On Wednesday, 19 March 2014 at 23:49:41 UTC, Joseph Rushton Wakeling wrote:
Hello all,

As some of you may already know, monarch_dodra and I have spent quite a lot of time over the last year discussing the state of std.random. To cut a long story short, there are significant problems that arise because the current RNGs are value types rather than reference types. We had quite a lot of back and forth on different design ideas, with a lot of helpful input from others in the community, but at the end of the day there are really only two broad approaches: create structs that implement reference semantics internally, or use classes. So, as an exercise, I decided to create a class-based std.random.


The preliminary (but comprehensive) results of this are now available here:
https://github.com/WebDrake/std.random2

Besides re-implementing random number generators as classes rather than structs, the new code splits std.random2 into a package of several different modules:

   * std.random2.generator, pseudo-random number generators;

   * std.random2.device, non-deterministic random sources;

* std.random2.distribution, random distributions such as uniform,
     normal, etc.;

* std.random2.adaptor, random "adaptors" such as randomShuffle,
     randomSample, etc.

* std.random2.traits, RNG-specific traits such as isUniformRNG
     and isSeedable.

A package.d file groups them together so one can still import all together via "import std.random2". I've also taken the liberty of following the new guideline to place import statements as locally as possible; it was striking how easy and clean this made things, and it should be easy to port that particular change back to std.random.

The new package implements all of the functions, templates and range objects from std.random except for the old std.random.uniformDistribution, whose name I have cannibalized for better purposes. Some have been updated: the MersenneTwisterEngine has been tweaked to match the corresponding code from Boost.Random, and this in turn has allowed the definition of a 64-bit Mersenne Twister (Mt19937_64) and an alternative 32-bit one (Mt11213b).

There are also a number of entirely new entries. std.random2.distribution contains not just existing functions such as dice and uniform, but also range-based random distribution classes UniformDistribution, NormalDistribution and DiscreteDistribution; the last of these is effectively a range-based version of dice, and is based on Chris Cain's excellent work here: https://github.com/D-Programming-Language/phobos/pull/1702

The principal weak point in terms of functionality is std.random2.device, where the implemented random devices (based on Posix' /std/random and /std/urandom) are really very primitive and just there to illustrate the principle. However, since their API is pretty simple (they're just input ranges with min and max defined) there should be plenty of opportunity to improve and extend the internals in future. Advice and patches are welcome for everything, but particularly here :-)

What's become quite apparent in the course of writing this package is how much more natural it is for ranges implementing randomness to be class objects. The basic fact that another range can store a copy of an RNG internally without creating a copy-by-value is merely the start: for example, in the case of the class implementation of RandomSample, we no longer need to have complications like,

    @property auto ref front()
    {
        assert(!empty);
// The first sample point must be determined here to avoid // having it always correspond to the first element of the // input. The rest of the sample points are determined each
        // time we call popFront().
        if (_skip == Skip.None)
        {
            initializeFront();
        }
        return _input.front;
    }

that were necessary to avoid bugs like https://d.puremagic.com/issues/show_bug.cgi?id=7936; because the class-based implementation copies by reference, we can just initialize everything in the constructor. Similarly, issues like https://d.puremagic.com/issues/show_bug.cgi?id=7067 and https://d.puremagic.com/issues/show_bug.cgi?id=8247 just vanish.

Obvious caveats about the approach include the fact that classes need to be new'd, and questions over whether allocation on the heap might create speed issues. The benchmarks I've run (code available in the repo) seem to suggest that at least the latter is not a worry, but these are obviously things that need to be considered. My own feeling is that ultimately it is a responsibility of the language to offer nice ways to allocate classes without necessarily relying on new or the GC.

A few remarks on design and other factors:

* The new range objects have been implemented as final classes for speed purposes. However, I tried another approach where the RNG
     class templates were abstract classes, and the individual
parameterizations were final-class subclasses of those rather than aliases. This was noticeably slower. My OO-fu is not really sufficient to explain this, so if anybody can offer a reason, I'd
     be happy to learn it.

* A design question I considered but have not yet pursued: since at least two functions require passing the RNG as the first parameter (dice and discreteDistribution), perhaps this should be made a general design pattern for everything? It would make it harder to adapt code using the existing std.random but would create a useful
     uniformity.

* I would have liked to ensure that every random distribution had both a range- and function-based version. However, I came to the conclusion that solely function-based versions should be avoided if either (i) the function would need to maintain internal state between calls, or (ii) the function would need to allocate memory per call. The first is why for example NormalDistribution exists only as a class/range. The second might in principle raise some objections to dice, but as dice seems to be a reasonably standard
     function, I kept it in.

* It might be good to implement helper functions for the individual RNGs (e.g. just as RandomSample has a randomSample helper function
     to deliver instances, so Mt19937 could have a corresponding
mt19937 helper function returning Mt19937 instances seeded in line
     with helper function parameters).

* Those with long memories may recall that when I originally wrote up my NormalDistribution code, it was written to allow various "normal engines" to be plugged in; mine was Box-Muller, but jerro also contributed a Ziggurat-based engine. This could still be provided here, although my own inclination is that it's probably best for Phobos to provide one single good-for-general-purpose-use
     implementation.

Known issues:

* While every bugfix I've made in the course of implementing this package has been propagated back to std.random where possible, this package is missing some of the more recent improvements to std.random by other people (e.g. I think it's missing Chris Cain's
     update to integer-based uniform()).

* The unittest coverage is overall pretty damn good, but there are weak spots in std.random.distribution and std.random2.device. Some of the "unittests" in these cases are no more than basic developer sanity checks that print results to console, and need to be replaced by well-defined, silent-unless-failed alternatives.

* Some of the .save functions are implemented with the help of rather odd private constructors; it would probably be much better to redo
     these in terms of public this(typeof(this)) constructors.

* The random devices _really_ need to be better. Consider the current
     versions as placeholders ... :-)

Finally, a note on authorship: since this is still based very substantially on std.random, I've made an effort to check git logs and ensure that authors and copyright records (and dates of contribution) are correct. My general principle here has been that listed authors should only include those who've made a substantial contribution (i.e. whole functions, large numbers of unittests, ...), not just various 1-line tweaks. But if anyone has any objection to any of the names, dates or other credits given, or if anybody would like their name removed (!), just let me know.

I owe a great debt of gratitude to many people here on the forums, and monarch_dodra in particular, for a huge amount of useful discussion, advice and feedback that has made its way into the current code. Thank you all for your time, thoughts, ideas and patience.

Anyway, please feel free to review, destroy and otherwise do fun stuff with this module. I hope that some of you will find it immediately useful, but please note that feedback and advice may result in breaking changes -- this is intended to wind up in Phobos, so it really needs to be perfect when it does so. Let's review it really well and make it happen!

Thanks and best wishes,

    -- Joe

It should be std.pseudorandom (except for /dev/random) :)

Still no cmwc rng... IMO cmwc should replace mt as default RNG. Faster. Looooonger period. More passed tests (if i'm right MT didn't pass testu01). And it is parametric to get faster result or longer period.

http://en.wikipedia.org/wiki/Multiply-with-carry#Complementary-multiply-with-carry_generators

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