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--------------------------------------------------------------------------------
{-# LANGUAGE UndecidableInstances #-}
{-# OPTIONS_GHC -fno-warn-orphans #-}
--------------------------------------------------------------------------------
module Xanthous.Random
( Choose(..)
, ChooseElement(..)
, Weighted(..)
, evenlyWeighted
, weightedBy
, subRand
, chance
, chooseSubset
, chooseRange
) where
--------------------------------------------------------------------------------
import Xanthous.Prelude
--------------------------------------------------------------------------------
import Data.List.NonEmpty (NonEmpty(..))
import Control.Monad.Random.Class (MonadRandom(getRandomR, getRandom))
import Control.Monad.Random (Rand, evalRand, mkStdGen, StdGen)
import Data.Functor.Compose
import Data.Random.Shuffle.Weighted
import Data.Random.Distribution
import Data.Random.Distribution.Uniform
import Data.Random.Distribution.Uniform.Exclusive
import Data.Random.Sample
import qualified Data.Random.Source as DRS
import Data.Interval ( Interval, lowerBound', Extended (Finite)
, upperBound', Boundary (Closed)
)
--------------------------------------------------------------------------------
instance {-# INCOHERENT #-} (Monad m, MonadRandom m) => DRS.MonadRandom m where
getRandomWord8 = getRandom
getRandomWord16 = getRandom
getRandomWord32 = getRandom
getRandomWord64 = getRandom
getRandomDouble = getRandom
getRandomNByteInteger n = getRandomR (0, 256 ^ n)
class Choose a where
type RandomResult a
choose :: MonadRandom m => a -> m (RandomResult a)
newtype ChooseElement a = ChooseElement a
instance MonoFoldable a => Choose (ChooseElement a) where
type RandomResult (ChooseElement a) = Maybe (Element a)
choose (ChooseElement xs) = do
chosenIdx <- getRandomR (0, olength xs - 1)
let pick _ (Just x) = Just x
pick (x, i) Nothing
| i == chosenIdx = Just x
| otherwise = Nothing
pure $ ofoldr pick Nothing $ zip (toList xs) [0..]
instance MonoFoldable a => Choose (NonNull a) where
type RandomResult (NonNull a) = Element a
choose
= fmap (fromMaybe (error "unreachable")) -- why not lol
. choose
. ChooseElement
. toNullable
instance Choose (NonEmpty a) where
type RandomResult (NonEmpty a) = a
choose = choose . fromNonEmpty @[_]
instance Choose (a, a) where
type RandomResult (a, a) = a
choose (x, y) = choose (x :| [y])
newtype Weighted w t a = Weighted (t (w, a))
deriving (Functor, Foldable) via (t `Compose` (,) w)
instance Traversable t => Traversable (Weighted w t) where
traverse f (Weighted twa) = Weighted <$> (traverse . traverse) f twa
evenlyWeighted :: [a] -> Weighted Int [] a
evenlyWeighted = Weighted . itoList
-- | Weight the elements of some functor by a function. Larger values of 'w' per
-- its 'Ord' instance will be more likely to be generated
weightedBy :: Functor t => (a -> w) -> t a -> Weighted w t a
weightedBy weighting xs = Weighted $ (weighting &&& id) <$> xs
instance (Num w, Ord w, Distribution Uniform w, Excludable w)
=> Choose (Weighted w [] a) where
type RandomResult (Weighted w [] a) = Maybe a
choose (Weighted ws) = sample $ headMay <$> weightedSample 1 ws
instance (Num w, Ord w, Distribution Uniform w, Excludable w)
=> Choose (Weighted w NonEmpty a) where
type RandomResult (Weighted w NonEmpty a) = a
choose (Weighted ws) =
sample
$ fromMaybe (error "unreachable") . headMay
<$> weightedSample 1 (toList ws)
subRand :: MonadRandom m => Rand StdGen a -> m a
subRand sub = evalRand sub . mkStdGen <$> getRandom
-- | Has a @n@ chance of returning 'True'
--
-- eg, chance 0.5 will return 'True' half the time
chance
:: (Num w, Ord w, Distribution Uniform w, Excludable w, MonadRandom m)
=> w
-> m Bool
chance n = choose $ weightedBy (bool 1 (n * 2)) bools
-- | Choose a random subset of *about* @w@ of the elements of the given
-- 'Witherable' structure
chooseSubset :: ( Num w, Ord w, Distribution Uniform w, Excludable w
, Witherable t
, MonadRandom m
) => w -> t a -> m (t a)
chooseSubset = filterA . const . chance
-- | Choose a random @n@ in the given interval
chooseRange
:: ( MonadRandom m
, Distribution Uniform n
, Enum n
, Bounded n, Show n, Ord n)
=> Interval n
-> m (Maybe n)
chooseRange int = traverse sample distribution
where
(lower, lowerBoundary) = lowerBound' int
lowerR = case lower of
Finite x -> if lowerBoundary == Closed
then x
else succ x
_ -> minBound
(upper, upperBoundary) = upperBound' int
upperR = case upper of
Finite x -> if upperBoundary == Closed
then x
else pred x
_ -> maxBound
distribution
| lowerR <= upperR = Just $ Uniform lowerR upperR
| otherwise = Nothing
--------------------------------------------------------------------------------
bools :: NonEmpty Bool
bools = True :| [False]
|
