* make more standard libraries work with `nimPreviewSlimSystem`
* typo
* part two
* Delete specutils.nim
* fixes more tests
* more fixes
* fixes tests
* fixes three more tests
* add formatfloat import
* fix
* last
* Improve rand(bool)
* Use sign test instead of mod 2
* Use mod 2 again, as it works for js
* Use right shift as suggested by the authors of xoroshiro
* Update random.nim
* General case doesn't need any right shift it was correct to begin with
* Update random.nim
* add comment
Co-authored-by: flywind <43030857+xflywind@users.noreply.github.com>
* Fix initrand to avoid random number sequences overlapping
* Minor fix
* Fix compile error on js backend
* Disable new test for js backend
* Minor fix
* tempfiles module uses random.initRand()
* Remove unused module import from lib/std/tempfiles.nim
* Initialize baseState in initRand()
* Run tests/stdlib/trandom.nim from tests/test_nimscript.nims
* baseState is initialized only with sysrand.urandom and quit if failed
* Add comments
* CIs: attempt to use csources_v1
* also updated the BSDs
* also updated azure pipelines
* std modules should not itself use the 'std/' import dir...
* compiler has to be careful with std/ for v1 booting
* Improve documentation for random
Use runnableExamples
Minor changes
* Apply suggestions
Remove echo
Use RNG in more places
* Fix skipRandomNumbers example
* Error -> Defect for defects
The distinction between Error and Defect is subjective,
context-dependent and somewhat arbitrary, so when looking at an
exception, it's hard to guess what it is - this happens often when
looking at a `raises` list _without_ opening the corresponding
definition and digging through layers of inheritance.
With the help of a little consistency in naming, it's at least possible
to start disentangling the two error types and the standard lib can set
a good example here.
* 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.)
