class MMAPProbEvidenceSampler extends ProbEvidenceSampler with OneTimeProbEvidenceSampler with OnlineLogStatistics
Special probability of evidence sampler used for marginal MAP. Unlike a regular probability of evidence sampler, this records its own variance. It does so in an online fashion, and computes it in log space to prevent underflow. Additionally, this algorithm may be run multiple times. The rolling mean and variance computation incorporates the samples taken from all runs.
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 MMAPProbEvidenceSampler
 OnlineLogStatistics
 OneTimeProbEvidenceSampler
 OneTimeProbEvidence
 OneTimeSampler
 OneTime
 ProbEvidenceSampler
 Sampler
 ProbEvidenceAlgorithm
 Algorithm
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final
def
!=(arg0: Any): Boolean
 Definition Classes
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final
def
##(): Int
 Definition Classes
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final
def
==(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

val
active: Boolean
 Attributes
 protected
 Definition Classes
 Algorithm

def
additionalEvidenceAlgorithm(evidence: List[NamedEvidence[_]]): ProbEvidenceSampler with OneTimeProbEvidenceSampler
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
 OneTimeProbEvidenceSampler → ProbEvidenceAlgorithm

final
def
asInstanceOf[T0]: T0
 Definition Classes
 Any

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
 ProbEvidenceSampler → ProbEvidenceAlgorithm → Algorithm

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

def
computedResult: Double
 Attributes
 protected
 Definition Classes
 ProbEvidenceSampler → ProbEvidenceAlgorithm

val
count: Int
 Attributes
 protected
 Definition Classes
 OnlineLogStatistics

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
doSample(): Unit
Perform sampling, but additionally update the variance and clear only elements that shouldn't be preserved.
Perform sampling, but additionally update the variance and clear only elements that shouldn't be preserved.
 Attributes
 protected
 Definition Classes
 MMAPProbEvidenceSampler → ProbEvidenceSampler → Sampler

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

def
finalize(): Unit
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 protected[java.lang]
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 @throws( classOf[java.lang.Throwable] )

final
def
getClass(): Class[_]
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def
hashCode(): Int
 Definition Classes
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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
logComputedResult: Double
 Attributes
 protected
 Definition Classes
 ProbEvidenceSampler

val
logM2: Double
 Attributes
 protected
 Definition Classes
 OnlineLogStatistics

val
logMean: Double
 Attributes
 protected
 Definition Classes
 OnlineLogStatistics

def
logProbEvidence: Double
The computed log probability of evidence.
The computed log probability of evidence.
 Definition Classes
 ProbEvidenceSampler → ProbEvidenceAlgorithm

val
lw: LikelihoodWeighter
 Definition Classes
 ProbEvidenceSampler

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

final
def
notify(): Unit
 Definition Classes
 AnyRef

final
def
notifyAll(): Unit
 Definition Classes
 AnyRef

val
numSamples: Int
 Definition Classes
 MMAPProbEvidenceSampler → OneTimeSampler
 val observations: List[ElemVal[_]]

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
record(logWeight: Double): Unit
Record the weight in the rolling mean and variance computation.
Record the weight in the rolling mean and variance computation.
 logWeight
Log of the weight to record.
 Definition Classes
 OnlineLogStatistics

def
resetCounts(): Unit
 Attributes
 protected
 Definition Classes
 ProbEvidenceSampler → Sampler

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
Observe the necessary values of MAP elements, then run the algorithm.
Observe the necessary values of MAP elements, then run the algorithm. After this is initialized, calling this method again is allowed. The additional samples are accounted for when returning the total log statistics.
 Definition Classes
 MMAPProbEvidenceSampler → OneTimeSampler → OneTime

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
successWeight: Double
 Attributes
 protected
 Definition Classes
 ProbEvidenceSampler

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

def
toString(): String
 Definition Classes
 AnyRef → Any

def
totalLogStatistics: LogStatistics
Return the combined statistics for the log probability of evidence over all runs of this sampler.
Return the combined statistics for the log probability of evidence over all runs of this sampler. If the number of observations is 0, the returned log mean is Infinity. If the number of observations is 0 or 1, the returned log variance is NaN.
 Definition Classes
 OnlineLogStatistics

var
totalWeight: Double
 Attributes
 protected
 Definition Classes
 ProbEvidenceSampler

val
universe: Universe
 Definition Classes
 ProbEvidenceSampler → ProbEvidenceAlgorithm

def
update(): Unit
 Attributes
 protected
 Definition Classes
 ProbEvidenceSampler → Sampler

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