// do a hard classification as though the reads were independent
// i.e. look more like the data in the BAM file
void PosteriorInference::StartAtHardClassify(ShortStack &total_theory, bool update_frequency, float start_frequency) {
// just to allocate
ResizeToMatch(total_theory);
if (update_frequency) {
max_freq = start_frequency;
max_ll = total_theory.PosteriorFrequencyLogLikelihood(max_freq, data_reliability, ALL_STRAND_KEY);
}
total_theory.UpdateResponsibility(max_freq, data_reliability);
}
void PosteriorInference::DetailedUpdateStep(ShortStack &total_theory, bool update_frequency){
DoPosteriorFrequencyScan(total_theory, update_frequency, ALL_STRAND_KEY); // update max frequency using new likelihoods -> estimate overall max likelihood
total_theory.UpdateResponsibility(max_freq, data_reliability); // update cluster responsibilities
}
void PosteriorInference::QuickUpdateStep(ShortStack &total_theory){
UpdateMaxFreqFromResponsibility(total_theory, ALL_STRAND_KEY);
total_theory.UpdateResponsibility(max_freq, data_reliability); // update cluster responsibilities
}
// initialize the ensemble
void PosteriorInference::StartAtNull(ShortStack &total_theory, bool update_frequency) {
DoPosteriorFrequencyScan(total_theory, update_frequency, ALL_STRAND_KEY);
total_theory.UpdateResponsibility(max_freq, data_reliability);
}
