# source:branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Korns/KornFunctionNine.cs@7095

Last change on this file since 7095 was 7095, checked in by sforsten, 11 years ago

#1669: Input variables of Korn functions have been adjusted according to the description.

File size: 3.3 KB
Line
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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
22using System;
23using System.Collections.Generic;
24using HeuristicLab.Data;
25
26namespace 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 out-of-sample 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 non-negatic 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}
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