#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.Collections;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis {
[StorableClass]
[Item("AverageThresholdCalculator", "")]
public class AverageThresholdCalculator : DiscriminantClassificationWeightCalculator {
public AverageThresholdCalculator()
: base() {
}
[StorableConstructor]
protected AverageThresholdCalculator(bool deserializing) : base(deserializing) { }
protected AverageThresholdCalculator(AverageThresholdCalculator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new AverageThresholdCalculator(this, cloner);
}
protected double[] threshold;
protected double[] classValues;
protected override IEnumerable DiscriminantCalculateWeights(IEnumerable discriminantSolutions) {
classValues = discriminantSolutions.First().Model.ClassValues.ToArray();
var modelThresholds = discriminantSolutions.Select(x => x.Model.Thresholds.ToArray());
threshold = new double[modelThresholds.First().GetLength(0)];
for (int i = 0; i < modelThresholds.First().GetLength(0); i++) {
threshold[i] = modelThresholds.Select(x => x[i]).Average();
}
return Enumerable.Repeat(1, discriminantSolutions.Count());
}
protected override double GetDiscriminantConfidence(IEnumerable solutions, int index, double estimatedClassValue) {
Dataset dataset = solutions.First().ProblemData.Dataset;
IList values = solutions.Select(s => s.Model.GetEstimatedValues(dataset, Enumerable.Repeat(index, 1)).First()).ToList();
if (values.Count <= 0)
return double.NaN;
double avg = values.Average();
return GetAverageConfidence(avg, estimatedClassValue);
}
public override IEnumerable GetDiscriminantConfidence(IEnumerable solutions, IEnumerable indices, IEnumerable estimatedClassValue) {
Dataset dataset = solutions.First().ProblemData.Dataset;
double[][] values = solutions.Select(s => s.Model.GetEstimatedValues(dataset, indices).ToArray()).ToArray();
double[] confidences = new double[indices.Count()];
double[] estimatedClassValueArr = estimatedClassValue.ToArray();
for (int i = 0; i < indices.Count(); i++) {
double avg = values.Select(x => x[i]).Average();
confidences[i] = GetAverageConfidence(avg, estimatedClassValueArr[i]);
}
return confidences;
}
protected double GetAverageConfidence(double avg, double estimatedClassValue) {
for (int i = 0; i < classValues.Length; i++) {
if (estimatedClassValue.Equals(classValues[i])) {
//special case: avgerage is higher than value of highest class
if (i == classValues.Length - 1 && avg > estimatedClassValue) {
return 1;
}
//special case: average is lower than value of lowest class
if (i == 0 && avg < estimatedClassValue) {
return 1;
}
//special case: average is not between threshold of estimated class value
if ((i < classValues.Length - 1 && avg >= threshold[i + 1]) || avg <= threshold[i]) {
return 0;
}
double thresholdToClassDistance, thresholdToAverageValueDistance;
if (avg >= classValues[i]) {
thresholdToClassDistance = threshold[i + 1] - classValues[i];
thresholdToAverageValueDistance = threshold[i + 1] - avg;
} else {
thresholdToClassDistance = classValues[i] - threshold[i];
thresholdToAverageValueDistance = avg - threshold[i];
}
return (1 / thresholdToClassDistance) * thresholdToAverageValueDistance;
}
}
return double.NaN;
}
}
}