org.knowceans.dirichlet.atm
Class AtmTopicSimilarities

java.lang.Object
  org.knowceans.dirichlet.lda.LdaTopicSimilarities
      org.knowceans.dirichlet.atm.AtmTopicSimilarities

public class AtmTopicSimilarities
extends LdaTopicSimilarities

FmLdaSimilarities calculates similarities between term and documents, both known and unknown. This is the interface for LDA queries, once the topics of an unknown string have been determined. This implementation supports both the symmetrised KL-divergence (= Jenson-Shannon distance) and a predictive likelihood.

By convention, conditional likelihoods are normalised along rows, i.e., p(col|row) = double[row][col]; If distributions are along columns, some methods provide a transposed flag.

Author:

heinrich

Field Summary

Fields inherited from class org.knowceans.dirichlet.lda.LdaTopicSimilarities

phi, phiPost, theta, thetaPost

Constructor Summary

AtmTopicSimilarities(AtmGibbsSampler lda, boolean terms, boolean authors, boolean pl, boolean js)
Initialise topic similarities using an existing lda gibbs sampler, whose phi and theta values are shared.

AtmTopicSimilarities(java.lang.String atmbase, boolean terms, boolean authors, boolean pl, boolean js)
Construct an LdaSimilaritiesCps object with path bases and action indicators for terms and documents processing

Method Summary

org.knowceans.map.IndexRanking	authorAuthors(int author, boolean mutLik, int max) Get the most similar authors for the author.
org.knowceans.map.IndexRanking	docDocs(int doc, boolean mutLik, int max) TODO: implement the search for documents.
org.knowceans.map.IndexRanking	docTerms(int doc, boolean mutLik, int max) Get the most similar terms for the doc.
org.knowceans.map.IndexRanking	queryAuthors(double[] topics, boolean mutLik, int max) Get the most similar documents for the query expressed as distribution over z.
org.knowceans.map.IndexRanking	queryDocs(double[] topics, boolean mutLik, int max) TODO: implement the search for documents.
org.knowceans.map.IndexRanking[]	queryTerms(double[][] topics, boolean mutLik, int max) Get the most similar terms for the queries expressed as array of distributions over z.
org.knowceans.map.IndexRanking	termDocs(int term, boolean mutLik, int max) Get the most similar docs for the term.
org.knowceans.map.IndexRanking	termTerms(int term, boolean mutLik, int max) Get the most similar terms for the term.

Methods inherited from class org.knowceans.dirichlet.lda.LdaTopicSimilarities

bestJsMatches, bestJsMatches, bestMutLikMatches, bestMutLikMatches, getPhi, getPhiPost, getTheta, getThetaPost, jsDistance, jsDistance, klDivergence, klDivergence, mutualLikelihood, mylog, posterior, queryDocs, queryTerms

Methods inherited from class java.lang.Object

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

Constructor Detail

AtmTopicSimilarities

public AtmTopicSimilarities(java.lang.String atmbase,
                            boolean terms,
                            boolean authors,
                            boolean pl,
                            boolean js)
                     throws java.io.IOException

Construct an LdaSimilaritiesCps object with path bases and action indicators for terms and documents processing

Parameters:

atmbase - path base of lda parameter set (path + filename excluding extensions .phi.zip and .theta.zip)

terms - load term matrix (phi)

authors - load document matrix (theta)

pl - configure for use with predictive likelihoods (syn. mutual likelihood, because it appears to be symmetric)

js - configure for use with jenson shannon likelihood

Throws:

java.io.IOException

AtmTopicSimilarities

public AtmTopicSimilarities(AtmGibbsSampler lda,
                            boolean terms,
                            boolean authors,
                            boolean pl,
                            boolean js)

Initialise topic similarities using an existing lda gibbs sampler, whose phi and theta values are shared.

Parameters:

lda -

terms -

authors -

pl -

js -

Method Detail

queryAuthors

public org.knowceans.map.IndexRanking queryAuthors(double[] topics,
                                                   boolean mutLik,
                                                   int max)

Get the most similar documents for the query expressed as distribution over z.

Parameters:

topics - distribution over z. multiple elements

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon)

max - maximum number of matches

Returns:

queryDocs

public org.knowceans.map.IndexRanking queryDocs(double[] topics,
                                                boolean mutLik,
                                                int max)

TODO: implement the search for documents.

Overrides:

queryDocs in class LdaTopicSimilarities

Parameters:

topics - distribution over z. multiple elements

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon)

max - maximum number of matches

Returns:

queryTerms

public org.knowceans.map.IndexRanking[] queryTerms(double[][] topics,
                                                   boolean mutLik,
                                                   int max)

Get the most similar terms for the queries expressed as array of distributions over z.

Overrides:

queryTerms in class LdaTopicSimilarities

Parameters:

topics - distribution over z.

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon)

max - maximum number of matches

Returns:

authorAuthors

public org.knowceans.map.IndexRanking authorAuthors(int author,
                                                    boolean mutLik,
                                                    int max)

Get the most similar authors for the author.

Parameters:

doc - document index

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon)

max - maximum number of matches

Returns:

docDocs

public org.knowceans.map.IndexRanking docDocs(int doc,
                                              boolean mutLik,
                                              int max)

TODO: implement the search for documents.

Overrides:

docDocs in class LdaTopicSimilarities

Parameters:

doc - document index

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon)

max - maximum number of matches

Returns:

termTerms

public org.knowceans.map.IndexRanking termTerms(int term,
                                                boolean mutLik,
                                                int max)

Get the most similar terms for the term.

Overrides:

termTerms in class LdaTopicSimilarities

Parameters:

doc - document index

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon)

max - maximum number of matches

Returns:

docTerms

public org.knowceans.map.IndexRanking docTerms(int doc,
                                               boolean mutLik,
                                               int max)

Get the most similar terms for the doc.

Overrides:

docTerms in class LdaTopicSimilarities

Parameters:

doc - document index

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon) *

max - maximum number of matches

Returns:

termDocs

public org.knowceans.map.IndexRanking termDocs(int term,
                                               boolean mutLik,
                                               int max)

Get the most similar docs for the term.

Overrides:

termDocs in class LdaTopicSimilarities

Parameters:

term - term index

mutLik - use mutual / predictive likelihood (otherwise jensen-shannon) *

max - maximum number of matches

Returns: