MPEBeliefPropagation

Instance Constructors

new MPEBeliefPropagation(universe: Universe)(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double)

Abstract Value Members

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

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[Double]

Returns the product of all messages from a source node's neighbors to itself.
Returns the product of all messages from a source node's neighbors to itself.

Definition Classes
BeliefPropagation
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.

Definition Classes
BeliefPropagation
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
MPEBeliefPropagation → FactoredAlgorithm
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
MPEBeliefPropagation → FactoredAlgorithm
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
val factorGraph: BasicFactorGraph

Definition Classes
MPEBeliefPropagation → BeliefPropagation
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def getBeliefsForElement[T](target: Element[T]): List[(Double, T)]

Get the belief for an element
Get the belief for an element

Attributes
protected[com.cra.figaro]
Definition Classes
ProbabilisticBeliefPropagation
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[Double]]

Returns the factors needed for BP.
Returns the factors needed for BP. Since BP operates on a complete factor graph, factors are created for all elements in the universe.

Definition Classes
ProbabilisticBeliefPropagation → FactoredAlgorithm
def getFinalFactorForElement[T](target: Element[T]): Factor[Double]

Get the final factor for an element
Get the final factor for an element

Definition Classes
ProbabilisticBeliefPropagation
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
def mostLikelyValue[T](target: Element[T]): T

Returns the most likely value for the target element.
Returns the most likely value for the target element.

Definition Classes
MPEBeliefPropagation → MPEAlgorithm
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val neededElements: List[Element[_]]
def newMessage(source: Node, target: Node): Factor[Double]

Attributes
protected[com.cra.figaro]
Definition Classes
ProbabilisticBeliefPropagation → BeliefPropagation
def normalize(factor: Factor[Double]): Factor[Double]

Normalize a factor
Normalize a factor

Definition Classes
ProbabilisticBeliefPropagation
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.

Definition Classes
BeliefPropagation
val semiring: LogMaxProductSemiring.type

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
MPEBeliefPropagation → BeliefPropagation → FactoredAlgorithm
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.

Definition Classes
BeliefPropagation
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
val targetElements: List[Element[_]]

Target elements that should not be eliminated but should be available for querying.
Target elements that should not be eliminated but should be available for querying.

Definition Classes
MPEBeliefPropagation → BeliefPropagation
def toString(): String

Definition Classes
AnyRef → Any
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
MPEBeliefPropagation → BeliefPropagation → FactoredAlgorithm → MPEAlgorithm
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( ... )

abstract class MPEBeliefPropagation extends MPEAlgorithm with ProbabilisticBeliefPropagation

Instance Constructors

new MPEBeliefPropagation(universe: Universe)(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double)

Abstract Value Members

abstract def doKill(): Unit

abstract def doResume(): Unit

abstract def doStart(): Unit

abstract def doStop(): Unit

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[Double]

def cleanUp(): Unit

def clone(): AnyRef

val debug: Boolean

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

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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val factorGraph: BasicFactorGraph

def finalize(): Unit

def getBeliefsForElement[T](target: Element[T]): List[(Double, T)]

final def getClass(): Class[_]

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

def getFinalFactorForElement[T](target: Element[T]): Factor[Double]

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

def mostLikelyValue[T](target: Element[T]): T

final def ne(arg0: AnyRef): Boolean

val neededElements: List[Element[_]]

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

def normalize(factor: Factor[Double]): Factor[Double]

final def notify(): Unit

final def notifyAll(): Unit

def resume(): Unit

def runStep(): Unit

val semiring: LogMaxProductSemiring.type

def start(): Unit

def starterElements: List[Element[_]]

def stop(): Unit

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

val targetElements: List[Element[_]]

def toString(): String

val universe: Universe

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from ProbabilisticBeliefPropagation

Inherited from BeliefPropagation[Double]

Inherited from FactoredAlgorithm[Double]

Inherited from MPEAlgorithm

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped