dk.bayes.dsl.epnaivebayes

EPNaiveBayesFactorGraph

Related Doc: package epnaivebayes

case class EPNaiveBayesFactorGraph[X](prior: SingleFactor[X], likelihoods: Seq[DoubleFactor[X, _]], paralllelMessagePassing: Boolean = false)(implicit multOp: multOp[X], divideOp: divideOp[X], isIdentical: isIdentical[X]) extends LazyLogging with Product with Serializable

Computes posterior of X for a naive bayes net. Variables: X, Y1|X, Y2|X,...Yn|X

It run Expectation Propagation algorithm. http://en.wikipedia.org/wiki/Expectation_propagation

paralllelMessagePassing

If true then messages between X variable and Y variables are sent in parallel

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Serializable, Serializable, Product, Equals, LazyLogging, Logging, AnyRef, Any
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Instance Constructors

  1. new EPNaiveBayesFactorGraph(prior: SingleFactor[X], likelihoods: Seq[DoubleFactor[X, _]], paralllelMessagePassing: Boolean = false)(implicit multOp: multOp[X], divideOp: divideOp[X], isIdentical: isIdentical[X])

    paralllelMessagePassing

    If true then messages between X variable and Y variables are sent in parallel

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. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  5. def calibrate(maxIter: Int = 100, threshold: Double = 1e-6): Int

    returns

    Number of iterations that factor graph was calibrated over.

  6. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  7. implicit val divideOp: divideOp[X]

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

    Definition Classes
    AnyRef

  9. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  11. def getMsgsUp(): Seq[X]

  12. def getPosterior(): X

  13. implicit val isIdentical: isIdentical[X]

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. val likelihoods: Seq[DoubleFactor[X, _]]

  16. lazy val logger: Logger

    Attributes
    protected
    Definition Classes
    LazyLogging → Logging

  17. implicit val multOp: multOp[X]

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

    Definition Classes
    AnyRef

  19. final def notify(): Unit

    Definition Classes
    AnyRef

  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  21. val paralllelMessagePassing: Boolean

    If true then messages between X variable and Y variables are sent in parallel

  22. val prior: SingleFactor[X]

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

    Definition Classes
    AnyRef

  24. final def wait(): Unit

    Definition Classes
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    Annotations
    @throws( ... )

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

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    @throws( ... )

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

    Definition Classes
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