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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 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

LocalLDAModel

Companion class LocalLDAModel

object LocalLDAModel extends Loader[LocalLDAModel] with Serializable

Local (non-distributed) model fitted by LDA.

This model stores the inferred topics only; it does not store info about the training dataset.

Annotations: @Since( "1.5.0" )
Source: LDAModel.scala

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Serializable, Serializable, Loader[LocalLDAModel], AnyRef, Any

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Any
def load(sc: SparkContext, path: String): LocalLDAModel
Load a model from the given path.
Load a model from the given path.
The model should have been saved by Saveable.save.
sc
Spark context used for loading model files.
path
Path specifying the directory to which the model was saved.
returns
Model instance

Definition Classes
LocalLDAModel → Loader
Annotations
@Since( "1.5.0" )
final def ne(arg0: AnyRef): Boolean

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LocalLDAModel

Companion class LocalLDAModel

object LocalLDAModel extends Loader[LocalLDAModel] with Serializable

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LocalLDAModel 

Companion class LocalLDAModel

object LocalLDAModel extends Loader[LocalLDAModel] with Serializable

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Inherited from Loader[LocalLDAModel]

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LocalLDAModel