Analyzes a function given a dataset/observables for constant terms and caches those in the dataset.
This optimizer should be used on a consistent combination of function (usually a pdf) and a dataset with observables. It then analyzes the function to find parts that can be precalculated because they are constant given the set of observables. These are cached inside the dataset and used in subsequent evaluations of the function on that dataset. The typical use case for this is inside likelihood minimization where many calls of the same pdf/dataset combination are made. norm_set must provide the normalization set of the function, which would typically be the set of observables in the dataset; this is used to make sure all object caches are created before analysis by evaluating the function on this set at the beginning of enableConstantTermsOptimization.
Definition at line 25 of file ConstantTermsOptimizer.h.
