monad-bayes 1.2.0 → 1.3.0

raw patch · 33 files changed

+193/−89 lines, 33 filesdep ~basedep ~hspecdep ~mtlPVP ok

version bump matches the API change (PVP)

Dependency ranges changed: base, hspec, mtl, transformers

API changes (from Hackage documentation)

Files

• CHANGELOG.md +7/−0
• benchmark/Single.hs +6/−2
• models/ConjugatePriors.hs +5/−2
• models/Dice.hs +5/−1
• monad-bayes.cabal +28/−23
• src/Control/Monad/Bayes/Class.hs +2/−12
• src/Control/Monad/Bayes/Enumerator.hs +3/−1
• src/Control/Monad/Bayes/Inference/Lazy/MH.hs +5/−4
• src/Control/Monad/Bayes/Inference/Lazy/WIS.hs +4/−4
• src/Control/Monad/Bayes/Integrator.hs +4/−0
• src/Control/Monad/Bayes/Population.hs +43/−5
• src/Control/Monad/Bayes/Sampler/Lazy.hs +45/−27
• src/Control/Monad/Bayes/Sampler/Strict.hs +2/−1
• src/Control/Monad/Bayes/Traced/Basic.hs +5/−1
• src/Control/Monad/Bayes/Traced/Static.hs +5/−1
• test/TestBenchmarks.hs +4/−2
• test/TestSSMFixtures.hs +4/−2
• test/TestWeighted.hs +1/−1
• test/fixtures/HMM10-MH.txt +1/−0
• test/fixtures/HMM10-RMSMC.txt +1/−0
• test/fixtures/HMM10-SMC.txt +1/−0
• test/fixtures/LDA10-MH.txt +1/−0
• test/fixtures/LDA10-RMSMC.txt +1/−0
• test/fixtures/LDA10-SMC.txt +1/−0
• test/fixtures/LR10-MH.txt +1/−0
• test/fixtures/LR10-RMSMC.txt +1/−0
• test/fixtures/LR10-SMC.txt +1/−0
• test/fixtures/SSM-PMMH.txt +1/−0
• test/fixtures/SSM-RMSMC.txt +1/−0
• test/fixtures/SSM-RMSMCBasic.txt +1/−0
• test/fixtures/SSM-RMSMCDynamic.txt +1/−0
• test/fixtures/SSM-SMC.txt +1/−0
• test/fixtures/SSM-SMC2.txt +1/−0

@@ -1,3 +1,10 @@+# 1.3.0++- Support for GHC 9.6+- Replaced transformers' `ListT` by+  (https://github.com/tweag/monad-bayes/pull/295)+- Naming fixes for `Sampler` and `SamplerT`+ # 1.2.0  - Renamed monad transformers idiomatically

@@ -1,12 +1,12 @@ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE ImportQualifiedPost #-} +import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Sampler.Strict import Data.Time (diffUTCTime, getCurrentTime) import Helper import Options.Applicative-  ( Applicative (liftA2),-    ParserInfo,+  ( ParserInfo,     auto,     execParser,     fullDesc,@@ -17,6 +17,10 @@     option,     short,   )+-- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md+-- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore++import Prelude hiding (Applicative (..))  infer :: Model -> Alg -> IO () infer model alg = do

@@ -4,12 +4,15 @@  module ConjugatePriors where -import Control.Applicative (Applicative (liftA2))+import Control.Applicative (Applicative (..)) import Control.Foldl (fold) import Control.Foldl qualified as F import Control.Monad.Bayes.Class (Bayesian (..), MonadDistribution (bernoulli, beta, gamma, normal), MonadMeasure, normalPdf) import Numeric.Log (Log (Exp))-import Prelude+-- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md+-- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore++import Prelude hiding (Applicative (..))  type GammaParams = (Double, Double) 

@@ -3,13 +3,17 @@ -- A toy model for dice rolling from http://dl.acm.org/citation.cfm?id=2804317 -- Exact results can be obtained using Dist monad -import Control.Applicative (liftA2)+import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Class   ( MonadDistribution (uniformD),     MonadFactor (score),     MonadMeasure,     condition,   )+-- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md+-- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore++import Prelude hiding (Applicative (..))  -- | A toss of a six-sided die. die :: (MonadDistribution m) => m Int

@@ -1,16 +1,16 @@-cabal-version:      2.2-name:               monad-bayes-version:            1.2.0-license:            MIT-license-file:       LICENSE.md-copyright:          2015-2020 Adam Scibior-maintainer:         dominic.steinitz@tweag.io-author:             Adam Scibior <adscib@gmail.com>-stability:          experimental-tested-with:        GHC ==9.0.2 || ==9.2.7 || ==9.4.5-homepage:           http://github.com/tweag/monad-bayes#readme-bug-reports:        https://github.com/tweag/monad-bayes/issues-synopsis:           A library for probabilistic programming.+cabal-version:   2.2+name:            monad-bayes+version:         1.3.0+license:         MIT+license-file:    LICENSE.md+copyright:       2015-2020 Adam Scibior+maintainer:      dominic.steinitz@tweag.io+author:          Adam Scibior <adscib@gmail.com>+stability:       experimental+tested-with:     GHC ==9.0.2 || ==9.2.7 || ==9.4.5 || ==9.6.4+homepage:        http://github.com/tweag/monad-bayes#readme+bug-reports:     https://github.com/tweag/monad-bayes/issues+synopsis:        A library for probabilistic programming. description:   A library for probabilistic programming using probability monads. The   emphasis is on composition of inference algorithms implemented in@@ -19,12 +19,14 @@   for a quick-start user guide and a reference overview of how it all   works" and the included [README](#readme). -category:           Statistics-build-type:         Simple-extra-source-files:+category:        Statistics+build-type:      Simple+extra-doc-files:   CHANGELOG.md   README.md +data-files:      test/fixtures/*.txt+ source-repository head   type:     git   location: https://github.com/tweag/monad-bayes.git@@ -36,7 +38,7 @@  common deps   build-depends:-    , base             >=4.15    && <4.18+    , base             >=4.15    && <4.19     , brick            >=1.0     && <2.0     , containers       >=0.5.10  && <0.7     , foldl            ^>=1.4@@ -51,7 +53,7 @@     , matrix           ^>=0.3     , monad-coroutine  ^>=0.9.0     , monad-extras     ^>=0.6-    , mtl              ^>=2.2.2+    , mtl              >=2.2.2   && <2.4     , mwc-random       >=0.13.6  && <0.16     , pipes            ^>=4.3     , pretty-simple    ^>=4.1@@ -61,22 +63,25 @@     , scientific       ^>=0.3     , statistics       >=0.14.0  && <0.17     , text             >=1.2     && <2.1+    , transformers     >=0.5.6   && <0.7     , vector           >=0.12.0  && <0.14     , vty              ^>=5.38  common test-deps   build-depends:     , abstract-par          ^>=0.3-    , criterion             >=1.5    && <1.7+    , criterion             >=1.5   && <1.7     , directory             ^>=1.3-    , hspec                 ^>=2.11+    , hspec                 >=2.10  && <2.12     , monad-bayes-    , optparse-applicative  >=0.17   && <0.19+    , optparse-applicative  >=0.17  && <0.19     , process               ^>=1.6     , QuickCheck            ^>=2.14-    , time                  >=1.9    && <1.13-    , transformers          ^>=0.5.6+    , time                  >=1.9   && <1.13     , typed-process         ^>=0.2++  autogen-modules: Paths_monad_bayes+  other-modules:   Paths_monad_bayes  library   import:             deps

@@ -77,11 +77,11 @@ import Control.Arrow (Arrow (second)) import Control.Monad (replicateM, when) import Control.Monad.Cont (ContT)-import Control.Monad.Except (ExceptT, lift)+import Control.Monad.Except (ExceptT) import Control.Monad.Identity (IdentityT)-import Control.Monad.List (ListT) import Control.Monad.Reader (ReaderT) import Control.Monad.State (StateT)+import Control.Monad.Trans.Class (lift) import Control.Monad.Writer (WriterT) import Data.Histogram qualified as H import Data.Histogram.Fill qualified as H@@ -389,16 +389,6 @@   score = lift . score  instance (MonadMeasure m) => MonadMeasure (StateT s m)--instance (MonadDistribution m) => MonadDistribution (ListT m) where-  random = lift random-  bernoulli = lift . bernoulli-  categorical = lift . categorical--instance (MonadFactor m) => MonadFactor (ListT m) where-  score = lift . score--instance (MonadMeasure m) => MonadMeasure (ListT m)  instance (MonadDistribution m) => MonadDistribution (ContT r m) where   random = lift random

@@ -32,16 +32,18 @@  import Control.Applicative (Alternative) import Control.Arrow (second)+import Control.Monad (MonadPlus) import Control.Monad.Bayes.Class   ( MonadDistribution (bernoulli, categorical, logCategorical, random),     MonadFactor (..),     MonadMeasure,   )-import Control.Monad.Writer+import Control.Monad.Writer (WriterT (..)) import Data.AEq (AEq, (===), (~==)) import Data.List (sortOn) import Data.Map qualified as Map import Data.Maybe (fromMaybe)+import Data.Monoid (Product (..)) import Data.Ord (Down (Down)) import Data.Vector qualified as VV import Data.Vector.Generic qualified as V

@@ -7,10 +7,11 @@  import Control.Monad.Bayes.Class (Log (ln)) import Control.Monad.Bayes.Sampler.Lazy-  ( SamplerT (runSamplerT),+  ( Sampler,     Tree (..),     Trees (..),     randomTree,+    runSampler,   ) import Control.Monad.Bayes.Weighted (WeightedT, runWeightedT) import Control.Monad.Extra (iterateM)@@ -18,7 +19,7 @@ import System.Random (RandomGen (split), getStdGen, newStdGen) import System.Random qualified as R -mh :: forall a. Double -> WeightedT SamplerT a -> IO [(a, Log Double)]+mh :: forall a. Double -> WeightedT Sampler a -> IO [(a, Log Double)] mh p m = do   -- Top level: produce a stream of samples.   -- Split the random number generator in two@@ -27,7 +28,7 @@   g <- newStdGen >> getStdGen   let (g1, g2) = split g   let t = randomTree g1-  let (x, w) = runSamplerT (runWeightedT m) t+  let (x, w) = runSampler (runWeightedT m) t   -- Now run step over and over to get a stream of (tree,result,weight)s.   let (samples, _) = runState (iterateM step (t, x, w)) g2   -- The stream of seeds is used to produce a stream of result/weight pairs.@@ -50,7 +51,7 @@       let t' = mutateTree p g1 t       -- Rerun the model with the new tree, to get a new       -- weight w'.-      let (x', w') = runSamplerT (runWeightedT m) t'+      let (x', w') = runSampler (runWeightedT m) t'       -- MH acceptance ratio. This is the probability of either       -- returning the new seed or the old one.       let ratio = w' / w

@@ -1,17 +1,17 @@ module Control.Monad.Bayes.Inference.Lazy.WIS where -import Control.Monad.Bayes.Sampler.Lazy (SamplerT, weightedsamples)+import Control.Monad.Bayes.Sampler.Lazy (SamplerT, weightedSamples) import Control.Monad.Bayes.Weighted (WeightedT) import Numeric.Log (Log (Exp)) import System.Random (Random (randoms), getStdGen, newStdGen) --- | WeightedT Importance Sampling+-- | Weighted Importance Sampling  -- | Likelihood weighted importance sampling first draws n weighted samples, --    and then samples a stream of results from that regarded as an empirical distribution-lwis :: Int -> WeightedT SamplerT a -> IO [a]+lwis :: Int -> WeightedT (SamplerT IO) a -> IO [a] lwis n m = do-  xws <- weightedsamples m+  xws <- weightedSamples m   let xws' = take n $ accumulate xws 0   let max' = snd $ last xws'   _ <- newStdGen

@@ -48,6 +48,10 @@ import Numeric.Log (Log (ln)) import Statistics.Distribution qualified as Statistics import Statistics.Distribution.Uniform qualified as Statistics+-- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md+-- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore++import Prelude hiding (Applicative (..))  newtype Integrator a = Integrator {getIntegrator :: Cont Double a}   deriving newtype (Functor, Applicative, Monad)

@@ -1,6 +1,7 @@ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE ImportQualifiedPost #-}+{-# LANGUAGE NamedFieldPuns #-} {-# LANGUAGE RankNTypes #-} {-# OPTIONS_GHC -Wno-deprecations #-} @@ -37,11 +38,12 @@   ) where +import Control.Applicative (Alternative) import Control.Arrow (second)-import Control.Monad (replicateM)+import Control.Monad (forM, replicateM) import Control.Monad.Bayes.Class-  ( MonadDistribution (categorical, logCategorical, random, uniform),-    MonadFactor,+  ( MonadDistribution (..),+    MonadFactor (..),     MonadMeasure,     factor,   )@@ -52,7 +54,9 @@     runWeightedT,     weightedT,   )-import Control.Monad.List (ListT (..), MonadIO, MonadTrans (..))+import Control.Monad.IO.Class+import Control.Monad.Trans.Class+import Data.Functor.Compose import Data.List (unfoldr) import Data.List qualified import Data.Maybe (catMaybes)@@ -62,6 +66,40 @@ import Numeric.Log qualified as Log import Prelude hiding (all, sum) +-- | The old-fashioned, broken list transformer, adding a list/nondeterminism/choice effect.+--   It is not a valid monad transformer, but it is a valid 'Applicative'.+newtype ListT m a = ListT {getListT :: Compose m [] a}+  deriving newtype (Functor, Applicative, Alternative)++listT :: m [a] -> ListT m a+listT = ListT . Compose++runListT :: ListT m a -> m [a]+runListT = getCompose . getListT++-- | This monad instance is _unlawful_,+-- it is only by accident and careful construction that it can be used here.+instance (Monad m) => Monad (ListT m) where+  ma >>= f = ListT $ Compose $ do+    as <- runListT ma+    fmap concat $ forM as $ runListT . f++instance MonadTrans ListT where+  lift = ListT . Compose . fmap pure++instance (MonadIO m) => MonadIO (ListT m) where+  liftIO = lift . liftIO++instance (MonadDistribution m) => MonadDistribution (ListT m) where+  random = lift random+  bernoulli = lift . bernoulli+  categorical = lift . categorical++instance (MonadFactor m) => MonadFactor (ListT m) where+  score = lift . score++instance (MonadMeasure m) => MonadMeasure (ListT m)+ -- | A collection of weighted samples, or particles. newtype PopulationT m a = PopulationT {getPopulationT :: WeightedT (ListT m) a}   deriving newtype (Functor, Applicative, Monad, MonadIO, MonadDistribution, MonadFactor, MonadMeasure)@@ -80,7 +118,7 @@  -- | Initialize 'PopulationT' with a concrete weighted sample. fromWeightedList :: (Monad m) => m [(a, Log Double)] -> PopulationT m a-fromWeightedList = PopulationT . weightedT . ListT+fromWeightedList = PopulationT . weightedT . listT  -- | Increase the sample size by a given factor. -- The weights are adjusted such that their sum is preserved.

@@ -1,4 +1,5 @@ {-# LANGUAGE BangPatterns #-}+{-# LANGUAGE DeriveFunctor #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE RankNTypes #-} {-# LANGUAGE ScopedTypeVariables #-}@@ -9,6 +10,9 @@ import Control.Monad (ap) import Control.Monad.Bayes.Class (MonadDistribution (random)) import Control.Monad.Bayes.Weighted (WeightedT, runWeightedT)+import Control.Monad.IO.Class+import Control.Monad.Identity (Identity (runIdentity))+import Control.Monad.Trans import Numeric.Log (Log (..)) import System.Random   ( RandomGen (split),@@ -17,10 +21,11 @@   ) import System.Random qualified as R --- | A 'Tree' is a lazy, infinitely wide and infinitely deep tree, labelled by Doubles--- | Our source of randomness will be a Tree, populated by uniform [0,1] choices for each label.--- | Often people just use a list or stream instead of a tree.--- | But a tree allows us to be lazy about how far we are going all the time.+-- | A 'Tree' is a lazy, infinitely wide and infinitely deep tree, labelled by Doubles.+--+--   Our source of randomness will be a Tree, populated by uniform [0,1] choices for each label.+--   Often people just use a list or stream instead of a tree.+--   But a tree allows us to be lazy about how far we are going all the time. data Tree = Tree   { currentUniform :: Double,     lazyUniforms :: Trees@@ -32,48 +37,61 @@     tailTrees :: Trees   } --- | A probability distribution over a is--- | a function 'Tree -> a'--- | The idea is that it uses up bits of the tree as it runs-newtype SamplerT a = SamplerT {runSamplerT :: Tree -> a}+-- | A probability distribution over @a@ is a function 'Tree -> a'.+--   The idea is that it uses up bits of the tree as it runs.+type Sampler = SamplerT Identity++runSampler :: Sampler a -> Tree -> a+runSampler = (runIdentity .) . runSamplerT++newtype SamplerT m a = SamplerT {runSamplerT :: Tree -> m a}   deriving (Functor) --- | Two key things to do with trees:--- | Split tree splits a tree in two (bijectively)--- | Get the label at the head of the tree and discard the rest+-- | Split a tree in two (bijectively). splitTree :: Tree -> (Tree, Tree) splitTree (Tree r (Trees t ts)) = (t, Tree r ts) --- | Preliminaries for the simulation methods. Generate a tree with uniform random labels. This uses 'split' to split a random seed+-- | Generate a tree with uniform random labels.+--+-- Preliminary for the simulation methods. This uses 'split' to split a random seed. randomTree :: (RandomGen g) => g -> Tree randomTree g = let (a, g') = R.random g in Tree a (randomTrees g')  randomTrees :: (RandomGen g) => g -> Trees randomTrees g = let (g1, g2) = split g in Trees (randomTree g1) (randomTrees g2) -instance Applicative SamplerT where-  pure = SamplerT . const+instance (Monad m) => Applicative (SamplerT m) where+  pure = lift . pure   (<*>) = ap --- | probabilities for a monad. -- | Sequencing is done by splitting the tree--- | and using different bits for different computations.-instance Monad SamplerT where+--   and using different bits for different computations.+instance (Monad m) => Monad (SamplerT m) where   return = pure-  (SamplerT m) >>= f = SamplerT \g ->+  (SamplerT m) >>= f = SamplerT \g -> do     let (g1, g2) = splitTree g-        (SamplerT m') = f (m g1)-     in m' g2+    a <- m g1+    let SamplerT m' = f a+    m' g2 -instance MonadDistribution SamplerT where-  random = SamplerT \(Tree r _) -> r+instance MonadTrans SamplerT where+  lift = SamplerT . const -sampler :: SamplerT a -> IO a-sampler m = newStdGen *> (runSamplerT m . randomTree <$> getStdGen)+instance (MonadIO m) => MonadIO (SamplerT m) where+  liftIO = lift . liftIO +-- | Sampling gets the label at the head of the tree and discards the rest.+instance (Monad m) => MonadDistribution (SamplerT m) where+  random = SamplerT \(Tree r _) -> pure r++-- | Runs a 'SamplerT' by creating a new 'StdGen'.+runSamplerTIO :: (MonadIO m) => SamplerT m a -> m a+runSamplerTIO m = liftIO newStdGen *> (runSamplerT m =<< randomTree <$> liftIO getStdGen)++-- | Draw a stream of independent samples. independent :: (Monad m) => m a -> m [a] independent = sequence . repeat --- | 'weightedsamples' runs a probability measure and gets out a stream of (result,weight) pairs-weightedsamples :: WeightedT SamplerT a -> IO [(a, Log Double)]-weightedsamples = sampler . independent . runWeightedT+-- | Runs a probability measure and gets out a stream of @(result,weight)@ pairs+weightedSamples :: (MonadIO m) => WeightedT (SamplerT m) a -> m [(a, Log Double)]+weightedSamples = runSamplerTIO . sequence . repeat . runWeightedT

@@ -42,13 +42,14 @@   ) import Control.Monad.Reader (MonadIO, ReaderT (..)) import Control.Monad.ST (ST)+import Control.Monad.Trans (MonadTrans) import Numeric.Log (Log (ln)) import System.Random.MWC.Distributions qualified as MWC import System.Random.Stateful (IOGenM (..), STGenM, StatefulGen, StdGen, initStdGen, mkStdGen, newIOGenM, newSTGenM, uniformDouble01M, uniformRM)  -- | The sampling interpretation of a probabilistic program -- Here m is typically IO or ST-newtype SamplerT g m a = SamplerT {runSamplerT :: ReaderT g m a} deriving (Functor, Applicative, Monad, MonadIO)+newtype SamplerT g m a = SamplerT {runSamplerT :: ReaderT g m a} deriving (Functor, Applicative, Monad, MonadIO, MonadTrans)  -- | convenient type synonym to show specializations of SamplerT -- to particular pairs of monad and RNG

@@ -17,7 +17,7 @@   ) where -import Control.Applicative (liftA2)+import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Class   ( MonadDistribution (random),     MonadFactor (..),@@ -34,6 +34,10 @@ import Control.Monad.Bayes.Weighted (WeightedT) import Data.Functor.Identity (Identity) import Data.List.NonEmpty as NE (NonEmpty ((:|)), toList)+-- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md+-- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore++import Prelude hiding (Applicative (..))  -- | Tracing monad that records random choices made in the program. data TracedT m a = TracedT

@@ -18,7 +18,7 @@   ) where -import Control.Applicative (liftA2)+import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Class   ( MonadDistribution (random),     MonadFactor (..),@@ -35,6 +35,10 @@ import Control.Monad.Bayes.Weighted (WeightedT) import Control.Monad.Trans (MonadTrans (..)) import Data.List.NonEmpty as NE (NonEmpty ((:|)), toList)+-- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md+-- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore++import Prelude hiding (Applicative (..))  -- | A tracing monad where only a subset of random choices are traced. --

@@ -3,12 +3,13 @@ import Control.Monad (forM_) import Data.Maybe (fromJust) import Helper+import Paths_monad_bayes (getDataDir) import System.IO (readFile') import System.IO.Error (catchIOError, isDoesNotExistError) import Test.Hspec  fixtureToFilename :: Model -> Alg -> String-fixtureToFilename model alg = fromJust (serializeModel model) ++ "-" ++ show alg ++ ".txt"+fixtureToFilename model alg = "/test/fixtures/" ++ fromJust (serializeModel model) ++ "-" ++ show alg ++ ".txt"  models :: [Model] models = [LR 10, HMM 10, LDA (5, 10)]@@ -21,7 +22,8 @@  testFixture :: Model -> Alg -> SpecWith () testFixture model alg = do-  let filename = "test/fixtures/" ++ fixtureToFilename model alg+  dataDir <- runIO getDataDir+  let filename = dataDir <> fixtureToFilename model alg   it ("should agree with the fixture " ++ filename) $ do     fixture <- catchIOError (readFile' filename) $ \e ->       if isDoesNotExistError e

@@ -3,16 +3,18 @@ import Control.Monad.Bayes.Sampler.Strict (sampleIOfixed) import NonlinearSSM import NonlinearSSM.Algorithms+import Paths_monad_bayes (getDataDir) import System.IO (readFile') import System.IO.Error (catchIOError, isDoesNotExistError) import Test.Hspec  fixtureToFilename :: Alg -> FilePath-fixtureToFilename alg = "test/fixtures/SSM-" ++ show alg ++ ".txt"+fixtureToFilename alg = "/test/fixtures/SSM-" ++ show alg ++ ".txt"  testFixture :: Alg -> SpecWith () testFixture alg = do-  let filename = fixtureToFilename alg+  dataDir <- runIO getDataDir+  let filename = dataDir <> fixtureToFilename alg   it ("should agree with the fixture " ++ filename) $ do     ys <- sampleIOfixed $ generateData t     fixture <- catchIOError (readFile' filename) $ \e ->

@@ -2,6 +2,7 @@  module TestWeighted (check, passed, result, model) where +import Control.Monad (unless, when) import Control.Monad.Bayes.Class   ( MonadDistribution (normal, uniformD),     MonadMeasure,@@ -9,7 +10,6 @@   ) import Control.Monad.Bayes.Sampler.Strict (sampleIOfixed) import Control.Monad.Bayes.Weighted (runWeightedT)-import Control.Monad.State (unless, when) import Data.AEq (AEq ((~==))) import Data.Bifunctor (second) import Numeric.Log (Log (Exp, ln))

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