SparseOnlineLDA

Instance Constructors

new SparseOnlineLDA(wordDomain: CategoricalDomain[String], numDocs: Int, numTopics: Int = 10, alpha: Double = 0.1, beta: Double = 0.1, batchSize: Int = 100, numSamples: Int = 5, burninSamples: Int = 2, initLearningRate: Double = 100.0, kappa: Double = 0.6, maxIterations: Int = 2000, printTopicInterval: Int = 10, topicsFileName: String = "lda.topics")(implicit random: Random)

Value Members

final def !=(arg0: AnyRef): Boolean

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

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
Any
val Ndk: Array[Int]
val Nk: Array[Double]
val alpha: Double
def approximateExpDigamma(x: Double): Double
final def asInstanceOf[T0]: T0

Definition Classes
Any
val batchSize: Int
val beta: Double
val betaSum: Double
val betas: MassesVariable
val burninSamples: Int
def clone(): AnyRef

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

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

Definition Classes
AnyRef → Any
val expDiGammaBeta: Double
def export(): Unit
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
val infoMsg: String
val initLearningRate: Double
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val kappa: Double
val maxIterations: Int
var maxTokens: Int
implicit val model: ItemizedDirectedModel
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
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val numDocs: Int
val numSamples: Int
val numTopics: Int
val numTypes: Int
val phis: Mixture[ProportionsVariable]
val printTopicInterval: Int
implicit val random: Random
def rescale(scale: Double): Unit
val samplingWeights: Array[Double]
var scale: Double
val skipIterations: Int
def sortAndPrune(cutoff: Double): Unit
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
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def toString(): String

Definition Classes
AnyRef → Any
val topicCoefficients: Array[Double]
val topicNormalizers: Array[Double]
def topicSummary(topicIndex: Int, numWords: Int = 10): String
def topicWords(topicIndex: Int, numWords: Int = 10): Seq[String]
def topicWordsArray(topicIndex: Int, numWords: Int): Array[String]
val topicsFileName: String
def topicsSummary(numWords: Int = 10): String
def train(docs: Stream[CategoricalSeqVariable[String]]): Unit
def train(batchDocs: Array[CategoricalSeqVariable[String]], iteration: Int): Unit
def train(ws: CategoricalSeqVariable[String]): Unit
val typeTopics: Array[Array[Int]]
val typeWeights: Array[Array[Double]]
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )
val wordDomain: CategoricalDomain[String]
var wordGradientLimit: Int
var wordGradientQueueTopics: Array[Int]
var wordGradientQueueTypes: Array[Int]
var wordGradientSize: Int
val wordWeightConstant: Int
var zs: Array[Int]

class SparseOnlineLDA extends AnyRef

Instance Constructors

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

val Ndk: Array[Int]

val Nk: Array[Double]

val alpha: Double

def approximateExpDigamma(x: Double): Double

final def asInstanceOf[T0]: T0

val batchSize: Int

val beta: Double

val betaSum: Double

val betas: MassesVariable

val burninSamples: Int

def clone(): AnyRef

var currChanges: Int

var currSamples: Int

var docsProcessed: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val expDiGammaBeta: Double

def export(): Unit

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

val infoMsg: String

val initLearningRate: Double

final def isInstanceOf[T0]: Boolean

val kappa: Double

val maxIterations: Int

var maxTokens: Int

implicit val model: ItemizedDirectedModel

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numDocs: Int

val numSamples: Int

val numTopics: Int

val numTypes: Int

val phis: Mixture[ProportionsVariable]

val printTopicInterval: Int

implicit val random: Random

def rescale(scale: Double): Unit

val samplingWeights: Array[Double]

var scale: Double

val skipIterations: Int

def sortAndPrune(cutoff: Double): Unit

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

def toString(): String

val topicCoefficients: Array[Double]

val topicNormalizers: Array[Double]

def topicSummary(topicIndex: Int, numWords: Int = 10): String

def topicWords(topicIndex: Int, numWords: Int = 10): Seq[String]

def topicWordsArray(topicIndex: Int, numWords: Int): Array[String]

val topicsFileName: String

def topicsSummary(numWords: Int = 10): String

def train(docs: Stream[CategoricalSeqVariable[String]]): Unit

def train(batchDocs: Array[CategoricalSeqVariable[String]], iteration: Int): Unit

def train(ws: CategoricalSeqVariable[String]): Unit

val typeTopics: Array[Array[Int]]

val typeWeights: Array[Array[Double]]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val wordDomain: CategoricalDomain[String]

var wordGradientLimit: Int

var wordGradientQueueTopics: Array[Int]

var wordGradientQueueTypes: Array[Int]

var wordGradientSize: Int

val wordWeightConstant: Int

var zs: Array[Int]

Inherited from AnyRef

Inherited from Any

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