edu.stanford.nlp.tmt.model.pldp.PLDP

Modeler

class Modeler [Document <: Document, DocumentCollection <: Iterable[Document]] extends AnyRef

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  1. Modeler
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Instance Constructors

  1. new Modeler (model: PLDP, documents: DocumentCollection, random: Random)

Value Members

  1. def != (arg0: AnyRef): Boolean

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  2. def != (arg0: Any): Boolean

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  3. def ## (): Int

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  4. def == (arg0: AnyRef): Boolean

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  5. def == (arg0: Any): Boolean

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  6. def asInstanceOf [T0] : T0

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  7. def clone (): AnyRef

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  8. def countLabelTopic (label: Int, topic: Int): Int

    How often was the given label and topic observed globally.

  9. def countLabelTopicTerm (label: Int, topic: Int, term: Int): Int

    How often was the given label and topic observed with the given term.

  10. val documents : DocumentCollection

  11. def eq (arg0: AnyRef): Boolean

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  12. def equals (arg0: Any): Boolean

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  13. def finalize (): Unit

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

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  15. def hashCode (): Int

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  16. def isInstanceOf [T0] : Boolean

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  17. val model : PLDP

  18. def ne (arg0: AnyRef): Boolean

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  19. def notify (): Unit

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  20. def notifyAll (): Unit

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  21. val pNewTopic : Array[Double]

    Fixed held-out probability of using a new topic for any given term.

  22. val random : Random

  23. def sample (doc: Document): Unit

    Resamples all assignments in the current document.

  24. def sampleActiveLabelTopic (doc: Document, term: Int): ActiveLabelTopic

    Resamples a new LabelTopic for the given term in the given doc.

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

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  26. def toString (): String

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  27. def updateLabelTopicTerm (label: Int, topic: Int, term: Int, update: Int): Unit

    Updates the given (label,topic,term) by += update

  28. def wait (): Unit

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

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  30. def wait (arg0: Long): Unit

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