org.apache.spark.mllib.clustering

LocalLDAModel

class LocalLDAModel extends LDAModel with Serializable

:: Experimental ::

Local LDA model. This model stores only the inferred topics. It may be used for computing topics for new documents, but it may give less accurate answers than the DistributedLDAModel.

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@Experimental()
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Serializable, Serializable, LDAModel, AnyRef, Any
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  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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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    @throws( ... )
  8. def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])]

    Return the topics described by weighted terms.

    Return the topics described by weighted terms.

    This limits the number of terms per topic. This is approximate; it may not return exactly the top-weighted terms for each topic. To get a more precise set of top terms, increase maxTermsPerTopic.

    maxTermsPerTopic

    Maximum number of terms to collect for each topic.

    returns

    Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.

    Definition Classes
    LocalLDAModelLDAModel
  9. def describeTopics(): Array[(Array[Int], Array[Double])]

    Return the topics described by weighted terms.

    Return the topics described by weighted terms.

    WARNING: If vocabSize and k are large, this can return a large object!

    returns

    Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.

    Definition Classes
    LDAModel
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def k: Int

    Number of topics

    Number of topics

    Definition Classes
    LocalLDAModelLDAModel
  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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

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  21. def toString(): String

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  22. def topicsMatrix: Matrix

    Inferred topics, where each topic is represented by a distribution over terms.

    Inferred topics, where each topic is represented by a distribution over terms. This is a matrix of size vocabSize x k, where each column is a topic. No guarantees are given about the ordering of the topics.

    Definition Classes
    LocalLDAModelLDAModel
  23. def vocabSize: Int

    Vocabulary size (number of terms or terms in the vocabulary)

    Vocabulary size (number of terms or terms in the vocabulary)

    Definition Classes
    LocalLDAModelLDAModel
  24. final def wait(): Unit

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

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

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Inherited from Serializable

Inherited from Serializable

Inherited from LDAModel

Inherited from AnyRef

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