c

com.cra.figaro.experimental.collapsedgibbs

CollapsedProbQueryGibbs

abstract class CollapsedProbQueryGibbs extends ProbQueryGibbs with CollapsedProbabilisticGibbs

CollapsedProbQueryGibbs only uses graph information and the list of targets to collapse some variables. extend with HeuristicCollapser or RecurringCollapser to implement other features described in Gogate et. al.

Linear Supertypes
CollapsedProbabilisticGibbs, ProbQueryGibbs, UnweightedSampler, StreamableProbQueryAlgorithm, ProbQueryAlgorithm, ProbQuerySampler, BaseProbQuerySampler[Element], BaseProbQueryAlgorithm[Element], ProbabilisticGibbs, Gibbs[Double], FactoredAlgorithm[Double], BaseUnweightedSampler, Sampler, Algorithm, AnyRef, Any
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Inherited
  1. CollapsedProbQueryGibbs
  2. CollapsedProbabilisticGibbs
  3. ProbQueryGibbs
  4. UnweightedSampler
  5. StreamableProbQueryAlgorithm
  6. ProbQueryAlgorithm
  7. ProbQuerySampler
  8. BaseProbQuerySampler
  9. BaseProbQueryAlgorithm
  10. ProbabilisticGibbs
  11. Gibbs
  12. FactoredAlgorithm
  13. BaseUnweightedSampler
  14. Sampler
  15. Algorithm
  16. AnyRef
  17. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new CollapsedProbQueryGibbs(universe: Universe, targets: Element[_]*)(dependentUniverses: List[(Universe, List[NamedEvidence[_]])], dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double, burnIn: Int, interval: Int, blockToSampler: BlockSamplerCreator, alphaIn: Int = 10, gammaIn: Int = 1000, upperBounds: Boolean = false)

Type Members

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

Abstract Value Members

  1. 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
  2. abstract def doDistribution[T](target: Element[T]): Stream[(Double, T)]
    Attributes
    protected
    Definition Classes
    BaseProbQueryAlgorithm
  3. abstract def doExpectation[T](target: Element[T], function: (T) ⇒ Double): Double
    Attributes
    protected
    Definition Classes
    BaseProbQueryAlgorithm
  4. abstract def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    Algorithm
  5. abstract def doProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double
    Attributes
    protected
    Definition Classes
    BaseProbQueryAlgorithm
  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

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. val blockToSampler: BlockSamplerCreator
    Definition Classes
    CollapsedProbQueryGibbs → ProbQueryGibbs
  14. val burnIn: Int
    Definition Classes
    CollapsedProbQueryGibbs → ProbQueryGibbs → Gibbs
  15. def chainMapper(chain: Chain[_, _]): Set[Variable[_]]
    Definition Classes
    ProbQueryGibbs
  16. def check[T](target: Element[T]): Unit
    Attributes
    protected
    Definition Classes
    BaseProbQueryAlgorithm
  17. 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
  18. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  19. def collapseVariables(): Unit

    Perform the collapsing step.

    Perform the collapsing step.

    Definition Classes
    CollapsedProbabilisticGibbs
  20. def computeDistribution[T](target: Element[T]): Stream[(Double, T)]

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Definition Classes
    BaseProbQuerySampler → BaseProbQueryAlgorithm
  21. def computeExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Definition Classes
    BaseProbQuerySampler → BaseProbQueryAlgorithm
  22. def computeProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Definition Classes
    BaseProbQueryAlgorithm
  23. def computeProjection[T](target: Element[T]): List[(T, Double)]
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    UnweightedSampler → BaseProbQueryAlgorithm
  24. 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
  25. 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
  26. val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double
    Definition Classes
    CollapsedProbQueryGibbs → ProbQueryGibbs → FactoredAlgorithm
  27. val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]
    Definition Classes
    CollapsedProbQueryGibbs → ProbQueryGibbs → FactoredAlgorithm
  28. def distribution[T](target: Element[T]): Stream[(Double, T)]

    Return an estimate of the marginal probability distribution over the target that lists each element with its probability.

    Return an estimate of the marginal probability distribution over the target that lists each element with its probability. The result is a lazy stream. It is up to the algorithm how the stream is ordered. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  29. def doProjection[T](target: Element[T]): List[(T, Double)]
    Attributes
    protected
    Definition Classes
    BaseProbQueryAlgorithm
  30. def doSample(): Unit
    Attributes
    protected
    Definition Classes
    ProbabilisticGibbs → BaseUnweightedSampler → Sampler
  31. 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
  32. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  34. def expectation[T](target: Element[T])(function: (T) ⇒ Double, c: Any = DummyImplicit): Double

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  35. def expectation[T](target: Element[T], function: (T) ⇒ Double): Double

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  36. val factors: List[Factor[Double]]

    List of all factors.

    List of all factors.

    Definition Classes
    Gibbs
  37. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  38. val gamma: Int
    Definition Classes
    CollapsedProbabilisticGibbs
  39. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  40. 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
  41. 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
  42. def getSampleCount: Int

    Number of samples taken

    Number of samples taken

    Definition Classes
    BaseUnweightedSampler
  43. def getTotalWeight: Double

    Total weight of samples taken, in log space

    Total weight of samples taken, in log space

    Definition Classes
    UnweightedSampler → BaseProbQuerySampler
  44. val globalGraph: VEGraph

    globalGraph lets us traverse the primal graph.

    globalGraph lets us traverse the primal graph.

    Definition Classes
    CollapsedProbabilisticGibbs
  45. 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).

    Definition Classes
    CollapsedProbabilisticGibbs
  46. 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
  47. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  48. def initUpdates(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  49. 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
    CollapsedProbQueryGibbs → ProbQueryGibbs → Algorithm
  50. val interval: Int
    Definition Classes
    CollapsedProbQueryGibbs → ProbQueryGibbs → Gibbs
  51. def isActive: Boolean
    Definition Classes
    Algorithm
  52. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. 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
  54. 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
  55. 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
  56. 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
  57. def mean(target: Element[Double]): Double

    Return the mean of the probability density function for the given continuous element.

    Return the mean of the probability density function for the given continuous element.

    Definition Classes
    BaseProbQueryAlgorithm
  58. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  59. def newLastUpdate[T](target: Element[T]): LastUpdate[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  60. def newTimesSeen[T](target: Element[T]): TimesSeen[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  61. final def notify(): Unit
    Definition Classes
    AnyRef
  62. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  63. val originalBlocks: List[Block]
    Definition Classes
    CollapsedProbabilisticGibbs
  64. def posteriorElement[T](target: Element[T], universe: Universe = Universe.universe): Element[T]

    Return an element representing the posterior probability distribution of the given element.

    Return an element representing the posterior probability distribution of the given element.

    Definition Classes
    ProbQueryAlgorithm
  65. def probability[T](target: Element[T], value: T): Double

    Return an estimate of the probability that the target produces the value.

    Return an estimate of the probability that the target produces the value. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  66. def probability[T](target: Element[T])(predicate: (T) ⇒ Boolean, c: Any = DummyImplicit): Double

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  67. def probability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  68. lazy val queryTargets: List[Element[_]]
    Definition Classes
    BaseUnweightedSampler
  69. 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
  70. def resetCounts(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler → Sampler
  71. 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
  72. def sample(): (Boolean, Sample)

    Produce a single sample.

    Produce a single sample.

    Definition Classes
    ProbabilisticGibbs → BaseUnweightedSampler
  73. def sampleAllBlocks(): Unit
    Definition Classes
    ProbabilisticGibbs
  74. var sampleCount: Int
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  75. def sampleFromPosterior[T](element: Element[T]): Stream[T]

    Sample an value from the posterior of this element

    Sample an value from the posterior of this element

    Definition Classes
    UnweightedSampler → StreamableProbQueryAlgorithm
  76. val semiring: LogSumProductSemiring

    Semiring for use in factors.

    Semiring for use in factors.

    Definition Classes
    ProbabilisticGibbs → Gibbs → FactoredAlgorithm
  77. 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
  78. 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
  79. 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
  80. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  81. val targetElements: List[Element[_]]
    Definition Classes
    ProbQueryGibbs → Gibbs
  82. 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
  83. 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
  84. def toString(): String
    Definition Classes
    AnyRef → Any
  85. val universe: Universe
    Definition Classes
    CollapsedProbQueryGibbs → ProbQueryGibbs → ProbQueryAlgorithm → ProbQuerySampler → Gibbs → FactoredAlgorithm → BaseUnweightedSampler
  86. def update(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler → Sampler
  87. def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  88. def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  89. 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
  90. val variables: Set[Variable[_]]

    Variables to sample at each time step.

    Variables to sample at each time step.

    Definition Classes
    Gibbs
  91. def variance(target: Element[Double]): Double

    Return the variance of the probability density function for the given continuous element.

    Return the variance of the probability density function for the given continuous element.

    Definition Classes
    BaseProbQueryAlgorithm
  92. 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
  93. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  94. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  95. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from CollapsedProbabilisticGibbs

Inherited from ProbQueryGibbs

Inherited from UnweightedSampler

Inherited from StreamableProbQueryAlgorithm

Inherited from ProbQueryAlgorithm

Inherited from ProbQuerySampler

Inherited from BaseProbQuerySampler[Element]

Inherited from BaseProbQueryAlgorithm[Element]

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