random-fu-0.0.3: Random number generation Source code Contents Index

Data.Random.Distribution.Discrete

Synopsis

discrete :: Distribution (Discrete p) a => [(p, a)] -> RVar a empirical :: (Num p, Ord a) => [a] -> Discrete p a newtype Discrete p a = Discrete [(p, a)] mapDiscreteWeights :: (p -> q) -> Discrete p e -> Discrete q e normalizeDiscreteWeights :: Fractional p => Discrete p e -> Discrete p e collectDiscreteEvents :: (Ord e, Num p, Ord p) => Discrete p e -> Discrete p e collectDiscreteEventsBy :: (e -> e -> Ordering) -> ([p] -> p) -> ([e] -> e) -> Discrete p e -> Discrete p e

Documentation

discrete :: Distribution (Discrete p) a => [(p, a)] -> RVar a Source

empirical :: (Num p, Ord a) => [a] -> Discrete p a Source

newtype Discrete p a Source

Constructors Discrete [(p, a)] Instances Num p => Monad (Discrete p) Functor (Discrete p) Traversable (Discrete p) Foldable (Discrete p) Num p => Applicative (Discrete p) (Num p, Ord p, Distribution Uniform p) => Distribution (Discrete p) a (Eq p, Eq a) => Eq (Discrete p a) (Show p, Show a) => Show (Discrete p a)

mapDiscreteWeights :: (p -> q) -> Discrete p e -> Discrete q e Source

Like fmap, but for the weights of a discrete distribution.

normalizeDiscreteWeights :: Fractional p => Discrete p e -> Discrete p e Source

Adjust all the weights of a discrete distribution so that they sum to unity. If not possible, returns the original distribution unchanged.

collectDiscreteEvents :: (Ord e, Num p, Ord p) => Discrete p e -> Discrete p e Source

Simplify a discrete distribution by combining equivalent events (the new event will have a weight equal to the sum of all the originals).

collectDiscreteEventsBy :: (e -> e -> Ordering) -> ([p] -> p) -> ([e] -> e) -> Discrete p e -> Discrete p e Source

Simplify a discrete distribution by combining equivalent events (the new event will have a weight equal to the sum of all the originals). The comparator function is used to identify events to combine. Once chosen, the events and their weights are combined (independently) by the provided weight and event aggregation functions.

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