LDA

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. def incrementalTrain(docs: RDD[(Long, Vector)], computedModel: LocalLDAModel, alphaAS: Double = 0.1, totalIter: Int = 150, useLightLDA: Boolean = false): DistributedLDAModel

    

    incremental train

    incremental train

    docs

    computedModel

    alphaAS

    totalIter

    useLightLDA

    returns

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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

    

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17. def toString(): String

    

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18. def train(docs: RDD[(Long, Vector)], totalIter: Int = 150, numTopics: Int = 2048, alpha: Double = 0.001, beta: Double = 0.01, alphaAS: Double = 0.1, useLightLDA: Boolean = false): DistributedLDAModel

    

    LDA training

    LDA training

    docs

    RDD of documents, which are term (word) count vectors paired with IDs. The term count vectors are "bags of words" with a fixed-size vocabulary (where the vocabulary size is the length of the vector). Document IDs must be unique and >= 0.

    totalIter

    the number of iterations

    numTopics

    the number of topics (5000+ for large data)

    alpha

    recommend to be (5.0 /numTopics)

    beta

    recommend to be in range 0.001 - 0.1

    alphaAS

    recommend to be in range 0.01 - 1.0

    useLightLDA

    use LightLDA sampling algorithm or not, recommend false for short text

    returns

    DistributedLDAModel

19. final def wait(): Unit

    

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

    

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

    

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