Algorithm::NaiveBayes - A Raku Naive Bayes classifier implementation
use Algorithm::NaiveBayes;
my $nb = Algorithm::NaiveBayes.new(solver => Algorithm::NaiveBayes::Multinomial);
$nb.add-document("Chinese Beijing Chinese", "China");
$nb.add-document("Chinese Chinese Shanghai", "China");
$nb.add-document("Chinese Macao", "China");
$nb.add-document("Tokyo Japan Chinese", "Japan");
my $model = $nb.train;
my @result = $model.predict("Chinese Chinese Chinese Tokyo Japan");
@result.say; # [China => -8.10769031284391 Japan => -8.90668134500126]
use Algorithm::NaiveBayes;
my $nb = Algorithm::NaiveBayes.new(solver => Algorithm::NaiveBayes::Bernoulli);
$nb.add-document("Chinese Beijing Chinese", "China");
$nb.add-document("Chinese Chinese Shanghai", "China");
$nb.add-document("Chinese Macao", "China");
$nb.add-document("Tokyo Japan Chinese", "Japan");
my $model = $nb.train;
my @result = $model.predict("Chinese Chinese Chinese Tokyo Japan");
@result.say; # [Japan => -3.81908500976888 China => -5.26217831993216]
Algorithm::NaiveBayes is a Raku Naive Bayes classifier implementation.
my $nb = Algorithm::NaiveBayes.new(); # default solver is Multinomial
my $nb = Algorithm::NaiveBayes.new(%options);
solver => Algorithm::NaiveBayes::Multinomial|Algorithm::NaiveBayes::Bernoulli
multi method add-document(%attributes, Str $label)
multi method add-document(Str @words, Str $label)
multi method add-document(Str $text, Str $label)
Adds a document used for training. %attributes is the key-value pair, where key is the word and value is the frequency of occurrence of the word in the document. @words is the bag-of-words. The bag-of-words is represented as a multiset of words occurrence in the document. $text is the plain text of the document. It will be splitted by whitespaces and processed as the bag-of-words internally.
method train(--> Algorithm::NaiveBayes::Model)
Starts training and returns an Algorithm::NaiveBayes::Model instance.
titsuki [email protected]
Copyright 2016 titsuki
This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0.
This algorithm is from Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schutze. Introduction to information retrieval. Vol. 1. No. 1. Cambridge: Cambridge university press, 2008.