On Wed, Jan 10, 2018 at 11:27 PM, Saagar Jha <saa...@saagarjha.com> wrote:
> Not a floating point expert, but are you sure this works? I have a feeling
> this would lead to a distribution that’s not uniform.
>
>
Yes, it would not be uniform, which is exactly what I meant by the last
part of: "Assuming you are ok with signed zero and infinities and "strange"
bias as result of IEEE 754"
Also, I think it's impossible to get a uniform distribution of all non-Nan
Double values (since they include +- infinity).
/Jens
> Saagar Jha
>
> On Jan 10, 2018, at 14:07, Jens Persson via swift-evolution <
> swift-evolution@swift.org> wrote:
>
> On Tue, Jan 9, 2018 at 10:07 PM, Jonathan Hull via swift-evolution <
> swift-evolution@swift.org> wrote:
>
>>
>> One additional question. How do you ergonomically get a Double which
>> doesn’t have a range, but also isn’t NaN?
>>
>>
> Assuming you are ok with signed zero and infinities and "strange" bias as
> result of IEEE 754:
>
> func randomNonNanDouble<R: RandomNumberGenerator>(using generator: R) ->
> Double {
> while true {
> let rndUInt64 = generator.next()
> let rndDouble = Double(bitPattern: rndUInt64)
> if rndDouble != Double.nan { return rndDouble }
> }
> }
>
> /Jens
>
> _______________________________________________
> swift-evolution mailing list
> swift-evolution@swift.org
> https://lists.swift.org/mailman/listinfo/swift-evolution
>
>
>
_______________________________________________
swift-evolution mailing list
swift-evolution@swift.org
https://lists.swift.org/mailman/listinfo/swift-evolution
