BeliefPropagation

Abstract Value Members

abstract val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

The algorithm to compute probability of specified evidence in a dependent universe.
The algorithm to compute probability of specified evidence in a dependent universe. We use () => Double to represent this algorithm instead of an instance of ProbEvidenceAlgorithm. Typical usage is to return the result of ProbEvidenceAlgorithm.computeProbEvidence when invoked.

Definition Classes
FactoredAlgorithm
abstract val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]

A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.
A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.

Definition Classes
FactoredAlgorithm
abstract def doKill(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doResume(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doStart(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doStop(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract val factorGraph: FactorGraph[T]

Attributes
protected[com.cra.figaro]
abstract def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[T]]

All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.
All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.

Definition Classes
FactoredAlgorithm
abstract val semiring: DivideableSemiRing[T]

Since BP uses division to compute messages, the semiring has to have a division function defined
Since BP uses division to compute messages, the semiring has to have a division function defined

Definition Classes
BeliefPropagation → FactoredAlgorithm
abstract val targetElements: List[Element[_]]

Target elements that should not be eliminated but should be available for querying.
abstract val universe: Universe

The universe on which this belief propagation algorithm should be applied.
The universe on which this belief propagation algorithm should be applied.

Definition Classes
BeliefPropagation → FactoredAlgorithm

Concrete 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
var active: Boolean

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

Definition Classes
Any
def belief(source: Node): Factor[T]

Returns the product of all messages from a source node's neighbors to itself.
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( ... )
val debug: Boolean

By default, implementations that inherit this trait have no debug information.
By default, implementations that inherit this trait have no debug information. Override this if you want a debugging option.
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 getNeededElements(starterElements: List[Element[_]], depth: Int): (List[Element[_]], Boolean)

Get the elements that are needed by the query target variables and the evidence variables.
Get the elements that are needed by the query target variables and the evidence variables. Also compute the values of those variables to the given depth. Only get factors for elements that are actually used by the target variables. This is more efficient. Also, it avoids problems when values of unused elements have not been computed.
In addition to getting all the needed elements, it determines if any of the conditioned, constrained, or dependent universe parent elements has * in its range. If any of these elements has * in its range, the lower and upper bounds of factors will be different, so we need to compute both. If they don't, we don't need to compute bounds.

Definition Classes
FactoredAlgorithm
def hashCode(): Int

Definition Classes
AnyRef → Any
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
BeliefPropagation → Algorithm
def isActive: Boolean

Definition Classes
Algorithm
final def isInstanceOf[T0]: Boolean

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

Definition Classes
AnyRef
def newMessage(source: Node, target: Node): Factor[T]

Attributes
protected[com.cra.figaro]
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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 runStep(): Unit

Runs this belief propagation algorithm for one iteration.
Runs this belief propagation algorithm for one iteration. An iteration consists of each node of the factor graph sending a message to each of its neighbors.
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 starterElements: List[Element[_]]

Elements towards which queries are directed.
Elements towards which queries are directed. By default, these are the target elements. This is overridden by DecisionVariableElimination, where it also includes utility variables.
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
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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( ... )

trait BeliefPropagation[T] extends FactoredAlgorithm[T]

Abstract Value Members

abstract val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

abstract val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]

abstract def doKill(): Unit

abstract def doResume(): Unit

abstract def doStart(): Unit

abstract def doStop(): Unit

abstract val factorGraph: FactorGraph[T]

abstract def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[T]]

abstract val semiring: DivideableSemiRing[T]

abstract val targetElements: List[Element[_]]

abstract val universe: Universe

Concrete 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

var active: Boolean

final def asInstanceOf[T0]: T0

def belief(source: Node): Factor[T]

def cleanUp(): Unit

def clone(): AnyRef

val debug: Boolean

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getNeededElements(starterElements: List[Element[_]], depth: Int): (List[Element[_]], Boolean)

def hashCode(): Int

def initialize(): Unit

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def kill(): Unit

final def ne(arg0: AnyRef): Boolean

def newMessage(source: Node, target: Node): Factor[T]

final def notify(): Unit

final def notifyAll(): Unit

def resume(): Unit

def runStep(): Unit

def start(): Unit

def starterElements: List[Element[_]]

def stop(): Unit

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 FactoredAlgorithm[T]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped