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.
This implementation uses the Portable Combined Generator of L'Ecuyer
[System.Random#LEcuyer] for 32-bit computers, transliterated by
Lennart Augustsson. It has a period of roughly 2.30584e18.
|The RandomGen class, and the StdGen generator
|class RandomGen g where|
|The class RandomGen provides a common interface to random number
|next :: g -> (Int, g)|
|The next operation returns an Int that is uniformly distributed
in the range returned by genRange (including both end points),
and a new generator.
|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#Burton, System.Random#Hellekalek]
are the only examples we know of).
|genRange :: g -> (Int, Int)|
The genRange operation yields the range of values returned by
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.
|data StdGen |
The System.Random library provides one instance of RandomGen, the
abstract data type StdGen.
The StdGen instance of RandomGen has a genRange of at least 30 bits.
The result of repeatedly using next should be at least as statistically
robust as the Minimal Standard Random Number Generator described by
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 and 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.
|mkStdGen :: Int -> StdGen|
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.
|The Random class
|class Random a where|
With a source of random number supply in hand, the Random class allows the
programmer to extract random values of a variety of types.
Minimal complete definition: randomR and random.
|randomR :: RandomGen g => (a, a) -> g -> (a, g)|
|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 :: RandomGen g => g -> (a, g)|
The same as randomR, but using a default range determined by the type:
- 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
- For Integer, the range is (arbitrarily) the range of Int.
|randomRs :: RandomGen g => (a, a) -> g -> [a]|
|Plural variant of randomR, producing an infinite list of
random values instead of returning a new generator.
|randoms :: RandomGen g => g -> [a]|
|Plural variant of random, producing an infinite list of
random values instead of returning a new generator.
|randomRIO :: (a, a) -> IO a|
|A variant of randomR that uses the global random number generator
|randomIO :: IO a|
|A variant of random that uses the global random number generator
|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|
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|
|Gets the global random number generator.
|setStdGen :: StdGen -> IO ()|
|Sets the global random number generator.
|newStdGen :: IO StdGen|
|Applies split to the current global random generator,
updates it with one of the results, and returns the other.
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,
5. Pierre L'Ecuyer, Efficient and portable combined random
number generators, Comm ACM, 31(6), Jun 1988, pp742-749.
The Web site http://random.mat.sbg.ac.at/ is a great source of information.
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