public abstract class LDAModel extends java.lang.Object implements Saveable
This abstraction permits for different underlying representations, including local and distributed data structures.
Modifier and Type  Method and Description 

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

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

abstract Vector 
docConcentration()
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").

protected abstract double 
gammaShape()
Shape parameter for random initialization of variational parameter gamma.

abstract int 
k()
Number of topics

abstract double 
topicConcentration()
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.

abstract Matrix 
topicsMatrix()
Inferred topics, where each topic is represented by a distribution over terms.

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

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
formatVersion, save
public abstract int k()
public abstract int vocabSize()
public abstract Vector docConcentration()
This is the parameter to a Dirichlet distribution.
public abstract double topicConcentration()
This is the parameter to a symmetric Dirichlet distribution.
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.
protected abstract double gammaShape()
public abstract Matrix topicsMatrix()
public abstract scala.Tuple2<int[],double[]>[] describeTopics(int maxTermsPerTopic)
maxTermsPerTopic
 Maximum number of terms to collect for each topic.public scala.Tuple2<int[],double[]>[] describeTopics()
WARNING: If vocabSize and k are large, this can return a large object!