Safe Haskell	None
Language	Haskell2010

Math.HiddenMarkovModel.Distribution

Documentation

newtype State Source

Constructors

State Int

Instances

Enum State Source
Eq State Source
Ord State Source
Read State Source
Show State Source
Ix State Source
NFData State Source

type family Emission distr Source

Instances

type Emission (Gaussian a) = Vector a Source
type Emission (Discrete prob symbol) = symbol Source

type family Probability distr Source

Instances

type Probability (Gaussian a) = a Source
type Probability (Discrete prob symbol) = prob Source

type family Trained distr Source

Instances

type Trained (Gaussian a) = GaussianTrained a Source
type Trained (Discrete prob symbol) = DiscreteTrained prob symbol Source

class (Container Vector (Probability distr), Product (Probability distr)) => Info distr where Source

Methods

numberOfStates :: distr -> Int Source

Instances

Field a => Info (Gaussian a) Source
(Container Vector prob, Product prob, Ord symbol) => Info (Discrete prob symbol) Source

class (Container Vector (Probability distr), Product (Probability distr)) => Generate distr where Source

Methods

generate :: (RandomGen g, Probability distr ~ prob, Emission distr ~ emission) => distr -> State -> State g emission Source

Instances

Field a => Generate (Gaussian a) Source
(Container Vector prob, Product prob, Ord symbol, Ord prob, Random prob) => Generate (Discrete prob symbol) Source

class (Container Vector (Probability distr), Product (Probability distr)) => EmissionProb distr where Source

Minimal complete definition

emissionProb

Methods

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

emissionStateProb :: distr -> Emission distr -> State -> Probability distr Source

Instances

(Numeric a, Field a) => EmissionProb (Gaussian a) Source
(Container Vector prob, Product prob, Ord symbol) => EmissionProb (Discrete prob symbol) Source

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

Associated Types

type Distribution tdistr Source

Methods

accumulateEmissions :: (Distribution tdistr ~ distr, Probability distr ~ prob) => [[(Emission distr, prob)]] -> tdistr Source

combine :: tdistr -> tdistr -> tdistr Source

normalize :: (Distribution tdistr ~ distr) => tdistr -> distr Source

Instances

(Numeric a, Field a) => Estimate (GaussianTrained a) Source
(Container Vector prob, Product prob, Ord symbol) => Estimate (DiscreteTrained prob symbol) Source

newtype Discrete prob symbol Source

Constructors

Discrete (Map symbol (Vector prob))

Instances

(Show prob, Show symbol, Storable prob) => Show (Discrete prob symbol) Source
(NFData prob, NFData symbol) => NFData (Discrete prob symbol) Source
(Field prob, Show prob, Read prob, CSVSymbol symbol) => CSV (Discrete prob symbol) Source
(Container Vector prob, Product prob, Ord symbol) => EmissionProb (Discrete prob symbol) Source
(Container Vector prob, Product prob, Ord symbol, Ord prob, Random prob) => Generate (Discrete prob symbol) Source
(Container Vector prob, Product prob, Ord symbol) => Info (Discrete prob symbol) Source
type Trained (Discrete prob symbol) = DiscreteTrained prob symbol Source
type Emission (Discrete prob symbol) = symbol Source
type Probability (Discrete prob symbol) = prob Source

newtype DiscreteTrained prob symbol Source

Constructors

DiscreteTrained (Map symbol (Vector prob))

Instances

(Show prob, Show symbol, Storable prob) => Show (DiscreteTrained prob symbol) Source
(NFData prob, NFData symbol) => NFData (DiscreteTrained prob symbol) Source
(Container Vector prob, Product prob, Ord symbol) => Estimate (DiscreteTrained prob symbol) Source
type Distribution (DiscreteTrained prob symbol) = Discrete prob symbol Source

newtype Gaussian a Source

Constructors

Gaussian (Array State (Vector a, Matrix a, a))

Instances

(Show a, Element a) => Show (Gaussian a) Source
(NFData a, Storable a) => NFData (Gaussian a) Source
(Field a, Eq a, Show a, Read a) => CSV (Gaussian a) Source
(Numeric a, Field a) => EmissionProb (Gaussian a) Source
Field a => Generate (Gaussian a) Source
Field a => Info (Gaussian a) Source
type Trained (Gaussian a) = GaussianTrained a Source
type Emission (Gaussian a) = Vector a Source
type Probability (Gaussian a) = a Source

newtype GaussianTrained a Source

Constructors

GaussianTrained (Map State (Vector a, Matrix a, a))

Instances

(Show a, Element a) => Show (GaussianTrained a) Source
(NFData a, Storable a) => NFData (GaussianTrained a) Source
(Numeric a, Field a) => Estimate (GaussianTrained a) Source
type Distribution (GaussianTrained a) = Gaussian a Source

gaussian :: Field prob => [(Vector prob, Matrix prob)] -> Gaussian prob Source

class CSV distr where Source

Methods

toCells :: distr -> [[String]] Source

parseCells :: Int -> CSVParser distr Source

Instances

(Field a, Eq a, Show a, Read a) => CSV (Gaussian a) Source
(Field prob, Show prob, Read prob, CSVSymbol symbol) => CSV (Discrete prob symbol) 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
CSVSymbol 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.