# source:stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionEleven.cs@14305

Last change on this file since 14305 was 14305, checked in by gkronber, 4 years ago

#2371: merged r14228, r14229 from trunk to stable

File size: 3.5 KB
RevLine
2/* HeuristicLab
[14186]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[7849]4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
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;
[12740]25using HeuristicLab.Common;
[14305]26using HeuristicLab.Random;
[7849]27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
[8238]29  public class KeijzerFunctionEleven : ArtificialRegressionDataDescriptor {
[7849]30
[8238]31    public override string Name { get { return "Keijzer 11 f(x, y) = xy + sin((x - 1)(y - 1))"; } }
[7849]32    public override string Description {
33      get {
[8238]34        return
35          "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
36          + "Authors: Maarten Keijzer" + Environment.NewLine
37          + "Function: f(x, y) = xy + sin((x - 1)(y - 1))" + Environment.NewLine
38          + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
39          + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
[9007]40          + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
[7849]41      }
42    }
43    protected override string TargetVariable { get { return "F"; } }
[8825]44    protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
[7849]45    protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
46    protected override int TrainingPartitionStart { get { return 0; } }
47    protected override int TrainingPartitionEnd { get { return 20; } }
[9007]48    protected override int TestPartitionStart { get { return 20; } }
49    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
[14305]50    public int Seed { get; private set; }
[7849]51
[14305]52    public KeijzerFunctionEleven() : this((int)System.DateTime.Now.Ticks) {
53    }
54    public KeijzerFunctionEleven(int seed) : base() {
55      Seed = seed;
56    }
[7849]57    protected override List<List<double>> GenerateValues() {
58      List<List<double>> data = new List<List<double>>();
[12740]59      List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.01m).Select(v => (double)v).ToList();
[9007]60      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
61
62      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
[14305]63      var rand = new MersenneTwister((uint)Seed);
[7849]64      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
[7849]67      }
68
69      double x, y;
70      List<double> results = new List<double>();
71      for (int i = 0; i < data[0].Count; i++) {
72        x = data[0][i];
73        y = data[1][i];
74        results.Add(x * y + Math.Sin((x - 1) * (y - 1)));
75      }