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source: branches/DataAnalysisCSVImport/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionFive.cs @ 8695

Last change on this file since 8695 was 8245, checked in by gkronber, 12 years ago

#1784 adapted value ranges for Korns benchmark instances to prevent generating NaN target values

File size: 3.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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 System.Linq;
25
26namespace HeuristicLab.Problems.Instances.DataAnalysis {
27  public class KornsFunctionFive : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Korns 5 y = 3.0 + (2.13 * log(X4))"; } }
30    public override string Description {
31      get {
32        return "Paper: Accuracy in Symbolic Regression" + Environment.NewLine
33        + "Authors: Michael F. Korns" + Environment.NewLine
34        + "Function: y = 3.0 + (2.13 * log(X4))" + Environment.NewLine
35        + "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine
36        + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine
37        + "Constants: random finit 64-bit IEEE double" + 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 logarithm only non-negatic values are created for the input variables!";
43      }
44    }
45    protected override string TargetVariable { get { return "Y"; } }
46    protected override string[] InputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
47    protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
48    protected override int TrainingPartitionStart { get { return 0; } }
49    protected override int TrainingPartitionEnd { get { return 10000; } }
50    protected override int TestPartitionStart { get { return 10000; } }
51    protected override int TestPartitionEnd { get { return 20000; } }
52
53    protected override List<List<double>> GenerateValues() {
54      List<List<double>> data = new List<List<double>>();
55      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
56      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
57      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
58      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
59      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
60
61      double x4;
62      List<double> results = new List<double>();
63      for (int i = 0; i < data[0].Count; i++) {
64        x4 = data[4][i];
65        results.Add(3.0 + (2.13 * Math.Log(x4)));
66      }
67      data.Add(results);
68
69      return data;
70    }
71  }
72}
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