Safe Haskell	None
Language	Haskell2010

Math.HiddenMarkovModel.Distribution

Synopsis

data family T typ sh prob
data family Trained typ sh prob
type family Emission typ prob
class Show typ where
- 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
class NFData typ where
- rnf :: (NFData sh, NFData prob, C sh) => T typ sh prob -> ()
- rnfTrained :: (NFData sh, NFData prob, C sh) => Trained typ sh prob -> ()
class Format typ where
- format :: (C sh, Output out, Real prob) => String -> T typ sh prob -> out
class Info typ where
- statesShape :: C sh => T typ sh prob -> sh
- statesShapeTrained :: C sh => Trained typ sh prob -> sh
class Generate typ where
- generate :: (Indexed sh, Real prob, Random prob, RandomGen g) => T typ sh prob -> Index sh -> State g (Emission typ prob)
class EmissionProb typ where
- 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
class EmissionProb typ => Estimate typ where
- 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
accumulateEmissionVectors :: (Estimate typ, C sh, Eq sh, Real prob) => T [] (Emission typ prob, Vector sh prob) -> Trained typ sh prob
data Discrete symbol
discreteFromList :: (Ord symbol, C sh, Eq sh, Real prob) => T [] (symbol, Vector sh prob) -> T (Discrete symbol) sh prob
data Gaussian emiSh
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
class ToCSV typ where
- toCells :: (C sh, Real prob, Show prob) => T typ sh prob -> [[String]]
class FromCSV typ where
- parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T typ sh prob)
type CSVParser = StateT CSVResult (Exceptional String)
class Ord symbol => CSVSymbol symbol where
- cellFromSymbol :: symbol -> String
- symbolFromCell :: String -> Maybe symbol

Documentation

data family T typ sh prob Source #

Instances

(Show typ, C sh, Show sh, Show prob, Storable prob) => Show (T typ sh prob) Source #
Instance details 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) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods rnf :: T typ sh prob -> () #
(Format typ, C sh, Real prob) => Format (T typ sh prob) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods format :: Output out => String -> T typ sh prob -> out #
newtype T (Gaussian emiSh) stateSh a Source #
Instance details 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 Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution newtype T (Discrete symbol) sh prob = Discrete (General (Set symbol) sh prob)

data family Trained typ sh prob Source #

Instances

(Show typ, C sh, Show sh, Show prob, Storable prob) => Show (Trained typ sh prob) Source #
Instance details 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) Source #
Instance details 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) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods rnf :: Trained typ sh prob -> () #
newtype Trained (Gaussian emiSh) stateSh a Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution newtype Trained (Gaussian emiSh) stateSh a = GaussianTrained (Array (stateSh, Hermitian (() :+: emiSh)) a)
newtype Trained (Discrete symbol) sh prob Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution newtype Trained (Discrete symbol) sh prob = DiscreteTrained (T symbol (Vector sh prob))

type family Emission typ prob Source #

Instances

type Emission (Gaussian emiSh) a Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution type Emission (Gaussian emiSh) a = Vector emiSh a
type Emission (Discrete symbol) prob Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution type Emission (Discrete symbol) prob = symbol

class Show typ where Source #

Methods

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

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

Instances

(C emiSh, Show emiSh) => Show (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Gaussian emiSh) sh prob -> ShowS Source # showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Gaussian emiSh) sh prob -> ShowS Source #
(Show symbol, Ord symbol) => Show (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Discrete symbol) sh prob -> ShowS Source # showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Discrete symbol) sh prob -> ShowS Source #

class NFData typ where Source #

Methods

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

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

Instances

NFData emiSh => NFData (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods rnf :: (NFData sh, NFData prob, C sh) => T (Gaussian emiSh) sh prob -> () Source # rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Gaussian emiSh) sh prob -> () Source #
NFData symbol => NFData (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods rnf :: (NFData sh, NFData prob, C sh) => T (Discrete symbol) sh prob -> () Source # rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Discrete symbol) sh prob -> () Source #

class Format typ where Source #

Methods

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

Instances

FormatArray emiSh => Format (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods format :: (C sh, Output out, Real prob) => String -> T (Gaussian emiSh) sh prob -> out Source #
(Show symbol, Ord symbol) => Format (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods format :: (C sh, Output out, Real prob) => String -> T (Discrete symbol) sh prob -> out Source #

class Info typ where Source #

Methods

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

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

Instances

Info (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods statesShape :: C sh => T (Gaussian emiSh) sh prob -> sh Source # statesShapeTrained :: C sh => Trained (Gaussian emiSh) sh prob -> sh Source #
Ord symbol => Info (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods statesShape :: C sh => T (Discrete symbol) sh prob -> sh Source # statesShapeTrained :: C sh => Trained (Discrete symbol) sh prob -> sh Source #

class Generate typ where Source #

Methods

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

Instances

(C emiSh, Eq emiSh) => Generate (Gaussian emiSh) Source #
Instance details 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) Source #
Ord symbol => Generate (Discrete symbol) Source #
Instance details 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) Source #

class EmissionProb typ where Source #

Minimal complete definition

mapStatesShape, emissionProb

Methods

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

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

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

Instances

(C emiSh, Eq emiSh) => EmissionProb (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Gaussian emiSh) sh0 prob -> T (Gaussian emiSh) sh1 prob Source # emissionProb :: (C sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Vector sh prob Source # emissionStateProb :: (Indexed sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Index sh -> prob Source #
Ord symbol => EmissionProb (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Discrete symbol) sh0 prob -> T (Discrete symbol) sh1 prob Source # emissionProb :: (C sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Vector sh prob Source # emissionStateProb :: (Indexed sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Index sh -> prob Source #

class EmissionProb typ => Estimate typ where Source #

Methods

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

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

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

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

Instances

(C emiSh, Eq emiSh) => Estimate (Gaussian emiSh) Source #
Instance details 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 Source # trainVector :: (C sh, Eq sh, Real prob) => Emission (Gaussian emiSh) prob -> Vector sh prob -> Trained (Gaussian emiSh) sh prob Source # combine :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob Source # normalize :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> T (Gaussian emiSh) sh prob Source #
Ord symbol => Estimate (Discrete symbol) Source #
Instance details 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 Source # trainVector :: (C sh, Eq sh, Real prob) => Emission (Discrete symbol) prob -> Vector sh prob -> Trained (Discrete symbol) sh prob Source # combine :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob Source # normalize :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> T (Discrete symbol) sh prob Source #

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

data Discrete symbol Source #

Instances

CSVSymbol symbol => FromCSV (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Discrete symbol) sh prob) Source #
CSVSymbol symbol => ToCSV (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods toCells :: (C sh, Real prob, Show prob) => T (Discrete symbol) sh prob -> [[String]] Source #
Ord symbol => Estimate (Discrete symbol) Source #
Instance details 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 Source # trainVector :: (C sh, Eq sh, Real prob) => Emission (Discrete symbol) prob -> Vector sh prob -> Trained (Discrete symbol) sh prob Source # combine :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob -> Trained (Discrete symbol) sh prob Source # normalize :: (C sh, Eq sh, Real prob) => Trained (Discrete symbol) sh prob -> T (Discrete symbol) sh prob Source #
Ord symbol => EmissionProb (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Discrete symbol) sh0 prob -> T (Discrete symbol) sh1 prob Source # emissionProb :: (C sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Vector sh prob Source # emissionStateProb :: (Indexed sh, Real prob) => T (Discrete symbol) sh prob -> Emission (Discrete symbol) prob -> Index sh -> prob Source #
Ord symbol => Generate (Discrete symbol) Source #
Instance details 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) Source #
Ord symbol => Info (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods statesShape :: C sh => T (Discrete symbol) sh prob -> sh Source # statesShapeTrained :: C sh => Trained (Discrete symbol) sh prob -> sh Source #
(Show symbol, Ord symbol) => Format (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods format :: (C sh, Output out, Real prob) => String -> T (Discrete symbol) sh prob -> out Source #
NFData symbol => NFData (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods rnf :: (NFData sh, NFData prob, C sh) => T (Discrete symbol) sh prob -> () Source # rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Discrete symbol) sh prob -> () Source #
(Show symbol, Ord symbol) => Show (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Discrete symbol) sh prob -> ShowS Source # showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Discrete symbol) sh prob -> ShowS Source #
newtype Trained (Discrete symbol) sh prob Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution newtype Trained (Discrete symbol) sh prob = DiscreteTrained (T symbol (Vector sh prob))
newtype T (Discrete symbol) sh prob Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution newtype T (Discrete symbol) sh prob = Discrete (General (Set symbol) sh prob)
type Emission (Discrete symbol) prob Source #
Instance details 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 Source #

data Gaussian emiSh Source #

Instances

emiSh ~ ShapeInt => FromCSV (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Gaussian emiSh) sh prob) Source #
Indexed emiSh => ToCSV (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods toCells :: (C sh, Real prob, Show prob) => T (Gaussian emiSh) sh prob -> [[String]] Source #
(C emiSh, Eq emiSh) => Estimate (Gaussian emiSh) Source #
Instance details 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 Source # trainVector :: (C sh, Eq sh, Real prob) => Emission (Gaussian emiSh) prob -> Vector sh prob -> Trained (Gaussian emiSh) sh prob Source # combine :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob -> Trained (Gaussian emiSh) sh prob Source # normalize :: (C sh, Eq sh, Real prob) => Trained (Gaussian emiSh) sh prob -> T (Gaussian emiSh) sh prob Source #
(C emiSh, Eq emiSh) => EmissionProb (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods mapStatesShape :: (C sh0, C sh1) => (sh0 -> sh1) -> T (Gaussian emiSh) sh0 prob -> T (Gaussian emiSh) sh1 prob Source # emissionProb :: (C sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Vector sh prob Source # emissionStateProb :: (Indexed sh, Real prob) => T (Gaussian emiSh) sh prob -> Emission (Gaussian emiSh) prob -> Index sh -> prob Source #
(C emiSh, Eq emiSh) => Generate (Gaussian emiSh) Source #
Instance details 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) Source #
Info (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods statesShape :: C sh => T (Gaussian emiSh) sh prob -> sh Source # statesShapeTrained :: C sh => Trained (Gaussian emiSh) sh prob -> sh Source #
FormatArray emiSh => Format (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods format :: (C sh, Output out, Real prob) => String -> T (Gaussian emiSh) sh prob -> out Source #
NFData emiSh => NFData (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods rnf :: (NFData sh, NFData prob, C sh) => T (Gaussian emiSh) sh prob -> () Source # rnfTrained :: (NFData sh, NFData prob, C sh) => Trained (Gaussian emiSh) sh prob -> () Source #
(C emiSh, Show emiSh) => Show (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods showsPrec :: (C sh, Show sh, Show prob, Storable prob) => Int -> T (Gaussian emiSh) sh prob -> ShowS Source # showsPrecTrained :: (C sh, Show sh, Show prob, Storable prob) => Int -> Trained (Gaussian emiSh) sh prob -> ShowS Source #
newtype Trained (Gaussian emiSh) stateSh a Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution newtype Trained (Gaussian emiSh) stateSh a = GaussianTrained (Array (stateSh, Hermitian (() :+: emiSh)) a)
newtype T (Gaussian emiSh) stateSh a Source #
Instance details 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 Source #
Instance details 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 Source #

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

input array must be non-empty

class ToCSV typ where Source #

Methods

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

Instances

Indexed emiSh => ToCSV (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods toCells :: (C sh, Real prob, Show prob) => T (Gaussian emiSh) sh prob -> [[String]] Source #
CSVSymbol symbol => ToCSV (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods toCells :: (C sh, Real prob, Show prob) => T (Discrete symbol) sh prob -> [[String]] Source #

class FromCSV typ where Source #

Methods

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

Instances

emiSh ~ ShapeInt => FromCSV (Gaussian emiSh) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Gaussian emiSh) sh prob) Source #
CSVSymbol symbol => FromCSV (Discrete symbol) Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods parseCells :: (C sh, Eq sh, Real prob, Read prob) => sh -> CSVParser (T (Discrete symbol) sh prob) Source #

type CSVParser = StateT CSVResult (Exceptional String) Source #

class Ord symbol => CSVSymbol symbol where Source #

Methods

cellFromSymbol :: symbol -> String Source #

symbolFromCell :: String -> Maybe symbol Source #

Instances

CSVSymbol Char Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods cellFromSymbol :: Char -> String Source # symbolFromCell :: String -> Maybe Char Source #
CSVSymbol Int Source #
Instance details Defined in Math.HiddenMarkovModel.Distribution Methods cellFromSymbol :: Int -> String Source # symbolFromCell :: String -> Maybe Int Source #
CSVSymbol Color Source #	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.
Instance details Defined in Math.HiddenMarkovModel.Example.TrafficLightPrivate Methods cellFromSymbol :: Color -> String Source # symbolFromCell :: String -> Maybe Color Source #