[15830] | 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 System.Linq;
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| 25 | using System.Threading;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 28 | using HeuristicLab.Problems.DataAnalysis;
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| 29 |
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| 30 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 31 | [StorableClass]
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| 32 | public class RegressionNodeModel : RegressionModel {
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| 33 | #region Properties
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| 34 | public double PruningStrength = double.NaN;
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| 35 |
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| 36 | [Storable]
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| 37 | private IReadOnlyList<string> Variables {
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| 38 | get {
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| 39 | if (IsLeaf && Model == null) return new List<string>();
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| 40 | if (IsLeaf) return Model.VariablesUsedForPrediction.ToList();
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| 41 | var set = new HashSet<string> {SplitAttribute};
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| 42 | var vl = Left.Variables;
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| 43 | var vr = Right.Variables;
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| 44 | for (var i = 0; i < vl.Count; i++) set.Add(vl[i]);
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| 45 | for (var i = 0; i < vr.Count; i++) set.Add(vr[i]);
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| 46 | return set.ToList();
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| 47 | }
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| 48 | }
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| 49 | [Storable]
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| 50 | internal int NumSamples { get; private set; }
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| 51 | [Storable]
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| 52 | internal bool IsLeaf { get; private set; }
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| 53 | [Storable]
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| 54 | internal IRegressionModel Model { get; private set; }
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| 55 |
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| 56 | [Storable]
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| 57 | public string SplitAttribute { get; private set; }
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| 58 | [Storable]
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| 59 | public double SplitValue { get; private set; }
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| 60 | [Storable]
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| 61 | public RegressionNodeModel Left { get; private set; }
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| 62 | [Storable]
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| 63 | public RegressionNodeModel Right { get; private set; }
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| 64 | [Storable]
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| 65 | public RegressionNodeModel Parent { get; private set; }
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| 66 | #endregion
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| 67 |
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| 68 | #region HLConstructors
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| 69 | [StorableConstructor]
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| 70 | protected RegressionNodeModel(bool deserializing) : base(deserializing) { }
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| 71 | protected RegressionNodeModel(RegressionNodeModel original, Cloner cloner) : base(original, cloner) {
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| 72 | IsLeaf = original.IsLeaf;
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| 73 | Model = cloner.Clone(original.Model);
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| 74 | SplitValue = original.SplitValue;
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| 75 | SplitAttribute = original.SplitAttribute;
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| 76 | Left = cloner.Clone(original.Left);
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| 77 | Right = cloner.Clone(original.Right);
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| 78 | Parent = cloner.Clone(original.Parent);
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| 79 | NumSamples = original.NumSamples;
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| 80 | }
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| 81 | private RegressionNodeModel(string targetAttr) : base(targetAttr) {
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| 82 | IsLeaf = true;
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| 83 | }
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| 84 | private RegressionNodeModel(RegressionNodeModel parent) : this(parent.TargetVariable) {
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| 85 | Parent = parent;
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| 86 | IsLeaf = true;
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| 87 | }
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| 88 | public override IDeepCloneable Clone(Cloner cloner) {
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| 89 | return new RegressionNodeModel(this, cloner);
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| 90 | }
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| 91 | public static RegressionNodeModel CreateNode(string targetAttr, RegressionTreeParameters regressionTreeParams) {
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| 92 | return regressionTreeParams.LeafModel.ProvidesConfidence ? new ConfidenceRegressionNodeModel(targetAttr) : new RegressionNodeModel(targetAttr);
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| 93 | }
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| 94 | private static RegressionNodeModel CreateNode(RegressionNodeModel parent, RegressionTreeParameters regressionTreeParams) {
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| 95 | return regressionTreeParams.LeafModel.ProvidesConfidence ? new ConfidenceRegressionNodeModel(parent) : new RegressionNodeModel(parent);
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| 96 | }
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| 97 | #endregion
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| 98 |
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| 99 | #region RegressionModel
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| 100 | public override IEnumerable<string> VariablesUsedForPrediction {
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| 101 | get { return Variables; }
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| 102 | }
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| 103 | public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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| 104 | if (!IsLeaf) return rows.Select(row => GetEstimatedValue(dataset, row));
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| 105 | if (Model == null) throw new NotSupportedException("The model has not been built correctly");
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| 106 | return Model.GetEstimatedValues(dataset, rows);
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| 107 | }
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| 108 | public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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| 109 | return new RegressionSolution(this, problemData);
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| 110 | }
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| 111 | #endregion
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| 112 |
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| 113 | internal void Split(RegressionTreeParameters regressionTreeParams, string splitAttribute, double splitValue, int numSamples) {
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| 114 | NumSamples = numSamples;
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| 115 | SplitAttribute = splitAttribute;
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| 116 | SplitValue = splitValue;
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| 117 | Left = CreateNode(this, regressionTreeParams);
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| 118 | Right = CreateNode(this, regressionTreeParams);
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| 119 | IsLeaf = false;
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| 120 | }
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| 121 |
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| 122 | internal void ToLeaf() {
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| 123 | IsLeaf = true;
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| 124 | Right = null;
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| 125 | Left = null;
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| 126 | }
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| 127 |
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| 128 | internal void SetLeafModel(IRegressionModel model) {
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| 129 | Model = model;
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| 130 | }
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| 131 |
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| 132 | internal IEnumerable<RegressionNodeModel> EnumerateNodes() {
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| 133 | var queue = new Queue<RegressionNodeModel>();
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| 134 | queue.Enqueue(this);
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| 135 | while (queue.Count != 0) {
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| 136 | var cur = queue.Dequeue();
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| 137 | yield return cur;
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| 138 | if (cur.Left == null && cur.Right == null) continue;
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| 139 | if (cur.Left != null) queue.Enqueue(cur.Left);
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| 140 | if (cur.Right != null) queue.Enqueue(cur.Right);
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| 141 | }
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| 142 | }
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| 143 |
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| 144 | #region Helpers
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| 145 | private double GetEstimatedValue(IDataset dataset, int row) {
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| 146 | if (!IsLeaf) return (dataset.GetDoubleValue(SplitAttribute, row) <= SplitValue ? Left : Right).GetEstimatedValue(dataset, row);
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| 147 | if (Model == null) throw new NotSupportedException("The model has not been built correctly");
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| 148 | return Model.GetEstimatedValues(dataset, new[] {row}).First();
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| 149 | }
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| 150 | #endregion
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| 151 |
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| 152 | [StorableClass]
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| 153 | private sealed class ConfidenceRegressionNodeModel : RegressionNodeModel, IConfidenceRegressionModel {
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| 154 | #region HLConstructors
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| 155 | [StorableConstructor]
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| 156 | private ConfidenceRegressionNodeModel(bool deserializing) : base(deserializing) { }
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| 157 | private ConfidenceRegressionNodeModel(ConfidenceRegressionNodeModel original, Cloner cloner) : base(original, cloner) { }
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| 158 | public ConfidenceRegressionNodeModel(string targetAttr) : base(targetAttr) { }
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| 159 | public ConfidenceRegressionNodeModel(RegressionNodeModel parent) : base(parent) { }
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| 160 | public override IDeepCloneable Clone(Cloner cloner) {
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| 161 | return new ConfidenceRegressionNodeModel(this, cloner);
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| 162 | }
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| 163 | #endregion
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| 164 |
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| 165 | public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
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| 166 | return IsLeaf ? ((IConfidenceRegressionModel)Model).GetEstimatedVariances(dataset, rows) : rows.Select(row => GetEstimatedVariance(dataset, row));
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| 167 | }
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| 168 |
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| 169 | private double GetEstimatedVariance(IDataset dataset, int row) {
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| 170 | if (!IsLeaf)
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| 171 | return ((IConfidenceRegressionModel)(dataset.GetDoubleValue(SplitAttribute, row) <= SplitValue ? Left : Right)).GetEstimatedVariances(dataset, row.ToEnumerable()).Single();
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| 172 | return ((IConfidenceRegressionModel)Model).GetEstimatedVariances(dataset, new[] {row}).First();
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| 173 | }
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| 174 |
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| 175 | public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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| 176 | return new ConfidenceRegressionSolution(this, problemData);
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| 177 | }
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| 178 | }
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| 179 | }
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| 180 | } |
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