Math.HiddenMarkovModel.Distribution

Documentation

data family T typ sh prob

Instances

(Show typ, C sh, Show sh, Show prob, Storable prob) => Show (T typ sh prob)

Defined in Math.HiddenMarkovModel.Distribution

Methods

showsPrec :: Int -> T typ sh prob -> ShowS

show :: T typ sh prob -> String

showList :: [T typ sh prob] -> ShowS

(NFData typ, NFData sh, NFData prob, C sh) => NFData (T typ sh prob)

Defined in Math.HiddenMarkovModel.Distribution

Methods

rnf :: T typ sh prob -> ()

(Format typ, C sh, Real prob) => Format (T typ sh prob)

Defined in Math.HiddenMarkovModel.Distribution

Methods

format :: Output out => String -> T typ sh prob -> out

newtype T (Gaussian emiSh) stateSh a

Defined in Math.HiddenMarkovModel.Distribution

newtype T (Gaussian emiSh) stateSh a = Gaussian (Array stateSh (a, Vector emiSh a, Upper emiSh a))

newtype T (Discrete symbol) sh prob

Defined in Math.HiddenMarkovModel.Distribution

newtype T (Discrete symbol) sh prob = Discrete (General (Set symbol) sh prob)

data family Trained typ sh prob

Instances

(Show typ, C sh, Show sh, Show prob, Storable prob) => Show (Trained typ sh prob)

Defined in Math.HiddenMarkovModel.Distribution

Methods

showsPrec :: Int -> Trained typ sh prob -> ShowS

show :: Trained typ sh prob -> String

showList :: [Trained typ sh prob] -> ShowS

(Estimate typ, C sh, Eq sh, Real prob) => Semigroup (Trained typ sh prob)

Defined in Math.HiddenMarkovModel.Distribution

Methods

(<>) :: Trained typ sh prob -> Trained typ sh prob -> Trained typ sh prob

sconcat :: NonEmpty (Trained typ sh prob) -> Trained typ sh prob

stimes :: Integral b => b -> Trained typ sh prob -> Trained typ sh prob

(NFData typ, NFData sh, NFData prob, C sh) => NFData (Trained typ sh prob)

Defined in Math.HiddenMarkovModel.Distribution

Methods

rnf :: Trained typ sh prob -> ()

newtype Trained (Gaussian emiSh) stateSh a

Defined in Math.HiddenMarkovModel.Distribution

newtype Trained (Gaussian emiSh) stateSh a = GaussianTrained (Array (stateSh, Hermitian (() :+: emiSh)) a)

newtype Trained (Discrete symbol) sh prob

Defined in Math.HiddenMarkovModel.Distribution

newtype Trained (Discrete symbol) sh prob = DiscreteTrained (T symbol (Vector sh prob))

type family Emission typ prob

Instances

type Emission (Gaussian emiSh) a

Defined in Math.HiddenMarkovModel.Distribution

type Emission (Gaussian emiSh) a = Vector emiSh a

type Emission (Discrete symbol) prob

Defined in Math.HiddenMarkovModel.Distribution

type Emission (Discrete symbol) prob = symbol

class Show typ where

Methods

showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T typ sh prob -> ShowS

showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained typ sh prob -> ShowS

Instances

(C emiSh, Show emiSh) => Show (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Gaussian emiSh) sh prob -> ShowS

showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Gaussian emiSh) sh prob -> ShowS

(Show symbol, Ord symbol) => Show (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Discrete symbol) sh prob -> ShowS

showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Discrete symbol) sh prob -> ShowS

class NFData typ where

Methods

rnf :: (NFData sh, NFData prob, C sh) => T typ sh prob -> ()

rnfTrained :: (NFData sh, NFData prob, C sh) => Trained typ sh prob -> ()

Instances

NFData emiSh => NFData (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

rnf :: (NFData sh, NFData prob, C sh) => T (Gaussian emiSh) sh prob -> ()

rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Gaussian emiSh) sh prob -> ()

NFData symbol => NFData (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

rnf :: (NFData sh, NFData prob, C sh) => T (Discrete symbol) sh prob -> ()

rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Discrete symbol) sh prob -> ()

class Format typ where

Methods

format :: (C sh, Output out, Real prob) => String -> T typ sh prob -> out

Instances

FormatArray emiSh => Format (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

format :: (C sh, Output out, Real prob) => String -> T (Gaussian emiSh) sh prob -> out

(Show symbol, Ord symbol) => Format (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

format :: (C sh, Output out, Real prob) => String -> T (Discrete symbol) sh prob -> out

class Info typ where

Methods

statesShape :: C sh => T typ sh prob -> sh

statesShapeTrained :: C sh => Trained typ sh prob -> sh

Instances

Info (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

statesShape :: C sh => T (Gaussian emiSh) sh prob -> sh

statesShapeTrained :: C sh => Trained (Gaussian emiSh) sh prob -> sh

Ord symbol => Info (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

statesShape :: C sh => T (Discrete symbol) sh prob -> sh

statesShapeTrained :: C sh => Trained (Discrete symbol) sh prob -> sh

class Generate typ where

Methods

generate :: (Indexed sh, Real prob, Random prob, RandomGen g) => T typ sh prob -> Index sh -> State g (Emission typ prob)

Instances

(C emiSh, Eq emiSh) => Generate (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

generate :: (Indexed sh, Real prob, Random prob, RandomGen g) => T (Gaussian emiSh) sh prob -> Index sh -> State g (Emission (Gaussian emiSh) prob)

Ord symbol => Generate (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

generate :: (Indexed sh, Real prob, Random prob, RandomGen g) => T (Discrete symbol) sh prob -> Index sh -> State g (Emission (Discrete symbol) prob)

class EmissionProb typ where

Minimal complete definition

mapStatesShape, emissionProb

Methods

mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T typ sh0 prob -> T typ sh1 prob

emissionProb :: (C sh, Real prob) => T typ sh prob -> Emission typ prob -> Vector sh prob

emissionStateProb :: (Indexed sh, Real prob) => T typ sh prob -> Emission typ prob -> Index sh -> prob

Instances

(C emiSh, Eq emiSh) => EmissionProb (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Gaussian emiSh) sh0 prob -> T (Gaussian emiSh) sh1 prob

emissionProb :: (C sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Vector sh prob

emissionStateProb :: (Indexed sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Index sh -> prob

Ord symbol => EmissionProb (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Discrete symbol) sh0 prob -> T (Discrete symbol) sh1 prob

emissionProb :: (C sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Vector sh prob

emissionStateProb :: (Indexed sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Index sh -> prob

class EmissionProb typ => Estimate typ where

Methods

accumulateEmissions :: (Indexed sh, Real prob, Index sh ~ state) => sh -> T [] (state, Emission typ prob) -> Trained typ sh prob

trainVector :: (C sh, Eq sh, Real prob) => Emission typ prob -> Vector sh prob -> Trained typ sh prob

combine :: (C sh, Eq sh, Real prob) => Trained typ sh prob -> Trained typ sh prob -> Trained typ sh prob

normalize :: (C sh, Eq sh, Real prob) => Trained typ sh prob -> T typ sh prob

Instances

(C emiSh, Eq emiSh) => Estimate (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

accumulateEmissions :: (Indexed sh, Real prob, Index sh ~ state) => sh -> T [] (state, Emission (Gaussian emiSh) prob) -> Trained (Gaussian emiSh) sh prob

trainVector :: (C sh, Eq sh, Real prob) => Emission (Gaussian emiSh) prob -> Vector sh prob -> Trained (Gaussian emiSh) sh prob

combine :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob

normalize :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> T (Gaussian emiSh) sh prob

Ord symbol => Estimate (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

accumulateEmissions :: (Indexed sh, Real prob, Index sh ~ state) => sh -> T [] (state, Emission (Discrete symbol) prob) -> Trained (Discrete symbol) sh prob

trainVector :: (C sh, Eq sh, Real prob) => Emission (Discrete symbol) prob -> Vector sh prob -> Trained (Discrete symbol) sh prob

combine :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob

normalize :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> T (Discrete symbol) sh prob

accumulateEmissionVectors :: (Estimate typ, C sh, Eq sh, Real prob) => T [] (Emission typ prob, Vector sh prob) -> Trained typ sh prob

data Discrete symbol

Instances

CSVSymbol symbol => FromCSV (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Discrete symbol) sh prob)

CSVSymbol symbol => ToCSV (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

toCells :: (C sh, Real prob, Show prob) => T (Discrete symbol) sh prob -> [[String]]

Ord symbol => Estimate (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

accumulateEmissions :: (Indexed sh, Real prob, Index sh ~ state) => sh -> T [] (state, Emission (Discrete symbol) prob) -> Trained (Discrete symbol) sh prob

trainVector :: (C sh, Eq sh, Real prob) => Emission (Discrete symbol) prob -> Vector sh prob -> Trained (Discrete symbol) sh prob

combine :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob

normalize :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> T (Discrete symbol) sh prob

Ord symbol => EmissionProb (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Discrete symbol) sh0 prob -> T (Discrete symbol) sh1 prob

emissionProb :: (C sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Vector sh prob

emissionStateProb :: (Indexed sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Index sh -> prob

Ord symbol => Generate (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

generate :: (Indexed sh, Real prob, Random prob, RandomGen g) => T (Discrete symbol) sh prob -> Index sh -> State g (Emission (Discrete symbol) prob)

Ord symbol => Info (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

statesShape :: C sh => T (Discrete symbol) sh prob -> sh

statesShapeTrained :: C sh => Trained (Discrete symbol) sh prob -> sh

(Show symbol, Ord symbol) => Format (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

format :: (C sh, Output out, Real prob) => String -> T (Discrete symbol) sh prob -> out

NFData symbol => NFData (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

rnf :: (NFData sh, NFData prob, C sh) => T (Discrete symbol) sh prob -> ()

rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Discrete symbol) sh prob -> ()

(Show symbol, Ord symbol) => Show (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Discrete symbol) sh prob -> ShowS

showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Discrete symbol) sh prob -> ShowS

newtype Trained (Discrete symbol) sh prob

Defined in Math.HiddenMarkovModel.Distribution

newtype Trained (Discrete symbol) sh prob = DiscreteTrained (T symbol (Vector sh prob))

newtype T (Discrete symbol) sh prob

Defined in Math.HiddenMarkovModel.Distribution

newtype T (Discrete symbol) sh prob = Discrete (General (Set symbol) sh prob)

type Emission (Discrete symbol) prob

Defined in Math.HiddenMarkovModel.Distribution

type Emission (Discrete symbol) prob = symbol

discreteFromList :: (Ord symbol, C sh, Eq sh, Real prob) => T [] (symbol, Vector sh prob) -> T (Discrete symbol) sh prob

data Gaussian emiSh

Instances

emiSh ~ ShapeInt => FromCSV (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Gaussian emiSh) sh prob)

Indexed emiSh => ToCSV (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

toCells :: (C sh, Real prob, Show prob) => T (Gaussian emiSh) sh prob -> [[String]]

(C emiSh, Eq emiSh) => Estimate (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

accumulateEmissions :: (Indexed sh, Real prob, Index sh ~ state) => sh -> T [] (state, Emission (Gaussian emiSh) prob) -> Trained (Gaussian emiSh) sh prob

trainVector :: (C sh, Eq sh, Real prob) => Emission (Gaussian emiSh) prob -> Vector sh prob -> Trained (Gaussian emiSh) sh prob

combine :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob

normalize :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> T (Gaussian emiSh) sh prob

(C emiSh, Eq emiSh) => EmissionProb (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Gaussian emiSh) sh0 prob -> T (Gaussian emiSh) sh1 prob

emissionProb :: (C sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Vector sh prob

emissionStateProb :: (Indexed sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Index sh -> prob

(C emiSh, Eq emiSh) => Generate (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

generate :: (Indexed sh, Real prob, Random prob, RandomGen g) => T (Gaussian emiSh) sh prob -> Index sh -> State g (Emission (Gaussian emiSh) prob)

Info (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

statesShape :: C sh => T (Gaussian emiSh) sh prob -> sh

statesShapeTrained :: C sh => Trained (Gaussian emiSh) sh prob -> sh

FormatArray emiSh => Format (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

format :: (C sh, Output out, Real prob) => String -> T (Gaussian emiSh) sh prob -> out

NFData emiSh => NFData (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

rnf :: (NFData sh, NFData prob, C sh) => T (Gaussian emiSh) sh prob -> ()

rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Gaussian emiSh) sh prob -> ()

(C emiSh, Show emiSh) => Show (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Gaussian emiSh) sh prob -> ShowS

showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Gaussian emiSh) sh prob -> ShowS

newtype Trained (Gaussian emiSh) stateSh a

Defined in Math.HiddenMarkovModel.Distribution

newtype Trained (Gaussian emiSh) stateSh a = GaussianTrained (Array (stateSh, Hermitian (() :+: emiSh)) a)

newtype T (Gaussian emiSh) stateSh a

Defined in Math.HiddenMarkovModel.Distribution

newtype T (Gaussian emiSh) stateSh a = Gaussian (Array stateSh (a, Vector emiSh a, Upper emiSh a))

type Emission (Gaussian emiSh) a

Defined in Math.HiddenMarkovModel.Distribution

type Emission (Gaussian emiSh) a = Vector emiSh a

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

gaussianTrained :: (C emiSh, Eq emiSh, C stateSh, Real prob) => Array stateSh (prob, Vector emiSh prob, Hermitian emiSh prob) -> Trained (Gaussian emiSh) stateSh prob

input array must be non-empty

class ToCSV typ where

Methods

toCells :: (C sh, Real prob, Show prob) => T typ sh prob -> [[String]]

Instances

Indexed emiSh => ToCSV (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

toCells :: (C sh, Real prob, Show prob) => T (Gaussian emiSh) sh prob -> [[String]]

CSVSymbol symbol => ToCSV (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

toCells :: (C sh, Real prob, Show prob) => T (Discrete symbol) sh prob -> [[String]]

class FromCSV typ where

Methods

parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T typ sh prob)

Instances

emiSh ~ ShapeInt => FromCSV (Gaussian emiSh)

Defined in Math.HiddenMarkovModel.Distribution

Methods

parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Gaussian emiSh) sh prob)

CSVSymbol symbol => FromCSV (Discrete symbol)

Defined in Math.HiddenMarkovModel.Distribution

Methods

parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (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

Defined in Math.HiddenMarkovModel.Distribution

Methods

cellFromSymbol :: Char -> String

symbolFromCell :: String -> Maybe Char

CSVSymbol Int

Defined in Math.HiddenMarkovModel.Distribution

Methods

cellFromSymbol :: Int -> String

symbolFromCell :: String -> Maybe 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.

Defined in Math.HiddenMarkovModel.Example.TrafficLightPrivate

Methods

cellFromSymbol :: Color -> String

symbolFromCell :: String -> Maybe Color