https://github.com/titsuki/raku-algorithm-lda

A Raku Latent Dirichlet Allocation implementation
https://github.com/titsuki/raku-algorithm-lda

lda perl6 raku rakulang

Last synced: 11 days ago
JSON representation

A Raku Latent Dirichlet Allocation implementation

• Host: GitHub
• URL: https://github.com/titsuki/raku-algorithm-lda
• Owner: titsuki
• License: artistic-2.0
• Created: 2018-11-26T04:01:28.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2021-05-05T03:58:50.000Z (over 3 years ago)
• Last Synced: 2024-11-05T18:56:46.466Z (about 2 months ago)
• Topics: lda, perl6, raku, rakulang
• Language: C
• Homepage:
• Size: 40 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: Changes
  • License: LICENSE

Awesome Lists containing this project

README

        
[![Build Status](https://travis-ci.org/titsuki/raku-Algorithm-LDA.svg?branch=master)](https://travis-ci.org/titsuki/raku-Algorithm-LDA)

NAME

====

Algorithm::LDA - A Raku Latent Dirichlet Allocation implementation.

SYNOPSIS

========

EXAMPLE 1

---------

    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

EXAMPLE 2

---------

    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

DESCRIPTION

===========

Algorithm::LDA is a Raku Latent Dirichlet Allocation implementation.

CONSTRUCTOR

-----------

### new

Defined as:

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

Constructs a new Algorithm::LDA instance.

METHODS

-------

### fit

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

AUTHOR

======

titsuki 

COPYRIGHT AND LICENSE

=====================

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.

  * 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.

  * Minka, Thomas. "Estimating a Dirichlet distribution." (2000): 4.