Packages

  • package root
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    root
  • package org
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    root
  • package apache
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    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

KernelDensity

class KernelDensity extends Serializable

Kernel density estimation. Given a sample from a population, estimate its probability density function at each of the given evaluation points using kernels. Only Gaussian kernel is supported.

Scala example:

val sample = sc.parallelize(Seq(0.0, 1.0, 4.0, 4.0))
val kd = new KernelDensity()
  .setSample(sample)
  .setBandwidth(3.0)
val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
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@Since( "1.4.0" )
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  1. new KernelDensity()

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  8. def estimate(points: Array[Double]): Array[Double]

    Estimates probability density function at the given array of points.

    Estimates probability density function at the given array of points.

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    @Since( "1.4.0" )
  9. def finalize(): Unit
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  16. def setBandwidth(bandwidth: Double): KernelDensity.this.type

    Sets the bandwidth (standard deviation) of the Gaussian kernel (default: 1.0).

    Sets the bandwidth (standard deviation) of the Gaussian kernel (default: 1.0).

    Annotations
    @Since( "1.4.0" )
  17. def setSample(sample: JavaRDD[Double]): KernelDensity.this.type

    Sets the sample to use for density estimation (for Java users).

    Sets the sample to use for density estimation (for Java users).

    Annotations
    @Since( "1.4.0" )
  18. def setSample(sample: RDD[Double]): KernelDensity.this.type

    Sets the sample to use for density estimation.

    Sets the sample to use for density estimation.

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    @Since( "1.4.0" )
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