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.Linq;
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24 | using System.Threading;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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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 | [Item("LinearLeaf", "A leaf type that uses linear models as leaf models. This is the standard for M5' regression")]
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33 | public class LinearLeaf : LeafBase {
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34 | #region Constructors & Cloning
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35 | [StorableConstructor]
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36 | protected LinearLeaf(bool deserializing) : base(deserializing) { }
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37 | protected LinearLeaf(LinearLeaf original, Cloner cloner) : base(original, cloner) { }
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38 | public LinearLeaf() { }
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39 | public override IDeepCloneable Clone(Cloner cloner) {
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40 | return new LinearLeaf(this, cloner);
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41 | }
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42 | #endregion
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43 |
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44 | #region IModelType
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45 | public override bool ProvidesConfidence {
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46 | get { return false; }
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47 | }
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48 | public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int noParameters) {
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49 | if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a linear model");
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50 | double rmse, cvRmse;
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51 | noParameters = pd.AllowedInputVariables.Count() + 1;
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52 | var res = LinearRegression.CreateLinearRegressionSolution(pd, out rmse, out cvRmse);
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53 | return res.Model;
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54 | }
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55 |
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56 | public override int MinLeafSize(IRegressionProblemData pd) {
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57 | return pd.AllowedInputVariables.Count() == 1 ? 2 : pd.AllowedInputVariables.Count() + 2;
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58 | }
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59 | #endregion
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60 | }
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61 | } |
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