[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.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 | using HeuristicLab.Optimization;
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| 32 | using System;
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| 33 |
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| 34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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| 35 | /// <summary>
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| 36 | /// Represents a symbolic classification model
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| 37 | /// </summary>
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| 38 | [StorableClass]
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| 39 | [Item(Name = "SymbolicDiscriminantFunctionClassificationModel", Description = "Represents a symbolic classification model unsing a discriminant function.")]
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| 40 | public class SymbolicDiscriminantFunctionClassificationModel : SymbolicDataAnalysisModel, ISymbolicDiscriminantFunctionClassificationModel {
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| 41 |
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| 42 | [Storable]
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| 43 | private double[] thresholds;
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| 44 | public IEnumerable<double> Thresholds {
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| 45 | get { return (IEnumerable<double>)thresholds.Clone(); }
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[5736] | 46 | private set { thresholds = value.ToArray(); }
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[5649] | 47 | }
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[5678] | 48 | [Storable]
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| 49 | private double[] classValues;
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| 50 | public IEnumerable<double> ClassValues {
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| 51 | get { return (IEnumerable<double>)classValues.Clone(); }
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[5736] | 52 | private set { classValues = value.ToArray(); }
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[5678] | 53 | }
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[5720] | 54 | [Storable]
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| 55 | private double lowerEstimationLimit;
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| 56 | public double LowerEstimationLimit { get { return lowerEstimationLimit; } }
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| 57 | [Storable]
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| 58 | private double upperEstimationLimit;
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| 59 | public double UpperEstimationLimit { get { return upperEstimationLimit; } }
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| 60 |
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[5649] | 61 | [StorableConstructor]
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| 62 | protected SymbolicDiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { }
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| 63 | protected SymbolicDiscriminantFunctionClassificationModel(SymbolicDiscriminantFunctionClassificationModel original, Cloner cloner)
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| 64 | : base(original, cloner) {
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| 65 | classValues = (double[])original.classValues.Clone();
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| 66 | thresholds = (double[])original.thresholds.Clone();
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[5720] | 67 | lowerEstimationLimit = original.lowerEstimationLimit;
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| 68 | upperEstimationLimit = original.upperEstimationLimit;
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[5649] | 69 | }
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[5720] | 70 | public SymbolicDiscriminantFunctionClassificationModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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| 71 | double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
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[5649] | 72 | : base(tree, interpreter) {
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[5736] | 73 | thresholds = new double[] { double.NegativeInfinity };
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| 74 | classValues = new double[] { 0.0 };
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[5720] | 75 | this.lowerEstimationLimit = lowerEstimationLimit;
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| 76 | this.upperEstimationLimit = upperEstimationLimit;
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[5649] | 77 | }
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| 78 |
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| 79 | public override IDeepCloneable Clone(Cloner cloner) {
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| 80 | return new SymbolicDiscriminantFunctionClassificationModel(this, cloner);
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| 81 | }
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| 82 |
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[5736] | 83 | public void SetThresholdsAndClassValues(IEnumerable<double> thresholds, IEnumerable<double> classValues) {
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| 84 | var classValuesArr = classValues.ToArray();
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| 85 | var thresholdsArr = thresholds.ToArray();
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| 86 | if (thresholdsArr.Length != classValuesArr.Length) throw new ArgumentException();
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| 87 |
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| 88 | this.classValues = classValuesArr;
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| 89 | this.thresholds = thresholdsArr;
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| 90 | OnThresholdsChanged(EventArgs.Empty);
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| 91 | }
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| 92 |
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[5649] | 93 | public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
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[5736] | 94 | return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows)
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| 95 | .LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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[5649] | 96 | }
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| 97 |
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| 98 | public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
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| 99 | foreach (var x in GetEstimatedValues(dataset, rows)) {
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| 100 | int classIndex = 0;
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[5678] | 101 | // find first threshold value which is larger than x => class index = threshold index + 1
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[5649] | 102 | for (int i = 0; i < thresholds.Length; i++) {
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| 103 | if (x > thresholds[i]) classIndex++;
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| 104 | else break;
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| 105 | }
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[5657] | 106 | yield return classValues.ElementAt(classIndex - 1);
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[5649] | 107 | }
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| 108 | }
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| 109 |
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| 110 | #region events
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| 111 | public event EventHandler ThresholdsChanged;
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| 112 | protected virtual void OnThresholdsChanged(EventArgs e) {
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| 113 | var listener = ThresholdsChanged;
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| 114 | if (listener != null) listener(this, e);
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| 115 | }
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[5720] | 116 | #endregion
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[5649] | 117 | }
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| 118 | }
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