NAME

Algorithm::HierarchicalPAM - A Perl 6 Hierarchical PAM (model 2) implementation.

SYNOPSIS

EXAMPLE 1

use Algorithm::HierarchicalPAM;
use Algorithm::HierarchicalPAM::Formatter;
use Algorithm::HierarchicalPAM::HierarchicalPAMModel;

my @documents = (
    "a b c",
    "d e f",
);
my ($documents, $vocabs) = Algorithm::HierarchicalPAM::Formatter.from-plain(@documents);
my Algorithm::HierarchicalPAM $hpam .= new(:$documents, :$vocabs);
my Algorithm::HierarchicalPAMModel $model = $hpam.fit(:num-super-topics(3), :num-sub-topics(5), :num-iterations(500));

$model.topic-word-matrix.say; # show topic-word matrix
$model.document-topic-matrix; # show document-topic matrix
$model.log-likelihood.say; # show likelihood 
$model.nbest-words-per-topic.say # show nbest words per topic

EXAMPLE 2

use Algorithm::HierarchicalPAM;
use Algorithm::HierarchicalPAM::Formatter;
use Algorithm::HierarchicalPAM::HierarchicalPAMModel;

# Note: You can get AP corpus as follows:
# $ wget "https://github.com/Blei-Lab/lda-c/blob/master/example/ap.tgz?raw=true" -O ap.tgz
# $ tar xvzf ap.tgz

my @vocabs = "./ap/vocab.txt".IO.lines;
my @documents = "./ap/ap.dat".IO.lines;
my $documents  = Algorithm::HierarchicalPAM::Formatter.from-libsvm(@documents);

my Algorithm::HierarchicalPAM $hpam .= new(:$documents, :@vocabs);
my Algorithm::HierarchicalPAM::HierarchicalPAMModel $model = $hpam.fit(:num-super-topics(10), :num-sub-topics(20), :num-iterations(500));

$model.topic-word-matrix.say; # show topic-word matrix
$model.document-topic-matrix; # show document-topic matrix
$model.log-likelihood.say; # show likelihood 
$model.nbest-words-per-topic.say # show nbest words per topic

DESCRIPTION

Algorithm::HierarchicalPAM - A Perl 6 Hierarchical PAM (model 2) implementation.

CONSTRUCTOR

new

Defined as:

submethod BUILD(:$!documents!, :$!vocabs! is raw) { }

Constructs a new Algorithm::HierarchicalPAM instance.

METHODS

fit

Defined as:

method fit(Int :$num-iterations = 500, Int :$num-super-topics!, Int :$num-sub-topics!, Num :$alpha = 0.1e0, Num :$beta = 0.1e0, Int :$seed --> Algorithm::HierarchicalPAM::HierarchicalPAMModel)

Returns an Algorithm::HierarchicalPAM::HierarchicalPAMModel instance.

:$num-iterations is the number of iterations for gibbs sampler
:$num-super-topics! is the number of super topics
:$num-sub-topics! is the number of sub topics
alpha is the prior for theta distribution (i.e., document-topic distribution)
beta is the prior for phi distribution (i.e., topic-word distribution)
seed is the seed for srand

AUTHOR

titsuki titsuki@cpan.org

COPYRIGHT AND LICENSE

This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0.

The algorithm is from:

Mimno, David, Wei Li, and Andrew McCallum. "Mixtures of hierarchical topics with pachinko allocation." Proceedings of the 24th international conference on Machine learning. ACM, 2007.
Minka, Thomas. "Estimating a Dirichlet distribution." (2000): 4.