#region License Information /* HeuristicLab * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification { /// /// Represents a symbolic classification model /// [StorableClass] [Item(Name = "SymbolicDiscriminantFunctionClassificationModel", Description = "Represents a symbolic classification model unsing a discriminant function.")] public class SymbolicDiscriminantFunctionClassificationModel : SymbolicClassificationModel, ISymbolicDiscriminantFunctionClassificationModel { [Storable] private double[] thresholds; public IEnumerable Thresholds { get { return (IEnumerable)thresholds.Clone(); } private set { thresholds = value.ToArray(); } } [Storable] private double[] classValues; public IEnumerable ClassValues { get { return (IEnumerable)classValues.Clone(); } private set { classValues = value.ToArray(); } } private IDiscriminantFunctionThresholdCalculator thresholdCalculator; [Storable] public IDiscriminantFunctionThresholdCalculator ThresholdCalculator { get { return thresholdCalculator; } private set { thresholdCalculator = value; } } [StorableConstructor] protected SymbolicDiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { } protected SymbolicDiscriminantFunctionClassificationModel(SymbolicDiscriminantFunctionClassificationModel original, Cloner cloner) : base(original, cloner) { classValues = (double[])original.classValues.Clone(); thresholds = (double[])original.thresholds.Clone(); thresholdCalculator = cloner.Clone(original.thresholdCalculator); } public SymbolicDiscriminantFunctionClassificationModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDiscriminantFunctionThresholdCalculator thresholdCalculator, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) : base(tree, interpreter, lowerEstimationLimit, upperEstimationLimit) { this.thresholds = new double[0]; this.classValues = new double[0]; this.ThresholdCalculator = thresholdCalculator; } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { // BackwardsCompatibility3.4 #region Backwards compatible code, remove with 3.5 if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator(); #endregion } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicDiscriminantFunctionClassificationModel(this, cloner); } public void SetThresholdsAndClassValues(IEnumerable thresholds, IEnumerable classValues) { var classValuesArr = classValues.ToArray(); var thresholdsArr = thresholds.ToArray(); if (thresholdsArr.Length != classValuesArr.Length) throw new ArgumentException(); this.classValues = classValuesArr; this.thresholds = thresholdsArr; OnThresholdsChanged(EventArgs.Empty); } public override void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable rows) { double[] classValues; double[] thresholds; var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, rows); thresholdCalculator.Calculate(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds); SetThresholdsAndClassValues(thresholds, classValues); } public IEnumerable GetEstimatedValues(Dataset dataset, IEnumerable rows) { return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows).LimitToRange(LowerEstimationLimit, UpperEstimationLimit); } public override IEnumerable GetEstimatedClassValues(Dataset dataset, IEnumerable rows) { if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current symbolic classification model."); foreach (var x in GetEstimatedValues(dataset, rows)) { int classIndex = 0; // find first threshold value which is larger than x => class index = threshold index + 1 for (int i = 0; i < thresholds.Length; i++) { if (x > thresholds[i]) classIndex++; else break; } yield return classValues.ElementAt(classIndex - 1); } } public override ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) { return CreateDiscriminantClassificationSolution(problemData); } public SymbolicDiscriminantFunctionClassificationSolution CreateDiscriminantClassificationSolution(IClassificationProblemData problemData) { return new SymbolicDiscriminantFunctionClassificationSolution(this, new ClassificationProblemData(problemData)); } IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) { return CreateDiscriminantClassificationSolution(problemData); } IDiscriminantFunctionClassificationSolution IDiscriminantFunctionClassificationModel.CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData) { return CreateDiscriminantClassificationSolution(problemData); } #region events public event EventHandler ThresholdsChanged; protected virtual void OnThresholdsChanged(EventArgs e) { var listener = ThresholdsChanged; if (listener != null) listener(this, e); } #endregion } }