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source: branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/Pruning/M5LinearPruning.cs @ 15470

Last change on this file since 15470 was 15470, checked in by bwerth, 5 years ago

#2847 worked on M5Regression

File size: 2.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2017 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.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26using HeuristicLab.Problems.DataAnalysis;
27
28namespace HeuristicLab.Algorithms.DataAnalysis {
29  [StorableClass]
30  [Item("M5LinearPruning", "M5Pruning using cheap linear models. This is standard for M5' regression")]
31  public class M5LinearPruning : PruningBase {
32    #region Constructors & Cloning
33    [StorableConstructor]
34    private M5LinearPruning(bool deserializing) : base(deserializing) { }
35    private M5LinearPruning(M5LinearPruning original, Cloner cloner) : base(original, cloner) { }
36    public M5LinearPruning() { }
37    public override IDeepCloneable Clone(Cloner cloner) {
38      return new M5LinearPruning(this, cloner);
39    }
40    #endregion
41
42    #region IPruningType
43    public override ILeafType<IRegressionModel> ModelType(ILeafType<IRegressionModel> leafType) {
44      return new LinearLeaf();
45    }
46
47    public override void GenerateHoldOutSet(IReadOnlyList<int> allrows, IRandom random, out IReadOnlyList<int> training, out IReadOnlyList<int> holdout) {
48      training = allrows;
49      holdout = allrows;
50    }
51    #endregion
52  }
53}
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