I’ve read TMVA documentation, slides and looked at the tutorials, but couldn’t find an example how to model a pdf.
I have background (mostly uniform in the detector, but with strange regions that I don’t want to model parametrically). On top of that known background (from a run without a source) there is a calibration signal that I want to model parametrically (that’s why I want background pdf to be calculated fast). I read that method of k nearest neighbours is fast enough, but couldn’t find an example how to get data from there.
There is a method
Double_t TMVA::MethodKNN::GetMvaValue(Double_t *err =
0, Double_t *errUpper =
but I’m not sure what its output is (it’s not written in its documentation).
There is also a PDEFoam method GetCellValue(), but I don’t understand its arguments and return value.
If my background decreases exponentially, will PDEFoam model it well? I read that it models pdf by constant cells.
Could someone please give an example how to initialize, train TMVA and get PDF value at the given point (maybe just give some methods names)? Maybe there is something other in ROOT for that? Thank you.