Diagrama de colaboración para org.knowceans.gibbstest.LdaGibbsSampler:

Métodos públicos
	LdaGibbsSampler (int[][] documents, int V)

void	initialState (int K)

void	gibbs (int K, double alpha, double beta)

double[][]	getTheta ()

double[][]	getPhi ()

int[][]	getZ ()

int[][]	getDocuments ()

int	getV ()

int	getK ()

void	configure (int iterations, int burnIn, int thinInterval, int sampleLag)

Métodos públicos estáticos
static void	hist (double[] data, int fmax)

static void	main (String[] args)

static String	shadeDouble (double d, double max)

Descripción detallada

Gibbs sampler for estimating the best assignments of topics for words and documents in a corpus. The algorithm is introduced in Tom Griffiths' paper "Gibbs sampling in the generative model of Latent Dirichlet Allocation" (2002).

Autor: heinrich

Documentación del constructor y destructor

org.knowceans.gibbstest.LdaGibbsSampler.LdaGibbsSampler	(	int	documents[][],
		int	V
	)

inline

Initialise the Gibbs sampler with data.

Parámetros

V	vocabulary size
data

Documentación de las funciones miembro

void org.knowceans.gibbstest.LdaGibbsSampler.configure	(	int	iterations,
		int	burnIn,
		int	thinInterval,
		int	sampleLag
	)

inline

Configure the gibbs sampler

Parámetros

iterations	number of total iterations
burnIn	number of burn-in iterations
thinInterval	update statistics interval
sampleLag	sample interval (-1 for just one sample at the end)

double [][] org.knowceans.gibbstest.LdaGibbsSampler.getPhi ( )

inline

Retrieve estimated topic–word associations. If sample lag > 0 then the mean value of all sampled statistics for phi[][] is taken.

Devuelve: phi multinomial mixture of topic words (K x V)

double [][] org.knowceans.gibbstest.LdaGibbsSampler.getTheta ( )

inline

Retrieve estimated document–topic associations. If sample lag > 0 then the mean value of all sampled statistics for theta[][] is taken.

Devuelve: theta multinomial mixture of document topics (M x K)

int [][] org.knowceans.gibbstest.LdaGibbsSampler.getZ ( )

inline

Added in by Doori Lee

void org.knowceans.gibbstest.LdaGibbsSampler.gibbs	(	int	K,
		double	alpha,
		double	beta
	)

inline

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.

Parámetros

K	number of topics
alpha	symmetric prior parameter on document–topic associations
beta	symmetric prior parameter on topic–term associations

static void org.knowceans.gibbstest.LdaGibbsSampler.hist	(	double[]	data,
		int	fmax
	)

inlinestatic

Print table of multinomial data

Parámetros

data	vector of evidence
fmax	max frequency in display

Devuelve: the scaled histogram bin values

void org.knowceans.gibbstest.LdaGibbsSampler.initialState ( int K )

inline

Initialisation: Must start with an assignment of observations to topics ? Many alternatives are possible, I chose to perform random assignments with equal probabilities

Parámetros

K	number of topics

Devuelve: z assignment of topics to words

static void org.knowceans.gibbstest.LdaGibbsSampler.main ( String[] args )

inlinestatic

Driver with example data.

Parámetros

args

static String org.knowceans.gibbstest.LdaGibbsSampler.shadeDouble	(	double	d,
		double	max
	)

inlinestatic

create a string representation whose gray value appears as an indicator of magnitude, cf. Hinton diagrams in statistics.

Parámetros

d	value
max	maximum value

Devuelve

La documentación para esta clase fue generada a partir del siguiente fichero:

vsm/vsm/extensions/lda_py4j/org/knowceans/gibbstest/LdaGibbsSampler.java

Métodos públicos

Métodos públicos estáticos

Descripción detallada

Documentación del constructor y destructor

Documentación de las funciones miembro