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  • 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.

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  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

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    spark
  • package stat
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    ml
  • package distribution
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    stat
  • MultivariateGaussian
c

org.apache.spark.ml.stat.distribution

MultivariateGaussian

class MultivariateGaussian extends Serializable

This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution. In the event that the covariance matrix is singular, the density will be computed in a reduced dimensional subspace under which the distribution is supported. (see here)

Annotations
@Since( "2.0.0" ) @DeveloperApi()
Source
MultivariateGaussian.scala
Linear Supertypes
Serializable, Serializable, AnyRef, Any
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  1. MultivariateGaussian
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Instance Constructors

  1. new MultivariateGaussian(mean: Vector, cov: Matrix)

    mean

    The mean vector of the distribution

    cov

    The covariance matrix of the distribution

    Annotations
    @Since( "2.0.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  5. def clone(): AnyRef
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    @throws( ... ) @native()
  6. val cov: Matrix
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    @Since( "2.0.0" )
  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: Any): Boolean
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  9. def finalize(): Unit
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. def logpdf(x: Vector): Double

    Returns the log-density of this multivariate Gaussian at given point, x

    Returns the log-density of this multivariate Gaussian at given point, x

    Annotations
    @Since( "2.0.0" )
  14. val mean: Vector
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    @Since( "2.0.0" )
  15. final def ne(arg0: AnyRef): Boolean
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  16. final def notify(): Unit
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    @native()
  17. final def notifyAll(): Unit
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    @native()
  18. def pdf(x: Vector): Double

    Returns density of this multivariate Gaussian at given point, x

    Returns density of this multivariate Gaussian at given point, x

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    @Since( "2.0.0" )
  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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