Haskell Hierarchical Libraries (base package)ContentsIndex
System.Random
Portability portable
Stability stable
Maintainer libraries@haskell.org
Contents
The RandomGen class, and the StdGen generator
The Random class
The global random number generator
References
Description
Random numbers.
Synopsis
class RandomGen g where
next :: g -> (Int, g)
split :: g -> (g, g)
genRange :: g -> (Int, Int)
data StdGen
mkStdGen :: Int -> StdGen
class Random a where
random :: RandomGen g => g -> (a, g)
randomR :: RandomGen g => (a, a) -> g -> (a, g)
randoms :: RandomGen g => g -> [a]
randomRs :: RandomGen g => (a, a) -> g -> [a]
randomIO :: IO a
randomRIO :: (a, a) -> IO a
getStdRandom :: (StdGen -> (a, StdGen)) -> IO a
getStdGen :: IO StdGen
setStdGen :: StdGen -> IO ()
newStdGen :: IO StdGen
Documentation

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 source.

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
RandomGen The class RandomGen provides a common interface to random number generators.
Methods
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)
Instances
RandomGen StdGen
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 just given.

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.

Instances
RandomGen StdGen
Show StdGen
Read StdGen
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.
  1. For bounded types (instances of Bounded, such as Char), the range is normally the whole type.
  2. For fractional types, the range is normally the semi-closed interval [0,1).
  3. For Integer, the range is (arbitrarily) the range of Int.
Methods
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
Instances
Random Int
Random Char
Random Bool
Random Integer
Random Double
Random Float
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
References
  • [1] FW Burton and RL Page, Distributed random number generation, Journal of Functional Programming, 2(2):203-212, April 1992.
  • [2] SK Park, and KW Miller, Random number generators - good ones are hard to find, Comm ACM 31(10), Oct 1988, pp1192-1201.
  • [3] DG Carta, Two fast implementations of the minimal standard random number generator, Comm ACM, 33(1), Jan 1990, pp87-88.
  • [4] P Hellekalek, Don't trust parallel Monte Carlo, Department of Mathematics, University of Salzburg, http://random.mat.sbg.ac.at/~peter/pads98.ps, 1998.

The Web site http://random.mat.sbg.ac.at/ is a great source of information.

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