LabeledLDA

def create (docParams: LabeledLDADocumentParams): Doc

Creates a document from the given document parameters.

def infer (doc: Doc): SparseArray[Double]

Does inference on the given document until convergence.

def pTopicTerm (topic: Int, term: Int): Double

Returns the probability of the given term in the given topic.

val params : LabeledLDAModelParams

The parameters used to create this model.

def reset (): Unit

Resets to the default state.

def state : ModelState

Gets the current state of this object.

def state_= (state: ModelState): Unit

Sets the current state of this object.

def != (arg0: AnyRef): Boolean

def != (arg0: Any): Boolean

def ## (): Int

def == (arg0: AnyRef): Boolean

def == (arg0: Any): Boolean

def asInstanceOf [T0] : T0

var checkers : List[Function0[_]]

def checkrep (): Unit

Assert invariants.

def clone (): AnyRef

def eq (arg0: AnyRef): Boolean

def equals (arg0: Any): Boolean

def finalize (): Unit

def getClass (): java.lang.Class[_]

def getTopicTermDistribution (topic: String): Array[Double]

Returns the distribution over terms for the given topic.

def getTopicTermDistribution (topic: Int): Array[Double]

Returns the distribution over terms for the given topic.

def hashCode (): Int

def infer (doc: String, labels: Array[String]): SparseArray[Double]

Does inference on the given document until convergence.

def infer (doc: LabeledLDADocumentParams): SparseArray[Double]

Does inference on the given document until convergence.

def isInstanceOf [T0] : Boolean

val log : (String) ⇒ Unit

Where log messages go.

def ne (arg0: AnyRef): Boolean

def notify (): Unit

def notifyAll (): Unit

val numTerms : Int

The number of terms in the model.

val numTopics : Int

The number of topics in the model.

def pTopicTerm (topic: String, term: String): Double

Returns the probability of the given term in the given topic.

def registerCheck (check: Function0[_]): Unit

Registers a function as a checker of invariants.

def synchronized [T0] (arg0: ⇒ T0): T0

def termIndex : Option[Index[String]]

The term index describing which terms are in the model.

def termIndex_= (index: Option[Index[String]]): Unit

def termSmoothDenom : Double

def termSmoothing : Array[Double]

Add-k prior counts for each term (eta in the model formulation).

def termSmoothing_= (smoothing: Array[Double]): Unit

def toString (): String

def tokenize (document: String): Iterable[Int]

Tokenizes the given input string using our stored tokenizer and term index, if available.

def tokenizer : Option[Tokenizer]

The tokenizer used to break input documents into terms.

def tokenizer_= (tokenizer: Option[Tokenizer]): Unit

var topicIndex : Option[Index[String]]

The term index describing which terms are in the model.

def topicName (topic: Int): String

Gets the name for this topic.

def topicSmoothing : Array[Double]

Prior counts for each topic (alpha in the model formulation).

def topicSmoothing_= (smoothing: Array[Double]): Unit

def wait (): Unit

def wait (arg0: Long, arg1: Int): Unit

def wait (arg0: Long): Unit