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ACME-THEDANIEL-Utils
view release on metacpan or search on metacpan
lib/ACME/THEDANIEL/Utils.pm view on Meta::CPAN
package ACME::THEDANIEL::Utils;
use 5.006;
use strict;
use warnings;
use Scalar::Util qw( looks_like_number );
use Carp qw ( croak );
=head1 NAME
ACME::THEDANIEL::Utils - The great new ACME::THEDANIEL::Utils!
=head1 VERSION
Version 0.01
=cut
our $VERSION = '0.01';
=head1 SYNOPSIS
Used to complete the Intermediate Perl textbook
=head1 SUBROUTINES/METHODS
=head2 sum
=cut
sub sum {
my $sum;
foreach my $num ( @_ ) {
if ( !looks_like_number( $num ) ) {
croak "Invalid input: $num"
}
$sum += $num;
}
return $sum;
}
=head1 AUTHOR
Daniel jones, C<< <dtj at someplace.com> >>
=head1 BUGS
Please report any bugs or feature requests to C<bug-acme-thedaniel-utils at rt.cpan.org>, or through
the web interface at L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=ACME-THEDANIEL-Utils>. I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc ACME::THEDANIEL::Utils
You can also look for information at:
=over 4
=item * RT: CPAN's request tracker (report bugs here)
L<http://rt.cpan.org/NoAuth/Bugs.html?Dist=ACME-THEDANIEL-Utils>
=item * AnnoCPAN: Annotated CPAN documentation
L<http://annocpan.org/dist/ACME-THEDANIEL-Utils>
=item * CPAN Ratings
L<http://cpanratings.perl.org/d/ACME-THEDANIEL-Utils>
=item * Search CPAN
L<http://search.cpan.org/dist/ACME-THEDANIEL-Utils/>
=back
=head1 ACKNOWLEDGEMENTS
Intermediate Perl, 2nd Edition.
=head1 LICENSE AND COPYRIGHT
Copyright 2017 Daniel jones.
This program is free software; you can redistribute it and/or modify it
under the terms of the the Artistic License (2.0). You may obtain a
copy of the full license at:
L<http://www.perlfoundation.org/artistic_license_2_0>
Any use, modification, and distribution of the Standard or Modified
Versions is governed by this Artistic License. By using, modifying or
distributing the Package, you accept this license. Do not use, modify,
or distribute the Package, if you do not accept this license.
If your Modified Version has been derived from a Modified Version made
by someone other than you, you are nevertheless required to ensure that
your Modified Version complies with the requirements of this license.
This license does not grant you the right to use any trademark, service
mark, tradename, or logo of the Copyright Holder.
This license includes the non-exclusive, worldwide, free-of-charge
patent license to make, have made, use, offer to sell, sell, import and
otherwise transfer the Package with respect to any patent claims
licensable by the Copyright Holder that are necessarily infringed by the
Package. If you institute patent litigation (including a cross-claim or
counterclaim) against any party alleging that the Package constitutes
direct or contributory patent infringement, then this Artistic License
to you shall terminate on the date that such litigation is filed.
Disclaimer of Warranty: THE PACKAGE IS PROVIDED BY THE COPYRIGHT HOLDER
AND CONTRIBUTORS "AS IS' AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES.
THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
PURPOSE, OR NON-INFRINGEMENT ARE DISCLAIMED TO THE EXTENT PERMITTED BY
YOUR LOCAL LAW. UNLESS REQUIRED BY LAW, NO COPYRIGHT HOLDER OR
CONTRIBUTOR WILL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, OR
CONSEQUENTIAL DAMAGES ARISING IN ANY WAY OUT OF THE USE OF THE PACKAGE,
EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
=cut
1; # End of ACME::THEDANIEL::Utils
ACME-YAPC-NA-2012
view release on metacpan or search on metacpan
lib/ACME/YAPC/NA/2012.pm view on Meta::CPAN
package ACME::YAPC::NA::2012;
use 5.006
use strict;
use warnings;
=head1 NAME
ACME::YAPC::NA::2012 - The great new ACME::YAPC::NA::2012!
=head1 VERSION
Version 0.01
=cut
our $VERSION = '0.02';
=head1 SYNOPSIS
Quick summary of what the module does.
Perhaps a little code snippet.
use ACME::YAPC::NA::2012;
my $foo = ACME::YAPC::NA::2012->new();
...
=head1 EXPORT
A list of functions that can be exported. You can delete this section
if you don't export anything, such as for a purely object-oriented module.
=head1 SUBROUTINES/METHODS
=head2 function1
=cut
sub function1 {
}
=head2 function2
=cut
sub function2 {
}
=head1 AUTHOR
Jacinta Richardson, C<< <jarich at cpan.org> >>
=head1 BUGS
Please report any bugs or feature requests to C<bug-acme-yapc-na-2012 at rt.cpan.org>, or through
the web interface at L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=ACME-YAPC-NA-2012>. I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc ACME::YAPC::NA::2012
You can also look for information at:
=over 4
=item * RT: CPAN's request tracker (report bugs here)
L<http://rt.cpan.org/NoAuth/Bugs.html?Dist=ACME-YAPC-NA-2012>
=item * AnnoCPAN: Annotated CPAN documentation
L<http://annocpan.org/dist/ACME-YAPC-NA-2012>
=item * CPAN Ratings
L<http://cpanratings.perl.org/d/ACME-YAPC-NA-2012>
=item * Search CPAN
L<http://search.cpan.org/dist/ACME-YAPC-NA-2012/>
=back
=head1 ACKNOWLEDGEMENTS
=head1 LICENSE AND COPYRIGHT
Copyright 2012 Jacinta Richardson.
This program is free software; you can redistribute it and/or modify it
under the terms of either: the GNU General Public License as published
by the Free Software Foundation; or the Artistic License.
See http://dev.perl.org/licenses/ for more information.
=cut
1; # End of ACME::YAPC::NA::2012
ACME-ltharris
view release on metacpan or search on metacpan
lib/ACME/ltharris.pm view on Meta::CPAN
package ACME::ltharris;
use 5.006;
use strict;
use warnings;
=head1 NAME
ACME::ltharris - The great new ACME::ltharris!
=head1 VERSION
Version 0.01
=cut
our $VERSION = '0.03'
=head1 SYNOPSIS
Quick summary of what the module does.
Perhaps a little code snippet.
use ACME::ltharris;
my $foo = ACME::ltharris->new();
...
=head1 EXPORT
A list of functions that can be exported. You can delete this section
if you don't export anything, such as for a purely object-oriented module.
=head1 SUBROUTINES/METHODS
=head2 function1
=cut
sub function1 {
}
=head2 function2
=cut
sub function2 {
}
=head1 AUTHOR
L.T. Harris, C<< <lth at ltharris.com> >>
=head1 BUGS
Please report any bugs or feature requests to C<bug-acme-ltharris at rt.cpan.org>, or through
the web interface at L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=ACME-ltharris>. I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc ACME::ltharris
You can also look for information at:
=over 4
=item * RT: CPAN's request tracker (report bugs here)
L<http://rt.cpan.org/NoAuth/Bugs.html?Dist=ACME-ltharris>
=item * AnnoCPAN: Annotated CPAN documentation
L<http://annocpan.org/dist/ACME-ltharris>
=item * CPAN Ratings
L<http://cpanratings.perl.org/d/ACME-ltharris>
=item * Search CPAN
L<http://search.cpan.org/dist/ACME-ltharris/>
=back
=head1 ACKNOWLEDGEMENTS
=head1 LICENSE AND COPYRIGHT
Copyright 2012 L.T. Harris.
This program is free software; you can redistribute it and/or modify it
under the terms of either: the GNU General Public License as published
by the Free Software Foundation; or the Artistic License.
See http://dev.perl.org/licenses/ for more information.
=cut
1; # End of ACME::ltharris
ADAMK-Release
view release on metacpan or search on metacpan
inc/Module/Install.pm view on Meta::CPAN
#line 1
package Module::Install;
# For any maintainers:
# The load order for Module::Install is a bit magic.
# It goes something like this...
#
# IF ( host has Module::Install installed, creating author mode ) {
# 1. Makefile.PL calls "use inc::Module::Install"
# 2. $INC{inc/Module/Install.pm} set to installed version of inc::Module::Install
# 3. The installed version of inc::Module::Install loads
# 4. inc::Module::Install calls "require Module::Install"
# 5. The ./inc/ version of Module::Install loads
# } ELSE {
# 1. Makefile.PL calls "use inc::Module::Install"
# 2. $INC{inc/Module/Install.pm} set to ./inc/ version of Module::Install
# 3. The ./inc/ version of Module::Install loads
# }
use 5.005;
use strict 'vars';
use Cwd ();
use File::Find ();
use File::Path ();
use vars qw{$VERSION $MAIN};
BEGIN {
# All Module::Install core packages now require synchronised versions.
# This will be used to ensure we don't accidentally load old or
# different versions of modules.
# This is not enforced yet, but will be some time in the next few
# releases once we can make sure it won't clash with custom
# Module::Install extensions.
$VERSION = '1.06';
# Storage for the pseudo-singleton
$MAIN = undef;
*inc::Module::Install::VERSION = *VERSION;
@inc::Module::Install::ISA = __PACKAGE__;
}
sub import {
my $class = shift;
my $self = $class->new(@_);
my $who = $self->_caller;
#-------------------------------------------------------------
# all of the following checks should be included in import(),
# to allow "eval 'require Module::Install; 1' to test
# installation of Module::Install. (RT #51267)
#-------------------------------------------------------------
# Whether or not inc::Module::Install is actually loaded, the
# $INC{inc/Module/Install.pm} is what will still get set as long as
# the caller loaded module this in the documented manner.
# If not set, the caller may NOT have loaded the bundled version, and thus
# they may not have a MI version that works with the Makefile.PL. This would
# result in false errors or unexpected behaviour. And we don't want that.
my $file = join( '/', 'inc', split /::/, __PACKAGE__ ) . '.pm';
unless ( $INC{$file} ) { die <<"END_DIE" }
Please invoke ${\__PACKAGE__} with:
use inc::${\__PACKAGE__};
not:
use ${\__PACKAGE__};
END_DIE
# This reportedly fixes a rare Win32 UTC file time issue, but
# as this is a non-cross-platform XS module not in the core,
# we shouldn't really depend on it. See RT #24194 for detail.
# (Also, this module only supports Perl 5.6 and above).
eval "use Win32::UTCFileTime" if $^O eq 'MSWin32' && $] >= 5.006;
# If the script that is loading Module::Install is from the future,
# then make will detect this and cause it to re-run over and over
# again. This is bad. Rather than taking action to touch it (which
# is unreliable on some platforms and requires write permissions)
# for now we should catch this and refuse to run.
if ( -f $0 ) {
my $s = (stat($0))[9];
# If the modification time is only slightly in the future,
# sleep briefly to remove the problem.
my $a = $s - time;
if ( $a > 0 and $a < 5 ) { sleep 5 }
# Too far in the future, throw an error.
my $t = time;
if ( $s > $t ) { die <<"END_DIE" }
Your installer $0 has a modification time in the future ($s > $t).
This is known to create infinite loops in make.
Please correct this, then run $0 again.
END_DIE
}
# Build.PL was formerly supported, but no longer is due to excessive
# difficulty in implementing every single feature twice.
if ( $0 =~ /Build.PL$/i ) { die <<"END_DIE" }
Module::Install no longer supports Build.PL.
It was impossible to maintain duel backends, and has been deprecated.
Please remove all Build.PL files and only use the Makefile.PL installer.
END_DIE
#-------------------------------------------------------------
# To save some more typing in Module::Install installers, every...
# use inc::Module::Install
# ...also acts as an implicit use strict.
$^H |= strict::bits(qw(refs subs vars));
#-------------------------------------------------------------
unless ( -f $self->{file} ) {
foreach my $key (keys %INC) {
delete $INC{$key} if $key =~ /Module\/Install/;
}
local $^W;
require "$self->{path}/$self->{dispatch}.pm";
File::Path::mkpath("$self->{prefix}/$self->{author}");
$self->{admin} = "$self->{name}::$self->{dispatch}"->new( _top => $self );
$self->{admin}->init;
@_ = ($class, _self => $self);
goto &{"$self->{name}::import"};
}
local $^W;
*{"${who}::AUTOLOAD"} = $self->autoload;
$self->preload;
# Unregister loader and worker packages so subdirs can use them again
delete $INC{'inc/Module/Install.pm'};
delete $INC{'Module/Install.pm'};
# Save to the singleton
$MAIN = $self;
return 1;
}
sub autoload {
my $self = shift;
my $who = $self->_caller;
my $cwd = Cwd::cwd();
my $sym = "${who}::AUTOLOAD";
$sym->{$cwd} = sub {
my $pwd = Cwd::cwd();
if ( my $code = $sym->{$pwd} ) {
# Delegate back to parent dirs
goto &$code unless $cwd eq $pwd;
}
unless ($$sym =~ s/([^:]+)$//) {
# XXX: it looks like we can't retrieve the missing function
# via $$sym (usually $main::AUTOLOAD) in this case.
# I'm still wondering if we should slurp Makefile.PL to
# get some context or not ...
my ($package, $file, $line) = caller;
die <<"EOT";
Unknown function is found at $file line $line.
Execution of $file aborted due to runtime errors.
If you're a contributor to a project, you may need to install
some Module::Install extensions from CPAN (or other repository).
If you're a user of a module, please contact the author.
EOT
}
my $method = $1;
if ( uc($method) eq $method ) {
# Do nothing
return;
} elsif ( $method =~ /^_/ and $self->can($method) ) {
# Dispatch to the root M:I class
return $self->$method(@_);
}
# Dispatch to the appropriate plugin
unshift @_, ( $self, $1 );
goto &{$self->can('call')};
};
}
sub preload {
my $self = shift;
unless ( $self->{extensions} ) {
$self->load_extensions(
"$self->{prefix}/$self->{path}", $self
);
}
my @exts = @{$self->{extensions}};
unless ( @exts ) {
@exts = $self->{admin}->load_all_extensions;
}
my %seen;
foreach my $obj ( @exts ) {
while (my ($method, $glob) = each %{ref($obj) . '::'}) {
next unless $obj->can($method);
next if $method =~ /^_/;
next if $method eq uc($method);
$seen{$method}++;
}
}
my $who = $self->_caller;
foreach my $name ( sort keys %seen ) {
local $^W;
*{"${who}::$name"} = sub {
${"${who}::AUTOLOAD"} = "${who}::$name";
goto &{"${who}::AUTOLOAD"};
};
}
}
sub new {
my ($class, %args) = @_;
delete $INC{'FindBin.pm'};
{
# to suppress the redefine warning
local $SIG{__WARN__} = sub {};
require FindBin;
}
# ignore the prefix on extension modules built from top level.
my $base_path = Cwd::abs_path($FindBin::Bin);
unless ( Cwd::abs_path(Cwd::cwd()) eq $base_path ) {
delete $args{prefix};
}
return $args{_self} if $args{_self};
$args{dispatch} ||= 'Admin';
$args{prefix} ||= 'inc';
$args{author} ||= ($^O eq 'VMS' ? '_author' : '.author');
$args{bundle} ||= 'inc/BUNDLES';
$args{base} ||= $base_path;
$class =~ s/^\Q$args{prefix}\E:://;
$args{name} ||= $class;
$args{version} ||= $class->VERSION;
unless ( $args{path} ) {
$args{path} = $args{name};
$args{path} =~ s!::!/!g;
}
$args{file} ||= "$args{base}/$args{prefix}/$args{path}.pm";
$args{wrote} = 0;
bless( \%args, $class );
}
sub call {
my ($self, $method) = @_;
my $obj = $self->load($method) or return;
splice(@_, 0, 2, $obj);
goto &{$obj->can($method)};
}
sub load {
my ($self, $method) = @_;
$self->load_extensions(
"$self->{prefix}/$self->{path}", $self
) unless $self->{extensions};
foreach my $obj (@{$self->{extensions}}) {
return $obj if $obj->can($method);
}
my $admin = $self->{admin} or die <<"END_DIE";
The '$method' method does not exist in the '$self->{prefix}' path!
Please remove the '$self->{prefix}' directory and run $0 again to load it.
END_DIE
my $obj = $admin->load($method, 1);
push @{$self->{extensions}}, $obj;
$obj;
}
sub load_extensions {
my ($self, $path, $top) = @_;
my $should_reload = 0;
unless ( grep { ! ref $_ and lc $_ eq lc $self->{prefix} } @INC ) {
unshift @INC, $self->{prefix};
$should_reload = 1;
}
foreach my $rv ( $self->find_extensions($path) ) {
my ($file, $pkg) = @{$rv};
next if $self->{pathnames}{$pkg};
local $@;
my $new = eval { local $^W; require $file; $pkg->can('new') };
unless ( $new ) {
warn $@ if $@;
next;
}
$self->{pathnames}{$pkg} =
$should_reload ? delete $INC{$file} : $INC{$file};
push @{$self->{extensions}}, &{$new}($pkg, _top => $top );
}
$self->{extensions} ||= [];
}
sub find_extensions {
my ($self, $path) = @_;
my @found;
File::Find::find( sub {
my $file = $File::Find::name;
return unless $file =~ m!^\Q$path\E/(.+)\.pm\Z!is;
my $subpath = $1;
return if lc($subpath) eq lc($self->{dispatch});
$file = "$self->{path}/$subpath.pm";
my $pkg = "$self->{name}::$subpath";
$pkg =~ s!/!::!g;
# If we have a mixed-case package name, assume case has been preserved
# correctly. Otherwise, root through the file to locate the case-preserved
# version of the package name.
if ( $subpath eq lc($subpath) || $subpath eq uc($subpath) ) {
my $content = Module::Install::_read($subpath . '.pm');
my $in_pod = 0;
foreach ( split //, $content ) {
$in_pod = 1 if /^=\w/;
$in_pod = 0 if /^=cut/;
next if ($in_pod || /^=cut/); # skip pod text
next if /^\s*#/; # and comments
if ( m/^\s*package\s+($pkg)\s*;/i ) {
$pkg = $1;
last;
}
}
}
push @found, [ $file, $pkg ];
}, $path ) if -d $path;
@found;
}
#####################################################################
# Common Utility Functions
sub _caller {
my $depth = 0;
my $call = caller($depth);
while ( $call eq __PACKAGE__ ) {
$depth++;
$call = caller($depth);
}
return $call;
}
# Done in evals to avoid confusing Perl::MinimumVersion
eval( $] >= 5.006 ? <<'END_NEW' : <<'END_OLD' ); die $@ if $@;
sub _read {
local *FH;
open( FH, '<', $_[0] ) or die "open($_[0]): $!";
my $string = do { local $/; <FH> };
close FH or die "close($_[0]): $!";
return $string;
}
END_NEW
sub _read {
local *FH;
open( FH, "< $_[0]" ) or die "open($_[0]): $!";
my $string = do { local $/; <FH> };
close FH or die "close($_[0]): $!";
return $string;
}
END_OLD
sub _readperl {
my $string = Module::Install::_read($_[0]);
$string =~ s/(?:\015{1,2}\012|\015|\012)/\n/sg;
$string =~ s/(\n)\n*__(?:DATA|END)__\b.*\z/$1/s;
$string =~ s/\n\n=\w+.+?\n\n=cut\b.+?\n+/\n\n/sg;
return $string;
}
sub _readpod {
my $string = Module::Install::_read($_[0]);
$string =~ s/(?:\015{1,2}\012|\015|\012)/\n/sg;
return $string if $_[0] =~ /\.pod\z/;
$string =~ s/(^|\n=cut\b.+?\n+)[^=\s].+?\n(\n=\w+|\z)/$1$2/sg;
$string =~ s/\n*=pod\b[^\n]*\n+/\n\n/sg;
$string =~ s/\n*=cut\b[^\n]*\n+/\n\n/sg;
$string =~ s/^\n+//s;
return $string;
}
# Done in evals to avoid confusing Perl::MinimumVersion
eval( $] >= 5.006 ? <<'END_NEW' : <<'END_OLD' ); die $@ if $@;
sub _write {
local *FH;
open( FH, '>', $_[0] ) or die "open($_[0]): $!";
foreach ( 1 .. $#_ ) {
print FH $_[$_] or die "print($_[0]): $!";
}
close FH or die "close($_[0]): $!";
}
END_NEW
sub _write {
local *FH;
open( FH, "> $_[0]" ) or die "open($_[0]): $!";
foreach ( 1 .. $#_ ) {
print FH $_[$_] or die "print($_[0]): $!";
}
close FH or die "close($_[0]): $!";
}
END_OLD
# _version is for processing module versions (eg, 1.03_05) not
# Perl versions (eg, 5.8.1).
sub _version ($) {
my $s = shift || 0;
my $d =()= $s =~ /(\.)/g;
if ( $d >= 2 ) {
# Normalise multipart versions
$s =~ s/(\.)(\d{1,3})/sprintf("$1%03d",$2)/eg;
}
$s =~ s/^(\d+)\.?//;
my $l = $1 || 0;
my @v = map {
$_ . '0' x (3 - length $_)
} $s =~ /(\d{1,3})\D?/g;
$l = $l . '.' . join '', @v if @v;
return $l + 0;
}
sub _cmp ($$) {
_version($_[1]) <=> _version($_[2]);
}
# Cloned from Params::Util::_CLASS
sub _CLASS ($) {
(
defined $_[0]
and
! ref $_[0]
and
$_[0] =~ m/^[^\W\d]\w*(?:::\w+)*\z/s
) ? $_[0] : undef;
}
1;
# Copyright 2008 - 2012 Adam Kennedy.
AE-AdHoc
view release on metacpan or search on metacpan
examples/port-probe-multi.pl view on Meta::CPAN
#!/usr/bin/perl -w
use strict;
use AE::AdHoc;
use AnyEvent::Socket;
use Getopt::Long;
my $timeout = 1;
GetOptions (
"timeout=s" => \$timeout,
"help" => \&usage,
) or usage();
my @probe = map {
/^(.*):(\d+)$/ or die "Expecting host:port. See $0 --help\n"; [$1, $2, $_];
} @ARGV;
usage() unless @probe;
# Real work
eval {
ae_recv {
tcp_connect $_->[0], $_->[1], ae_goal("$_->[0]:$_->[1]") for @probe;
} $timeout;
};
die $@ if $@ and $@ !~ /^Timeout/;
my @offline = sort keys %{ AE::AdHoc->goals };
my (@alive, @reject);
my $results = AE::AdHoc->results;
foreach (keys %$results) {
# tcp_connect will not feed any args if connect failed
ref $results->{$_}->[0]
? push @alive, $_
: push @reject, $_;
};
print "Connected: @alive\n" if @alive;
print "Rejected: @reject\n" if @reject;
print "Timed out: @offline\n" if @offline;
# /Real work
sub usage {
print <<"USAGE";
Probe tcp connection to several hosts at once
Usage: $0 [ options ] host:port host:port ...
Options may include:
--timeout <seconds> - may be fractional as well
--help - this message
USAGE
exit 1;
};
AES128
view release on metacpan or search on metacpan
lib/AES128.pm view on Meta::CPAN
package AES128;
use 5.016001;
use strict;
use warnings;
require Exporter;
our @ISA = qw(Exporter);
# Items to export into callers namespace by default. Note: do not export
# names by default without a very good reason. Use EXPORT_OK instead.
# Do not simply export all your public functions/methods/constants.
# This allows declaration use AES128 ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.
our %EXPORT_TAGS = ( 'all' => [ qw(
AES128_CTR_encrypt AES128_CTR_decrypt
) ] );
our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );
our @EXPORT = qw(
);
our $VERSION = '0.02';
require XSLoader;
XSLoader::load('AES128', $VERSION);
# Preloaded methods go here.
1;
__END__
# Below is stub documentation for your module. You'd better edit it!
=head1 NAME
AES128 - 128BIT CTR mode AES algorithm.
=head1 SYNOPSIS
# ------------------------ simple version ----------------------------------
use AES128 qw/:all/;
my $plain_text = "There's more than one way to do it.";
my $key = "my secret aes key.";
my $encrypted = AES128_CTR_encrypt($plain_text, $key);
my $plain = AES128_CTR_decrypt($encrypted, $key);
# ------------ server/client key exchange -----------------------------------
use MicroECC;
use AES128 qw/:all/;
use Digest::SHA qw/sha256/;
my $curve = MicroECC::secp256r1();
my ($server_pubkey, $server_privkey) = MicroECC::make_key($curve);
# Generate shared secret with client public key.
my $shared_secret = MicroECC::shared_secret($client_pubkey, $server_privkey);
my $key = sha256($shared_secret);
my $plain_text = "There's more than one way to do it.";
my $encrypted = AES128_CTR_encrypt($plain_text, $key);
my $plain = AES128_CTR_decrypt($encrypted, $key);
=head1 DESCRIPTION
Perl wrapper for the tiny-AES-c library (https://github.com/kokke/tiny-AES-c)
Since 128bit key length is secure enough for most applications and ECB is NOT secure,
this module supports 128bit key length and CTR mode only.
=head2 EXPORT
None by default.
=head1 SEE ALSO
The tiny-AES-c library: https://github.com/kokke/tiny-AES-c
=head1 AUTHOR
Jeff Zhang, <10395708@qq.com>
=head1 COPYRIGHT AND LICENSE
Copyright (C) 2019 by Jeff
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself, either Perl version 5.16.1 or,
at your option, any later version of Perl 5 you may have available.
=cut
AFS-Command
view release on metacpan or search on metacpan
lib/AFS/Command/BOS.pm view on Meta::CPAN
#
# $Id$
#
# (c) 2003-2004 Morgan Stanley and Co.
# See ..../src/LICENSE for terms of distribution.
#
package AFS::Command::BOS;
require 5.6.0;
use strict;
use English;
use AFS::Command::Base;
use AFS::Object;
use AFS::Object::BosServer;
use AFS::Object::Instance;
our @ISA = qw(AFS::Command::Base);
our $VERSION = '1.99';
sub getdate {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "getdate";
my $directory = $args{dir} || '/usr/afs/bin';
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
while ( defined($_ = $self->{handle}->getline()) ) {
chomp;
next unless m:File $directory/(\S+) dated ([^,]+),:;
my $file = AFS::Object->new
(
file => $1,
date => $2,
);
if ( /\.BAK dated ([^,]+),/ ) {
$file->_setAttribute( bak => $1 );
}
if ( /\.OLD dated ([^,\.]+)/ ) {
$file->_setAttribute( old => $1 );
}
$result->_addFile($file);
}
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
sub getlog {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "getlog";
my $redirect = undef;
my $redirectname = undef;
if ( $args{redirect} ) {
$redirectname = delete $args{redirect};
$redirect = IO::File->new(">$redirectname") || do {
$self->_Carp("Unable to write to $redirectname: $ERRNO");
return;
};
}
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
my $log = "";
while ( defined($_ = $self->{handle}->getline()) ) {
next if /^Fetching log file/;
if ( $redirect ) {
$redirect->print($_);
} else {
$log .= $_;
}
}
if ( $redirect ) {
$redirect->close()|| do {
$self->_Carp("Unable to close $redirectname: $ERRNO");
$errors++
};
$result->_setAttribute( log => $redirectname );
} else {
$result->_setAttribute( log => $log );
}
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
sub getrestart {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "getrestart";
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
while ( defined($_ = $self->{handle}->getline()) ) {
if ( /restarts at (.*)/ || /restarts (never)/ ) {
$result->_setAttribute( restart => $1 );
} elsif ( /binaries at (.*)/ || /binaries (never)/ ) {
$result->_setAttribute( binaries => $1 );
}
}
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
sub listhosts {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "listhosts";
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
my @hosts = ();
while ( defined($_ = $self->{handle}->getline()) ) {
chomp;
if ( /Cell name is (\S+)/i ) {
$result->_setAttribute( cell => $1 );
}
if ( /Host \d+ is (\S+)/i ) {
push(@hosts,$1);
}
}
$result->_setAttribute( hosts => \@hosts );
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
sub listkeys {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "listkeys";
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
while ( defined($_ = $self->{handle}->getline()) ) {
chomp;
if ( /key (\d+)/ ) {
my $key = AFS::Object->new( index => $1 );
if ( /has cksum (\d+)/ ) {
$key->_setAttribute( cksum => $1 );
} elsif ( /is \'([^\']+)\'/ ) {
$key->_setAttribute( value => $1 );
}
$result->_addKey($key);
}
if ( /last changed on (.*)\./ ) {
$result->_setAttribute( keyschanged => $1 );
}
}
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
sub listusers {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "listusers";
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
while ( defined($_ = $self->{handle}->getline()) ) {
chomp;
if ( /^SUsers are: (.*)/ ) {
$result->_setAttribute( susers => [split(/\s+/,$1)] );
}
}
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
#
# XXX -- we might want to provide parsing of the bos salvage output,
# but for now, this is a non-parsed command.
#
# sub salvage {
# my $self = shift;
# my (%args) = @_;
# my $result = AFS::Object::BosServer->new();
# $self->{operation} = "salvage";
# return unless $self->_parse_arguments(%args);
# return unless $self->_save_stderr();
# my $errors = 0;
# $errors++ unless $self->_exec_cmds();
# while ( defined($_ = $self->{handle}->getline()) ) {
# }
# $errors++ unless $self->_reap_cmds();
# $errors++ unless $self->_restore_stderr();
# return if $errors;
# return $result;
# }
sub status {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
$self->{operation} = "status";
return unless $self->_parse_arguments(%args);
return unless $self->_save_stderr();
my $errors = 0;
$errors++ unless $self->_exec_cmds();
my $instance = undef;
while ( defined($_ = $self->{handle}->getline()) ) {
chomp;
if ( /inappropriate access/ ) {
$result->_setAttribute( access => 1 );
next;
}
if ( /Instance (\S+),/ ) {
if ( defined $instance ) {
$result->_addInstance($instance);
}
$instance = AFS::Object::Instance->new( instance => $1 );
#
# This is ugly, since the order and number of these
# strings varies.
#
if ( /\(type is (\S+)\)/ ) {
$instance->_setAttribute( type => $1 );
}
if ( /(disabled|temporarily disabled|temporarily enabled),/ ) {
$instance->_setAttribute( state => $1 );
}
if ( /stopped for too many errors/ ) {
$instance->_setAttribute( errorstop => 1 );
}
if ( /has core file/ ) {
$instance->_setAttribute( core => 1 );
}
if ( /currently (.*)\.$/ ) {
$instance->_setAttribute( status => $1 );
}
}
if ( /Auxiliary status is: (.*)\.$/ ) {
$instance->_setAttribute( auxiliary => $1 );
}
if ( /Process last started at (.*) \((\d+) proc starts\)/ ) {
$instance->_setAttribute
(
startdate => $1,
startcount => $2,
);
}
if ( /Last exit at (.*)/ ) {
$instance->_setAttribute( exitdate => $1 );
}
if ( /Last error exit at ([^,]+),/ ) {
$instance->_setAttribute( errorexitdate => $1 );
if ( /due to shutdown request/ ) {
$instance->_setAttribute( errorexitdue => 'shutdown' );
}
if ( /due to signal (\d+)/ ) {
$instance->_setAttribute
(
errorexitdue => 'signal',
errorexitsignal => $1,
);
}
if ( /by exiting with code (\d+)/ ) {
$instance->_setAttribute
(
errorexitdue => 'code',
errorexitcode => $1,
);
}
}
if ( /Command\s+(\d+)\s+is\s+\'(.*)\'/ ) {
my $command = AFS::Object->new
(
index => $1,
command => $2,
);
$instance->_addCommand($command);
}
if ( /Notifier\s+is\s+\'(.*)\'/ ) {
$instance->_setAttribute( notifier => $1 );
}
}
if ( defined $instance ) {
$result->_addInstance($instance);
}
$errors++ unless $self->_reap_cmds();
$errors++ unless $self->_restore_stderr();
return if $errors;
return $result;
}
1;
AFS-Monitor
view release on metacpan or search on metacpan
examples/Meltdown.pl view on Meta::CPAN
#!/usr/bin/perl
#
# Meltdown.pl - Used to collect stats on running AFS process with rxdebug.
#
# Original Implementation:
# Unknown - Meltdown.csh, Meltdown.awk
#
# Change History:
# Jul 02, 2002 - Rex Basham - Added check for wproc, left out during the
# original conversion from awk/csh scripts.
# Mar 21, 2002 - Rex Basham - Merged original csh and awk scripts
# and converted to perl
# Mar 23, 2002 - Rex Basham - Fixed the display format and added
# field format expansion
# August 2004 - SLAC - modified to use the Perl rxdebu function
#
# Aug 30, 2006 - Jeff Blaine - Added CSV stats output mode
#
# Parameters are -s <server> -p <port> -t <sleeptime in seconds>
# and -C to enable CSV-output mode
#
# Example:
# Meltdown.pl -s point -p 7000 -t 300
#
# Check the server 'point' on port '7000' with 5 minutes between
# rxdebug commands.
#
use blib;
use AFS::Monitor;
sub Usage {
print STDERR "\n\n$progName: collect rxdebug stats on AFS process.\n";
print STDERR "usage: $progName [options]\n";
print STDERR "options:\n";
print STDERR " -s <server> (required parameter, no default).\n";
print STDERR " -p <port> (default: 7000).\n";
print STDERR " -t <interval> (default: 1200 seconds).\n";
print STDERR " -C \n";
print STDERR " -h (help: show this help message).\n\n";
print STDERR "Example: $progName -s point -p 7000\n";
print STDERR "Collect statistics on server point for port 7000\n";
print STDERR "Refresh interval will default to 20 minutes (1200 seconds)\n\n";
exit 0;
} # Usage
sub Check_data {
#
# If a value is going to overflow the field length,
# then bump the field length to match the value.
# It won't be pretty but we'll have valid data.
#
(length $wproc > $Ln[0]) ? ($Ln[0] = length $wproc) : "";
(length $nobuf > $Ln[1]) ? ($Ln[1] = length $nobuf) : "";
(length $wpack > $Ln[2]) ? ($Ln[2] = length $wpack) : "";
(length $fpack > $Ln[3]) ? ($Ln[3] = length $fpack) : "";
(length $calls > $Ln[4]) ? ($Ln[4] = length $calls) : "";
(length $delta > $Ln[5]) ? ($Ln[5] = length $delta) : "";
(length $data > $Ln[6]) ? ($Ln[6] = length $data) : "";
(length $resend > $Ln[7]) ? ($Ln[7] = length $resend) : "";
(length $idle > $Ln[8]) ? ($Ln[8] = length $idle) : "";
} # Check_data
sub Header {
if ($csvmode != 1) {
print "\nhh:mm:ss wproc nobufs wpack fpack calls delta data resends idle\n";
} else { # assume CSV mode...
print "\nhh:mm:ss,wproc,nobufs,wpack,fpack,calls,delta,data,resends,idle\n";
}
} # Header
#
# don't buffer the output
#
$| = 1;
#
# snag program name (drop the full pathname) :
#
$progName = $0;
$tmpName= "";
GETPROG: while ($letr = chop($progName)) {
$_ = $letr;
/\// && last GETPROG;
$tmpName .= $letr;
}
$progName = reverse($tmpName);
#
# set the defaults for server, port, and delay interval
#
$server = "";
$port = 7000;
$delay = 1200;
$csvmove = 0;
#
# any parms?
#
while ($_ = shift(@ARGV)) {
GETPARMS: {
/^-[pP]/ && do {
$port = shift(@ARGV);
last GETPARMS;
};
/^-[sS]/ && do {
$server = shift(@ARGV);
last GETPARMS;
};
/^-[tT]/ && do {
$delay = shift(@ARGV);
last GETPARMS;
};
/^-C/ && do {
$csvmode = 1;
last GETPARMS;
};
/^-[hH\?]/ && do {
&Usage();
};
/^-/ && do {
&Usage();
}
}
}
#
# if they didn't give us a server name, we can't run
#
if ($server eq "") {
&Usage();
}
else {
print "\nServer: $server, Port: $port, Interval $delay seconds\n";
system date;
}
#
# clear the counters for the first run
#
$wproc = 0;
$wpack = 0;
$fpack = 0;
$calls = 0;
$data = 0;
$resend = 0;
$nobuf = 0;
$idle = 0;
$oldcall = 0;
#
# set the default field format lengths for
# wproc,nobuf,wpack,fpack,calls,delta,data,resend,idle
#
@Ln = (5,8,6,8,9,6,9,8,4);
#
# force header display on first call
#
$firstrun = 1;
#
# run until we get cancelled
#
while (1) {
#
# show the column headers for every 20 lines of data
#
if ($firstrun == 1) {
Header;
$firstrun = 0;
}
if ($linecnt >= 20) {
if ($csvmode != 1) {
Header;
}
$linecnt = 1;
}
else {
$linecnt++;
}
#
# snag the current time
#
($sec,$min,$hour,$mday,$month,$year,$wday,$yday,$isDST) = localtime();
#
# fire the command and collect the stats
#
$rx = rxdebug(servers => $server,
port => $port,
rxstats => 1,
noconns => 1
);
# $wpack doesn't seem to have a corresponding value.
# It is always zero.
$tstats = $rx->{tstats};
$wproc = $tstats->{nWaiting};
$fpack = $tstats->{nFreePackets};
$calls = $tstats->{callsExecuted};
if ($oldcall > 0) {
$delta = $calls - $oldcall;
}
else {
$delta = 0;
}
$oldcall = $calls;
$rxstats = $rx->{rxstats};
$data = $rxstats->{dataPacketsSent};
$resend = $rxstats->{dataPacketsReSent};
$nobuf = $rxstats->{noPacketBuffersOnRead};
$idle = $tstats->{idleThreads};
#
# verify and fix field format lengths
#
Check_data;
if ($csvmode != 1) {
#
# output the timestamp and current results
#
printf "%2.2d:%2.2d:%2.2d ", $hour,$min,$sec;
printf "%-$Ln[0].0f %-$Ln[1].0f %-$Ln[2].0f %-$Ln[3].0f ",
$wproc,$nobuf,$wpack,$fpack;
printf "%-$Ln[4].0f %-$Ln[5].0f %-$Ln[6].0f %-$Ln[7].0f %-$Ln[8].0f\n",
$calls,$delta,$data,$resend,$idle;
} else { # must be csv mode then...
printf "%2.2d:%2.2d:%2.2d,", $hour,$min,$sec;
printf "$wproc,$nobuf,$wpack,$fpack";
printf "$calls,$delta,$data,$resend,$idle\n";
}
#
# delay for the required interval
#
sleep($delay);
}
exit();
AFS-PAG
view release on metacpan or search on metacpan
lib/AFS/PAG.pm view on Meta::CPAN
# Perl bindings for the PAG functions in libkafs.
#
# This is the Perl boostrap file for the AFS::PAG module, nearly all of which
# is implemented in XS. For the actual source, see PAG.xs. This file
# contains the bootstrap and export code and the documentation.
#
# Written by Russ Allbery <rra@cpan.org>
# Copyright 2013
# The Board of Trustees of the Leland Stanford Junior University
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
package AFS::PAG;
use 5.008;
use strict;
use warnings;
use base qw(DynaLoader);
use Exporter qw(import);
our (@EXPORT_OK, $VERSION);
# Set all import-related variables in a BEGIN block for robustness.
BEGIN {
@EXPORT_OK = qw(hasafs haspag setpag unlog);
$VERSION = '1.02';
}
# Load the binary module.
bootstrap AFS::PAG $VERSION;
1;
__END__
=for stopwords
Allbery AFS PAG libkafs libkopenafs Kerberos aklog UID kdestroy
=head1 NAME
AFS::PAG - Perl bindings for AFS PAG manipulation
=head1 SYNOPSIS
use AFS::PAG qw(hasafs setpag unlog);
if (hasafs()) {
setpag();
system('aklog') == 0
or die "cannot get tokens\n";
do_afs_things();
unlog();
}
=head1 DESCRIPTION
AFS is a distributed file system allowing cross-platform sharing of files
among multiple computers. It associates client credentials (called AFS
tokens) with a Process Authentication Group, or PAG. AFS::PAG makes
available in Perl the PAG manipulation functions provided by the libkafs
or libkopenafs libraries.
With the functions provided by this module, a Perl program can detect
whether AFS is available on the local system (hasafs()) and whether it is
currently running inside a PAG (haspag()). It can also create a new PAG
and put the current process in it (setpag()) and remove any AFS tokens in
the current PAG (unlog()).
Note that this module doesn't provide a direct way to obtain new AFS
tokens. Programs that need AFS tokens should normally obtain Kerberos
tickets (via whatever means) and then run the program B<aklog>, which
comes with most AFS distributions. This program will create AFS tokens
from the current Kerberos ticket cache and store them in the current PAG.
To isolate those credentials from the rest of the system, call setpag()
before running B<aklog>.
=head1 FUNCTIONS
This module provides the following functions, none of which are exported
by default:
=over 4
=item hasafs()
Returns true if the local host is running an AFS client and false
otherwise.
=item haspag()
Returns true if the current process is running inside a PAG and false
otherwise. AFS tokens obtained outside of a PAG are visible to any
process on the system outside of a PAG running as the same UID. AFS
tokens obtained inside a PAG are visible to any process in the same PAG,
regardless of UID.
=item setpag()
Creates a new, empty PAG and put the current process in it. This should
normally be called before obtaining new AFS tokens to isolate those tokens
from other processes on the system. Returns true on success and throws
an exception on failure.
=item unlog()
Deletes all AFS tokens in the current PAG, similar to the action of
B<kdestroy> on a Kerberos ticket cache. Returns true on success and
throws an exception on failure.
=back
=head1 DIAGNOSTICS
=over 4
=item PAG creation failed: %s
setpag() failed. The end of the error message will be a translation of
the system call error number.
=item Token deletion failed: %s
unlog() failed. The end of the error message will be a translation of
the system call error number.
=back
=head1 RESTRICTIONS
This module currently doesn't provide the k_pioctl() or pioctl() function
to make lower-level AFS system calls. It also doesn't provide the libkafs
functions to obtain AFS tokens from Kerberos tickets directly without using
an external ticket cache. This prevents use of internal Kerberos ticket
caches (such as memory caches), since the Kerberos tickets used to generate
AFS tokens have to be visible to an external B<aklog> program.
=head1 AUTHOR
Russ Allbery <rra@cpan.org>
=head1 SEE ALSO
aklog(1)
The current version of this module is always available from its web site
at L<http://www.eyrie.org/~eagle/software/afs-pag/>.
=cut
AFS
view release on metacpan or search on metacpan
src/ACL/ACL.pm view on Meta::CPAN
package AFS::ACL;
#------------------------------------------------------------------------------
# RCS-Id: "@(#)$RCS-Id: src/ACL/ACL.pm 7a64d4d Wed May 1 22:05:49 2013 +0200 Norbert E Gruener$"
#
# © 2001-2011 Norbert E. Gruener <nog@MPA-Garching.MPG.de>
#
# This library is free software; you can redistribute it and/or modify it
# under the same terms as Perl itself.
#------------------------------------------------------------------------------
use AFS ();
use vars qw(@ISA $VERSION);
@ISA = qw(AFS);
$VERSION = 'v2.6.4';
sub new {
my ($this, $class);
# this whole construct is to please the old version from Roland
if ($_[0] =~ /AFS::ACL/) {
$this = shift;
$class = ref($this) || $this;
}
else {
$class = 'AFS::ACL';
}
my $pos_rights = shift;
my $neg_rights = shift;
my $self = [{}, {}];
if (defined $pos_rights) { %{$self->[0]} = %$pos_rights; }
if (defined $neg_rights) { %{$self->[1]} = %$neg_rights; }
bless $self, $class;
}
sub copy {
my $self = shift;
my $class = ref($self) || $self;
my $new = [{}, {}];
%{$new->[0]} = %{$self->[0]};
%{$new->[1]} = %{$self->[1]};
bless $new, $class;
}
sub apply {
my $self = shift;
my $path = shift;
my $follow = shift;
$follow = 1 unless defined $follow;
AFS::setacl($path, $self, $follow);
}
sub retrieve {
my $class = shift;
my $path = shift;
my $follow = shift;
$follow = 1 unless defined $follow;
AFS::_getacl($path, $follow);
}
sub modifyacl {
my $self = shift;
my $path = shift;
my $follow = shift;
my $newacl;
$follow = 1 unless defined $follow;
if ($newacl = AFS::_getacl($path, $follow)) {
$newacl->add($self);
AFS::setacl($path, $newacl, $follow);
}
else { return 0; }
}
sub copyacl {
my $class = shift;
my $from = shift;
my $to = shift;
my $follow = shift;
my $acl;
$follow = 1 unless defined $follow;
if ($acl = AFS::_getacl($from, $follow)) { AFS::setacl($to, $acl, $follow); }
else { return 0; }
}
sub cleanacl {
my $class = shift;
my $path = shift;
my $follow = shift;
my $acl;
$follow = 1 unless defined $follow;
if (! defined ($acl = AFS::_getacl($path, $follow))) { return 0; }
if ($acl->is_clean) { return 1; }
AFS::setacl($path, $acl, $follow);
}
sub crights {
my $class = shift;
AFS::crights(@_);
}
sub ascii2rights {
my $class = shift;
AFS::ascii2rights(@_);
}
sub rights2ascii {
my $class = shift;
AFS::rights2ascii(@_);
}
# old form DEPRECATED !!!!
sub addacl {
my $self = shift;
my $macl = shift;
foreach my $key ($macl->keys) { $self->set($key, $macl->get($key)); }
foreach my $key ($macl->nkeys) { $self->nset($key, $macl->nget($key)); }
return $self;
}
sub add {
my $self = shift;
my $acl = shift;
foreach my $user ($acl->get_users) { $self->set($user, $acl->get_rights($user)); }
foreach my $user ($acl->nget_users) { $self->nset($user, $acl->nget_rights($user)); }
return $self;
}
sub is_clean {
my $self = shift;
foreach ($self->get_users, $self->nget_users) { return 0 if (m/^-?\d+$/); }
return 1;
}
# comment Roland Schemers: I hope I don't have to debug these :-)
sub empty { $_[0] = bless [ {},{} ]; }
sub get_users { CORE::keys %{$_[0]->[0]}; }
sub length { int(CORE::keys %{$_[0]->[0]}); }
sub get_rights { ${$_[0]->[0]}{$_[1]}; }
sub exists { CORE::exists ${$_[0]->[0]}{$_[1]}; }
sub set { ${$_[0]->[0]}{$_[1]} = $_[2]; }
sub remove { delete ${$_[0]->[0]}{$_[1]}; }
sub clear { $_[0]->[0] = {}; }
sub keys { CORE::keys %{$_[0]->[0]}; } # old form: DEPRECATED !!!!
sub get { ${$_[0]->[0]}{$_[1]}; } # old form: DEPRECATED !!!!
sub del { delete ${$_[0]->[0]}{$_[1]}; } # old form: DEPRECATED !!!!
# comment Roland Schemers: same for negative entries
sub nget_users { CORE::keys %{$_[0]->[1]}; }
sub nlength { int(CORE::keys %{$_[0]->[1]}); }
sub nget_rights { ${$_[0]->[1]}{$_[1]}; }
sub nexists { CORE::exists ${$_[0]->[1]}{$_[1]}; }
sub nset { ${$_[0]->[1]}{$_[1]} = $_[2]; }
sub nremove { delete ${$_[0]->[1]}{$_[1]}; }
sub nclear { $_[0]->[1] = {}; }
sub nkeys { CORE::keys %{$_[0]->[1]}; } # old form: DEPRECATED !!!!
sub nget { ${$_[0]->[1]}{$_[1]}; } # old form: DEPRECATED !!!!
sub ndel { delete ${$_[0]->[1]}{$_[1]}; } # old form: DEPRECATED !!!!
1;
AHA
view release on metacpan or search on metacpan
example/lava_lamp.pl view on Meta::CPAN
#!/usr/bin/perl
=head1 NAME
lava_lamp.pl --mode [watch|list|notify] --type [problem|recovery] \
--name [AIN|switch name] --label <label> --debug \
--config <path-to-perl-config>
=head1 DESCRIPTION
Simple example how to use L<"AHA"> for controlling AVM AHA switches. I.e.
it is used for using a Lava Lamp as a Nagios Notification handler.
It also tries to check that:
=over
=item *
The lamp can be switched on only during certain time periods
=item *
The lamp doesn't run longer than a maximum time (e.g. 6 hours)
(C<$LAMP_MAX_TIME>)
=item *
That the lamp is not switched on again after being switched off within a
certain time period (C<$LAMP_REST_TIME>)
=item *
That manual switches are detected and recorded
=back
This script knows three modes:
=over
=item watch
The "watch" mode is used for ensuring that the lamp is not switched on for
certain time i.e. during the night. The Variable C<$LAMP_ON_TIME_TABLE> can be
used to customize the time ranges on a weekday basis.
=item notify
The "notify" mode is used by a notification handler, e.g. from Nagios or from
Jenkins. In this mode, the C<type> parameter is used for signaling whether the
lamp should be switched on ("problem") or off ("recovery").
=item list
This scripts logs all activities in a log file C<$LOG_FILE>. With the "list"
mode, all history entries can be viewed.
=back
=cut
# ===========================================================================
# Configuration section
# Configuration required for accessing the switch.
my $SWITCH_CONFIG =
{
# AVM AHA Host for controlling the devices
host => "fritz.box",
# AVM AHA Password for connecting to the $AHA_HOST
password => "s!cr!t",
# AVM AHA user role (undef if no roles are in use)
user => undef,
# Name of AVM AHA switch
id => "Lava Lamp"
};
# Time how long the lamp should be at least be kept switched off (seconds)
my $LAMP_REST_TIME = 60 * 60;
# Maximum time a lamp can be on
my $LAMP_MAX_TIME = 5 * 60 * 60; # 5 hours
# When the lamp can be switched on. The values can contain multiple time
# windows defined as arrays
my $LAMP_ON_TIME_TABLE =
{
"Sun" => [ ["7:55", "23:00"] ],
"Mon" => [ ["6:55", "23:00"] ],
"Tue" => [ ["13:55", "23:00"] ],
"Wed" => [ ["13:55", "23:00"] ],
"Thu" => [ ["13:55", "23:00"] ],
"Fri" => [ ["6:55", "23:00"] ],
"Sat" => [ ["7:55", "23:00"] ],
};
# File holding the lamp's status
my $STATUS_FILE = "/var/run/lamp.status";
# Log file where to log to
my $LOG_FILE = "/var/log/lamp.log";
# Stop file, when, if exists, keeps the lamp off
my $OFF_FILE = "/tmp/lamp_off";
# Time back in passed assumed when switching was done manually (seconds)
# I.e. if a manual state change is detected, it is assumed that it was back
# that amount of seconds in the past (5 minutes here)
my $MANUAL_DELTA = 5 * 60;
# Maximum number of history entries to store
my $MAX_HISTORY_ENTRIES = 1000;
# ============================================================================
# End of configuration
use Storable qw(fd_retrieve store_fd store retrieve);
use Data::Dumper;
use feature qw(say);
use Fcntl qw(:flock);
use Getopt::Long;
use strict;
my %opts = ();
GetOptions(\%opts, 'type=s','mode=s','debug!','name=s','label=s','config=s');
my $DEBUG = $opts{debug};
read_config_file($opts{config}) if $opts{config};
init_status();
my $mode = $opts{'mode'} || "list";
# List mode doesnt need a connection
list() and exit if $mode eq "list";
# Open status and lock
my $status = fetch_status();
# Name and connection parameters
my $lamp = open_lamp($SWITCH_CONFIG,$opts{name});
# Check current switch state
my $is_on = $lamp->is_on();
# Log a manual switch which might has happened in between checks or notification
log_manual_switch($status,$is_on);
if ($mode eq "watch") {
# Watchdog mode If the lamp is on but out of the period, switch it
# off. Also, if it is running alredy for too long. $off_file can be used
# to switch it always off.
my $in_period = check_on_period();
if ($is_on && (-e $OFF_FILE ||
!$in_period ||
lamp_on_for_too_long($status))) {
# Switch off lamp whether the stop file is switched on when we are off the
# time window
$lamp->off();
update_status($status,0,$mode);
} elsif (!$is_on && $in_period && has_trigger($status)) {
$lamp->on();
update_status($status,1,"notif",undef,trigger_label($status));
delete_trigger($status);
}
} elsif ($mode eq "notif") {
my $type = $opts{type} || die "No notification type given";
if (lc($type) =~ /^(problem|custom)$/ && !$is_on) {
if (check_on_period()) {
# If it is a problem and the lamp is not on, switch it on,
# but only if the lamp is not 'hot' (i.e. was not switch off only
# $LAMP_REST_TIME
my $last_hist = get_last_entry($status);
my $rest_time = time - $LAMP_REST_TIME;
if (!$last_hist || $last_hist->[0] < $rest_time) {
$lamp->on();
update_status($status,1,$mode,time,$opts{label});
} else {
info("Lamp not switched on because the lamp was switched off just before ",
time - $last_hist->[0]," seconds");
}
} else {
# Notification received offtime, remember to switch on the lamp
# when in time
info("Notification received in an off-period: type = ",$type," | ",$opts{label});
set_trigger($status,$opts{label});
}
} elsif (lc($type) eq 'recovery') {
if ($is_on) {
# If it is a recovery switch it off
$lamp->off();
update_status($status,0,$mode,time,$opts{label});
} else {
# It's already off, but remove any trigger marker
delete_trigger($status);
}
} else {
info("Notification: No state change. Type = ",$type,", State = ",$is_on ? "On" : "Off",
" | Check Period: ",check_on_period());
}
} else {
die "Unknow mode '",$mode,"'";
}
if ($DEBUG) {
info(Dumper($status));
}
# Logout, we are done
close_lamp($lamp);
store_status($status);
# ================================================================================================
sub info {
if (open (F,">>$LOG_FILE")) {
print F scalar(localtime),": ",join("",@_),"\n";
close F;
}
}
# List the status file
sub list {
my $status = retrieve $STATUS_FILE;
my $hist_entries = $status->{hist};
for my $hist (@{$hist_entries}) {
print scalar(localtime($hist->[0])),": ",$hist->[1] ? "On " : "Off"," -- ",$hist->[2]," : ",$hist->[3],"\n";
}
print "Content: ",Dumper($status) if $DEBUG;
return 1;
}
# Create empty status file if necessary
sub init_status {
my $status = {};
$status->{hist} = [];
if (! -e $STATUS_FILE) {
store $status,$STATUS_FILE;
}
}
sub log_manual_switch {
my $status = shift;
my $is_on = shift;
my $last = get_last_entry($status);
if ($last && $is_on != $last->[1]) {
# Change has been manualy in between the interval. Add an approx history entry
update_status($status,$is_on,"manual",estimate_manual_time($status));
}
}
sub update_status {
my $status = shift;
my $is_on = shift;
my $mode = shift;
my $time = shift || time;
my $label = shift;
my $hist = $status->{hist};
push @{$hist},[ $time, $is_on, $mode, $label];
info($is_on ? "On " : "Off"," -- ",$mode, $label ? ": " . $label : "");
}
sub estimate_manual_time {
my $status = shift;
my $last_hist = get_last_entry($status);
if ($last_hist) {
my $now = time;
my $last = $last_hist->[0];
my $calc = $now - $MANUAL_DELTA;
return $calc > $last ? $calc : $now - int(($now - $last) / 2);
} else {
return time - $MANUAL_DELTA;
}
}
sub get_last_entry {
my $status = shift;
if ($status) {
my $hist = $status->{hist};
return $hist && @$hist ? $hist->[$#{$hist}] : undef;
}
return undef;
}
sub check_on_period {
my ($min,$hour,$wd) = (localtime)[1,2,6];
my $day = qw(Sun Mon Tue Wed Thu Fri Sat)[$wd];
my $periods = $LAMP_ON_TIME_TABLE->{$day};
for my $period (@$periods) {
my ($low,$high) = @$period;
my ($lh,$lm) = split(/:/,$low);
my ($hh,$hm) = split(/:/,$high);
my $m = $hour * 60 + $min;
return 1 if $m >= ($lh * 60 + $lm) && $m <= ($hh * 60 + $hm);
}
return 0;
}
sub lamp_on_for_too_long {
my $status = shift;
# Check if the lamp was on for more than max time in the duration now - max
# time + 1 hour
my $current = time;
my $low_time = $current - $LAMP_MAX_TIME - $LAMP_REST_TIME;
my $on_time = 0;
my $hist = $status->{hist};
my $i = $#{$hist};
while ($current > $low_time && $i >= 0) {
my $t = $hist->[$i]->[0];
$on_time += $current - $t if $hist->[$i]->[1];
$current = $t;
$i--;
}
if ($on_time >= $LAMP_MAX_TIME) {
info("Lamp was on for " . $on_time . "s in the last " . ($LAMP_MAX_TIME + $LAMP_REST_TIME) . "s and is switched off now");
return 1;
} else {
return 0;
}
}
sub read_config_file {
my $file = shift;
open (F,$file) || die "Cannot read config file ",$file,": ",$!;
my $config = join "",<F>;
close F;
eval $config;
die "Error evaluating $config: ",$@ if $@;
}
sub delete_trigger {
my $status = shift;
delete $status->{trigger_mark};
delete $status->{trigger_label};
}
sub set_trigger {
my $status = shift;
my $label = shift;
$status->{trigger_mark} = 1;
$status->{trigger_label} = $label;
}
sub has_trigger {
return shift->{trigger_mark};
}
sub trigger_label {
return shift->{trigger_label};
}
# ====================================================
# Status file handling including locking
my $status_fh;
sub fetch_status {
open ($status_fh,"+<$STATUS_FILE") || die "Cannot open $STATUS_FILE: $!";
$status = fd_retrieve($status_fh) || die "Cannot read $STATUS_FILE: $!";
flock($status_fh,2);
return $status;
}
sub store_status {
my $status = shift;
# Truncate history if necessary
truncate_hist($status);
# Store status and unlock
seek($status_fh, 0, 0); truncate($status_fh, 0);
store_fd $status,$status_fh;
close $status_fh;
}
sub truncate_hist {
my $status = shift;
my $hist = $status->{hist};
my $len = scalar(@$hist);
splice @$hist,0,$len - $MAX_HISTORY_ENTRIES if $len > $MAX_HISTORY_ENTRIES;
$status->{hist} = $hist;
}
# ==========================================================================
# Customize the following call and class in order to use a different
# switch than AVM AHA's
sub open_lamp {
my $config = shift;
my $name = shift || $config->{id};
return new Lamp($name,
$config->{host},
$config->{password},
$config->{user});
}
sub close_lamp {
my $lamp = shift;
$lamp->logout();
}
package Lamp;
use AHA;
sub new {
my $class = shift;
my $name = shift;
my $host = shift;
my $password = shift;
my $user = shift;
my $aha = new AHA($host,$password,$user);
my $switch = new AHA::Switch($aha,$name);
my $self = {
aha => $aha,
switch => $switch
};
return bless $self,$class;
}
sub is_on {
shift->{switch}->is_on();
}
sub on {
shift->{switch}->on();
}
sub off {
shift->{switch}->off();
}
sub logout {
shift->{aha}->logout();
}
=head1 LICENSE
lava_lampl.pl is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 2 of the License, or
(at your option) any later version.
lava_lamp.pl is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with lava_lamp.pl. If not, see <http://www.gnu.org/licenses/>.
=head1 AUTHOR
roland@cpan.org
=cut
AI-ANN
view release on metacpan or search on metacpan
examples/benchmark.pl view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
use Benchmark qw(:all);
use AI::ANN::Neuron;
my %data = (id => 1, inputs => [ 4*rand()-2, 4*rand()-2, 4*rand()-2,
4*rand()-2, 4*rand()-2 ],
neurons => [ 4*rand()-2, 4*rand()-2, 4*rand()-2,
4*rand()-2, 4*rand()-2 ]);
my $object1 = new AI::ANN::Neuron ( %data, inline_c => 0 );
my $object2 = new AI::ANN::Neuron ( %data, inline_c => 1 );
my @data = ( [ 4*rand()-2, 4*rand()-2, 4*rand()-2, 4*rand()-2, 4*rand()-2 ],
[ 4*rand()-2, 4*rand()-2, 4*rand()-2, 4*rand()-2, 4*rand()-2 ]);
cmpthese( -1, { 'pure_perl' => sub{$object1->execute(@data)},
'inline_c' => sub{$object2->execute(@data)} });
use Math::Libm qw(erf M_PI);
use Inline C => <<'END_C';
#include <math.h>
double afunc[4001];
double dafunc[4001];
void generate_globals() {
int i;
for (i=0;i<=4000;i++) {
afunc[i] = 2 * (erf(i/1000.0-2));
dafunc[i] = 4 / sqrt(M_PI) * pow(exp(-1 * ((i/1000.0-2))), 2);
}
}
double afunc_c (float input) {
return afunc[(int) floor((input)*1000)+2000];
}
double dafunc_c (float input) {
return dafunc[(int) floor((input)*1000)+2000];
}
END_C
timethis(-1, 'generate_globals()');
sub afunc_pp {
return 2 * erf(int((shift)*1000)/1000);
}
sub dafunc_pp {
return 4 / sqrt(M_PI) * exp( -1 * ((int((shift)*1000)/1000) ** 2) );
}
cmpthese( -1, { 'afunc_c' => sub{afunc_c(4*rand()-2)},
'afunc_pp' => sub{afunc_pp(4*rand()-2)} });
cmpthese( -1, { 'dafunc_c' => sub{dafunc_c(4*rand()-2)},
'dafunc_pp' => sub{dafunc_pp(4*rand()-2)} });
AI-CBR
view release on metacpan or search on metacpan
lib/AI/CBR.pm view on Meta::CPAN
package AI::CBR;
use warnings;
use strict;
=head1 NAME
AI::CBR - Framework for Case-Based Reasoning
=head1 VERSION
Version 0.02
=cut
our $VERSION = '0.02';
=head1 SYNOPSIS
use AI::CBR::Sim qw(sim_eq ...);
use AI::CBR::Case;
use AI::CBR::Retrieval;
my $case = AI::CBR::Case->new(...);
my $r = AI::CBR::Retrieval->new($case, \@case_base);
...
=head1 DESCRIPTION
Framework for Case-Based Reasoning in Perl.
For an overview, please see my slides from YAPC::EU 2009.
In brief, you need to specifiy an L<AI::CBR::Case>
with the help of similarity functions from L<AI::CBR::Sim>.
Then you can find similar cases from a case-base
with L<AI::CBR::Retrieval>.
The technical documentation can be found in the
individual modules of this distribution.
=head1 SEE ALSO
=over 4
=item * L<AI::CBR::Sim>
=item * L<AI::CBR::Case>
=item * L<AI::CBR::Case::Compound>
=item * L<AI::CBR::Retrieval>
=back
=head1 AUTHOR
Darko Obradovic, C<< <dobradovic at gmx.de> >>
=head1 BUGS
Please report any bugs or feature requests to C<bug-ai-cbr at rt.cpan.org>, or through
the web interface at L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-CBR>. I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc AI::CBR
You can also look for information at:
=over 4
=item * RT: CPAN's request tracker
L<http://rt.cpan.org/NoAuth/Bugs.html?Dist=AI-CBR>
=item * AnnoCPAN: Annotated CPAN documentation
L<http://annocpan.org/dist/AI-CBR>
=item * CPAN Ratings
L<http://cpanratings.perl.org/d/AI-CBR>
=item * Search CPAN
L<http://search.cpan.org/dist/AI-CBR>
=back
=head1 COPYRIGHT & LICENSE
Copyright 2009 Darko Obradovic, all rights reserved.
This program is free software; you can redistribute it and/or modify it
under the same terms as Perl itself.
=cut
1; # End of AI::CBR::Case
AI-CRM114
view release on metacpan or search on metacpan
lib/AI/CRM114.pm view on Meta::CPAN
package AI::CRM114;
use 5.008000;
use strict;
use warnings;
use IPC::Run qw /run/;
our @ISA = qw();
our $VERSION = '0.01';
sub new {
my $class = shift;
my $self = { cmd => 'crm', @_ };
bless $self, $class;
return $self;
}
sub classify {
my ($self, $flags, $files, $text) = @_;
my $code = qq#-{
isolate (:stats:);
classify <@$flags> ( @$files ) (:stats:);
output /:*:stats:/
}#;
my $o = "";
my $h = run [$self->{cmd}, $code], \$text, \$o;
my ($file, $prob, $pr) = $o =~
/Best match to file \S+ \((.*?)\) +prob: *([0-9.]+) +pR: *([0-9.-]+)/;
wantarray ? ($file, $prob, $pr) : $file;
}
sub learn {
my ($self, $flags, $file, $text) = @_;
my $code = qq#-{ learn <@$flags> ( $file ) }#;
my $o = "";
my $h = run [$self->{cmd}, $code], \$text, \$o;
}
1;
__END__
=head1 NAME
AI::CRM114 - Wrapper for the statistical data classifier CRM114
=head1 SYNOPSIS
use AI::CRM114;
my $crm = AI::CRM114->new(cmd => '/path/to/crm');
# Learn new text
$crm->learn(['osb'], 'spam.css', 'MAKE MONEY FAST');
# Classify some text
my $class = $crm->classify(['osb'], ['a.css', 'b.css'], $text);
=head1 DESCRIPTION
The CRM114 Discriminator, is a collection of tools to classify data,
e.g. for use in spam filters. This module is a simple wrapper around
the command line executable. Feedback is very welcome, the interface
is unstable. Use with caution.
=head1 METHODS
=over
=item AI::CRM114->new(%options)
Creates a new instance of this class. The following options are
available:
=over
=item cmd => '/path/to/crm'
Specifies the path to the crm executable.
=back
=item $crm->learn(\@flags, $file, $text)
Learn that the text belongs to the file using the specified flags.
Permissable flags are specified in the C<QUICKREF.txt> file that
comes with CRM114. Examples include C<winnow>, C<microgroom>, and
C<osbf>.
=item classify(\@flags, \@files, $text)
Attempt to correlate the text to one of the files using the
specified flags. Permissable flags are specified in the C<QUICKREF.txt>
file that comes with CRM114. Examples include C<unique>, C<fscm>, and
C<svm>.
In scalar context, returns the path of the best matching file.
In list context, returns a list containing the path of the best file,
and the probability and pR values as reported in C<(:stats:)>.
=back
=head1 SEE ALSO
* http://crm114.sourceforge.net/
* http://crm114.sourceforge.net/docs/QUICKREF.txt
=head1 AUTHOR / COPYRIGHT / LICENSE
Copyright (c) 2009 Bjoern Hoehrmann <bjoern@hoehrmann.de>.
This module is licensed under the same terms as Perl itself.
=cut
AI-Calibrate
view release on metacpan or search on metacpan
lib/AI/Calibrate.pm view on Meta::CPAN
package AI::Calibrate;
use 5.008008;
use strict;
use warnings;
use vars qw($VERSION);
$VERSION = "1.5";
require Exporter;
our @ISA = qw(Exporter);
# This allows declaration:
# use AI::Calibrate ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.
our %EXPORT_TAGS = (
'all' => [
qw(
calibrate
score_prob
print_mapping
)
]
);
our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );
our @EXPORT = qw( );
use constant DEBUG => 0;
# Structure slot names
use constant SCORE => 0;
use constant PROB => 1;
=head1 NAME
AI::Calibrate - Perl module for producing probabilities from classifier scores
=head1 SYNOPSIS
use AI::Calibrate ':all';
... train a classifier ...
... test classifier on $points ...
$calibrated = calibrate($points);
=head1 DESCRIPTION
Classifiers usually return some sort of an instance score with their
classifications. These scores can be used as probabilities in various
calculations, but first they need to be I<calibrated>. Naive Bayes, for
example, is a very useful classifier, but the scores it produces are usually
"bunched" around 0 and 1, making these scores poor probability estimates.
Support vector machines have a similar problem. Both classifier types should
be calibrated before their scores are used as probability estimates.
This module calibrates classifier scores using a method called the Pool
Adjacent Violators (PAV) algorithm. After you train a classifier, you take a
(usually separate) set of test instances and run them through the classifier,
collecting the scores assigned to each. You then supply this set of instances
to the calibrate function defined here, and it will return a set of ranges
mapping from a score range to a probability estimate.
For example, assume you have the following set of instance results from your
classifier. Each result is of the form C<[ASSIGNED_SCORE, TRUE_CLASS]>:
my $points = [
[.9, 1],
[.8, 1],
[.7, 0],
[.6, 1],
[.55, 1],
[.5, 1],
[.45, 0],
[.4, 1],
[.35, 1],
[.3, 0 ],
[.27, 1],
[.2, 0 ],
[.18, 0],
[.1, 1 ],
[.02, 0]
];
If you then call calibrate($points), it will return this structure:
[
[.9, 1 ],
[.7, 3/4 ],
[.45, 2/3 ],
[.3, 1/2 ],
[.2, 1/3 ],
[.02, 0 ]
]
This means that, given a SCORE produced by the classifier, you can map the
SCORE onto a probability like this:
SCORE >= .9 prob = 1
.9 > SCORE >= .7 prob = 3/4
.7 > SCORE >= .45 prob = 2/3
.45 > SCORE >= .3 prob = 3/4
.2 > SCORE >= .7 prob = 3/4
.02 > SCORE prob = 0
For a realistic example of classifier calibration, see the test file
t/AI-Calibrate-NB.t, which uses the AI::NaiveBayes1 module to train a Naive
Bayes classifier then calibrates it using this module.
=cut
=head1 FUNCTIONS
=over 4
=item B<calibrate>
This is the main calibration function. The calling form is:
my $calibrated = calibrate( $data, $sorted);
$data looks like: C<[ [score, class], [score, class], [score, class]...]>
Each score is a number. Each class is either 0 (negative class) or 1
(positive class).
$sorted is boolean (0 by default) indicating whether the data are already
sorted by score. Unless this is set to 1, calibrate() will sort the data
itself.
Calibrate returns a reference to an ordered list of references:
[ [score, prob], [score, prob], [score, prob] ... ]
Scores will be in descending numerical order. See the DESCRIPTION section for
how this structure is interpreted. You can pass this structure to the
B<score_prob> function, along with a new score, to get a probability.
=cut
sub calibrate {
my($data, $sorted) = @_;
if (DEBUG) {
print "Original data:\n";
for my $pair (@$data) {
my($score, $prob) = @$pair;
print "($score, $prob)\n";
}
}
# Copy the data over so PAV can clobber the PROB field
my $new_data = [ map([@$_], @$data) ];
# If not already sorted, sort data decreasing by score
if (!$sorted) {
$new_data = [ sort { $b->[SCORE] <=> $a->[SCORE] } @$new_data ];
}
PAV($new_data);
if (DEBUG) {
print("After PAV, vector is:\n");
print_vector($new_data);
}
my(@result);
my( $last_prob, $last_score);
push(@$new_data, [-1e10, 0]);
for my $pair (@$new_data) {
print "Seeing @$pair\n" if DEBUG;
my($score, $prob) = @$pair;
if (defined($last_prob) and $prob < $last_prob) {
print("Pushing [$last_score, $last_prob]\n") if DEBUG;
push(@result, [$last_score, $last_prob] );
}
$last_prob = $prob;
$last_score = $score;
}
return \@result;
}
sub PAV {
my ( $result ) = @_;
for ( my $i = 0; $i < @$result - 1; $i++ ) {
if ( $result->[$i][PROB] < $result->[ $i + 1 ][PROB] ) {
$result->[$i][PROB] =
( $result->[$i][PROB] + $result->[ $i + 1 ][PROB] ) / 2;
$result->[ $i + 1 ][PROB] = $result->[$i][PROB];
print "Averaging elements $i and ", $i + 1, "\n" if DEBUG;
for ( my $j = $i - 1; $j >= 0; $j-- ) {
if ( $result->[$j][PROB] < $result->[ $i + 1 ][PROB] ) {
my $d = ( $i + 1 ) - $j + 1;
flatten( $result, $j, $d );
}
else {
last;
}
}
}
}
}
sub print_vector {
my($vec) = @_;
for my $pair (@$vec) {
print join(", ", @$pair), "\n";
}
}
sub flatten {
my ( $vec, $start, $len ) = @_;
if (DEBUG) {
print "Flatten called on vec, $start, $len\n";
print "Vector before: \n";
print_vector($vec);
}
my $sum = 0;
for my $i ( $start .. $start + $len-1 ) {
$sum += $vec->[$i][PROB];
}
my $avg = $sum / $len;
print "Sum = $sum, avg = $avg\n" if DEBUG;
for my $i ( $start .. $start + $len -1) {
$vec->[$i][PROB] = $avg;
}
if (DEBUG) {
print "Vector after: \n";
print_vector($vec);
}
}
=item B<score_prob>
This is a simple utility function that takes the structure returned by
B<calibrate>, along with a new score, and returns the probability estimate.
Example calling form:
$p = score_prob($calibrated, $score);
Once you have a trained, calibrated classifier, you could imagine using it
like this:
$calibrated = calibrate( $calibration_set );
print "Input instances, one per line:\n";
while (<>) {
chomp;
my(@fields) = split;
my $score = classifier(@fields);
my $prob = score_prob($score);
print "Estimated probability: $prob\n";
}
=cut
sub score_prob {
my($calibrated, $score) = @_;
my $last_prob = 1.0;
for my $tuple (@$calibrated) {
my($bound, $prob) = @$tuple;
return $prob if $score >= $bound;
$last_prob = $prob;
}
# If we drop off the end, probability estimate is zero
return 0;
}
=item B<print_mapping>
This is a simple utility function that takes the structure returned by
B<calibrate> and prints out a simple list of lines describing the mapping
created.
Example calling form:
print_mapping($calibrated);
Sample output:
1.00 > SCORE >= 1.00 prob = 1.000
1.00 > SCORE >= 0.71 prob = 0.667
0.71 > SCORE >= 0.39 prob = 0.000
0.39 > SCORE >= 0.00 prob = 0.000
These ranges are not necessarily compressed/optimized, as this sample output
shows.
=back
=cut
sub print_mapping {
my($calibrated) = @_;
my $last_bound = 1.0;
for my $tuple (@$calibrated) {
my($bound, $prob) = @$tuple;
printf("%0.3f > SCORE >= %0.3f prob = %0.3f\n",
$last_bound, $bound, $prob);
$last_bound = $bound;
}
if ($last_bound != 0) {
printf("%0.3f > SCORE >= %0.3f prob = %0.3f\n",
$last_bound, 0, 0);
}
}
=head1 DETAILS
The PAV algorithm is conceptually straightforward. Given a set of training
cases ordered by the scores assigned by the classifier, it first assigns a
probability of one to each positive instance and a probability of zero to each
negative instance, and puts each instance in its own group. It then looks, at
each iteration, for adjacent violators: adjacent groups whose probabilities
locally increase rather than decrease. When it finds such groups, it pools
them and replaces their probability estimates with the average of the group's
values. It continues this process of averaging and replacement until the
entire sequence is monotonically decreasing. The result is a sequence of
instances, each of which has a score and an associated probability estimate,
which can then be used to map scores into probability estimates.
For further information on the PAV algorithm, you can read the section in my
paper referenced below.
=head1 EXPORT
This module exports three functions: calibrate, score_prob and print_mapping.
=head1 BUGS
None known. This implementation is straightforward but inefficient (its time
is O(n^2) in the length of the data series). A linear time algorithm is
known, and in a later version of this module I'll probably implement it.
=head1 SEE ALSO
The AI::NaiveBayes1 perl module.
My paper "PAV and the ROC Convex Hull" has a good discussion of the PAV
algorithm, including examples:
L<http://home.comcast.net/~tom.fawcett/public_html/papers/PAV-ROCCH-dist.pdf>
If you want to read more about the general issue of classifier calibration,
here are some good papers, which are freely available on the web:
I<"Transforming classifier scores into accurate multiclass probability estimates">
by Bianca Zadrozny and Charles Elkan
I<"Predicting Good Probabilities With Supervised Learning">
by A. Niculescu-Mizil and R. Caruana
=head1 AUTHOR
Tom Fawcett, E<lt>tom.fawcett@gmail.comE<gt>
=head1 COPYRIGHT AND LICENSE
Copyright (C) 2008-2012 by Tom Fawcett
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself, either Perl version 5.8.8 or,
at your option, any later version of Perl 5 you may have available.
=cut
1;
AI-Categorizer
view release on metacpan or search on metacpan
#!/usr/bin/perl
# This script is a fairly simple demonstration of how AI::Categorizer
# can be used. There are lots of other less-simple demonstrations
# (actually, they're doing much simpler things, but are probably
# harder to follow) in the tests in the t/ subdirectory. The
# eg/categorizer script can also be a good example if you're willing
# to figure out a bit how it works.
#
# This script reads a training corpus from a directory of plain-text
# documents, trains a Naive Bayes categorizer on it, then tests the
# categorizer on a set of test documents.
use strict;
use AI::Categorizer;
use AI::Categorizer::Collection::Files;
use AI::Categorizer::Learner::NaiveBayes;
use File::Spec;
die("Usage: $0 <corpus>\n".
" A sample corpus (data set) can be downloaded from\n".
" http://www.cpan.org/authors/Ken_Williams/data/reuters-21578.tar.gz\n".
" or http://www.limnus.com/~ken/reuters-21578.tar.gz\n")
unless @ARGV == 1;
my $corpus = shift;
my $training = File::Spec->catfile( $corpus, 'training' );
my $test = File::Spec->catfile( $corpus, 'test' );
my $cats = File::Spec->catfile( $corpus, 'cats.txt' );
my $stopwords = File::Spec->catfile( $corpus, 'stopwords' );
my %params;
if (-e $stopwords) {
$params{stopword_file} = $stopwords;
} else {
warn "$stopwords not found - no stopwords will be used.\n";
}
if (-e $cats) {
$params{category_file} = $cats;
} else {
die "$cats not found - can't proceed without category information.\n";
}
# In a real-world application these Collection objects could be of any
# type (any Collection subclass). Or you could create each Document
# object manually. Or you could let the KnowledgeSet create the
# Collection objects for you.
$training = AI::Categorizer::Collection::Files->new( path => $training, %params );
$test = AI::Categorizer::Collection::Files->new( path => $test, %params );
# We turn on verbose mode so you can watch the progress of loading &
# training. This looks nicer if you have Time::Progress installed!
print "Loading training set\n";
my $k = AI::Categorizer::KnowledgeSet->new( verbose => 1 );
$k->load( collection => $training );
print "Training categorizer\n";
my $l = AI::Categorizer::Learner::NaiveBayes->new( verbose => 1 );
$l->train( knowledge_set => $k );
print "Categorizing test set\n";
my $experiment = $l->categorize_collection( collection => $test );
print $experiment->stats_table;
# If you want to get at the specific assigned categories for a
# specific document, you can do it like this:
my $doc = AI::Categorizer::Document->new
( content => "Hello, I am a pretty generic document with not much to say." );
my $h = $l->categorize( $doc );
print ("For test document:\n",
" Best category = ", $h->best_category, "\n",
" All categories = ", join(', ', $h->categories), "\n");
AI-Chat
view release on metacpan or search on metacpan
lib/AI/Chat.pm view on Meta::CPAN
package AI::Chat;
use strict;
use warnings;
use Carp;
use HTTP::Tiny;
use JSON::PP;
our $VERSION = '0.2';
$VERSION = eval $VERSION;
my $http = HTTP::Tiny->new;
# Create Chat object
sub new {
my $class = shift;
my %attr = @_;
$attr{'error'} = '';
$attr{'api'} = 'OpenAI' unless $attr{'api'};
$attr{'error'} = 'Invalid API' unless $attr{'api'} eq 'OpenAI';
$attr{'error'} = 'API Key missing' unless $attr{'key'};
$attr{'model'} = 'gpt-3.5-turbo-0125' unless $attr{'model'};
return bless \%attr, $class;
}
# Define endpoints for APIs
my %url = (
'OpenAI' => 'https://api.openai.com/v1/chat/completions',
);
# Define HTTP Headers for APIs
my %header = (
'OpenAI' => &_get_header_openai,
);
# Returns true if last operation was success
sub success {
my $self = shift;
return !$self->{'error'};
}
# Returns error if last operation failed
sub error {
my $self = shift;
return $self->{'error'};
}
# Header for calling OpenAI
sub _get_header_openai {
my $self = shift;
$self->{'key'} = '' unless defined $self->{'key'};
return {
'Authorization' => 'Bearer ' . $self->{'key'},
'Content-type' => 'application/json'
};
}
# Get a reply from a single prompt
sub prompt {
my ($self, $prompt, $temperature) = @_;
$self->{'error'} = '';
unless ($prompt) {
$self->{'error'} = "Missing prompt calling 'prompt' method";
return undef;
}
$temperature = 1.0 unless $temperature;
my @messages;
push @messages, {
role => 'system',
content => $self->{'role'},
} if $self->{'role'};
push @messages, {
role => 'user',
content => $prompt,
};
my $response = $http->post($url{$self->{'api'}}, {
'headers' => {
'Authorization' => 'Bearer ' . $self->{'key'},
'Content-type' => 'application/json'
},
content => encode_json {
model => $self->{'model'},
messages => [ @messages ],
temperature => $temperature,
}
});
if ($response->{'content'} =~ 'invalid_api_key') {
croak 'Incorrect API Key - check your API Key is correct';
}
if ($self->{'debug'} and !$response->{'success'}) {
croak $response if $self->{'debug'} eq 'verbose';
croak $response->{'content'};
}
my $reply = decode_json($response->{'content'});
return $reply->{'choices'}[0]->{'message'}->{'content'};
}
__END__
=head1 NAME
AI::Chat - Interact with AI Chat APIs
=head1 VERSION
Version 0.2
=head1 SYNOPSIS
use AI::Chat;
my $chat = AI::Chat->new(
key => 'your-api-key',
api => 'OpenAI',
model => 'gpt-3.5-turbo-0125',
);
my $reply = $chat->prompt("What is the meaning of life?");
print $reply;
=head1 DESCRIPTION
This module provides a simple interface for interacting with AI Chat APIs,
currently supporting OpenAI.
The AI chat agent can be given a I<role> and then passed I<prompts>. It will
reply to the prompts in natural language. Being AI, the responses are
non-deterministic, that is, the same prompt will result in diferent responses
on different occasions.
Further control of the creativity of the responses is possible by specifying
at optional I<temperature> parameter.
=head1 API KEYS
A free OpenAI API can be obtained from L<https://platform.openai.com/account/api-keys>
=head1 MODELS
Although the API Key is free, each use incurs a cost. This is dependent on the
number of tokens in the prompt and the reply. Different models have different costs.
The default model C<gpt-3.5-turbo-0125> is the lowest cost of the useful models and
is a good place to start using this module.
See also L<https://platform.openai.com/docs/models/overview>
=head1 METHODS
=head2 new
my $chat = AI::Chat->new(%params);
Creates a new AI::Chat object.
=head3 Parameters
=over 4
=item key
C<required> Your API key for the chosen service.
=item api
The API to use (currently only 'OpenAI' is supported).
=item model
The language model to use (default: 'gpt-3.5-turbo-0125').
See L<https://platform.openai.com/docs/models/overview>
=item role
The role to use for the bot in conversations.
This tells the bot what it's purpose when answering prompts.
For example: "You are a world class copywriter famed for
creating content that is immediately engaging with a
lighthearted, storytelling style".
=item debug
Used for testing. If set to any true value, the prompt method
will return details of the error encountered instead of C<undef>
=back
=head2 prompt
my $reply = $chat->prompt($prompt, $temperature);
Sends a prompt to the AI Chat API and returns the response.
=head3 Parameters
=over 4
=item prompt
C<required> The prompt to send to the AI.
=item temperature
The creativity level of the response (default: 1.0).
Temperature ranges from 0 to 2. The higher the temperature,
the more creative the bot will be in it's responses.
=back
=head2 success
my $success = $chat->success();
Returns true if the last operation was successful.
=head2 error
my $error = $chat->error();
Returns the error message if the last operation failed.
=head1 SEE ALSO
L<https://openai.com> - OpenAI official website
=head1 AUTHOR
Ian Boddison <ian at boddison.com>
=head1 BUGS
Please report any bugs or feature requests to C<bug-ai-chat at rt.cpan.org>, or through
the web interface at L<https://rt.cpan.org/NoAuth/ReportBug.html?Queue=bug-ai-chat>. I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc AI::Chat
You can also look for information at:
=over 4
=item * RT: CPAN's request tracker (report bugs here)
L<https://rt.cpan.org/NoAuth/Bugs.html?Dist=AI-Chat>
=item * Search CPAN
L<https://metacpan.org/release/AI::Chat>
=back
=head1 COPYRIGHT AND LICENSE
Copyright (C) 2024 by Ian Boddison
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.
=cut
AI-Classifier-Japanese
view release on metacpan or search on metacpan
lib/AI/Classifier/Japanese.pm view on Meta::CPAN
package AI::Classifier::Japanese;
use 5.008005;
use strict;
use warnings;
our $VERSION = "0.01";
use Mouse;
use Text::MeCab;
use Algorithm::NaiveBayes;
my $nb = Algorithm::NaiveBayes->new;
sub add_training_text {
my ($self, $text, $category) = @_;
my $words_freq_ref = &_convert_text_to_bow($text);
$nb->add_instance(
attributes => $words_freq_ref,
label => $category
);
}
sub train {
$nb->train;
}
sub labels {
$nb->labels;
}
sub predict {
my ($self, $text) = @_;
my $words_freq_ref = &_convert_text_to_bow($text);
my $result_ref = $nb->predict(
attributes => $words_freq_ref
);
}
sub _convert_text_to_bow {
my $text = shift;
my $words_ref = &_parse_text($text);
my $words_freq_ref = {};
foreach (@$words_ref) {
$words_freq_ref->{$_}++;
}
return $words_freq_ref;
}
sub _parse_text {
my $text = shift;
my $mecab = Text::MeCab->new();
my $node = $mecab->parse($text);
my $words_ref = [];
while ($node) {
if (&_is_keyword($node->posid)) {
push @$words_ref, $node->surface;
}
$node = $node->next;
}
return $words_ref;
}
sub save_state {
my ($self, $path) = @_;
$nb->save_state($path);
}
sub restore_state {
my ($self, $path) = @_;
$nb = Algorithm::NaiveBayes->restore_state($path);
}
sub _is_keyword {
my $posid = shift;
return &_is_noun($posid) || &_is_verb($posid) || &_is_adj($posid);
}
# See: http://mecab.googlecode.com/svn/trunk/mecab/doc/posid.html
sub _is_interjection {
return $_[0] == 2;
}
sub _is_adj {
return 10 <= $_[0] && $_[0] < 13;
}
sub _is_aux {
return $_[0] == 25;
}
sub _is_conjunction {
return $_[0] == 26;
}
sub _is_particls {
return 27 <= $_[0] && $_[0] < 31;
}
sub _is_verb {
return 31 <= $_[0] && $_[0] < 34;
}
sub _is_noun {
return 36 <= $_[0] && $_[0] < 68;
}
sub _is_prenominal_adj {
return $_[0] == 68;
}
__PACKAGE__->meta->make_immutable();
1;
__END__
=encoding utf-8
=head1 NAME
AI::Classifier::Japanese - the combination wrapper of Algorithm::NaiveBayes and
Text::MeCab.
=head1 SYNOPSIS
use AI::Classifier::Japanese;
# Create new instance
my $classifier = AI::Classifier::Japanese->new();
# Add training text
$classifier->add_training_text("ãŸã®ã—ã„．楽ã—ã„ï¼", 'positive');
$classifier->add_training_text("ã¤ã‚‰ã„．辛ã„ï¼", 'negative');
# Train
$classifier->train;
# Test
my $result_ref = $classifier->predict("ãŸã®ã—ã„");
print $result_ref->{'positive'}; # => Confidence value
=head1 DESCRIPTION
AI::Classifier::Japanese is a Japanese-text category classifier module using Naive Bayes and MeCab.
This module is based on Algorithm::NaiveBayes.
Only noun, verb and adjective are currently supported.
=head1 METHODS
=over
=item C<< my $classifier = AI::Classifier::Japanese->new(); >>
Create new instance of AI::Classifier::Japanese.
=item C<< $classifier->add_training_text($text, $category); >>
Add training text.
=item C<< $classifier->train; >>
Train.
=item C<< my $result_ref = $classifier->predict($text); >>
Test and returns a predicted result hash reference which has a confidence value for each category.
=item C<< $classifier->save_state($params_path); >>
Save parameters.
=item C<< $classifier->restore_state($params_path); >>
Restore parameters from a file.
=item C<< my @labels = $classifier->labels; >>
Get category labels as an array reference.
=back
=head1 LICENSE
Copyright (C) Shinichi Goto.
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.
=head1 AUTHOR
Shinichi Goto E<lt>shingtgt @ GMAIL COME<gt>
=cut
AI-Classifier
view release on metacpan or search on metacpan
lib/AI/Classifier/Text.pm view on Meta::CPAN
package AI::Classifier::Text;
{
$AI::Classifier::Text::VERSION = '0.03';
}
use strict;
use warnings;
use 5.010;
use Moose;
use MooseX::Storage;
use AI::Classifier::Text::Analyzer;
use Module::Load (); # don't overwrite our sub load() with Module::Load::load()
with Storage(format => 'Storable', io => 'File');
has classifier => (is => 'ro', required => 1 );
has analyzer => ( is => 'ro', default => sub{ AI::Classifier::Text::Analyzer->new() } );
# for store/load only, don't touch unless you really know what you're doing
has classifier_class => (is => 'bare');
before store => sub {
my $self = shift;
$self->{classifier_class} = $self->classifier->meta->name;
};
around load => sub {
my ($orig, $class) = (shift, shift);
my $self = $class->$orig(@_);
Module::Load::load($self->{classifier_class});
return $self;
};
sub classify {
my( $self, $text, $features ) = @_;
return $self->classifier->classify( $self->analyzer->analyze( $text, $features ) );
}
__PACKAGE__->meta->make_immutable;
1;
=pod
=head1 NAME
AI::Classifier::Text - A convenient class for text classification
=head1 VERSION
version 0.03
=head1 SYNOPSIS
my $cl = AI::Classifier::Text->new(classifier => AI::NaiveBayes->new(...));
my $res = $cl->classify("do cats eat bats?");
$res = $cl->classify("do cats eat bats?", { new_user => 1 });
$cl->store('some-file');
# later
my $cl = AI::Classifier::Text->load('some-file');
my $res = $cl->classify("do cats eat bats?");
=head1 DESCRIPTION
AI::Classifier::Text combines a lexical analyzer (by default being
L<AI::Classifier::Text::Analyzer>) and a classifier (like AI::NaiveBayes) to
perform text classification.
This is partially based on AI::TextCategorizer.
=head1 ATTRIBUTES
=over 4
=item C<classifier>
An object that'll perform classification of supplied feature vectors. Has to
define a C<classify()> method, which accepts a hash refence. The return value of
C<AI::Classifier::Text->classify()> will be the return value of C<classifier>'s
C<classify()> method.
This attribute has to be supplied to the C<new()> method during object creation.
=item C<analyzer>
The class performing lexical analysis of the text in order to produce a feature
vector. This defaults to C<AI::Classifier::Text::Analyzer>.
=back
=head1 METHODS
=over 4
=item C<< new(classifier => $foo) >>
Creates a new C<AI::Classifier::Text> object. The classifier argument is mandatory.
=item C<classify($document, $features)>
Categorize the given document. A lexical analyzer will be used to extract
features from C<$document>, and in addition to that the features from
C<$features> hash reference will be added. The return value comes directly from
the C<classifier> object's C<classify> method.
=back
=head1 SEE ALSO
AI::NaiveBayes (3), AI::Categorizer(3)
=head1 AUTHOR
Zbigniew Lukasiak <zlukasiak@opera.com>, Tadeusz Sośnierz <tsosnierz@opera.com>
=head1 COPYRIGHT AND LICENSE
This software is copyright (c) 2012 by Opera Software ASA.
This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.
=cut
__END__
# ABSTRACT: A convenient class for text classification
AI-CleverbotIO
view release on metacpan or search on metacpan
lib/AI/CleverbotIO.pm view on Meta::CPAN
package AI::CleverbotIO;
use strict;
use warnings;
{ our $VERSION = '0.002'; }
use Moo;
use Ouch;
use Log::Any ();
use Data::Dumper;
use JSON::PP qw< decode_json >;
has endpoints => (
is => 'ro',
default => sub {
return {
ask => 'https://cleverbot.io/1.0/ask',
create => 'https://cleverbot.io/1.0/create',
};
},
);
has key => (
is => 'ro',
required => 1,
);
has logger => (
is => 'ro',
lazy => 1,
builder => 'BUILD_logger',
);
has nick => (
is => 'rw',
lazy => 1,
predicate => 1,
);
has user => (
is => 'ro',
required => 1,
);
has ua => (
is => 'ro',
lazy => 1,
builder => 'BUILD_ua',
);
sub BUILD_logger {
return Log::Any->get_logger;
}
sub BUILD_ua {
my $self = shift;
require HTTP::Tiny;
return HTTP::Tiny->new;
}
sub ask {
my ($self, $question) = @_;
my %ps = (
key => $self->key,
text => $question,
user => $self->user,
);
$ps{nick} = $self->nick if $self->has_nick;
return $self->_parse_response(
$self->ua->post_form($self->endpoints->{ask}, \%ps));
}
sub create {
my $self = shift;
$self->nick(shift) if @_;
# build request parameters
my %ps = (
key => $self->key,
user => $self->user,
);
$ps{nick} = $self->nick if $self->has_nick && length $self->nick;
my $data =
$self->_parse_response(
$self->ua->post_form($self->endpoints->{create}, \%ps));
$self->nick($data->{nick}) if exists($data->{nick});
return $data;
}
sub _parse_response {
my ($self, $response) = @_;
{
local $Data::Dumper::Indent = 1;
$self->logger->debug('got response: ' . Dumper($response));
}
ouch 500, 'no response (possible bug in HTTP::Tiny though?)'
unless ref($response) eq 'HASH';
my $status = $response->{status};
ouch $status, $response->{reason}
if ($status != 200) && ($status != 400);
my $data = __decode_content($response);
return $data if $response->{success};
ouch 400, $data->{status};
} ## end sub _parse_response
sub __decode_content {
my $response = shift;
my $encoded = $response->{content};
if (!$encoded) {
my $url = $response->{url} // '*unknown url, check HTTP::Tiny*';
ouch 500, "response status $response->{status}, nothing from $url)";
}
my $decoded = eval { decode_json($encoded) }
or ouch 500, "response status $response->{status}, exception: $@";
return $decoded;
} ## end sub __decode_content
1;
AI-ConfusionMatrix
view release on metacpan or search on metacpan
lib/AI/ConfusionMatrix.pm view on Meta::CPAN
package AI::ConfusionMatrix;
$AI::ConfusionMatrix::VERSION = '0.010';
use strict;
use warnings;
use Carp;
use Exporter 'import';
our @EXPORT= qw (getConfusionMatrix makeConfusionMatrix);
use strict;
use Tie::File;
# ABSTRACT: Make a confusion matrix
sub makeConfusionMatrix {
my ($matrix, $file, $delem) = @_;
unless(defined $delem) {
$delem = ',';
}
carp ('First argument must be a hash reference') if ref($matrix) ne 'HASH';
my %cmData = genConfusionMatrixData($matrix);
# This ties @output_array to the output file. Each output_array item represents a line in the output file
tie my @output_array, 'Tie::File', $file or carp "$!";
# Empty the file
@output_array = ();
my @columns = @{$cmData{columns}};
map {$output_array[0] .= $delem . $_} join $delem, (@columns, 'TOTAL', 'TP', 'FP', 'FN', 'SENS', 'ACC');
my $line = 1;
my @expected = sort keys %{$matrix};
for my $expected (@expected) {
$output_array[$line] = $expected;
my $lastIndex = 0;
my $index;
for my $predicted (sort keys %{$matrix->{$expected}}) {
# Calculate the index of the label in the output_array of columns
$index = _findIndex($predicted, \@columns);
# Print some of the delimiter to get to the column of the next value predicted
$output_array[$line] .= $delem x ($index - $lastIndex) . $matrix->{$expected}{$predicted};
$lastIndex = $index;
}
# Get to the columns of the stats
$output_array[$line] .= $delem x (scalar(@columns) - $lastIndex + 1);
$output_array[$line] .= join $delem, (
$cmData{stats}{$expected}{'total'},
$cmData{stats}{$expected}{'tp'},
$cmData{stats}{$expected}{'fp'},
$cmData{stats}{$expected}{'fn'},
sprintf('%.2f%%', $cmData{stats}{$expected}{'sensitivity'}),
sprintf('%.2f%%', $cmData{stats}{$expected}{'acc'})
);
++$line;
}
# Print the TOTAL row to the csv file
$output_array[$line] = 'TOTAL' . $delem;
map {$output_array[$line] .= $cmData{totals}{$_} . $delem} (@columns);
$output_array[$line] .= join $delem, (
$cmData{totals}{'total'},
$cmData{totals}{'tp'},
$cmData{totals}{'fp'},
$cmData{totals}{'fn'},
sprintf('%.2f%%', $cmData{totals}{'sensitivity'}),
sprintf('%.2f%%', $cmData{totals}{'acc'})
);
untie @output_array;
}
sub getConfusionMatrix {
my ($matrix) = @_;
carp ('First argument must be a hash reference') if ref($matrix) ne 'HASH';
return genConfusionMatrixData($matrix);
}
sub genConfusionMatrixData {
my $matrix = shift;
my @expected = sort keys %{$matrix};
my %stats;
my %totals;
my @columns;
for my $expected (@expected) {
$stats{$expected}{'fn'} = 0;
$stats{$expected}{'tp'} = 0;
# Ensure that the False Positive counter is defined to be able to compute the total later
unless(defined $stats{$expected}{'fp'}) {
$stats{$expected}{'fp'} = 0;
}
for my $predicted (keys %{$matrix->{$expected}}) {
$stats{$expected}{'total'} += $matrix->{$expected}->{$predicted};
$stats{$expected}{'tp'} += $matrix->{$expected}->{$predicted} if $expected eq $predicted;
if ($expected ne $predicted) {
$stats{$expected}{'fn'} += $matrix->{$expected}->{$predicted};
$stats{$predicted}{'fp'} += $matrix->{$expected}->{$predicted};
}
$totals{$predicted} += $matrix->{$expected}->{$predicted};
# Add the label to the array of columns if it does not contain it already
push @columns, $predicted unless _findIndex($predicted, \@columns);
}
$stats{$expected}{'acc'} = ($stats{$expected}{'tp'} * 100) / $stats{$expected}{'total'};
}
for my $expected (@expected) {
$totals{'total'} += $stats{$expected}{'total'};
$totals{'tp'} += $stats{$expected}{'tp'};
$totals{'fn'} += $stats{$expected}{'fn'};
$totals{'fp'} += $stats{$expected}{'fp'};
$stats{$expected}{'sensitivity'} = ($stats{$expected}{'tp'} * 100) / ($stats{$expected}{'tp'} + $stats{$expected}{'fp'});
}
$totals{'acc'} = ($totals{'tp'} * 100) / $totals{'total'};
$totals{'sensitivity'} = ($totals{'tp'} * 100) / ($totals{'tp'} + $totals{'fp'});
return (
columns => [sort @columns],
stats => \%stats,
totals => \%totals
);
}
sub _findIndex {
my ($string, $array) = @_;
for (0 .. @$array - 1) {
return $_ + 1 if ($string eq @{$array}[$_]);
}
}
=head1 NAME
AI::ConfusionMatrix - make a confusion matrix
=head1 SYNOPSIS
my %matrix;
# Loop over your predictions
# [...]
$matrix{$expected}{$predicted} += 1;
# [...]
makeConfusionMatrix(\%matrix, 'output.csv');
=head1 DESCRIPTION
This module prints a L<confusion matrix|https://en.wikipedia.org/wiki/Confusion_matrix> from a hash reference. This module tries to be generic enough to be used within a lot of machine learning projects.
=head3 Functions:
=head4 C<makeConfusionMatrix($hash_ref, $file [, $delimiter ])>
This function makes a confusion matrix from C<$hash_ref> and writes it to C<$file>. C<$file> can be a filename or a file handle opened with the C<w+> mode. If C<$delimiter> is present, it is used as a custom separator for the fields in the confusion ...
Examples:
makeConfusionMatrix(\%matrix, 'output.csv');
makeConfusionMatrix(\%matrix, 'output.csv', ';');
makeConfusionMatrix(\%matrix, *$fh);
The hash reference must look like this :
$VAR1 = {
'value_expected1' => {
'value_predicted1' => number_of_predictions
},
'value_expected2' => {
'value_predicted1' => number_of_predictions,
'value_predicted2' => number_of_predictions
},
'value_expected3' => {
'value_predicted3' => number_of_predictions
}
};
The output will be in CSV. Here is an example:
,1974,1978,2002,2003,2005,TOTAL,TP,FP,FN,SENS,ACC
1974,3,1,,,2,6,3,4,3,42.86%,50.00%
1978,1,5,,,,6,5,4,1,55.56%,83.33%
2002,2,2,8,,,12,8,1,4,88.89%,66.67%
2003,1,,,7,2,10,7,0,3,100.00%,70.00%
2005,,1,1,,6,8,6,4,2,60.00%,75.00%
TOTAL,7,9,9,7,10,42,29,13,13,69.05%,69.05%
Prettified:
| | 1974 | 1978 | 2002 | 2003 | 2005 | TOTAL | TP | FP | FN | SENS | ACC |
|-------|------|------|------|------|------|-------|----|----|----|---------|--------|
| 1974 | 3 | 1 | | | 2 | 6 | 3 | 4 | 3 | 42.86% | 50.00% |
| 1978 | 1 | 5 | | | | 6 | 5 | 4 | 1 | 55.56% | 83.33% |
| 2002 | 2 | 2 | 8 | | | 12 | 8 | 1 | 4 | 88.89% | 66.67% |
| 2003 | 1 | | | 7 | 2 | 10 | 7 | 0 | 3 | 100.00% | 70.00% |
| 2005 | | 1 | 1 | | 6 | 8 | 6 | 4 | 2 | 60.00% | 75.00% |
| TOTAL | 7 | 9 | 9 | 7 | 10 | 42 | 29 | 13 | 13 | 69.05% | 69.05% |
=over
=item TP:
True Positive
=item FP:
False Positive
=item FN:
False Negative
=item SENS
Sensitivity. Number of true positives divided by the number of positives.
=item ACC:
Accuracy
=back
=head4 C<getConfusionMatrix($hash_ref)>
Get the data used to compute the table above.
Example:
my %cm = getConfusionMatrix(\%matrix);
=head1 AUTHOR
Vincent Lequertier <vi.le@autistici.org>
=head1 LICENSE
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.
=cut
1;
# vim: set ts=4 sw=4 tw=0 fdm=marker :
AI-DecisionTree
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Instance/Instance.pm view on Meta::CPAN
package AI::DecisionTree::Instance;
use strict;
use vars qw($VERSION @ISA);
$VERSION = '0.11';
use DynaLoader ();
@ISA = qw(DynaLoader);
bootstrap AI::DecisionTree::Instance $VERSION;
1;
__END__
=head1 NAME
AI::DecisionTree::Instance - C-struct wrapper for training instances
=head1 SYNOPSIS
use AI::DecisionTree::Instance;
my $i = new AI::DecisionTree::Instance([3,5], 7, 'this_instance');
$i->value_int(0) == 3;
$i->value_int(1) == 5;
$i->result_int == 7;
=head1 DESCRIPTION
This class is just a simple Perl wrapper around a C struct embodying a
single training instance. Its purpose is to reduce memory usage. In
a "typical" training set with about 1000 instances, memory usage can
be reduced by about a factor of 5 (from 43.7M to 8.2M in my test
program).
A fairly tight loop is also implemented that helps speed up the
C<train()> AI::DecisionTree method by about a constant factor of 4.
Please do not consider this interface stable - I change it whenever I
have a new need in AI::DecisionTree.
=head1 AUTHOR
Ken Williams, ken@mathforum.org
=head1 SEE ALSO
AI::DecisionTree
=cut
AI-Embedding
view release on metacpan or search on metacpan
lib/AI/Embedding.pm view on Meta::CPAN
package AI::Embedding;
use strict;
use warnings;
use HTTP::Tiny;
use JSON::PP;
use Data::CosineSimilarity;
our $VERSION = '1.11';
$VERSION = eval $VERSION;
my $http = HTTP::Tiny->new;
# Create Embedding object
sub new {
my $class = shift;
my %attr = @_;
$attr{'error'} = '';
$attr{'api'} = 'OpenAI' unless $attr{'api'};
$attr{'error'} = 'Invalid API' unless $attr{'api'} eq 'OpenAI';
$attr{'error'} = 'API Key missing' unless $attr{'key'};
$attr{'model'} = 'text-embedding-ada-002' unless $attr{'model'};
return bless \%attr, $class;
}
# Define endpoints for APIs
my %url = (
'OpenAI' => 'https://api.openai.com/v1/embeddings',
);
# Define HTTP Headers for APIs
my %header = (
'OpenAI' => &_get_header_openai,
);
# Returns true if last operation was success
sub success {
my $self = shift;
return !$self->{'error'};
}
# Returns error if last operation failed
sub error {
my $self = shift;
return $self->{'error'};
}
# Header for calling OpenAI
sub _get_header_openai {
my $self = shift;
$self->{'key'} = '' unless defined $self->{'key'};
return {
'Authorization' => 'Bearer ' . $self->{'key'},
'Content-type' => 'application/json'
};
}
# Fetch Embedding response
sub _get_embedding {
my ($self, $text) = @_;
my $response = $http->post($url{$self->{'api'}}, {
'headers' => {
'Authorization' => 'Bearer ' . $self->{'key'},
'Content-type' => 'application/json'
},
content => encode_json {
input => $text,
model => $self->{'model'},
}
});
if ($response->{'content'} =~ 'invalid_api_key') {
die 'Incorrect API Key - check your API Key is correct';
}
return $response;
}
# TODO:
# Make 'headers' use $header{$self->{'api'}}
# Currently hard coded to OpenAI
# Added purely for testing - IGNORE!
sub _test {
my $self = shift;
# return $self->{'api'};
return $header{$self->{'api'}};
}
# Return Embedding as a CSV string
sub embedding {
my ($self, $text, $verbose) = @_;
my $response = $self->_get_embedding($text);
if ($response->{'success'}) {
my $embedding = decode_json($response->{'content'});
return join (',', @{$embedding->{'data'}[0]->{'embedding'}});
}
$self->{'error'} = 'HTTP Error - ' . $response->{'reason'};
return $response if defined $verbose;
return undef;
}
# Return Embedding as an array
sub raw_embedding {
my ($self, $text, $verbose) = @_;
my $response = $self->_get_embedding($text);
if ($response->{'success'}) {
my $embedding = decode_json($response->{'content'});
return @{$embedding->{'data'}[0]->{'embedding'}};
}
$self->{'error'} = 'HTTP Error - ' . $response->{'reason'};
return $response if defined $verbose;
return undef;
}
# Return Test Embedding
sub test_embedding {
my ($self, $text, $dimension) = @_;
$self->{'error'} = '';
$dimension = 1536 unless defined $dimension;
if ($text) {
srand scalar split /\s+/, $text;
}
my @vector;
for (1...$dimension) {
push @vector, rand(2) - 1;
}
return join ',', @vector;
}
# Convert a CSV Embedding into a hashref
sub _make_vector {
my ($self, $embed_string) = @_;
if (!defined $embed_string) {
$self->{'error'} = 'Nothing to compare!';
return;
}
my %vector;
my @embed = split /,/, $embed_string;
for (my $i = 0; $i < @embed; $i++) {
$vector{'feature' . $i} = $embed[$i];
}
return \%vector;
}
# Return a comparator to compare to a set vector
sub comparator {
my($self, $embed) = @_;
$self->{'error'} = '';
my $vector1 = $self->_make_vector($embed);
return sub {
my($embed2) = @_;
my $vector2 = $self->_make_vector($embed2);
return $self->_compare_vector($vector1, $vector2);
};
}
# Compare 2 Embeddings
sub compare {
my ($self, $embed1, $embed2) = @_;
my $vector1 = $self->_make_vector($embed1);
my $vector2;
if (defined $embed2) {
$vector2 = $self->_make_vector($embed2);
} else {
$vector2 = $self->{'comparator'};
}
if (!defined $vector2) {
$self->{'error'} = 'Nothing to compare!';
return;
}
if (scalar keys %$vector1 != scalar keys %$vector2) {
$self->{'error'} = 'Embeds are unequal length';
return;
}
return $self->_compare_vector($vector1, $vector2);
}
# Compare 2 Vectors
sub _compare_vector {
my ($self, $vector1, $vector2) = @_;
my $cs = Data::CosineSimilarity->new;
$cs->add( label1 => $vector1 );
$cs->add( label2 => $vector2 );
return $cs->similarity('label1', 'label2')->cosine;
}
1;
__END__
=encoding utf8
=head1 NAME
AI::Embedding - Perl module for working with text embeddings using various APIs
=head1 VERSION
Version 1.11
=head1 SYNOPSIS
use AI::Embedding;
my $embedding = AI::Embedding->new(
api => 'OpenAI',
key => 'your-api-key'
);
my $csv_embedding = $embedding->embedding('Some sample text');
my $test_embedding = $embedding->test_embedding('Some sample text');
my @raw_embedding = $embedding->raw_embedding('Some sample text');
my $cmp = $embedding->comparator($csv_embedding2);
my $similarity = $cmp->($csv_embedding1);
my $similarity_with_other_embedding = $embedding->compare($csv_embedding1, $csv_embedding2);
=head1 DESCRIPTION
The L<AI::Embedding> module provides an interface for working with text embeddings using various APIs. It currently supports the L<OpenAI|https://www.openai.com> L<Embeddings API|https://platform.openai.com/docs/guides/embeddings/what-are-embeddings>...
Embeddings allow the meaning of passages of text to be compared for similarity. This is more natural and useful to humans than using traditional keyword based comparisons.
An Embedding is a multi-dimensional vector representing the meaning of a piece of text. The Embedding vector is created by an AI Model. The default model (OpenAI's C<text-embedding-ada-002>) produces a 1536 dimensional vector. The resulting vector...
=head2 Comparator
Embeddings are used to compare similarity of meaning between two passages of text. A typical work case is to store a number of pieces of text (e.g. articles or blogs) in a database and compare each one to some user supplied search text. L<AI::Embed...
Alternatively, the C<comparator> method can be called with one Embedding. The C<comparator> returns a reference to a method that takes a single Embedding to be compared to the Embedding from which the Comparator was created.
When comparing multiple Embeddings to the same Embedding (such as search text) it is faster to use a C<comparator>.
=head1 CONSTRUCTOR
=head2 new
my $embedding = AI::Embedding->new(
api => 'OpenAI',
key => 'your-api-key',
model => 'text-embedding-ada-002',
);
Creates a new AI::Embedding object. It requires the 'key' parameter. The 'key' parameter is the API key provided by the service provider and is required.
Parameters:
=over
=item *
C<key> - B<required> The API Key
=item *
C<api> - The API to use. Currently only 'OpenAI' is supported and this is the default.
=item *
C<model> - The language model to use. Defaults to C<text-embedding-ada-002> - see L<OpenAI docs|https://platform.openai.com/docs/guides/embeddings/what-are-embeddings>
=back
=head1 METHODS
=head2 success
Returns true if the last method call was successful
=head2 error
Returns the last error message or an empty string if B<success> returned true
=head2 embedding
my $csv_embedding = $embedding->embedding('Some text passage', [$verbose]);
Generates an embedding for the given text and returns it as a comma-separated string. The C<embedding> method takes a single parameter, the text to generate the embedding for.
Returns a (rather long) string that can be stored in a C<TEXT> database field.
If the method call fails it sets the L</"error"> message and returns C<undef>. If the optional C<verbose> parameter is true, the complete L<HTTP::Tiny> response object is also returned to aid with debugging issues when using this module.
=head2 raw_embedding
my @raw_embedding = $embedding->raw_embedding('Some text passage', [$verbose]);
Generates an embedding for the given text and returns it as an array. The C<raw_embedding> method takes a single parameter, the text to generate the embedding for.
It is not normally necessary to use this method as the Embedding will almost always be used as a single homogeneous unit.
If the method call fails it sets the L</"error"> message and returns C<undef>. If the optional C<verbose> parameter is true, the complete L<HTTP::Tiny> response object is also returned to aid with debugging issues when using this module.
=head2 test_embedding
my $test_embedding = $embedding->test_embedding('Some text passage', $dimensions);
Used for testing code without making a chargeable call to the API.
Provides a CSV string of the same size and format as L<embedding> but with meaningless random data.
Returns a random embedding. Both parameters are optional. If a text string is provided, the returned embedding will always be the same random embedding otherwise it will be random and different every time. The C<dimension> parameter controls the n...
=head2 comparator
$embedding->comparator($csv_embedding2);
Sets a vector as a C<comparator> for future comparisons and returns a reference to a method for using the C<comparator>.
The B<comparator> method takes a single parameter, the comma-separated Embedding string to use as the comparator.
The following two are functionally equivalent. However, where multiple Embeddings are to be compared to a single Embedding, using a L<Comparator> is significantly faster.
my $similarity = $embedding->compare($csv_embedding1, $csv_embedding2);
my $cmp = $embedding->comparator($csv_embedding2);
my $similarity = $cmp->($csv_embedding1);
See L</"Comparator">
The returned method reference returns the cosine similarity between the Embedding used to call the C<comparator> method and the Embedding supplied to the method reference. See L<compare> for an explanation of the cosine similarity.
=head2 compare
my $similarity_with_other_embedding = $embedding->compare($csv_embedding1, $csv_embedding2);
Compares two embeddings and returns the cosine similarity between them. The B<compare> method takes two parameters: $csv_embedding1 and $csv_embedding2 (both comma-separated embedding strings).
Returns the cosine similarity as a floating-point number between -1 and 1, where 1 represents identical embeddings, 0 represents no similarity, and -1 represents opposite embeddings.
The absolute number is not usually relevant for text comparision. It is usually sufficient to rank the comparison results in order of high to low to reflect the best match to the worse match.
=head1 SEE ALSO
L<https://openai.com> - OpenAI official website
=head1 AUTHOR
Ian Boddison <ian at boddison.com>
=head1 BUGS
Please report any bugs or feature requests to C<bug-ai-embedding at rt.cpan.org>, or through
the web interface at L<https://rt.cpan.org/NoAuth/ReportBug.html?Queue=bug-ai-embedding>. I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc AI::Embedding
You can also look for information at:
=over 4
=item * RT: CPAN's request tracker (report bugs here)
L<https://rt.cpan.org/NoAuth/Bugs.html?Dist=AI-Embedding>
=item * Search CPAN
L<https://metacpan.org/release/AI::Embedding>
=back
=head1 ACKNOWLEDGEMENTS
Thanks to the help and support provided by members of Perl Monks L<https://perlmonks.org/>.
Especially L<Ken Cotterill (KCOTT)|https://metacpan.org/author/KCOTT> for assistance with unit tests and L<Hugo van der Sanden (HVDS)|https://metacpan.org/author/HVDS> for suggesting the current C<comparator> implementaion.
=head1 COPYRIGHT AND LICENSE
This software is copyright (c) 2023 by Ian Boddison.
This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.
=cut
AI-Evolve-Befunge
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lib/AI/Evolve/Befunge.pm view on Meta::CPAN
package AI::Evolve::Befunge;
use strict;
use warnings;
our $VERSION = "0.03";
=head1 NAME
AI::Evolve::Befunge - practical evolution of Befunge AI programs
=head1 SYNOPSIS
use aliased 'AI::Evolve::Befunge::Population' => 'Population';
use AI::Evolve::Befunge::Util qw(v nonquiet);
$pop = Population->new();
while(1) {
my $gen = $pop->generation;
nonquiet("generation $gen\n");
$pop->fight();
$pop->breed();
$pop->migrate();
$pop->save();
$pop->generation($gen+1);
}
=head1 DESCRIPTION
This software project provides all of the necessary tools to grow a
population of AI creatures which are fit to perform a task.
Normally, end users can use the "evolve" script as a frontend. If
that's what you're after, please see the documentation contained
within that script. Otherwise, read on.
This particular file (AI/Evolve/Befunge.pm) does not contain any code;
it exists mainly to provide a version number to keep Build.PL happy.
The rest of this file acts as a quick-start guide to the rest of the
codebase.
The important bits from a user's standpoint are the Population object
(which drives the main process of evolving AI), and the Physics plugin
(which implements the rules of the universe those AI live in). There
are sections below containing more detail on what these two things
are, and how they work.
=head1 POPULATIONS
The Population object is the main user interface to this project.
Basically you just keep running it over and over, and presumably, the
result gets better and better.
The important thing to remember here is that the results take time -
it will probably take several weeks of solid processing time before you
begin to see any promising results at all. It takes a lot of random
code generation before it starts to generate code that does what you
want it to do.
If you don't know anything about Befunge, I recommend you read up on
that first, before trying to understand how this works.
The individuals of this population (which we call Critters) may be of
various sizes, and may make heavy or light use of threads and stacks.
Each one is issued a certain number of "tokens" (which you can think
of as blood sugar or battery power). Just being born takes a certain
number of tokens, depending on the code size. After that, doing things
(like executing a befunge command, pushing a value to the stack,
spawning a thread) all take a certain number of tokens to accomplish.
When the number of tokens drops to 0, the critter dies. So it had
better accomplish its task before that happens.
After a population fights it out for a while, the winners are chosen
(who continue to live) and everyone else dies. Then a new population
is generated from the winners, some random mutation (random
generation of code, as well as potentially resizing the codebase)
occurs, and the whole process starts over for the next generation.
=head1 PHYSICS
At the other end of all of this is the Physics plugin. The Physics
plugin implements the rules of the universe inhabited by these AI
creatures. It provides a scoring mechanism through which multiple
critters may be weighed against eachother. It provides a set of
commands which may be used by the critters to do useful things within
its universe (such as make a move in a board game, do a spellcheck,
or request a google search).
Physics engines register themselves with the Physics database (which
is managed by Physics.pm). The arguments they pass to
register_physics() get wrapped up in a hash reference, which is copied
for you whenever you call Physics->new("pluginname"). The "commands"
argument is particularly important: this is where you add special
befunge commands and provide references to callback functions to
implement them.
One special attribute, "generations", is set by the Population code
and can determine some of the parameters for more complex Physics
plugins. For instance, a "Go" game might wish to increase the board
size, or enable more complex rules, once a certain amount of evolution
has occurred.
Rather than describing the entire API in detail, I suggest you read
through the "othello" and "ttt" modules provided along with this
distribution. They are small and simple, and should make good
examples.
=head1 MIGRATION
Further performance may be improved through the use of migration.
Migration is a very simple form of parallel processing. It should scale
nearly linearly, and is a very effective means of increasing performance.
The idea is, you run multiple populations on multiple machines (one per
machine). The only requirement is that each Population has a different
"hostname" setting. And that's not really a requirement, it's just useful
for tracking down which host a critter came from.
When a Population object has finished processing a generation, there is
a chance that one or more (up to 3) of the surviving critters will be
written out to a special directory (which acts as an "outbox").
A separate networking program (implemented by Migrator.pm and spawned
automatically when creating a Population object) may pick up these
critters and broadcast them to some or all of the other nodes in a cluster
(deleting them from the "outbox" folder at the same time). The instances
of this networking program on the other nodes will receive them, and write
them out to another special directory (which acts as an "inbox").
When a Population object has finished processing a generation, another
thing it does is checks the "inbox" directory for any new incoming
critters. If they are detected, they are imported into the population
(and deleted from the "inbox").
Imported critters will compete in the next generation. If they win,
they will be involved in the reproduction process and may contribute to
the local gene pool.
On the server end, a script called "migrationd" is provided to accept
connections and distribute critters between nodes. The config file
specifies which server to connect to. See the CONFIG FILE section,
below.
=head1 PRACTICAL APPLICATION
So, the purpose is to evolve some nice smart critters, but you're
probably wondering, once you get them, what do you do with them?
Well, once you get some critters that perform well, you can always
write up a production program which creates the Physics and Critter
objects and runs them directly, over and over and over to your heart's
content. After you have reached your goal, you need not continue to
evolve or test new critters.
=head1 CONFIG FILE
You can find an example config file ("example.conf") in the source
tarball. It contains all of the variables with their default values,
and descriptions of each. It lets you configure many important
parameters about how the evolutionary process works, so you probably
want to copy and edit it.
This file can be copied to ".ai-evolve-befunge" in your home
directory, or "/etc/ai-evolve-befunge.conf" for sitewide use. If both
files exist, they are both loaded, in such a way that the homedir
settings supercede the ones from /etc. If the "AIEVOLVEBEFUNGE"
environment variable is set, that too is loaded as a config file, and
its settings take priority over the other files (if any).
=cut
1;
AI-ExpertSystem-Advanced
view release on metacpan or search on metacpan
examples/backward.pl view on Meta::CPAN
#!/usr/bin/perl
#
# backward.pl
#
# Author(s): Pablo Fischer (pfischer@cpan.org)
# Created: 12/13/2009 15:20:43 PST 15:20:43
use strict;
use warnings;
use Data::Dumper;
use AI::ExpertSystem::Advanced;
use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;
my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
{
filename => 'examples/knowledge_db_one.yaml'
});
my $ai = AI::ExpertSystem::Advanced->new(
viewer_class => 'terminal',
knowledge_db => $yaml_kdb,
goals_to_check => ['J']);
$ai->backward();
$ai->summary();
AI-ExpertSystem-Simple
view release on metacpan or search on metacpan
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
package AI::ExpertSystem::Simple;
use strict;
use warnings;
use XML::Twig;
use AI::ExpertSystem::Simple::Rule;
use AI::ExpertSystem::Simple::Knowledge;
use AI::ExpertSystem::Simple::Goal;
our $VERSION = '1.2';
sub new {
my ($class) = @_;
die "Simple->new() takes no arguments" if scalar(@_) != 1;
my $self = {};
$self->{_rules} = ();
$self->{_knowledge} = ();
$self->{_goal} = undef;
$self->{_filename} = undef;
$self->{_ask_about} = undef;
$self->{_told_about} = undef;
$self->{_log} = ();
$self->{_number_of_rules} = 0;
$self->{_number_of_attributes} = 0;
$self->{_number_of_questions} = 0;
return bless $self, $class;
}
sub reset {
my ($self) = @_;
die "Simple->reset() takes no arguments" if scalar(@_) != 1;
foreach my $name (keys %{$self->{_rules}}) {
$self->{_rules}->{$name}->reset();
}
foreach my $name (keys %{$self->{_knowledge}}) {
$self->{_knowledge}->{$name}->reset();
}
$self->{_ask_about} = undef;
$self->{_told_about} = undef;
$self->{_log} = ();
}
sub load {
my ($self, $filename) = @_;
die "Simple->load() takes 1 argument" if scalar(@_) != 2;
die "Simple->load() argument 1 (FILENAME) is undefined" if !defined($filename);
if(-f $filename and -r $filename) {
my $twig = XML::Twig->new(
twig_handlers => { goal => sub { $self->_goal(@_) },
rule => sub { $self->_rule(@_) },
question => sub { $self->_question(@_) } }
);
$twig->safe_parsefile($filename);
die "Simple->load() XML parse failed: $@" if $@;
$self->{_filename} = $filename;
$self->_add_to_log( "Read in $filename" );
$self->_add_to_log( "There are " . $self->{_number_of_rules} . " rules" );
$self->_add_to_log( "There are " . $self->{_number_of_attributes} . " attributes" );
$self->_add_to_log( "There are " . $self->{_number_of_questions} . " questions" );
$self->_add_to_log( "The goal attibutes is " . $self->{_goal}->name() );
return 1;
} else {
die "Simple->load() unable to use file";
}
}
sub _goal {
my ($self, $t, $node) = @_;
my $attribute = undef;
my $text = undef;
my $x = ($node->children('attribute'))[0];
$attribute = $x->text();
$x = ($node->children('text'))[0];
$text = $x->text();
$self->{_goal} = AI::ExpertSystem::Simple::Goal->new($attribute, $text);
eval { $t->purge(); }
}
sub _rule {
my ($self, $t, $node) = @_;
my $name = undef;
my $x = ($node->children('name'))[0];
$name = $x->text();
if(!defined($self->{_rules}->{$name})) {
$self->{_rules}->{$name} = AI::ExpertSystem::Simple::Rule->new($name);
$self->{_number_of_rules}++;
}
foreach $x ($node->get_xpath('//condition')) {
my $attribute = undef;
my $value = undef;
my $y = ($x->children('attribute'))[0];
$attribute = $y->text();
$y = ($x->children('value'))[0];
$value = $y->text();
$self->{_rules}->{$name}->add_condition($attribute, $value);
if(!defined($self->{_knowledge}->{$attribute})) {
$self->{_number_of_attributes}++;
$self->{_knowledge}->{$attribute} = AI::ExpertSystem::Simple::Knowledge->new($attribute);
}
}
foreach $x ($node->get_xpath('//action')) {
my $attribute = undef;
my $value = undef;
my $y = ($x->children('attribute'))[0];
$attribute = $y->text();
$y = ($x->children('value'))[0];
$value = $y->text();
$self->{_rules}->{$name}->add_action($attribute, $value);
if(!defined($self->{_knowledge}->{$attribute})) {
$self->{_number_of_attributes}++;
$self->{_knowledge}->{$attribute} = AI::ExpertSystem::Simple::Knowledge->new($attribute);
}
}
eval { $t->purge(); }
}
sub _question {
my ($self, $t, $node) = @_;
my $attribute = undef;
my $text = undef;
my @responses = ();
$self->{_number_of_questions}++;
my $x = ($node->children('attribute'))[0];
$attribute = $x->text();
$x = ($node->children('text'))[0];
$text = $x->text();
foreach $x ($node->children('response')) {
push(@responses, $x->text());
}
if(!defined($self->{_knowledge}->{$attribute})) {
$self->{_number_of_attributes}++;
$self->{_knowledge}->{$attribute} = AI::ExpertSystem::Simple::Knowledge->new($attribute);
}
$self->{_knowledge}->{$attribute}->set_question($text, @responses);
eval { $t->purge(); }
}
sub process {
my ($self) = @_;
die "Simple->process() takes no arguments" if scalar(@_) != 1;
my $n = $self->{_goal}->name();
if($self->{_knowledge}->{$n}->is_value_set()) {
return 'finished';
}
if($self->{_ask_about}) {
my %answers = ();
$answers{$self->{_ask_about}}->{value} = $self->{_told_about};
$answers{$self->{_ask_about}}->{setter} = '';
$self->{_ask_about} = undef;
$self->{_told_about} = undef;
while(%answers) {
my %old_answers = %answers;
%answers = ();
foreach my $answer (keys(%old_answers)) {
my $n = $answer;
my $v = $old_answers{$answer}->{value};
my $s = $old_answers{$answer}->{setter};
$self->_add_to_log( "Setting '$n' to '$v'" );
$self->{_knowledge}->{$n}->set_value($v,$s);
foreach my $key (keys(%{$self->{_rules}})) {
if($self->{_rules}->{$key}->state() eq 'active') {
my $state = $self->{_rules}->{$key}->given($n, $v);
if($state eq 'completed') {
$self->_add_to_log( "Rule '$key' has completed" );
my %y = $self->{_rules}->{$key}->actions();
foreach my $k (keys(%y)) {
$self->_add_to_log( "Rule '$key' is setting '$k' to '$y{$k}'" );
$answers{$k}->{value} = $y{$k};
$answers{$k}->{setter} = $key;
}
} elsif($state eq 'invalid') {
$self->_add_to_log( "Rule '$key' is now inactive" );
}
}
}
}
}
return 'continue';
} else {
my %scoreboard = ();
foreach my $rule (keys(%{$self->{_rules}})) {
if($self->{_rules}->{$rule}->state() eq 'active') {
my @listofquestions = $self->{_rules}->{$rule}->unresolved();
my $ok = 1;
my @questionstoask = ();
foreach my $name (@listofquestions) {
if($self->{_knowledge}->{$name}->has_question()) {
push(@questionstoask, $name);
} else {
$ok = 0;
}
}
if($ok == 1) {
foreach my $name (@questionstoask) {
$scoreboard{$name}++;
}
}
}
}
my $max_value = 0;
foreach my $name (keys(%scoreboard)) {
if($scoreboard{$name} > $max_value) {
$max_value = $scoreboard{$name};
$self->{_ask_about} = $name;
}
}
return $self->{_ask_about} ? 'question' : 'failed';
}
}
sub get_question {
my ($self) = @_;
die "Simple->get_question() takes no arguments" if scalar(@_) != 1;
return $self->{_knowledge}->{$self->{_ask_about}}->get_question();
}
sub answer {
my ($self, $value) = @_;
die "Simple->answer() takes 1 argument" if scalar(@_) != 2;
die "Simple->answer() argument 1 (VALUE) is undefined" if ! defined($value);
$self->{_told_about} = $value;
}
sub get_answer {
my ($self) = @_;
die "Simple->get_answer() takes no arguments" if scalar(@_) != 1;
my $n = $self->{_goal}->name();
return $self->{_goal}->answer($self->{_knowledge}->{$n}->get_value());
}
sub log {
my ($self) = @_;
die "Simple->log() takes no arguments" if scalar(@_) != 1;
my @return = ();
@return = @{$self->{_log}} if defined @{$self->{_log}};
$self->{_log} = ();
return @return;
}
sub _add_to_log {
my ($self, $message) = @_;
push( @{$self->{_log}}, $message );
}
sub explain {
my ($self) = @_;
die "Simple->explain() takes no arguments" if scalar(@_) != 1;
my $name = $self->{_goal}->name();
my $rule = $self->{_knowledge}->{$name}->get_setter();
my $value = $self->{_knowledge}->{$name}->get_value();
my $x = "The goal '$name' was set to '$value' by " . ($rule ? "rule '$rule'" : 'asking a question' );
$self->_add_to_log( $x );
my @processed_rules;
push( @processed_rules, $rule ) if $rule;
$self->_explain_this( $rule, '', @processed_rules );
}
sub _explain_this {
my ($self, $rule, $depth, @processed_rules) = @_;
$self->_add_to_log( "${depth}Explaining rule '$rule'" );
my %dont_do_these = map{ $_ => 1 } @processed_rules;
my @check_these_rules = ();
my %conditions = $self->{_rules}->{$rule}->conditions();
foreach my $name (sort keys %conditions) {
my $value = $conditions{$name};
my $setter = $self->{_knowledge}->{$name}->get_setter();
my $x = "$depth Condition '$name' was set to '$value' by " . ($setter ? "rule '$setter'" : 'asking a question' );
$self->_add_to_log( $x );
if($setter) {
unless($dont_do_these{$setter}) {
$dont_do_these{$setter} = 1;
push( @check_these_rules, $setter );
}
}
}
my %actions = $self->{_rules}->{$rule}->actions();
foreach my $name (sort keys %actions) {
my $value = $actions{$name};
my $x = "$depth Action set '$name' to '$value'";
$self->_add_to_log( $x );
}
@processed_rules = keys %dont_do_these;
foreach my $x ( @check_these_rules ) {
push( @processed_rules, $self->_explain_this( $x, "$depth ", keys %dont_do_these ) );
}
return @processed_rules;
}
1;
=head1 NAME
AI::ExpertSystem::Simple - A simple expert system shell
=head1 VERSION
This document refers to verion 1.2 of AI::ExpertSystem::Simple, released June 10, 2003
=head1 SYNOPSIS
This class implements a simple expert system shell that reads the rules from an XML
knowledge base and questions the user as it attempts to arrive at a conclusion.
=head1 DESCRIPTION
=head2 Overview
This class is where all the work is being done and the other three classes are only
there for support. At present there is little you can do with it other than run it. Future
version will make subclassing of this class feasable and features like logging will be introduced.
To see how to use this class there is a simple shell in the bin directory which allows you
to consult the example knowledge bases and more extensive documemtation in the docs directory.
There is a Ruby version that reads the same XML knowledge bases, if you are interested.
=head2 Constructors and initialisation
=over 4
=item new( )
The constructor takes no arguments and just initialises a few basic variables.
=back
=head2 Public methods
=over 4
=item reset( )
Resets the system back to its initial state so that a new consoltation can be run
=item load( FILENAME )
This method takes the FILENAME of an XML knowledgebase and attempts to parse it to set up the data structures
required for a consoltation.
=item process( )
Once the knowledgebase is loaded the consultation is run by repeatedly calling this method.
It returns four results:
=over 4
=item "question"
The system has a question to ask of the user.
The question and list of valid responses is available from the get_question( ) method and the users response should be returned via the answer( ) method.
Then simply call the process( ) method again.
=item "continue"
The system has calculated some data but has nothing to ask the user but has still not finished.
This response will be removed in future versions.
Simply call the process( ) method again.
=item "finished"
The consoltation has finished and the system has an answer for the user which is available from the answer( ) method.
=item "failed"
The consoltation has finished and the system has failed to find an answer for the user. It happens.
=back
=item get_question( )
If the process( ) method has returned "question" then this method will return the question to ask the user
and a list of valid responses.
=item answer( VALUE )
The user has been presented with the question from the get_question( ) method along with a set of
valid responses and the users selection is returned by this method.
=item get_answer( )
If the process( ) method has returned "finished" then the answer to the users query will be
returned by this method.
=item log( )
Returns a list of the actions undertaken so far and clears the log.
=item explain( )
Explain how the given answer was arrived at. The explanation is added to the log.
=back
=head2 Private methods
=over 4
=item _goal
A private method to get the goal data from the knowledgebase.
=item _rule
A private method to get the rule data from the knowledgebase.
=item _question
A private method to get the question data from the knowledgebase.
=item _add_to_log
A private method to add a message to the log.
=item _explain_this
A private method to explain how a single attribute was set.
=back
=head1 ENVIRONMENT
None
=head1 DIAGNOSTICS
=over 4
=item Simple->new() takes no arguments
When the constructor is initialised it requires no arguments. This message is given if
some arguments were supplied.
=item Simple->reset() takes no arguments
When the method is called it requires no arguments. This message is given if
some arguments were supplied.
=item Simple->load() takes 1 argument
When the method is called it requires one argument. This message is given if more or
less arguments were supplied.
=item Simple->load() argument 1 (FILENAME) is undefined
The corrct number of arguments were supplied with the method call, however the first
argument, FILENAME, was undefined.
=item Simple->load() XML parse failed
XML Twig encountered some errors when trying to parse the XML knowledgebase.
=item Simple->load() unable to use file
The file supplied to the load( ) method could not be used as it was either not a file
or not readable.
=item Simple->process() takes no arguments
When the method is called it requires no arguments. This message is given if
some arguments were supplied.
=item Simple->get_question() takes no arguments
When the method is called it requires no arguments. This message is given if
some arguments were supplied.
=item Simple->answer() takes 1 argument
When the method is called it requires one argument. This message is given if more or
less arguments were supplied.
=item Simple->answer() argument 1 (VALUE) is undefined
The corrct number of arguments were supplied with the method call, however the first
argument, VALUE, was undefined.
=item Simple->get_answer() takes no arguments
When the method is called it requires no arguments. This message is given if
some arguments were supplied.
=item Simple->log() takes no arguments
When the method is called it requires no arguments. This message is given if
some arguments were supplied.
=item Simple->explain() takes no arguments
When the method is called it requires no arguments. This message is given if
some arguments were supplied.
=back
=head1 BUGS
None
=head1 FILES
See the Simple.t file in the test directory and simpleshell in the bin directory.
=head1 SEE ALSO
AI::ExpertSystem::Simple::Goal - A utility class
AI::ExpertSystem::Simple::Knowledge - A utility class
AI::ExpertSystem::Simple::Rule - A utility class
=head1 AUTHORS
Peter Hickman (peterhi@ntlworld.com)
=head1 COPYRIGHT
Copyright (c) 2003, Peter Hickman. All rights reserved.
This module is free software. It may be used, redistributed and/or
modified under the same terms as Perl itself.
AI-FANN-Evolving
view release on metacpan or search on metacpan
lib/AI/FANN/Evolving.pm view on Meta::CPAN
package AI::FANN::Evolving;
use strict;
use warnings;
use AI::FANN ':all';
use List::Util 'shuffle';
use File::Temp 'tempfile';
use AI::FANN::Evolving::Gene;
use AI::FANN::Evolving::Chromosome;
use AI::FANN::Evolving::Experiment;
use AI::FANN::Evolving::Factory;
use Algorithm::Genetic::Diploid;
use base qw'Algorithm::Genetic::Diploid::Base';
our $VERSION = '0.4';
our $AUTOLOAD;
my $log = __PACKAGE__->logger;
my %enum = (
'train' => {
# 'FANN_TRAIN_INCREMENTAL' => FANN_TRAIN_INCREMENTAL, # only want batch training
'FANN_TRAIN_BATCH' => FANN_TRAIN_BATCH,
'FANN_TRAIN_RPROP' => FANN_TRAIN_RPROP,
'FANN_TRAIN_QUICKPROP' => FANN_TRAIN_QUICKPROP,
},
'activationfunc' => {
'FANN_LINEAR' => FANN_LINEAR,
# 'FANN_THRESHOLD' => FANN_THRESHOLD, # can not be used during training
# 'FANN_THRESHOLD_SYMMETRIC' => FANN_THRESHOLD_SYMMETRIC, # can not be used during training
# 'FANN_SIGMOID' => FANN_SIGMOID, # range is between 0 and 1
# 'FANN_SIGMOID_STEPWISE' => FANN_SIGMOID_STEPWISE, # range is between 0 and 1
'FANN_SIGMOID_SYMMETRIC' => FANN_SIGMOID_SYMMETRIC,
'FANN_SIGMOID_SYMMETRIC_STEPWISE' => FANN_SIGMOID_SYMMETRIC_STEPWISE,
# 'FANN_GAUSSIAN' => FANN_GAUSSIAN, # range is between 0 and 1
'FANN_GAUSSIAN_SYMMETRIC' => FANN_GAUSSIAN_SYMMETRIC,
'FANN_GAUSSIAN_STEPWISE' => FANN_GAUSSIAN_STEPWISE,
# 'FANN_ELLIOT' => FANN_ELLIOT, # range is between 0 and 1
'FANN_ELLIOT_SYMMETRIC' => FANN_ELLIOT_SYMMETRIC,
# 'FANN_LINEAR_PIECE' => FANN_LINEAR_PIECE, # range is between 0 and 1
'FANN_LINEAR_PIECE_SYMMETRIC' => FANN_LINEAR_PIECE_SYMMETRIC,
'FANN_SIN_SYMMETRIC' => FANN_SIN_SYMMETRIC,
'FANN_COS_SYMMETRIC' => FANN_COS_SYMMETRIC,
# 'FANN_SIN' => FANN_SIN, # range is between 0 and 1
# 'FANN_COS' => FANN_COS, # range is between 0 and 1
},
'errorfunc' => {
'FANN_ERRORFUNC_LINEAR' => FANN_ERRORFUNC_LINEAR,
'FANN_ERRORFUNC_TANH' => FANN_ERRORFUNC_TANH,
},
'stopfunc' => {
'FANN_STOPFUNC_MSE' => FANN_STOPFUNC_MSE,
# 'FANN_STOPFUNC_BIT' => FANN_STOPFUNC_BIT,
}
);
my %constant;
for my $hashref ( values %enum ) {
while( my ( $k, $v ) = each %{ $hashref } ) {
$constant{$k} = $v;
}
}
my %default = (
'error' => 0.0001,
'epochs' => 5000,
'train_type' => 'ordinary',
'epoch_printfreq' => 100,
'neuron_printfreq' => 0,
'neurons' => 15,
'activation_function' => FANN_SIGMOID_SYMMETRIC,
);
=head1 NAME
AI::FANN::Evolving - artificial neural network that evolves
=head1 METHODS
=over
=item new
Constructor requires 'file', or 'data' and 'neurons' arguments. Optionally takes
'connection_rate' argument for sparse topologies. Returns a wrapper around L<AI::FANN>.
=cut
sub new {
my $class = shift;
my %args = @_;
my $self = {};
bless $self, $class;
$self->_init(%args);
# de-serialize from a file
if ( my $file = $args{'file'} ) {
$self->{'ann'} = AI::FANN->new_from_file($file);
$log->debug("instantiating from file $file");
return $self;
}
# build new topology from input data
elsif ( my $data = $args{'data'} ) {
$log->debug("instantiating from data $data");
$data = $data->to_fann if $data->isa('AI::FANN::Evolving::TrainData');
# prepare arguments
my $neurons = $args{'neurons'} || ( $data->num_inputs + 1 );
my @sizes = (
$data->num_inputs,
$neurons,
$data->num_outputs
);
# build topology
if ( $args{'connection_rate'} ) {
$self->{'ann'} = AI::FANN->new_sparse( $args{'connection_rate'}, @sizes );
}
else {
$self->{'ann'} = AI::FANN->new_standard( @sizes );
}
# finalize the instance
return $self;
}
# build new ANN using argument as a template
elsif ( my $ann = $args{'ann'} ) {
$log->debug("instantiating from template $ann");
# copy the wrapper properties
%{ $self } = %{ $ann };
# instantiate the network dimensions
$self->{'ann'} = AI::FANN->new_standard(
$ann->num_inputs,
$ann->num_inputs + 1,
$ann->num_outputs,
);
# copy the AI::FANN properties
$ann->template($self->{'ann'});
return $self;
}
else {
die "Need 'file', 'data' or 'ann' argument!";
}
}
=item template
Uses the object as a template for the properties of the argument, e.g.
$ann1->template($ann2) applies the properties of $ann1 to $ann2
=cut
sub template {
my ( $self, $other ) = @_;
# copy over the simple properties
$log->debug("copying over simple properties");
my %scalar_properties = __PACKAGE__->_scalar_properties;
for my $prop ( keys %scalar_properties ) {
my $val = $self->$prop;
$other->$prop($val);
}
# copy over the list properties
$log->debug("copying over list properties");
my %list_properties = __PACKAGE__->_list_properties;
for my $prop ( keys %list_properties ) {
my @values = $self->$prop;
$other->$prop(@values);
}
# copy over the layer properties
$log->debug("copying over layer properties");
my %layer_properties = __PACKAGE__->_layer_properties;
for my $prop ( keys %layer_properties ) {
for my $i ( 0 .. $self->num_layers - 1 ) {
for my $j ( 0 .. $self->layer_num_neurons($i) - 1 ) {
my $val = $self->$prop($i,$j);
$other->$prop($i,$j,$val);
}
}
}
return $self;
}
=item recombine
Recombines (exchanges) properties between the two objects at the provided rate, e.g.
$ann1->recombine($ann2,0.5) means that on average half of the object properties are
exchanged between $ann1 and $ann2
=cut
sub recombine {
my ( $self, $other, $rr ) = @_;
# recombine the simple properties
my %scalar_properties = __PACKAGE__->_scalar_properties;
for my $prop ( keys %scalar_properties ) {
if ( rand(1) < $rr ) {
my $vals = $self->$prop;
my $valo = $other->$prop;
$other->$prop($vals);
$self->$prop($valo);
}
}
# copy over the list properties
my %list_properties = __PACKAGE__->_list_properties;
for my $prop ( keys %list_properties ) {
if ( rand(1) < $rr ) {
my @values = $self->$prop;
my @valueo = $other->$prop;
$other->$prop(@values);
$self->$prop(@valueo);
}
}
# copy over the layer properties
my %layer_properties = __PACKAGE__->_layer_properties;
for my $prop ( keys %layer_properties ) {
for my $i ( 0 .. $self->num_layers - 1 ) {
for my $j ( 0 .. $self->layer_num_neurons($i) - 1 ) {
my $val = $self->$prop($i,$j);
$other->$prop($i,$j,$val);
}
}
}
return $self;
}
=item mutate
Mutates the object by the provided mutation rate
=cut
sub mutate {
my ( $self, $mu ) = @_;
$log->debug("going to mutate at rate $mu");
# mutate the simple properties
$log->debug("mutating scalar properties");
my %scalar_properties = __PACKAGE__->_scalar_properties;
for my $prop ( keys %scalar_properties ) {
my $handler = $scalar_properties{$prop};
my $val = $self->$prop;
if ( ref $handler ) {
$self->$prop( $handler->($val,$mu) );
}
else {
$self->$prop( _mutate_enum($handler,$val,$mu) );
}
}
# mutate the list properties
$log->debug("mutating list properties");
my %list_properties = __PACKAGE__->_list_properties;
for my $prop ( keys %list_properties ) {
my $handler = $list_properties{$prop};
my @values = $self->$prop;
if ( ref $handler ) {
$self->$prop( map { $handler->($_,$mu) } @values );
}
else {
$self->$prop( map { _mutate_enum($handler,$_,$mu) } @values );
}
}
# mutate the layer properties
$log->debug("mutating layer properties");
my %layer_properties = __PACKAGE__->_layer_properties;
for my $prop ( keys %layer_properties ) {
my $handler = $layer_properties{$prop};
for my $i ( 1 .. $self->num_layers ) {
for my $j ( 1 .. $self->layer_num_neurons($i) ) {
my $val = $self->$prop($i,$j);
if ( ref $handler ) {
$self->$prop( $handler->($val,$mu) );
}
else {
$self->$prop( _mutate_enum($handler,$val,$mu) );
}
}
}
}
return $self;
}
sub _mutate_double {
my ( $value, $mu ) = @_;
my $scale = 1 + ( rand( 2 * $mu ) - $mu );
return $value * $scale;
}
sub _mutate_int {
my ( $value, $mu ) = @_;
if ( rand(1) < $mu ) {
my $inc = ( int(rand(2)) * 2 ) - 1;
while( ( $value < 0 ) xor ( ( $value + $inc ) < 0 ) ) {
$inc = ( int(rand(2)) * 2 ) - 1;
}
return $value + $inc;
}
return $value;
}
sub _mutate_enum {
my ( $enum_name, $value, $mu ) = @_;
if ( rand(1) < $mu ) {
my ($newval) = shuffle grep { $_ != $value } values %{ $enum{$enum_name} };
$value = $newval if defined $newval;
}
return $value;
}
sub _list_properties {
(
# cascade_activation_functions => 'activationfunc',
cascade_activation_steepnesses => \&_mutate_double,
)
}
sub _layer_properties {
(
# neuron_activation_function => 'activationfunc',
# neuron_activation_steepness => \&_mutate_double,
)
}
sub _scalar_properties {
(
training_algorithm => 'train',
train_error_function => 'errorfunc',
train_stop_function => 'stopfunc',
learning_rate => \&_mutate_double,
learning_momentum => \&_mutate_double,
quickprop_decay => \&_mutate_double,
quickprop_mu => \&_mutate_double,
rprop_increase_factor => \&_mutate_double,
rprop_decrease_factor => \&_mutate_double,
rprop_delta_min => \&_mutate_double,
rprop_delta_max => \&_mutate_double,
cascade_output_change_fraction => \&_mutate_double,
cascade_candidate_change_fraction => \&_mutate_double,
cascade_output_stagnation_epochs => \&_mutate_int,
cascade_candidate_stagnation_epochs => \&_mutate_int,
cascade_max_out_epochs => \&_mutate_int,
cascade_max_cand_epochs => \&_mutate_int,
cascade_num_candidate_groups => \&_mutate_int,
bit_fail_limit => \&_mutate_double, # 'fann_type',
cascade_weight_multiplier => \&_mutate_double, # 'fann_type',
cascade_candidate_limit => \&_mutate_double, # 'fann_type',
)
}
=item defaults
Getter/setter to influence default ANN configuration
=cut
sub defaults {
my $self = shift;
my %args = @_;
for my $key ( keys %args ) {
$log->info("setting $key to $args{$key}");
if ( $key eq 'activation_function' ) {
$args{$key} = $constant{$args{$key}};
}
$default{$key} = $args{$key};
}
return %default;
}
sub _init {
my $self = shift;
my %args = @_;
for ( qw(error epochs train_type epoch_printfreq neuron_printfreq neurons activation_function) ) {
$self->{$_} = $args{$_} // $default{$_};
}
return $self;
}
=item clone
Clones the object
=cut
sub clone {
my $self = shift;
$log->debug("cloning...");
# we delete the reference here so we can use
# Algorithm::Genetic::Diploid::Base's cloning method, which
# dumps and loads from YAML. This wouldn't work if the
# reference is still attached because it cannot be
# stringified, being an XS data structure
my $ann = delete $self->{'ann'};
my $clone = $self->SUPER::clone;
# clone the ANN by writing it to a temp file in "FANN/FLO"
# format and reading that back in, then delete the file
my ( $fh, $file ) = tempfile();
close $fh;
$ann->save($file);
$clone->{'ann'} = __PACKAGE__->new_from_file($file);
unlink $file;
# now re-attach the original ANN to the invocant
$self->{'ann'} = $ann;
return $clone;
}
=item train
Trains the AI on the provided data object
=cut
sub train {
my ( $self, $data ) = @_;
if ( $self->train_type eq 'cascade' ) {
$log->debug("cascade training");
# set learning curve
$self->cascade_activation_functions( $self->activation_function );
# train
$self->{'ann'}->cascadetrain_on_data(
$data,
$self->neurons,
$self->neuron_printfreq,
$self->error,
);
}
else {
$log->debug("normal training");
# set learning curves
$self->hidden_activation_function( $self->activation_function );
$self->output_activation_function( $self->activation_function );
# train
$self->{'ann'}->train_on_data(
$data,
$self->epochs,
$self->epoch_printfreq,
$self->error,
);
}
}
=item enum_properties
Returns a hash whose keys are names of enums and values the possible states for the
enum
=cut
=item error
Getter/setter for the error rate. Default is 0.0001
=cut
sub error {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting error threshold to $value");
return $self->{'error'} = $value;
}
else {
$log->debug("getting error threshold");
return $self->{'error'};
}
}
=item epochs
Getter/setter for the number of training epochs, default is 500000
=cut
sub epochs {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting training epochs to $value");
return $self->{'epochs'} = $value;
}
else {
$log->debug("getting training epochs");
return $self->{'epochs'};
}
}
=item epoch_printfreq
Getter/setter for the number of epochs after which progress is printed. default is 1000
=cut
sub epoch_printfreq {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting epoch printfreq to $value");
return $self->{'epoch_printfreq'} = $value;
}
else {
$log->debug("getting epoch printfreq");
return $self->{'epoch_printfreq'}
}
}
=item neurons
Getter/setter for the number of neurons. Default is 15
=cut
sub neurons {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting neurons to $value");
return $self->{'neurons'} = $value;
}
else {
$log->debug("getting neurons");
return $self->{'neurons'};
}
}
=item neuron_printfreq
Getter/setter for the number of cascading neurons after which progress is printed.
default is 10
=cut
sub neuron_printfreq {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting neuron printfreq to $value");
return $self->{'neuron_printfreq'} = $value;
}
else {
$log->debug("getting neuron printfreq");
return $self->{'neuron_printfreq'};
}
}
=item train_type
Getter/setter for the training type: 'cascade' or 'ordinary'. Default is ordinary
=cut
sub train_type {
my $self = shift;
if ( @_ ) {
my $value = lc shift;
$log->debug("setting train type to $value");
return $self->{'train_type'} = $value;
}
else {
$log->debug("getting train type");
return $self->{'train_type'};
}
}
=item activation_function
Getter/setter for the function that maps inputs to outputs. default is
FANN_SIGMOID_SYMMETRIC
=back
=cut
sub activation_function {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting activation function to $value");
return $self->{'activation_function'} = $value;
}
else {
$log->debug("getting activation function");
return $self->{'activation_function'};
}
}
# this is here so that we can trap method calls that need to be
# delegated to the FANN object. at this point we're not even
# going to care whether the FANN object implements these methods:
# if it doesn't we get the normal error for unknown methods, which
# the user then will have to resolve.
sub AUTOLOAD {
my $self = shift;
my $method = $AUTOLOAD;
$method =~ s/.+://;
# ignore all caps methods
if ( $method !~ /^[A-Z]+$/ ) {
# determine whether to invoke on an object or a package
my $invocant;
if ( ref $self ) {
$invocant = $self->{'ann'};
}
else {
$invocant = 'AI::FANN';
}
# determine whether to pass in arguments
if ( @_ ) {
my $arg = shift;
$arg = $constant{$arg} if exists $constant{$arg};
return $invocant->$method($arg);
}
else {
return $invocant->$method;
}
}
}
1;
AI-FANN
view release on metacpan or search on metacpan
lib/AI/FANN.pm view on Meta::CPAN
package AI::FANN;
our $VERSION = '0.10';
use strict;
use warnings;
use Carp;
require XSLoader;
XSLoader::load('AI::FANN', $VERSION);
use Exporter qw(import);
{
my @constants = _constants();
our %EXPORT_TAGS = ( 'all' => [ @constants ] );
our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );
require constant;
for my $constant (@constants) {
constant->import($constant, $constant);
}
}
sub num_neurons {
@_ == 1 or croak "Usage: AI::FANN::get_neurons(self)";
my $self = shift;
if (wantarray) {
map { $self->layer_num_neurons($_) } (0 .. $self->num_layers - 1);
}
else {
$self->total_neurons;
}
}
1;
__END__
=head1 NAME
AI::FANN - Perl wrapper for the Fast Artificial Neural Network library
=head1 SYNOPSIS
Train...
use AI::FANN qw(:all);
# create an ANN with 2 inputs, a hidden layer with 3 neurons and an
# output layer with 1 neuron:
my $ann = AI::FANN->new_standard(2, 3, 1);
$ann->hidden_activation_function(FANN_SIGMOID_SYMMETRIC);
$ann->output_activation_function(FANN_SIGMOID_SYMMETRIC);
# create the training data for a XOR operator:
my $xor_train = AI::FANN::TrainData->new( [-1, -1], [-1],
[-1, 1], [1],
[1, -1], [1],
[1, 1], [-1] );
$ann->train_on_data($xor_train, 500000, 1000, 0.001);
$ann->save("xor.ann");
Run...
use AI::FANN;
my $ann = AI::FANN->new_from_file("xor.ann");
for my $a (-1, 1) {
for my $b (-1, 1) {
my $out = $ann->run([$a, $b]);
printf "xor(%f, %f) = %f\n", $a, $b, $out->[0];
}
}
=head1 DESCRIPTION
WARNING: THIS IS A VERY EARLY RELEASE,
MAY CONTAIN CRITICAL BUGS!!!
AI::FANN is a Perl wrapper for the Fast Artificial Neural Network
(FANN) Library available from L<http://fann.sourceforge.net>:
Fast Artificial Neural Network Library is a free open source neural
network library, which implements multilayer artificial neural
networks in C with support for both fully connected and sparsely
connected networks. Cross-platform execution in both fixed and
floating point are supported. It includes a framework for easy
handling of training data sets. It is easy to use, versatile, well
documented, and fast. PHP, C++, .NET, Python, Delphi, Octave, Ruby,
Pure Data and Mathematica bindings are available. A reference manual
accompanies the library with examples and recommendations on how to
use the library. A graphical user interface is also available for
the library.
AI::FANN object oriented interface provides an almost direct map to
the C library API. Some differences have been introduced to make it
more perlish:
=over 4
=item *
Two classes are used: C<AI::FANN> that wraps the C C<struct fann> type
and C<AI::FANN::TrainData> that wraps C<struct fann_train_data>.
=item *
Prefixes and common parts on the C function names referring to those
structures have been removed. For instance C
C<fann_train_data_shuffle> becomes C<AI::FANN::TrainData::shuffle> that
will be usually called as...
$train_data->shuffle;
=item *
Pairs of C get/set functions are wrapped in Perl with dual accessor
methods named as the attribute (and without any C<set_>/C<get_>
prefix). For instance:
$ann->bit_fail_limit($limit); # sets the bit_fail_limit
$bfl = $ann->bit_fail_limit; # gets the bit_fail_limit
Pairs of get/set functions requiring additional indexing arguments are
also wrapped inside dual accessors:
# sets:
$ann->neuron_activation_function($layer_ix, $neuron_ix, $actfunc);
# gets:
$af = $ann->neuron_activation_function($layer_ix, $neuron_ix);
Important: note that on the Perl version, the optional value argument
is moved to the last position (on the C version of the C<set_> method
it is usually the second argument).
=item *
Some functions have been renamed to make the naming more consistent
and to follow Perl conventions:
C Perl
-----------------------------------------------------------
fann_create_from_file => new_from_file
fann_create_standard => new_standard
fann_get_num_input => num_inputs
fann_get_activation_function => neuron_activation_function
fann_set_activation_function => ^^^
fann_set_activation_function_layer => layer_activation_function
fann_set_activation_function_hidden => hidden_activation_function
fann_set_activation_function_output => output_activation_function
=item *
Boolean methods return true on success and undef on failure.
=item *
Any error reported from the C side is automaticaly converter to a Perl
exception. No manual error checking is required after calling FANN
functions.
=item *
Memory management is automatic, no need to call destroy methods.
=item *
Doubles are used for computations (using floats or fixed
point types is not supported).
=back
=head1 CONSTANTS
All the constants defined in the C documentation are exported from the module:
# import all...
use AI::FANN ':all';
# or individual constants...
use AI::FANN qw(FANN_TRAIN_INCREMENTAL FANN_GAUSSIAN);
The values returned from this constant subs yield the integer value on
numerical context and the constant name when used as strings.
The constants available are:
# enum fann_train_enum:
FANN_TRAIN_INCREMENTAL
FANN_TRAIN_BATCH
FANN_TRAIN_RPROP
FANN_TRAIN_QUICKPROP
# enum fann_activationfunc_enum:
FANN_LINEAR
FANN_THRESHOLD
FANN_THRESHOLD_SYMMETRIC
FANN_SIGMOID
FANN_SIGMOID_STEPWISE
FANN_SIGMOID_SYMMETRIC
FANN_SIGMOID_SYMMETRIC_STEPWISE
FANN_GAUSSIAN
FANN_GAUSSIAN_SYMMETRIC
FANN_GAUSSIAN_STEPWISE
FANN_ELLIOT
FANN_ELLIOT_SYMMETRIC
FANN_LINEAR_PIECE
FANN_LINEAR_PIECE_SYMMETRIC
FANN_SIN_SYMMETRIC
FANN_COS_SYMMETRIC
FANN_SIN
FANN_COS
# enum fann_errorfunc_enum:
FANN_ERRORFUNC_LINEAR
FANN_ERRORFUNC_TANH
# enum fann_stopfunc_enum:
FANN_STOPFUNC_MSE
FANN_STOPFUNC_BIT
=head1 CLASSES
The classes defined by this package are:
=head2 AI::FANN
Wraps C C<struct fann> types and provides the following methods
(consult the C documentation for a full description of their usage):
=over 4
=item AI::FANN->new_standard(@layer_sizes)
-
=item AI::FANN->new_sparse($connection_rate, @layer_sizes)
-
=item AI::FANN->new_shortcut(@layer_sizes)
-
=item AI::FANN->new_from_file($filename)
-
=item $ann->save($filename)
-
=item $ann->run($input)
C<input> is an array with the input values.
returns an array with the values on the output layer.
$out = $ann->run([1, 0.6]);
print "@$out\n";
=item $ann->randomize_weights($min_weight, $max_weight)
=item $ann->train($input, $desired_output)
C<$input> and C<$desired_output> are arrays.
=item $ann->test($input, $desired_output)
C<$input> and C<$desired_output> are arrays.
It returns an array with the values of the output layer.
=item $ann->reset_MSE
-
=item $ann->train_on_file($filename, $max_epochs, $epochs_between_reports, $desired_error)
-
=item $ann->train_on_data($train_data, $max_epochs, $epochs_between_reports, $desired_error)
C<$train_data> is a AI::FANN::TrainData object.
=item $ann->cascadetrain_on_file($filename, $max_neurons, $neurons_between_reports, $desired_error)
-
=item $ann->cascadetrain_on_data($train_data, $max_neurons, $neurons_between_reports, $desired_error)
C<$train_data> is a AI::FANN::TrainData object.
=item $ann->train_epoch($train_data)
C<$train_data> is a AI::FANN::TrainData object.
=item $ann->print_connections
-
=item $ann->print_parameters
-
=item $ann->cascade_activation_functions()
returns a list of the activation functions used for cascade training.
=item $ann->cascade_activation_functions(@activation_functions)
sets the list of activation function to use for cascade training.
=item $ann->cascade_activation_steepnesses()
returns a list of the activation steepnesses used for cascade training.
=item $ann->cascade_activation_steepnesses(@activation_steepnesses)
sets the list of activation steepnesses to use for cascade training.
=item $ann->training_algorithm
=item $ann->training_algorithm($training_algorithm)
-
=item $ann->train_error_function
=item $ann->train_error_function($error_function)
-
=item $ann->train_stop_function
=item $ann->train_stop_function($stop_function)
-
=item $ann->learning_rate
=item $ann->learning_rate($rate)
-
=item $ann->learning_momentum
=item $ann->learning_momentum($momentun)
-
=item $ann->bit_fail_limit
=item $ann->bit_fail_limit($bfl)
-
=item $ann->quickprop_decay
=item $ann->quickprop_decay($qpd)
-
=item $ann->quickprop_mu
=item $ann->quickprop_mu($qpmu)
-
=item $ann->rprop_increase_factor
=item $ann->rprop_increase_factor($factor)
-
=item $ann->rprop_decrease_factor
=item $ann->rprop_decrease_factor($factor)
-
=item $ann->rprop_delta_min
=item $ann->rprop_delta_min($min)
-
=item $ann->rprop_delta_max
=item $ann->rprop_delta_max($max)
-
=item $ann->num_inputs
-
=item $ann->num_outputs
-
=item $ann->total_neurons
-
=item $ann->total_connections
-
=item $ann->MSE
-
=item $ann->bit_fail
-
=item cascade_output_change_fraction
=item cascade_output_change_fraction($fraction)
-
=item $ann->cascade_output_stagnation_epochs
=item $ann->cascade_output_stagnation_epochs($epochs)
-
=item $ann->cascade_candidate_change_fraction
=item $ann->cascade_candidate_change_fraction($fraction)
-
=item $ann->cascade_candidate_stagnation_epochs
=item $ann->cascade_candidate_stagnation_epochs($epochs)
-
=item $ann->cascade_weight_multiplier
=item $ann->cascade_weight_multiplier($multiplier)
-
=item $ann->cascade_candidate_limit
=item $ann->cascade_candidate_limit($limit)
-
=item $ann->cascade_max_out_epochs
=item $ann->cascade_max_out_epochs($epochs)
-
=item $ann->cascade_max_cand_epochs
=item $ann->cascade_max_cand_epochs($epochs)
-
=item $ann->cascade_num_candidates
-
=item $ann->cascade_num_candidate_groups
=item $ann->cascade_num_candidate_groups($groups)
-
=item $ann->neuron_activation_function($layer_index, $neuron_index)
=item $ann->neuron_activation_function($layer_index, $neuron_index, $activation_function)
-
=item $ann->layer_activation_function($layer_index, $activation_function)
-
=item $ann->hidden_activation_function($layer_index, $activation_function)
-
=item $ann->output_activation_function($layer_index, $activation_function)
-
=item $ann->neuron_activation_steepness($layer_index, $neuron_index)
=item $ann->neuron_activation_steepness($layer_index, $neuron_index, $activation_steepness)
-
=item $ann->layer_activation_steepness($layer_index, $activation_steepness)
-
=item $ann->hidden_activation_steepness($layer_index, $activation_steepness)
-
=item $ann->output_activation_steepness($layer_index, $activation_steepness)
-
=item $ann->num_layers
returns the number of layers on the ANN
=item $ann->layer_num_neurons($layer_index)
return the number of neurons on layer C<$layer_index>.
=item $ann->num_neurons
return a list with the number of neurons on every layer
=back
=head2 AI::FANN::TrainData
Wraps C C<struct fann_train_data> and provides the following method:
=over 4
=item AI::FANN::TrainData->new_from_file($filename)
-
=item AI::FANN::TrainData->new($input1, $output1 [, $input2, $output2, ...])
C<$inputx> and C<$outputx> are arrays with the values of the input and
output layers.
=item AI::FANN::TrainData->new_empty($num_data, $num_inputs, $num_outputs)
returns a new AI::FANN::TrainData object of the sizes indicated on the
arguments. The initial values of the data contained inside the object
are random and should be set before using the train data object for
training an ANN.
=item $train->data($index)
returns two arrays with the values of the input and output layer
respectively for that index.
=item $train->data($index, $input, $output)
C<$input> and C<$output> are two arrays.
The input and output layers at the index C<$index> are set to the
values on these arrays.
=item $train->shuffle
-
=item $train->scale_input($new_min, $new_max)
-
=item $train->scale_output($new_min, $new_max)
-
=item $train->scale($new_min, $new_max)
-
=item $train->subset($pos, $length)
-
=item $train->num_inputs
-
=item $train->num_outputs
-
=item $train->length
-
=back
=head1 INSTALLATION
See the README file for instruction on installing this module.
=head1 BUGS
Only tested on Linux.
I/O is not performed through PerlIO because the C library doesn't have
the required infrastructure to do that.
Send bug reports to my email address or use the CPAN RT system.
=head1 SEE ALSO
FANN homepage at L<http://leenissen.dk/fann/index.php>.
=head1 COPYRIGHT AND LICENSE
Copyright (C) 2006-2008 by Salvador FandiE<ntilde>o
(sfandino@yahoo.com).
This Perl module is free software; you can redistribute it and/or
modify it under the same terms as Perl itself, either Perl version
5.8.8 or, at your option, any later version of Perl 5 you may have
available.
The Fast Artificial Neural Network Library (FANN)
Copyright (C) 2003-2006 Steffen Nissen (lukesky@diku.dk) and others.
Distributed under the GNU Lesser General Public License.
=cut
AI-Fuzzy
view release on metacpan or search on metacpan
package AI::Fuzzy;
use strict;
use vars qw($VERSION);
use AI::Fuzzy::Set;
use AI::Fuzzy::Axis;
use AI::Fuzzy::Label;
$VERSION = '0.05';
1;
__END__
=head1 NAME
AI::Fuzzy - Perl extension for Fuzzy Logic
=head1 SYNOPSIS
use AI::Fuzzy;
my $f = new AI::Fuzzy::Axis;
my $l = new AI::Fuzzy::Label("toddler", 1, 1.5, 3.5);
$f->addlabel("baby", -1, 1, 2.5);
$f->addlabel($l);
$f->addlabel("little kid", 2, 7, 12);
$f->addlabel("kid", 6, 10, 14);
$f->addlabel("teenager", 12, 16, 20);
$f->addlabel("young adult", 18, 27, 35);
$f->addlabel("adult", 25, 50, 75);
$f->addlabel("senior", 60, 80, 110);
$f->addlabel("relic", 100, 150, 200);
for (my $x = 0; $x<50; $x+=4) {
print "$x years old => " . $f->labelvalue($x) . "\n";
}
$a = new AI::Fuzzy::Set( x1 => .3, x2 => .5, x3 => .8, x4 => 0, x5 => 1);
$b = new AI::Fuzzy::Set( x5 => .3, x6 => .5, x7 => .8, x8 => 0, x9 => 1);
print "a is: " . $a->as_string . "\n";
print "b is: " . $b->as_string . "\n";
print "a is equal to b" if ($a->equal($b));
my $c = $a->complement();
print "complement of a is: " . $c->as_string . "\n";
$c = $a->union($b);
print "a union b is: " . $c->as_string . "\n";
$c = $a->intersection($b);
print "a intersection b is: " . $c->as_string . "\n";
__END__
=head1 DESCRIPTION
AI::Fuzzy really consists of three modules - AI::Fuzzy::Axis, AI::Fuzzy::Label, and
AI::Fuzzy::Set.
A fuzzy set is simply a mathematical set to which members can
I<partially> belong. For example, a particular shade of gray may
partially belong to the set of dark colors, whereas black would have
full membership, and lemon yellow would have almost no membership.
A fuzzy axis holds fuzzy labels and can be used to classify values
by examining the degree to which they belong to several labels, and
selecting the most appropriate. For example, it can decide whether
to call water at 60 degrees Farenheight "cold", "cool", or "warm".
A fuzzy label classifies a particular range of the Axis. In the above example
the label is one of "cold", "cool", or "warm". A fuzzy label defines how
much a crisp value belongs to the classifier such as "cold", "warm", or "cool".
=head2 Fuzzy Sets
AI::Fuzzy:Set has these methods:
$fs = B<new> AI::Fuzzy::Set;
# here, "Bob" is unquestionably tall.. the others less so.
$fs_tall_people = B<new> AI::Fuzzy::Set( Lester=>.34, Bob=>1.00, Max=>.86 );
# $x will be .86
$x = B<membership> $fs_tall_people, "Max";
# get list of members, sorted from least membership to greatest:
@shortest_first = B<members> $fs_tall_people;
$fs = B<new> AI::Fuzzy::Set( x1 => .3, x2 => .5, x3 => .8, x4 => 0, x5 => 1);
B<complement>, B<union>, B<intersection>
Thesie are the fuzzy set version of the typical functions.
B<equal>
Returns true if the sets have the same elements and those elements
are all equal.
B<as_string>
Prints the set as tuples:
$b = new AI::Fuzzy::Set( x5 => .3, x6 => .5, x7 => .8, x8 => 0, x9 => 1);
print "b is: " . $b->as_string . "\n";
prints:
b is: x8/0, x5/0.3, x6/0.5, x7/0.8, x9/1
=head2 Fuzzy Labels
A Fuzzy::Label label has four attributes: the text of the label (it
can be any scalar, really), and three numbers: low, mid, high if you
imagine a cartesian plane (remember graph paper in algebra?) of all
possible values, the label applies to a particular range. the graph
might look something like this:
|Y * (mid, 1)
| / \
| / \
| / \
| / \
-|-------*-------------*------- X
(low,0) (high,0)
the Y value is applicability of the label for a given X value
the mid number is the "pure" value. eg, orange is at 0 or 360
degrees on the color wheel. the label applies 100% at the mid
point.
the low and high numbers are the two points at which
the label ceases to apply.
note that labels can overlap, and that the
mid number isn't always in the exact center, so the slope
of the two sides may vary...
$fl = new AI::Fuzzy::Label ( "hot", 77, 80, 100 );
$fx = new AI::Fuzzy::Label ( "cold", 0, 10, 200 );
# what I consider hot. :) (in Farenheit, of course!)
if ( $fl->lessthan($fx) ) {
print "the laws of nature have changed\n";
}
# there is a lessthan, greaterthan, lessequal, greaterequal, and between
# that functions as above or using <,>,<=,>=
$a = $fl->applicability($value);
# $a is now the degree to which this label applies to $value
=head2 Fuzzy Axis
A Fuzzy::Axis maintains a hash of labels. Thus you can now look at how
values apply to the full range of labels. The graph of an Axis might
look like this:
|Y * (mid, 1)
| /\/ \ /|
| /- -\ / /\ \ / |
| / \-/ / \ \ / | (some function on some range of x)
| | / \ /\ ---*-|
-|---------*-----------*------- X
(low,0) (high,0)
the Y value is still the applicability of the label for a given X value,
but there are three labels on this Axis. A different X value may
put your value into a new label.
$fl = new AI::Fuzzy::Axis;
$fl->addlabel($label);
# add a label created as in AI::Fuzzy::Label docs
$a = $fl->applicability($label, $value);
# $a is now the degree to which $label applies to $value
$l = $fl->label ("labelname");
# returns the label object named "labelname"
$l = $fl->labelvalue ($value);
# applies a label to $value
@l = $fl->labelvalue($value);
# returns a list of labels and their applicability values
$s = new AI::Fuzzy::Set( $fl->label($value) );
# same thing, but now it's an object
@range = $fl->range();
# returns a list of labels, sorted by their midpoints
# eg: ("cold", "cool", "lukewarm", "warm", "hot")
=head1 AUTHOR
Tom Scanlan <tscanlan@openreach.com>,
current maintainer
Michal Wallace (sabren@manifestation.com),
original author
=head1 SEE ALSO
Move along, nothing to "see also" here...
=head1 BUGS
Please send any bugs to Tom Scanlan <tscanlan@openreach.com>
=cut
AI-FuzzyEngine
view release on metacpan or search on metacpan
lib/AI/FuzzyEngine.pm view on Meta::CPAN
package AI::FuzzyEngine;
use 5.008009;
use version 0.77; our $VERSION = version->declare('v0.2.2');
use strict;
use warnings;
use Carp;
use Scalar::Util;
use List::Util;
use List::MoreUtils;
use AI::FuzzyEngine::Variable;
sub new {
my ($class) = @_;
my $self = bless {}, $class;
$self->{_variables} = [];
return $self;
}
sub variables { @{ shift->{_variables} } };
sub and {
my ($self, @vals) = @_;
# PDL awareness: any element is a piddle?
return List::Util::min(@vals) if _non_is_a_piddle(@vals);
_check_for_PDL();
my $vals = $self->_cat_array_of_piddles(@vals);
return $vals->mv(-1, 0)->minimum;
}
sub or {
my ($self, @vals) = @_;
# PDL awareness: any element is a piddle?
return List::Util::max(@vals) if _non_is_a_piddle(@vals);
_check_for_PDL();
my $vals = $self->_cat_array_of_piddles(@vals);
return $vals->mv(-1, 0)->maximum;
}
sub not {
my ($self, $val) = @_;
return 1-$val;
}
sub true { return 1 }
sub false { return 0 }
sub new_variable {
my ($self, @pars) = @_;
my $variable_class = $self->_class_of_variable();
my $var = $variable_class->new($self, @pars);
push @{$self->{_variables}}, $var;
Scalar::Util::weaken $self->{_variables}->[-1];
return $var;
}
sub reset {
my ($self) = @_;
$_->reset() for $self->variables();
return $self;
}
sub _class_of_variable { 'AI::FuzzyEngine::Variable' }
sub _non_is_a_piddle {
return List::MoreUtils::none {ref $_ eq 'PDL'} @_;
}
my $_PDL_is_imported;
sub _check_for_PDL {
return if $_PDL_is_imported;
die "PDL not loaded" unless $INC{'PDL.pm'};
die "PDL::Core not loaded" unless $INC{'PDL/Core.pm'};
$_PDL_is_imported = 1;
}
sub _cat_array_of_piddles {
my ($class, @vals) = @_;
# TODO: Rapid return if @_ == 1 (isa piddle)
# TODO: join "-", ndims -> Schnellcheck auf gleiche Dim.
# All elements must get piddles
my @pdls = map { PDL::Core::topdl($_) } @vals;
# Get size of wrapping piddle (using a trick)
# applying valid expansion rules for element wise operations
my $zeros = PDL->pdl(0);
# v-- does not work due to threading mechanisms :-((
# $zeros += $_ for @pdls;
# Avoid threading!
for my $p (@pdls) {
croak "Empty piddles are not allowed" if $p->isempty();
eval { $zeros = $zeros + $p->zeros(); 1
} or croak q{Can't expand piddles to same size};
}
# Now, cat 'em by expanding them on the fly
my $vals = PDL::cat( map {$_ + $zeros} @pdls );
return $vals;
};
1;
=pod
=head1 NAME
AI::FuzzyEngine - A Fuzzy Engine, PDL aware
=head1 SYNOPSIS
=head2 Regular Perl - without PDL
use AI::FuzzyEngine;
# Engine (or factory) provides fuzzy logical arithmetic
my $fe = AI::FuzzyEngine->new();
# Disjunction:
my $a = $fe->or ( 0.2, 0.5, 0.8, 0.7 ); # 0.8
# Conjunction:
my $b = $fe->and( 0.2, 0.5, 0.8, 0.7 ); # 0.2
# Negation:
my $c = $fe->not( 0.4 ); # 0.6
# Always true:
my $t = $fe->true(); # 1.0
# Always false:
my $f = $fe->false(); # 0.0
# These functions are constitutive for the operations
# on the fuzzy sets of the fuzzy variables:
# VARIABLES (AI::FuzzyEngine::Variable)
# input variables need definition of membership functions of their sets
my $flow = $fe->new_variable( 0 => 2000,
small => [0, 1, 500, 1, 1000, 0 ],
med => [ 400, 0, 1000, 1, 1500, 0 ],
huge => [ 1000, 0, 1500, 1, 2000, 1],
);
my $cap = $fe->new_variable( 0 => 1800,
avg => [0, 1, 1500, 1, 1700, 0 ],
high => [ 1500, 0, 1700, 1, 1800, 1],
);
# internal variables need sets, but no membership functions
my $saturation = $fe->new_variable( # from => to may be ommitted
low => [],
crit => [],
over => [],
);
# But output variables need membership functions for their sets:
my $green = $fe->new_variable( -5 => 5,
decrease => [-5, 1, -2, 1, 0, 0 ],
ok => [ -2, 0 0, 1, 2, 0 ],
increase => [ 0, 0, 2, 1, 5, 1],
);
# Reset FuzzyEngine (resets all variables)
$fe->reset();
# Reset a fuzzy variable directly
$flow->reset;
# Membership functions can be changed via the set's variable.
# This might be useful during parameter identification algorithms
# Changing a function resets the respective variable.
$flow->change_set( med => [500, 0, 1000, 1, 1500, 0] );
# Fuzzification of input variables
$flow->fuzzify( 600 );
$cap->fuzzify( 1000 );
# Membership degrees of the respective sets are now available:
my $flow_is_small = $flow->small(); # 0.8
my $flow_is_med = $flow->med(); # 0.2
my $flow_is_huge = $flow->huge(); # 0.0
# RULES and their application
# a) If necessary, calculate some internal variables first.
# They will not be defuzzified (in fact, $saturation can't)
# Implicit application of 'and'
# Multiple calls to a membership function
# are similar to 'or' operations:
$saturation->low( $flow->small(), $cap->avg() );
$saturation->low( $flow->small(), $cap->high() );
$saturation->low( $flow->med(), $cap->high() );
# Explicite 'or', 'and' or 'not' possible:
$saturation->crit( $fe->or( $fe->and( $flow->med(), $cap->avg() ),
$fe->and( $flow->huge(), $cap->high() ),
),
);
$saturation->over( $fe->not( $flow->small() ),
$fe->not( $flow->med() ),
$flow->huge(),
$cap->high(),
);
$saturation->over( $flow->huge(), $fe->not( $cap->high() ) );
# b) deduce output variable(s) (here: from internal variable $saturation)
$green->decrease( $saturation->low() );
$green->ok( $saturation->crit() );
$green->increase( $saturation->over() );
# All sets provide their respective membership degrees:
my $saturation_is_over = $saturation->over(); # This is no defuzzification!
my $green_is_ok = $green->ok();
# Defuzzification ( is a matter of the fuzzy variable )
my $delta_green = $green->defuzzify(); # -5 ... 5
=head2 Using PDL and its threading capability
use PDL;
use AI::FuzzyEngine;
# (Probably a stupide example)
my $fe = AI::FuzzyEngine->new();
# Declare variables as usual
my $severity = $fe->new_variable( 0 => 10,
low => [0, 1, 3, 1, 5, 0 ],
high => [ 3, 0, 5, 1, 10, 1],
);
my $threshold = $fe->new_variable( 0 => 1,
low => [0, 1, 0.2, 1, 0.8, 0, ],
high => [ 0.2, 0, 0.8, 1, 1, 1],
);
my $problem = $fe->new_variable( -0.5 => 2,
no => [-0.5, 0, 0, 1, 0.5, 0, 1, 0],
yes => [ 0, 0, 0.5, 1, 1, 1, 1.5, 1, 2, 0],
);
# Input data is a pdl of arbitrary dimension
my $data = pdl( [0, 4, 6, 10] );
$severity->fuzzify( $data );
# Membership degrees are piddles now:
print 'Severity is high: ', $severity->high, "\n";
# [0 0.5 1 1]
# Other variables might be piddles of other dimensions,
# but all variables must be expandible to a common 'wrapping' piddle
# ( in this case a 4x2 matrix with 4 colums and 2 rows)
my $level = pdl( [0.6],
[0.2],
);
$threshold->fuzzify( $level );
print 'Threshold is low: ', $threshold->low(), "\n";
# [
# [0.33333333]
# [ 1]
# ]
# Apply some rules
$problem->yes( $severity->high, $threshold->low );
$problem->no( $fe->not( $problem->yes ) );
# Fuzzy results are represented by the membership degrees of sets
print 'Problem yes: ', $problem->yes, "\n";
# [
# [ 0 0.33333333 0.33333333 0.33333333]
# [ 0 0.5 1 1]
# ]
# Defuzzify the output variables
# Caveat: This includes some non-threadable operations up to now
my $problem_ratings = $problem->defuzzify();
print 'Problems rated: ', $problem_ratings;
# [
# [ 0 0.60952381 0.60952381 0.60952381]
# [ 0 0.75 1 1]
# ]
=head1 EXPORT
Nothing is exported or exportable.
=head1 DESCRIPTION
This module is yet another implementation of a fuzzy inference system.
The aim was to be able to code rules (no string parsing),
but avoid operator overloading,
and make it possible to split calculation into multiple steps.
All intermediate results (memberships of sets of variables)
should be available.
Beginning with v0.2.0 it is PDL aware,
meaning that it can handle piddles (PDL objects)
when running the fuzzy operations.
More information on PDL can be found at L<http://pdl.perl.org/>.
Credits to Ala Qumsieh and his L<AI::FuzzyInference>,
that showed me that fuzzy is no magic.
I learned a lot by analyzing his code,
and he provides good information and links to learn more about Fuzzy Logics.
=head2 Fuzzy stuff
The L<AI::FuzzyEngine> object defines and provides
the elementary operations for fuzzy sets.
All membership degrees of sets are values from 0 to 1.
Up to now there is no choice with regard to how to operate on sets:
=over 2
=item C<< $fe->or( ... ) >> (Disjunction)
is I<Maximum> of membership degrees
=item C<< $fe->and( ... ) >> (Conjunction)
is I<Minimum> of membership degrees
=item C<< $fe->not( $var->$set ) >> (Negation)
is I<1-degree> of membership degree
=item Aggregation of rules (Disjunction)
is I<Maximum>
=item True C<< $fe->true() >> and false C<< $fe->false() >>
are provided for convenience.
=back
Defuzzification is based on
=over 2
=item Implication
I<Clip> membership function of a set according to membership degree,
before the implicated memberships of all sets of a variable are taken for defuzzification:
=item Defuzzification
I<Centroid> of aggregated (and clipped) membership functions
=back
=head2 Public functions
Creation of an C<AI::FuzzyEngine> object by
my $fe = AI::FuzzyEngine->new();
This function has no parameters. It provides the fuzzy methods
C<or>, C<and> and C<not>, as listed above.
If needed, I will introduce alternative fuzzy operations,
they will be configured as arguments to C<new>.
Once built, the engine can create fuzzy variables by C<new_variable>:
my $var = $fe->new_variable( $from => $to,
$name_of_set1 => [$x11, $y11, $x12, $y12, ... ],
$name_of_set2 => [$x21, $y21, $x22, $y22, ... ],
...
);
Result is an L<AI::FuzzyEngine::Variable>.
The name_of_set strings are taken to assign corresponding methods
for the respective fuzzy variables.
They must be valid function identifiers.
Same name_of_set can used for different variables without conflict.
Take care:
There is no check for conflicts with predefined class methods.
Fuzzy variables provide a method to fuzzify input values:
$var->fuzzify( $val );
according to the defined sets and their membership functions.
The memberships of the sets of C<$var> are accessible
by the respective functions:
my $membership_degree = $var->$name_of_set();
Membership degrees can be assigned directly (within rules for example):
$var->$name_of_set( $membership_degree );
If multiple membership_degrees are given, they are "anded":
$var->$name_of_set( $degree1, $degree2, ... ); # "and"
By this, simple rules can be coded directly:
my $var_3->zzz( $var_1->xxx, $var_2->yyy, ... ); # "and"
this implements the fuzzy implication
if $var_1->xxx and $var_2->yyy and ... then $var_3->zzz
The membership degrees of a variable's sets can be reset to undef:
$var->reset(); # resets a variable
$fe->reset(); # resets all variables
The fuzzy engine C<$fe> has all variables registered
that have been created by its C<new_variable> method.
A variable can be defuzzified:
my $out_value = $var->defuzzify();
Membership functions can be replaced via a set's variable:
$var->change_set( $name_of_set => [$x11n, $y11n, $x12n, $y12n, ... ] );
The variable will be reset when replacing a membership function
of any of its sets.
Interdependencies with other variables are not checked
(it might happen that the results of any rules are no longer valid,
so it needs some recalculations).
Sometimes internal variables are used that need neither fuzzification
nor defuzzification.
They can be created by a simplified call to C<new_variable>:
my $var_int = $fe->new_variable( $name_of_set1 => [],
$name_of_set2 => [],
...
);
Hence, they can not use the methods C<fuzzify> or C<defuzzify>.
Fuzzy operations are simple operations on floating values between 0 and 1:
my $conjunction = $fe->and( $var1->xxx, $var2->yyy, ... );
my $disjunction = $fe->or( $var1->xxx, $var2->yyy, ... );
my $negated = $fe->not( $var1->zzz );
There is no magic.
A sequence of rules for the same set can be implemented as follows:
$var_3->zzz( $var_1->xxx, $var_2->yyy, ... );
$var_3->zzz( $var_4->aaa, $var_5->bbb, ... );
The subsequent application of C<< $var_3->zzz(...) >>
corresponds to "or" operations (aggregation of rules).
Only a reset can reset C<$var_3>.
=head2 PDL awareness
Membership degrees of sets might be either scalars or piddles now.
$var_a->memb_fun_a( 5 ); # degree of memb_fun_a is a scalar
$var_a->memb_fun_b( pdl(7, 8) ); # degree of memb_fun_b is a piddle
Empty piddles are not allowed, behaviour with bad values is not tested.
Fuzzification (hence calculating degrees) accepts piddles:
$var_b->fuzzify( pdl([1, 2], [3, 4]) );
Defuzzification returns a piddle if any of the membership
degrees of the function's sets is a piddle:
my $val = $var_a->defuzzify(); # $var_a returns a 1dim piddle with two elements
So do the fuzzy operations as provided by the fuzzy engine C<$fe> itself.
Any operation on more then one piddle expands those to common
dimensions, if possible, or throws a PDL error otherwise.
The way expansion is done is best explained by code
(see C<< AI::FuzzyEngine->_cat_array_of_piddles(@pdls) >>).
Assuming all piddles are in C<@pdls>,
calculation goes as follows:
# Get the common dimensions
my $zeros = PDL->pdl(0);
# Note: $zeros += $_->zeros() for @pdls does not work here
$zeros = $zeros + $_->zeros() for @pdls;
# Expand all piddles
@pdls = map {$_ + $zeros} @pdls;
Defuzzification uses some heavy non-threading code,
so there might be a performance penalty for big piddles.
=head2 Todos
=over 2
=item Add optional alternative implementations of fuzzy operations
=item More checks on input arguments and allowed method calls
=item PDL awareness: Use threading in C<< $variable->defuzzify >>
=item Divide tests into API tests and test of internal functions
=back
=head1 CAVEATS / BUGS
This is my first module.
I'm happy about feedback that helps me to learn
and improve my contributions to the Perl ecosystem.
Please report any bugs or feature requests to
C<bug-ai-fuzzyengine at rt.cpan.org>, or through
the web interface at
L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-FuzzyEngine>.
I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.
=head1 SUPPORT
You can find documentation for this module with the perldoc command.
perldoc AI::FuzzyEngine
=head1 AUTHOR
Juergen Mueck, jmueck@cpan.org
=head1 COPYRIGHT
Copyright (c) Juergen Mueck 2013. All rights reserved.
This library is free software; you can redistribute it and/or
modify it under the same terms as Perl itself.
=cut
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