Torch.Indef.Static.Tensor.Random.TH

Documentation

_random :: Dimensions d => Tensor d -> Generator -> IO ()

Static call to _random

random :: forall d. Dimensions d => Generator -> IO (Tensor d)

Static call to random

_clampedRandom :: Dimensions d => Tensor d -> Generator -> Ord2Tuple Integer -> IO ()

Static call to _clampedRandom

clampedRandom :: forall d. Dimensions d => Generator -> Ord2Tuple Integer -> IO (Tensor d)

Static call to clampedRandom

_cappedRandom :: Dimensions d => Tensor d -> Generator -> Integer -> IO ()

Static call to _cappedRandom

cappedRandom :: forall d. Dimensions d => Generator -> Word64 -> IO (Tensor d)

Static call to cappedRandom

_geometric :: Dimensions d => Tensor d -> Generator -> HsAccReal -> IO ()

Static call to _geometric

geometric :: forall d. Dimensions d => Generator -> OpenUnit HsAccReal -> IO (Tensor d)

Static call to geometric

_bernoulli :: Dimensions d => Tensor d -> Generator -> HsAccReal -> IO ()

Static call to _bernoulli

bernoulli :: forall d. Dimensions d => Generator -> ClosedUnit HsAccReal -> IO (Tensor d)

Static call to bernoulli

_bernoulli_FloatTensor :: Dimensions d => Tensor d -> Generator -> FloatTensor d -> IO ()

Static call to _bernoulli_FloatTensor

bernoulli_FloatTensor :: forall d. Dimensions d => Generator -> FloatTensor d -> IO (Tensor d)

Static call to bernoulli_FloatTensor

_bernoulli_DoubleTensor :: Dimensions d => Tensor d -> Generator -> DoubleTensor d -> IO ()

Static call to _bernoulli_DoubleTensor

bernoulli_DoubleTensor :: forall d. Dimensions d => Generator -> DoubleTensor d -> IO (Tensor d)

Static call to bernoulli_DoubleTensor

_uniform :: Dimensions d => Tensor d -> Generator -> Ord2Tuple HsAccReal -> IO ()

Static call to _uniform

uniform :: forall d. Dimensions d => Generator -> Ord2Tuple HsAccReal -> IO (Tensor d)

Static call to uniform

_normal :: Dimensions d => Tensor d -> Generator -> HsAccReal -> Positive HsAccReal -> IO ()

Static call to _normal

normal :: forall d. Dimensions d => Generator -> HsAccReal -> Positive HsAccReal -> IO (Tensor d)

Static call to normal

_normal_means :: Dimensions d => Tensor d -> Generator -> Tensor d -> Positive HsAccReal -> IO ()

Static call to _normal_means

normal_means :: forall d. Dimensions d => Generator -> Tensor d -> Positive HsAccReal -> IO (Tensor d)

Static call to normal_means

_normal_stddevs :: Dimensions d => Tensor d -> Generator -> HsAccReal -> Tensor d -> IO ()

Static call to _normal_stddevs

normal_stddevs :: forall d. Dimensions d => Generator -> HsAccReal -> Tensor d -> IO (Tensor d)

Static call to normal_stddevs

_normal_means_stddevs :: Dimensions d => Tensor d -> Generator -> Tensor d -> Tensor d -> IO ()

Static call to _normal_means_stddevs

normal_means_stddevs :: forall d. Dimensions d => Generator -> Tensor d -> Tensor d -> IO (Tensor d)

Static call to normal_means_stddevs

_exponential :: Dimensions d => Tensor d -> Generator -> HsAccReal -> IO ()

Static call to _exponential

exponential :: forall d. Dimensions d => Generator -> HsAccReal -> IO (Tensor d)

Static call to exponential

_standard_gamma :: Dimensions d => Tensor d -> Generator -> Tensor d -> IO ()

Static call to _standard_gamma

standard_gamma :: forall d. Dimensions d => Generator -> Tensor d -> IO (Tensor d)

Static call to standard_gamma

_cauchy :: Dimensions d => Tensor d -> Generator -> HsAccReal -> HsAccReal -> IO ()

Static call to _cauchy

cauchy :: forall d. Dimensions d => Generator -> HsAccReal -> HsAccReal -> IO (Tensor d)

Static call to cauchy

_logNormal :: Dimensions d => Tensor d -> Generator -> HsAccReal -> Positive HsAccReal -> IO ()

Static call to _logNormal

logNormal :: forall d. Dimensions d => Generator -> HsAccReal -> Positive HsAccReal -> IO (Tensor d)

Static call to logNormal

_multinomial :: Dimensions d => IndexTensor d -> Generator -> Tensor d -> Int -> Int -> IO ()

Static call to _multinomial

_multinomialAliasSetup :: Dimensions d => Tensor d -> IndexTensor d -> Tensor d -> IO ()

Static call to _multinomialAliasSetup

_multinomialAliasDraw :: Dimensions d => IndexTensor d -> Generator -> IndexTensor d -> Tensor d -> IO ()

Static call to _multinomialAliasDraw

data OpenUnit x

Datatype to represent the open unit interval: 0 < x < 1. Any OpenUnit inhabitant must satisfy being in the interval.

FIXME: replace with numhask.

Instances

Eq x => Eq (OpenUnit x)

Defined in Torch.Types.Numeric

Methods

(==) :: OpenUnit x -> OpenUnit x -> Bool

(/=) :: OpenUnit x -> OpenUnit x -> Bool

Ord x => Ord (OpenUnit x)

Defined in Torch.Types.Numeric

Methods

compare :: OpenUnit x -> OpenUnit x -> Ordering

(<) :: OpenUnit x -> OpenUnit x -> Bool

(<=) :: OpenUnit x -> OpenUnit x -> Bool

(>) :: OpenUnit x -> OpenUnit x -> Bool

(>=) :: OpenUnit x -> OpenUnit x -> Bool

max :: OpenUnit x -> OpenUnit x -> OpenUnit x

min :: OpenUnit x -> OpenUnit x -> OpenUnit x

Show x => Show (OpenUnit x)

Defined in Torch.Types.Numeric

Methods

showsPrec :: Int -> OpenUnit x -> ShowS

show :: OpenUnit x -> String

showList :: [OpenUnit x] -> ShowS

openUnit :: (Ord x, Num x) => x -> Maybe (OpenUnit x)

smart constructor to place a number in the open unit interval.

openUnitValue :: OpenUnit x -> x

Get a value from the open unit interval.

data ClosedUnit x

Datatype to represent the closed unit interval: 0 =< x =< 1. Any ClosedUnit inhabitant must satisfy being in the interval.

FIXME: replace with numhask.

Instances

Eq x => Eq (ClosedUnit x)

Defined in Torch.Types.Numeric

Methods

(==) :: ClosedUnit x -> ClosedUnit x -> Bool

(/=) :: ClosedUnit x -> ClosedUnit x -> Bool

Ord x => Ord (ClosedUnit x)

Defined in Torch.Types.Numeric

Methods

compare :: ClosedUnit x -> ClosedUnit x -> Ordering

(<) :: ClosedUnit x -> ClosedUnit x -> Bool

(<=) :: ClosedUnit x -> ClosedUnit x -> Bool

(>) :: ClosedUnit x -> ClosedUnit x -> Bool

(>=) :: ClosedUnit x -> ClosedUnit x -> Bool

max :: ClosedUnit x -> ClosedUnit x -> ClosedUnit x

min :: ClosedUnit x -> ClosedUnit x -> ClosedUnit x

Show x => Show (ClosedUnit x)

Defined in Torch.Types.Numeric

Methods

showsPrec :: Int -> ClosedUnit x -> ShowS

show :: ClosedUnit x -> String

showList :: [ClosedUnit x] -> ShowS

closedUnit :: (Ord x, Num x) => x -> Maybe (ClosedUnit x)

smart constructor to place a number in the closed unit interval.

closedUnitValue :: ClosedUnit x -> x

Get a value from the closed unit interval.

data Positive x

Datatype to represent a generic positive number: 0 =< x.

FIXME: replace with numhask.

Instances

Eq x => Eq (Positive x)

Defined in Torch.Types.Numeric

Methods

(==) :: Positive x -> Positive x -> Bool

(/=) :: Positive x -> Positive x -> Bool

Ord x => Ord (Positive x)

Defined in Torch.Types.Numeric

Methods

compare :: Positive x -> Positive x -> Ordering

(<) :: Positive x -> Positive x -> Bool

(<=) :: Positive x -> Positive x -> Bool

(>) :: Positive x -> Positive x -> Bool

(>=) :: Positive x -> Positive x -> Bool

max :: Positive x -> Positive x -> Positive x

min :: Positive x -> Positive x -> Positive x

Show x => Show (Positive x)

Defined in Torch.Types.Numeric

Methods

showsPrec :: Int -> Positive x -> ShowS

show :: Positive x -> String

showList :: [Positive x] -> ShowS

positive :: (Ord x, Num x) => x -> Maybe (Positive x)

smart constructor to place a number in a positive bound.

positiveValue :: Positive x -> x

Get a value from the positive bound.

data Ord2Tuple x

Datatype to represent an ordered pair of numbers, (a, b), where a <= b.

FIXME: replace with numhask.

Instances

Eq x => Eq (Ord2Tuple x)

Defined in Torch.Types.Numeric

Methods

(==) :: Ord2Tuple x -> Ord2Tuple x -> Bool

(/=) :: Ord2Tuple x -> Ord2Tuple x -> Bool

Show x => Show (Ord2Tuple x)

Defined in Torch.Types.Numeric

Methods

showsPrec :: Int -> Ord2Tuple x -> ShowS

show :: Ord2Tuple x -> String

showList :: [Ord2Tuple x] -> ShowS

ord2Tuple :: (Ord x, Num x) => (x, x) -> Maybe (Ord2Tuple x)

smart constructor to place two values in an ordered tuple.

ord2TupleValue :: Ord2Tuple x -> (x, x)

Get the values of an ordered tuple.

multivariate_normal

Arguments

:: All KnownDim '[n, p]

=> Generator

-> Tensor '[p]

mu

-> Tensor '[p, p]

eigenvec

-> Tensor '[p]

eigenval

-> IO (Tensor '[n, p])

find the multivariate normal distribution given mu, an eigenvector and eigenvalues.