Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package stat
    Definition Classes
    mllib
  • package distribution
    Definition Classes
    stat
  • package test
    Definition Classes
    stat
  • KernelDensity
  • MultivariateOnlineSummarizer
  • MultivariateStatisticalSummary
  • Statistics
c

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 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: see Reliability weights (Wikipedia).

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

  1. new MultivariateOnlineSummarizer()

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. 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" )
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. def count: Long

    Sample size.

    Sample size.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  12. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. def max: Vector

    Maximum value of each dimension.

    Maximum value of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  15. def mean: Vector

    Sample mean of each dimension.

    Sample mean of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  16. 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" )
  17. def min: Vector

    Minimum value of each dimension.

    Minimum value of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def normL1: Vector

    L1 norm of each dimension.

    L1 norm of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.2.0" )
  20. def normL2: Vector

    L2 (Euclidean) norm of each dimension.

    L2 (Euclidean) norm of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.2.0" )
  21. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. 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" )
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. def variance: Vector

    Unbiased estimate of sample variance of each dimension.

    Unbiased estimate of sample variance of each dimension.

    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from MultivariateStatisticalSummary

Inherited from AnyRef

Inherited from Any

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