Class LDAModel
Object
org.apache.spark.mllib.clustering.LDAModel
 All Implemented Interfaces:
Saveable
 Direct Known Subclasses:
DistributedLDAModel
,LocalLDAModel
Latent Dirichlet Allocation (LDA) model.
This abstraction permits for different underlying representations, including local and distributed data structures.

Method Summary
Modifier and TypeMethodDescriptionscala.Tuple2<int[],
double[]>[] Return the topics described by weighted terms.abstract scala.Tuple2<int[],
double[]>[] describeTopics
(int maxTermsPerTopic) Return the topics described by weighted terms.abstract Vector
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").abstract int
k()
Number of topicsabstract double
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.abstract Matrix
Inferred topics, where each topic is represented by a distribution over terms.abstract int
Vocabulary size (number of terms or terms in the vocabulary)

Method Details

describeTopics
public abstract scala.Tuple2<int[],double[]>[] describeTopics(int maxTermsPerTopic) Return the topics described by weighted terms. 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.

describeTopics
public scala.Tuple2<int[],double[]>[] describeTopics()Return the topics described by weighted terms.WARNING: If vocabSize and k are large, this can return a large object!
 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.

docConcentration
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").This is the parameter to a Dirichlet distribution.
 Returns:
 (undocumented)

k
public abstract int k()Number of topics 
topicConcentration
public abstract double topicConcentration()Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.This is the parameter to a symmetric Dirichlet distribution.
 Returns:
 (undocumented)
 Note:
 The topics' distributions over terms are called "beta" in the original LDA paper by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.

topicsMatrix
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. Returns:
 (undocumented)

vocabSize
public abstract int vocabSize()Vocabulary size (number of terms or terms in the vocabulary)
