Statistics

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. def clone(): AnyRef

   

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6. final def eq(arg0: AnyRef): 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. def moments(input: Output, axes: Seq[Int], weights: Output = null, keepDims: Boolean = false, name: String = "Moments"): (Output, Output)

    

    $OpDocStatisticsMoments

    $OpDocStatisticsMoments

    input

    Input tensor.

    axes

    Axes along which to compute the mean and variance.

    weights

    Optional tensor of positive weights that can be broadcast with input, to weigh the samples. Defaults to null, meaning that equal weighting is used (i.e., all samples have weight equal to 1).

    keepDims

    If true, retain the reduced axes.

    name

    Name for the created op.

    returns

    Tuple containing the created op outputs: (i) the mean tensor, and (ii) the variance tensor.

    Definition Classes

    Statistics

13. def momentsFromSufficientStatistics(counts: Output, meanSS: Output, varSS: Output, shift: Output = null, name: String = "MomentsFromSufficientStatistics"): (Output, Output)

    

    $OpDocStatisticsMomentsFromSufficientStatistics

    $OpDocStatisticsMomentsFromSufficientStatistics

    counts

    Total number of elements over which the provided sufficient statistics were computed.

    meanSS

    Mean sufficient statistics: the (possibly shifted) sum of the elements.

    varSS

    Variance sufficient statistics: the (possibly shifted) sum of squares of the elements.

    shift

    The shift by which the mean must be corrected, or null if no shift was used.

    name

    Name for the created op.

    returns

    Tuple containing the created op outputs: (i) the mean tensor, and (ii) the variance tensor.

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    Statistics

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

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. def sufficientStatistics(input: Output, axes: Output, shift: Output = null, keepDims: Boolean = false, name: String = "SufficientStatistics"): (Output, Output, Output, Output)

    

    $OpDocStatisticsSufficientStatistics

    $OpDocStatisticsSufficientStatistics

    input

    Input tensor.

    axes

    Tensor containing the axes along which to compute the mean and variance.

    shift

    Optional tensor containing the value by which to shift the data for numerical stability. Defaults to null, meaning that no shift needs to be performed. A shift close to the true mean provides the most numerically stable results.

    keepDims

    If true, retain the reduced axes.

    name

    Name for the created op.

    returns

    Tuple containing the following created op outputs:

    • Count: The number of elements to average over.
    • Mean Sufficient Statistic: The (possibly shifted) sum of the elements in the tensor.
    • Variance Sufficient Statistic: The (possibly shifted) sum of squares of the elements in the tensor.
    • Shift: The shift by which the mean must be corrected, or null if no shift was used.

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    Statistics

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

    

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19. def toString(): String

    

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20. final def wait(): Unit

    

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

    

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

    

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