source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/RealWorldProblems/ChemicalOne.cs @ 7044

Last change on this file since 7044 was 7044, checked in by sforsten, 9 years ago

#1669: real world problems have been added

File size: 2.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Collections;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Data;
26
27namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
28  public class ChemicalOne : RegressionRealWorldBenchmark {
29
30    private const string fileName = "chemical-I.csv";
31
32    public ChemicalOne() {
33      Name = "chemical-I";
34      //Description = "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
35      //  + "Authors: Maarten Keijzer" + Environment.NewLine
36      //  + "Function: f(x) = log(x)" + Environment.NewLine
37      //  + "range(train): x = [0:1:100]" + Environment.NewLine
38      //  + "range(test): x = [0:0.1:100]" + Environment.NewLine
39      //  + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
40    }
41
42    protected override List<IList> GetData() {
43      csvFileParser = RegressionRealWorldBenchmark.getParserForFile(fileName);
44
45      targetVariable = csvFileParser.VariableNames.Last();
46      inputVariables = new List<string>(csvFileParser.VariableNames.Take(csvFileParser.Columns - 1));
47      int trainingPartEnd = csvFileParser.Rows * 2 / 3;
48      trainingPartition = new IntRange(0, trainingPartEnd);
49      testPartition = new IntRange(trainingPartEnd + 1, csvFileParser.Rows);
50
51      return csvFileParser.Values.Skip(csvFileParser.Columns - 1).Union(csvFileParser.Values.Take(csvFileParser.Columns - 1)).ToList();
52    }
53  }
54}
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