ad-4.2.1.1: Automatic Differentiation

Safe HaskellNone
LanguageHaskell2010

Numeric.AD.Mode.Forward.Double

Contents

Synopsis

Documentation

data AD s a Source

Instances

Bounded a => Bounded (AD s a) 
Enum a => Enum (AD s a) 
Eq a => Eq (AD s a) 
Floating a => Floating (AD s a) 
Fractional a => Fractional (AD s a) 
Num a => Num (AD s a) 
Ord a => Ord (AD s a) 
Read a => Read (AD s a) 
Real a => Real (AD s a) 
RealFloat a => RealFloat (AD s a) 
RealFrac a => RealFrac (AD s a) 
Show a => Show (AD s a) 
Erf a => Erf (AD s a) 
InvErf a => InvErf (AD s a) 
Mode a => Mode (AD s a) 
Typeable (* -> * -> *) AD 
type Scalar (AD s a) = Scalar a 

data ForwardDouble Source

Instances

Gradient

grad :: Traversable f => (forall s. f (AD s ForwardDouble) -> AD s ForwardDouble) -> f Double -> f Double Source

Compute the gradient of a function using forward mode AD.

Note, this performs O(n) worse than grad for n inputs, in exchange for better space utilization.

grad' :: Traversable f => (forall s. f (AD s ForwardDouble) -> AD s ForwardDouble) -> f Double -> (Double, f Double) Source

Compute the gradient and answer to a function using forward mode AD.

Note, this performs O(n) worse than grad' for n inputs, in exchange for better space utilization.

gradWith :: Traversable f => (Double -> Double -> b) -> (forall s. f (AD s ForwardDouble) -> AD s ForwardDouble) -> f Double -> f b Source

Compute the gradient of a function using forward mode AD and combine the result with the input using a user-specified function.

Note, this performs O(n) worse than gradWith for n inputs, in exchange for better space utilization.

gradWith' :: Traversable f => (Double -> Double -> b) -> (forall s. f (AD s ForwardDouble) -> AD s ForwardDouble) -> f Double -> (Double, f b) Source

Compute the gradient of a function using forward mode AD and the answer, and combine the result with the input using a user-specified function.

Note, this performs O(n) worse than gradWith' for n inputs, in exchange for better space utilization.

>>> gradWith' (,) sum [0..4]
(10.0,[(0.0,1.0),(1.0,1.0),(2.0,1.0),(3.0,1.0),(4.0,1.0)])

Jacobian

jacobian :: (Traversable f, Traversable g) => (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f Double -> g (f Double) Source

Compute the Jacobian using Forward mode AD. This must transpose the result, so jacobianT is faster and allows more result types.

>>> jacobian (\[x,y] -> [y,x,x+y,x*y,exp x * sin y]) [pi,1]
[[0.0,1.0],[1.0,0.0],[1.0,1.0],[1.0,3.141592653589793],[19.472221418841606,12.502969588876512]]

jacobian' :: (Traversable f, Traversable g) => (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f Double -> g (Double, f Double) Source

Compute the Jacobian using Forward mode AD along with the actual answer.

jacobianWith :: (Traversable f, Traversable g) => (Double -> Double -> b) -> (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f Double -> g (f b) Source

Compute the Jacobian using Forward mode AD and combine the output with the input. This must transpose the result, so jacobianWithT is faster, and allows more result types.

jacobianWith' :: (Traversable f, Traversable g) => (Double -> Double -> b) -> (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f Double -> g (Double, f b) Source

Compute the Jacobian using Forward mode AD combined with the input using a user specified function, along with the actual answer.

Transposed Jacobian

jacobianT :: (Traversable f, Functor g) => (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f Double -> f (g Double) Source

A fast, simple, transposed Jacobian computed with forward-mode AD.

jacobianWithT :: (Traversable f, Functor g) => (Double -> Double -> b) -> (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f Double -> f (g b) Source

A fast, simple, transposed Jacobian computed with Forward mode AD that combines the output with the input.

Derivatives

diff :: (forall s. AD s ForwardDouble -> AD s ForwardDouble) -> Double -> Double Source

The diff function calculates the first derivative of a scalar-to-scalar function by forward-mode AD

>>> diff sin 0
1.0

diff' :: (forall s. AD s ForwardDouble -> AD s ForwardDouble) -> Double -> (Double, Double) Source

The diff' function calculates the result and first derivative of scalar-to-scalar function by Forward mode AD

diff' sin == sin &&& cos
diff' f = f &&& d f

>>> diff' sin 0
(0.0,1.0)

>>> diff' exp 0
(1.0,1.0)

diffF :: Functor f => (forall s. AD s ForwardDouble -> f (AD s ForwardDouble)) -> Double -> f Double Source

The diffF function calculates the first derivatives of scalar-to-nonscalar function by Forward mode AD

>>> diffF (\a -> [sin a, cos a]) 0
[1.0,-0.0]

diffF' :: Functor f => (forall s. AD s ForwardDouble -> f (AD s ForwardDouble)) -> Double -> f (Double, Double) Source

The diffF' function calculates the result and first derivatives of a scalar-to-non-scalar function by Forward mode AD

>>> diffF' (\a -> [sin a, cos a]) 0
[(0.0,1.0),(1.0,-0.0)]

Directional Derivatives

du :: Functor f => (forall s. f (AD s ForwardDouble) -> AD s ForwardDouble) -> f (Double, Double) -> Double Source

Compute the directional derivative of a function given a zipped up Functor of the input values and their derivatives

du' :: Functor f => (forall s. f (AD s ForwardDouble) -> AD s ForwardDouble) -> f (Double, Double) -> (Double, Double) Source

Compute the answer and directional derivative of a function given a zipped up Functor of the input values and their derivatives

duF :: (Functor f, Functor g) => (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f (Double, Double) -> g Double Source

Compute a vector of directional derivatives for a function given a zipped up Functor of the input values and their derivatives.

duF' :: (Functor f, Functor g) => (forall s. f (AD s ForwardDouble) -> g (AD s ForwardDouble)) -> f (Double, Double) -> g (Double, Double) Source

Compute a vector of answers and directional derivatives for a function given a zipped up Functor of the input values and their derivatives.