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 classification
    Definition Classes
    mllib
  • package clustering
    Definition Classes
    mllib
  • package evaluation
    Definition Classes
    mllib
  • package feature
    Definition Classes
    mllib
  • package fpm
    Definition Classes
    mllib
  • package linalg
    Definition Classes
    mllib
  • package optimization
    Definition Classes
    mllib
  • package pmml
    Definition Classes
    mllib
  • package random
    Definition Classes
    mllib
  • package rdd
    Definition Classes
    mllib
  • package recommendation
    Definition Classes
    mllib
  • package regression
    Definition Classes
    mllib
  • package stat
    Definition Classes
    mllib
  • package distribution
  • package test
  • KernelDensity
  • MultivariateOnlineSummarizer
  • MultivariateStatisticalSummary
  • Statistics
  • package tree

    This package contains the default implementation of the decision tree algorithm, which supports:

    This package contains the default implementation of the decision tree algorithm, which supports:

    • binary classification,
    • regression,
    • information loss calculation with entropy and Gini for classification and variance for regression,
    • both continuous and categorical features.
    Definition Classes
    mllib
  • package util
    Definition Classes
    mllib

package stat

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Visibility
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Package Members

  1. package distribution
  2. package test

Type Members

  1. class KernelDensity extends Serializable

    Kernel density estimation.

    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))
    Annotations
    @Since("1.4.0")
  2. class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable

    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.

    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")
  3. trait MultivariateStatisticalSummary extends AnyRef

    Trait for multivariate statistical summary of a data matrix.

    Trait for multivariate statistical summary of a data matrix.

    Annotations
    @Since("1.0.0")

Value Members

  1. object Statistics

    API for statistical functions in MLlib.

    API for statistical functions in MLlib.

    Annotations
    @Since("1.1.0")

Ungrouped