abstract class Filtering extends Algorithm
The general class of filtering algorithms. A filtering algorithm is provided with an initial model, represented by a universe encoding the probability distribution over the initial state, and a transition model, which maps a state to a universe encoding the probability distribution over the new state. An implementation of Filtering must implement the advanceTime, computeCurrentDistribution, and computeCurrentExpectation methods.
Querying and asserting evidence to a filtering algorithm are done using references. This is because references are stable over time, while the particular elements they refer to are not.
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abstract
def
advanceTime(evidence: Seq[NamedEvidence[_]]): Unit
Advance the filtering one time step, conditioning on the given evidence at the new time point.

abstract
def
computeCurrentDistribution[T](reference: Reference[T]): Stream[(Double, T)]
Returns the distribution over the element referred to by the reference at the current time point.
Returns the distribution over the element referred to by the reference at the current time point.
 Attributes
 protected

abstract
def
computeCurrentExpectation[T](reference: Reference[T], function: (T) ⇒ Double): Double
Returns the expectation of the element referred to by the reference under the given function at the current time point.
Returns the expectation of the element referred to by the reference under the given function at the current time point.
 Attributes
 protected

abstract
def
currentDistribution[T](reference: Reference[T]): Stream[(Double, T)]
Returns the distribution over the element referred to by the reference at the current time point.

abstract
def
currentExpectation[T](reference: Reference[T], function: (T) ⇒ Double): Double
Returns the expectation of the element referred to by the reference under the given function at the current time point.

abstract
def
currentProbability[T](reference: Reference[T], predicate: (T) ⇒ Boolean): Double
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.

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

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

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

abstract
def
doStop(): Unit
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 protected[com.cra.figaro.algorithm]
 Definition Classes
 Algorithm
Concrete Value Members

final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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val
active: Boolean
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 Definition Classes
 Algorithm

final
def
asInstanceOf[T0]: T0
 Definition Classes
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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
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 protected[java.lang]
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 @throws( ... )

def
computeCurrentProbability[T](reference: Reference[T], predicate: (T) ⇒ Boolean): Double
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.
 Attributes
 protected

def
currentProbability[T](reference: Reference[T], value: T): Double
Returns the probability that the element referred to by the reference produces the given value at the current time point.

final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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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
 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
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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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
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

final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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