module Math.HiddenMarkovModel.Example.SineWave
where
import qualified Math.HiddenMarkovModel as HMM
import qualified Math.HiddenMarkovModel.Distribution as Distr
import qualified Numeric.Container as NC
import qualified Data.Packed.Matrix as Matrix
import qualified Data.Packed.Vector as Vector
import qualified Data.NonEmpty.Class as NonEmptyC
import qualified Data.NonEmpty as NonEmpty
import Data.Function.HT (nest)
import Data.Tuple.HT (mapSnd)
hmm :: HMM.Gaussian Double
hmm =
HMM.Cons {
HMM.initial = Vector.fromList [1/4, 1/4, 1/4, 1/4],
HMM.transition =
Matrix.fromLists $
[0.9, 0.0, 0.0, 0.1] :
[0.1, 0.9, 0.0, 0.0] :
[0.0, 0.1, 0.9, 0.0] :
[0.0, 0.0, 0.1, 0.9] :
[],
HMM.distribution =
Distr.gaussian $
(Vector.fromList [ 0], Matrix.fromLists [[1]]) :
(Vector.fromList [ 1], Matrix.fromLists [[1]]) :
(Vector.fromList [ 0], Matrix.fromLists [[1]]) :
(Vector.fromList [1], Matrix.fromLists [[1]]) :
[]
}
sineWaveLabeled :: NonEmpty.T [] (HMM.State, Double)
sineWaveLabeled =
NonEmpty.mapTail (take 200) $
fmap (\x -> (HMM.state $ mod (floor (x*2/pi+0.5)) 4, sin x)) $
NonEmptyC.iterate (0.1+) 0
sineWave :: NonEmpty.T [] Double
sineWave = fmap snd sineWaveLabeled
revealed :: NonEmpty.T [] HMM.State
revealed = HMM.reveal hmmTrainedSupervised $ fmap NC.scalar sineWave
hmmTrainedSupervised :: HMM.Gaussian Double
hmmTrainedSupervised =
HMM.finishTraining $ HMM.trainSupervised 4 $
fmap (mapSnd NC.scalar) sineWaveLabeled
hmmTrainedUnsupervised :: HMM.Gaussian Double
hmmTrainedUnsupervised =
HMM.finishTraining $ HMM.trainUnsupervised hmm $ fmap NC.scalar sineWave
hmmIterativelyTrained :: HMM.Gaussian Double
hmmIterativelyTrained =
nest 100
(\model ->
HMM.finishTraining $ HMM.trainUnsupervised model $
fmap NC.scalar sineWave)
hmm