OnlineLDAOptimizer

Instance Constructors

new OnlineLDAOptimizer()

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
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def getKappa: Double

Learning rate: exponential decay rate
Learning rate: exponential decay rate

Annotations
@Since( "1.4.0" )
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" )
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.

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@Since( "1.5.0" )
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" )
def hashCode(): Int

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def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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protected
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Logging
final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

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protected
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Logging
def log: Logger

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protected
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Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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Logging
def logDebug(msg: ⇒ String): Unit

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protected
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Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

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protected
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Logging
def logError(msg: ⇒ String): Unit

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protected
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Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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protected
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Logging
def logInfo(msg: ⇒ String): Unit

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protected
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Logging
def logName: String

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Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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protected
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Logging
def logTrace(msg: ⇒ String): Unit

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Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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protected
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Logging
def logWarning(msg: ⇒ String): Unit

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protected
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Logging
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def toString(): String

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final def wait(): Unit

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

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

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@throws( ... )

Related Doc: package clustering

final class OnlineLDAOptimizer extends LDAOptimizer with Logging

Instance Constructors

new OnlineLDAOptimizer()

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 getKappa: Double

def getMiniBatchFraction: Double

def getOptimizeDocConcentration: Boolean

def getTau0: Double

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def setMiniBatchFraction(miniBatchFraction: Double): OnlineLDAOptimizer.this.type

def setOptimizeDocConcentration(optimizeDocConcentration: Boolean): OnlineLDAOptimizer.this.type

def setTau0(tau0: Double): OnlineLDAOptimizer.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 Logging

Inherited from LDAOptimizer

Inherited from AnyRef

Inherited from Any

Ungrouped