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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/MibaFriction/NvhRating.cs @ 15887

Last change on this file since 15887 was 15855, checked in by fbaching, 7 years ago

#2908: Change setter to private access level to prevent external changes of value; add constructor parameter for configuration of ResourceRegressionDataDescriptor
Add constructor to all existing instances.

File size: 2.7 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
22namespace HeuristicLab.Problems.Instances.DataAnalysis {
23  public class NvhRating : ResourceRegressionDataDescriptor {
24    public NvhRating(string resourceName) : base(resourceName) { }
25    public override string Name { get { return "NvhRating"; } }
26    public override string Description {
27      get {
28        return "Noise-vibration-harshness rating (1..8), Publication: E. Lughofer, G. K. Kronberger, M. Kommenda, S. Saminger-Platz, " +
29               "A. Promberger, F. Nickel, S. M. Winkler, M. Affenzeller - Robust Fuzzy Modeling and Symbolic Regression " +
30               "for Establishing Accurate and Interpretable Prediction Models in Supervising Tribological Systems - " +
31               "Proceedings of the 8th International Joint Conference on Computational Intelligence, Porto, Portugal, 2016, pp. 51-63";
32      }
33    }
34    protected override string TargetVariable { get { return "NVH_Rating"; } }
35    protected override string[] VariableNames {
36      get { return new string[] { "Partition", "Source1", "Source2", "x1", "Material_Cat", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16", "Material", "Grooving", "Oil", "x17", "x18", "x19", "x20", "x21", "x22", "NVH_Rating"
37 };
38      }
39    }
40
41    protected override string[] AllowedInputVariables {
42      get { return new string[] { "Material_Cat",
43        "x1", "x2", "x3", "x4", "x5", "x6", "x8", "x9", "x10", "x11", "x12", "x13", "x14", "x15", "x16",
44        "Material", "Grooving", "Oil",
45        "x17", "x18", "x19", "x21", "x22" }; }
46    }
47    protected override int TrainingPartitionStart { get { return 0; } }
48    protected override int TrainingPartitionEnd { get { return 2636; } }
49    protected override int TestPartitionStart { get { return 2636; } }
50    protected override int TestPartitionEnd { get { return 3943; } }
51  }
52}
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