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source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs @ 17764

Last change on this file since 17764 was 17181, checked in by swagner, 5 years ago

#2875: Merged r17180 from trunk to stable

File size: 3.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 KeijzerFunctionTen : ArtificialRegressionDataDescriptor {
30
31    public override string Name { get { return "Keijzer 10 f(x, y) = x ^ y"; } }
32    public override string Description {
33      get {
34        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
35        + "Authors: Maarten Keijzer" + Environment.NewLine
36        + "Function: f(x, y) = x ^ y" + Environment.NewLine
37        + "range(train): 100 Train cases x,y = rnd(0, 1)" + Environment.NewLine
38        + "range(test): x,y = [0:0.01:1]" + Environment.NewLine
39        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
40      }
41    }
42    protected override string TargetVariable { get { return "F"; } }
43    protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
44    protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
45    protected override int TrainingPartitionStart { get { return 0; } }
46    protected override int TrainingPartitionEnd { get { return 100; } }
47    protected override int TestPartitionStart { get { return 100; } }
48    protected override int TestPartitionEnd { get { return 100 + (101 * 101); } }
49    public int Seed { get; private set; }
50
51    public KeijzerFunctionTen() : this((int)System.DateTime.Now.Ticks) {
52    }
53    public KeijzerFunctionTen(int seed) : base() {
54      Seed = seed;
55    }
56    protected override List<List<double>> GenerateValues() {
57      List<List<double>> data = new List<List<double>>();
58
59      List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(0, 1, 0.01m).Select(v => (double)v).ToList();
60      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
61
62      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
63      var rand = new MersenneTwister((uint)Seed);
64      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
65        data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 100, 0, 1).ToList());
66        data[i].AddRange(combinations[i]);
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(Math.Pow(x, y));
75      }
76      data.Add(results);
77
78      return data;
79    }
80  }
81}
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