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source: trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/M5Utilities/RegressionTreeParameters.cs @ 17078

Last change on this file since 17078 was 16855, checked in by gkronber, 6 years ago

#2847: moved M5 regression into a separate plugin as it depends on HL.DataAnalysis.Algorithms.Glmnet plugin

File size: 3.5 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;
23using System.Collections.Generic;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Problems.DataAnalysis;
27using HEAL.Attic;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [StorableType("A6293516-C146-469D-B248-31B866A1D94F")]
31  public sealed class RegressionTreeParameters : Item {
32    private readonly ISplitter splitter;
33    private readonly IPruning pruning;
34    private readonly ILeafModel leafModel;
35    private readonly int minLeafSize;
36    private readonly IRegressionProblemData problemData;
37    private readonly IRandom random;
38    public ISplitter Splitter {
39      get { return splitter; }
40    }
41    public IPruning Pruning {
42      get { return pruning; }
43    }
44    public ILeafModel LeafModel {
45      get { return leafModel; }
46    }
47    public int MinLeafSize {
48      get { return minLeafSize; }
49    }
50    private IRegressionProblemData ProblemData {
51      get { return problemData; }
52    }
53    public IRandom Random {
54      get { return random; }
55    }
56    public IEnumerable<string> AllowedInputVariables {
57      get { return ProblemData.AllowedInputVariables; }
58    }
59    public string TargetVariable {
60      get { return ProblemData.TargetVariable; }
61    }
62    public IDataset Data {
63      get { return ProblemData.Dataset; }
64    }
65
66    #region Constructors & Cloning
67    [StorableConstructor]
68    private RegressionTreeParameters(StorableConstructorFlag _) : base(_) { }
69    private RegressionTreeParameters(RegressionTreeParameters original, Cloner cloner) : base(original, cloner) {
70      problemData = cloner.Clone(original.problemData);
71      random = cloner.Clone(original.random);
72      leafModel = cloner.Clone(original.leafModel);
73      splitter = cloner.Clone(original.splitter);
74      pruning = cloner.Clone(original.pruning);
75      minLeafSize = original.minLeafSize;
76    }
77
78    public RegressionTreeParameters(IPruning pruning, int minleafSize, ILeafModel leafModel,
79      IRegressionProblemData problemData, IRandom random, ISplitter splitter) {
80      this.problemData = problemData;
81      this.random = random;
82      this.leafModel = leafModel;
83      this.splitter = splitter;
84      this.pruning = pruning;
85      minLeafSize = Math.Max(pruning.MinLeafSize(problemData, leafModel), Math.Max(minleafSize, leafModel.MinLeafSize(problemData)));
86    }
87    public RegressionTreeParameters(ILeafModel modeltype, IRegressionProblemData problemData, IRandom random) {
88      this.problemData = problemData;
89      this.random = random;
90      leafModel = modeltype;
91    }
92    public override IDeepCloneable Clone(Cloner cloner) {
93      return new RegressionTreeParameters(this, cloner);
94    }
95    #endregion
96  }
97}
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