source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwelve.cs @ 15475

Last change on this file since 15475 was 15475, checked in by mkommend, 2 years ago

#2853: Merged r15461 into stable.

File size: 3.6 KB
RevLine 
[7849]1#region License Information
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
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;
[12740]25using HeuristicLab.Common;
[14305]26using HeuristicLab.Random;
[7849]27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
[8238]29  public class KeijzerFunctionTwelve : ArtificialRegressionDataDescriptor {
[7849]30
[8238]31    public override string Name { get { return "Keijzer 12 f(x, y) = x^4 - x³ + y² / 2 - y"; } }
[7849]32    public override string Description {
33      get {
34        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
[9007]35               + "Authors: Maarten Keijzer" + Environment.NewLine
36               + "Function: f(x, y) = x^4 - x³ + y² / 2 - y" + Environment.NewLine
37               + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
[15475]38               + "range(test): x,y = [-3:0.1:3]" + Environment.NewLine
39               + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine
40               + "Comments: Reduced test set compared to original publication!";
[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; } }
[15475]49    protected override int TestPartitionEnd { get { return 20 + (61 * 61); } }
[14305]50    public int Seed { get; private set; }
[7849]51
[14305]52    public KeijzerFunctionTwelve() : this((int)System.DateTime.Now.Ticks) {
53    }
54    public KeijzerFunctionTwelve(int seed) : base() {
55      Seed = seed;
56    }
[7849]57    protected override List<List<double>> GenerateValues() {
58      List<List<double>> data = new List<List<double>>();
[15475]59      List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.1m).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);
[9007]64
[7849]65      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
[14305]66        data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 20, -3, 3).ToList());
[9007]67        data[i].AddRange(combinations[i]);
[7849]68      }
69
70      double x, y;
71      List<double> results = new List<double>();
72      for (int i = 0; i < data[0].Count; i++) {
73        x = data[0][i];
74        y = data[1][i];
75        results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + Math.Pow(y, 2) / 2 - y);
76      }
77      data.Add(results);
78
79      return data;
80    }
81  }
82}
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