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# LazyVariableElimination 

#### class LazyVariableElimination extends FactoredAlgorithm[Double] with LazyAlgorithm

Algorithm that lazily performs variable elimination. This algorithm is a lazy algorithm that can be run to any depth. Given a depth, it expands the model up to that depth and creates factors for the expanded elements. It also creates factors that capture the effect of parts of the model that have not been expanded on the query targets. These factors are used to compute lower or upper bounds on the queries. Then it uses ordinary variable elimination to solve these factors.

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### Instance Constructors

1. new LazyVariableElimination(targetElements: Element[_]*)(implicit universe: Universe)

### Value Members

1. final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
2. final def ##(): Int
Definition Classes
AnyRef → Any
3. final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
4. val active: Boolean
Attributes
protected
Definition Classes
Algorithm
5. final def asInstanceOf[T0]: T0
Definition Classes
Any
6. 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
7. def clone(): AnyRef
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
8. val comparator: Option[(Double, Double) ⇒ Boolean]

Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated.

Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated. Such values are stored in a factor that maps values of the other variables to a value of the eliminated variable. This factor is produced by finding the value of the variable that "maximizes" the entry associated with the value in the product factor resulting from eliminating this variable, for some maximization function. The recordingFunction determines which of two entries is greater according to the maximization function. It returns true iff the second entry is greater. The recording function is an option so that variable elimination algorithms that do not use it can ignore it.

9. var currentResult: Factor[(Double, Double)]
10. var debug: Boolean
11. val dependentAlgorithm: Null
12. val dependentUniverses: List[Nothing]
13. val depth: Int

The current depth to which the algorithm should be run.

The current depth to which the algorithm should be run.

Definition Classes
LazyAlgorithm
14. def doElimination(allFactors: List[Factor[Double]], targetVariables: Seq[Variable[_]]): Set[Factor[Double]]
Attributes
protected
15. def doKill(): Unit

Kill the algorithm.

Kill the algorithm.

Definition Classes
LazyAlgorithmAlgorithm
16. def doResume(): Unit

Resume the algorithm by increasing the depth and running again.

Resume the algorithm by increasing the depth and running again.

Definition Classes
LazyAlgorithmAlgorithm
17. def doStart(): Unit

Start the algorithm.

Start the algorithm. This will run the algorithm to one depth.

Definition Classes
LazyAlgorithmAlgorithm
18. def doStop(): Unit

Stop the algorithm.

Stop the algorithm.

Definition Classes
LazyAlgorithmAlgorithm
19. def eliminationOrder(allVars: Set[Variable[_]], factors: Traversable[Factor[Double]], toPreserve: Traversable[Variable[_]]): List[Variable[_]]

Method for choosing the elimination order.

Method for choosing the elimination order. The default order chooses first the variable that minimizes the number of extra factor entries that would be created when it is eliminated. Override this method if you want a different rule.

20. final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
21. def equals(arg0: Any): Boolean
Definition Classes
AnyRef → Any
22. def finalize(): Unit
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
23. def finishNoBounds(factorsAfterElimination: Set[Factor[Double]]): Factor[(Double, Double)]

Postprocess the factors produced by eliminating variables, assuming the entire model has been expanded so the lower and upper bounds are the same.

24. def finishWithBounds(lowerFactors: Set[Factor[Double]], upperFactors: Set[Factor[Double]]): Factor[(Double, Double)]

Postprocess the factors produced by eliminating variables, when the lower and upper bounds may be different.

Postprocess the factors produced by eliminating variables, when the lower and upper bounds may be different.

lowerFactors

the factors produced with the upperBounds flag = false

upperFactors

the factors produced with the upperBounds flag = true

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

Create the necessary factors.

Create the necessary factors.

neededElements

elements that have been expanded that need factors created

targetElements

query targets

upperBounds

flag indicating whether lower (false) or upper (true) bounds should be computed for unexpanded parts of the model

Definition Classes
LazyVariableEliminationFactoredAlgorithm
27. 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
28. def hashCode(): Int
Definition Classes
AnyRef → Any
29. 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
30. def isActive: Boolean
Definition Classes
Algorithm
31. final def isInstanceOf[T0]: Boolean
Definition Classes
Any
32. 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
33. final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
34. final def notify(): Unit
Definition Classes
AnyRef
35. final def notifyAll(): Unit
Definition Classes
AnyRef
36. def probabilityBounds[T](target: Element[_], value: T): (Double, Double)

Returns the lower and upper bounds of the probability of the target.

37. def pump(): Unit

Increase the depth and run the algorithm again.

Increase the depth and run the algorithm again.

Definition Classes
LazyAlgorithm
38. var recordingFactors: List[Factor[_]]
Attributes
protected
39. 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
40. def run(depth: Int): Unit

Run the algorithm to the given depth.

Run the algorithm to the given depth.

Definition Classes
LazyVariableEliminationLazyAlgorithm
41. val semiring
42. var showTiming: Boolean
43. 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
44. 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
45. final def synchronized[T0](arg0: ⇒ T0): T0
Definition Classes
AnyRef
46. var targetFactors: Map[Element[_], Factor[(Double, Double)]]
47. def toString(): String
Definition Classes
AnyRef → Any
48. implicit val universe
49. final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )
50. final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )
51. final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )