OnlineExpectationMaximization

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

abstract val initial: Universe

abstract val targetParameters: Seq[Parameter[_]]

abstract val terminationCriteria: () ⇒ EMTerminationCriteria

abstract val transition: () ⇒ Universe

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

var active: Boolean

final def asInstanceOf[T0]: T0

def cleanUp(): Unit

Called when the algorithm is killed.

def clone(): AnyRef

var currentUniverse: Universe

var debug: Boolean

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

def initialize(): Unit

Called when the algorithm is started before running any steps.

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def iteration(): Unit

def kill(): Unit

Kill the algorithm so that it is inactive.

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

Resume the computation of the algorithm, if it has been stopped.

def start(): Unit

Start the algorithm and make it active.

def stop(): Unit

Stop the algorithm from computing.

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

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

def toString(): String

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

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit