edu.stanford.nlp.tmt.model.llda

CVB0LabeledLDA

class CVB0LabeledLDA extends LabeledLDA[SoftAssignmentModelState, CVB0LabeledLDADocument, (String, Array[Array[Double]])] with SoftAssignmentModel[LabeledLDAModelParams, LabeledLDADocumentParams, CVB0LabeledLDADocument]

CVB0 learning and inference for LabeledLDA.

Linear Supertypes
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. Hide All
  2. Show all
  1. CVB0LabeledLDA
  2. SoftAssignmentModel
  3. LabeledLDA
  4. DirichletTopicSmoothing
  5. DirichletTermSmoothing
  6. ClosedTopicSet
  7. TopicModel
  8. RepCheck
  9. Stateful
  10. AnyRef
  11. Any
Visibility
  1. Public
  2. All

Instance Constructors

  1. new CVB0LabeledLDA (params: LabeledLDAModelParams, seed: Long, log: (String) ⇒ Unit)

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 asCVB0LDA : CVB0LDA

    Creates a view of this model as a standard CVB0LDA model for doing inference unconstrained by an observed set of topics on each document.

  7. def asInstanceOf [T0] : T0

    Attributes
    final
    Definition Classes
    Any
  8. var checkers : List[Function0[_]]

    Attributes
    protected
    Definition Classes
    RepCheck
  9. def checkrep (): Unit

    Assert invariants.

    Assert invariants.

    Attributes
    protected final
    Definition Classes
    RepCheck
  10. def clone (): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  11. val countTopic : Array[Double]

    How many times each topic is seen overall.

    How many times each topic is seen overall.

    Definition Classes
    SoftAssignmentModel
  12. val countTopicTerm : Array[Array[Double]]

    How many times each term is seen in each topic.

    How many times each term is seen in each topic.

    Definition Classes
    SoftAssignmentModel
  13. def create (dp: LabeledLDADocumentParams): CVB0LabeledLDADocument

    Creates a document from the given document parameters.

    Creates a document from the given document parameters.

    Definition Classes
    CVB0LabeledLDATopicModel
  14. def doAssignments (doc: CVB0LabeledLDADocument, learn: Boolean = true): Unit

  15. def doCounts (doc: CVB0LabeledLDADocument): Unit

  16. def eq (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef
  17. def equals (arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  18. def finalize (): Unit

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  19. def getClass (): java.lang.Class[_]

    Attributes
    final
    Definition Classes
    AnyRef → Any
  20. 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
  21. 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
  22. def hashCode (): Int

    Definition Classes
    AnyRef → Any
  23. def infer (doc: CVB0LabeledLDADocument): SparseArray[Double]

    Returns an array of per-label probabilities.

    Returns an array of per-label probabilities. Loops while the largest difference between iterations in probabilities for any given topic is greater than delta (default 1e-5).

    Definition Classes
    CVB0LabeledLDALabeledLDA
  24. def infer (doc: CVB0LabeledLDADocument, delta: Double): SparseArray[Double]

    Returns an array of per-label probabilities.

    Returns an array of per-label probabilities. Loops while the largest difference between iterations in probabilities for any given topic is greater than delta (default 1e-5).

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

    Does inference on the given document until convergence.

    Does inference on the given document until convergence.

    Definition Classes
    LabeledLDA
  26. def infer (doc: LabeledLDADocumentParams): SparseArray[Double]

    Does inference on the given document until convergence.

    Does inference on the given document until convergence.

    Definition Classes
    LabeledLDA
  27. def isInstanceOf [T0] : Boolean

    Attributes
    final
    Definition Classes
    Any
  28. val log : (String) ⇒ Unit

    Where log messages go.

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

    Definition Classes
    CVB0LabeledLDATopicModel
  29. def ne (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef
  30. def notify (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
  31. def notifyAll (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
  32. val numTerms : Int

    The number of terms in the model.

    The number of terms in the model.

    Definition Classes
    LabeledLDATopicModel
  33. val numTopics : Int

    The number of topics in the model.

    The number of topics in the model.

    Definition Classes
    LabeledLDAClosedTopicSet
  34. 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
    final
    Definition Classes
    SoftAssignmentModelClosedTopicSet
  35. 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
  36. val params : LabeledLDAModelParams

    The parameters used to create this model.

    The parameters used to create this model.

    Definition Classes
    CVB0LabeledLDALabeledLDATopicModel
  37. 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
  38. def reset (): Unit

    Resets to the default state.

    Resets to the default state.

    Definition Classes
    CVB0LabeledLDASoftAssignmentModelStateful
  39. val seed : Long

  40. def state : SoftAssignmentModelState

    Gets the current state of this object.

    Gets the current state of this object.

    Definition Classes
    SoftAssignmentModelStateful
  41. def state_= (state: SoftAssignmentModelState): Unit

    Sets the current state of this object.

    Sets the current state of this object.

    Definition Classes
    SoftAssignmentModelStateful
  42. def summary : Iterator[String]

    Returns human-readable summary of the current topic model.

    Returns human-readable summary of the current topic model.

    Definition Classes
    SoftAssignmentModel
  43. def synchronized [T0] (arg0: ⇒ T0): T0

    Attributes
    final
    Definition Classes
    AnyRef
  44. 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
  45. def termIndex_= (index: Option[Index[String]]): Unit

    Attributes
    protected final
    Definition Classes
    TopicModel
  46. def termSmoothDenom : Double

    Attributes
    protected
    Definition Classes
    DirichletTermSmoothing
  47. 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
  48. def termSmoothing_= (smoothing: Array[Double]): Unit

    Attributes
    protected
    Definition Classes
    DirichletTermSmoothing
  49. def toString (): String

    Definition Classes
    AnyRef → Any
  50. 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
  51. 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
  52. def tokenizer_= (tokenizer: Option[Tokenizer]): Unit

    Attributes
    protected final
    Definition Classes
    TopicModel
  53. 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
  54. def topicName (topic: Int): String

    Gets the name for this topic.

    Gets the name for this topic.

    Definition Classes
    ClosedTopicSet
  55. 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
  56. def topicSmoothing_= (smoothing: Array[Double]): Unit

    Attributes
    protected
    Definition Classes
    DirichletTopicSmoothing
  57. def wait (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()
  58. def wait (arg0: Long, arg1: Int): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()
  59. def wait (arg0: Long): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from SoftAssignmentModel[LabeledLDAModelParams, LabeledLDADocumentParams, CVB0LabeledLDADocument]

Inherited from LabeledLDA[SoftAssignmentModelState, CVB0LabeledLDADocument, (String, Array[Array[Double]])]

Inherited from DirichletTopicSmoothing

Inherited from DirichletTermSmoothing

Inherited from ClosedTopicSet

Inherited from TopicModel[LabeledLDAModelParams, SoftAssignmentModelState, LabeledLDADocumentParams, CVB0LabeledLDADocument, (String, Array[Array[Double]])]

Inherited from RepCheck

Inherited from Stateful[SoftAssignmentModelState]

Inherited from AnyRef

Inherited from Any