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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RationalPolynomialThreeDimensional.cs @ 12012

Last change on this file since 12012 was 12012, checked in by ascheibe, 9 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 3.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 RationalPolynomialThreeDimensional : ArtificialRegressionDataDescriptor {
28
29    public override string Name { get { return "Vladislavleva-5 F5(X1, X2, X3) = 30 * ((X1 - 1) * (X3 -1)) / (X2² * (X1 - 10))"; } }
30    public override string Description {
31      get {
32        return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
33        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
34        + "Function: F5(X1, X2, X3) = 30 * ((X1 - 1) * (X3 -1)) / (X2² * (X1 - 10))" + Environment.NewLine
35        + "Training Data: 300 points X1, X3 = Rand(0.05, 2), X2 = Rand(1, 2)" + Environment.NewLine
36        + "Test Data: (14*12*14) points X1, X3 = (-0.05:0.15:2.05), X2 = (0.95:0.1:2.05)" + Environment.NewLine
37        + "Function Set: +, -, *, /, square, x^eps, x + eps, x * eps";
38      }
39    }
40    protected override string TargetVariable { get { return "Y"; } }
41    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "Y" }; } }
42    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3" }; } }
43    protected override int TrainingPartitionStart { get { return 0; } }
44    protected override int TrainingPartitionEnd { get { return 300; } }
45    protected override int TestPartitionStart { get { return 300; } }
46    protected override int TestPartitionEnd { get { return 300 + (15*12*15); } }
47
48    protected override List<List<double>> GenerateValues() {
49      List<List<double>> data = new List<List<double>>();
50
51      int n = 300;
52      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 0.05, 2).ToList());
53      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 1, 2).ToList());
54      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 0.05, 2).ToList());
55
56      List<List<double>> testData = new List<List<double>>() {
57        ValueGenerator.GenerateSteps(-0.05m, 2.05m, 0.15m).Select(v => (double)v).ToList(),
58        ValueGenerator.GenerateSteps( 0.95m, 2.05m, 0.1m).Select(v => (double)v).ToList(),
59        ValueGenerator.GenerateSteps(-0.05m, 2.05m, 0.15m).Select(v => (double)v).ToList()
60      };
61
62      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
63
64      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
65        data[i].AddRange(combinations[i]);
66      }
67
68      double x1, x2, x3;
69      List<double> results = new List<double>();
70      for (int i = 0; i < data[0].Count; i++) {
71        x1 = data[0][i];
72        x2 = data[1][i];
73        x3 = data[2][i];
74        results.Add(30 * ((x1 - 1) * (x3 - 1)) / (Math.Pow(x2, 2) * (x1 - 10)));
75      }
76      data.Add(results);
77
78      return data;
79    }
80  }
81}
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