cc.factorie.app.topics.lda

LDA

class LDA extends AnyRef

Typical recommended value for alpha1 is 50/numTopics.

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RecursiveLDA
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Instance Constructors

  1. new LDA(numTopics: Int, alpha1: Double, beta1: Double, burnIn: Int)(implicit random: Random)

  2. new LDA(wordSeqDomain: CategoricalSeqDomain[String], numTopics: Int = 10, alpha1: Double = 0.1, beta1: Double = 0.01, burnIn: Int = 100)(implicit model: MutableDirectedModel, random: Random)

Type Members

  1. class Zs extends DiscreteSeqVariable

Value Members

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

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

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  3. final def ##(): Int

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

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

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  6. object ZDomain extends variable.DiscreteDomain

    The per-word variable that indicates which topic it comes from.

  7. object ZSeqDomain extends DiscreteSeqDomain

  8. def addDocument(doc: Doc, random: Random): Unit

    Add a document to the LDA model.

  9. def addDocumentsFromWordZs(file: File, minDocLength: Int, random: Random): Unit

  10. val alphas: MassesVariable

    The prior over per-document topic distribution

  11. final def asInstanceOf[T0]: T0

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  12. val beta1: Double

  13. val betas: MassesVariable

    The prior over per-topic word distribution

  14. val burnIn: Int

  15. def clone(): AnyRef

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    @throws( ... )

  16. var diagnosticName: String

  17. var docLengthCounts: Array[Int]

  18. def documents: Iterable[Doc]

  19. final def eq(arg0: AnyRef): Boolean

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  20. def equals(arg0: Any): Boolean

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  21. def finalize(): Unit

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  22. final def getClass(): Class[_]

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  23. def getDocument(name: String): Doc

  24. def hashCode(): Int

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  25. def inferDocumentTheta(doc: Doc, iterations: Int = 10): Unit

    Infer doc.

    Infer doc.theta. If the document is not already part of this LDA, do not add it and do not collapse anything that would effect this LDA.

  26. def inferTopics(iterations: Int = 60, fitAlphaInterval: Int = Int.MaxValue, diagnosticInterval: Int = 10, diagnosticShowPhrases: Boolean = false): Unit

    Run a collapsed Gibbs sampler to estimate the parameters of the LDA model.

  27. def inferTopicsMultithreaded(numThreads: Int, iterations: Int = 60, fitAlphaInterval: Int = Int.MaxValue, diagnosticInterval: Int = 10, diagnosticShowPhrases: Boolean = false): Unit

  28. final def isInstanceOf[T0]: Boolean

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  29. var maxDocSize: Int

  30. def maximizePhisAndThetas(): Unit

  31. implicit val model: MutableDirectedModel

  32. def nameDocumentMap: Map[String, Doc]

  33. final def ne(arg0: AnyRef): Boolean

    Definition Classes
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  34. def newZs: Zs

  35. final def notify(): Unit

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  36. final def notifyAll(): Unit

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  37. val phis: Mixture[ProportionsVariable]

    The per-topic distribution over words.

    The per-topic distribution over words. FiniteMixture is a Seq of Dirichlet-distributed Proportions.

  38. implicit val random: Random

  39. def removeDocument(doc: Doc): Unit

  40. def saveWordsZs(file: File): Unit

  41. def setupDocument(doc: Doc, m: MutableDirectedModel, random: Random): Unit

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  42. final def synchronized[T0](arg0: ⇒ T0): T0

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  43. def toString(): String

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  44. def topicSummary(topicIndex: Int, numWords: Int = 10): String

  45. def topicWords(topicIndex: Int, numWords: Int = 10): Seq[String]

  46. def topicWordsArray(topicIndex: Int, numWords: Int): Array[String]

  47. def topicsPhraseCounts: TopicPhraseCounts

  48. def topicsSummary(numWords: Int = 10): String

  49. def topicsWordsAndPhrasesSummary(numWords: Int = 10, numPhrases: Int = 10): String

  50. final def wait(): Unit

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

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  52. final def wait(arg0: Long): Unit

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  53. def wordDomain: CategoricalDomain[String]

  54. val wordSeqDomain: CategoricalSeqDomain[String]

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