haskell98-2.0.0.3: Compatibility with Haskell 98

Safe HaskellTrustworthy
LanguageHaskell98

Random

Contents

Synopsis

Random number generators

class RandomGen g where Source

The class RandomGen provides a common interface to random number generators.

Minimal complete definition: next and split.

Methods

next :: g -> (Int, g) Source

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) Source

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 (["Random#Burton", "Random#Hellekalek"] are the only examples we know of).

genRange :: g -> (Int, Int) Source

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.

Instances

Standard random number generators

data StdGen Source

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 ["Random#Park", "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 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, reads 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.

Instances

mkStdGen :: Int -> StdGen Source

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.

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 Source

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 Source

Gets the global random number generator.

setStdGen :: StdGen -> IO () Source

Sets the global random number generator.

newStdGen :: IO StdGen Source

Applies split to the current global random generator, updates it with one of the results, and returns the other.

Random values of various types

class Random a where Source

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.

Methods

randomR :: RandomGen g => (a, a) -> g -> (a, g) Source

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) Source

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 [0,1).
  • For Integer, the range is (arbitrarily) the range of Int.

randomRs :: RandomGen g => (a, a) -> g -> [a] Source

Plural variant of randomR, producing an infinite list of random values instead of returning a new generator.

randoms :: RandomGen g => g -> [a] Source

Plural variant of random, producing an infinite list of random values instead of returning a new generator.

randomRIO :: (a, a) -> IO a Source

A variant of randomR that uses the global random number generator (see "Random#globalrng").

randomIO :: IO a Source

A variant of random that uses the global random number generator (see "Random#globalrng").

Instances

References

  1. FW #Burton# Burton and RL Page, Distributed random number generation, Journal of Functional Programming, 2(2):203-212, April 1992.
  2. SK #Park# Park, and KW Miller, /Random number generators - good ones are hard to find/, Comm ACM 31(10), Oct 1988, pp1192-1201.
  3. DG #Carta# Carta, /Two fast implementations of the minimal standard random number generator/, Comm ACM, 33(1), Jan 1990, pp87-88.
  4. P #Hellekalek# Hellekalek, Don't trust parallel Monte Carlo, Department of Mathematics, University of Salzburg, http://random.mat.sbg.ac.at/~peter/pads98.ps, 1998.
  5. Pierre #LEcuyer# 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.