Algorithm::LDA - A Raku Latent Dirichlet Allocation implementation.
use Algorithm::LDA;
use Algorithm::LDA::Formatter;
use Algorithm::LDA::LDAModel;
my @documents = (
"a b c",
"d e f",
);
my ($documents, $vocabs) = Algorithm::LDA::Formatter.from-plain(@documents);
my Algorithm::LDA $lda .= new(:$documents, :$vocabs);
my Algorithm::LDA::LDAModel $model = $lda.fit(:num-topics(3), :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
use Algorithm::LDA;
use Algorithm::LDA::Formatter;
use Algorithm::LDA::LDAModel;
# 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::LDA::Formatter.from-libsvm(@documents);
my Algorithm::LDA $lda .= new(:$documents, :@vocabs);
my Algorithm::LDA::LDAModel $model = $lda.fit(:num-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
$model.nbest-words-per-topic.say # show nbest words per topic
Algorithm::LDA is a Raku Latent Dirichlet Allocation implementation.
Defined as:
submethod BUILD(:$!documents!, :$!vocabs! is raw) { }
Constructs a new Algorithm::LDA instance.
Defined as:
method fit(Int :$num-iterations = 500, Int :$num-topics!, Num :$alpha = 0.1e0, Num :$beta = 0.1e0, Int :$seed --> Algorithm::LDA::LDAModel)
Returns an Algorithm::LDA::LDAModel instance.
-
:$num-ierations
is the number of iterations for gibbs sampler -
:$num-topics!
is the number of 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
titsuki [email protected]
Copyright 2018 titsuki
This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0.
The algorithm is from:
-
Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
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Li, Wei, and Andrew McCallum. "Pachinko allocation: DAG-structured mixture models of topic correlations." Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
-
Wallach, Hanna M., et al. "Evaluation methods for topic models." Proceedings of the 26th annual international conference on machine learning. ACM, 2009.
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Minka, Thomas. "Estimating a Dirichlet distribution." (2000): 4.