edu.stanford.nlp.tmt.model.lda

LDA

trait LDA [ModelState, Doc <: LDADocument[DocState], DocState] extends TopicModel[LDAModelParams, ModelState, LDADocumentParams, Doc, DocState] with ClosedTopicSet with DirichletTermSmoothing with DirichletTopicSmoothing

LDA models are a TopicModel on a fixed set of k topics with dirichlet term and topic smoothing.

Linear Supertypes
DirichletTopicSmoothing, DirichletTermSmoothing, ClosedTopicSet, TopicModel[LDAModelParams, ModelState, LDADocumentParams, Doc, DocState], RepCheck, Stateful[ModelState], AnyRef, Any
Known Subclasses
CVB0LDA, GibbsLDA
Ordering
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Inherited
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  1. LDA
  2. DirichletTopicSmoothing
  3. DirichletTermSmoothing
  4. ClosedTopicSet
  5. TopicModel
  6. RepCheck
  7. Stateful
  8. AnyRef
  9. Any
Visibility
  1. Public
  2. All

Abstract Value Members

  1. def create (docParams: LDADocumentParams): Doc

    Creates a document from the given document parameters.

    Creates a document from the given document parameters.

    Attributes
    abstract
    Definition Classes
    TopicModel

  2. def infer (doc: Doc): Array[Double]

    Does inference on the given document until convergence.

    Does inference on the given document until convergence.

    Attributes
    abstract

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

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

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

    Attributes
    abstract
    Definition Classes
    ClosedTopicSet

  4. val params : LDAModelParams

    The parameters used to create this model.

    The parameters used to create this model.

    Attributes
    abstract
    Definition Classes
    LDA → TopicModel

  5. def reset (): Unit

    Resets to the default state.

    Resets to the default state.

    Attributes
    abstract
    Definition Classes
    Stateful

  6. def state : ModelState

    Gets the current state of this object.

    Gets the current state of this object.

    Attributes
    abstract
    Definition Classes
    Stateful

  7. def state_= (state: ModelState): Unit

    Sets the current state of this object.

    Sets the current state of this object.

    Attributes
    abstract
    Definition Classes
    Stateful

Concrete Value Members

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

    Attributes
    final
    Definition Classes
    AnyRef

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

    Attributes
    final
    Definition Classes
    Any

  3. def ## (): Int

    Attributes
    final
    Definition Classes
    AnyRef → Any

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

    Attributes
    final
    Definition Classes
    AnyRef

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

    Attributes
    final
    Definition Classes
    Any

  6. def asInstanceOf [T0] : T0

    Attributes
    final
    Definition Classes
    Any

  7. var checkers : List[Function0[_]]

    Attributes
    protected
    Definition Classes
    RepCheck

  8. def checkrep (): Unit

    Assert invariants.

    Assert invariants.

    Attributes
    protected final
    Definition Classes
    RepCheck

  9. def clone (): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()

  10. def computeCrossEntropy (doc: LDADocumentParams): (Double, Int)

    Computes the total cross-entropy of the terms in the second half of the document based on an estimate of theta from the terms in the fisrt half of the doucment.

    Computes the total cross-entropy of the terms in the second half of the document based on an estimate of theta from the terms in the fisrt half of the doucment. Returns (sum crossEntropy, numTerms). This is used as the basis of computePerplexity.

  11. def computeLogPW (doc: Doc): Double

    Computes the log probability for the current document.

    Computes the log probability for the current document. This measure treats the assignment to theta and the model counts as observed. Returns sum_i P(w_i | theta*, beta*). Beta maps from (topic,term) to probability.

  12. def computePerplexity (docs: Traversable[LDADocumentParams]): Double

    Computes the average per-word perplexity of the given dataset.

  13. def eq (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef

  14. def equals (arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  15. def finalize (): Unit

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Attributes
    final
    Definition Classes
    AnyRef → Any

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

    Returns the distribution over terms for the given topic.

    Returns the distribution over terms for the given topic. The return value of this method is assumed to have already incorporated the corresponding getTermSmoothing to the appropriate extent.

    Attributes
    final
    Definition Classes
    ClosedTopicSet

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

    Returns the distribution over terms for the given topic.

    Returns the distribution over terms for the given topic. The return value of this method is assumed to have already incorporated the corresponding getTermSmoothing to the appropriate extent.

    Definition Classes
    ClosedTopicSet

  19. def hashCode (): Int

    Definition Classes
    AnyRef → Any

  20. def infer (doc: String): Array[Double]

    Does inference on the given document until convergence.

  21. def infer (doc: LDADocumentParams): Array[Double]

    Does inference on the given document until convergence.

  22. def isInstanceOf [T0] : Boolean

    Attributes
    final
    Definition Classes
    Any

  23. val log : (String) ⇒ Unit

    Where log messages go.

    Where log messages go. Defaults to System.err.println.

    Definition Classes
    TopicModel

  24. def ne (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef

  25. def notify (): Unit

    Attributes
    final
    Definition Classes
    AnyRef

  26. def notifyAll (): Unit

    Attributes
    final
    Definition Classes
    AnyRef

  27. val numTerms : Int

    The number of terms in the model.

    The number of terms in the model.

    Definition Classes
    LDA → TopicModel

  28. val numTopics : Int

    The number of topics in the model.

    The number of topics in the model.

    Definition Classes
    LDA → ClosedTopicSet

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

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

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

    Definition Classes
    ClosedTopicSet

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

    Registers a function as a checker of invariants.

    Registers a function as a checker of invariants.

    Attributes
    protected
    Definition Classes
    RepCheck

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

    Attributes
    final
    Definition Classes
    AnyRef

  32. def termIndex : Option[Index[String]]

    The term index describing which terms are in the model.

    The term index describing which terms are in the model.

    Attributes
    final
    Definition Classes
    TopicModel

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

    Attributes
    protected final
    Definition Classes
    TopicModel

  34. def termSmoothDenom : Double

    Attributes
    protected
    Definition Classes
    DirichletTermSmoothing

  35. def termSmoothing : Array[Double]

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

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

    Attributes
    final
    Definition Classes
    DirichletTermSmoothing

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

    Attributes
    protected
    Definition Classes
    DirichletTermSmoothing

  37. def toString (): String

    Definition Classes
    AnyRef → Any

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

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

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

    Attributes
    protected
    Definition Classes
    TopicModel

  39. def tokenizer : Option[Tokenizer]

    The tokenizer used to break input documents into terms.

    The tokenizer used to break input documents into terms.

    Attributes
    final
    Definition Classes
    TopicModel

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

    Attributes
    protected final
    Definition Classes
    TopicModel

  41. var topicIndex : Option[Index[String]]

    The term index describing which terms are in the model.

    The term index describing which terms are in the model.

    Definition Classes
    ClosedTopicSet

  42. def topicName (topic: Int): String

    Gets the name for this topic.

    Gets the name for this topic.

    Definition Classes
    ClosedTopicSet

  43. def topicSmoothing : Array[Double]

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

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

    Attributes
    final
    Definition Classes
    DirichletTopicSmoothing

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

    Attributes
    protected
    Definition Classes
    DirichletTopicSmoothing

  45. def wait (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

  47. def wait (arg0: Long): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from DirichletTopicSmoothing

Inherited from DirichletTermSmoothing

Inherited from ClosedTopicSet

Inherited from TopicModel[LDAModelParams, ModelState, LDADocumentParams, Doc, DocState]

Inherited from RepCheck

Inherited from Stateful[ModelState]

Inherited from AnyRef

Inherited from Any