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

A Raku Hierarchical PAM (model 2) implementation.
https://github.com/titsuki/raku-algorithm-hierarchicalpam

Last synced: 10 days ago
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A Raku Hierarchical PAM (model 2) implementation.

• Host: GitHub
• URL: https://github.com/titsuki/raku-algorithm-hierarchicalpam
• Owner: titsuki
• License: artistic-2.0
• Created: 2019-03-31T07:56:43.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-26T15:35:40.000Z (over 4 years ago)
• Last Synced: 2024-11-05T21:50:25.227Z (about 2 months ago)
• Language: C
• Homepage:
• Size: 22.5 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Changelog: Changes
  • License: LICENSE

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README

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

NAME

====

Algorithm::HierarchicalPAM - A Raku 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 Raku 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 

COPYRIGHT AND LICENSE

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

Copyright 2019 titsuki

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.