This library deals with the common task of pseudo-random
number generation. The library makes it possible to generate
repeatable results, by starting with a specified initial random
number generator; or to get different results on each run by using the
system-initialised generator, or by supplying a seed from some other
The library is split into two layers:
- A core random number generator provides a supply of bits. The class
RandomGen provides a common interface to such generators.
- The class Random provides a way to extract particular values from
a random number generator. For example, the Float instance of Random
allows one to generate random values of type Float.
- Comment found in this file when merging with Library Report:
The June 1988 (v31 #6) issue of the Communications of the ACM has an
article by Pierre L'Ecuyer called, Efficient and Portable Combined
Random Number Generators. Here is the Portable Combined Generator of
L'Ecuyer for 32-bit computers. It has a period of roughly 2.30584e18.
Transliterator: Lennart Augustsson
|The RandomGen class, and the StdGen generator|
|class RandomGen g where|
The class RandomGen provides a common interface to random number generators.|
|next :: g -> (Int, g)|
|The next operation allows one to extract at least 30 bits (one Int's
worth) from the generator, returning a new generator as well. The
integer returned may be positive or negative.|
|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 ([1,4] are the only examples we know of).|
|genRange :: g -> (Int, Int)|
|data StdGen |
The System.Random library provides one instance of RandomGen, the
abstract data type StdGen.
The result of repeatedly using next should be at least as statistically robust
as the Minimal Standard Random Number Generator described by
[System.Random#Park, System.Random#Carta]. Until more
is known about implementations of split, all we require is that split deliver
generators that are (a) not identical and (b) independently robust in the sense
The show/Read instances of StdGen provide a primitive way to save the
state of a random number generator. It is required that read (show g) == g.
In addition, read may be used to map an arbitrary string (not necessarily one
produced by show) onto a value of type StdGen. In general, the read
instance of StdGen has the following properties:
- It guarantees to succeed on any string.
- It guarantees to consume only a finite portion of the string.
- Different argument strings are likely to result in different results.
The function mkStdGen provides an alternative way of producing an initial
generator, by mapping an Int into a generator. Again, distinct arguments
should be likely to produce distinct generators.
Programmers may, of course, supply their own instances of RandomGen.
|mkStdGen :: Int -> StdGen|
|The Random class|
|class Random a where|
The Random class
With a source of random number supply in hand, the Random class allows the
programmer to extract random values of a variety of types.
- randomR takes a range (lo,hi) and a random number generator g, and returns
a random value uniformly distributed in the closed interval [lo,hi], together
with a new generator. It is unspecified what happens if lo>hi. For continuous
types there is no requirement that the values lo and hi are ever produced,
but they may be, depending on the implementation and the interval.
- random does the same as randomR, but does not take a range.
- For bounded types (instances of Bounded, such as Char), the range is
normally the whole type.
- For fractional types, the range is normally the semi-closed interval [0,1).
- For Integer, the range is (arbitrarily) the range of Int.
|random :: RandomGen g => g -> (a, g)|
|Minimal complete definition: random and randomR|
|randomR :: RandomGen g => (a, a) -> g -> (a, g)|
|randoms :: RandomGen g => g -> [a]|
|Default methods |
|randomRs :: RandomGen g => (a, a) -> g -> [a]|
|randomIO :: IO a|
|randomRIO :: (a, a) -> IO a|
|The global random number generator|
|There is a single, implicit, global random number generator of type
StdGen, held in some global variable maintained by the IO monad. It is
initialised automatically in some system-dependent fashion, for example, by
using the time of day, or Linux's kernel random number generator. To get
deterministic behaviour, use setStdGen.
|getStdRandom :: (StdGen -> (a, StdGen)) -> IO a|
getStdRandom uses the supplied function to get a value from the current
global random generator, and updates the global generator with the new generator
returned by the function. For example, rollDice gets a random integer between 1 and 6:
rollDice :: IO Int
rollDice = getStdRandom (randomR (1,6))
|getStdGen :: IO StdGen|
|getStdGen gets the global random number generator.|
|setStdGen :: StdGen -> IO ()|
|setStdGen sets the global random number generator.|
|newStdGen :: IO StdGen|
-  FW Burton and RL Page, Distributed random number generation,
Journal of Functional Programming, 2(2):203-212, April 1992.
-  SK Park, and KW Miller, Random number generators -
good ones are hard to find, Comm ACM 31(10), Oct 1988, pp1192-1201.
-  DG Carta, Two fast implementations of the minimal standard
random number generator, Comm ACM, 33(1), Jan 1990, pp87-88.
-  P Hellekalek, Don't trust parallel Monte Carlo,
Department of Mathematics, University of Salzburg,
The Web site http://random.mat.sbg.ac.at/ is a great source of information.
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