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com.cra.figaro.experimental.collapsedgibbs

RecurringCollapseStrategy

trait RecurringCollapseStrategy extends BaseUnweightedSampler with HeuristicCollapseStrategy

In the paper, the authors recommend updating the marginals every N samples and re-collapsing every few iterations. In practice, this is pretty slow. This trait will keep a running tally of samples of each of the used variables and re-collapse the factor graph (starting from the initial graph) periodically.

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Inherited
  1. RecurringCollapseStrategy
  2. HeuristicCollapseStrategy
  3. CollapsedProbabilisticGibbs
  4. ProbabilisticGibbs
  5. Gibbs
  6. FactoredAlgorithm
  7. BaseUnweightedSampler
  8. Sampler
  9. Algorithm
  10. AnyRef
  11. 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

    add a factor to the list

    Definition Classes
    CollapsedProbabilisticGibbs
  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.

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

    Definition Classes
    CollapsedProbabilisticGibbs
  9. val alphaChoose2: Double

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

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

    Definition Classes
    CollapsedProbabilisticGibbs
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val blockSamplerCreate: BlockSamplerCreator
    Definition Classes
    CollapsedProbabilisticGibbs
  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.

    Perform the collapsing step.

    Definition Classes
    HeuristicCollapseStrategyCollapsedProbabilisticGibbs
  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.

    Definition Classes
    CollapsedProbabilisticGibbs
  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 distributionDistance[T, U](var1: Variable[T], var2: Variable[U]): Double

    Hellinger distance is defined in the source paper (amongst other places).

    Hellinger distance is defined in the source paper (amongst other places). It's the sum over all values of X1 and X2 of (sqrt(P(X1,X2)) - sqrt(P(X1)*P(X2)))^2

    Definition Classes
    HeuristicCollapseStrategy
  19. def doSample(): Unit
    Attributes
    protected
    Definition Classes
    ProbabilisticGibbsBaseUnweightedSamplerSampler
  20. 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. }

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

    List of all factors.

    List of all factors.

    Definition Classes
    Gibbs
  24. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. val gamma: Int
    Definition Classes
    CollapsedProbabilisticGibbs
  26. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  27. 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
  28. 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
  29. def getSampleCount: Int

    Number of samples taken

    Number of samples taken

    Definition Classes
    BaseUnweightedSampler
  30. val globalGraph: VEGraph

    globalGraph lets us traverse the primal graph.

    globalGraph lets us traverse the primal graph.

    Definition Classes
    CollapsedProbabilisticGibbs
  31. def graphHeuristicFunction[T](var1: Variable[T]): Double

    Compute the score of a given variable.

    Compute the score of a given variable.

    Definition Classes
    HeuristicCollapseStrategyCollapsedProbabilisticGibbs
  32. 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.

    Definition Classes
    CollapsedProbabilisticGibbs
  33. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  34. val hellingerDistances: Map[(Int, Int), Double]
    Definition Classes
    HeuristicCollapseStrategy
  35. def initUpdates(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  36. 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
    HeuristicCollapseStrategyAlgorithm
  37. def isActive: Boolean
    Definition Classes
    Algorithm
  38. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  39. 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
  40. def makeResultFactor(factorsAfterElimination: MultiSet[Factor[Double]]): Factor[Double]

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

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

    Definition Classes
    CollapsedProbabilisticGibbs
  41. def marginalize(resultFactor: Factor[Double]): List[Factor[Double]]

    Marginalize all factors to their component variables.

    Marginalize all factors to their component variables.

    Definition Classes
    CollapsedProbabilisticGibbs
  42. def marginalizeToTarget(factor: Factor[Double], target: Variable[_]): Factor[Double]

    Marginalize a factor to a particular variable.

    Marginalize a factor to a particular variable.

    Definition Classes
    CollapsedProbabilisticGibbs
  43. val marginals: Map[Int, Map[Int, Double]]
    Definition Classes
    HeuristicCollapseStrategy
  44. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  45. def newLastUpdate[T](target: Element[T]): LastUpdate[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  46. def newTimesSeen[T](target: Element[T]): TimesSeen[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  47. final def notify(): Unit
    Definition Classes
    AnyRef
  48. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  49. val numSamplesSeenSoFar: Int
    Definition Classes
    HeuristicCollapseStrategy
  50. val originalBlocks: List[Block]
    Definition Classes
    CollapsedProbabilisticGibbs
  51. val pairwiseMarignals: Map[(Int, Int), Map[(Int, Int), Double]]
    Definition Classes
    HeuristicCollapseStrategy
  52. lazy val queryTargets: List[Element[_]]
    Definition Classes
    BaseUnweightedSampler
  53. def removeFactor[T](factor: Factor[T], map: Map[Variable[_], MultiSet[Factor[T]]]): Unit

    remove a factor from the list

    remove a factor from the list

    Definition Classes
    CollapsedProbabilisticGibbs
  54. def resetCounts(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSamplerSampler
  55. def resetMarginals(): Unit

    We override resetMarginals() to do nothing so that we don't get rid of our saved marginal data every time we re-initialize.

    We override resetMarginals() to do nothing so that we don't get rid of our saved marginal data every time we re-initialize. If we haven't built the marginal maps yes, build them, else do nothing.

    Definition Classes
    RecurringCollapseStrategyHeuristicCollapseStrategy
  56. 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
  57. def sample(): (Boolean, Sample)

    Produce a single sample.

    Produce a single sample.

    Definition Classes
    ProbabilisticGibbsBaseUnweightedSampler
  58. def sampleAllBlocks(): Unit

    Every sampleRecurrence many samples, we alter the marginals and PairwiseMarginal Maps to record the current sample.

    Every sampleRecurrence many samples, we alter the marginals and PairwiseMarginal Maps to record the current sample. Every sampleReset many samples, we re-initialize.

    Definition Classes
    RecurringCollapseStrategyProbabilisticGibbs
  59. def sampleAllBlocksWithTracking(): Unit

    Sample all blocks, then store that sample in the marginal and p.m.

    Sample all blocks, then store that sample in the marginal and p.m. maps.

    Definition Classes
    HeuristicCollapseStrategy
  60. var sampleCount: Int
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  61. val sampleRecurrence: Int

    How often we want to save one of our samples for marginal estimation.

  62. val sampleReset: Int

    How often we want to re-collapse.

  63. val semiring: LogSumProductSemiring

    Semiring for use in factors.

    Semiring for use in factors.

    Definition Classes
    ProbabilisticGibbsGibbsFactoredAlgorithm
  64. 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.

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

    Definition Classes
    CollapsedProbabilisticGibbs
  65. 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
  66. 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
  67. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  68. 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.

    Definition Classes
    CollapsedProbabilisticGibbs
  69. val targs: Seq[Element[_]]

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

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

    Definition Classes
    CollapsedProbabilisticGibbs
  70. def toString(): String
    Definition Classes
    AnyRef → Any
  71. val totalSamples: Int
    Definition Classes
    HeuristicCollapseStrategy
  72. val trackingSamples: Int
    Definition Classes
    HeuristicCollapseStrategy
  73. val universe: Universe
    Definition Classes
    BaseUnweightedSampler
  74. def update(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSamplerSampler
  75. def updateDistances(): Unit

    Use the marginal maps to compute Hellinger maps.

    Use the marginal maps to compute Hellinger maps.

    Definition Classes
    HeuristicCollapseStrategy
  76. def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  77. def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  78. val upperB: Boolean

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

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

    Definition Classes
    CollapsedProbabilisticGibbs
  79. val variables: Set[Variable[_]]

    Variables to sample at each time step.

    Variables to sample at each time step.

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

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

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

    Definition Classes
    CollapsedProbabilisticGibbs
  81. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  82. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  83. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from HeuristicCollapseStrategy

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