On Saturday, 22 March 2014 at 23:56:35 UTC, bearophile wrote:
They seem good.


Excellent!

There may need to be some attention to the internals of uniform01. Its correctness depends on whether one can always trust a float-based RNG to return values in [min, max) or whether [min, max] is also going to be supplied by some.

More ideas:

"Three suggestions for std.random":
https://d.puremagic.com/issues/show_bug.cgi?id=4851

I think all std.random functions now support a default RNG. There were some bugs related to that (e.g. the "can't use Xorshift" one) that I fixed last year.

The problem you identify with,

    int r = randomCover(data, rndGen).front;

always returning the same value, is down to the fact that rndGen is being copied inside the RandomCover struct by value, so of course the original rndGen is never updated and each of these calls will produce the same result. The new std.random2 fixes that, because the RNGs are reference types.

However, I'd have thought that

    int r = data.sample(1, rndGen).front;

would have been a more efficient way to implement "choice", as it can operate on any input range, as long as it has the .length property; and it ought to be _much_ faster than even a single call to randomCover.

One could always use this as a default option, with a specialization where data is a RandomAccessRange to use the more efficient

    int r = data[uniform!"[)"(0, data.length)];

"Strongly pure random generator":
https://d.puremagic.com/issues/show_bug.cgi?id=5249

.front and .popFront at least are pure for _all_ the RNGs currently implemented in std.random2.generator. See e.g.:
https://github.com/WebDrake/std.random2/blob/master/std/random2/generator.d#L266-L272
https://github.com/WebDrake/std.random2/blob/master/std/random2/generator.d#L506-L517
https://github.com/WebDrake/std.random2/blob/master/std/random2/generator.d#L821-L834

Of course this is not strongly pure in line with your request, but it should enable use of these RNGs in many other scenarios where purity is important.

I hope a gaussian (normal distribution) generator is planned or present.

https://github.com/WebDrake/std.random2/blob/master/std/random2/distribution.d#L326

This is a range implementation; there will also be a function implementation, which will probably follow the inefficient Box-Muller variant that uses 2 uniform random variates to generate a single normal variate (as per the example you posted in your feature request).

Reply via email to