On Wednesday, 23 November 2016 at 11:44:44 UTC, Joseph Rushton
Wakeling wrote:
Yes, most uses of RNGs in std.random involve calling `front`
and then `popFront()` (although it would probably be better the
other way round). But it's readily possible to imagine
range-based use-cases for random distributions along the lines
of,
myRNG.normalDistribution(0.0, 5.0).filter!(a => a >
0).somethingElse.take(20);
But what I'd say more broadly is that of what I've seen so far,
mir.random is conflating too many breaking changes without
giving thought for their impact (for example, converting the
`isUniformRNG` check to rely on a UDA is IMO problematic; I can
file a GitHub issue explaining the reasons for this). Allowing
for the wider goals of the exercise, it could be worth giving
some thought to which of these breakages is really needed to
support your use-cases, and which can be avoided.
Added RandomRangeAdaptor for URBGs:
https://github.com/libmir/mir-random/blob/master/source/random/algorithm.d
