jtobin

import Control.Monad.Par

-- Using the Par monad
logRosenbrockDensity :: [Double] -> Double
logRosenbrockDensity [x0, x1] = runPar $ do
    [a, b]   <- sequence [new, new]
    fork $ put a (-100*(x1 - (x0^2))^2 / 20)
    fork $ put b (-0.05*(1-x0)^2)
    [a', b'] <- sequence [get a, get b]
    return $ a' + b'

jtobin

logRosenbrockDensity :: [Double] -> Double
logRosenbrockDensity [x0, x1] = (-1)*(100*(x1 - (x0^2))^2 + (1 - x0)^2) / 20

jtobin

$ time ./Rosenbrock 500 > trace.dat  # using an ensemble containing 200 particles
(flat-mcmc) Rosenbrock density
20259 / 100000 (0.20259) proposals accepted

real	0m0.428s
user	0m0.367s
sys	0m0.016s

jtobin

target :: [Double] -> Double
target xs = go 0 0 xs
  where go t0 t1 []         = -0.5 * t0 / h - 0.5 * h * t1
        go t0 t1 (u:us:uss) = go (t0 + (us - u)^2) (t1 + v (us + u)) uss
        h   = 1 / fromIntegral (length xs)
        v x = (1 - x^2)^2

jtobin

./SPDE 1000000 1000 +RTS -N4 -qg > trace.dat 

MUT     time  4723.78s  (1620.24s elapsed)
GC      time  623.24s   (596.64s  elapsed)
Total   time  5347.02s  (2216.89s elapsed)

jtobin

./SPDE 1000000 1000 > trace.dat 

MUT     time  2769.65s  (2770.68s elapsed)
GC      time  600.57s   (599.46s  elapsed)
Total   time  3370.22s  (3370.13s elapsed)

jtobin

import Numeric.AD

gTarget :: [Double] -> [Double]
gTarget = grad target

jtobin

{-# OPTIONS_GHC -Wall #-}
{-# LANGUAGE TemplateHaskell #-}

import Data.List 
import Data.Hashable (Hashable)
import Data.HashMap.Strict hiding (filter)
import Control.Monad
import Test.QuickCheck
import Test.QuickCheck.All
import Criterion.Main

jtobin

=== prop_i0ResultElementsAreInBoth from ui.hs:64 ===
+++ OK, passed 1000 tests.

=== prop_i1ResultElementsAreInBoth from ui.hs:68 ===
+++ OK, passed 1000 tests.

=== prop_i2ResultElementsAreInBoth from ui.hs:72 ===
+++ OK, passed 1000 tests.

=== prop_u0ResultElementsInAtLeastOne from ui.hs:77 ===

jtobin

{-# OPTIONS_GHC -Wall #-}

import Data.Function
import Data.List

main :: IO ()
main = print $ minimumBy (compare `on` sumDiffs) portfolios

-- Data ------------------------------------------------------------------------