Portability GHC only experimental ekmett@gmail.com None

Description

This module provides reverse-mode Automatic Differentiation using post-hoc linear time topological sorting.

For reverse mode AD we use `StableName` to recover sharing information from the tape to avoid combinatorial explosion, and thus run asymptotically faster than it could without such sharing information, but the use of side-effects contained herein is benign.

Synopsis

grad :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f a -> f aSource

The `grad` function calculates the gradient of a non-scalar-to-scalar function with reverse-mode AD in a single pass.

````>>> ````grad (\[x,y,z] -> x*y+z) [1,2,3]
```[2,1,1]
```

grad' :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f a -> (a, f a)Source

The `grad'` function calculates the result and gradient of a non-scalar-to-scalar function with reverse-mode AD in a single pass.

````>>> ````grad' (\[x,y,z] -> 4*x*exp y+cos z) [1,2,3]
```(28.566231899122155,[29.5562243957226,29.5562243957226,-0.1411200080598672])
```

gradWith :: (Traversable f, Num a) => (a -> a -> b) -> (forall s. Mode s => f (AD s a) -> AD s a) -> f a -> f bSource

`grad g f` function calculates the gradient of a non-scalar-to-scalar function `f` with reverse-mode AD in a single pass. The gradient is combined element-wise with the argument using the function `g`.

``` `grad` = `gradWith` (_ dx -> dx)
`id` = `gradWith` const
```

gradWith' :: (Traversable f, Num a) => (a -> a -> b) -> (forall s. Mode s => f (AD s a) -> AD s a) -> f a -> (a, f b)Source

`grad' g f` calculates the result and gradient of a non-scalar-to-scalar function `f` with reverse-mode AD in a single pass the gradient is combined element-wise with the argument using the function `g`.

``grad'` == `gradWith'` (_ dx -> dx)`

# Jacobian

jacobian :: (Traversable f, Functor g, Num a) => (forall s. Mode s => f (AD s a) -> g (AD s a)) -> f a -> g (f a)Source

The `jacobian` function calculates the jacobian of a non-scalar-to-non-scalar function with reverse AD lazily in `m` passes for `m` outputs.

````>>> ````jacobian (\[x,y] -> [y,x,x*y]) [2,1]
```[[0,1],[1,0],[1,2]]
```
````>>> ````jacobian (\[x,y] -> [exp y,cos x,x+y]) [1,2]
```[[0.0,7.38905609893065],[-0.8414709848078965,0.0],[1.0,1.0]]
```

jacobian' :: (Traversable f, Functor g, Num a) => (forall s. Mode s => f (AD s a) -> g (AD s a)) -> f a -> g (a, f a)Source

The `jacobian'` function calculates both the result and the Jacobian of a nonscalar-to-nonscalar function, using `m` invocations of reverse AD, where `m` is the output dimensionality. Applying `fmap snd` to the result will recover the result of `jacobian` | An alias for `gradF'`

ghci> jacobian' ([x,y] -> [y,x,x*y]) [2,1] [(1,[0,1]),(2,[1,0]),(2,[1,2])]

jacobianWith :: (Traversable f, Functor g, Num a) => (a -> a -> b) -> (forall s. Mode s => f (AD s a) -> g (AD s a)) -> f a -> g (f b)Source

'jacobianWith g f' calculates the Jacobian of a non-scalar-to-non-scalar function `f` with reverse AD lazily in `m` passes for `m` outputs.

Instead of returning the Jacobian matrix, the elements of the matrix are combined with the input using the `g`.

``` `jacobian` = `jacobianWith` (_ dx -> dx)
`jacobianWith` `const` = (f x -> `const` x `<\$>` f x)
```

jacobianWith' :: (Traversable f, Functor g, Num a) => (a -> a -> b) -> (forall s. Mode s => f (AD s a) -> g (AD s a)) -> f a -> g (a, f b)Source

`jacobianWith` g f' calculates both the result and the Jacobian of a nonscalar-to-nonscalar function `f`, using `m` invocations of reverse AD, where `m` is the output dimensionality. Applying `fmap snd` to the result will recover the result of `jacobianWith`

Instead of returning the Jacobian matrix, the elements of the matrix are combined with the input using the `g`.

``jacobian'` == `jacobianWith'` (_ dx -> dx)`

# Hessian

hessian :: (Traversable f, Num a) => (forall s. Mode s => f (AD s a) -> AD s a) -> f a -> f (f a)Source

Compute the `hessian` via the `jacobian` of the gradient. gradient is computed in reverse mode and then the `jacobian` is computed in reverse mode.

However, since the `grad f :: f a -> f a` is square this is not as fast as using the forward-mode `jacobian` of a reverse mode gradient provided by `hessian`.

````>>> ````hessian (\[x,y] -> x*y) [1,2]
```[[0,1],[1,0]]
```

hessianF :: (Traversable f, Functor g, Num a) => (forall s. Mode s => f (AD s a) -> g (AD s a)) -> f a -> g (f (f a))Source

Compute the order 3 Hessian tensor on a non-scalar-to-non-scalar function via the reverse-mode Jacobian of the reverse-mode Jacobian of the function.

Less efficient than `hessianF`.

````>>> ````hessianF (\[x,y] -> [x*y,x+y,exp x*cos y]) [1,2]
```[[[0.0,1.0],[1.0,0.0]],[[0.0,0.0],[0.0,0.0]],[[-1.1312043837568135,-2.4717266720048188],[-2.4717266720048188,1.1312043837568135]]]
```

# Derivatives

diff :: Num a => (forall s. Mode s => AD s a -> AD s a) -> a -> aSource

Compute the derivative of a function.

````>>> ````diff sin 0
```1.0
```
````>>> ````cos 0
```1.0
```

diff' :: Num a => (forall s. Mode s => AD s a -> AD s a) -> a -> (a, a)Source

The `diff'` function calculates the value and derivative, as a pair, of a scalar-to-scalar function.

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

diffF :: (Functor f, Num a) => (forall s. Mode s => AD s a -> f (AD s a)) -> a -> f aSource

Compute the derivatives of a function that returns a vector with regards to its single input.

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

diffF' :: (Functor f, Num a) => (forall s. Mode s => AD s a -> f (AD s a)) -> a -> f (a, a)Source

Compute the derivatives of a function that returns a vector with regards to its single input as well as the primal answer.

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