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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/ClassificationDataDescriptor.cs @ 9363

Last change on this file since 9363 was 9208, checked in by sforsten, 12 years ago

#1941:

  • added wisconsin breast cancer problem instance
  • corrected iris dataset
  • changed classification data descriptors to be able to set training and test partition as well as input and target variables (in the same way as it is done in regression)
File size: 2.1 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
22
23using HeuristicLab.Problems.DataAnalysis;
24namespace HeuristicLab.Problems.Instances.DataAnalysis {
25  public abstract class ClassificationDataDescriptor : IDataDescriptor {
26    public abstract string Name { get; }
27    public abstract string Description { get; }
28
29    protected abstract string TargetVariable { get; }
30    protected abstract string[] VariableNames { get; }
31    protected abstract string[] AllowedInputVariables { get; }
32    protected abstract int TrainingPartitionStart { get; }
33    protected abstract int TrainingPartitionEnd { get; }
34    protected abstract int TestPartitionStart { get; }
35    protected abstract int TestPartitionEnd { get; }
36
37    public IClassificationProblemData GenerateClassificationData(Dataset dataset) {
38      IClassificationProblemData claData = new ClassificationProblemData(dataset, AllowedInputVariables, TargetVariable);
39      claData.Name = this.Name;
40      claData.Description = this.Description;
41      claData.TrainingPartition.Start = this.TrainingPartitionStart;
42      claData.TrainingPartition.End = this.TrainingPartitionEnd;
43      claData.TestPartition.Start = this.TestPartitionStart;
44      claData.TestPartition.End = this.TestPartitionEnd;
45      return claData;
46    }
47  }
48}
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