| Safe Haskell | Safe |
|---|---|
| Language | Haskell2010 |
Stochastic.Uniform
- xorshift128plus :: Integer -> UniformRandom
- data UniformRandom
- nWayAllocate :: Integer -> Integer -> UniformRandom -> ([UniformRandom], UniformRandom)
- splitAllocate :: Integer -> UniformRandom -> (UniformRandom, UniformRandom)
- class RandomGen g where
Documentation
xorshift128plus :: Integer -> UniformRandom Source
For information on the performance of the xorshift-128-plus PRNG, please see: Vigna et al.
data UniformRandom Source
Instances
nWayAllocate :: Integer -> Integer -> UniformRandom -> ([UniformRandom], UniformRandom) Source
splitAllocate :: Integer -> UniformRandom -> (UniformRandom, UniformRandom) Source
class RandomGen g where
The class RandomGen provides a common interface to random number
generators.
Methods
The next operation returns an Int that is uniformly distributed
in the range returned by genRange (including both end points),
and a new generator.
The genRange operation yields the range of values returned by
the generator.
It is required that:
The second condition ensures that genRange cannot examine its
argument, and hence the value it returns can be determined only by the
instance of RandomGen. That in turn allows an implementation to make
a single call to genRange to establish a generator's range, without
being concerned that the generator returned by (say) next might have
a different range to the generator passed to next.
The default definition spans the full range of Int.
split :: g -> (g, g)
The split operation allows one to obtain two distinct random number
generators. This is very useful in functional programs (for example, when
passing a random number generator down to recursive calls), but very
little work has been done on statistically robust implementations of
split ([System.Random, System.Random]
are the only examples we know of).