Conversion to Float

The "obvious" approach of converting to a float, then multiplying by a scale factor is slow, and has the potential to give an answer of `1.0`.

The easiest option is:

``` julia
import Base: significand_mask, exponent_one
f1(u::UInt64) = reinterpret(Float64, exponent_one(Float64) | significand_mask(Float64) & u) - 1.0
```

unfortunately this has the downside that the last bit will always be zero, so you only get 52 bits of randomness per float: see https://github.com/JuliaLang/julia/issues/16344.

A slightly more advanced option (based on [this proposal](http://stackoverflow.com/a/35351145/392585)) is:

``` julia
import Base: significand_mask, significand_bits, exponent_half
function f2(u::UInt64)
f = reinterpret(Float64, exponent_half(Float64) | 0x001f_ffff_ffff_ffff & u)
if (u >> significand_bits(Float64)) &1 == 1
f-= 0.5
end
f
end
```

This gets us 53 bits, but introduces a branch. There might be some clever stuff we can do here though.

Start with the current float-conversion approach described in the issue and compare the f1 and f2 alternatives, including the linked Julia issue about the lost last bit. The change is complete when conversion avoids producing 1.0 while preserving 53 bits of randomness without an unacceptable performance cost.

Rédigé par le modèle d'indexation à partir du texte de l'issue.