void BasicSkewGenerator::UpdateSkewGenerator(ShortStack &total_theory) {
// put everything to null
ResetUpdate();
//for (unsigned int i_read=0; i_read<total_theory.my_hypotheses.size(); i_read++){
for (unsigned int i_ndx = 0; i_ndx < total_theory.valid_indexes.size(); i_ndx++) {
unsigned int i_read = total_theory.valid_indexes[i_ndx];
AddCrossUpdate(total_theory.my_hypotheses[i_read]);
}
DoLatentUpdate(); // new latent predictors for sigma
}
void BasicSigmaGenerator::SimplePrior(){
// sum r^2
ZeroAccumulator();
float basic_weight = prior_weight; // prior strength
for (unsigned int i_level = 0; i_level<accumulated_sigma.size(); i_level++)
{
// expected variance per level
// this is fairly arbitrary as we expect the data to overcome our weak prior here
float square_level = i_level*i_level+1.0f; // avoid zero division
// approximate quadratic increase in sigma- should be linear, but empirically we see more than expected
float sigma_square = prior_sigma_regression[0]+prior_sigma_regression[1]*square_level;
sigma_square *=sigma_square; // push squared value
PushLatent(basic_weight, (float) i_level, sigma_square, true);
}
DoLatentUpdate(); // the trivial model for sigma by intensity done
}
// important: residuals do not need to be reset before this operation (predictions have been corrected for bias already)
void BasicSigmaGenerator::UpdateSigmaGenerator(ShortStack &total_theory) {
// put everything to null
ResetUpdate();
// float k_zero = 1.0f;
for (unsigned int i_ndx = 0; i_ndx < total_theory.valid_indexes.size(); i_ndx++) {
unsigned int i_read = total_theory.valid_indexes[i_ndx];
AddCrossUpdate(total_theory.my_hypotheses[i_read]);
}
// now that I've established basic weight, I can update
for (unsigned int i_ndx = 0; i_ndx < total_theory.valid_indexes.size(); i_ndx++) {
unsigned int i_read = total_theory.valid_indexes[i_ndx];
// additional variability from cluster shifting
// bayesian multidimensional normal
AddShiftCrossUpdate(total_theory.my_hypotheses[i_read], k_zero);
}
DoLatentUpdate(); // new latent predictors for sigma
}
