* Resolve things raised in https://github.com/nim-lang/Nim/issues/10081 ?
CDF is a standard ident in all things related to random numbers/sampling,
and full words "cumulativeDistributionFunction" would be silly long, in
this case, IMO. We use lowercase `cdf` to make it not look like a type,
remove all looping from `sample` letting callers do it. Besides just
side-stepping any `sampleSize` name choice, callers may want to filter
out samples anyway which this makes slightly simpler.
Also add two variants of `cumsum`, value return and in-place update
distinguished by the var-ness of the first argument. Add tests for
`int` and `float` for both `cumsum` and the new `sample`. (The sample
tests exercise the value return mode of `cumsum`.)
Functionality pre-this-PR `sample(a, w)` is now the almost as simple
`for i in 0..<n: sample(a, w.cumsum)`, but this new code factoring is
almost surely better. The statistical tests pass, as before.
* Address Araq comment in https://github.com/nim-lang/Nim/pull/10084
We can always add in some `var` version later if desired to save
memory, but this change now at least firms up the `sample` interface.
* Rename `cumsum` -> `cumsummed` to honor NEP1 style. Re-instate `cumsum` as
the in-place transformation. Test both in `tests/stdlib/tmath.nim` and use
`cumsummed` in the example code for sample since that's a simpler example.
* Fix requests from https://github.com/nim-lang/Nim/pull/10084 :
example in lib/pure/math.nim and comment whitespace in lib/pure/random.nim
* Add the ability to sample elements from an openArray according to a parallel
array of weights/unnormalized probabilities (any sort of histogram, basically).
Also add a non-thread safe version for convenience.
* Address Araq comments on https://github.com/nim-lang/Nim/pull/10072
* import at top of file and space after '#'.
* Put in a check for non-zero total weight.
* Clarify constraint on `w`.
* Rename `rand(openArray[T])` to `sample(openArray[T])` to `sample`, deprecating
old name and name new (openArray[T], openArray[U]) variants `sample`.
* Rename caller-provided state version of rand(openArray[T]) and also clean
up doc comments.
* Add test for new non-uniform array sampler. 3 sd bound makes it 99% likely
that it will still pass in the future if the random number generator changes.
We cannot both have a tight bound to check distribution *and* loose check to
ensure resilience to RNG changes. (We cannot *guarantee* resilience, anyway.
There's always a small chance any test hits a legitimate random fluctuation.)
