trait OneTimeProbEvidenceBeliefPropagation extends OneTimeProbabilisticBeliefPropagation with OneTimeProbEvidence with ProbEvidenceBeliefPropagation
Trait for One Time BP evidence algorithms.
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 OneTimeProbEvidenceBeliefPropagation
 ProbEvidenceBeliefPropagation
 OneTimeProbEvidence
 ProbEvidenceAlgorithm
 OneTimeProbabilisticBeliefPropagation
 OneTime
 ProbabilisticBeliefPropagation
 BeliefPropagation
 FactoredAlgorithm
 Algorithm
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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
iterations: Int
 Definition Classes
 OneTimeProbabilisticBeliefPropagation

abstract
val
semiring: LogConvertibleSemiRing[Double]
Since BP uses division to compute messages, the semiring has to have a division function defined and must be log convertable.
Since BP uses division to compute messages, the semiring has to have a division function defined and must be log convertable. Note that BP operates in log space and any semiring must be log convertible If you define a nonlog semiring, it will automatically convert, and convert it back to normal space at the end If you define a log semiring, it won't convert to log or convert from log. In other words, it outputs the answer in the space specified by the semiring
 Definition Classes
 BeliefPropagation → FactoredAlgorithm

abstract
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
 BeliefPropagation

abstract
val
universe: Universe
 Definition Classes
 ProbEvidenceAlgorithm
Concrete 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

val
active: Boolean
 Attributes
 protected
 Definition Classes
 Algorithm

def
additionalEvidenceAlgorithm(evidence: List[NamedEvidence[_]]): ProbEvidenceAlgorithm
The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence.
The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence. This algorithm can be different to the one defined in this class. (For example, a onetime algorithm can use an anytime algorithm for additional evidence.)
 Definition Classes
 OneTimeProbEvidenceBeliefPropagation → ProbEvidenceAlgorithm

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
Removes the evidence provided in the constructor from the universe.
Removes the evidence provided in the constructor from the universe.
 Definition Classes
 ProbEvidenceAlgorithm → Algorithm

def
clone(): AnyRef
 Attributes
 protected[java.lang]
 Definition Classes
 AnyRef
 Annotations
 @throws( ... )

def
computedResult(): Double
Compute the evidence of the model.
Compute the evidence of the model. Returns the probability of evidence on the model. This assumes that BP has already been run on this algorithm instance.
 Definition Classes
 ProbEvidenceBeliefPropagation → ProbEvidenceAlgorithm

def
convertFactors(factors: List[Factor[Double]]): List[Factor[Double]]
 Attributes
 protected
 Definition Classes
 ProbabilisticBeliefPropagation

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
denominator: Double
 Definition Classes
 ProbEvidenceAlgorithm

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

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

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

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

final
def
eq(arg0: AnyRef): Boolean
 Definition Classes
 AnyRef

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

val
evidence: List[NamedEvidence[_]]
 Definition Classes
 ProbEvidenceAlgorithm

val
factorGraph: FactorGraph[Double]
 Attributes
 protected[com.cra.figaro]
 Definition Classes
 BeliefPropagation

def
factorToBeliefs[T](factor: Factor[Double]): List[Tuple2[Double, _]]
 Attributes
 protected[com.cra.figaro]
 Definition Classes
 ProbabilisticBeliefPropagation

def
finalize(): Unit
 Attributes
 protected[java.lang]
 Definition Classes
 AnyRef
 Annotations
 @throws( classOf[java.lang.Throwable] )

def
findNodeForElement[T](target: Element[T]): Node
 Attributes
 protected[com.cra.figaro]
 Definition Classes
 ProbabilisticBeliefPropagation

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, parameterized: Boolean = false): (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
getNewMessageFactorToVar(fn: FactorNode, vn: VariableNode): Factor[Double]
 Attributes
 protected
 Definition Classes
 BeliefPropagation

def
getNewMessageVarToFactor(vn: VariableNode, fn: FactorNode): Factor[Double]
 Attributes
 protected
 Definition Classes
 BeliefPropagation

def
hashCode(): Int
 Definition Classes
 AnyRef → Any

def
initialize(): Unit
Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end.
Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end. This is achieved with the initialize method, called when the algorithm starts, and the cleanUp method, called when the algorithm is killed.
 Definition Classes
 ProbEvidenceAlgorithm → 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
logProbEvidence: Double
The computed log probability of evidence.
The computed log probability of evidence.
 Definition Classes
 ProbEvidenceAlgorithm

def
logSpaceSemiring(): LogConvertibleSemiRing[Double]
Returns the log space version of the semiring (or the semiring if already in log space)
Returns the log space version of the semiring (or the semiring if already in log space)
 Attributes
 protected
 Definition Classes
 BeliefPropagation

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

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
probAdditionalEvidence(evidence: List[NamedEvidence[_]]): ProbEvidenceAlgorithm
Returns an algorithm to compute the probability of the additional evidence provided.
Returns an algorithm to compute the probability of the additional evidence provided.
 Definition Classes
 ProbEvidenceAlgorithm

def
probEvidence: Double
The computed probability of evidence.
The computed probability of evidence.
 Definition Classes
 ProbEvidenceAlgorithm

def
probabilityOfEvidence(): Double
Returns the probability of evidence of the universe on which the algorithm operates.
Returns the probability of evidence of the universe on which the algorithm operates. Throws AlgorithmInactiveException if the algorithm is not active.
 Definition Classes
 OneTimeProbEvidence

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
run(): Unit
Run the algorithm, performing its computation to completion.
Run the algorithm, performing its computation to completion.
 Definition Classes
 OneTimeProbabilisticBeliefPropagation → OneTime

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

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

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( ... )