[5649] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 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.Collections.Generic;
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| 23 | using System.Linq;
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| 24 | using HeuristicLab.Common;
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| 25 | using HeuristicLab.Core;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Operators;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 | using HeuristicLab.Optimization;
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| 31 | using System;
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| 32 |
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| 33 | namespace HeuristicLab.Problems.DataAnalysis {
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| 34 | /// <summary>
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| 35 | /// Represents discriminant function classification data analysis models.
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| 36 | /// </summary>
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| 37 | [StorableClass]
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| 38 | [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")]
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| 39 | public class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
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| 40 | [Storable]
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| 41 | private IRegressionModel model;
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| 42 | [Storable]
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| 43 | private double[] classValues;
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[5678] | 44 | // class values are not necessarily sorted in ascending order
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| 45 | public IEnumerable<double> ClassValues {
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| 46 | get { return (double[])classValues.Clone(); }
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| 47 | set {
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| 48 | if (value == null) throw new ArgumentException();
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| 49 | double[] newValue = value.ToArray();
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| 50 | if (newValue.Length != classValues.Length) throw new ArgumentException();
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| 51 | classValues = newValue;
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| 52 | }
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| 53 | }
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| 54 | [Storable]
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| 55 | private double[] thresholds;
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| 56 | public IEnumerable<double> Thresholds {
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| 57 | get { return (IEnumerable<double>)thresholds.Clone(); }
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| 58 | set {
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| 59 | thresholds = value.ToArray();
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| 60 | OnThresholdsChanged(EventArgs.Empty);
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| 61 | }
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| 62 | }
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[5649] | 63 |
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[5678] | 64 |
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[5649] | 65 | [StorableConstructor]
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[5681] | 66 | protected DiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { }
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[5649] | 67 | protected DiscriminantFunctionClassificationModel(DiscriminantFunctionClassificationModel original, Cloner cloner)
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| 68 | : base(original, cloner) {
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| 69 | model = cloner.Clone(original.model);
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| 70 | classValues = (double[])original.classValues.Clone();
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[5678] | 71 | thresholds = (double[])original.thresholds.Clone();
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[5649] | 72 | }
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[5678] | 73 | public DiscriminantFunctionClassificationModel(IRegressionModel model, IEnumerable<double> classValues, IEnumerable<double> thresholds)
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[5649] | 74 | : base() {
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| 75 | this.name = ItemName;
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| 76 | this.description = ItemDescription;
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| 77 | this.model = model;
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| 78 | this.classValues = classValues.ToArray();
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[5678] | 79 | this.thresholds = thresholds.ToArray();
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[5649] | 80 | }
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| 81 |
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| 82 | public override IDeepCloneable Clone(Cloner cloner) {
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| 83 | return new DiscriminantFunctionClassificationModel(this, cloner);
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| 84 | }
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| 85 |
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| 86 | public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
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| 87 | return model.GetEstimatedValues(dataset, rows);
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| 88 | }
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| 89 |
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| 90 | public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
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| 91 | foreach (var x in GetEstimatedValues(dataset, rows)) {
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| 92 | int classIndex = 0;
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[5678] | 93 | // find first threshold value which is larger than x => class index = threshold index + 1
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[5649] | 94 | for (int i = 0; i < thresholds.Length; i++) {
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| 95 | if (x > thresholds[i]) classIndex++;
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| 96 | else break;
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| 97 | }
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| 98 | yield return classValues.ElementAt(classIndex);
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| 99 | }
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| 100 | }
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[5678] | 101 | #region events
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| 102 | public event EventHandler ThresholdsChanged;
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| 103 | protected virtual void OnThresholdsChanged(EventArgs e) {
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| 104 | var listener = ThresholdsChanged;
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| 105 | if (listener != null) listener(this, e);
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| 106 | }
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[5649] | 107 | #endregion
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| 108 | }
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| 109 | }
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