learning-hmm 0.3.0.1 → 0.3.1.0

raw patch · 6 files changed

+59/−1 lines, 6 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

+ Learning.HMM: baumWelch' :: (Eq s, Eq o) => HMM s o -> [o] -> (HMM s o, LogLikelihood)

+ Learning.IOHMM: baumWelch' :: (Eq i, Eq s, Eq o) => IOHMM i s o -> [i] -> [o] -> (IOHMM i s o, LogLikelihood)

Files

CHANGES.md +5/−0
learning-hmm.cabal +1/−1
src/Learning/HMM.hs +16/−0
src/Learning/HMM/Internal.hs +9/−0
src/Learning/IOHMM.hs +19/−0
src/Learning/IOHMM/Internal.hs +9/−0

CHANGES.md view

@@ -1,6 +1,11 @@ Revision history for Haskell package learning-hmm === +## Version 0.3.1.0+- Add function `baumWelch'` that performs the Baum-Welch algorithm and returns+  a result locally maximizing its likelihood. This behaviour is different from+  that of `baumWelch`, which returns a list of intermediate results.+ ## Version 0.3.0.0 - Add `Learning.IOHMM` which represents a class of input-output HMM - Delete `Learning.HMM.new`

learning-hmm.cabal view

@@ -1,5 +1,5 @@ name:                learning-hmm-version:             0.3.0.1+version:             0.3.1.0 stability:           experimental  synopsis:            Yet another library for hidden Markov models

src/Learning/HMM.hs view

@@ -5,6 +5,7 @@   , withEmission   , viterbi   , baumWelch+  , baumWelch'   , simulate   ) where @@ -121,6 +122,21 @@   checkModelIn "baumWelch" model `seq`   checkDataIn "baumWelch" model xs `seq`   map (first $ fromInternal ss os) $ I.baumWelch model' xs'+  where+    ss     = states model+    os     = outputs model+    os'    = V.fromList os+    model' = toInternal model+    xs'    = U.fromList $ fromJust $ mapM (`V.elemIndex` os') xs++-- | @baumWelch' model xs@ performs the Baum-Welch algorithm using the+--   observed outputs @xs@, and returns a model locally maximizing its log+--   likelihood.+baumWelch' :: (Eq s, Eq o) => HMM s o -> [o] -> (HMM s o, LogLikelihood)+baumWelch' model xs =+  checkModelIn "baumWelch" model `seq`+  checkDataIn "baumWelch" model xs `seq`+  first (fromInternal ss os) $ I.baumWelch' model' xs'   where     ss     = states model     os     = outputs model

src/Learning/HMM/Internal.hs view

@@ -5,6 +5,7 @@   , withEmission   , viterbi   , baumWelch+  , baumWelch'   -- , baumWelch1   -- , forward   -- , backward@@ -162,6 +163,14 @@     n = U.length xs     step (m, _)     = baumWelch1 m n xs     (models, logLs) = unzip $ iterate step (model, undefined)++baumWelch' :: HMM -> U.Vector Int -> (HMM, LogLikelihood)+baumWelch' model xs = go (undefined, -1/0) (baumWelch1 model n xs)+  where+    n = U.length xs+    go (m, l) (m', l')+      | l' - l > 1.0e-9 = go (m', l') (baumWelch1 m' n xs)+      | otherwise       = (m, l')  -- | Perform one step of the Baum-Welch algorithm and return the updated --   model and the likelihood of the old model.

src/Learning/IOHMM.hs view

@@ -5,6 +5,7 @@   , withEmission   , viterbi   , baumWelch+  , baumWelch'   , simulate   ) where @@ -140,6 +141,24 @@   checkModelIn "baumWelch" model `seq`   checkDataIn "baumWelch" model xs ys `seq`   map (first $ fromInternal is ss os) $ I.baumWelch model' $ U.zip xs' ys'+  where+    is     = inputs model+    is'    = V.fromList is+    ss     = states model+    os     = outputs model+    os'    = V.fromList os+    model' = toInternal model+    xs'    = U.fromList $ fromJust $ mapM (`V.elemIndex` is') xs+    ys'    = U.fromList $ fromJust $ mapM (`V.elemIndex` os') ys++-- | @baumWelch' model xs@ performs the Baum-Welch algorithm using the+--   inputs @xs@ and outputs @ys@, and returns a model locally maximizing+--   its log likelihood.+baumWelch' :: (Eq i, Eq s, Eq o) => IOHMM i s o -> [i] -> [o] -> (IOHMM i s o, LogLikelihood)+baumWelch' model xs ys =+  checkModelIn "baumWelch" model `seq`+  checkDataIn "baumWelch" model xs ys `seq`+  first (fromInternal is ss os) $ I.baumWelch' model' $ U.zip xs' ys'   where     is     = inputs model     is'    = V.fromList is

src/Learning/IOHMM/Internal.hs view

@@ -5,6 +5,7 @@   , withEmission   , viterbi   , baumWelch+  , baumWelch'   -- , baumWelch1   -- , forward   -- , backward@@ -168,6 +169,14 @@     n = U.length xys     step (m, _)     = baumWelch1 m n xys     (models, logLs) = unzip $ iterate step (model, undefined)++baumWelch' :: IOHMM -> U.Vector (Int, Int) -> (IOHMM, LogLikelihood)+baumWelch' model xys = go (undefined, -1/0) (baumWelch1 model n xys)+  where+    n = U.length xys+    go (m, l) (m', l')+      | l' - l > 1.0e-9 = go (m', l') (baumWelch1 m' n xys)+      | otherwise       = (m, l')  -- | Perform one step of the Baum-Welch algorithm and return the updated --   model and the likelihood of the old model.