Safe Haskell | Safe-Infered |
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- data Categorical p a
- categorical :: (Num p, Distribution (Categorical p) a) => [(p, a)] -> RVar a
- categoricalT :: (Num p, Distribution (Categorical p) a) => [(p, a)] -> RVarT m a
- weightedCategorical :: (Fractional p, Eq p, Distribution (Categorical p) a) => [(p, a)] -> RVar a
- weightedCategoricalT :: (Fractional p, Eq 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)]
- totalWeight :: Num p => Categorical p a -> p
- numEvents :: Categorical p a -> Int
- fromWeightedList :: (Fractional p, Eq p) => [(p, a)] -> Categorical p a
- fromObservations :: (Fractional p, Eq p, Ord a) => [a] -> Categorical p a
- mapCategoricalPs :: (Num p, Num q) => (p -> q) -> Categorical p e -> Categorical q e
- normalizeCategoricalPs :: (Fractional p, Eq 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

data Categorical p a Source

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 at least eliminate negative weights before sampling).

Num p => Monad (Categorical p) | |

Functor (Categorical p) | |

Fractional p => Applicative (Categorical p) | |

Foldable (Categorical p) | |

Traversable (Categorical p) | |

(Fractional p, Ord p, Distribution Uniform p) => Distribution (Categorical p) a | |

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

(Num p, Read p, Read a) => Read (Categorical p a) | |

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

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.

weightedCategorical :: (Fractional p, Eq 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.

weightedCategoricalT :: (Fractional p, Eq 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.

toList :: Num p => Categorical p a -> [(p, a)]Source

totalWeight :: Num p => Categorical p a -> pSource

numEvents :: Categorical p a -> IntSource

fromWeightedList :: (Fractional p, Eq p) => [(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, Eq 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 :: (Num p, Num q) => (p -> q) -> Categorical p e -> Categorical q eSource

Like `fmap`

, but for the probabilities of a categorical distribution.

normalizeCategoricalPs :: (Fractional p, Eq p) => Categorical p e -> Categorical p eSource

Adjust all the weights of a categorical distribution so that they sum to unity and remove all events whose probability is zero.

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

Simplify a categorical distribution by combining equivalent events (the new event 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.