Copyright | (c) 2012 Aleksey Khudyakov |
---|---|
License | BSD3 |
Maintainer | alexey.skladnoy@gmail.com |
Stability | experimental |
Portability | portable |
Safe Haskell | None |
Language | Haskell98 |
Monadic wrapper for CondesedTable
.
- data CondensedTable v a :: (* -> *) -> * -> *
- type CondensedTableV = CondensedTable Vector
- type CondensedTableU = CondensedTable Vector
- genFromTable :: (MonadPrim m, Vector v a) => CondensedTable v a -> Rand m a
- tableFromProbabilities :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32) => v (a, Double) -> CondensedTable v a
- tableFromWeights :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32) => v (a, Double) -> CondensedTable v a
- tableFromIntWeights :: (Vector v (a, Word32), Vector v a, Vector v Word32) => v (a, Word32) -> CondensedTable v a
- tablePoisson :: Double -> CondensedTableU Int
- tableBinomial :: Int -> Double -> CondensedTableU Int
Condensed tables
data CondensedTable v a :: (* -> *) -> * -> *
A lookup table for arbitrary discrete distributions. It allows
the generation of random variates in O(1). Note that probability
is quantized in units of 1/2^32
, and all distributions with
infinite support (e.g. Poisson) should be truncated.
type CondensedTableV = CondensedTable Vector
A CondensedTable
that uses boxed vectors, and is able to hold
any type of element.
type CondensedTableU = CondensedTable Vector
A CondensedTable
that uses unboxed vectors.
genFromTable :: (MonadPrim m, Vector v a) => CondensedTable v a -> Rand m a Source
Constructors for tables
tableFromProbabilities :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32) => v (a, Double) -> CondensedTable v a
Generate a condensed lookup table from a list of outcomes with given probabilities. The vector should be non-empty and the probabilites should be non-negative and sum to 1. If this is not the case, this algorithm will construct a table for some distribution that may bear no resemblance to what you intended.
tableFromWeights :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32) => v (a, Double) -> CondensedTable v a
Same as tableFromProbabilities
but treats number as weights not
probilities. Non-positive weights are discarded, and those
remaining are normalized to 1.
tableFromIntWeights :: (Vector v (a, Word32), Vector v a, Vector v Word32) => v (a, Word32) -> CondensedTable v a
Generate a condensed lookup table from integer weights. Weights
should sum to 2^32
. If they don't, the algorithm will alter the
weights so that they do. This approach should work reasonably well
for rounding errors.
Disrete distributions
tablePoisson :: Double -> CondensedTableU Int
Create a lookup table for the Poisson distibution. Note that table construction may have significant cost. For λ < 100 it takes as much time to build table as generation of 1000-30000 variates.
:: Int | Number of tries |
-> Double | Probability of success |
-> CondensedTableU Int |
Create a lookup table for the binomial distribution.