txt: ## dist_cpan-TITSUKI-Algorithm-HierarchicalPAM.md
  [![Build Status](https://travis-ci.org/titsuki/p6-Algorithm-HierarchicalPAM.svg?branch=master)](https://travis-ci.org/titsuki/p6-Algorithm-HierarchicalPAM)
  
  # 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](mailto:titsuki@cpan.org)
  
  # 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.
  