Math.HiddenMarkovModel.Distribution

Documentation

type family Emission distr

type family Probability distr

type family StateShape distr

class Real (Probability distr) => Info distr where

Methods

statesShape :: distr -> StateShape distr

Instances

(Indexed stateSh, Eq stateSh, Real a) => Info (Gaussian emiSh stateSh a)

(C sh, Real prob, Ord symbol) => Info (Discrete symbol sh prob)

class Real (Probability distr) => Generate distr where

Methods

generate :: (RandomGen g, Emission distr ~ emission, StateShape distr ~ sh) => distr -> Index sh -> State g emission

Instances

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => Generate (Gaussian emiSh stateSh a)

(Indexed sh, Real prob, Ord symbol, Ord prob, Random prob) => Generate (Discrete symbol sh prob)

class (Indexed (StateShape distr), Real (Probability distr)) => EmissionProb distr where

Methods

emissionProb :: distr -> Emission distr -> Vector (StateShape distr) (Probability distr)

emissionStateProb :: distr -> Emission distr -> Index (StateShape distr) -> Probability distr

Instances

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => EmissionProb (Gaussian emiSh stateSh a)

(Indexed sh, Real prob, Ord symbol) => EmissionProb (Discrete symbol sh prob)

class (Distribution tdistr ~ distr, Trained distr ~ tdistr, EmissionProb distr) => Estimate tdistr distr where

Associated Types

type Distribution tdistr

type Trained distr

Methods

accumulateEmissions :: (Probability distr ~ prob, StateShape distr ~ sh) => Array sh [(Emission distr, prob)] -> tdistr

combine :: tdistr -> tdistr -> tdistr

normalize :: tdistr -> distr

Instances

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => Estimate (GaussianTrained emiSh stateSh a) (Gaussian emiSh stateSh a)

(Indexed sh, Eq sh, Real prob, Ord symbol) => Estimate (DiscreteTrained symbol sh prob) (Discrete symbol sh prob)

newtype Discrete symbol sh prob

Constructors

Discrete (Map symbol (Vector sh prob))

Instances

(Show symbol, Show sh, Show prob, Storable prob, C sh) => Show (Discrete symbol sh prob)

(NFData sh, NFData prob, NFData symbol) => NFData (Discrete symbol sh prob)

(FormatArray sh, Real prob, Format symbol) => Format (Discrete symbol sh prob)

(C sh, Real prob, Show prob, Read prob, CSVSymbol symbol) => FromCSV (Discrete symbol sh prob)

(C sh, Real prob, Show prob, Read prob, CSVSymbol symbol) => ToCSV (Discrete symbol sh prob)

(Indexed sh, Real prob, Ord symbol) => EmissionProb (Discrete symbol sh prob)

(Indexed sh, Real prob, Ord symbol, Ord prob, Random prob) => Generate (Discrete symbol sh prob)

(C sh, Real prob, Ord symbol) => Info (Discrete symbol sh prob)

(Indexed sh, Eq sh, Real prob, Ord symbol) => Estimate (DiscreteTrained symbol sh prob) (Discrete symbol sh prob)

newtype DiscreteTrained symbol sh prob

Constructors

DiscreteTrained (Map symbol (Vector sh prob))

Instances

(Show symbol, Show sh, Show prob, Storable prob, C sh) => Show (DiscreteTrained symbol sh prob)

(NFData sh, NFData prob, NFData symbol) => NFData (DiscreteTrained symbol sh prob)

(Indexed sh, Eq sh, Real prob, Ord symbol) => Estimate (DiscreteTrained symbol sh prob) (Discrete symbol sh prob)

newtype Gaussian emiSh stateSh a

Constructors

Gaussian (Array stateSh (Vector emiSh a, UpperTriangular emiSh a, a))

Instances

(Show emiSh, Show stateSh, Show a, Storable a, C emiSh, C stateSh) => Show (Gaussian emiSh stateSh a)

(NFData emiSh, NFData stateSh, C stateSh, NFData a, Storable a) => NFData (Gaussian emiSh stateSh a)

(FormatArray emiSh, C stateSh, Real a) => Format (Gaussian emiSh stateSh a)

(~ * emiSh ZeroInt, Indexed stateSh, Real a, Eq a, Show a, Read a) => FromCSV (Gaussian emiSh stateSh a)

(Indexed emiSh, Indexed stateSh, Real a, Eq a, Show a, Read a) => ToCSV (Gaussian emiSh stateSh a)

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => EmissionProb (Gaussian emiSh stateSh a)

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => Generate (Gaussian emiSh stateSh a)

(Indexed stateSh, Eq stateSh, Real a) => Info (Gaussian emiSh stateSh a)

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => Estimate (GaussianTrained emiSh stateSh a) (Gaussian emiSh stateSh a)

newtype GaussianTrained emiSh stateSh a

Constructors

GaussianTrained (Array stateSh (Maybe (Vector emiSh a, HermitianMatrix emiSh a, a)))

Instances

(Show emiSh, Show stateSh, Show a, Storable a, C emiSh, C stateSh) => Show (GaussianTrained emiSh stateSh a)

(NFData emiSh, NFData stateSh, C stateSh, NFData a, Storable a) => NFData (GaussianTrained emiSh stateSh a)

(C emiSh, Eq emiSh, Indexed stateSh, Eq stateSh, Real a) => Estimate (GaussianTrained emiSh stateSh a) (Gaussian emiSh stateSh a)

gaussian :: (C emiSh, C stateSh, Real prob) => Array stateSh (Vector emiSh prob, HermitianMatrix emiSh prob) -> Gaussian emiSh stateSh prob

class ToCSV distr where

Methods

toCells :: distr -> [[String]]

Instances

(Indexed emiSh, Indexed stateSh, Real a, Eq a, Show a, Read a) => ToCSV (Gaussian emiSh stateSh a)

(C sh, Real prob, Show prob, Read prob, CSVSymbol symbol) => ToCSV (Discrete symbol sh prob)

class FromCSV distr where

Methods

parseCells :: StateShape distr -> CSVParser distr

Instances

(~ * emiSh ZeroInt, Indexed stateSh, Real a, Eq a, Show a, Read a) => FromCSV (Gaussian emiSh stateSh a)

(C sh, Real prob, Show prob, Read prob, CSVSymbol symbol) => FromCSV (Discrete symbol sh prob)

type CSVParser = StateT CSVResult (Exceptional String)

class Ord symbol => CSVSymbol symbol where

Methods

cellFromSymbol :: symbol -> String

symbolFromCell :: String -> Maybe symbol

Instances

CSVSymbol Char

CSVSymbol Int

CSVSymbol Color

Using show and read is not always a good choice since they must format and parse Haskell expressions which is not of much use to the outside world.