org.knowceans.dirichlet.atm
Class AtmGibbsSampler

java.lang.Object
  org.knowceans.dirichlet.lda.LdaGibbsSampler
      org.knowceans.dirichlet.atm.AtmGibbsSampler

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

AtmGibbsQuerySampler

public class AtmGibbsSampler
extends LdaGibbsSampler

Gibbs sampler for estimating the best assignments of topics for words and authors of documents in a corpus. The algorithm is introduced in Steyvers, M.; Smyth, P.; Rosen-Zvi, M. & Griffiths, T. "Probabilistic Author-Topic models for information discovery". Proc. ACM SIGKDD, 2004.

Author:

heinrich

See Also:

Serialized Form

Field Summary

protected AtmMarkovState	atmstate State variables of the Lda gibbs sampler.
private static long	serialVersionUID

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

backupIteration, conf, dispcol, numstats, phisum, rand, state, thetasum

Constructor Summary

protected	AtmGibbsSampler() For subclasses who know what they do...
	AtmGibbsSampler(AmqCorpus corpus, AtmMarkovState state, ExtLdaConfiguration conf, java.util.Random rand) Initialise the sampler with an existing state.
	AtmGibbsSampler(AmqCorpus corpus, ExtLdaConfiguration conf) Initialise the corpus with
	AtmGibbsSampler(AmqCorpus corpus, ExtLdaConfiguration conf, java.util.Random rand) Initialise the corpus with
protected	AtmGibbsSampler(AtmMarkovState state, ExtLdaConfiguration conf, java.util.Random rand) Initialise the sampler with an existing state.
	AtmGibbsSampler(int[][] documents, int V, int[][] authors, int A, double alpha, double beta, int K, int iterations) Initialise the Gibbs sampler with data and standard values.

Method Summary

AtmMarkovState	getState() Get the current state of the markov chain.
protected void	gibbs() Main method: Select initial state ?
int[][]	gibbsAtm(int[][] w, int V, int[][] ad, int A, int[][] z, int K, int[][] x, double alpha, double beta, int iter) Native implementation of the Gibbs sampling procedure.
void	gibbsAtmHeap(AtmMarkovState s, ExtLdaConfiguration c) Native gibbs sampling on the jvm heap
int[][]	gibbsAtmHeap(int[][] w, int[][] ad, int[][] z, int[][] x, int[][] nw, int[] nwsum, int[][] nd, int[] ndsum, double alpha, double beta, int iter) Native gibbs sampling on the jvm heap.
protected void	initialState() Initialisation: Random assignments with equal probabilities
static void	main(java.lang.String[] args)
void	run() Run the sampler after initialisation.
protected int[]	sampleAtmFullConditional(AtmMarkovState s, int m, int n) Sample an actor--topic pair (x_i, z_i) from the full conditional distribution: p(x_i = q,z_i = j|z_-i, w, x_i, a_d, r_d) = (cwt_mj + beta)/(cwtsum_j + W * beta) * (cat_qjr + alpha)/(catsum_qr + K * alpha)
protected void	sampleCorpus(AtmMarkovState s) Sample once through the corpus and update the corresponding state.
void	saveState(java.lang.String file) Saves the current state of the markov chain and the parameters to a file.

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

getPhi, getTheta, gibbs, gibbsHeap, gibbsHeap, load, output, sampleCorpus, sampleLdaFullConditional, save, updateParams, updatePhi, updateTheta, writeParameters

Methods inherited from class java.lang.Object

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

Field Detail

serialVersionUID

private static final long serialVersionUID

See Also:

Constant Field Values

atmstate

protected AtmMarkovState atmstate

State variables of the Lda gibbs sampler. The contract is to set this pointer equal to the pointer on LdaMarkovState LdaGibbsSampler.state.

Constructor Detail

AtmGibbsSampler

protected AtmGibbsSampler()

For subclasses who know what they do...

AtmGibbsSampler

public AtmGibbsSampler(int[][] documents,
                       int V,
                       int[][] authors,
                       int A,
                       double alpha,
                       double beta,
                       int K,
                       int iterations)

Initialise the Gibbs sampler with data and standard values. (For backwards compatibility).

Parameters:

documents -

V -

authors -

A -

alpha -

beta -

K -

iterations -

AtmGibbsSampler

public AtmGibbsSampler(AmqCorpus corpus,
                       ExtLdaConfiguration conf)

Initialise the corpus with

Parameters:

corpus -

conf -

AtmGibbsSampler

public AtmGibbsSampler(AmqCorpus corpus,
                       ExtLdaConfiguration conf,
                       java.util.Random rand)

Initialise the corpus with

Parameters:

corpus -

conf -

rand -

AtmGibbsSampler

protected AtmGibbsSampler(AtmMarkovState state,
                          ExtLdaConfiguration conf,
                          java.util.Random rand)

Initialise the sampler with an existing state.

Parameters:

corpus -

conf -

rand -

AtmGibbsSampler

public AtmGibbsSampler(AmqCorpus corpus,
                       AtmMarkovState state,
                       ExtLdaConfiguration conf,
                       java.util.Random rand)

Initialise the sampler with an existing state.

Parameters:

corpus -

state -

conf -

rand -

Method Detail

initialState

protected void initialState()

Initialisation: Random assignments with equal probabilities

Overrides:

initialState in class LdaGibbsSampler

run

public void run()

Run the sampler after initialisation.

Overrides:

run in class LdaGibbsSampler

gibbs

protected void gibbs()

Main method: Select initial state ? Repeat a large number of times: 1. Select an element 2. Update conditional on other elements. If appropriate, output summary for each run.

Overrides:

gibbs in class LdaGibbsSampler

gibbsAtm

public int[][] gibbsAtm(int[][] w,
                        int V,
                        int[][] ad,
                        int A,
                        int[][] z,
                        int K,
                        int[][] x,
                        double alpha,
                        double beta,
                        int iter)

Native implementation of the Gibbs sampling procedure. In the same class to allow subclass access.

Parameters:

w - words

V - vocabulary size

ad - author document associations

A - author count

z - topic word associations

K - tobic count

x - author word associations

alpha -

beta -

iter -

Returns:

the new assinments z

gibbsAtmHeap

public int[][] gibbsAtmHeap(int[][] w,
                            int[][] ad,
                            int[][] z,
                            int[][] x,
                            int[][] nw,
                            int[] nwsum,
                            int[][] nd,
                            int[] ndsum,
                            double alpha,
                            double beta,
                            int iter)

Native gibbs sampling on the jvm heap.

Parameters:

w - [in] words

ad - [in] document authors

z - [in/out] topic associations

x - [in/out] author word associations

nw - [in/out] topic-word counts

nwsum - [in/out] summed topic-word counts (total words per topic)

nd - [in/out] author-topic counts

ndsum - [in] author words (total words per author)

alpha -

beta -

iter -

Returns:

gibbsAtmHeap

public void gibbsAtmHeap(AtmMarkovState s,
                         ExtLdaConfiguration c)

Native gibbs sampling on the jvm heap

Parameters:

s - [in/out] state

c - [in] configuration

saveState

public void saveState(java.lang.String file)

Saves the current state of the markov chain and the parameters to a file.

Overrides:

saveState in class LdaGibbsSampler

Parameters:

file -

sampleCorpus

protected void sampleCorpus(AtmMarkovState s)

Sample once through the corpus and update the corresponding state. The parameter is used to choose the state to be sampled from: query vs. corpus; choice of chain for multichain sampling.

Parameters:

s -

sampleAtmFullConditional

protected int[] sampleAtmFullConditional(AtmMarkovState s,
                                         int m,
                                         int n)

Sample an actor--topic pair (x_i, z_i) from the full conditional distribution: p(x_i = q,z_i = j|z_-i, w, x_i, a_d, r_d) = (cwt_mj + beta)/(cwtsum_j + W * beta) * (cat_qjr + alpha)/(catsum_qr + K * alpha)

Parameters:

s - state

m - document

n - word

Returns:

int[] in the order {z x}

main

public static void main(java.lang.String[] args)

getState

public AtmMarkovState getState()

Get the current state of the markov chain.

Overrides:

getState in class LdaGibbsSampler

Returns: