Copyright (c) Edward Kmett 2010-2014 BSD3 ekmett@gmail.com experimental GHC only None Haskell2010

Description

Synopsis

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:

````>>> ````take 10 \$ findZero (\x->x^2-4) 1
```[1.0,2.5,2.05,2.000609756097561,2.0000000929222947,2.000000000000002,2.0]
```
````>>> ````last \$ take 10 \$ findZero ((+1).(^2)) (1 :+ 1)
```0.0 :+ 1.0
```

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:

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

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.

````>>> ````last \$ take 10 \$ fixedPoint cos 1
```0.7390851332151607
```

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.

````>>> ````last \$ take 10 \$ extremum cos 1
```0.0
```

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.

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)
````>>> ````rosenbrock [0,0]
```1
`>>> ````rosenbrock (conjugateGradientDescent rosenbrock [0, 0] !! 5) < 0.1
```True
```

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