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
t

org.apache.spark.mllib.stat

MultivariateStatisticalSummary

trait MultivariateStatisticalSummary extends AnyRef

Trait for multivariate statistical summary of a data matrix.

Annotations
@Since( "1.0.0" )
Source
MultivariateStatisticalSummary.scala
Linear Supertypes
AnyRef, Any
Known Subclasses
MultivariateOnlineSummarizer
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Abstract Value Members

  1. abstract def count: Long

    Sample size.

    Sample size.

    Annotations
    @Since( "1.0.0" )
  2. abstract def max: Vector

    Maximum value of each column.

    Maximum value of each column.

    Annotations
    @Since( "1.0.0" )
  3. abstract def mean: Vector

    Sample mean vector.

    Sample mean vector.

    Annotations
    @Since( "1.0.0" )
  4. abstract def min: Vector

    Minimum value of each column.

    Minimum value of each column.

    Annotations
    @Since( "1.0.0" )
  5. abstract def normL1: Vector

    L1 norm of each column

    L1 norm of each column

    Annotations
    @Since( "1.2.0" )
  6. abstract def normL2: Vector

    Euclidean magnitude of each column

    Euclidean magnitude of each column

    Annotations
    @Since( "1.2.0" )
  7. abstract def numNonzeros: Vector

    Number of nonzero elements (including explicitly presented zero values) in each column.

    Number of nonzero elements (including explicitly presented zero values) in each column.

    Annotations
    @Since( "1.0.0" )
  8. abstract def variance: Vector

    Sample variance vector.

    Sample variance vector. Should return a zero vector if the sample size is 1.

    Annotations
    @Since( "1.0.0" )
  9. abstract def weightSum: Double

    Sum of weights.

    Sum of weights.

    Annotations
    @Since( "3.0.0" )

Concrete Value Members

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