commit 016183757f1fa9dba2b2a95047215d36de9cbcea
parent f6785395c4f316074129f35880b0b94da6e5e054
Author: Marco Zocca <marco.zocca@recordunion.com>
Date: Tue, 30 Jan 2018 09:56:35 +0100
upd docs, add Weibull
Diffstat:
2 files changed, 26 insertions(+), 10 deletions(-)
diff --git a/mwc-probability.cabal b/mwc-probability.cabal
@@ -3,8 +3,8 @@ version: 2.0.0
homepage: http://github.com/jtobin/mwc-probability
license: MIT
license-file: LICENSE
-author: Jared Tobin
-maintainer: jared@jtobin.ca
+author: Jared Tobin, Marco Zocca
+maintainer: jared@jtobin.ca, zocca.marco gmail
category: Math
build-type: Simple
cabal-version: >= 1.10
diff --git a/src/System/Random/MWC/Probability.hs b/src/System/Random/MWC/Probability.hs
@@ -4,10 +4,10 @@
-- |
-- Module: System.Random.MWC.Probability
--- Copyright: (c) 2015 Jared Tobin
+-- Copyright: (c) 2015-2017 Jared Tobin, Marco Zocca
-- License: MIT
--
--- Maintainer: Jared Tobin <jared@jtobin.ca>
+-- Maintainer: Jared Tobin <jared@jtobin.ca>, Marco Zocca <zocca.marco gmail>
-- Stability: unstable
-- Portability: ghc
--
@@ -42,8 +42,18 @@
-- For example, @'uniform'@ is a distribution over the 0-1 interval, but @fmap
-- (+ 1) uniform@ is the translated distribution over the 1-2 interval.
--
--- >>> sample (fmap (+ 1) uniform) gen
+-- >>> create >>= sample (fmap (+ 1) uniform)
-- 1.5480073474340754
+--
+-- == Running the examples
+--
+-- In the following we will assume an interactive GHCi session; the user should first declare a random number generator:
+--
+-- >>> gen <- create
+--
+-- which will be reused throughout all examples.
+-- Note: creating a random generator is an expensive operation, so it should be only performed once in the code (usually in the top-level IO action, e.g `main`).
+
module System.Random.MWC.Probability (
module MWC
@@ -62,6 +72,7 @@ module System.Random.MWC.Probability (
, laplace
, gamma
, inverseGamma
+ , weibull
, chiSquare
, beta
, student
@@ -97,14 +108,13 @@ import System.Random.MWC.CondensedTable
-- | A probability distribution characterized by a sampling function.
--
--- >>> gen <- create
-- >>> sample uniform gen
-- 0.4208881170464097
newtype Prob m a = Prob { sample :: Gen (PrimState m) -> m a }
-- | Sample from a model 'n' times.
--
--- >>> create >>= samples 2 uniform
+-- >>> samples 2 uniform gen
-- [0.6738707766845254,0.9730405951541817]
samples :: PrimMonad m => Int -> Prob m a -> Gen (PrimState m) -> m [a]
samples n model gen = replicateM n (sample model gen)
@@ -147,7 +157,6 @@ instance PrimMonad m => PrimMonad (Prob m) where
-- | The uniform distribution over a type.
--
--- >>> gen <- create
-- >>> sample uniform gen :: IO Double
-- 0.29308497534914946
-- >>> sample uniform gen :: IO Bool
@@ -207,6 +216,13 @@ laplace mu sigma = do
{-# INLINABLE laplace #-}
+-- | The Weibull distribution with provided shape and scale parameters.
+weibull :: (Floating a, Variate a, PrimMonad m) => a -> a -> Prob m a
+weibull a b = do
+ x <- uniform
+ return $ (- 1/a * log (1 - x)) ** 1/b
+
+
-- | The gamma distribution with shape parameter a and scale parameter b.
--
-- This is the parameterization used more traditionally in frequentist
@@ -307,10 +323,10 @@ categorical ps = do
-- avoided as they are very computationally intensive. The following
-- code illustrates this behaviour.
--
--- >>> create >>= samples 10 (zipf 1.1)
+-- >>> samples 10 (zipf 1.1) gen
-- [11315371987423520,2746946,653,609,2,13,85,4,256184577853,50]
--
--- >>> create >>= samples 10 (zipf 1.5)
+-- >>> samples 10 (zipf 1.5) gen
-- [19,3,3,1,1,2,1,191,2,1]
zipf :: (PrimMonad m, Integral b) => Double -> Prob m b
zipf a = do
