunctionFifteen : RegressionBenchmark {


28 


29  public KeijzerFunctionFifteen() {


30  Name = "Keijzer 15 f(x) = 8 / (2 + x^2 + y^2)";


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


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


33  + "Function: f(x, y) = 8 / (2 + x^2 + y^2)" + Environment.NewLine


34  + "range(train): 20 Testcases x,y = rnd(3, 3)" + Environment.NewLine


35  + "range(test): x,y = [3:0.01:3]" + Environment.NewLine


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


37  targetVariable = "F";


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


39  trainingPartition = new IntRange(0, 20);


40  testPartition = new IntRange(21, 621);


41  }


42 


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


44  double x, y;


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


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


47  x = data[0][i];


48  y = data[1][i];


49  results.Add(8 / (2 + Math.Pow(x, 2) + Math.Pow(y, 2)));


50  }


51  return results;


52  }


53 


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


55  DoubleRange range = new DoubleRange(3, 3);


56 


57  List<double> oneVariableTestData = RegressionBenchmark.GenerateSteps(range, 0.01);


58  List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };


59  testData = RegressionBenchmark.AllCombinationsOf(testData);


60 


61  for (int i = 0; i < InputVariable.Count; i++) {


62  dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(20, range));


63  dataList[i].AddRange(testData[i]);


64  }


65 


66  return dataList;


67  }


68  }


69  }

