learning-hmm 0.3.1.1 → 0.3.1.2

raw patch · 4 files changed

+12/−9 lines, 4 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

CHANGES.md +3/−0
learning-hmm.cabal +1/−1
src/Learning/HMM/Internal.hs +4/−4
src/Learning/IOHMM/Internal.hs +4/−4

CHANGES.md view

@@ -1,6 +1,9 @@ Revision history for Haskell package learning-hmm === +## Version 0.3.1.2+- Default the limit of Baum-Welch iteration to 10000 (in `baumWelch'`)+ ## Version 0.3.1.1 - Bug fix release

learning-hmm.cabal view

@@ -1,5 +1,5 @@ name:                learning-hmm-version:             0.3.1.1+version:             0.3.1.2 stability:           experimental  synopsis:            Yet another library for hidden Markov models

src/Learning/HMM/Internal.hs view

@@ -146,12 +146,12 @@     (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)+baumWelch' model xs = go (10000 :: Int) (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')+    go k (m, l) (m', l')+      | k > 0 && l' - l > 1.0e-9 = go (k - 1) (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/Internal.hs view

@@ -152,12 +152,12 @@     (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)+baumWelch' model xys = go (10000 :: Int) (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')+    go k (m, l) (m', l')+      | k > 0 && l' - l > 1.0e-9 = go (k - 1) (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.