mwc-random-0.13.1.0: Fast, high quality pseudo random number generation

Portabilityportable
Stabilityexperimental
Maintainerbos@serpentine.com
Safe HaskellNone

System.Random.MWC.CondensedTable

Contents

Description

Table-driven generation of random variates. This approach can generate random variates in O(1) time for the supported distributions, at a modest cost in initialization time.

Synopsis

Condensed tables

data CondensedTable v a Source

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 VectorSource

A CondensedTable that uses boxed vectors, and is able to hold any type of element.

type CondensedTableU = CondensedTable VectorSource

A CondensedTable that uses unboxed vectors.

genFromTable :: (PrimMonad m, Vector v a) => CondensedTable v a -> Gen (PrimState m) -> m aSource

Generate a random value using a condensed table.

Constructors for tables

tableFromProbabilities :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32) => v (a, Double) -> CondensedTable v aSource

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 aSource

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 aSource

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 IntSource

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.

tableBinomialSource

Arguments

:: Int

Number of tries

-> Double

Probability of success

-> CondensedTableU Int 

Create a lookup table for the binomial distribution.

References