Because rollInt > nextInt.
sbt:
"games.datastrophic" %% "dice" % "0.2.5",An example:
val (newDice, randomInt) = dice.rollInt(N)This works well with State:
for {
amount <- State[Dice, Int](_.rollInt(math.max(1, math.pow(value, 1d / 9).toInt)))
spread <- State[Dice, ValueSpread](_.unsafeRollOneOf(List(SpreadEqually, SpreadStepOne, SpreadSquare)))
bar <- foo(amount, spread)
} yield barWIP, but used in our game: Blackout Age for almost every random thing there.
This implementation fails PractRand at 8/16 MB. Do not use for cryptographic purposes.
> sbt run | ./RNG_test stdin -tlmin 4MB
RNG_test using PractRand version 0.94
RNG = RNG_stdin, seed = unknown
test set = core, folding = standard(unknown format)
rng=RNG_stdin, seed=unknown
length= 4 megabytes (2^22 bytes), time= 0.3 seconds
no anomalies in 124 test result(s)
rng=RNG_stdin, seed=unknown
length= 8 megabytes (2^23 bytes), time= 0.7 seconds
Test Name Raw Processed Evaluation
FPF-14+6/16:cross R= +11.0 p = 2.6e-9 VERY SUSPICIOUS
...and 134 test result(s) without anomalies
rng=RNG_stdin, seed=unknown
length= 16 megabytes (2^24 bytes), time= 1.3 seconds
Test Name Raw Processed Evaluation
DC6-9x1Bytes-1 R= +5.8 p = 4.3e-3 unusual
FPF-14+6/16:all R= +6.1 p = 3.1e-5 mildly suspicious
FPF-14+6/16:cross R= +26.2 p = 4.8e-21 FAIL !!
...and 148 test result(s) without anomalies
Internal RNG is based on The PCG Paper, implementation is adapted from PCG-Java.
Watch this great lecture of the paper's author to learn how that's different from java.util.Random.
