Safe Haskell	None
Language	Haskell2010

Math.HiddenMarkovModel.Example.Circle

Description

Warning: do not import that module, it is only intended for demonstration

Example of an HMM with continuous emissions with two-dimensional observations. We train a model to accept a parametric curve of a circle with a certain speed. This is like Math.HiddenMarkovModel.Example.SineWave but in two dimensions.

The four hidden states correspond to the four quadrants.

Synopsis

data State
- = Q1
- | Q2
- | Q3
- | Q4
type StateSet = Enumeration State
stateSet :: StateSet
data Coordinate
- = X
- | Y
type CoordinateSet = Enumeration Coordinate
coordinateSet :: CoordinateSet
type HMM = Gaussian CoordinateSet StateSet Double
hmm :: HMM
stateVector :: Double -> Double -> Double -> Double -> Vector StateSet Double
circleLabeled :: T [] (State, Vector CoordinateSet Double)
circle :: T [] (Vector CoordinateSet Double)
revealed :: T [] State
reconstructDistribution :: Gaussian CoordinateSet () Double
reconstructModel :: HMM
hmmTrainedSupervised :: HMM
hmmTrainedUnsupervised :: HMM
hmmIterativelyTrained :: HMM

Documentation

data State Source #

Constructors

Q1
Q2
Q3
Q4

Instances

Bounded State Source #
Instance details Defined in Math.HiddenMarkovModel.Example.CirclePrivate Methods minBound :: State # maxBound :: State #
Enum State Source #
Instance details 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 Source #
Instance details Defined in Math.HiddenMarkovModel.Example.CirclePrivate Methods (==) :: State -> State -> Bool # (/=) :: State -> State -> Bool #
Ord State Source #
Instance details 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 #

type StateSet = Enumeration State Source #

stateSet :: StateSet Source #

data Coordinate Source #

Constructors

X
Y

Instances

Bounded Coordinate Source #
Instance details Defined in Math.HiddenMarkovModel.Example.CirclePrivate Methods minBound :: Coordinate # maxBound :: Coordinate #
Enum Coordinate Source #
Instance details 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 Source #
Instance details Defined in Math.HiddenMarkovModel.Example.CirclePrivate Methods (==) :: Coordinate -> Coordinate -> Bool # (/=) :: Coordinate -> Coordinate -> Bool #
Ord Coordinate Source #
Instance details 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 Source #

coordinateSet :: CoordinateSet Source #

type HMM = Gaussian CoordinateSet StateSet Double Source #

hmm :: HMM Source #

stateVector :: Double -> Double -> Double -> Double -> Vector StateSet Double Source #

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

circle :: T [] (Vector CoordinateSet Double) Source #

revealed :: T [] State Source #

reconstructDistribution :: Gaussian CoordinateSet () Double Source #

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

reconstructModel :: HMM Source #

Generate labeled emission sequences and use them for supervised training.

hmmTrainedSupervised :: HMM Source #

hmmTrainedUnsupervised :: HMM Source #

hmmIterativelyTrained :: HMM Source #