Because the diagnose matrices are computed separately for each T-stage, they are not aligned with the patient data stored in the model anymore.
A solution could be to store the diagnose matrices in the patient data DataFrame and filter that by T-stage when model.diagnose_matrices[t_stage] is called.
This has benefits for both the Bayesian network implementation and the mixture model. And if I didn't overlook anything, this should be possible without breaking changes.
Because the diagnose matrices are computed separately for each T-stage, they are not aligned with the patient data stored in the model anymore.
A solution could be to store the diagnose matrices in the patient data
DataFrameand filter that by T-stage whenmodel.diagnose_matrices[t_stage]is called.This has benefits for both the Bayesian network implementation and the mixture model. And if I didn't overlook anything, this should be possible without breaking changes.