Data.Random.Distribution.Categorical
- categorical :: (Num p, Distribution (Categorical p) a) => [(p, a)] -> RVar a
- categoricalT :: (Num p, Distribution (Categorical p) a) => [(p, a)] -> RVarT m a
- fromList :: Num p => [(p, a)] -> Categorical p a
- toList :: Num p => Categorical p a -> [(p, a)]
- fromWeightedList :: (Fractional p, Ord a) => [(p, a)] -> Categorical p a
- fromObservations :: (Fractional p, Ord a) => [a] -> Categorical p a
- mapCategoricalPs :: (p -> q) -> Categorical p e -> Categorical q e
- normalizeCategoricalPs :: Fractional p => Categorical p e -> Categorical p e
- collectEvents :: (Ord e, Num p, Ord p) => Categorical p e -> Categorical p e
- collectEventsBy :: Num p => (e -> e -> Ordering) -> ([(p, e)] -> (p, e)) -> Categorical p e -> Categorical p e
Documentation
categorical :: (Num p, Distribution (Categorical p) a) => [(p, a)] -> RVar aSource
Construct a Categorical random variable from a list of probabilities
and categories, where the probabilities all sum to 1.
categoricalT :: (Num p, Distribution (Categorical p) a) => [(p, a)] -> RVarT m aSource
Construct a Categorical random process from a list of probabilities
and categories, where the probabilities all sum to 1.
fromList :: Num p => [(p, a)] -> Categorical p aSource
Construct a Categorical distribution from a list of weighted categories.
fromWeightedList :: (Fractional p, Ord a) => [(p, a)] -> Categorical p aSource
Construct a Categorical distribution from a list of weighted categories,
where the weights do not necessarily sum to 1.
fromObservations :: (Fractional p, Ord a) => [a] -> Categorical p aSource
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.
mapCategoricalPs :: (p -> q) -> Categorical p e -> Categorical q eSource
Like fmap, but for the probabilities of a categorical distribution.
normalizeCategoricalPs :: Fractional p => Categorical p e -> Categorical p eSource
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 eSource
Simplify a categorical distribution by combining equivalent categories (the new category will have a probability equal to the sum of all the originals).
collectEventsBy :: Num p => (e -> e -> Ordering) -> ([(p, e)] -> (p, e)) -> Categorical p e -> Categorical p eSource
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.