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source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RationalPolynomialTwoDimensional.cs @ 14389

Last change on this file since 14389 was 14305, checked in by gkronber, 8 years ago

#2371: merged r14228, r14229 from trunk to stable

File size: 3.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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.Common;
26using HeuristicLab.Random;
27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
29  public class RationalPolynomialTwoDimensional : ArtificialRegressionDataDescriptor {
30
31    public override string Name { get { return "Vladislavleva-8 F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)³ - (X2 -3)) / ((X2 - 2)^4 + 10)"; } }
32    public override string Description {
33      get {
34        return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
35        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
36        + "Function: F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)³ - (X2 -3)) / ((X2 - 2)^4 + 10)" + Environment.NewLine
37        + "Training Data: 50 points X1, X2 = Rand(0.05, 6.05)" + Environment.NewLine
38        + "Test Data: 34*34 points X1, X2 = (-0.25:0.2:6.35)" + Environment.NewLine
39        + "Function Set: +, -, *, /, square, x^eps, x + eps, x * eps";
40      }
41    }
42    protected override string TargetVariable { get { return "Y"; } }
43    protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
44    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
45    protected override int TrainingPartitionStart { get { return 0; } }
46    protected override int TrainingPartitionEnd { get { return 50; } }
47    protected override int TestPartitionStart { get { return 50; } }
48    protected override int TestPartitionEnd { get { return 50 + (34 * 34); } }
49
50    public int Seed { get; private set; }
51
52    public RationalPolynomialTwoDimensional() : this((int)DateTime.Now.Ticks) { }
53
54    public RationalPolynomialTwoDimensional(int seed) : base() {
55      Seed = seed;
56    }
57    protected override List<List<double>> GenerateValues() {
58      List<List<double>> data = new List<List<double>>();
59
60      List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(-0.25m, 6.35m, 0.2m).Select(v => (double)v).ToList();
61
62      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
63      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
64      var rand = new MersenneTwister((uint)Seed);
65
66      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
67        data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 50, 0.05, 6.05).ToList());
68        data[i].AddRange(combinations[i]);
69      }
70
71      double x1, x2;
72      List<double> results = new List<double>();
73      for (int i = 0; i < data[0].Count; i++) {
74        x1 = data[0][i];
75        x2 = data[1][i];
76        results.Add((Math.Pow(x1 - 3, 4) + Math.Pow(x2 - 3, 3) - x2 + 3) / (Math.Pow(x2 - 2, 4) + 10));
77      }
78      data.Add(results);
79
80      return data;
81    }
82  }
83}
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