Copyright | (c) Edward Kmett 2010-2015 |
---|---|

License | BSD3 |

Maintainer | ekmett@gmail.com |

Stability | experimental |

Portability | GHC only |

Safe Haskell | None |

Language | Haskell2010 |

- findZero :: (Fractional a, Eq a) => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a]
- findZeroNoEq :: Fractional a => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a]
- inverse :: (Fractional a, Eq a) => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> a -> [a]
- inverseNoEq :: Fractional a => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> a -> [a]
- fixedPoint :: (Fractional a, Eq a) => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a]
- fixedPointNoEq :: Fractional a => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a]
- extremum :: (Fractional a, Eq a) => (forall s. AD s (On (Forward (Forward a))) -> AD s (On (Forward (Forward a)))) -> a -> [a]
- extremumNoEq :: Fractional a => (forall s. AD s (On (Forward (Forward a))) -> AD s (On (Forward (Forward a)))) -> a -> [a]
- gradientDescent :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> f a -> [f a]
- constrainedDescent :: forall f a. (Traversable f, RealFloat a, Floating a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> [CC f a] -> f a -> [(a, f a)]
- data CC f a where
- eval :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> f a -> a
- gradientAscent :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> f a -> [f a]
- conjugateGradientDescent :: (Traversable f, Ord a, Fractional a) => (forall s. Chosen s => f (Or s (On (Forward (Forward a))) (Kahn a)) -> Or s (On (Forward (Forward a))) (Kahn a)) -> f a -> [f a]
- conjugateGradientAscent :: (Traversable f, Ord a, Fractional a) => (forall s. Chosen s => f (Or s (On (Forward (Forward a))) (Kahn a)) -> Or s (On (Forward (Forward a))) (Kahn a)) -> f a -> [f a]
- stochasticGradientDescent :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Scalar a) -> f (Reverse s a) -> Reverse s a) -> [f (Scalar a)] -> f a -> [f a]

# Newton's Method (Forward AD)

findZero :: (Fractional a, Eq a) => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a] Source #

The `findZero`

function finds a zero of a scalar function using
Newton's method; its output is a stream of increasingly accurate
results. (Modulo the usual caveats.) If the stream becomes constant
("it converges"), no further elements are returned.

Examples:

`>>>`

[1.0,2.5,2.05,2.000609756097561,2.0000000929222947,2.000000000000002,2.0]`take 10 $ findZero (\x->x^2-4) 1`

`>>>`

0.0 :+ 1.0`last $ take 10 $ findZero ((+1).(^2)) (1 :+ 1)`

findZeroNoEq :: Fractional a => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a] Source #

The `findZeroNoEq`

function behaves the same as `findZero`

except that it
doesn't truncate the list once the results become constant. This means it
can be used with types without an `Eq`

instance.

inverse :: (Fractional a, Eq a) => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> a -> [a] Source #

The `inverse`

function inverts a scalar function using
Newton's method; its output is a stream of increasingly accurate
results. (Modulo the usual caveats.) If the stream becomes
constant ("it converges"), no further elements are returned.

Example:

`>>>`

10.0`last $ take 10 $ inverse sqrt 1 (sqrt 10)`

inverseNoEq :: Fractional a => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> a -> [a] Source #

The `inverseNoEq`

function behaves the same as `inverse`

except that it
doesn't truncate the list once the results become constant. This means it
can be used with types without an `Eq`

instance.

fixedPoint :: (Fractional a, Eq a) => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a] Source #

The `fixedPoint`

function find a fixedpoint of a scalar
function using Newton's method; its output is a stream of
increasingly accurate results. (Modulo the usual caveats.)

If the stream becomes constant ("it converges"), no further elements are returned.

`>>>`

0.7390851332151607`last $ take 10 $ fixedPoint cos 1`

fixedPointNoEq :: Fractional a => (forall s. AD s (Forward a) -> AD s (Forward a)) -> a -> [a] Source #

The `fixedPointNoEq`

function behaves the same as `fixedPoint`

except that
it doesn't truncate the list once the results become constant. This means it
can be used with types without an `Eq`

instance.

extremum :: (Fractional a, Eq a) => (forall s. AD s (On (Forward (Forward a))) -> AD s (On (Forward (Forward a)))) -> a -> [a] Source #

The `extremum`

function finds an extremum of a scalar
function using Newton's method; produces a stream of increasingly
accurate results. (Modulo the usual caveats.) If the stream
becomes constant ("it converges"), no further elements are returned.

`>>>`

0.0`last $ take 10 $ extremum cos 1`

extremumNoEq :: Fractional a => (forall s. AD s (On (Forward (Forward a))) -> AD s (On (Forward (Forward a)))) -> a -> [a] Source #

The `extremumNoEq`

function behaves the same as `extremum`

except that it
doesn't truncate the list once the results become constant. This means it
can be used with types without an `Eq`

instance.

# Gradient Ascent/Descent (Reverse AD)

gradientDescent :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> f a -> [f a] Source #

The `gradientDescent`

function performs a multivariate
optimization, based on the naive-gradient-descent in the file
`stalingrad/examples/flow-tests/pre-saddle-1a.vlad`

from the
VLAD compiler Stalingrad sources. Its output is a stream of
increasingly accurate results. (Modulo the usual caveats.)

It uses reverse mode automatic differentiation to compute the gradient.

constrainedDescent :: forall f a. (Traversable f, RealFloat a, Floating a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> [CC f a] -> f a -> [(a, f a)] Source #

`constrainedDescent obj fs env`

optimizes the convex function `obj`

subject to the convex constraints `f <= 0`

where `f `

. This is
done using a log barrier to model constraints (i.e. Boyd, Chapter 11.3).
The returned optimal point for the objective function must satisfy `elem`

fs`fs`

,
but the initial environment, `env`

, needn't be feasible.

Convex constraint, CC, is a GADT wrapper that hides the existential
(`s`

) which is so prevalent in the rest of the API. This is an
engineering convenience for managing the skolems.

eval :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> f a -> a Source #

gradientAscent :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Reverse s a) -> Reverse s a) -> f a -> [f a] Source #

Perform a gradient descent using reverse mode automatic differentiation to compute the gradient.

conjugateGradientDescent :: (Traversable f, Ord a, Fractional a) => (forall s. Chosen s => f (Or s (On (Forward (Forward a))) (Kahn a)) -> Or s (On (Forward (Forward a))) (Kahn a)) -> f a -> [f a] Source #

Perform a conjugate gradient descent using reverse mode automatic differentiation to compute the gradient, and using forward-on-forward mode for computing extrema.

`>>>`

`let sq x = x * x`

`>>>`

`let rosenbrock [x,y] = sq (1 - x) + 100 * sq (y - sq x)`

`>>>`

1`rosenbrock [0,0]`

`>>>`

True`rosenbrock (conjugateGradientDescent rosenbrock [0, 0] !! 5) < 0.1`

conjugateGradientAscent :: (Traversable f, Ord a, Fractional a) => (forall s. Chosen s => f (Or s (On (Forward (Forward a))) (Kahn a)) -> Or s (On (Forward (Forward a))) (Kahn a)) -> f a -> [f a] Source #

Perform a conjugate gradient ascent using reverse mode automatic differentiation to compute the gradient.

stochasticGradientDescent :: (Traversable f, Fractional a, Ord a) => (forall s. Reifies s Tape => f (Scalar a) -> f (Reverse s a) -> Reverse s a) -> [f (Scalar a)] -> f a -> [f a] Source #

The `stochasticGradientDescent`

function approximates
the true gradient of the constFunction by a gradient at
a single example. As the algorithm sweeps through the training
set, it performs the update for each training example.

It uses reverse mode automatic differentiation to compute the gradient The learning rate is constant through out, and is set to 0.001