nak.inference.bp.BeliefPropagation

Beliefs

case class Beliefs(model: Model, beliefs: IndexedSeq[DenseVector[Double]], messages: IndexedSeq[IndexedSeq[DenseVector[Double]]], factorLogPartitions: IndexedSeq[Double]) extends Product with Serializable

The result object for BeliefPropagation, useful for getting information about marginals and edge marginals

model

the Factor model used to perform inference

beliefs

beliefs for each variable, for each assignment to each variable. normalized, not in log space

messages

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Instance Constructors

  1. new Beliefs(model: Model, beliefs: IndexedSeq[DenseVector[Double]], messages: IndexedSeq[IndexedSeq[DenseVector[Double]]], factorLogPartitions: IndexedSeq[Double])

    model

    the Factor model used to perform inference

    beliefs

    beliefs for each variable, for each assignment to each variable. normalized, not in log space

    messages

Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. val beliefs: IndexedSeq[DenseVector[Double]]

    beliefs for each variable, for each assignment to each variable.

    beliefs for each variable, for each assignment to each variable. normalized, not in log space

  6. def clone(): AnyRef

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

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  8. val factorLogPartitions: IndexedSeq[Double]

  9. def factorMarginalFor(f: Factor): Factor

    returns a factor representing the factor marginal for the given marginal.

    returns a factor representing the factor marginal for the given marginal. That is, f(assignment) will give the marginal probability of any given assignment.

    If the factor is not in the original model, this still works, but it doesn't mean much unless logApply returns 0.0 for all values.

    f

    the factor

    returns

    the edge marginal factor

  10. def finalize(): Unit

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

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  12. final def isInstanceOf[T0]: Boolean

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  13. val logPartition: Double

  14. def marginalFor[T](v: Variable[T]): Counter[T, Double]

  15. val messages: IndexedSeq[IndexedSeq[DenseVector[Double]]]

  16. val model: Model

    the Factor model used to perform inference

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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

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  21. final def wait(): Unit

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

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

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