org.apache.spark.mllib.clustering
Class LocalLDAModel

Object
  org.apache.spark.mllib.clustering.LDAModel
      org.apache.spark.mllib.clustering.LocalLDAModel

All Implemented Interfaces:

java.io.Serializable

public class LocalLDAModel
extends LDAModel
implements scala.Serializable

:: Experimental ::

Local LDA model. This model stores only the inferred topics. It may be used for computing topics for new documents, but it may give less accurate answers than the DistributedLDAModel.

param: topics Inferred topics (vocabSize x k matrix).

See Also:

Serialized Form

Method Summary

scala.Tuple2<int[],double[]>[]	describeTopics(int maxTermsPerTopic) Return the topics described by weighted terms.
int	k() Number of topics
Matrix	topicsMatrix() Inferred topics, where each topic is represented by a distribution over terms.
int	vocabSize() Vocabulary size (number of terms or terms in the vocabulary)

Methods inherited from class org.apache.spark.mllib.clustering.LDAModel

describeTopics

Methods inherited from class Object

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

Method Detail

k

public int k()

Description copied from class: LDAModel

Number of topics

Specified by:

k in class LDAModel

vocabSize

public int vocabSize()

Description copied from class: LDAModel

Vocabulary size (number of terms or terms in the vocabulary)

Specified by:

vocabSize in class LDAModel

topicsMatrix

public Matrix topicsMatrix()

Description copied from class: LDAModel

Inferred topics, where each topic is represented by a distribution over terms. This is a matrix of size vocabSize x k, where each column is a topic. No guarantees are given about the ordering of the topics.

Specified by:

topicsMatrix in class LDAModel

Returns:

(undocumented)

describeTopics

public scala.Tuple2<int[],double[]>[] describeTopics(int maxTermsPerTopic)

Description copied from class: LDAModel

Return the topics described by weighted terms.

This limits the number of terms per topic. This is approximate; it may not return exactly the top-weighted terms for each topic. To get a more precise set of top terms, increase maxTermsPerTopic.

Specified by:

describeTopics in class LDAModel

Parameters:

maxTermsPerTopic - Maximum number of terms to collect for each topic.

Returns:

Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.