Class

org.apache.spark.mllib.stat

MultivariateOnlineSummarizer

Related Doc: package stat

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class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable

:: DeveloperApi :: MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector format in an online fashion.

Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of the corresponding joint dataset.

A numerically stable algorithm is implemented to compute the mean and variance of instances: Reference: variance-wiki Zero elements (including explicit zero values) are skipped when calling add(), to have time complexity O(nnz) instead of O(n) for each column.

For weighted instances, the unbiased estimation of variance is defined by the reliability weights: https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Reliability_weights.

Annotations
@Since( "1.1.0" ) @DeveloperApi()
Source
MultivariateOnlineSummarizer.scala
Linear Supertypes
Serializable, Serializable, MultivariateStatisticalSummary, AnyRef, Any
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  1. MultivariateOnlineSummarizer
  2. Serializable
  3. Serializable
  4. MultivariateStatisticalSummary
  5. AnyRef
  6. Any
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Instance Constructors

  1. new MultivariateOnlineSummarizer()

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Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def add(sample: Vector): MultivariateOnlineSummarizer.this.type

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    Add a new sample to this summarizer, and update the statistical summary.

    Add a new sample to this summarizer, and update the statistical summary.

    sample

    The sample in dense/sparse vector format to be added into this summarizer.

    returns

    This MultivariateOnlineSummarizer object.

    Annotations
    @Since( "1.1.0" )
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. def count: Long

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    Sample size.

    Sample size.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  8. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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    AnyRef → Any
  13. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  14. def max: Vector

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    Maximum value of each dimension.

    Maximum value of each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  15. def mean: Vector

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    Sample mean of each dimension.

    Sample mean of each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  16. def merge(other: MultivariateOnlineSummarizer): MultivariateOnlineSummarizer.this.type

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    Merge another MultivariateOnlineSummarizer, and update the statistical summary.

    Merge another MultivariateOnlineSummarizer, and update the statistical summary. (Note that it's in place merging; as a result, this object will be modified.)

    other

    The other MultivariateOnlineSummarizer to be merged.

    returns

    This MultivariateOnlineSummarizer object.

    Annotations
    @Since( "1.1.0" )
  17. def min: Vector

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    Minimum value of each dimension.

    Minimum value of each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  18. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  19. def normL1: Vector

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    L1 norm of each dimension.

    L1 norm of each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.2.0" )
  20. def normL2: Vector

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    L2 (Euclidian) norm of each dimension.

    L2 (Euclidian) norm of each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.2.0" )
  21. final def notify(): Unit

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    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  23. def numNonzeros: Vector

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    Number of nonzero elements in each dimension.

    Number of nonzero elements in each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  24. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  25. def toString(): String

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    Definition Classes
    AnyRef → Any
  26. def variance: Vector

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    Unbiased estimate of sample variance of each dimension.

    Unbiased estimate of sample variance of each dimension.

    Definition Classes
    MultivariateOnlineSummarizerMultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  27. final def wait(): Unit

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    AnyRef
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    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  29. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

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