LREval

Instance Constructors

new LREval(wSeqDomain: CategoricalSeqDomain[String], zDomain: variable.DiscreteDomain, alpha: Array[Double], alphaSum: Double, beta: Double, betaSum: Double, topicCounts: Array[Int], typeTopicCounts: Array[HashMap[Int, Int]])

wSeqDomain
word domain
zDomain
topic domain
alpha
an array storing the alpha parameters of the Dirichlet prior on the document-topic distributions
alphaSum
the sum of the alpha parameters, i.e., the concentration parameter of the Dirichlet distribution
beta
parameter of the Dirichlet prior on the topic-word distributions; same for every word type
betaSum
the sum of the beta parameters
topicCounts
an array storing the count for every topic
typeTopicCounts
for every word type this data structure stores the number of times the word appears in each topic; only topics with non-zero counts should be specified.

Type Members

class Zs extends DiscreteSeqVariable

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
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
val alpha: Array[Double]

an array storing the alpha parameters of the Dirichlet prior on the document-topic distributions
val alphaSum: Double

the sum of the alpha parameters, i.
the sum of the alpha parameters, i.e., the concentration parameter of the Dirichlet distribution
final def asInstanceOf[T0]: T0

Definition Classes
Any
val beta: Double

parameter of the Dirichlet prior on the topic-word distributions; same for every word type
val betaSum: Double

the sum of the beta parameters
def calcLR(testFile: String, numParticles: Int, useResampling: Boolean)(implicit random: Random): Double

Sets up the test documents and computes the information rate
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def doCalc(testDocs: ArrayBuffer[Document], numParticles: Int, useResampling: Boolean)(implicit random: Random): Double

Computes the information rate
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
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 hashCode(): Int

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

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

Definition Classes
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final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numTopics: Int
def sampleAtPosition(position: Int, doc: Doc, docTopicCounts: Array[Int], decrDocCounts: Boolean, decrTopicCounts: Boolean)(implicit random: Random): Unit

Samples a topic a assignment for a given position in a document.
def setUpDocs(testFile: String)(implicit random: Random): ArrayBuffer[Document]

Creates documents from a file
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
val topicCounts: Array[Int]

an array storing the count for every topic
val typeTopicCounts: Array[HashMap[Int, Int]]

for every word type this data structure stores the number of times the word appears in each topic; only topics with non-zero counts should be specified.
val wSeqDomain: CategoricalSeqDomain[String]

word domain
final def wait(): Unit

Definition Classes
AnyRef
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 zDomain: variable.DiscreteDomain

topic domain
object zSeqDomain extends DiscreteSeqDomain

class LREval extends AnyRef

Instance Constructors

new LREval(wSeqDomain: CategoricalSeqDomain[String], zDomain: variable.DiscreteDomain, alpha: Array[Double], alphaSum: Double, beta: Double, betaSum: Double, topicCounts: Array[Int], typeTopicCounts: Array[HashMap[Int, Int]])

Type Members

class Zs extends DiscreteSeqVariable

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 alpha: Array[Double]

val alphaSum: Double

final def asInstanceOf[T0]: T0

val beta: Double

val betaSum: Double

def calcLR(testFile: String, numParticles: Int, useResampling: Boolean)(implicit random: Random): Double

def clone(): AnyRef

def doCalc(testDocs: ArrayBuffer[Document], numParticles: Int, useResampling: Boolean)(implicit random: Random): Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numTopics: Int

def sampleAtPosition(position: Int, doc: Doc, docTopicCounts: Array[Int], decrDocCounts: Boolean, decrTopicCounts: Boolean)(implicit random: Random): Unit

def setUpDocs(testFile: String)(implicit random: Random): ArrayBuffer[Document]

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

def toString(): String

val topicCounts: Array[Int]

val typeTopicCounts: Array[HashMap[Int, Int]]

val wSeqDomain: CategoricalSeqDomain[String]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val zDomain: variable.DiscreteDomain

object zSeqDomain extends DiscreteSeqDomain

Inherited from AnyRef

Inherited from Any

Ungrouped