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 |
|
---|
22 | using System;
|
---|
23 | using System.Collections.Generic;
|
---|
24 | using System.Linq;
|
---|
25 |
|
---|
26 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
|
---|
27 | public class KornsFunctionSix : ArtificialRegressionDataDescriptor {
|
---|
28 |
|
---|
29 | public override string Name { get { return "Korns 6 y = 1.3 + (0.13 * sqrt(X0))"; } }
|
---|
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 = 1.3 + (0.13 * sqrt(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.\"" + Environment.NewLine + Environment.NewLine
|
---|
42 | + "Note: Because of the square root only non-negatic values are created for the input variables!";
|
---|
43 | }
|
---|
44 | }
|
---|
45 | protected override string TargetVariable { get { return "Y"; } }
|
---|
46 | protected override string[] VariableNames { 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, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
|
---|
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, -50, 50).ToList());
|
---|
60 |
|
---|
61 | double x0;
|
---|
62 | List<double> results = new List<double>();
|
---|
63 | for (int i = 0; i < data[0].Count; i++) {
|
---|
64 | x0 = data[0][i];
|
---|
65 | results.Add(1.3 + (0.13 * Math.Sqrt(x0)));
|
---|
66 | }
|
---|
67 | data.Add(results);
|
---|
68 |
|
---|
69 | return data;
|
---|
70 | }
|
---|
71 | }
|
---|
72 | }
|
---|