dk.bayes.model.clustergraph

Cluster

class Cluster extends AnyRef

Represents cluster in a cluster graph.

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Instance Constructors

  1. new Cluster(id: Int, typeId: Int, factor: Factor)

    id

    Unique cluster id

    typeId

    Unique id of a cluster type. Clusters of the same cluster type usually share the same cluster initial potentials. TypeId can be used for learning parameters in dynamic bayesian networks, with multiple prior, transition and emission parameters.

    factor

    Initial cluster potentials

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. def addEdge(edge: Edge): Unit

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. final def getClass(): Class[_]

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  13. def getEdges(): Seq[Edge]

  14. def getFactor(): Factor

  15. def hashCode(): Int

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  16. val id: Int

    Unique cluster id

  17. final def isInstanceOf[T0]: Boolean

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  18. final def ne(arg0: AnyRef): Boolean

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def resetMessages(): Unit

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

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  23. def toString(): String

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  24. val typeId: Int

    Unique id of a cluster type.

    Unique id of a cluster type. Clusters of the same cluster type usually share the same cluster initial potentials. TypeId can be used for learning parameters in dynamic bayesian networks, with multiple prior, transition and emission parameters.

  25. def updateFactor(newFactor: Factor): Unit

  26. final def wait(): Unit

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

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  28. final def wait(arg0: Long): Unit

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