class SufficientStatisticsSemiring extends Semiring[(Double, Map[Parameter[_], Seq[Double]])]

Sum and product operations defined for sufficient statistics. Statistics consist of a probability and counts of the number of times various values have been seen.

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Semiring[(Double, Map[Parameter[_], Seq[Double]])], AnyRef, Any
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Instance Constructors

  1. new SufficientStatisticsSemiring(parameterMap: Map[Parameter[_], Seq[Double]])

    parameterMap

    Map of parameters to their sufficient statistics. Expectation Maximization determines the parameterMap automatically from the parameters.

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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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_]
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  10. def hashCode(): Int
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  11. final def isInstanceOf[T0]: Boolean
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  12. final def ne(arg0: AnyRef): Boolean
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  13. final def notify(): Unit
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  14. final def notifyAll(): Unit
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  15. val one: (Double, Map[Parameter[_], Seq[Double]])

    1 probability and a vector of zeros for all parameters.

    1 probability and a vector of zeros for all parameters. The vector for a parameter must be of length equal to number of possible observations of the parameter.

    Definition Classes
    SufficientStatisticsSemiringSemiring
  16. def product(xVector: (Double, Map[Parameter[_], Seq[Double]]), yVector: (Double, Map[Parameter[_], Seq[Double]])): (Double, Map[Parameter[_], Seq[Double]])

    Probabilities are multiplied using standard multiplication.

    Probabilities are multiplied using standard multiplication. Sufficient statistics for each parameter are summed together.

    Definition Classes
    SufficientStatisticsSemiringSemiring
  17. def sum(xVector: (Double, Map[Parameter[_], Seq[Double]]), yVector: (Double, Map[Parameter[_], Seq[Double]])): (Double, Map[Parameter[_], Seq[Double]])

    Probabilities are added using standard addition.

    Probabilities are added using standard addition. Sufficient statistics for each parameter are weighted by their respective probabilities and summed together, then divided by the sum of both probabilities.

    Definition Classes
    SufficientStatisticsSemiringSemiring
  18. def sumMany(xs: Traversable[(Double, Map[Parameter[_], Seq[Double]])]): (Double, Map[Parameter[_], Seq[Double]])

    Sum of many entries.

    Sum of many entries. Typically, this would be implemented by the ordinary sum, but there may be more efficient implementations.

    Definition Classes
    Semiring
  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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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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  24. val zero: (Double, Map[Parameter[_], Seq[Double]])

    0 probability and a vector of zeros for all parameters.

    0 probability and a vector of zeros for all parameters. The vector for a parameter must be of length equal to number of possible observations of the parameter.

    Definition Classes
    SufficientStatisticsSemiringSemiring

Inherited from Semiring[(Double, Map[Parameter[_], Seq[Double]])]

Inherited from AnyRef

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