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package root

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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 clustering

Definition Classes
mllib
BisectingKMeans
BisectingKMeansModel
DistanceMeasure
DistributedLDAModel
EMLDAOptimizer
GaussianMixture
GaussianMixtureModel
KMeans
KMeansModel
LDA
LDAModel
LDAOptimizer
LocalLDAModel
OnlineLDAOptimizer
PowerIterationClustering
PowerIterationClusteringModel
StreamingKMeans
StreamingKMeansModel

org.apache.spark.mllib.clustering

PowerIterationClustering

Companion class PowerIterationClustering

object PowerIterationClustering extends Logging with Serializable

Annotations: @Since( "1.3.0" )
Source: PowerIterationClustering.scala

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case class Assignment(id: Long, cluster: Int) extends Product with Serializable
Cluster assignment.
Cluster assignment.
id
node id
cluster
assigned cluster id

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@Since( "1.3.0" )

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final def !=(arg0: Any): Boolean

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def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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Packages

PowerIterationClustering

Companion class PowerIterationClustering

object PowerIterationClustering extends Logging with Serializable

Type Members

Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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Packages

PowerIterationClustering 

Companion class PowerIterationClustering

object PowerIterationClustering extends Logging with Serializable

Type Members

Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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PowerIterationClustering