source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/LeafTypes/ConstantLeaf.cs @ 17209

Last change on this file since 17209 was 17209, checked in by gkronber, 4 months ago

#2994: merged r17132:17198 from trunk to branch

File size: 2.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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.Linq;
24using System.Threading;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Problems.DataAnalysis;
28using HEAL.Attic;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  [StorableType("F3E94907-C5FF-4658-A870-8013C61DD2E1")]
32  [Item("ConstantLeaf", "A leaf type that uses constant models as leaf models")]
33  public class ConstantLeaf : LeafBase {
34    #region Constructors & Cloning
35    [StorableConstructor]
36    protected ConstantLeaf(StorableConstructorFlag _) : base(_) { }
37    protected ConstantLeaf(ConstantLeaf original, Cloner cloner) : base(original, cloner) { }
38    public ConstantLeaf() { }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new ConstantLeaf(this, cloner);
41    }
42    #endregion
43
44    #region IModelType
45    public override bool ProvidesConfidence {
46      get { return false; }
47    }
48    public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int numberOfParameters) {
49      if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a linear model");
50      numberOfParameters = 1;
51      return new PreconstructedLinearModel(pd.Dataset.GetDoubleValues(pd.TargetVariable).Average(), pd.TargetVariable);
52    }
53
54    public override int MinLeafSize(IRegressionProblemData pd) {
55      return 0;
56    }
57    #endregion
58  }
59}
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