1 -----------------------------------------------------------------------------
3 -- Module : System.Random
4 -- Copyright : (c) The University of Glasgow 2001
5 -- License : BSD-style (see the file libraries/base/LICENSE)
7 -- Maintainer : libraries@haskell.org
9 -- Portability : portable
13 -----------------------------------------------------------------------------
20 -- * The 'RandomGen' class, and the 'StdGen' generator
22 RandomGen(next, split, genRange)
26 -- * The 'Random' class
27 , Random ( random, randomR,
31 -- * The global random number generator
48 import CPUTime ( getCPUTime )
49 import Foreign.Ptr ( Ptr, nullPtr )
51 import System.CPUTime ( getCPUTime )
52 import System.Time ( getClockTime, ClockTime(..) )
54 import Data.Char ( isSpace, chr, ord )
55 import System.IO.Unsafe ( unsafePerformIO )
57 import Numeric ( readDec )
59 -- The standard nhc98 implementation of Time.ClockTime does not match
60 -- the extended one expected in this module, so we lash-up a quick
63 data ClockTime = TOD Integer ()
64 foreign import ccall "time.h time" readtime :: Ptr () -> IO Int
65 getClockTime :: IO ClockTime
66 getClockTime = do t <- readtime nullPtr; return (TOD (toInteger t) ())
71 This library deals with the common task of pseudo-random
72 number generation. The library makes it possible to generate
73 repeatable results, by starting with a specified initial random
74 number generator; or to get different results on each run by using the
75 system-initialised generator, or by supplying a seed from some other
78 The library is split into two layers:
80 * A core /random number generator/ provides a supply of bits. The class
81 'RandomGen' provides a common interface to such generators.
83 * The class 'Random' provides a way to extract particular values from
84 a random number generator. For example, the 'Float' instance of 'Random'
85 allows one to generate random values of type 'Float'.
87 This implementation uses the Portable Combined Generator of L'Ecuyer
88 ["System.Random\#LEcuyer"] for 32-bit computers, transliterated by
89 Lennart Augustsson. It has a period of roughly 2.30584e18.
93 -- | The class 'RandomGen' provides a common interface to random number
96 class RandomGen g where
98 -- |The 'next' operation returns an 'Int' that is uniformly distributed
99 -- in the range returned by 'genRange' (including both end points),
100 -- and a new generator.
101 next :: g -> (Int, g)
103 -- |The 'split' operation allows one to obtain two distinct random number
104 -- generators. This is very useful in functional programs (for example, when
105 -- passing a random number generator down to recursive calls), but very
106 -- little work has been done on statistically robust implementations of
107 -- 'split' (["System.Random\#Burton", "System.Random\#Hellekalek"]
108 -- are the only examples we know of).
111 -- |The 'genRange' operation yields the range of values returned by
114 -- It is required that:
116 -- * If @(a,b) = 'genRange' g@, then @a < b@.
118 -- * 'genRange' is not strict.
120 -- The second condition ensures that 'genRange' cannot examine its
121 -- argument, and hence the value it returns can be determined only by the
122 -- instance of 'RandomGen'. That in turn allows an implementation to make
123 -- a single call to 'genRange' to establish a generator's range, without
124 -- being concerned that the generator returned by (say) 'next' might have
125 -- a different range to the generator passed to 'next'.
126 genRange :: g -> (Int,Int)
129 genRange g = (minBound,maxBound)
131 {- |The "System.Random" library provides one instance of 'RandomGen', the
132 abstract data type 'StdGen'.
134 The 'StdGen' instance of 'RandomGen' has a 'genRange' of at least 30 bits.
136 The result of repeatedly using 'next' should be at least as statistically
137 robust as the /Minimal Standard Random Number Generator/ described by
138 ["System.Random\#Park", "System.Random\#Carta"].
139 Until more is known about implementations of 'split', all we require is
140 that 'split' deliver generators that are (a) not identical and
141 (b) independently robust in the sense just given.
143 The 'Show' and 'Read' instances of 'StdGen' provide a primitive way to save the
144 state of a random number generator.
145 It is required that @'read' ('show' g) == g@.
147 In addition, 'read' may be used to map an arbitrary string (not necessarily one
148 produced by 'show') onto a value of type 'StdGen'. In general, the 'read'
149 instance of 'StdGen' has the following properties:
151 * It guarantees to succeed on any string.
153 * It guarantees to consume only a finite portion of the string.
155 * Different argument strings are likely to result in different results.
162 instance RandomGen StdGen where
165 genRange _ = stdRange
167 instance Show StdGen where
168 showsPrec p (StdGen s1 s2) =
173 instance Read StdGen where
174 readsPrec _p = \ r ->
177 _ -> [stdFromString r] -- because it shouldn't ever fail.
180 (s1, r1) <- readDec (dropWhile isSpace r)
181 (s2, r2) <- readDec (dropWhile isSpace r1)
182 return (StdGen s1 s2, r2)
185 If we cannot unravel the StdGen from a string, create
186 one based on the string given.
188 stdFromString :: String -> (StdGen, String)
189 stdFromString s = (mkStdGen num, rest)
190 where (cs, rest) = splitAt 6 s
191 num = foldl (\a x -> x + 3 * a) 1 (map ord cs)
195 The function 'mkStdGen' provides an alternative way of producing an initial
196 generator, by mapping an 'Int' into a generator. Again, distinct arguments
197 should be likely to produce distinct generators.
199 Programmers may, of course, supply their own instances of 'RandomGen'.
201 mkStdGen :: Int -> StdGen -- why not Integer ?
203 | s < 0 = mkStdGen (-s)
204 | otherwise = StdGen (s1+1) (s2+1)
206 (q, s1) = s `divMod` 2147483562
207 s2 = q `mod` 2147483398
209 createStdGen :: Integer -> StdGen
211 | s < 0 = createStdGen (-s)
212 | otherwise = StdGen (fromInteger (s1+1)) (fromInteger (s2+1))
214 (q, s1) = s `divMod` 2147483562
215 s2 = q `mod` 2147483398
217 -- FIXME: 1/2/3 below should be ** (vs@30082002) XXX
220 With a source of random number supply in hand, the 'Random' class allows the
221 programmer to extract random values of a variety of types.
223 Minimal complete definition: 'randomR' and 'random'.
228 -- | Takes a range /(lo,hi)/ and a random number generator
229 -- /g/, and returns a random value uniformly distributed in the closed
230 -- interval /[lo,hi]/, together with a new generator. It is unspecified
231 -- what happens if /lo>hi/. For continuous types there is no requirement
232 -- that the values /lo/ and /hi/ are ever produced, but they may be,
233 -- depending on the implementation and the interval.
234 randomR :: RandomGen g => (a,a) -> g -> (a,g)
236 -- | The same as 'randomR', but using a default range determined by the type:
238 -- * For bounded types (instances of 'Bounded', such as 'Char'),
239 -- the range is normally the whole type.
241 -- * For fractional types, the range is normally the semi-closed interval
244 -- * For 'Integer', the range is (arbitrarily) the range of 'Int'.
245 random :: RandomGen g => g -> (a, g)
247 -- | Plural variant of 'randomR', producing an infinite list of
248 -- random values instead of returning a new generator.
249 randomRs :: RandomGen g => (a,a) -> g -> [a]
250 randomRs ival g = x : randomRs ival g' where (x,g') = randomR ival g
252 -- | Plural variant of 'random', producing an infinite list of
253 -- random values instead of returning a new generator.
254 randoms :: RandomGen g => g -> [a]
255 randoms g = (\(x,g') -> x : randoms g') (random g)
257 -- | A variant of 'randomR' that uses the global random number generator
258 -- (see "System.Random#globalrng").
259 randomRIO :: (a,a) -> IO a
260 randomRIO range = getStdRandom (randomR range)
262 -- | A variant of 'random' that uses the global random number generator
263 -- (see "System.Random#globalrng").
265 randomIO = getStdRandom random
268 instance Random Int where
269 randomR (a,b) g = randomIvalInteger (toInteger a, toInteger b) g
270 random g = randomR (minBound,maxBound) g
272 instance Random Char where
274 case (randomIvalInteger (toInteger (ord a), toInteger (ord b)) g) of
276 random g = randomR (minBound,maxBound) g
278 instance Random Bool where
280 case (randomIvalInteger (toInteger (bool2Int a), toInteger (bool2Int b)) g) of
281 (x, g) -> (int2Bool x, g)
289 random g = randomR (minBound,maxBound) g
291 instance Random Integer where
292 randomR ival g = randomIvalInteger ival g
293 random g = randomR (toInteger (minBound::Int), toInteger (maxBound::Int)) g
295 instance Random Double where
296 randomR ival g = randomIvalDouble ival id g
297 random g = randomR (0::Double,1) g
299 -- hah, so you thought you were saving cycles by using Float?
300 instance Random Float where
301 random g = randomIvalDouble (0::Double,1) realToFrac g
302 randomR (a,b) g = randomIvalDouble (realToFrac a, realToFrac b) realToFrac g
304 mkStdRNG :: Integer -> IO StdGen
307 (TOD sec _) <- getClockTime
308 return (createStdGen (sec * 12345 + ct + o))
310 randomIvalInteger :: (RandomGen g, Num a) => (Integer, Integer) -> g -> (a, g)
311 randomIvalInteger (l,h) rng
312 | l > h = randomIvalInteger (h,l) rng
313 | otherwise = case (f n 1 rng) of (v, rng') -> (fromInteger (l + v `mod` k), rng')
324 f (n-1) (fromIntegral x + acc * b) g'
326 randomIvalDouble :: (RandomGen g, Fractional a) => (Double, Double) -> (Double -> a) -> g -> (a, g)
327 randomIvalDouble (l,h) fromDouble rng
328 | l > h = randomIvalDouble (h,l) fromDouble rng
330 case (randomIvalInteger (toInteger (minBound::Int), toInteger (maxBound::Int)) rng) of
334 fromDouble ((l+h)/2) +
335 fromDouble ((h-l) / realToFrac intRange) *
336 fromIntegral (x::Int)
341 intRange = toInteger (maxBound::Int) - toInteger (minBound::Int)
343 iLogBase :: Integer -> Integer -> Integer
344 iLogBase b i = if i < b then 1 else 1 + iLogBase b (i `div` b)
346 stdRange :: (Int,Int)
347 stdRange = (0, 2147483562)
349 stdNext :: StdGen -> (Int, StdGen)
350 -- Returns values in the range stdRange
351 stdNext (StdGen s1 s2) = (z', StdGen s1'' s2'')
352 where z' = if z < 1 then z + 2147483562 else z
356 s1' = 40014 * (s1 - k * 53668) - k * 12211
357 s1'' = if s1' < 0 then s1' + 2147483563 else s1'
360 s2' = 40692 * (s2 - k' * 52774) - k' * 3791
361 s2'' = if s2' < 0 then s2' + 2147483399 else s2'
363 stdSplit :: StdGen -> (StdGen, StdGen)
364 stdSplit std@(StdGen s1 s2)
367 -- no statistical foundation for this!
368 left = StdGen new_s1 t2
369 right = StdGen t1 new_s2
371 new_s1 | s1 == 2147483562 = 1
374 new_s2 | s2 == 1 = 2147483398
377 StdGen t1 t2 = snd (next std)
379 -- The global random number generator
381 {- $globalrng #globalrng#
383 There is a single, implicit, global random number generator of type
384 'StdGen', held in some global variable maintained by the 'IO' monad. It is
385 initialised automatically in some system-dependent fashion, for example, by
386 using the time of day, or Linux's kernel random number generator. To get
387 deterministic behaviour, use 'setStdGen'.
390 -- |Sets the global random number generator.
391 setStdGen :: StdGen -> IO ()
392 setStdGen sgen = writeIORef theStdGen sgen
394 -- |Gets the global random number generator.
395 getStdGen :: IO StdGen
396 getStdGen = readIORef theStdGen
398 theStdGen :: IORef StdGen
399 theStdGen = unsafePerformIO $ do
403 -- |Applies 'split' to the current global random generator,
404 -- updates it with one of the results, and returns the other.
405 newStdGen :: IO StdGen
408 let (a,b) = split rng
412 {- |Uses the supplied function to get a value from the current global
413 random generator, and updates the global generator with the new generator
414 returned by the function. For example, @rollDice@ gets a random integer
418 > rollDice = getStdRandom (randomR (1,6))
422 getStdRandom :: (StdGen -> (a,StdGen)) -> IO a
425 let (v, new_rng) = f rng
431 1. FW #Burton# Burton and RL Page, /Distributed random number generation/,
432 Journal of Functional Programming, 2(2):203-212, April 1992.
434 2. SK #Park# Park, and KW Miller, /Random number generators -
435 good ones are hard to find/, Comm ACM 31(10), Oct 1988, pp1192-1201.
437 3. DG #Carta# Carta, /Two fast implementations of the minimal standard
438 random number generator/, Comm ACM, 33(1), Jan 1990, pp87-88.
440 4. P #Hellekalek# Hellekalek, /Don\'t trust parallel Monte Carlo/,
441 Department of Mathematics, University of Salzburg,
442 <http://random.mat.sbg.ac.at/~peter/pads98.ps>, 1998.
444 5. Pierre #LEcuyer# L'Ecuyer, /Efficient and portable combined random
445 number generators/, Comm ACM, 31(6), Jun 1988, pp742-749.
447 The Web site <http://random.mat.sbg.ac.at/> is a great source of information.