Experimental approach to reference-type random number generators

[bearophile]

Joseph Rushton Wakeling:

>     auto uniDist = uniformDistribution(0.0, 1.0);
>     auto biasedDice = diceDistribution(10, 5, 7);
>     foreach(u, d; lockstep(uniDist, biasedDice).take(10))
>     {
>         writeln(u);
>         writeln(d);
>     }
>
> (I'm sure you can find something to improve in how I've written 
> the second example, but you get the idea of how the 
> distributions could behave:-)

Perhaps it's better for it to only accept a range (despite this 
causes a memory allocation):

diceDistribution([10, 5, 7])

It's usable like this:

[0.3, 0.5, 0.1, 0.1].diceDistribution.map!(i => 
"ACGT"[i]).take(10).writeln;

diceDistribution() is much more efficient than calling dice() 
many times in map.

In the enhancement request I have also suggested to support an 
optional list of items, so the code becomes:

[0.3, 0.5, 0.1, 0.1].diceDistribution("ACGT").take(10).writeln;

The name "diceDistribution" is rather long, but at the moment I 
don't know of better names. "diceDist" is shorter, but less clear.

I usually don't have a continuous distribution to sample (beside 
the most common ones, like the gaussian), so for me a 
diceDistribution is enough for now.

Bye,
bearophile