AI-Categorizer

 view release on metacpan or  search on metacpan

Changes  view on Meta::CPAN

 - Added learners for SVMs, Decision Trees, and a pass-through to
   Weka.

 - Added a virtual class for binary classifiers.

 - Wrote documentation for lots of the undocumented classes.

 - Added a PNG file giving an overview diagram of the classes.

 - Added a script 'categorizer' to provide a simple command-line
   interface to AI::Categorizer

 - save_state() and restore_state() now save to a directory, not a
   file.

 - Removed F1(), precision(), recall(), etc. from Util package since
   they're in Statistics::Contingency.  Added random_elements() to
   Util.

 - Collection::Files now warns when no category information is known

eg/categorizer  view on Meta::CPAN

#
# Copyright 2002 Ken Williams, under the same license as the
# AI::Categorizer distribution.


use strict;
use AI::Categorizer;
use Benchmark;
my $HAVE_YAML = eval "use YAML; 1";

my ($opt, $do_stage, $outfile) = parse_command_line(@ARGV);
@ARGV = grep !/^-\d$/, @ARGV;

my $c = eval {new AI::Categorizer(%$opt)};
if ($@ and $@ =~ /^The following parameter/) {
  die "$@\nPlease see the AI::Categorizer documentation for a description of parameters accepted.\n";
}
die $@ if $@;

%$do_stage = map {$_, 1} 1..5 unless keys %$do_stage;

eg/categorizer  view on Meta::CPAN

  }
  my $start = new Benchmark;
  $c->$section();
  my $end = new Benchmark;
  my $summary = timestr(timediff($end, $start));
  my ($rss, $vsz) = memory_usage();
  print "$summary (memory: rss=$rss, vsz=$vsz)\n" if $c->verbose;
  print $out_fh "Stage $stage: $summary (memory: rss=$rss, vsz=$vsz)\n" if $out_fh;
}

sub parse_command_line {
  my (%opt, %do_stage);

  while (@_) {
    if ($_[0] =~ /^-(\d+)$/) {
      shift;
      $do_stage{$1} = 1;
      
    } elsif ( $_[0] eq '--config_file' ) {
      die "--config_file requires the YAML module from CPAN to be installed.\n" unless $HAVE_YAML;
      shift;

lib/AI/Categorizer/Learner/Weka.pm  view on Meta::CPAN

  }

=head1 DESCRIPTION

This class doesn't implement any machine learners of its own, it
merely passes the data through to the Weka machine learning system
(http://www.cs.waikato.ac.nz/~ml/weka/).  This can give you access to
a collection of machine learning algorithms not otherwise implemented
in C<AI::Categorizer>.

Currently this is a simple command-line wrapper that calls C<java>
subprocesses.  In the future this may be converted to an
C<Inline::Java> wrapper for better performance (faster running
times).  However, if you're looking for really great performance,
you're probably looking in the wrong place - this Weka wrapper is
intended more as a way to try lots of different machine learning
methods.

=head1 METHODS

This class inherits from the C<AI::Categorizer::Learner> class, so all