Math.HiddenMarkovModel.Example.CirclePrivate

Documentation

>>> import qualified Math.HiddenMarkovModel as HMM
>>> import qualified Data.NonEmpty as NonEmpty
>>> import Data.Eq.HT (equating)
>>> 
>>> checkTraining :: (Int, HMM) -> Bool
>>> checkTraining (maxDiff,hmm_) =
>>> maxDiff >=
>>> (length $ filter id $ NonEmpty.flatten $
>>> NonEmpty.zipWith (/=)
>>> (HMM.reveal hmm_ circle) (fmap fst circleLabeled))

data State

Constructors

Q1

Q2

Q3

Q4

Instances

Bounded State

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

minBound :: State

maxBound :: State

Enum State

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

succ :: State -> State

pred :: State -> State

toEnum :: Int -> State

fromEnum :: State -> Int

enumFrom :: State -> [State]

enumFromThen :: State -> State -> [State]

enumFromTo :: State -> State -> [State]

enumFromThenTo :: State -> State -> State -> [State]

Eq State

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

(==) :: State -> State -> Bool

(/=) :: State -> State -> Bool

Ord State

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

compare :: State -> State -> Ordering

(<) :: State -> State -> Bool

(<=) :: State -> State -> Bool

(>) :: State -> State -> Bool

(>=) :: State -> State -> Bool

max :: State -> State -> State

min :: State -> State -> State

Show State

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

showsPrec :: Int -> State -> ShowS

show :: State -> String

showList :: [State] -> ShowS

type StateSet = Enumeration State

stateSet :: StateSet

data Coordinate

Constructors

X

Y

Instances

Bounded Coordinate

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

minBound :: Coordinate

maxBound :: Coordinate

Enum Coordinate

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

succ :: Coordinate -> Coordinate

pred :: Coordinate -> Coordinate

toEnum :: Int -> Coordinate

fromEnum :: Coordinate -> Int

enumFrom :: Coordinate -> [Coordinate]

enumFromThen :: Coordinate -> Coordinate -> [Coordinate]

enumFromTo :: Coordinate -> Coordinate -> [Coordinate]

enumFromThenTo :: Coordinate -> Coordinate -> Coordinate -> [Coordinate]

Eq Coordinate

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

(==) :: Coordinate -> Coordinate -> Bool

(/=) :: Coordinate -> Coordinate -> Bool

Ord Coordinate

Defined in Math.HiddenMarkovModel.Example.CirclePrivate

Methods

compare :: Coordinate -> Coordinate -> Ordering

(<) :: Coordinate -> Coordinate -> Bool

(<=) :: Coordinate -> Coordinate -> Bool

(>) :: Coordinate -> Coordinate -> Bool

(>=) :: Coordinate -> Coordinate -> Bool

max :: Coordinate -> Coordinate -> Coordinate

min :: Coordinate -> Coordinate -> Coordinate

type CoordinateSet = Enumeration Coordinate

coordinateSet :: CoordinateSet

type HMM = Gaussian CoordinateSet StateSet Double

hmm :: HMM

checkTraining (0, hmm)

stateVector :: Double -> Double -> Double -> Double -> Vector StateSet Double

circleLabeled :: T [] (State, Vector CoordinateSet Double)

circle :: T [] (Vector CoordinateSet Double)

revealed :: T [] State

>>> take 20 $ NonEmpty.flatten revealed
[Q1,Q1,Q1,Q1,Q2,Q2,Q2,Q3,Q3,Q3,Q4,Q4,Q4,Q1,Q1,Q1,Q2,Q2,Q2,Q3]

equating (take 1000 . NonEmpty.flatten) revealed $ fmap fst circleLabeled

reconstructDistribution :: Gaussian CoordinateSet () Double

Sample multivariate normal distribution and reconstruct it from the samples. You should obtain the same parameters.

reconstructModel :: HMM

Generate labeled emission sequences and use them for supervised training.

checkTraining (0, reconstructModel)

hmmTrainedSupervised :: HMM

checkTraining (0, hmmTrainedSupervised)

hmmTrainedUnsupervised :: HMM

checkTraining (0, hmmTrainedUnsupervised)

hmmIterativelyTrained :: HMM

checkTraining (40, hmmIterativelyTrained)