thanks for the info.
My first, little, basically trivially success, is replacing the
Bernoulli-distribution for the default case:
inline bool bernoulli(std::mt19937_64& g) noexcept {
typedef std::mt19937_64::result_type uint_t;
constexpr uint_t half = std::numeric_limits<uint_t>::max() / 2;
return g() < half;
}
Checked that it reproduces the behaviour of gcc 4.7.4 and 5.4.
Replacing std::uniform_int_distribution (only for std::mt19937_64) seems harder,
and yet I have problems understanding the code.
One could just generalise the above, and since only 32-bits intervals
are used, that likely would suffice for a decent distribution.
However g++ seems to do something more advanced.
On Sun, Apr 09, 2017 at 05:31:54PM -0600, Martin Sebor wrote:
> It's worth keeping in mind that copying libstdc++ code will have
> licensing implications. A different caveat is that conforming C++
> programs can define explicit specializations of standard library
> templates only for user-defined types. I.e., it's not valid to
> define one's own std::uniform_int_ditribution class template or
> specialization of it on a fundamental type such as int.
>
I just want to extract the mathematical content, and then write simple
code for it, like above.
If somebody knows some underlying papers describing the algorithms for
uniform_int_distribution and seeding std::mt19937_64, that would be great.
> Another option is to browse the sources online:
>
> https://gcc.gnu.org/viewcvs/gcc/trunk/libstdc%2B%2B-v3/include/bits/random.h?view=markup
>
Thanks, I found that most useful.
Oliver
