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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionEleven.cs @ 9650

Last change on this file since 9650 was 9456, checked in by swagner, 12 years ago

Updated copyright year and added some missing license headers (#1889)

File size: 3.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 KornsFunctionEleven : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Korns 11 y = 6.87 + (11 * cos(7.23 * X0³))"; } }
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 = 6.87 + (11 * cos(7.23 * X0 * X0 * X0))" + 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[] VariableNames { 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;
59      List<double> results = new List<double>();
60      for (int i = 0; i < data[0].Count; i++) {
61        x0 = data[0][i];
62        results.Add(6.87 + (11 * Math.Cos(7.23 * x0 * x0 * x0)));
63      }
64      data.Add(results);
65
66      return data;
67    }
68  }
69}
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