dk.bayes.clustergraph.factor

Factor

Related Docs: object Factor | package factor

trait Factor extends AnyRef

Represents potentials over a set of variables.

Linear Supertypes
AnyRef, Any
Known Subclasses
MultiFactor, SingleFactor
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Abstract Value Members

  1. abstract def copy(values: Array[Double]): Factor

    Returns a copy of this factor with new values.

  2. abstract def getValue(assignment: Array[Int]): Double

    Returns potential value for a given assignment to all factor variables.

    Returns potential value for a given assignment to all factor variables.

    assignment

    Array[variable value index]

  3. abstract def getValues(): Array[Double]

    Returns factor potentials.

    Returns factor potentials.

    Values indexing:

    variable0 (dim=3) variable1(dim=2) variable2(dim=2) value ------------------------- 0 0 0 val0 0 0 1 val1 0 1 0 val2 0 1 1 val3 1 0 0 val4 1 0 1 val5 1 1 0 val6 1 1 1 val7 2 0 0 val8 2 0 1 val9 2 1 0 val10 2 1 1 val11

  4. abstract def getVariables(): Array[Var]

    Returns factor variables.

  5. abstract def mapAssignments[T](f: (Array[Int]) ⇒ T)(implicit arg0: Manifest[T]): Array[T]

    Maps all factor assignments to an array of objects.

    Maps all factor assignments to an array of objects.

    f

    Mapping function. (Assignment) => Array element

  6. abstract def marginal(varId: Int): SingleFactor

    Returns marginal factor for a given variable id.

  7. abstract def normalise(): Factor

    Returns new factor with all potential values normalised to 1.

  8. abstract def product(singleFactor: SingleFactor): Factor

    Returns product of this factor and single factor.

  9. abstract def withEvidence(evidence: (Int, Int)): Factor

    Returns new factor with zero values for all potential entries inconsistent with evidence.

    Returns new factor with zero values for all potential entries inconsistent with evidence.

    evidence

    Tuple2[variable id, variable value index]

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

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  2. final def ##(): Int

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