cc.mallet.topics

Class SimpleLDA

java.lang.Object

cc.mallet.topics.SimpleLDA

All Implemented Interfaces:

java.io.Serializable

public class SimpleLDA
extends java.lang.Object
implements java.io.Serializable

A simple implementation of Latent Dirichlet Allocation using Gibbs sampling. This code is slower than the regular Mallet LDA implementation, but provides a better starting place for understanding how sampling works and for building new topic models.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_BETA
int	showTopicsInterval
int	wordsPerTopic

Constructor Summary

Constructors

Constructor and Description
SimpleLDA(int numberOfTopics)
SimpleLDA(int numberOfTopics, double alphaSum, double beta)
SimpleLDA(int numberOfTopics, double alphaSum, double beta, Randoms random)
SimpleLDA(LabelAlphabet topicAlphabet, double alphaSum, double beta, Randoms random)

Method Summary

Modifier and Type	Method and Description
void	addInstances(InstanceList training)
Alphabet	getAlphabet()
java.util.ArrayList<TopicAssignment>	getData()
int	getNumTopics()
LabelAlphabet	getTopicAlphabet()
int[]	getTopicTotals()
int[][]	getTypeTopicCounts()
static void	main(java.lang.String[] args)
double	modelLogLikelihood()
void	printDocumentTopics(java.io.File file, double threshold, int max)
void	printState(java.io.File f)
void	printState(java.io.PrintStream out)
void	sample(int iterations)
void	setRandomSeed(int seed)
void	setTopicDisplay(int interval, int n)
java.lang.String	topWords(int numWords)
void	write(java.io.File f)

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_BETA

public static final double DEFAULT_BETA

See Also:

Constant Field Values

showTopicsInterval

public int showTopicsInterval

wordsPerTopic

public int wordsPerTopic

Constructor Detail

SimpleLDA

public SimpleLDA(int numberOfTopics)

SimpleLDA

public SimpleLDA(int numberOfTopics,
                 double alphaSum,
                 double beta)

SimpleLDA

public SimpleLDA(int numberOfTopics,
                 double alphaSum,
                 double beta,
                 Randoms random)

SimpleLDA

public SimpleLDA(LabelAlphabet topicAlphabet,
                 double alphaSum,
                 double beta,
                 Randoms random)

Method Detail

getAlphabet

public Alphabet getAlphabet()

getTopicAlphabet

public LabelAlphabet getTopicAlphabet()

getNumTopics

public int getNumTopics()

getData

public java.util.ArrayList<TopicAssignment> getData()

setTopicDisplay

public void setTopicDisplay(int interval,
                            int n)

setRandomSeed

public void setRandomSeed(int seed)

getTypeTopicCounts

public int[][] getTypeTopicCounts()

getTopicTotals

public int[] getTopicTotals()

addInstances

public void addInstances(InstanceList training)

sample

public void sample(int iterations)
            throws java.io.IOException

Throws:

java.io.IOException

modelLogLikelihood

public double modelLogLikelihood()

topWords

public java.lang.String topWords(int numWords)

printDocumentTopics

public void printDocumentTopics(java.io.File file,
                                double threshold,
                                int max)
                         throws java.io.IOException

Parameters:

file - The filename to print to

threshold - Only print topics with proportion greater than this number

max - Print no more than this many topics

Throws:

java.io.IOException

printState

public void printState(java.io.File f)
                throws java.io.IOException

Throws:

java.io.IOException

printState

public void printState(java.io.PrintStream out)

write

public void write(java.io.File f)

main

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

Throws:

java.io.IOException