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source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionTwo.cs @ 11946

Last change on this file since 11946 was 11170, checked in by ascheibe, 10 years ago

#2115 updated copyright year in stable branch

File size: 3.4 KB
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[7849]1#region License Information
2/* HeuristicLab
[11170]3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[7849]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 KornsFunctionTwo : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Korns 2 y = 0.23 + (14.2 * ((X3 + X1) / (3.0 * 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 = 0.23 + (14.2 * ((X3 + X1) / (3.0 * X4)))" + Environment.NewLine
[8225]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
[7849]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"; } }
[8825]45    protected override string[] VariableNames { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
[7849]46    protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
47    protected override int TrainingPartitionStart { get { return 0; } }
[8225]48    protected override int TrainingPartitionEnd { get { return 10000; } }
49    protected override int TestPartitionStart { get { return 10000; } }
50    protected override int TestPartitionEnd { get { return 20000; } }
[7849]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 x1, x3, x4;
59      List<double> results = new List<double>();
60      for (int i = 0; i < data[0].Count; i++) {
61        x1 = data[1][i];
62        x3 = data[3][i];
63        x4 = data[4][i];
64        results.Add(0.23 + (14.2 * ((x3 + x1) / (3.0 * x4))));
65      }
66      data.Add(results);
67
68      return data;
69    }
70  }
71}
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