Packages

c

org.apache.spark.mllib.clustering

OnlineLDAOptimizer

final class OnlineLDAOptimizer extends LDAOptimizer with Logging

:: 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.

Annotations
@Since( "1.4.0" ) @DeveloperApi()
Source
LDAOptimizer.scala
Linear Supertypes
Logging, LDAOptimizer, AnyRef, Any
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  1. OnlineLDAOptimizer
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Instance Constructors

  1. new OnlineLDAOptimizer()

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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    Any
  5. def clone(): AnyRef
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    protected[lang]
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    @throws( ... ) @native()
  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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    protected[lang]
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
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    @native()
  10. def getKappa: Double

    Learning rate: exponential decay rate

    Learning rate: exponential decay rate

    Annotations
    @Since( "1.4.0" )
  11. 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" )
  12. 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" )
  13. 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.

    Annotations
    @Since( "1.4.0" )
  14. def hashCode(): Int
    Definition Classes
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    @native()
  15. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  16. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
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    Logging
  17. final def isInstanceOf[T0]: Boolean
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    Any
  18. def isTraceEnabled(): Boolean
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    protected
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  19. def log: Logger
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    Logging
  20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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  21. def logDebug(msg: ⇒ String): Unit
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  22. def logError(msg: ⇒ String, throwable: Throwable): Unit
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  23. def logError(msg: ⇒ String): Unit
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  24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
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  25. def logInfo(msg: ⇒ String): Unit
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  26. def logName: String
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  27. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
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    protected
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  28. def logTrace(msg: ⇒ String): Unit
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    protected
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    Logging
  29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
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  30. def logWarning(msg: ⇒ String): Unit
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    protected
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    Logging
  31. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  32. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  33. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  34. def setKappa(kappa: Double): OnlineLDAOptimizer.this.type

    Learning rate: exponential decay rate---should be between (0.5, 1.0] to guarantee asymptotic convergence.

    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" )
  35. 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.

    Annotations
    @Since( "1.4.0" )
    Note

    This should be adjusted in synch with LDA.setMaxIterations() so the entire corpus is used. Specifically, set both so that maxIterations * miniBatchFraction is at least 1. Default: 0.05, i.e., 5% of total documents.

  36. 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" )
  37. 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.

    Annotations
    @Since( "1.4.0" )
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
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  40. final def wait(): Unit
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    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  42. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()

Inherited from Logging

Inherited from LDAOptimizer

Inherited from AnyRef

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