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 KornFunctionNine : RegressionToyBenchmark {


28 


29  public KornFunctionNine() {


30  Name = "Korn 9 y = ((sqrt(X0) / log(X1)) * (exp(X2) / square(X3)))";


31  Description = "Paper: Accuracy in Symbolic Regression" + Environment.NewLine


32  + "Authors: Michael F. Korns" + Environment.NewLine


33  + "Function: y = ((sqrt(X0) / log(X1)) * (exp(X2) / square(X3)))" + Environment.NewLine


34  + "Real Numbers: 3.45, .982, 100.389, and all other real constants" + Environment.NewLine


35  + "Row Features: x1, x2, x9, and all other features" + Environment.NewLine


36  + "Binary Operators: +, , *, /" + Environment.NewLine


37  + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, log, exp" + Environment.NewLine


38  + "\"Our testing regimen uses only statistical best practices outofsample testing techniques. "


39  + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "


40  + "For each test a training matrix is filled with random numbers between 50 and +50. The test case "


41  + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine


42  + "Note: Because of the square root and the logarithm only nonnegatic values are created for the input variables!";


43  targetVariable = "Y";


44  inputVariables = new List<string>() { "X0", "X1", "X2", "X3", "X4" };


45  trainingPartition = new IntRange(0, 5000);


46  testPartition = new IntRange(5001, 10000);


47  }


48 


49  protected override List<double> GenerateTarget(List<List<double>> data) {


50  double x0, x1, x2, x3;


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


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


53  x0 = data[0][i];


54  x1 = data[1][i];


55  x2 = data[2][i];


56  x3 = data[3][i];


57  results.Add(((Math.Sqrt(x0) / Math.Log(x1)) * (Math.Exp(x2) / Math.Pow(x3, 2))));


58  }


59  return results;


60  }


61 


62  protected override List<List<double>> GenerateInput() {


63  List<List<double>> dataList = new List<List<double>>();


64  DoubleRange range = new DoubleRange(0, 50);


65  for (int i = 0; i < inputVariables.Count; i++) {


66  dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, range));


67  }


68 


69  return dataList;


70  }


71  }


72  }

