Copyright	(c) Sam Stites 2017
License	BSD3
Maintainer	sam@stites.io
Stability	experimental
Portability	non-portable
Safe Haskell	None
Language	Haskell2010

Torch.Core.Random

Description

Random number generation for single values. FFI over TH/THRandom.h

Synopsis

data Generator
data Seed
newRNG :: IO Generator
copy :: Generator -> Generator -> IO Generator
seed :: Generator -> IO Seed
manualSeed :: Generator -> Seed -> IO ()
initialSeed :: Generator -> IO Seed
random :: Generator -> IO Seed
random64 :: Generator -> IO Seed
uniform :: Generator -> Double -> Double -> IO Double
uniformFloat :: Generator -> Float -> Float -> IO Float
normal :: Generator -> Double -> Double -> IO Double
exponential :: Generator -> Double -> IO Double
standard_gamma :: Generator -> Double -> IO Double
cauchy :: Generator -> Double -> Double -> IO Double
logNormal :: Generator -> Double -> Double -> IO Double
geometric :: Generator -> Double -> IO Int
bernoulli :: Generator -> Double -> IO Int

Documentation

data Generator #

Representation of a CPU-bound random number generator

Instances

Eq Generator
Instance details Defined in Torch.Types.TH Methods (==) :: Generator -> Generator -> Bool # (/=) :: Generator -> Generator -> Bool #
Show Generator
Instance details Defined in Torch.Types.TH Methods showsPrec :: Int -> Generator -> ShowS # show :: Generator -> String # showList :: [Generator] -> ShowS #

data Seed #

Representation of a CPU-bound random seed

Instances

Bounded Seed
Instance details Defined in Torch.Types.TH Methods minBound :: Seed # maxBound :: Seed #
Enum Seed
Instance details Defined in Torch.Types.TH Methods succ :: Seed -> Seed # pred :: Seed -> Seed # toEnum :: Int -> Seed # fromEnum :: Seed -> Int # enumFrom :: Seed -> [Seed] # enumFromThen :: Seed -> Seed -> [Seed] # enumFromTo :: Seed -> Seed -> [Seed] # enumFromThenTo :: Seed -> Seed -> Seed -> [Seed] #
Eq Seed
Instance details Defined in Torch.Types.TH Methods (==) :: Seed -> Seed -> Bool # (/=) :: Seed -> Seed -> Bool #
Integral Seed
Instance details Defined in Torch.Types.TH Methods quot :: Seed -> Seed -> Seed # rem :: Seed -> Seed -> Seed # div :: Seed -> Seed -> Seed # mod :: Seed -> Seed -> Seed # quotRem :: Seed -> Seed -> (Seed, Seed) # divMod :: Seed -> Seed -> (Seed, Seed) # toInteger :: Seed -> Integer #
Num Seed
Instance details Defined in Torch.Types.TH Methods (+) :: Seed -> Seed -> Seed # (-) :: Seed -> Seed -> Seed # (*) :: Seed -> Seed -> Seed # negate :: Seed -> Seed # abs :: Seed -> Seed # signum :: Seed -> Seed # fromInteger :: Integer -> Seed #
Ord Seed
Instance details Defined in Torch.Types.TH Methods compare :: Seed -> Seed -> Ordering # (<) :: Seed -> Seed -> Bool # (<=) :: Seed -> Seed -> Bool # (>) :: Seed -> Seed -> Bool # (>=) :: Seed -> Seed -> Bool # max :: Seed -> Seed -> Seed # min :: Seed -> Seed -> Seed #
Read Seed
Instance details Defined in Torch.Types.TH Methods readsPrec :: Int -> ReadS Seed # readList :: ReadS [Seed] # readPrec :: ReadPrec Seed # readListPrec :: ReadPrec [Seed] #
Real Seed
Instance details Defined in Torch.Types.TH Methods toRational :: Seed -> Rational #
Show Seed
Instance details Defined in Torch.Types.TH Methods showsPrec :: Int -> Seed -> ShowS # show :: Seed -> String # showList :: [Seed] -> ShowS #

newRNG :: IO Generator Source #

Construct a new Generator

copy :: Generator -> Generator -> IO Generator Source #

Copy a Generator state to a new generator

seed :: Generator -> IO Seed Source #

Get the current Seed of a Generator

manualSeed :: Generator -> Seed -> IO () Source #

Manually set the seed Seed of a Generator

initialSeed :: Generator -> IO Seed Source #

Get the first Seed that initialized a given Generator

random :: Generator -> IO Seed Source #

random64 :: Generator -> IO Seed Source #

uniform Source #

Arguments

:: Generator
-> Double	lower bound
-> Double	upper bound
-> IO Double

Returns a random double according to uniform distribution on [a,b).

uniformFloat Source #

Arguments

:: Generator
-> Float	lower bound
-> Float	upper bound
-> IO Float

Returns a random float according to uniform distribution on [a,b).

normal Source #

Arguments

:: Generator
-> Double	mean
-> Double	stddev (must be positive)
-> IO Double

Returns a random real number according to a normal distribution with the given mean and standard deviation stdv. stdv must be positive, but this is not yet enforced.

TODO: add a newtype Pos a = Pos { getPos :: a } package with a smart constructor export

exponential :: Generator -> Double -> IO Double Source #

Returns a random real number according to the exponential distribution p(x) = lambda * exp(-lambda * x)

standard_gamma :: Generator -> Double -> IO Double Source #

cauchy Source #

Arguments

:: Generator
-> Double	median
-> Double	sigma
-> IO Double

Returns a random real number according to the Cauchy distribution p(x) = sigma/(pi*(sigma^2 + (x-median)^2))

logNormal Source #

Arguments

:: Generator
-> Double	mean
-> Double	stddev (must be positive)
-> IO Double

Returns a random real number according to the log-normal distribution, with the given mean and standard deviation stdv. mean and stdv are the corresponding mean and standard deviation of the underlying normal distribution, and not of the returned distribution.

stdv must be positive.

geometric :: Generator -> Double -> IO Int Source #

Returns a random integer number according to a geometric distribution p(i) = (1-p) * p^(i-1). p must satisfy 0 < p < 1.

bernoulli :: Generator -> Double -> IO Int Source #

Returns 1 with probability p and 0 with probability 1-p. p must satisfy 0 <= p <= 1.

TODO: By default p is equal to 0.5 -- this isn't encoded in the API