Stability	experimental
Portability	non-portable (multi-parameter type classes, generalized newtype deriving, flexible instances)
Safe Haskell	None
Language	Haskell2010
Extensions	TypeSynonymInstances FlexibleInstances ConstrainedClassMethods MultiParamTypeClasses GeneralizedNewtypeDeriving

Control.Monad.Markov.Internal

Description

A Markov chain monad, built on top of MonadRandom.

The interface is defined by MonadMarkov.

For example code, see the "example" directory.

Synopsis

newtype MarkovT s m a = MarkovT {
- unMarkovT :: StateT s (ReaderT (s -> [(s, Rational)]) m) a
}
type MarkovStd s = MarkovR s StdGen
type MarkovStdT s m = MarkovRT s StdGen m
type MarkovR s g = MarkovT s (Rand g)
type MarkovRT s g m = MarkovT s (RandT g m)
type Markov s a = MarkovT s Identity a
type TransTable a = a -> [(a, Rational)]
newtype MarkovT s m a = MarkovT {
- unMarkovT :: StateT s (ReaderT (s -> [(s, Rational)]) m) a
}
withMarkovT :: (s -> s) -> MarkovT s m a -> MarkovT s m a
evalMarkovT :: Monad m => MarkovT s m a -> s -> TransTable s -> m a
evalMarkov :: Markov s a -> s -> TransTable s -> a
runMarkovT :: MarkovT s m a -> s -> TransTable s -> m (a, s)
runMarkov :: Markov s a -> s -> TransTable s -> (a, s)
runMarkovRT :: Functor m => MarkovRT s g m a -> g -> s -> TransTable s -> m (a, g, s)
runMarkovR :: MarkovR s g a -> g -> s -> TransTable s -> (a, g, s)
runMarkovStdT :: Functor m => MarkovStdT s m a -> Int -> s -> TransTable s -> m (a, StdGen, s)
runMarkovStd :: MarkovStd s a -> Int -> s -> TransTable s -> (a, StdGen, s)
evalMarkovRT :: Monad m => MarkovRT s g m a -> g -> s -> TransTable s -> m a
evalMarkovR :: MarkovR s g a -> g -> s -> TransTable s -> a
evalMarkovStdT :: Monad m => MarkovStdT s m a -> Int -> s -> TransTable s -> m a
evalMarkovStd :: MarkovStd s a -> Int -> s -> TransTable s -> a
evalMarkovIO :: MarkovStdT s IO a -> s -> TransTable s -> IO a
module Control.Monad.Markov.Class

Documentation

newtype MarkovT s m a Source #

A monad transformer which adds access to a state and a probabilistic transition function to an existing monad.

Parameterized by:

s - The state.
m - The inner monad.

The return function leaves the state unchanged, while >>= uses the final state of the first computation as the initial state of the second.

Constructors

MarkovT
Fields unMarkovT :: StateT s (ReaderT (s -> [(s, Rational)]) m) a

Instances

MonadRandom m => MonadMarkov s (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods nextState :: MarkovT s m s Source #
Monad m => Monad (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods (>>=) :: MarkovT s m a -> (a -> MarkovT s m b) -> MarkovT s m b # (>>) :: MarkovT s m a -> MarkovT s m b -> MarkovT s m b # return :: a -> MarkovT s m a # fail :: String -> MarkovT s m a #
Functor m => Functor (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods fmap :: (a -> b) -> MarkovT s m a -> MarkovT s m b # (<$) :: a -> MarkovT s m b -> MarkovT s m a #
Monad m => Applicative (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods pure :: a -> MarkovT s m a # (<>) :: MarkovT s m (a -> b) -> MarkovT s m a -> MarkovT s m b # liftA2 :: (a -> b -> c) -> MarkovT s m a -> MarkovT s m b -> MarkovT s m c # (>) :: MarkovT s m a -> MarkovT s m b -> MarkovT s m b # (<*) :: MarkovT s m a -> MarkovT s m b -> MarkovT s m a #
MonadPlus m => MonadPlus (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods mzero :: MarkovT s m a # mplus :: MarkovT s m a -> MarkovT s m a -> MarkovT s m a #
MonadIO m => MonadIO (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods liftIO :: IO a -> MarkovT s m a #
MonadRandom m => MonadRandom (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods getRandomR :: Random a => (a, a) -> MarkovT s m a # getRandom :: Random a => MarkovT s m a # getRandomRs :: Random a => (a, a) -> MarkovT s m [a] # getRandoms :: Random a => MarkovT s m [a] #
MonadInterleave m => MonadInterleave (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods interleave :: MarkovT s m a -> MarkovT s m a #
MonadPlus m => Alternative (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods empty :: MarkovT s m a # (<\|>) :: MarkovT s m a -> MarkovT s m a -> MarkovT s m a # some :: MarkovT s m a -> MarkovT s m [a] # many :: MarkovT s m a -> MarkovT s m [a] #

type MarkovStd s = MarkovR s StdGen Source #

Basic Markov monad, using the standard random generator StdGen

type MarkovStdT s m = MarkovRT s StdGen m Source #

Markov monad transformer, using the standard random generator

type MarkovR s g = MarkovT s (Rand g) Source #

Basic markov monad laid over the RandT random value monad.

type MarkovRT s g m = MarkovT s (RandT g m) Source #

Markov monad transformer, laid over the RandT random value monad.

type Markov s a = MarkovT s Identity a Source #

A basic Markov monad

type TransTable a = a -> [(a, Rational)] Source #

a transition function, from a state, to a weighted list of states. The total weight of states must not be 0.

newtype MarkovT s m a Source #

A monad transformer which adds access to a state and a probabilistic transition function to an existing monad.

Parameterized by:

s - The state.
m - The inner monad.

The return function leaves the state unchanged, while >>= uses the final state of the first computation as the initial state of the second.

Constructors

MarkovT
Fields unMarkovT :: StateT s (ReaderT (s -> [(s, Rational)]) m) a

Instances

MonadRandom m => MonadMarkov s (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods nextState :: MarkovT s m s Source #
Monad m => Monad (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods (>>=) :: MarkovT s m a -> (a -> MarkovT s m b) -> MarkovT s m b # (>>) :: MarkovT s m a -> MarkovT s m b -> MarkovT s m b # return :: a -> MarkovT s m a # fail :: String -> MarkovT s m a #
Functor m => Functor (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods fmap :: (a -> b) -> MarkovT s m a -> MarkovT s m b # (<$) :: a -> MarkovT s m b -> MarkovT s m a #
Monad m => Applicative (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods pure :: a -> MarkovT s m a # (<>) :: MarkovT s m (a -> b) -> MarkovT s m a -> MarkovT s m b # liftA2 :: (a -> b -> c) -> MarkovT s m a -> MarkovT s m b -> MarkovT s m c # (>) :: MarkovT s m a -> MarkovT s m b -> MarkovT s m b # (<*) :: MarkovT s m a -> MarkovT s m b -> MarkovT s m a #
MonadPlus m => MonadPlus (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods mzero :: MarkovT s m a # mplus :: MarkovT s m a -> MarkovT s m a -> MarkovT s m a #
MonadIO m => MonadIO (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods liftIO :: IO a -> MarkovT s m a #
MonadRandom m => MonadRandom (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods getRandomR :: Random a => (a, a) -> MarkovT s m a # getRandom :: Random a => MarkovT s m a # getRandomRs :: Random a => (a, a) -> MarkovT s m [a] # getRandoms :: Random a => MarkovT s m [a] #
MonadInterleave m => MonadInterleave (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods interleave :: MarkovT s m a -> MarkovT s m a #
MonadPlus m => Alternative (MarkovT s m) Source #
Instance details Defined in Control.Monad.Markov.Internal Methods empty :: MarkovT s m a # (<\|>) :: MarkovT s m a -> MarkovT s m a -> MarkovT s m a # some :: MarkovT s m a -> MarkovT s m [a] # many :: MarkovT s m a -> MarkovT s m [a] #

withMarkovT :: (s -> s) -> MarkovT s m a -> MarkovT s m a Source #

withMarkovT f m executes action m on a state modified by applying f.

evalMarkovT Source #

Arguments

:: Monad m
=> MarkovT s m a	computation to execute
-> s	initial state
-> TransTable s	transition function to use
-> m a

Evaluate a Markov chain computation with a given initial state and transition table, and return the final value, discarding the final state.

evalMarkov Source #

Arguments

:: Markov s a	computation to execute
-> s	initial state
-> TransTable s	transition function to use
-> a

Evaluate a Markov chain computation with a given initial state and transition table, and return the final value, discarding the final state.

runMarkovT Source #

Arguments

:: MarkovT s m a	computation to execute
-> s	initial state
-> TransTable s	transition function to use
-> m (a, s)

Unwrap a Markov monad computation as a function.

runMarkov Source #

Arguments

:: Markov s a	computation to execute
-> s	initial state
-> TransTable s	transition function to use
-> (a, s)

Unwrap a Markov monad computation as a function.

runMarkovRT Source #

Arguments

:: Functor m
=> MarkovRT s g m a	computation to execute
-> g	generator to use
-> s	initial state
-> TransTable s	transition function to use
-> m (a, g, s)

Run a Markov computation using the generator g, returning the result, the updated generator, and the the final state.

runMarkovR Source #

Arguments

:: MarkovR s g a	computation to execute
-> g	generator to use
-> s	initial state
-> TransTable s	transition function to use
-> (a, g, s)

Run a Markov computation using the generator g, returning the result, the updated generator, and the the final state.

runMarkovStdT Source #

Arguments

:: Functor m
=> MarkovStdT s m a	computation to execute
-> Int	seed for generator
-> s	initial state
-> TransTable s	transition function to use
-> m (a, StdGen, s)

Run a Markov computation using the random number generator used by getStdRandom, initialised with the seed seed, returning the result, the updated generator, and the the final state.

runMarkovStd Source #

Arguments

:: MarkovStd s a	computation to execute
-> Int	seed for generator
-> s	initial state
-> TransTable s	transition function to use
-> (a, StdGen, s)

Run a Markov computation using the random number generator used by getStdRandom, initialised with the seed seed, returning the result, the updated generator, and the the final state.

evalMarkovRT Source #

Arguments

:: Monad m
=> MarkovRT s g m a	computation to execute
-> g	generator to use
-> s	initial state
-> TransTable s	transition function to use
-> m a

Evaluate a MarkovRT computation using the random generator g.

evalMarkovR Source #

Arguments

:: MarkovR s g a	computation to execute
-> g	generator to use
-> s	initial state
-> TransTable s	transition function to use
-> a

Evaluate a MarkovR computation using the random generator g.

evalMarkovStdT Source #

Arguments

:: Monad m
=> MarkovStdT s m a	computation to execute
-> Int	seed for generator
-> s	initial state
-> TransTable s	transition function to use
-> m a

Evaluate a MarkovRT computation using the standard random generator, initialized with seed.

evalMarkovStd Source #

Arguments

:: MarkovStd s a	computation to execute
-> Int	seed for generator
-> s	initial state
-> TransTable s	transition function to use
-> a

Evaluate a MarkovRT computation using the standard random generator, initialized with seed.

evalMarkovIO Source #

Arguments

:: MarkovStdT s IO a	computation to execute
-> s	initial state
-> TransTable s	transition function to use
-> IO a

Evaluate a Markov computation in the IO monad, using the standard random generator supplied by getStdGen.

module Control.Monad.Markov.Class