public SemiSupervisedClassificationStatisticsAggregator(int nClasses, double a, double b)
{
nClasses_ = nClasses;
a_ = a;
b_ = b;
GaussianAggregator2d = new GaussianAggregator2d(a, b);
HistogramAggregator = new HistogramAggregator(nClasses);
}
public double ComputeInformationGain(SemiSupervisedClassificationStatisticsAggregator allStatistics, SemiSupervisedClassificationStatisticsAggregator leftStatistics, SemiSupervisedClassificationStatisticsAggregator rightStatistics)
{
double informationGainLabelled;
{
double entropyBefore = allStatistics.HistogramAggregator.Entropy();
HistogramAggregator leftHistogram = leftStatistics.HistogramAggregator;
HistogramAggregator rightHistogram = rightStatistics.HistogramAggregator;
int nTotalSamples = leftHistogram.SampleCount + rightHistogram.SampleCount;
if (nTotalSamples <= 1)
{
informationGainLabelled = 0;
}
else
{
double entropyAfter = (leftHistogram.SampleCount * leftHistogram.Entropy() + rightHistogram.SampleCount * rightHistogram.Entropy()) / nTotalSamples;
informationGainLabelled = entropyBefore - entropyAfter;
}
}
double informationGainUnlabelled;
{
double entropyBefore = ((SemiSupervisedClassificationStatisticsAggregator)(allStatistics)).GaussianAggregator2d.GetPdf().Entropy();
GaussianAggregator2d leftGaussian = leftStatistics.GaussianAggregator2d;
GaussianAggregator2d rightGaussian = rightStatistics.GaussianAggregator2d;
int nTotalSamples = leftGaussian.SampleCount + rightGaussian.SampleCount;
double entropyAfter = (leftGaussian.SampleCount * leftGaussian.GetPdf().Entropy() + rightGaussian.SampleCount * rightGaussian.GetPdf().Entropy()) / nTotalSamples;
informationGainUnlabelled = entropyBefore - entropyAfter;
}
double gain =
informationGainLabelled
+ alpha_ * informationGainUnlabelled;
return(gain);
}