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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/FeatureSelection/FeatureSelectionInstanceProvider.cs @ 9093

Last change on this file since 9093 was 9093, checked in by gkronber, 11 years ago

#1999 added a provider and a configurable problem instance for testing feature selection

File size: 2.0 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;
24
25namespace HeuristicLab.Problems.Instances.DataAnalysis {
26  public class FeatureSelectionInstanceProvider : ArtificialRegressionInstanceProvider {
27    public override string Name {
28      get { return "Feature Selection Problems"; }
29    }
30    public override string Description {
31      get { return "A set of artificial feature selection benchmark problems"; }
32    }
33    public override Uri WebLink {
34      get { return new Uri("http://dev.heuristiclab.com"); }
35    }
36    public override string ReferencePublication {
37      get { return ""; }
38    }
39
40    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
41      List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
42      var sizes = new int[] { 50, 100, 200 };
43      var pp = new double[] { 0.1, 0.25, 0.5 };
44      var noiseRatios = new double[] { 0.01, 0.05, 0.1, 0.2 };
45      foreach (var size in sizes) {
46        foreach (var p in pp) {
47          foreach (var noiseRatio in noiseRatios) {
48            descriptorList.Add(new FeatureSelection(size, p, noiseRatio));
49          }
50        }
51      }
52      return descriptorList;
53    }
54  }
55}
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