OnlineExpectationMaximizationWithFactors

Instance Constructors

new OnlineExpectationMaximizationWithFactors(initial: Universe, transition: () ⇒ Universe, targetParameters: Parameter[_]*)(terminationCriteria: () ⇒ EMTerminationCriteria)

Value Members

final def !=(arg0: Any): Boolean

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

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

Definition Classes
AnyRef → Any
var active: Boolean

Attributes
protected
Definition Classes
Algorithm
final def asInstanceOf[T0]: T0

Definition Classes
Any
def cleanUp(): Unit

Called when the algorithm is killed.
Called when the algorithm is killed. By default, does nothing. Can be overridden.

Definition Classes
Algorithm
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
var currentUniverse: Universe

Attributes
protected
Definition Classes
OnlineExpectationMaximization
var debug: Boolean

Definition Classes
ExpectationMaximization
def doExpectationStep(): Map[Parameter[_], Seq[Double]]

Definition Classes
OnlineExpectationMaximizationWithFactors → ExpectationMaximization
def doKill(): Unit

Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
ExpectationMaximization → Algorithm
def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit

Attributes
protected
Definition Classes
ExpectationMaximization
def doResume(): Unit

Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
ExpectationMaximization → Algorithm
def doStart(): Unit

Definition Classes
OnlineExpectationMaximization → ExpectationMaximization → Algorithm
def doStop(): Unit

Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
ExpectationMaximization → Algorithm
def em(): Unit

Attributes
protected
Definition Classes
ExpectationMaximization
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
val initial: Universe

Definition Classes
OnlineExpectationMaximizationWithFactors → OnlineExpectationMaximization → Online
def initialize(): Unit

Called when the algorithm is started before running any steps.
Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.

Definition Classes
Algorithm
def isActive: Boolean

Definition Classes
Algorithm
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def iteration(): Unit

Definition Classes
ExpectationMaximization
def kill(): Unit

Kill the algorithm so that it is inactive.
Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
var lastIterationStatistics: Map[Parameter[_], Seq[Double]]

Attributes
protected
Definition Classes
OnlineExpectationMaximization
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val paramMap: Map[Parameter[_], Seq[Double]]

Attributes
protected
Definition Classes
ExpectationMaximization
def resume(): Unit

Resume the computation of the algorithm, if it has been stopped.
Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
def start(): Unit

Start the algorithm and make it active.
Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.

Definition Classes
Algorithm
def stop(): Unit

Stop the algorithm from computing.
Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
var sufficientStatistics: Map[Parameter[_], Seq[Double]]

Definition Classes
ExpectationMaximization
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val targetParameters: Parameter[_]*

Definition Classes
OnlineExpectationMaximizationWithFactors → ExpectationMaximization
val terminationCriteria: () ⇒ EMTerminationCriteria

Definition Classes
OnlineExpectationMaximizationWithFactors → ExpectationMaximization
def toString(): String

Definition Classes
AnyRef → Any
val transition: () ⇒ Universe

Definition Classes
OnlineExpectationMaximizationWithFactors → OnlineExpectationMaximization → Online
def update(evidence: Seq[NamedEvidence[_]] = Seq()): Unit

Observe new evidence and perform one expectation step and one maximization step
Observe new evidence and perform one expectation step and one maximization step

Definition Classes
OnlineExpectationMaximization → Online
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( ... )

Related Doc: package learning

class OnlineExpectationMaximizationWithFactors extends OnlineExpectationMaximization

Instance Constructors

new OnlineExpectationMaximizationWithFactors(initial: Universe, transition: () ⇒ Universe, targetParameters: Parameter[_]*)(terminationCriteria: () ⇒ EMTerminationCriteria)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

var active: Boolean

final def asInstanceOf[T0]: T0

def cleanUp(): Unit

def clone(): AnyRef

var currentUniverse: Universe

var debug: Boolean

def doExpectationStep(): Map[Parameter[_], Seq[Double]]

def doKill(): Unit

def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit

def doResume(): Unit

def doStart(): Unit

def doStop(): Unit

def em(): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

val initial: Universe

def initialize(): Unit

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def iteration(): Unit

def kill(): Unit

var lastIterationStatistics: Map[Parameter[_], Seq[Double]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val paramMap: Map[Parameter[_], Seq[Double]]

def resume(): Unit

def start(): Unit

def stop(): Unit

var sufficientStatistics: Map[Parameter[_], Seq[Double]]

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

val targetParameters: Parameter[_]*

val terminationCriteria: () ⇒ EMTerminationCriteria

def toString(): String

val transition: () ⇒ Universe

def update(evidence: Seq[NamedEvidence[_]] = Seq()): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from OnlineExpectationMaximization

Inherited from ExpectationMaximization

Inherited from ParameterLearner

Inherited from Online

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped