GibbsSolver

Instance Constructors

new GibbsSolver(problem: Problem, toEliminate: Set[Variable[_]], toPreserve: Set[Variable[_]], _factors: List[Factor[Double]], _numSamples: Int, _burnIn: Int, _interval: Int, blockToSampler: BlockSamplerCreator)

Type Members

type LastUpdate[T] = (T, Int)

Attributes
protected
Definition Classes
BaseUnweightedSampler
type Sample = Map[Element[_], Any]

A sample is a map from elements to their values.
A sample is a map from elements to their values.

Definition Classes
BaseUnweightedSampler
class StarSampleException extends AlgorithmException

Definition Classes
ProbabilisticGibbs
type TimesSeen[T] = Map[T, Int]

Attributes
protected
Definition Classes
BaseUnweightedSampler

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
var allLastUpdates: Map[Element[_], LastUpdate[_]]

Attributes
protected
Definition Classes
BaseUnweightedSampler
var allTimesSeen: Map[Element[_], TimesSeen[_]]

Attributes
protected
Definition Classes
BaseUnweightedSampler
final def asInstanceOf[T0]: T0

Definition Classes
Any
var blockSamplers: List[BlockSampler]

Attributes
protected
Definition Classes
ProbabilisticGibbs
val blockToSampler: BlockSamplerCreator
def burnIn(): Int

Number of samples to throw away initially.
Number of samples to throw away initially.

Definition Classes
GibbsSolver → Gibbs
def chainMapper(chain: Chain[_, _]): Set[Variable[_]]
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 createBlocks(): List[Block]

Method to create a blocking scheme given information about the model and factors.
Method to create a blocking scheme given information about the model and factors.

Definition Classes
GibbsSolver → Gibbs
val currentSamples: Map[Variable[_], Int]

The most recent set of samples, used for sampling variables conditioned on the values of other variables.
The most recent set of samples, used for sampling variables conditioned on the values of other variables.

Definition Classes
Gibbs
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
GibbsSolver → 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
GibbsSolver → 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 doSample(): Unit

Attributes
protected
Definition Classes
ProbabilisticGibbs → BaseUnweightedSampler → 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
AnyRef → Any
var factors: List[Factor[Double]]

List of all factors.
List of all factors.

Definition Classes
Gibbs
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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

All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.
All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.

Definition Classes
ProbabilisticGibbs → FactoredAlgorithm
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 getSampleCount: Int

Number of samples taken
Number of samples taken

Definition Classes
BaseUnweightedSampler
def go(): List[Factor[Double]]
def hashCode(): Int

Definition Classes
AnyRef → Any
def initUpdates(): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler
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
GibbsSolver → Algorithm
def initializeBlocks(): Unit
def interval(): Int

Iterations thrown away between samples.
Iterations thrown away between samples.

Definition Classes
GibbsSolver → Gibbs
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

Definition Classes
AnyRef
def newLastUpdate[T](target: Element[T]): LastUpdate[T]

Attributes
protected
Definition Classes
BaseUnweightedSampler
def newTimesSeen[T](target: Element[T]): TimesSeen[T]

Attributes
protected
Definition Classes
BaseUnweightedSampler
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numSamples(): Int
lazy val queryTargets: List[Element[_]]

Definition Classes
BaseUnweightedSampler
def resetCounts(): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler → 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

Run the algorithm, performing its computation to completion.
Run the algorithm, performing its computation to completion.

Definition Classes
GibbsSolver → OneTime
def sample(): (Boolean, Sample)

Produce a single sample.
Produce a single sample.

Definition Classes
ProbabilisticGibbs → BaseUnweightedSampler
def sampleAllBlocks(): Unit

Definition Classes
ProbabilisticGibbs
var sampleCount: Int

Attributes
protected
Definition Classes
BaseUnweightedSampler
val semiring: LogSumProductSemiring

Semiring for use in factors.
Semiring for use in factors.

Definition Classes
ProbabilisticGibbs → Gibbs → 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 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

Elements whose samples will be recorded at each iteration.
Elements whose samples will be recorded at each iteration.

Definition Classes
GibbsSolver → Gibbs
def toString(): String

Definition Classes
AnyRef → Any
val universe: Universe

Definition Classes
BaseUnweightedSampler
def update(): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler → Sampler
def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler
def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler
var variables: Set[Variable[_]]

Variables to sample at each time step.
Variables to sample at each time step.

Definition Classes
Gibbs
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 GibbsSolver extends BaseUnweightedSampler with ProbabilisticGibbs with OneTime

Instance Constructors

new GibbsSolver(problem: Problem, toEliminate: Set[Variable[_]], toPreserve: Set[Variable[_]], _factors: List[Factor[Double]], _numSamples: Int, _burnIn: Int, _interval: Int, blockToSampler: BlockSamplerCreator)

Type Members

type LastUpdate[T] = (T, Int)

type Sample = Map[Element[_], Any]

class StarSampleException extends AlgorithmException

type TimesSeen[T] = Map[T, Int]

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

var active: Boolean

var allLastUpdates: Map[Element[_], LastUpdate[_]]

var allTimesSeen: Map[Element[_], TimesSeen[_]]

final def asInstanceOf[T0]: T0

var blockSamplers: List[BlockSampler]

val blockToSampler: BlockSamplerCreator

def burnIn(): Int

def chainMapper(chain: Chain[_, _]): Set[Variable[_]]

def cleanUp(): Unit

def clone(): AnyRef

def createBlocks(): List[Block]

val currentSamples: Map[Variable[_], Int]

val dependentAlgorithm: Null

val dependentUniverses: Null

def doKill(): Unit

def doResume(): Unit

def doSample(): Unit

def doStart(): Unit

def doStop(): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

var factors: List[Factor[Double]]

def finalize(): Unit

final def getClass(): Class[_]

def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[Double]]

def getNeededElements(starterElements: List[Element[_]], depth: Int, parameterized: Boolean = false): (List[Element[_]], Boolean)

def getSampleCount: Int

def go(): List[Factor[Double]]

def hashCode(): Int

def initUpdates(): Unit

def initialize(): Unit

def initializeBlocks(): Unit

def interval(): Int

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def kill(): Unit

final def ne(arg0: AnyRef): Boolean

def newLastUpdate[T](target: Element[T]): LastUpdate[T]

def newTimesSeen[T](target: Element[T]): TimesSeen[T]

final def notify(): Unit

final def notifyAll(): Unit

def numSamples(): Int

lazy val queryTargets: List[Element[_]]

def resetCounts(): Unit

def resume(): Unit

def run(): Unit

def sample(): (Boolean, Sample)

def sampleAllBlocks(): Unit

var sampleCount: Int

val semiring: LogSumProductSemiring

def start(): Unit

def stop(): Unit

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

val targetElements: Null

def toString(): String

val universe: Universe

def update(): Unit

def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit

def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit

var variables: Set[Variable[_]]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from OneTime

Inherited from ProbabilisticGibbs

Inherited from Gibbs[Double]