[13824] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16453] | 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[13824] | 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.Collections.Generic;
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| 23 | using System.Linq;
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| 24 | using HeuristicLab.Common;
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[16462] | 25 | using HEAL.Fossil;
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[13824] | 26 |
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| 27 | namespace HeuristicLab.Problems.DataAnalysis {
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| 28 | /// <summary>
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| 29 | /// Represents a regression data analysis solution that supports confidence estimates
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| 30 | /// </summary>
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[16462] | 31 | [StorableType("C2D0DE07-E8F0-4850-AAF3-E2885EC1DDB6")]
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[14099] | 32 | public class ConfidenceRegressionSolution : RegressionSolution, IConfidenceRegressionSolution {
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[13824] | 33 | protected readonly Dictionary<int, double> varianceEvaluationCache;
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| 34 |
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[14099] | 35 | public new IConfidenceRegressionModel Model {
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| 36 | get { return (IConfidenceRegressionModel)base.Model; }
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[13824] | 37 | set { base.Model = value; }
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| 38 | }
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| 39 |
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| 40 | [StorableConstructor]
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[16462] | 41 | protected ConfidenceRegressionSolution(StorableConstructorFlag _) : base(_) {
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[13824] | 42 | varianceEvaluationCache = new Dictionary<int, double>();
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| 43 | }
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[14099] | 44 | protected ConfidenceRegressionSolution(ConfidenceRegressionSolution original, Cloner cloner)
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[13824] | 45 | : base(original, cloner) {
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| 46 | varianceEvaluationCache = new Dictionary<int, double>(original.varianceEvaluationCache);
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| 47 | }
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[14099] | 48 | public ConfidenceRegressionSolution(IConfidenceRegressionModel model, IRegressionProblemData problemData)
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[13824] | 49 | : base(model, problemData) {
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| 50 | varianceEvaluationCache = new Dictionary<int, double>(problemData.Dataset.Rows);
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| 51 | }
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| 52 |
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| 53 | public override IDeepCloneable Clone(Cloner cloner) {
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[14099] | 54 | return new ConfidenceRegressionSolution(this, cloner);
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[13824] | 55 | }
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| 56 |
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| 57 | public IEnumerable<double> EstimatedVariances {
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| 58 | get { return GetEstimatedVariances(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
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| 59 | }
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| 60 | public IEnumerable<double> EstimatedTrainingVariances {
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| 61 | get { return GetEstimatedVariances(ProblemData.TrainingIndices); }
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| 62 | }
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| 63 | public IEnumerable<double> EstimatedTestVariances {
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| 64 | get { return GetEstimatedVariances(ProblemData.TestIndices); }
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| 65 | }
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| 66 |
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| 67 | public IEnumerable<double> GetEstimatedVariances(IEnumerable<int> rows) {
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| 68 | var rowsToEvaluate = rows.Except(varianceEvaluationCache.Keys);
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| 69 | var rowsEnumerator = rowsToEvaluate.GetEnumerator();
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| 70 | var valuesEnumerator = Model.GetEstimatedVariances(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();
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| 71 |
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| 72 | while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
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| 73 | varianceEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
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| 74 | }
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| 75 |
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| 76 | return rows.Select(row => varianceEvaluationCache[row]);
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| 77 | }
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| 78 |
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| 79 | protected override void OnProblemDataChanged() {
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| 80 | varianceEvaluationCache.Clear();
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| 81 | base.OnProblemDataChanged();
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| 82 | }
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| 83 |
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| 84 | protected override void OnModelChanged() {
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| 85 | varianceEvaluationCache.Clear();
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| 86 | base.OnModelChanged();
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| 87 | }
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| 88 | }
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| 89 | } |
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