ad 0.20 → 0.21

raw patch · 3 files changed

+35/−1 lines, 3 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• Numeric/AD.hs +22/−0
• Numeric/AD/Forward.hs +12/−0
• ad.cabal +1/−1

@@ -32,6 +32,10 @@     -- * Jacobians (Forward Mode)     , jacobianT, jacobianWithT +    -- * Hessians (Forward-On-Reverse Mode)+    , hessianProduct+    , hessianProduct'+     -- * Derivatives (Forward Mode)     , diff     , diffF@@ -83,6 +87,7 @@ import Numeric.AD.Forward  (diff, diff', diffF, diffF', du, du', duF, duF', diffM, diffM', jacobianT, jacobianWithT)  import Numeric.AD.Tower    (diffsF, diffs0F , diffs, diffs0, taylor, taylor0, maclaurin, maclaurin0) import Numeric.AD.Reverse  (grad, grad', gradWith, gradWith', gradM, gradM', gradWithM, gradWithM', gradF, gradF', gradWithF, gradWithF')+import Numeric.AD.Internal.Composition (compose, decompose)  import qualified Numeric.AD.Forward as Forward import qualified Numeric.AD.Reverse as Reverse@@ -135,4 +140,21 @@         size :: Foldable f => f a -> Int         size = foldr' (\_ b -> 1 + b) 0 {-# INLINE jacobianWith' #-}++-- | @'hessianProduct' f wv@ computes the product of the hessian @H@ of a non-scalar-to-scalar function @f@ at @w = 'fst' <$> wv@ with a vector @v = snd <$> wv@ using \"Pearlmutter\'s method\" from <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.6143>, which states:+--+-- > H v = (d/dr) grad_w (w + r v) | r = 0+-- +-- Or in other words, we take the directional derivative of the gradient.+hessianProduct :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f (a, a) -> f a+hessianProduct f = duF (grad (decompose . f . fmap compose))++-- | @'hessianProduct'' f wv@ computes both the gradient of a non-scalar-to-scalar @f@ at @w = 'fst' <$> wv@ and the product of the hessian @H@ at @w@ with a vector @v = snd <$> wv@ using \"Pearlmutter\'s method\". The outputs are returned wrapped in the same functor.+--+-- > H v = (d/dr) grad_w (w + r v) | r = 0+-- +-- Or in other words, we take the directional derivative of the gradient.+-- +hessianProduct' :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f (a, a) -> f (a, a)+hessianProduct' f = duF' (grad (decompose . f . fmap compose)) 

@@ -27,6 +27,9 @@     -- * Transposed Jacobian     , jacobianT     , jacobianWithT+    -- * Hessian Product+    , hessianProduct+    , hessianProduct'     -- * Derivatives     , diff     , diff'@@ -50,6 +53,7 @@ import Control.Monad (liftM) import Numeric.AD.Classes import Numeric.AD.Internal+import Numeric.AD.Internal.Composition import Numeric.AD.Internal.Forward  du :: (Functor f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f (a, a) -> a@@ -159,3 +163,11 @@ gradWith' :: (Traversable f, Num a) => (a -> a -> b) -> (forall s. Mode s => f (AD s a) -> AD s a) -> f a -> (a, f b) gradWith' g f = bindWith' g (tangent . f) {-# INLINE gradWith' #-}++-- | Compute the product of a vector with the Hessian using forward-on-forward-mode AD. +hessianProduct :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f (a, a) -> f a+hessianProduct f = duF (grad (decompose . f . fmap compose))++-- | Compute the gradient and hessian product using forward-on-forward-mode AD. +hessianProduct' :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f (a, a) -> f (a, a)+hessianProduct' f = duF' (grad (decompose . f . fmap compose))

@@ -1,5 +1,5 @@ Name:         ad-Version:      0.20+Version:      0.21 License:      BSD3 License-File: LICENSE Copyright:    Edward Kmett 2010