I can see that's certainly something that can be gotten wrong; the in-place sort is kind-of nice; but you end up hitting most numbers twice, and with a longer array (say a set of 75 bingo balls) you can move the same number multiple times.
which gives numbers at the end a higher chance of being at the start when all is done; and almost guaranteed to not be at the end. I use sha2 as a stream of bits; and when it runs out of bits, invokes a callback to get more salt ( that is optional, although adding Date.now() is sufficient to increase the overall entropy slightly ). This can also be used as a generator procedural content generation, since you can specify the initial salt and subsequent salts, or even copy the state, and fork two streams using slightly different progressive entropy. https://github.com/d3x0r/-/blob/master/org.d3x0r.common/ salty_random_generator.js And then to shuffle, for each number in the array, assign a random number; hang in a binary tree from least to most; then gather the tree back out. requires additional memory approximately the 4xsize of the original array tough temporarily. (don't seem to have a JS version, but the C version ports pretty easily) https://github.com/d3x0r/SACK/blob/master/src/utils/cardodds/shuffle.c This allows any number to result in any position equally. The SHA2 hash generates very good random number characteristics; evaluating with diehard tests; chi-square etc. Allowing more salt to be introduced periodically destroys any overall period (even if it is in the realm of 256 bits). if you were shuffling a deck of cards (52) you only need random values that are 6-7 bits at a time... It is slower than say http://www.pcg-random.org/using-pcg.html or mersenne, both of which I found to be very poor. pcg generates zeros like 70% of the time. While a good shuffle should be able to start from the initial state and generate a good shuffled result, it is slightly better to progressively shuffle the previous result array into new positions than to start from an initial state and compute a 1-off. On Sun, Apr 29, 2018 at 3:27 PM, Isiah Meadows <[email protected]> wrote: > BTW, I added this to my list of various proposed array additions (as a > weak one) [1]. > > I did do a little reading up and found that in general, there's a > major glitch that makes shuffling very hard to get right (issues even > Underscore's `_.shuffle` doesn't address), specifically that of the > size of the random number generator vs how many permutations it can > hit. Specifically, if you want any properly unbiased shuffles covering > all permutations of arrays larger than 34 entries (and that's not a > lot), you can't use any major engines' `Math.random` (which typically > have a max seed+period of 128 bits). You have to roll your own > implementation, and you need at least something like xorshift1024* [2] > (1024-bit max seed/period size) for up to 170 entries, MT19937 > [3]/WELL19937 [4] (seed/period up to 2^19937-1) for up to 2080 > entries, or MT44497/WELL44497 for up to 4199 entries. Keep in mind the > minimum seed/period size in bits grows roughly `ceil(log2(fact(N)))` > where `N` is the length of the array [5], and the only way you can > guarantee you can even generate all possible permutations of all > arrays (ignoring potential bias) is through a true hardware generator > (with its potentially infinite number of possibilities). Also, another > concern is that the loop's bottleneck is specifically the random > number generation, so you can't get too slow without resulting in a > *very* slow shuffle. > > If you want anything minimally biased and much larger than that, > you'll need a cryptographically secure pseudorandom number generator > just to cover the possible states. (This is about where the > intersection meets between basic statistics and cryptography, since > cipher blocks are frequently that long.) But I'm leaving that as out > of scope of that proposal. > > [1]: https://github.com/isiahmeadows/array-additions- > proposal#arrayprototypeshuffle > [2]: https://en.wikipedia.org/wiki/Xorshift#xorshift* > [3]: https://en.wikipedia.org/wiki/Mersenne_Twister > [4]: https://en.wikipedia.org/wiki/Well_equidistributed_long-period_linear > [5]: http://www.wolframalpha.com/input/?i=plot+ceiling(log2(x!) > )+where+0+%3C+x+%3C+10000 > > ----- > > Isiah Meadows > [email protected] > > Looking for web consulting? Or a new website? > Send me an email and we can get started. > www.isiahmeadows.com > > > On Sun, Apr 29, 2018 at 4:01 PM, Alexander Lichter <[email protected]> wrote: > > On 29.04.2018 18:34, Naveen Chawla wrote: > >> > >> I don't think there's such a thing as "real random" in digital algos, > just > >> "pseudo random". > > > > You are right. I meant 'unbiased' pseudo randomness here. > > > >> Apart from card games, what's the use case? > > > > There are a lot of potential use cases. The best that comes into my mind > is > > sampling test data. > > > > > > On 29.04.2018 19:01, Isiah Meadows wrote: > >> > >> I think this would be better suited for a library function rather than a > >> language feature. I could see this also being useful also for > >> randomized displays, but that's about it. And I'm not sure what an > >> engine could provide here that a library couldn't - you can't really > >> get much faster than what's in the language (minus bounds checking, > >> but the likely frequent cache misses will eclipse that greatly), and > >> it's not unlocking any real new possibilities. > > > > As Tab Atkins Jr. already pointed out it's not about performance > benefits. > > It's about how error-prone custom shuffle implementations are/can be. > > > > _______________________________________________ > > es-discuss mailing list > > [email protected] > > https://mail.mozilla.org/listinfo/es-discuss > _______________________________________________ > es-discuss mailing list > [email protected] > https://mail.mozilla.org/listinfo/es-discuss >
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