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source: branches/2817-BinPackingSpeedup/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionEight.cs

Last change on this file was 16140, checked in by abeham, 6 years ago

#2817: updated to trunk r15680

File size: 4.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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;
25using HeuristicLab.Random;
26
27namespace HeuristicLab.Problems.Instances.DataAnalysis {
28  public class KornsFunctionEight : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Korns 8 y = 6.87 + (11 * sqrt(7.23 * X0 * X3 * X4))"; } }
31    public override string Description {
32      get {
33        return "Paper: Accuracy in Symbolic Regression" + Environment.NewLine
34        + "Authors: Michael F. Korns" + Environment.NewLine
35        + "Function: y = 6.87 + (11 * sqrt(7.23 * X0 * X3 * X4))" + Environment.NewLine
36        + "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine
37        + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine
38        + "Constants: random finit 64-bit IEEE double" + Environment.NewLine
39        + "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. "
40        + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
41        + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
42        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
43        + "Note: Because of the square root only non-negatic values are created for the input variables!";
44      }
45    }
46    protected override string TargetVariable { get { return "Y"; } }
47    protected override string[] VariableNames { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
48    protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
49    protected override int TrainingPartitionStart { get { return 0; } }
50    protected override int TrainingPartitionEnd { get { return 10000; } }
51    protected override int TestPartitionStart { get { return 10000; } }
52    protected override int TestPartitionEnd { get { return 20000; } }
53
54    public int Seed { get; private set; }
55
56    public KornsFunctionEight() : this((int)System.DateTime.Now.Ticks) {
57    }
58    public KornsFunctionEight(int seed) : base() {
59      Seed = seed;
60    }
61
62    protected override List<List<double>> GenerateValues() {
63      List<List<double>> data = new List<List<double>>();
64      var rand = new MersenneTwister((uint)Seed);
65
66      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
67      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList());
68      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList());
69      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
70      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
71
72      double x0, x3, x4;
73      List<double> results = new List<double>();
74      for (int i = 0; i < data[0].Count; i++) {
75        x0 = data[0][i];
76        x3 = data[3][i];
77        x4 = data[4][i];
78        results.Add(6.87 + (11 * Math.Sqrt(7.23 * x0 * x3 * x4)));
79      }
80      data.Add(results);
81
82      return data;
83    }
84  }
85}
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