[15430] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17209] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[15430] | 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.Problems.DataAnalysis;
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[16847] | 28 | using HEAL.Attic;
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[15430] | 29 |
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| 30 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[16847] | 31 | [StorableType("347CA25D-FB37-4C4F-9B61-9D79288B2B28")]
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[17080] | 32 | [Item("LinearLeaf", "A leaf type that uses linear models as leaf models. This is the standard for decision tree regression")]
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[15830] | 33 | public class LinearLeaf : LeafBase {
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[15430] | 34 | #region Constructors & Cloning
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| 35 | [StorableConstructor]
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[16847] | 36 | protected LinearLeaf(StorableConstructorFlag _) : base(_) { }
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[15830] | 37 | protected LinearLeaf(LinearLeaf original, Cloner cloner) : base(original, cloner) { }
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[15430] | 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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[15830] | 45 | public override bool ProvidesConfidence {
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[16852] | 46 | get { return true; }
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[15614] | 47 | }
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[16847] | 48 | public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int numberOfParameters) {
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[15430] | 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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[16847] | 51 | numberOfParameters = pd.AllowedInputVariables.Count() + 1;
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| 52 | var res = LinearRegression.CreateSolution(pd, out rmse, out cvRmse);
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[15967] | 53 | return res.Model;
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[15430] | 54 | }
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| 55 |
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[15830] | 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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[15430] | 58 | }
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| 59 | #endregion
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| 60 | }
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| 61 | } |
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