org.apache.spark.streamdm.classifiers.trees

GaussianEstimator

class GaussianEstimator extends Serializable

Gaussian incremental estimator that uses incremental method, more resilient to floating point imprecision. For more info see Donald Knuth's "The Art of Computer Programming, Volume 2: Seminumerical Algorithms", section 4.2.2.

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Instance Constructors

  1. new GaussianEstimator(that: GaussianEstimator)

  2. new GaussianEstimator(weightSum: Double = 0.0, mean: Double = 0.0, varianceSum: Double = 0.0)

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. final def asInstanceOf[T0]: T0

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  7. var blockMean: Double

  8. var blockVarianceSum: Double

  9. var blockWeightSum: Double

  10. def clone(): AnyRef

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    @throws( ... )

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

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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  15. def getMean(): Double

    Returns the mean value

    Returns the mean value

    returns

    the mean value

  16. def hashCode(): Int

    Definition Classes
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  17. final def isInstanceOf[T0]: Boolean

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  18. var mean: Double

  19. def merge(that: GaussianEstimator, trySplit: Boolean): GaussianEstimator

    Merge current GaussianEstimator with another one.

    Merge current GaussianEstimator with another one.

    that

    the GaussianEstimator to be merged

    trySplit

    flag indicating whether the Hoeffding Tree tries to split

    returns

    the new GaussianEstimator

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

    Definition Classes
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  21. val normal_constant: Double

  22. final def notify(): Unit

    Definition Classes
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  23. final def notifyAll(): Unit

    Definition Classes
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  24. def observe(value: Double, weight: Double): Unit

    Observe the data and update the Gaussian estimator

    Observe the data and update the Gaussian estimator

    value

    value of a feature

    weight

    weight of the Example

  25. def probabilityDensity(value: Double): Double

    Returns the cumulative probability of the input value in the current distribution.

    Returns the cumulative probability of the input value in the current distribution.

    value

    the value

    returns

    the cumulative probability

  26. def stdDev(): Double

    Returns the standard deviation

    Returns the standard deviation

    returns

    the standard deviation

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

    Definition Classes
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  28. def toString(): String

    Definition Classes
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  29. def totalWeight(): Double

    Returns the total weight

    Returns the total weight

    returns

    the total weight

  30. def tripleWeights(splitValue: Double): Array[Double]

    Returns an array of weights which have the sum less than, equal to, and greater than the split value.

    Returns an array of weights which have the sum less than, equal to, and greater than the split value.

    splitValue

    the value of the split

    returns

    the resulting Array of values

  31. def variance(): Double

    Returns the variance

    Returns the variance

    returns

    the variance

  32. var varianceSum: Double

  33. final def wait(): Unit

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

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

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  36. var weightSum: Double

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