Data.Random.Distribution.Categorical

Documentation

categorical :: Distribution (Categorical p) a => [(p, a)] -> RVar a

Construct a Categorical random variable from a list of probabilities and categories, where the probabilities all sum to 1.

categoricalT :: Distribution (Categorical p) a => [(p, a)] -> RVarT m a

Construct a Categorical random process from a list of probabilities and categories, where the probabilities all sum to 1.

weightedCategorical :: Fractional p => [(p, a)] -> Categorical p a

Construct a Categorical distribution from a list of weighted categories, where the weights do not necessarily sum to 1.

empirical :: (Fractional p, Ord a) => [a] -> Categorical p a

Construct a Categorical distribution from a list of observed outcomes. Equivalent events will be grouped and counted, and the probabilities of each event in the returned distribution will be proportional to the number of occurrences of that event.

newtype Categorical p a

Categorical distribution; a list of events with corresponding probabilities. The sum of the probabilities must be 1, and no event should have a zero or negative probability (at least, at time of sampling; very clever users can do what they want with the numbers before sampling, just make sure that if you're one of those clever ones, you normalize before sampling).

Constructors

Categorical [(p, a)]

Instances

Fractional p => Monad (Categorical p)

Functor (Categorical p)

Fractional p => Applicative (Categorical p)

Foldable (Categorical p)

Traversable (Categorical p)

(Fractional p, Ord p, Distribution StdUniform p) => Distribution (Categorical p) a

(Eq p, Eq a) => Eq (Categorical p a)

(Show p, Show a) => Show (Categorical p a)

mapCategoricalPs :: (p -> q) -> Categorical p e -> Categorical q e

Like fmap, but for the probabilities of a categorical distribution.

normalizeCategoricalPs :: Fractional p => Categorical p e -> Categorical p e

Adjust all the weights of a categorical distribution so that they sum to unity.

collectEvents :: (Ord e, Num p, Ord p) => Categorical p e -> Categorical p e

Simplify a categorical distribution by combining equivalent categories (the new category will have a probability equal to the sum of all the originals).

collectEventsBy :: (e -> e -> Ordering) -> ([(p, e)] -> (p, e)) -> Categorical p e -> Categorical p e

Simplify a categorical 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 by the provided probability and event aggregation function.