org.apache.spark.mllib.stat

MultivariateOnlineSummarizer

class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable

:: DeveloperApi :: MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for samples in sparse or dense vector format in a 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 sample mean and variance: 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.

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

  1. new MultivariateOnlineSummarizer()

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. def add(sample: Vector): MultivariateOnlineSummarizer.this.type

    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" )

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. def count: Long

    Sample size.

    Sample size.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )

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

    Definition Classes
    AnyRef

  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. def max: Vector

    Maximum value of each dimension.

    Maximum value of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )

  17. def mean: Vector

    Sample mean of each dimension.

    Sample mean of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )

  18. def merge(other: MultivariateOnlineSummarizer): MultivariateOnlineSummarizer.this.type

    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" )

  19. def min: Vector

    Minimum value of each dimension.

    Minimum value of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )

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

    Definition Classes
    AnyRef

  21. def normL1: Vector

    L1 norm of each dimension.

    L1 norm of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.2.0" )

  22. def normL2: Vector

    L2 (Euclidian) norm of each dimension.

    L2 (Euclidian) norm of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.2.0" )

  23. final def notify(): Unit

    Definition Classes
    AnyRef

  24. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  25. def numNonzeros: Vector

    Number of nonzero elements in each dimension.

    Number of nonzero elements in each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )

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

    Definition Classes
    AnyRef

  27. def toString(): String

    Definition Classes
    AnyRef → Any

  28. def variance: Vector

    Sample variance of each dimension.

    Sample variance of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )

  29. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
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    @throws( ... )

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

    Definition Classes
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    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from MultivariateStatisticalSummary

Inherited from AnyRef

Inherited from Any

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