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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionOne.cs @ 16373

Last change on this file since 16373 was 15583, checked in by swagner, 7 years ago

#2640: Updated year of copyrights in license headers

File size: 3.6 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 KornsFunctionOne : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Korns 1 y = 1.57 + (24.3 * X3)"; } }
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 = 1.57 + (24.3 * X3)" + Environment.NewLine
36        + "Row Features: x1, x2, x9, and all other features" + Environment.NewLine
37        + "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine
38        + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine
39        + "Constants: random finit 64-bit IEEE double" + Environment.NewLine
40        + "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. "
41        + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
42        + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
43        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"";
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    public int Seed { get; private set; }
54
55    public KornsFunctionOne() : this((int)System.DateTime.Now.Ticks) {
56    }
57    public KornsFunctionOne(int seed) : base() {
58      Seed = seed;
59    }
60    protected override List<List<double>> GenerateValues() {
61      List<List<double>> data = new List<List<double>>();
62      var rand = new MersenneTwister((uint)Seed);
63
64      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
65        data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList());
66      }
67
68      double x3;
69      List<double> results = new List<double>();
70      for (int i = 0; i < data[0].Count; i++) {
71        x3 = data[3][i];
72        results.Add(1.57 + (24.3 * x3));
73      }
74      data.Add(results);
75
76      return data;
77    }
78  }
79}
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