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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/ArtificialClassificationDataDescriptor.cs @ 16169

Last change on this file since 16169 was 15583, checked in by swagner, 6 years ago

#2640: Updated year of copyrights in license headers

File size: 2.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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.Generic;
23using HeuristicLab.Problems.DataAnalysis;
24
25namespace HeuristicLab.Problems.Instances.DataAnalysis {
26  public abstract class ArtificialClassificationDataDescriptor : IDataDescriptor {
27    public abstract string Name { get; }
28    public abstract string Description { get; }
29
30    protected abstract string TargetVariable { get; }
31    protected abstract string[] VariableNames { get; }
32    protected abstract string[] AllowedInputVariables { get; }
33    protected abstract int TrainingPartitionStart { get; }
34    protected abstract int TrainingPartitionEnd { get; }
35    protected abstract int TestPartitionStart { get; }
36    protected abstract int TestPartitionEnd { get; }
37
38    public IClassificationProblemData GenerateClassificationData() {
39      Dataset dataset = new Dataset(VariableNames, this.GenerateValues());
40
41      ClassificationProblemData claData = new ClassificationProblemData(dataset, AllowedInputVariables, TargetVariable);
42      claData.Name = this.Name;
43      claData.Description = this.Description;
44      claData.TrainingPartition.Start = this.TrainingPartitionStart;
45      claData.TrainingPartition.End = this.TrainingPartitionEnd;
46      claData.TestPartition.Start = this.TestPartitionStart;
47      claData.TestPartition.End = this.TestPartitionEnd;
48      return claData;
49    }
50
51    protected abstract List<List<double>> GenerateValues();
52  }
53}
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