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# CollapsedProbabilisticGibbs 

#### trait CollapsedProbabilisticGibbs extends BaseUnweightedSampler with ProbabilisticGibbs

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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
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

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
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

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
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

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
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( ... )