EMLDAOptimizer

:: DeveloperApi ::

Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.

Currently, the underlying implementation uses Expectation-Maximization (EM), implemented according to the Asuncion et al. (2009) paper referenced below.

References:

Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
- This class implements their "smoothed" LDA model.
Paper which clearly explains several algorithms, including EM: Asuncion, Welling, Smyth, and Teh. "On Smoothing and Inference for Topic Models." UAI, 2009.

Annotations: @Since( "1.4.0" ) @DeveloperApi()
Source: LDAOptimizer.scala

Linear Supertypes

LDAOptimizer, AnyRef, Any

Instance Constructors

new EMLDAOptimizer()

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
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final def ==(arg0: Any): Boolean

Definition Classes
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final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getKeepLastCheckpoint: Boolean

If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).

Annotations
@Since( "2.0.0" )
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
AnyRef
def setKeepLastCheckpoint(keepLastCheckpoint: Boolean): EMLDAOptimizer.this.type

If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up). Deleting the checkpoint can cause failures if a data partition is lost, so set this bit with care. Note that checkpoints will be cleaned up via reference counting, regardless.
Default: true

Annotations
@Since( "2.0.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package clustering

final class EMLDAOptimizer extends LDAOptimizer

Instance Constructors

new EMLDAOptimizer()

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getKeepLastCheckpoint: Boolean

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def setKeepLastCheckpoint(keepLastCheckpoint: Boolean): EMLDAOptimizer.this.type

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from LDAOptimizer

Inherited from AnyRef

Inherited from Any

Ungrouped