At the moment I am using distribution functions from boost.random and add some template specialization to the 64bit RNGs provided here. This works well in principle, but has some disadvantages:
It is not easy to understand when these specializations are active, see e.g. the discussion in #77.
It does not work well with classes that are derived from random_64bit_generator, c.f. #65
At the moment I see three possibilities:
Vendor in the distribution functions from boost.random and remove the general behavior in favor of code making use of the richer interface of random_64bit_generator. License wise this is fine according to https://www.gnu.org/licenses/license-list.en.html#boost.
Go for a function interface normal(rng, n, mean, sd) etc., similar to the already functional sample() interface.
Extend random_64bit_generator further with methods normal(mean, sd) etc. In this case it would make sense to move the implementation of these methods into separate *.ipp files included after the class declaration. Due to the large tables for the Ziggurat method, this would increase the RNG objects size quite a bit.
At the moment I am using distribution functions from
boost.randomand add some template specialization to the 64bit RNGs provided here. This works well in principle, but has some disadvantages:random_64bit_generator, c.f. #65At the moment I see three possibilities:
boost.randomand remove the general behavior in favor of code making use of the richer interface ofrandom_64bit_generator. License wise this is fine according to https://www.gnu.org/licenses/license-list.en.html#boost.normal(rng, n, mean, sd)etc., similar to the already functionalsample()interface.random_64bit_generatorfurther with methodsnormal(mean, sd)etc. In this case it would make sense to move the implementation of these methods into separate*.ippfiles included after the class declaration. Due to the large tables for the Ziggurat method, this would increase the RNG objects size quite a bit.All three approaches would allow implementing the Ziggurat variant from https://github.com/cd-mcfarland/fast_prng and MT2001 for Gamma (c.f. #22).