org.knowceans.sandbox.hlda
Class NestedCrpNode

java.lang.Object
  org.knowceans.sandbox.hlda.NestedCrpNode

public class NestedCrpNode
extends java.lang.Object

NestedCrpNode models a nested Chinese restaurant process (CRP). Each level of the NCRP tree can be considered a multinomial over the assignment of z[level]. On the first level, the root is taken with probability one. On the second level, N branches have been formed by the CRP sampling on level 0, and correspondingly the topic assignment to one of the brances follows the occupation numbers.

Author:

heinrich

Field Summary

private java.util.Vector<NestedCrpNode>	children children = list of occupied tables in the nested CRP
int	depth depth L of the nested CRP
private double	gamma concentration parameter of the CRP
int	id
int	level level l in the nesting structure (root = 0)
static int	maxid
double	newChildLikelihood probability for creation of a new child
(package private) int[]	nw c_m,ell number of times term i is assigned to this node (= "the topic indexed by c_m,ell" from the HLDA paper).
(package private) int	nwsum number of terms assigned to this node ("tree topic").
int	occupation occupation count (customers at the table).
NestedCrpNode	parent parent restaurant (root = null)
private double[]	phiBeta topic--word assignments for this node (phi with Griffiths; beta with Blei)
private double[]	phiBetaSum sum of phiBeta (used for averaging)
double	prob sampling probability for this node for the sampleHierarchical() routine.
HldaGibbsSampler	sampler backref to sampler (to avoid duplicate variables)

Constructor Summary

	NestedCrpNode(HldaGibbsSampler samp, int depth, double gamma) initialise a nested CRP root node = initialise a nested CRP.
protected	NestedCrpNode(NestedCrpNode parent) initialise a CRP node deeper than root

Method Summary

private void	constructProb(int doc)
(package private) double[]	getPhiBeta() get the topic--word assignments for this node.
private NestedCrpNode	sampleCrp() returns the child node sampled from the CRP.
NestedCrpNode[]	sampleHierarchical() given tbe probability assignments in the nodes and the L-vector newprob for new CRP paths, sample a path hierarchically.
NestedCrpNode[]	samplePath() sample once from the nested CRP and update the occupation numbers
NestedCrpNode[]	samplePath(int doc) calculates the word conditionals, which, in conjunction with the CRP prior, yield the path probabilities.
private NestedCrpNode	sampleProb(boolean crp) Sample according to probability assignments in the prob field.
private void	updateCounts(NestedCrpNode[] path, int doc) updates the counts for the current sample.

Methods inherited from class java.lang.Object

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

Field Detail

maxid

public static int maxid

sampler

public HldaGibbsSampler sampler

backref to sampler (to avoid duplicate variables)

level

public int level

level l in the nesting structure (root = 0)

id

public int id

depth

public int depth

depth L of the nested CRP

parent

public NestedCrpNode parent

parent restaurant (root = null)

occupation

public int occupation

occupation count (customers at the table). Is initially set to one (creation = first occupation)

prob

public double prob

sampling probability for this node for the sampleHierarchical() routine.

newChildLikelihood

public double newChildLikelihood

probability for creation of a new child

phiBeta

private double[] phiBeta

topic--word assignments for this node (phi with Griffiths; beta with Blei)

phiBetaSum

private double[] phiBetaSum

sum of phiBeta (used for averaging)

nw

int[] nw

c_m,ell number of times term i is assigned to this node (= "the topic indexed by c_m,ell" from the HLDA paper). TODO: put in sampler class and index from there with the c.

nwsum

int nwsum

number of terms assigned to this node ("tree topic").

children

private java.util.Vector<NestedCrpNode> children

children = list of occupied tables in the nested CRP

gamma

private double gamma

concentration parameter of the CRP

Constructor Detail

NestedCrpNode

public NestedCrpNode(HldaGibbsSampler samp,
                     int depth,
                     double gamma)

initialise a nested CRP root node = initialise a nested CRP.

Parameters:

depth - L of the tree structure

gamma - concentration parameter of the underlying CRP (expected occupation number per child node) XXX: strongly unbalanced through hierarchy, but this means that branching occurs first at the top and later at the bottom when more data arrives!

NestedCrpNode

protected NestedCrpNode(NestedCrpNode parent)

initialise a CRP node deeper than root

Parameters:

parent -

Method Detail

getPhiBeta

double[] getPhiBeta()

get the topic--word assignments for this node.

Returns:

samplePath

public NestedCrpNode[] samplePath(int doc)

calculates the word conditionals, which, in conjunction with the CRP prior, yield the path probabilities. The word conditional factors are stored in the hierarchy, so the sampling process directly can access them. Use only in root node.

Parameters:

doc -

updateCounts

private void updateCounts(NestedCrpNode[] path,
                          int doc)

updates the counts for the current sample.

Parameters:

path -

doc -

constructProb

private void constructProb(int doc)

Parameters:

doc -

samplePath

public NestedCrpNode[] samplePath()

sample once from the nested CRP and update the occupation numbers

Returns:

node objects

sampleHierarchical

public NestedCrpNode[] sampleHierarchical()

given tbe probability assignments in the nodes and the L-vector newprob for new CRP paths, sample a path hierarchically. This works for the word conditional in the HLDA model because it can be factored over levels.

Returns:

sampleProb

private NestedCrpNode sampleProb(boolean crp)

Sample according to probability assignments in the prob field.

Parameters:

crp - true if the prior term should be used.

Returns:

sampleCrp

private NestedCrpNode sampleCrp()

returns the child node sampled from the CRP.

Returns: