Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionThirteen.cs @ 8225

Last change on this file since 8225 was 8225, checked in by gkronber, 11 years ago

#1784: Adapted Korns instances.

File size: 3.5 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 KornsFunctionThirteen : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Korns 13 y = 32.0 - (3.0 * ((tan(X0) / tan(X1)) * (tan(X2) / tan(X3))))"; } }
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 = 32.0 - (3.0 * ((tan(X0) / tan(X1)) * (tan(X2) / tan(X3))))" + 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.\"";
42      }
43    }
44    protected override string TargetVariable { get { return "Y"; } }
45    protected override string[] InputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
46    protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
47    protected override int TrainingPartitionStart { get { return 0; } }
48    protected override int TrainingPartitionEnd { get { return 10000; } }
49    protected override int TestPartitionStart { get { return 10000; } }
50    protected override int TestPartitionEnd { get { return 20000; } }
51
52    protected override List<List<double>> GenerateValues() {
53      List<List<double>> data = new List<List<double>>();
54      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
55        data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
56      }
57
58      double x0, x1, x2, x3;
59      List<double> results = new List<double>();
60      for (int i = 0; i < data[0].Count; i++) {
61        x0 = data[0][i];
62        x1 = data[1][i];
63        x2 = data[2][i];
64        x3 = data[3][i];
65        results.Add(32.0 - (3.0 * ((Math.Tan(x0) / Math.Tan(x1)) * (Math.Tan(x2) / Math.Tan(x3)))));
66      }
67      data.Add(results);
68
69      return data;
70    }
71  }
72}
Note: See TracBrowser for help on using the repository browser.