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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Nguyen/NguyenFunctionFive.cs @ 7485

Last change on this file since 7485 was 7485, checked in by sforsten, 12 years ago

#1708:

  • changes according to mkommend's reviewing comments have been made
File size: 2.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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 HeuristicLab.Data;
25
26namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
27  public class NguyenFunctionFive : RegressionToyBenchmark {
28
29    public NguyenFunctionFive() {
30      Name = "Nguyen F5 = sin(x^2)cos(x) - 1";
31      Description = "Paper: Semantically-based Crossover in Genetic Programming: Application to Real-valued Symbolic Regression" + Environment.NewLine
32        + "Authors: Nguyen Quang Uy · Nguyen Xuan Hoai · Michael O’Neill · R.I. McKay · Edgar Galvan-Lopez" + Environment.NewLine
33        + "Function: F5 = sin(x^2)cos(x) - 1" + Environment.NewLine
34        + "Fitcases: 20 random points ⊆ [-1, 1]" + Environment.NewLine
35        + "Non-terminals: +, -, *, /, sin, cos, exp, log (protected version)" + Environment.NewLine
36        + "Terminals: X, 1 for single variable problems, and X, Y for bivariable problems";
37      targetVariable = "Y";
38      inputVariables = new List<string>() { "X" };
39      trainingPartition = new IntRange(0, 20);
40      testPartition = new IntRange(250, 350);
41    }
42
43    protected override List<double> GenerateTarget(List<List<double>> data) {
44      double x;
45      List<double> results = new List<double>();
46      for (int i = 0; i < data[0].Count; i++) {
47        x = data[0][i];
48        results.Add(Math.Sin(Math.Pow(x, 2)) * Math.Cos(x) - 1);
49      }
50      return results;
51    }
52
53    protected override List<List<double>> GenerateInput() {
54      List<List<double>> dataList = new List<List<double>>();
55      DoubleRange range = new DoubleRange(-1, 1);
56      dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(500, range));
57
58      return dataList;
59    }
60  }
61}
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