org.apache.spark.mllib.clustering

Class EMLDAOptimizer

Object

org.apache.spark.mllib.clustering.EMLDAOptimizer

All Implemented Interfaces:

LDAOptimizer

public final class EMLDAOptimizer
extends Object
implements LDAOptimizer

:: DeveloperApi ::

Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.

Currently, the underlying implementation uses Expectation-Maximization (EM), implemented according to the Asuncion et al. (2009) paper referenced below.

References: - Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. - This class implements their "smoothed" LDA model. - Paper which clearly explains several algorithms, including EM: Asuncion, Welling, Smyth, and Teh. "On Smoothing and Inference for Topic Models." UAI, 2009.

Constructor Summary

Constructors

Constructor and Description
EMLDAOptimizer()

Method Summary

Modifier and Type	Method and Description
boolean	getKeepLastCheckpoint() If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
EMLDAOptimizer	setKeepLastCheckpoint(boolean keepLastCheckpoint) If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

EMLDAOptimizer

public EMLDAOptimizer()

Method Detail

getKeepLastCheckpoint

public boolean getKeepLastCheckpoint()

If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).

Returns:

(undocumented)

setKeepLastCheckpoint

public EMLDAOptimizer setKeepLastCheckpoint(boolean keepLastCheckpoint)

If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up). Deleting the checkpoint can cause failures if a data partition is lost, so set this bit with care.

Default: true

Parameters:

keepLastCheckpoint - (undocumented)

Returns:

(undocumented)

Note:

Checkpoints will be cleaned up via reference counting, regardless.