Safe Haskell	None
Language	Haskell2010

Backprop.Learn.Model.Regression

Contents

Linear Regression
Reshape
ARIMA

Synopsis

linReg :: (KnownNat i, KnownNat o) => Model (Just (LRp i o)) Nothing (R i) (R o)
logReg :: (KnownNat i, KnownNat o) => Model (Just (LRp i o)) Nothing (R i) (R o)
data LRp (i :: Nat) (o :: Nat) = LRp {
- _lrAlpha :: !(R o)
- _lrBeta :: !(L o i)
}
lrAlpha :: forall i o. Lens' (LRp i o) (R o)
lrBeta :: forall i o i. Lens (LRp i o) (LRp i o) (L o i) (L o i)
runLRp :: (KnownNat i, KnownNat o, Reifies s W) => BVar s (LRp i o) -> BVar s (R i) -> BVar s (R o)
reshapeLRpInput :: (ContGen d, PrimMonad m, KnownNat i, KnownNat i', KnownNat o) => d -> Gen (PrimState m) -> LRp i o -> m (LRp i' o)
reshapeLRpOutput :: (ContGen d, PrimMonad m, KnownNat i, KnownNat o, KnownNat o') => d -> Gen (PrimState m) -> LRp i o -> m (LRp i o')
expandLRpInput :: (PrimMonad m, ContGen d, KnownNat i, KnownNat j, KnownNat o) => LRp i o -> d -> Gen (PrimState m) -> m (LRp (i + j) o)
expandLRpOutput :: (PrimMonad m, ContGen d, KnownNat i, KnownNat o, KnownNat p) => LRp i o -> d -> Gen (PrimState m) -> m (LRp i (o + p))
premuteLRpInput :: (KnownNat i, KnownNat o) => Vector i' (Finite i) -> LRp i o -> LRp i' o
permuteLRpOutput :: (KnownNat i, KnownNat o) => Vector o' (Finite o) -> LRp i o -> LRp i o'
arima :: forall p d q. (KnownNat p, KnownNat d, KnownNat q) => Model (Just (ARIMAp p q)) (Just (ARIMAs p d q)) Double Double
autoregressive :: KnownNat p => Model (Just (ARIMAp p 0)) (Just (ARIMAs p 0 0)) Double Double
movingAverage :: KnownNat q => Model (Just (ARIMAp 0 q)) (Just (ARIMAs 0 0 q)) Double Double
arma :: (KnownNat p, KnownNat q) => Model (Just (ARIMAp p q)) (Just (ARIMAs p 0 q)) Double Double
data ARIMAp :: Nat -> Nat -> Type where
- ARIMAp :: {..} -> ARIMAp p q
data ARIMAs :: Nat -> Nat -> Nat -> Type where
- ARIMAs :: {..} -> ARIMAs p d q
arimaPhi :: forall a b a. Lens (ARIMAp a b) (ARIMAp a b) (R a) (R a)
arimaTheta :: forall a b b. Lens (ARIMAp a b) (ARIMAp a b) (R b) (R b)
arimaConstant :: forall a b. Lens' (ARIMAp a b) Double
arimaYPred :: forall a b c. Lens' (ARIMAs a b c) Double
arimaYHist :: forall a b c a b. Lens (ARIMAs a b c) (ARIMAs a b c) (R ((+) a b)) (R ((+) a b))
arimaEHist :: forall a b c c. Lens (ARIMAs a b c) (ARIMAs a b c) (R c) (R c)

Linear Regression

linReg :: (KnownNat i, KnownNat o) => Model (Just (LRp i o)) Nothing (R i) (R o) Source #

logReg :: (KnownNat i, KnownNat o) => Model (Just (LRp i o)) Nothing (R i) (R o) Source #

data LRp (i :: Nat) (o :: Nat) Source #

Linear Regression parameter

Constructors

LRp
Fields _lrAlpha :: !(R o) _lrBeta :: !(L o i)

Instances

(PrimMonad m, KnownNat i, KnownNat o) => LinearInPlace m Double (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (.+.=) :: Ref m (LRp i o) -> LRp i o -> m () # (.=) :: Ref m (LRp i o) -> Double -> m () # (.+=) :: Ref m (LRp i o) -> (Double, LRp i o) -> m () #
(KnownNat o, KnownNat i) => Linear Double (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (.+.) :: LRp i o -> LRp i o -> LRp i o # zeroL :: LRp i o # (.*) :: Double -> LRp i o -> LRp i o #
(KnownNat o, KnownNat i) => Metric Double (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (<.>) :: LRp i o -> LRp i o -> Double # norm_inf :: LRp i o -> Double # norm_0 :: LRp i o -> Double # norm_1 :: LRp i o -> Double # norm_2 :: LRp i o -> Double # quadrance :: LRp i o -> Double #
(PrimMonad m, KnownNat i, KnownNat o) => Mutable m (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Associated Types type Ref m (LRp i o) = (v :: Type) # Methods thawRef :: LRp i o -> m (Ref m (LRp i o)) # freezeRef :: Ref m (LRp i o) -> m (LRp i o) # copyRef :: Ref m (LRp i o) -> LRp i o -> m () # modifyRef :: Ref m (LRp i o) -> (LRp i o -> LRp i o) -> m () # modifyRef' :: Ref m (LRp i o) -> (LRp i o -> LRp i o) -> m () # updateRef :: Ref m (LRp i o) -> (LRp i o -> (LRp i o, b)) -> m b # updateRef' :: Ref m (LRp i o) -> (LRp i o -> (LRp i o, b)) -> m b #
(KnownNat i, KnownNat o, PrimMonad m) => Learnable m (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression
(KnownNat i, KnownNat o) => Floating (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods pi :: LRp i o # exp :: LRp i o -> LRp i o # log :: LRp i o -> LRp i o # sqrt :: LRp i o -> LRp i o # (**) :: LRp i o -> LRp i o -> LRp i o # logBase :: LRp i o -> LRp i o -> LRp i o # sin :: LRp i o -> LRp i o # cos :: LRp i o -> LRp i o # tan :: LRp i o -> LRp i o # asin :: LRp i o -> LRp i o # acos :: LRp i o -> LRp i o # atan :: LRp i o -> LRp i o # sinh :: LRp i o -> LRp i o # cosh :: LRp i o -> LRp i o # tanh :: LRp i o -> LRp i o # asinh :: LRp i o -> LRp i o # acosh :: LRp i o -> LRp i o # atanh :: LRp i o -> LRp i o # log1p :: LRp i o -> LRp i o # expm1 :: LRp i o -> LRp i o # log1pexp :: LRp i o -> LRp i o # log1mexp :: LRp i o -> LRp i o #
(KnownNat i, KnownNat o) => Fractional (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (/) :: LRp i o -> LRp i o -> LRp i o # recip :: LRp i o -> LRp i o # fromRational :: Rational -> LRp i o #
(KnownNat i, KnownNat o) => Num (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (+) :: LRp i o -> LRp i o -> LRp i o # (-) :: LRp i o -> LRp i o -> LRp i o # (*) :: LRp i o -> LRp i o -> LRp i o # negate :: LRp i o -> LRp i o # abs :: LRp i o -> LRp i o # signum :: LRp i o -> LRp i o # fromInteger :: Integer -> LRp i o #
(KnownNat o, KnownNat i) => Show (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods showsPrec :: Int -> LRp i o -> ShowS # show :: LRp i o -> String # showList :: [LRp i o] -> ShowS #
Generic (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Associated Types type Rep (LRp i o) :: Type -> Type # Methods from :: LRp i o -> Rep (LRp i o) x # to :: Rep (LRp i o) x -> LRp i o #
NFData (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods rnf :: LRp i o -> () #
(KnownNat o, KnownNat i) => Backprop (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods zero :: LRp i o -> LRp i o # add :: LRp i o -> LRp i o -> LRp i o # one :: LRp i o -> LRp i o #
(KnownNat o, KnownNat i) => Binary (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods put :: LRp i o -> Put # get :: Get (LRp i o) # putList :: [LRp i o] -> Put #
(KnownNat i, KnownNat o) => Regularize (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods rnorm_1 :: LRp i o -> Double Source # rnorm_2 :: LRp i o -> Double Source # lasso :: Double -> LRp i o -> LRp i o Source # ridge :: Double -> LRp i o -> LRp i o Source #
(KnownNat o, KnownNat i) => Initialize (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (LRp i o) Source #
type Ref m (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression type Ref m (LRp i o) = GRef m (LRp i o)
type Rep (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression type Rep (LRp i o) = D1 (MetaData "LRp" "Backprop.Learn.Model.Regression" "backprop-learn-0.1.0.0-LYs2l1OGpKTGmGWQXOoOXm" False) (C1 (MetaCons "LRp" PrefixI True) (S1 (MetaSel (Just "_lrAlpha") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (R o)) :*: S1 (MetaSel (Just "_lrBeta") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (L o i))))

lrAlpha :: forall i o. Lens' (LRp i o) (R o) Source #

lrBeta :: forall i o i. Lens (LRp i o) (LRp i o) (L o i) (L o i) Source #

runLRp :: (KnownNat i, KnownNat o, Reifies s W) => BVar s (LRp i o) -> BVar s (R i) -> BVar s (R o) Source #

Reshape

reshapeLRpInput :: (ContGen d, PrimMonad m, KnownNat i, KnownNat i', KnownNat o) => d -> Gen (PrimState m) -> LRp i o -> m (LRp i' o) Source #

Reshape an LRp to take more or less inputs. If more, new parameters are initialized randomly according to the given distribution.

reshapeLRpOutput :: (ContGen d, PrimMonad m, KnownNat i, KnownNat o, KnownNat o') => d -> Gen (PrimState m) -> LRp i o -> m (LRp i o') Source #

Reshape an LRp to return more or less outputs If more, new parameters are initialized randomly according to the given distribution.

expandLRpInput :: (PrimMonad m, ContGen d, KnownNat i, KnownNat j, KnownNat o) => LRp i o -> d -> Gen (PrimState m) -> m (LRp (i + j) o) Source #

Adjust an LRp to take extra inputs, initialized randomly.

Initial contributions to each output is randomized.

expandLRpOutput :: (PrimMonad m, ContGen d, KnownNat i, KnownNat o, KnownNat p) => LRp i o -> d -> Gen (PrimState m) -> m (LRp i (o + p)) Source #

Adjust an LRp to return extra ouputs, initialized randomly

premuteLRpInput :: (KnownNat i, KnownNat o) => Vector i' (Finite i) -> LRp i o -> LRp i' o Source #

Premute (or remove) inputs

Removed inputs will simply have their contributions removed from each output.

permuteLRpOutput :: (KnownNat i, KnownNat o) => Vector o' (Finite o) -> LRp i o -> LRp i o' Source #

Premute (or remove) outputs

ARIMA

arima :: forall p d q. (KnownNat p, KnownNat d, KnownNat q) => Model (Just (ARIMAp p q)) (Just (ARIMAs p d q)) Double Double Source #

autoregressive :: KnownNat p => Model (Just (ARIMAp p 0)) (Just (ARIMAs p 0 0)) Double Double Source #

movingAverage :: KnownNat q => Model (Just (ARIMAp 0 q)) (Just (ARIMAs 0 0 q)) Double Double Source #

arma :: (KnownNat p, KnownNat q) => Model (Just (ARIMAp p q)) (Just (ARIMAs p 0 q)) Double Double Source #

data ARIMAp :: Nat -> Nat -> Type where Source #

ARIMA parmaeters

Constructors

ARIMAp
Fields :: { _arimaPhi :: !(R p) , _arimaTheta :: !(R q) , _arimaConstant :: !Double } -> ARIMAp p q

Instances

(KnownNat p, KnownNat q, PrimMonad m) => LinearInPlace m Double (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (.+.=) :: Ref m (ARIMAp p q) -> ARIMAp p q -> m () # (.=) :: Ref m (ARIMAp p q) -> Double -> m () # (.+=) :: Ref m (ARIMAp p q) -> (Double, ARIMAp p q) -> m () #
(KnownNat a, KnownNat b) => Linear Double (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (.+.) :: ARIMAp a b -> ARIMAp a b -> ARIMAp a b # zeroL :: ARIMAp a b # (.*) :: Double -> ARIMAp a b -> ARIMAp a b #
(KnownNat a, KnownNat b) => Metric Double (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (<.>) :: ARIMAp a b -> ARIMAp a b -> Double # norm_inf :: ARIMAp a b -> Double # norm_0 :: ARIMAp a b -> Double # norm_1 :: ARIMAp a b -> Double # norm_2 :: ARIMAp a b -> Double # quadrance :: ARIMAp a b -> Double #
(PrimMonad m, KnownNat p, KnownNat q) => Mutable m (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Associated Types type Ref m (ARIMAp p q) = (v :: Type) # Methods thawRef :: ARIMAp p q -> m (Ref m (ARIMAp p q)) # freezeRef :: Ref m (ARIMAp p q) -> m (ARIMAp p q) # copyRef :: Ref m (ARIMAp p q) -> ARIMAp p q -> m () # modifyRef :: Ref m (ARIMAp p q) -> (ARIMAp p q -> ARIMAp p q) -> m () # modifyRef' :: Ref m (ARIMAp p q) -> (ARIMAp p q -> ARIMAp p q) -> m () # updateRef :: Ref m (ARIMAp p q) -> (ARIMAp p q -> (ARIMAp p q, b)) -> m b # updateRef' :: Ref m (ARIMAp p q) -> (ARIMAp p q -> (ARIMAp p q, b)) -> m b #
(KnownNat p, KnownNat q, PrimMonad m) => Learnable m (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression
Floating (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods pi :: ARIMAp p q # exp :: ARIMAp p q -> ARIMAp p q # log :: ARIMAp p q -> ARIMAp p q # sqrt :: ARIMAp p q -> ARIMAp p q # (**) :: ARIMAp p q -> ARIMAp p q -> ARIMAp p q # logBase :: ARIMAp p q -> ARIMAp p q -> ARIMAp p q # sin :: ARIMAp p q -> ARIMAp p q # cos :: ARIMAp p q -> ARIMAp p q # tan :: ARIMAp p q -> ARIMAp p q # asin :: ARIMAp p q -> ARIMAp p q # acos :: ARIMAp p q -> ARIMAp p q # atan :: ARIMAp p q -> ARIMAp p q # sinh :: ARIMAp p q -> ARIMAp p q # cosh :: ARIMAp p q -> ARIMAp p q # tanh :: ARIMAp p q -> ARIMAp p q # asinh :: ARIMAp p q -> ARIMAp p q # acosh :: ARIMAp p q -> ARIMAp p q # atanh :: ARIMAp p q -> ARIMAp p q # log1p :: ARIMAp p q -> ARIMAp p q # expm1 :: ARIMAp p q -> ARIMAp p q # log1pexp :: ARIMAp p q -> ARIMAp p q # log1mexp :: ARIMAp p q -> ARIMAp p q #
Fractional (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (/) :: ARIMAp p q -> ARIMAp p q -> ARIMAp p q # recip :: ARIMAp p q -> ARIMAp p q # fromRational :: Rational -> ARIMAp p q #
Num (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (+) :: ARIMAp p q -> ARIMAp p q -> ARIMAp p q # (-) :: ARIMAp p q -> ARIMAp p q -> ARIMAp p q # (*) :: ARIMAp p q -> ARIMAp p q -> ARIMAp p q # negate :: ARIMAp p q -> ARIMAp p q # abs :: ARIMAp p q -> ARIMAp p q # signum :: ARIMAp p q -> ARIMAp p q # fromInteger :: Integer -> ARIMAp p q #
(KnownNat a, KnownNat b) => Show (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods showsPrec :: Int -> ARIMAp a b -> ShowS # show :: ARIMAp a b -> String # showList :: [ARIMAp a b] -> ShowS #
Generic (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Associated Types type Rep (ARIMAp a b) :: Type -> Type # Methods from :: ARIMAp a b -> Rep (ARIMAp a b) x # to :: Rep (ARIMAp a b) x -> ARIMAp a b #
NFData (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods rnf :: ARIMAp a b -> () #
Backprop (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods zero :: ARIMAp a b -> ARIMAp a b # add :: ARIMAp a b -> ARIMAp a b -> ARIMAp a b # one :: ARIMAp a b -> ARIMAp a b #
(KnownNat a, KnownNat b) => Binary (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods put :: ARIMAp a b -> Put # get :: Get (ARIMAp a b) # putList :: [ARIMAp a b] -> Put #
(KnownNat p, KnownNat q) => Regularize (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods rnorm_1 :: ARIMAp p q -> Double Source # rnorm_2 :: ARIMAp p q -> Double Source # lasso :: Double -> ARIMAp p q -> ARIMAp p q Source # ridge :: Double -> ARIMAp p q -> ARIMAp p q Source #
(KnownNat a, KnownNat b) => Initialize (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (ARIMAp a b) Source #
type Ref m (ARIMAp p q) Source #
Instance details Defined in Backprop.Learn.Model.Regression type Ref m (ARIMAp p q) = GRef m (ARIMAp p q)
type Rep (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression type Rep (ARIMAp a b) = D1 (MetaData "ARIMAp" "Backprop.Learn.Model.Regression" "backprop-learn-0.1.0.0-LYs2l1OGpKTGmGWQXOoOXm" False) (C1 (MetaCons "ARIMAp" PrefixI True) (S1 (MetaSel (Just "_arimaPhi") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (R a)) :: (S1 (MetaSel (Just "_arimaTheta") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (R b)) :: S1 (MetaSel (Just "_arimaConstant") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Double))))

data ARIMAs :: Nat -> Nat -> Nat -> Type where Source #

ARIMA state

Constructors

ARIMAs
Fields :: { _arimaYPred :: !Double , _arimaYHist :: !(R (p + d)) , _arimaEHist :: !(R q) } -> ARIMAs p d q

Instances

(KnownNat p, KnownNat d, KnownNat q, PrimMonad m) => LinearInPlace m Double (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (.+.=) :: Ref m (ARIMAs p d q) -> ARIMAs p d q -> m () # (.=) :: Ref m (ARIMAs p d q) -> Double -> m () # (.+=) :: Ref m (ARIMAs p d q) -> (Double, ARIMAs p d q) -> m () #
KnownNat c => Linear Double (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (.+.) :: ARIMAs a b c -> ARIMAs a b c -> ARIMAs a b c # zeroL :: ARIMAs a b c # (.*) :: Double -> ARIMAs a b c -> ARIMAs a b c #
KnownNat c => Metric Double (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (<.>) :: ARIMAs a b c -> ARIMAs a b c -> Double # norm_inf :: ARIMAs a b c -> Double # norm_0 :: ARIMAs a b c -> Double # norm_1 :: ARIMAs a b c -> Double # norm_2 :: ARIMAs a b c -> Double # quadrance :: ARIMAs a b c -> Double #
(PrimMonad m, KnownNat p, KnownNat d, KnownNat q) => Mutable m (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Associated Types type Ref m (ARIMAs p d q) = (v :: Type) # Methods thawRef :: ARIMAs p d q -> m (Ref m (ARIMAs p d q)) # freezeRef :: Ref m (ARIMAs p d q) -> m (ARIMAs p d q) # copyRef :: Ref m (ARIMAs p d q) -> ARIMAs p d q -> m () # modifyRef :: Ref m (ARIMAs p d q) -> (ARIMAs p d q -> ARIMAs p d q) -> m () # modifyRef' :: Ref m (ARIMAs p d q) -> (ARIMAs p d q -> ARIMAs p d q) -> m () # updateRef :: Ref m (ARIMAs p d q) -> (ARIMAs p d q -> (ARIMAs p d q, b)) -> m b # updateRef' :: Ref m (ARIMAs p d q) -> (ARIMAs p d q -> (ARIMAs p d q, b)) -> m b #
(KnownNat p, KnownNat d, KnownNat q, PrimMonad m) => Learnable m (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression
Floating (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods pi :: ARIMAs p d q # exp :: ARIMAs p d q -> ARIMAs p d q # log :: ARIMAs p d q -> ARIMAs p d q # sqrt :: ARIMAs p d q -> ARIMAs p d q # (**) :: ARIMAs p d q -> ARIMAs p d q -> ARIMAs p d q # logBase :: ARIMAs p d q -> ARIMAs p d q -> ARIMAs p d q # sin :: ARIMAs p d q -> ARIMAs p d q # cos :: ARIMAs p d q -> ARIMAs p d q # tan :: ARIMAs p d q -> ARIMAs p d q # asin :: ARIMAs p d q -> ARIMAs p d q # acos :: ARIMAs p d q -> ARIMAs p d q # atan :: ARIMAs p d q -> ARIMAs p d q # sinh :: ARIMAs p d q -> ARIMAs p d q # cosh :: ARIMAs p d q -> ARIMAs p d q # tanh :: ARIMAs p d q -> ARIMAs p d q # asinh :: ARIMAs p d q -> ARIMAs p d q # acosh :: ARIMAs p d q -> ARIMAs p d q # atanh :: ARIMAs p d q -> ARIMAs p d q # log1p :: ARIMAs p d q -> ARIMAs p d q # expm1 :: ARIMAs p d q -> ARIMAs p d q # log1pexp :: ARIMAs p d q -> ARIMAs p d q # log1mexp :: ARIMAs p d q -> ARIMAs p d q #
Fractional (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (/) :: ARIMAs p d q -> ARIMAs p d q -> ARIMAs p d q # recip :: ARIMAs p d q -> ARIMAs p d q # fromRational :: Rational -> ARIMAs p d q #
Num (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods (+) :: ARIMAs p d q -> ARIMAs p d q -> ARIMAs p d q # (-) :: ARIMAs p d q -> ARIMAs p d q -> ARIMAs p d q # (*) :: ARIMAs p d q -> ARIMAs p d q -> ARIMAs p d q # negate :: ARIMAs p d q -> ARIMAs p d q # abs :: ARIMAs p d q -> ARIMAs p d q # signum :: ARIMAs p d q -> ARIMAs p d q # fromInteger :: Integer -> ARIMAs p d q #
KnownNat c => Show (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods showsPrec :: Int -> ARIMAs a b c -> ShowS # show :: ARIMAs a b c -> String # showList :: [ARIMAs a b c] -> ShowS #
Generic (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Associated Types type Rep (ARIMAs a b c) :: Type -> Type # Methods from :: ARIMAs a b c -> Rep (ARIMAs a b c) x # to :: Rep (ARIMAs a b c) x -> ARIMAs a b c #
NFData (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods rnf :: ARIMAs a b c -> () #
Backprop (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods zero :: ARIMAs a b c -> ARIMAs a b c # add :: ARIMAs a b c -> ARIMAs a b c -> ARIMAs a b c # one :: ARIMAs a b c -> ARIMAs a b c #
KnownNat c => Binary (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods put :: ARIMAs a b c -> Put # get :: Get (ARIMAs a b c) # putList :: [ARIMAs a b c] -> Put #
Regularize (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods rnorm_1 :: ARIMAs p d q -> Double Source # rnorm_2 :: ARIMAs p d q -> Double Source # lasso :: Double -> ARIMAs p d q -> ARIMAs p d q Source # ridge :: Double -> ARIMAs p d q -> ARIMAs p d q Source #
KnownNat c => Initialize (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (ARIMAs a b c) Source #
type Ref m (ARIMAs p d q) Source #
Instance details Defined in Backprop.Learn.Model.Regression type Ref m (ARIMAs p d q) = GRef m (ARIMAs p d q)
type Rep (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression type Rep (ARIMAs a b c) = D1 (MetaData "ARIMAs" "Backprop.Learn.Model.Regression" "backprop-learn-0.1.0.0-LYs2l1OGpKTGmGWQXOoOXm" False) (C1 (MetaCons "ARIMAs" PrefixI True) (S1 (MetaSel (Just "_arimaYPred") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Double) :: (S1 (MetaSel (Just "_arimaYHist") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (R (a + b))) :: S1 (MetaSel (Just "_arimaEHist") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (R c)))))

arimaPhi :: forall a b a. Lens (ARIMAp a b) (ARIMAp a b) (R a) (R a) Source #

arimaTheta :: forall a b b. Lens (ARIMAp a b) (ARIMAp a b) (R b) (R b) Source #

arimaConstant :: forall a b. Lens' (ARIMAp a b) Double Source #

arimaYPred :: forall a b c. Lens' (ARIMAs a b c) Double Source #

arimaYHist :: forall a b c a b. Lens (ARIMAs a b c) (ARIMAs a b c) (R ((+) a b)) (R ((+) a b)) Source #

arimaEHist :: forall a b c c. Lens (ARIMAs a b c) (ARIMAs a b c) (R c) (R c) Source #