1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2017 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Problems.DataAnalysis;
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27 | using HEAL.Attic;
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28 |
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29 | namespace HeuristicLab.Algorithms.DataAnalysis {
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30 | [StorableType("A6293516-C146-469D-B248-31B866A1D94F")]
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31 | public class RegressionTreeParameters : Item {
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32 | private readonly ISplitter splitter;
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33 | private readonly IPruning pruning;
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34 | private readonly ILeafModel leafModel;
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35 | private readonly int minLeafSize;
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36 | private readonly IRegressionProblemData problemData;
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37 | private readonly IRandom random;
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38 | public ISplitter Splitter {
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39 | get { return splitter; }
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40 | }
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41 | public IPruning Pruning {
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42 | get { return pruning; }
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43 | }
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44 | public ILeafModel LeafModel {
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45 | get { return leafModel; }
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46 | }
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47 | public int MinLeafSize {
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48 | get { return minLeafSize; }
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49 | }
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50 | private IRegressionProblemData ProblemData {
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51 | get { return problemData; }
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52 | }
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53 | public IRandom Random {
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54 | get { return random; }
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55 | }
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56 | public IEnumerable<string> AllowedInputVariables {
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57 | get { return ProblemData.AllowedInputVariables; }
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58 | }
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59 | public string TargetVariable {
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60 | get { return ProblemData.TargetVariable; }
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61 | }
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62 | public IDataset Data {
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63 | get { return ProblemData.Dataset; }
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64 | }
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65 |
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66 | #region Constructors & Cloning
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67 | [StorableConstructor]
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68 | private RegressionTreeParameters(StorableConstructorFlag _) : base(_) { }
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69 | private RegressionTreeParameters(RegressionTreeParameters original, Cloner cloner) : base(original, cloner) {
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70 | problemData = cloner.Clone(original.problemData);
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71 | random = cloner.Clone(original.random);
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72 | leafModel = cloner.Clone(original.leafModel);
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73 | splitter = cloner.Clone(original.splitter);
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74 | pruning = cloner.Clone(original.pruning);
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75 | minLeafSize = original.minLeafSize;
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76 | }
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77 |
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78 | public RegressionTreeParameters(IPruning pruning, int minleafSize, ILeafModel leafModel,
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79 | IRegressionProblemData problemData, IRandom random, ISplitter splitter) {
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80 | this.problemData = problemData;
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81 | this.random = random;
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82 | this.leafModel = leafModel;
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83 | this.splitter = splitter;
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84 | this.pruning = pruning;
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85 | minLeafSize = Math.Max(pruning.MinLeafSize(problemData, leafModel), Math.Max(minleafSize, leafModel.MinLeafSize(problemData)));
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86 | }
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87 | public RegressionTreeParameters(ILeafModel modeltype, IRegressionProblemData problemData, IRandom random) {
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88 | this.problemData = problemData;
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89 | this.random = random;
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90 | leafModel = modeltype;
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91 | }
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92 | public override IDeepCloneable Clone(Cloner cloner) {
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93 | return new RegressionTreeParameters(this, cloner);
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94 | }
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95 | #endregion
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96 | }
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97 | } |
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