BPSolver

Instance Constructors

new BPSolver(problem: Problem, toEliminate: Set[Variable[_]], toPreserve: Set[Variable[_]], factors: List[Factor[Double]], iters: Int, semiring: LogConvertibleSemiRing[Double])

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
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( ... )
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 dependentAlgorithm: Null

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
BPSolver → FactoredAlgorithm
val dependentUniverses: Null

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
BPSolver → FactoredAlgorithm
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
var 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 generateGraph(): Unit
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 go(): (List[Factor[Double]], Map[Variable[_], Factor[_]])
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
BPSolver → BeliefPropagation → Algorithm
def isActive: Boolean

Definition Classes
Algorithm
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def iterations: Int

Definition Classes
BPSolver → OneTimeProbabilisticBeliefPropagation
val iters: Int
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 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
def makeRecordingFactor[U](v: Variable[U]): Factor[U]
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 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
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 non-log 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
BPSolver → 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: Null

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
BPSolver → BeliefPropagation
def toString(): String

Definition Classes
AnyRef → Any
val tupleFactor: Factor[Double]
val tupleVar: Variable[_]
val universe: Null

The universe on which this belief propagation algorithm should be applied.
The universe on which this belief propagation algorithm should be applied.

Definition Classes
BPSolver → BeliefPropagation → FactoredAlgorithm
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( ... )

Related Doc: package solver

class BPSolver extends OneTimeProbabilisticBeliefPropagation

Instance Constructors

new BPSolver(problem: Problem, toEliminate: Set[Variable[_]], toPreserve: Set[Variable[_]], factors: List[Factor[Double]], iters: Int, semiring: LogConvertibleSemiRing[Double])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

def convertFactors(factors: List[Factor[Double]]): List[Factor[Double]]

val debug: Boolean

val dependentAlgorithm: Null

val dependentUniverses: Null

def doKill(): Unit

def doResume(): Unit

def doStart(): Unit

def doStop(): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

var factorGraph: FactorGraph[Double]

def factorToBeliefs[T](factor: Factor[Double]): List[Tuple2[Double, _]]

def finalize(): Unit

def findNodeForElement[T](target: Element[T]): Node

def generateGraph(): 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, parameterized: Boolean = false): (List[Element[_]], Boolean)

def getNewMessageFactorToVar(fn: FactorNode, vn: VariableNode): Factor[Double]

def getNewMessageVarToFactor(vn: VariableNode, fn: FactorNode): Factor[Double]

def go(): (List[Factor[Double]], Map[Variable[_], Factor[_]])

def hashCode(): Int

def initialize(): Unit

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def iterations: Int

val iters: Int

def kill(): Unit

def logSpaceSemiring(): LogConvertibleSemiRing[Double]

def makeRecordingFactor[U](v: Variable[U]): Factor[U]

final def ne(arg0: AnyRef): Boolean

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 run(): Unit

def runStep(): Unit

val semiring: LogConvertibleSemiRing[Double]

def start(): Unit

def starterElements: List[Element[_]]

def stop(): Unit

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

val targetElements: Null

def toString(): String

val tupleFactor: Factor[Double]

val tupleVar: Variable[_]

val universe: Null

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from OneTimeProbabilisticBeliefPropagation

Inherited from OneTime

Inherited from ProbabilisticBeliefPropagation

Inherited from BeliefPropagation[Double]

Inherited from FactoredAlgorithm[Double]

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped