org.apache.spark.mllib.clustering

OnlineLDAOptimizer

final class OnlineLDAOptimizer extends LDAOptimizer

:: DeveloperApi ::

An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic distribution adaptively.

Original Online LDA paper: Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.

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@Since( "1.4.0" ) @DeveloperApi()
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LDAOptimizer, AnyRef, Any
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Instance Constructors

  1. new OnlineLDAOptimizer()

Value Members

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

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  3. final def ##(): Int

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

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  10. def finalize(): Unit

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

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  12. def getKappa: Double

    Learning rate: exponential decay rate

    Learning rate: exponential decay rate

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    @Since( "1.4.0" )
  13. def getMiniBatchFraction: Double

    Mini-batch fraction, which sets the fraction of document sampled and used in each iteration

    Mini-batch fraction, which sets the fraction of document sampled and used in each iteration

    Annotations
    @Since( "1.4.0" )
  14. def getOptimizeDocConcentration: Boolean

    Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training.

    Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training.

    Annotations
    @Since( "1.5.0" )
  15. def getTau0: Double

    A (positive) learning parameter that downweights early iterations.

    A (positive) learning parameter that downweights early iterations. Larger values make early iterations count less.

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    @Since( "1.4.0" )
  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def setKappa(kappa: Double): OnlineLDAOptimizer.this.type

    Learning rate: exponential decay rate---should be between (0.

    Learning rate: exponential decay rate---should be between (0.5, 1.0] to guarantee asymptotic convergence. Default: 0.51, based on the original Online LDA paper.

    Annotations
    @Since( "1.4.0" )
  22. def setMiniBatchFraction(miniBatchFraction: Double): OnlineLDAOptimizer.this.type

    Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in each iteration.

    Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in each iteration.

    Note that this should be adjusted in synch with LDA.setMaxIterations() so the entire corpus is used. Specifically, set both so that maxIterations * miniBatchFraction >= 1.

    Default: 0.05, i.e., 5% of total documents.

    Annotations
    @Since( "1.4.0" )
  23. def setOptimizeDocConcentration(optimizeDocConcentration: Boolean): OnlineLDAOptimizer.this.type

    Sets whether to optimize docConcentration parameter during training.

    Sets whether to optimize docConcentration parameter during training.

    Default: false

    Annotations
    @Since( "1.5.0" )
  24. def setTau0(tau0: Double): OnlineLDAOptimizer.this.type

    A (positive) learning parameter that downweights early iterations.

    A (positive) learning parameter that downweights early iterations. Larger values make early iterations count less. Default: 1024, following the original Online LDA paper.

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    @Since( "1.4.0" )
  25. final def synchronized[T0](arg0: ⇒ T0): T0

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  26. def toString(): String

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  27. final def wait(): Unit

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

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

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Inherited from LDAOptimizer

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