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package AI::Categorizer::Learner::NaiveBayes;

use strict;
use AI::Categorizer::Learner;
use base qw(AI::Categorizer::Learner);
use Params::Validate qw(:types);
use Algorithm::NaiveBayes;

__PACKAGE__->valid_params
  (
   threshold => {type => SCALAR, default => 0.3},
  );

sub create_model {
  my $self = shift;
  my $m = $self->{model} = Algorithm::NaiveBayes->new;

  foreach my $d ($self->knowledge_set->documents) {
    $m->add_instance(attributes => $d->features->as_hash,
		     label      => [ map $_->name, $d->categories ]);
  }
  $m->train;
}

sub get_scores {
  my ($self, $newdoc) = @_;

  return ($self->{model}->predict( attributes => $newdoc->features->as_hash ),
	  $self->{threshold});
}

sub threshold {
  my $self = shift;
  $self->{threshold} = shift if @_;
  return $self->{threshold};
}

sub save_state {
  my $self = shift;
  local $self->{knowledge_set};  # Don't need the knowledge_set to categorize
  $self->SUPER::save_state(@_);
}

sub categories {
  my $self = shift;
  return map AI::Categorizer::Category->by_name( name => $_ ), $self->{model}->labels;
}

1;

__END__

=head1 NAME

AI::Categorizer::Learner::NaiveBayes - Naive Bayes Algorithm For AI::Categorizer

=head1 SYNOPSIS

  use AI::Categorizer::Learner::NaiveBayes;
  
  # Here $k is an AI::Categorizer::KnowledgeSet object
  
  my $nb = new AI::Categorizer::Learner::NaiveBayes(...parameters...);
  $nb->train(knowledge_set => $k);
  $nb->save_state('filename');
  
  ... time passes ...
  
  $nb = AI::Categorizer::Learner::NaiveBayes->restore_state('filename');
  my $c = new AI::Categorizer::Collection::Files( path => ... );
  while (my $document = $c->next) {
    my $hypothesis = $nb->categorize($document);
    print "Best assigned category: ", $hypothesis->best_category, "\n";
    print "All assigned categories: ", join(', ', $hypothesis->categories), "\n";
  }

=head1 DESCRIPTION

This is an implementation of the Naive Bayes decision-making
algorithm, applied to the task of document categorization (as defined
by the AI::Categorizer module).  See L<AI::Categorizer> for a complete
description of the interface.

This module is now a wrapper around the stand-alone
C<Algorithm::NaiveBayes> module.  I moved the discussion of Bayes'
Theorem into that module's documentation.

=head1 METHODS

This class inherits from the C<AI::Categorizer::Learner> class, so all
of its methods are available unless explicitly mentioned here.

=head2 new()

Creates a new Naive Bayes Learner and returns it.  In addition to the
parameters accepted by the C<AI::Categorizer::Learner> class, the
Naive Bayes subclass accepts the following parameters:

=over 4

=item * threshold

Sets the score threshold for category membership.  The default is
currently 0.3.  Set the threshold lower to assign more categories per