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


-- | Learning Algorithms
--   
--   Learning Algorithms
@package learn
@version 0.1.1


-- | This library provides tools for doing single parameter Bayesian
--   inference.
--   
--   Example ghci use:
--   
--   <pre>
--   # Generate a discrete simulation of the uniform prior for the bias
--   # of a single coin on [0,1] using a grid of 100 points.
--   let prior = uniformPrior 100
--   # Update the prior as if we flipped the coin 100 times and got 25 heads.
--   let posterior = coins prior 25 75
--   # Optionally, import Graphics.Gnuplot.Simple and convert posterior 
--   # to a list of tuples for plotting using listify
--   :m + Graphics.Gnuplot.Simple
--   plotList [] (listify posterior)
--   # As you'd expect, nearly all of the probability mass is concentrated
--   # between a bias of 0.2 and 0.3
--   </pre>
module Numeric.Learn
type Prob = Ratio Integer
newtype Distro a
Distro :: Map a Prob -> Distro a
runDistro :: Distro a -> Map a Prob
data Result
Positive :: Result
Negative :: Result

-- | Takes a list of elements and generates a uniform Distro over them.
uniformPDF :: Ord k => [k] -> Distro k

-- | Generates a simulated uniform distro over [0,1] with user supplied
--   granularity.
uniformPrior :: Integer -> Distro Prob

-- | Updates a distro with a new trial that either succeeded or failed.
singleCoin :: Distro Prob -> Result -> Distro Prob

-- | Updates a distro with many trials, all of which either succeeded or
--   failed.
coins :: Distro Prob -> Int -> Int -> Distro Prob

-- | Convert a Distro to a list of tuples for easier plotting.
listify :: Distro k -> [(k, Prob)]
instance Eq a => Eq (Distro a)
instance Ord a => Ord (Distro a)
instance (Ord a, Read a) => Read (Distro a)
instance Show a => Show (Distro a)