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com.cra.figaro.experimental.collapsedgibbs

CollapsedProbabilisticGibbs

trait CollapsedProbabilisticGibbs extends BaseUnweightedSampler with ProbabilisticGibbs

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Inherited
  1. CollapsedProbabilisticGibbs
  2. ProbabilisticGibbs
  3. Gibbs
  4. FactoredAlgorithm
  5. BaseUnweightedSampler
  6. Sampler
  7. Algorithm
  8. AnyRef
  9. Any
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Type Members

  1. class StarSampleException extends AlgorithmException
    Definition Classes
    ProbabilisticGibbs
  2. type LastUpdate[T] = (T, Int)
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  3. 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
  4. type TimesSeen[T] = Map[T, Int]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler

Abstract Value Members

  1. abstract def burnIn(): Int

    Number of samples to throw away initially.

    Number of samples to throw away initially.

    Definition Classes
    Gibbs
  2. abstract 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
    Gibbs
  3. abstract val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

    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
    FactoredAlgorithm
  4. abstract val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]

    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
    FactoredAlgorithm
  5. abstract def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  6. abstract def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  7. abstract def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  8. abstract def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  9. abstract def interval(): Int

    Iterations thrown away between samples.

    Iterations thrown away between samples.

    Definition Classes
    Gibbs
  10. abstract val targetElements: List[Element[_]]

    Elements whose samples will be recorded at each iteration.

    Elements whose samples will be recorded at each iteration.

    Definition Classes
    Gibbs

Concrete 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. def addFactor[T](factor: Factor[T], map: Map[Variable[_], MultiSet[Factor[T]]]): Unit

    add a factor to the list

  6. var allLastUpdates: Map[Element[_], LastUpdate[_]]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  7. var allTimesSeen: Map[Element[_], TimesSeen[_]]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  8. val alpha: Int

    Only variables with alpha or fewer neighbors in the primal graph are candidates for collapsing.

  9. val alphaChoose2: Double

    We use ( alpha C 2 ) often, may as well store it.

  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val blockSamplerCreate: BlockSamplerCreator
  12. val blockSamplers: List[BlockSampler]
    Attributes
    protected
    Definition Classes
    ProbabilisticGibbs
  13. 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
  14. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  15. def collapseVariables(): Unit

    Perform the collapsing step.

  16. def correctBlocks(originalBlocks: List[Block]): List[Block]

    We want to alter the original blocks so that we filter out any variables which have been eliminated.

    We want to alter the original blocks so that we filter out any variables which have been eliminated. If the original blocks overlapped a lot, then there'll be a lot of redundancy in the filtered blocks, so we take a further step of eliminating any block xs which is fully contained in another block ys.

  17. 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
  18. def doSample(): Unit
    Attributes
    protected
    Definition Classes
    ProbabilisticGibbsBaseUnweightedSamplerSampler
  19. def eliminate(variable: Variable[_], factors: MultiSet[Factor[Double]], map: Map[Variable[_], MultiSet[Factor[Double]]]): Unit

    Eliminate a variable.

    Eliminate a variable. This follows the same approach as in VariableElimination.scala. }

  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. val factors: List[Factor[Double]]

    List of all factors.

    List of all factors.

    Definition Classes
    Gibbs
  23. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. val gamma: Int
  25. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  26. 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
    ProbabilisticGibbsFactoredAlgorithm
  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 getSampleCount: Int

    Number of samples taken

    Number of samples taken

    Definition Classes
    BaseUnweightedSampler
  29. val globalGraph: VEGraph

    globalGraph lets us traverse the primal graph.

  30. def graphHeuristicFunction[T](var1: Variable[T]): Double

    The heuristic of a node is how many edges would be added to the primal graph by removing that variable.

    The heuristic of a node is how many edges would be added to the primal graph by removing that variable. Because we make a clique over the variable's neighbors. Since we only eliminate variables with alpha or fewer neighbors, this is capped at (alpha C 2). So we return the number of edges as a percentage of (alpha C 2).

  31. def graphTerm[T](var1: Variable[T]): Double

    Returns how many edges would be added to the primal graph by removing var1.

    Returns how many edges would be added to the primal graph by removing var1. Note: this is number of edges added, NOT net edges added and removed. Source paper is somewhat ambiguous on whether this should be added or net.

  32. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  33. def initUpdates(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  34. 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
  35. def isActive: Boolean
    Definition Classes
    Algorithm
  36. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  37. 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
  38. def makeResultFactor(factorsAfterElimination: MultiSet[Factor[Double]]): Factor[Double]

    Combine all the remaining factors into one 'result factor', as in VE.

  39. def marginalize(resultFactor: Factor[Double]): List[Factor[Double]]

    Marginalize all factors to their component variables.

  40. def marginalizeToTarget(factor: Factor[Double], target: Variable[_]): Factor[Double]

    Marginalize a factor to a particular variable.

  41. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  42. def newLastUpdate[T](target: Element[T]): LastUpdate[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  43. def newTimesSeen[T](target: Element[T]): TimesSeen[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  44. final def notify(): Unit
    Definition Classes
    AnyRef
  45. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  46. val originalBlocks: List[Block]
  47. lazy val queryTargets: List[Element[_]]
    Definition Classes
    BaseUnweightedSampler
  48. def removeFactor[T](factor: Factor[T], map: Map[Variable[_], MultiSet[Factor[T]]]): Unit

    remove a factor from the list

  49. def resetCounts(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSamplerSampler
  50. 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
  51. def sample(): (Boolean, Sample)

    Produce a single sample.

    Produce a single sample.

    Definition Classes
    ProbabilisticGibbsBaseUnweightedSampler
  52. def sampleAllBlocks(): Unit
    Definition Classes
    ProbabilisticGibbs
  53. var sampleCount: Int
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  54. val semiring: LogSumProductSemiring

    Semiring for use in factors.

    Semiring for use in factors.

    Definition Classes
    ProbabilisticGibbsGibbsFactoredAlgorithm
  55. def sortByHeuristic(varList: List[Variable[_]], HeuristicMap: Map[Variable[_], Double]): List[Variable[_]]

    Sort variables by the target heuristic, if they have fewer than alpha neighbors and are not targets.

  56. 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
  57. 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
  58. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  59. val targetVariables: List[Variable[_]]

    List of variables corresponding to target elements.

    List of variables corresponding to target elements. Creating these is memoized, so we don't need to worry about duplicates.

  60. val targs: Seq[Element[_]]

    Store which elements are our target variables so that subclasses can make use of them.

  61. def toString(): String
    Definition Classes
    AnyRef → Any
  62. val universe: Universe
    Definition Classes
    BaseUnweightedSampler
  63. def update(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSamplerSampler
  64. def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  65. def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  66. val upperB: Boolean

    Store which elements are our target variables so that subclasses can make use of them.

  67. val variables: Set[Variable[_]]

    Variables to sample at each time step.

    Variables to sample at each time step.

    Definition Classes
    Gibbs
  68. val varsInOrder: List[Variable[_]]

    We need a list of variables in order so we can access them by index.

  69. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  70. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  71. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ProbabilisticGibbs

Inherited from Gibbs[Double]

Inherited from FactoredAlgorithm[Double]

Inherited from BaseUnweightedSampler

Inherited from Sampler

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped