<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><br class=""><div><blockquote type="cite" class=""><div class="">On Sep 8, 2017, at 9:52 AM, Alejandro Alonso via swift-evolution <<a href="mailto:swift-evolution@swift.org" class="">swift-evolution@swift.org</a>> wrote:</div><br class="Apple-interchange-newline"><div class="">
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Hello swift evolution, I would like to propose a unified approach to `random()` in Swift. I have a simple implementation here <a href="https://gist.github.com/Azoy/5d294148c8b97d20b96ee64f434bb4f5" class="">https://gist.github.com/Azoy/5d294148c8b97d20b96ee64f434bb4f5</a>.
This implementation is a simple wrapper over existing random functions so existing code bases will not be affected. Also, this approach introduces a new random feature for Linux users that give them access to upper bounds, as well as a lower bound for both
Glibc and Darwin users. This change would be implemented within Foundation.
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<div class="">I believe this simple change could have a very positive impact on new developers learning Swift and experienced developers being able to write single random declarations.</div>
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<div class="">I’d like to hear about your ideas on this proposal, or any implementation changes if need be.</div>
</div></div></div></blockquote><br class=""></div><div>My 2c:</div><div><br class=""></div><div>- I’d love to see some random number initializers get added as initializers on the numeric types. This is a long standing hole and it would be hugely valuable to fill it.</div>- I’d love to see several of the most common random kinds supported, and I agree it would be nice (but not required IMO) for the default to be cryptographically secure.<div class="">- We should avoid the temptation to nuke this mosquito with a heavy handed solution designed to solve all of the world’s problems: For example, the C++ random number stuff is crazily over-general. The stdlib should aim to solve (e.g.) the top 3 most common cases, and let a more specialized external library solve the fully general problem (e.g. seed management, every distribution imaginable, etc).</div><div class=""><br class=""></div><div class="">In terms of approach, I’d suggest looking at other libraries that are conceptually similar, e.g. the “simple random data” APIs for numpy:</div><div class=""><a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html" class="">https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html</a></div><div class=""><br class=""></div><div class="">Things like “return a random number from 0 to 1.0, 0 to N, 0 to INTMAX, sample from a normal/gaussian distribution, and maybe one more should be enough.</div><div class=""><br class=""></div><div class="">-Chris</div><div class=""><br class=""><div class=""><br class=""></div></div></body></html>