-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | Reservoir Computing, fast RNNs
--   
--   Please see the README on Github at
--   <a>https://github.com/masterdezign/rc#readme</a>
@package rc
@version 0.1.0.1

module RC.Helpers

-- | Prepend a row of ones
--   
--   <pre>
--   &gt;&gt;&gt; addBiases $ (2&gt;&lt;3) [20..26]
--   (3&gt;&lt;3)
--    [  1.0,  1.0,  1.0
--    , 20.0, 21.0, 22.0
--    , 23.0, 24.0, 25.0 ]
--   </pre>
addBiases :: Matrix Double -> Matrix Double
randList :: (Random a, Floating a) => Int -> StdGen -> [a]

-- | Random matrix with elements in the range of [minVal; maxVal]
randMatrix :: StdGen -> (Int, Int) -> (Double, Double) -> Matrix Double

-- | Random sparse matrix
--   
--   NB: at the moment, the matrix is stored in memory as an ordinary
--   (dense) matrix.
randSparse :: StdGen -> (Int, Int) -> (R, R) -> R -> Matrix R

-- | Hard sigmoid
hsigmoid :: (Fractional a, Ord a) => (a, a, a) -> a -> a


-- | <h1>Nonlinear transient computing</h1>
--   
--   This module was developed as a part of author's PhD project:
--   <a>https://www.researchgate.net/project/Theory-and-Modeling-of-Complex-Nonlinear-Delay-Dynamics-Applied-to-Neuromorphic-Computing</a>
module RC.NTC

-- | Creates an untrained NTC network
new :: StdGen -> NTCParameters -> (Int, Int, Int) -> NTC

-- | NTC training: learn the readout weights offline
learn :: NTC -> Int -> Matrix Double -> Matrix Double -> Either String NTC

-- | Run prediction using a "clean" (uninitialized) reservoir and then
--   forget the reservoir's state. This can be used for both forecasting
--   and classification tasks.
predict :: NTC -> Int -> Matrix Double -> Either String (Matrix Double)

-- | Default NTC parameters
par0 :: NTCParameters

-- | Customizable NTC parameters
data NTCParameters
Par :: (Matrix Double -> Matrix Double) -> (Double, Double) -> (StdGen -> (Int, Int) -> (Double, Double) -> Matrix Double) -> (Matrix Double -> Matrix Double) -> Par -> NTCParameters

-- | Modify data before masking (e.g. compression)
[_preprocess] :: NTCParameters -> Matrix Double -> Matrix Double

-- | Input weights (mask) range
[_inputWeightsRange] :: NTCParameters -> (Double, Double)
[_inputWeightsGenerator] :: NTCParameters -> StdGen -> (Int, Int) -> (Double, Double) -> Matrix Double

-- | Modify data before training or prediction (e.g. add biases)
[_postprocess] :: NTCParameters -> Matrix Double -> Matrix Double
[_reservoirModel] :: NTCParameters -> Par

-- | Model parameters
data Par :: *

-- | Mackey-Glass model (no external input)
MackeyGlass :: Double -> Double -> Par
[_beta] :: Par -> Double
[_gamma] :: Par -> Double
RC :: (Double -> Double) -> Double -> BandpassFiltering -> Par

-- | Nonlinear transformation
[_fnl] :: Par -> Double -> Double

-- | External forcing weight
[_rho] :: Par -> Double
[_filt] :: Par -> BandpassFiltering

-- | Bandpass filter (linear) parameters
data BandpassFiltering :: *
BandpassFiltering :: Double -> Double -> BandpassFiltering

-- | System response time, s (epsilon = tau / tau_D)
[_tau] :: BandpassFiltering -> Double

-- | Integration time, s (delta = tau_D / theta)
[_theta] :: BandpassFiltering -> Double