#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Optimization;
using System;
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 : SymbolicDataAnalysisModel, 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(); }
}
[Storable]
private double lowerEstimationLimit;
public double LowerEstimationLimit { get { return lowerEstimationLimit; } }
[Storable]
private double upperEstimationLimit;
public double UpperEstimationLimit { get { return upperEstimationLimit; } }
[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();
lowerEstimationLimit = original.lowerEstimationLimit;
upperEstimationLimit = original.upperEstimationLimit;
}
public SymbolicDiscriminantFunctionClassificationModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
: base(tree, interpreter) {
thresholds = new double[] { double.NegativeInfinity };
classValues = new double[] { 0.0 };
this.lowerEstimationLimit = lowerEstimationLimit;
this.upperEstimationLimit = upperEstimationLimit;
}
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 IEnumerable GetEstimatedValues(Dataset dataset, IEnumerable rows) {
return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows)
.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
}
public IEnumerable GetEstimatedClassValues(Dataset dataset, IEnumerable rows) {
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);
}
}
#region events
public event EventHandler ThresholdsChanged;
protected virtual void OnThresholdsChanged(EventArgs e) {
var listener = ThresholdsChanged;
if (listener != null) listener(this, e);
}
#endregion
}
}