model

type ANode struct {
	// Start index (inclusive)
	Start int `json:"s"`
	// End index (exclusive)
	End int `json:"e"`
	// Name of unit being aligned.
	Name string `json:"n"`
	// Value is an arbitrary object associated to an interval.
	Value interface{} `json:"v,omitempty"`
	// Pointers to child alignments one level down.
	Children []*ANode `json:"-"`
}

type Aligner interface {

	// Alignment info for all available levels.
	Alignment() []*ANode
}

type Alignment [][]*ANode

type FloatObs struct {
	// contains filtered or unexported fields
}

type FloatObsSequence struct {
	// contains filtered or unexported fields
}

type FloatObserver struct {
	Values [][]float64
	Labels []SimpleLabel
	// contains filtered or unexported fields
}

type IntObs struct {
	// contains filtered or unexported fields
}

type Labeler interface {

	// Human-readable name.
	String() string

	// Compare labels.
	IsEqual(label Labeler) bool
}

type Modeler interface {

	// The model name.
	Name() string

	// Dimensionality of the observation vector.
	Dim() int

	Trainer
	Predictor
	Scorer
	Sampler
}

type Obs interface {

	// The observation's id.
	ID() string
	// The observation's value.
	Value() interface{}
	// The observation's label.
	Label() Labeler
}

type Observer interface {

	// Returns channel of observations.
	// The sequence ends when the channel closes.
	ObsChan() (<-chan Obs, error)
}

type Predictor interface {
	Predict(x Observer) ([]Labeler, error)
}

type Sampler interface {
	// Returns a sample drawn from the underlying distribution.
	Sample(*rand.Rand) Obs

	// Returns a sample of size "size" drawn from the underlying distribution.
	// The sequence ends when the channel closes.
	SampleChan(r *rand.Rand, size int) <-chan Obs
}

type Scorer interface {
	LogProb(x Obs) float64
}

type Seq struct {
	Vectors    [][]float64 `json:"vectors"`
	Labels     []string    `json:"labels"`
	ID         string      `json:"id"`
	Alignments []*ANode    `json:"alignments,omitempty"`
}

type SeqObserver struct {
	// contains filtered or unexported fields
}

type SimpleLabel string

type Trainer interface {

	// Updates model using weighted samples: x[i] * w(x[i]).
	Update(x Observer, w func(Obs) float64) error

	// Updates model using a single weighted sample.
	UpdateOne(o Obs, w float64)

	// Estimates model parameters.
	Estimate() error

	// Clears all model parameters.
	Clear()
}