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 KeijzerFunctionSix : RegressionBenchmark {


28 


29  public KeijzerFunctionSix() {


30  Name = "Keijzer 6 f(x) = (30 * x * z) / ((x  10) * 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) = (30 * x * z) / ((x  10) * y^2)" + Environment.NewLine


34  + "range(train): 1000 points x,z = rnd(1, 1), y = rnd(1, 2)" + Environment.NewLine


35  + "range(test): 10000 points x,z = rnd(1, 1), y = rnd(1, 2)" + 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, 1000);


40  testPartition = new IntRange(1001, 11000);


41  }


42 


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


44  double x, y, z;


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  z = data[2][i];


50  results.Add((30 * x * z) / ((x  10) * Math.Pow(y, 2)));


51  }


52  return results;


53  }


54 


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


56  DoubleRange rangeXZ = new DoubleRange(1, 1);


57  DoubleRange rangeY = new DoubleRange(1, 2);


58  dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, rangeXZ));


59  dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, rangeY));


60  dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, rangeXZ));


61 


62  return dataList;


63  }


64  }


65  }

