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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFive.cs @ 18242

Last change on this file since 18242 was 17180, checked in by swagner, 5 years ago

#2875: Removed years in copyrights

File size: 3.3 KB
RevLine 
[7849]1#region License Information
2/* HeuristicLab
[17180]3 * Copyright (C) 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;
[14228]25using HeuristicLab.Random;
[7849]26
27namespace HeuristicLab.Problems.Instances.DataAnalysis {
[8238]28  public class KeijzerFunctionFive : ArtificialRegressionDataDescriptor {
[7849]29
[8238]30    public override string Name { get { return "Keijzer 5 f(x) = (30 * x * z) / ((x - 10)  * y²)"; } }
[7849]31    public override string Description {
32      get {
33        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
34        + "Authors: Maarten Keijzer" + Environment.NewLine
[8238]35        + "Function: f(x) = (30 * x * z) / ((x - 10)  * y²)" + Environment.NewLine
[7849]36        + "range(train): 1000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
37        + "range(test): 10000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
38        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
39      }
40    }
41    protected override string TargetVariable { get { return "F"; } }
[8825]42    protected override string[] VariableNames { get { return new string[] { "X", "Y", "Z", "F" }; } }
[7849]43    protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y", "Z" }; } }
44    protected override int TrainingPartitionStart { get { return 0; } }
45    protected override int TrainingPartitionEnd { get { return 1000; } }
46    protected override int TestPartitionStart { get { return 1000; } }
47    protected override int TestPartitionEnd { get { return 11000; } }
[14229]48    public int Seed { get; private set; }
[7849]49
[14228]50    public KeijzerFunctionFive() : this((int)System.DateTime.Now.Ticks) {
51    }
52    public KeijzerFunctionFive(int seed) : base() {
53      Seed = seed;
54    }
[7849]55    protected override List<List<double>> GenerateValues() {
56      List<List<double>> data = new List<List<double>>();
[14228]57      var rand = new MersenneTwister((uint)Seed);
[7849]58
[14228]59      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -1, 1).ToList());
60      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList());
61      data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -1, 1).ToList());
62
[7849]63      double x, y, z;
64      List<double> results = new List<double>();
65      for (int i = 0; i < data[0].Count; i++) {
66        x = data[0][i];
67        y = data[1][i];
68        z = data[2][i];
69        results.Add((30 * x * z) / ((x - 10) * Math.Pow(y, 2)));
70      }
71      data.Add(results);
72
73      return data;
74    }
75  }
76}
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