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found more than 265 distributions - search limited to the first 2001 files matching your query
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AE-AdHoc
view release on metacpan or search on metacpan
examples/port-probe-multi.pl view on Meta::CPAN
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
AES128
view release on metacpan or search on metacpan
my(%replace, %need, %hints, %warnings, %depends);
my $replace = 0;
my($hint, $define, $function);
sub find_api
{
my $code = shift;
$code =~ s{
/ (?: \*[^*]*\*+(?:[^$ccs][^*]*\*+)* / | /[^\r\n]*)
| "[^"\\]*(?:\\.[^"\\]*)*"
}
}
else {
eval {
require File::Find;
File::Find::find(sub {
$File::Find::name =~ /($srcext)$/i
and push @files, $File::Find::name;
}, '.');
};
if ($@) {
close PATCH if $patch_opened;
exit 0;
sub try_use { eval "use @_;"; return $@ eq '' }
sub mydiff
{
local *F = shift;
my($file, $str) = @_;
my $diff;
}
print F $diff;
}
sub run_diff
{
my($prog, $file, $str) = @_;
my $tmp = 'dppptemp';
my $suf = 'aaa';
my $diff = '';
}
return undef;
}
sub rec_depend
{
my($func, $seen) = @_;
return () unless exists $depends{$func};
$seen = {%{$seen||{}}};
return () if $seen->{$func}++;
my %s;
grep !$s{$_}++, map { ($_, rec_depend($_, $seen)) } @{$depends{$func}};
}
sub parse_version
{
my $ver = shift;
if ($ver =~ /^(\d+)\.(\d+)\.(\d+)$/) {
return ($1, $2, $3);
}
return ($r, $v, $s);
}
sub format_version
{
my $ver = shift;
$ver =~ s/$/000000/;
my($r,$v,$s) = $ver =~ /(\d+)\.(\d{3})(\d{3})/;
}
return sprintf "%d.%d.%d", $r, $v, $s;
}
sub info
{
$opt{quiet} and return;
print @_, "\n";
}
sub diag
{
$opt{quiet} and return;
$opt{diag} and print @_, "\n";
}
sub warning
{
$opt{quiet} and return;
print "*** ", @_, "\n";
}
sub error
{
print "*** ERROR: ", @_, "\n";
}
my %given_hints;
my %given_warnings;
sub hint
{
$opt{quiet} and return;
my $func = shift;
my $rv = 0;
if (exists $warnings{$func} && !$given_warnings{$func}++) {
print " --- hint for $func ---\n", $hint;
}
$rv;
}
sub usage
{
my($usage) = do { local(@ARGV,$/)=($0); <> } =~ /^=head\d$HS+SYNOPSIS\s*^(.*?)\s*^=/ms;
my %M = ( 'I' => '*' );
$usage =~ s/^\s*perl\s+\S+/$^X $0/;
$usage =~ s/([A-Z])<([^>]+)>/$M{$1}$2$M{$1}/g;
ENDUSAGE
exit 2;
}
sub strip
{
my $self = do { local(@ARGV,$/)=($0); <> };
my($copy) = $self =~ /^=head\d\s+COPYRIGHT\s*^(.*?)^=\w+/ms;
$copy =~ s/^(?=\S+)/ /gms;
$self =~ s/^$HS+Do NOT edit.*?(?=^-)/$copy/ms;
#if (PERL_BCDVERSION <= 0x5004005)
/* Replace: 1 */
# define PL_DBsignal DBsignal
# define PL_DBsingle DBsingle
# define PL_DBsub DBsub
# define PL_DBtrace DBtrace
# define PL_Sv Sv
# define PL_bufend bufend
# define PL_bufptr bufptr
# define PL_compiling compiling
ccstack = top_si->si_cxstack;
cxix = DPPP_dopoptosub_at(ccstack, top_si->si_cxix);
}
if (cxix < 0)
return NULL;
/* caller() should not report the automatic calls to &DB::sub */
if (PL_DBsub && GvCV(PL_DBsub) && cxix >= 0 &&
ccstack[cxix].blk_sub.cv == GvCV(PL_DBsub))
count++;
if (!count--)
break;
cxix = DPPP_dopoptosub_at(ccstack, cxix - 1);
if (CxTYPE(cx) == CXt_SUB || CxTYPE(cx) == CXt_FORMAT) {
const I32 dbcxix = DPPP_dopoptosub_at(ccstack, cxix - 1);
/* We expect that ccstack[dbcxix] is CXt_SUB, anyway, the
field below is defined for any cx. */
/* caller() should not report the automatic calls to &DB::sub */
if (PL_DBsub && GvCV(PL_DBsub) && dbcxix >= 0 && ccstack[dbcxix].blk_sub.cv == GvCV(PL_DBsub))
cx = &ccstack[dbcxix];
}
return cx;
}
AFS-Command
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lib/AFS/Command/BOS.pm view on Meta::CPAN
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();
lib/AFS/Command/BOS.pm view on Meta::CPAN
return if $errors;
return $result;
}
sub getlog {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
lib/AFS/Command/BOS.pm view on Meta::CPAN
return if $errors;
return $result;
}
sub getrestart {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
lib/AFS/Command/BOS.pm view on Meta::CPAN
return if $errors;
return $result;
}
sub listhosts {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
lib/AFS/Command/BOS.pm view on Meta::CPAN
return if $errors;
return $result;
}
sub listkeys {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
lib/AFS/Command/BOS.pm view on Meta::CPAN
return if $errors;
return $result;
}
sub listusers {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
lib/AFS/Command/BOS.pm view on Meta::CPAN
#
# 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();
lib/AFS/Command/BOS.pm view on Meta::CPAN
# return if $errors;
# return $result;
# }
sub status {
my $self = shift;
my (%args) = @_;
my $result = AFS::Object::BosServer->new();
AFS-Monitor
view release on metacpan or search on metacpan
examples/Meltdown.pl view on Meta::CPAN
#
use blib;
use AFS::Monitor;
sub Usage {
print STDERR "\n\n$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";
examples/Meltdown.pl view on Meta::CPAN
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.
#
examples/Meltdown.pl view on Meta::CPAN
(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";
}
AFS-PAG
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t/lib/Test/RRA.pm view on Meta::CPAN
# Calls plan skip_all, which will terminate the program.
#
# $description - Short description of the tests
#
# Returns: undef
sub skip_unless_author {
my ($description) = @_;
if (!$ENV{AUTHOR_TESTING}) {
plan skip_all => "$description only run for author";
}
return;
t/lib/Test/RRA.pm view on Meta::CPAN
# plan skip_all, which will terminate the program.
#
# $description - Short description of the tests
#
# Returns: undef
sub skip_unless_automated {
my ($description) = @_;
for my $env (qw(AUTOMATED_TESTING RELEASE_TESTING AUTHOR_TESTING)) {
return if $ENV{$env};
}
plan skip_all => "$description normally skipped";
t/lib/Test/RRA.pm view on Meta::CPAN
#
# $module - Name of the module to load
# @imports - Any arguments to import, possibly including a version
#
# Returns: undef
sub use_prereq {
my ($module, @imports) = @_;
# If the first import looks like a version, pass it as a bare string.
my $version = q{};
if (@imports >= 1 && $imports[0] =~ m{ \A \d+ (?: [.][\d_]+ )* \z }xms) {
AFS
view release on metacpan or search on metacpan
src/ACL/ACL.pm view on Meta::CPAN
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;
src/ACL/ACL.pm view on Meta::CPAN
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 $newacl;
src/ACL/ACL.pm view on Meta::CPAN
AFS::setacl($path, $newacl, $follow);
}
else { return 0; }
}
sub copyacl {
my $class = shift;
my $from = shift;
my $to = shift;
my $follow = shift;
src/ACL/ACL.pm view on Meta::CPAN
$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 $acl;
src/ACL/ACL.pm view on Meta::CPAN
if (! defined ($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]}{$_[1]}; } # old form: DEPRECATED !!!!
1;
AHA
view release on metacpan or search on metacpan
example/lava_lamp.pl view on Meta::CPAN
store_status($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];
example/lava_lamp.pl view on Meta::CPAN
} 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;
example/lava_lamp.pl view on Meta::CPAN
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;
example/lava_lamp.pl view on Meta::CPAN
} 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;
example/lava_lamp.pl view on Meta::CPAN
}
# ==========================================================================
# 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;
example/lava_lamp.pl view on Meta::CPAN
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
AI-ANN
view release on metacpan or search on metacpan
examples/benchmark.pl view on Meta::CPAN
}
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)} });
AI-CBR
view release on metacpan or search on metacpan
lib/AI/CBR/Case.pm view on Meta::CPAN
=back
=cut
sub new {
my ($class, %attributes) = @_;
# set default weights if unspecified
foreach (keys %attributes) {
$attributes{$_}->{weight} = $DEFAULT_WEIGHT unless defined $attributes{$_}->{weight};
lib/AI/CBR/Case.pm view on Meta::CPAN
This will overwrite existing values, and can thus be used as a faster method
for generating new cases with the same specification.
=cut
sub set_values {
my ($self, %values) = @_;
foreach (keys %values) {
$self->{$_}->{value} = $values{$_};
}
}
AI-CRM114
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lib/AI/CRM114.pm view on Meta::CPAN
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:);
lib/AI/CRM114.pm view on Meta::CPAN
/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 $o = "";
AI-Calibrate
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lib/AI/Calibrate.pm view on Meta::CPAN
how this structure is interpreted. You can pass this structure to the
B<score_prob> function, along with a new score, to get a probability.
=cut
sub calibrate {
my($data, $sorted) = @_;
if (DEBUG) {
print "Original data:\n";
for my $pair (@$data) {
lib/AI/Calibrate.pm view on Meta::CPAN
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] =
lib/AI/Calibrate.pm view on Meta::CPAN
}
}
}
}
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);
lib/AI/Calibrate.pm view on Meta::CPAN
print "Estimated probability: $prob\n";
}
=cut
sub score_prob {
my($calibrated, $score) = @_;
my $last_prob = 1.0;
for my $tuple (@$calibrated) {
lib/AI/Calibrate.pm view on Meta::CPAN
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",
AI-Categorizer
view release on metacpan or search on metacpan
lib/AI/Categorizer.pm view on Meta::CPAN
delayed => 1 },
collection => { class => 'AI::Categorizer::Collection::Files',
delayed => 1 },
);
sub new {
my $package = shift;
my %args = @_;
my %defaults;
if (exists $args{data_root}) {
$defaults{training_set} = File::Spec->catfile($args{data_root}, 'training');
lib/AI/Categorizer.pm view on Meta::CPAN
}
return $package->SUPER::new(%defaults, %args);
}
#sub dump_parameters {
# my $p = shift()->SUPER::dump_parameters;
# delete $p->{stopwords} if $p->{stopword_file};
# return $p;
#}
sub knowledge_set { shift->{knowledge_set} }
sub learner { shift->{learner} }
# Combines several methods in one sub
sub run_experiment {
my $self = shift;
$self->scan_features;
$self->read_training_set;
$self->train;
$self->evaluate_test_set;
print $self->stats_table;
}
sub scan_features {
my $self = shift;
return unless $self->knowledge_set->scan_first;
$self->knowledge_set->scan_features( path => $self->{training_set} );
$self->knowledge_set->save_features( "$self->{progress_file}-01-features" );
}
sub read_training_set {
my $self = shift;
$self->knowledge_set->restore_features( "$self->{progress_file}-01-features" )
if -e "$self->{progress_file}-01-features";
$self->knowledge_set->read( path => $self->{training_set} );
$self->_save_progress( '02', 'knowledge_set' );
return $self->knowledge_set;
}
sub train {
my $self = shift;
$self->_load_progress( '02', 'knowledge_set' );
$self->learner->train( knowledge_set => $self->{knowledge_set} );
$self->_save_progress( '03', 'learner' );
return $self->learner;
}
sub evaluate_test_set {
my $self = shift;
$self->_load_progress( '03', 'learner' );
my $c = $self->create_delayed_object('collection', path => $self->{test_set} );
$self->{experiment} = $self->learner->categorize_collection( collection => $c );
$self->_save_progress( '04', 'experiment' );
return $self->{experiment};
}
sub stats_table {
my $self = shift;
$self->_load_progress( '04', 'experiment' );
return $self->{experiment}->stats_table;
}
sub progress_file {
shift->{progress_file};
}
sub verbose {
shift->{verbose};
}
sub _save_progress {
my ($self, $stage, $node) = @_;
return unless $self->{progress_file};
my $file = "$self->{progress_file}-$stage-$node";
warn "Saving to $file\n" if $self->{verbose};
$self->{$node}->save_state($file);
}
sub _load_progress {
my ($self, $stage, $node) = @_;
return unless $self->{progress_file};
my $file = "$self->{progress_file}-$stage-$node";
warn "Loading $file\n" if $self->{verbose};
$self->{$node} = $self->contained_class($node)->restore_state($file);
AI-Chat
view release on metacpan or search on metacpan
lib/AI/Chat.pm view on Meta::CPAN
$VERSION = eval $VERSION;
my $http = HTTP::Tiny->new;
# Create Chat object
sub new {
my $class = shift;
my %attr = @_;
$attr{'error'} = '';
lib/AI/Chat.pm view on Meta::CPAN
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";
lib/AI/Chat.pm view on Meta::CPAN
return $self->chat(\@messages, $temperature);
}
# Get a reply from a full chat
sub chat {
my ($self, $chat, $temperature) = @_;
if (ref($chat) ne 'ARRAY') {
$self->{'error'} = 'chat method requires an arrayref';
return undef;
AI-Classifier-Japanese
view release on metacpan or search on metacpan
lib/AI/Classifier/Japanese.pm view on Meta::CPAN
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 = [];
lib/AI/Classifier/Japanese.pm view on Meta::CPAN
$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();
AI-Classifier
view release on metacpan or search on metacpan
lib/AI/Classifier/Text.pm view on Meta::CPAN
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;
AI-CleverbotIO
view release on metacpan or search on metacpan
lib/AI/CleverbotIO.pm view on Meta::CPAN
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',
};
},
lib/AI/CleverbotIO.pm view on Meta::CPAN
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,
lib/AI/CleverbotIO.pm view on Meta::CPAN
$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 = (
lib/AI/CleverbotIO.pm view on Meta::CPAN
$self->nick($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));
lib/AI/CleverbotIO.pm view on Meta::CPAN
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 $decoded;
} ## end sub __decode_content
1;
AI-ConfusionMatrix
view release on metacpan or search on metacpan
lib/AI/ConfusionMatrix.pm view on Meta::CPAN
use strict;
use Tie::File;
# ABSTRACT: Make a confusion matrix
sub makeConfusionMatrix {
my ($matrix, $file, $delem) = @_;
unless(defined $delem) {
$delem = ',';
}
lib/AI/ConfusionMatrix.pm view on Meta::CPAN
);
untie @output_array;
}
sub getConfusionMatrix {
my ($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;
lib/AI/ConfusionMatrix.pm view on Meta::CPAN
stats => \%stats,
totals => \%totals
);
}
sub _findIndex {
my ($string, $array) = @_;
for (0 .. @$array - 1) {
return $_ + 1 if ($string eq @{$array}[$_]);
}
}
AI-DecisionTree
view release on metacpan or search on metacpan
lib/AI/DecisionTree.pm view on Meta::CPAN
use AI::DecisionTree::Instance;
use Carp;
use vars qw(@ISA);
sub new {
my $package = shift;
return bless {
noise_mode => 'fatal',
prune => 1,
purge => 1,
lib/AI/DecisionTree.pm view on Meta::CPAN
instances => [],
name_gen => 0,
}, $package;
}
sub nodes { $_[0]->{nodes} }
sub noise_mode { $_[0]->{noise_mode} }
sub depth { $_[0]->{depth} }
sub add_instance {
my ($self, %args) = @_;
croak "Missing 'attributes' parameter" unless $args{attributes};
croak "Missing 'result' parameter" unless defined $args{result};
$args{name} = $self->{name_gen}++ unless exists $args{name};
lib/AI/DecisionTree.pm view on Meta::CPAN
$_ ||= 0 foreach @attributes;
push @{$self->{instances}}, AI::DecisionTree::Instance->new(\@attributes, _hlookup($self->{results}, $args{result}), $args{name});
}
sub _hlookup {
$_[0] ||= {}; # Autovivify as a hash
my ($hash, $key) = @_;
unless (exists $hash->{$key}) {
$hash->{$key} = 1 + keys %$hash;
}
return $hash->{$key};
}
sub _create_lookup_hashes {
my $self = shift;
my $h = $self->{results};
$self->{results_reverse} = [ undef, sort {$h->{$a} <=> $h->{$b}} keys %$h ];
foreach my $attr (keys %{$self->{attribute_values}}) {
my $h = $self->{attribute_values}{$attr};
$self->{attribute_values_reverse}{$attr} = [ undef, sort {$h->{$a} <=> $h->{$b}} keys %$h ];
}
}
sub train {
my ($self, %args) = @_;
if (not @{ $self->{instances} }) {
croak "Training data has been purged, can't re-train" if $self->{tree};
croak "Must add training instances before calling train()";
}
lib/AI/DecisionTree.pm view on Meta::CPAN
$self->prune_tree if $self->{prune};
$self->do_purge if $self->purge;
return 1;
}
sub do_purge {
my $self = shift;
delete @{$self}{qw(instances attribute_values attribute_values_reverse results results_reverse)};
}
sub copy_instances {
my ($self, %opt) = @_;
croak "Missing 'from' parameter to copy_instances()" unless exists $opt{from};
my $other = $opt{from};
croak "'from' parameter is not a decision tree" unless UNIVERSAL::isa($other, __PACKAGE__);
lib/AI/DecisionTree.pm view on Meta::CPAN
$self->{$_} = $other->{$_};
}
$self->_create_lookup_hashes;
}
sub set_results {
my ($self, $hashref) = @_;
foreach my $instance (@{$self->{instances}}) {
my $name = $instance->name;
croak "No result given for instance '$name'" unless exists $hashref->{$name};
$instance->set_result( $self->{results}{ $hashref->{$name} } );
}
}
sub instances { $_[0]->{instances} }
sub purge {
my $self = shift;
$self->{purge} = shift if @_;
return $self->{purge};
}
lib/AI/DecisionTree.pm view on Meta::CPAN
# $attr_value2 => $node2, ... }
# }
# or
# { result => $result }
sub _expand_node {
my ($self, %args) = @_;
my $instances = $args{instances};
print STDERR '.' if $self->{verbose};
$self->{depth} = $self->{curr_depth} if $self->{curr_depth} > $self->{depth};
lib/AI/DecisionTree.pm view on Meta::CPAN
}
return \%node;
}
sub best_attr {
my ($self, $instances) = @_;
# 0 is a perfect score, entropy(#instances) is the worst possible score
my ($best_score, $best_attr) = (@$instances * $self->entropy( map $_->result_int, @$instances ), undef);
lib/AI/DecisionTree.pm view on Meta::CPAN
}
return $best_attr;
}
sub entropy2 {
shift;
my ($counts, $total) = @_;
# Entropy is defined with log base 2 - we just divide by log(2) at the end to adjust.
my $sum = 0;
$sum += $_ * log($_) foreach values %$counts;
return +(log($total) - $sum/$total)/log(2);
}
sub entropy {
shift;
my %count;
$count{$_}++ foreach @_;
lib/AI/DecisionTree.pm view on Meta::CPAN
my $sum = 0;
$sum += $_ * log($_) foreach values %count;
return +(log(@_) - $sum/@_)/log(2);
}
sub prune_tree {
my $self = shift;
# We use a minimum-description-length approach. We calculate the
# score of each node:
# n = number of nodes below
lib/AI/DecisionTree.pm view on Meta::CPAN
my $r = keys %{ $self->{results} };
my $i = $self->{tree}{instances};
my $exception_cost = log($r) * log($i) / log(2)**2;
# Pruning can turn a branch into a leaf
my $maybe_prune = sub {
my ($self, $node) = @_;
return unless $node->{children}; # Can't prune leaves
my $nodes_below = $self->nodes_below($node);
my $tree_cost = 2 * $nodes_below - 1; # $edges_below == $nodes_below - 1
lib/AI/DecisionTree.pm view on Meta::CPAN
};
$self->_traverse($maybe_prune);
}
sub exceptions {
my ($self, $node) = @_;
return $node->{exceptions} if exists $node->{exeptions};
my $count = 0;
if ( exists $node->{result} ) {
lib/AI/DecisionTree.pm view on Meta::CPAN
}
return $node->{exceptions} = $count;
}
sub nodes_below {
my ($self, $node) = @_;
return $node->{nodes_below} if exists $node->{nodes_below};
my $count = 0;
$self->_traverse( sub {$count++}, $node );
return $node->{nodes_below} = $count - 1;
}
# This is *not* for external use, I may change it.
sub _traverse {
my ($self, $callback, $node, $parent, $node_name) = @_;
$node ||= $self->{tree};
ref($callback) ? $callback->($self, $node, $parent, $node_name) : $self->$callback($node, $parent, $node_name);
lib/AI/DecisionTree.pm view on Meta::CPAN
foreach my $child ( keys %{$node->{children}} ) {
$self->_traverse($callback, $node->{children}{$child}, $node, $child);
}
}
sub get_result {
my ($self, %args) = @_;
croak "Missing 'attributes' or 'callback' parameter" unless $args{attributes} or $args{callback};
$self->train unless $self->{tree};
my $tree = $self->{tree};
lib/AI/DecisionTree.pm view on Meta::CPAN
$tree = $tree->{children}{ $instance_val }
or return undef;
}
}
sub as_graphviz {
my ($self, %args) = @_;
my $colors = delete $args{leaf_colors} || {};
require GraphViz;
my $g = GraphViz->new(%args);
my $id = 1;
my $add_edge = sub {
my ($self, $node, $parent, $node_name) = @_;
# We use stringified reference names for node names, as a convenient hack.
if ($node->{split_on}) {
$g->add_node( "$node",
lib/AI/DecisionTree.pm view on Meta::CPAN
$self->_traverse( $add_edge );
return $g;
}
sub rule_tree {
my $self = shift;
my ($tree) = @_ ? @_ : $self->{tree};
# build tree:
# [ question, { results => [ question, { ... } ] } ]
lib/AI/DecisionTree.pm view on Meta::CPAN
map { $_ => $self->rule_tree($tree->{children}{$_}) } keys %{$tree->{children}},
}
];
}
sub rule_statements {
my $self = shift;
my ($stmt, $tree) = @_ ? @_ : ('', $self->{tree});
return("$stmt -> '$tree->{result}'") if exists $tree->{result};
my @out;
lib/AI/DecisionTree.pm view on Meta::CPAN
return @out;
}
### Some instance accessor stuff:
sub _result {
my ($self, $instance) = @_;
my $int = $instance->result_int;
return $self->{results_reverse}[$int];
}
sub _delete_value {
my ($self, $instance, $attr) = @_;
my $val = $self->_value($instance, $attr);
return unless defined $val;
$instance->set_value($self->{attributes}{$attr}, 0);
return $val;
}
sub _value {
my ($self, $instance, $attr) = @_;
return unless exists $self->{attributes}{$attr};
my $val_int = $instance->value_int($self->{attributes}{$attr});
return $self->{attribute_values_reverse}{$attr}[$val_int];
}
AI-Embedding
view release on metacpan or search on metacpan
lib/AI/Embedding.pm view on Meta::CPAN
$VERSION = eval $VERSION;
my $http = HTTP::Tiny->new;
# Create Embedding object
sub new {
my $class = shift;
my %attr = @_;
$attr{'error'} = '';
lib/AI/Embedding.pm view on Meta::CPAN
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'},
lib/AI/Embedding.pm view on Meta::CPAN
# 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'});
lib/AI/Embedding.pm view on Meta::CPAN
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'});
lib/AI/Embedding.pm view on Meta::CPAN
return $response if defined $verbose;
return undef;
}
# Return Test Embedding
sub test_embedding {
my ($self, $text, $dimension) = @_;
$self->{'error'} = '';
$dimension = 1536 unless defined $dimension;
lib/AI/Embedding.pm view on Meta::CPAN
}
return join ',', @vector;
}
# Convert a CSV Embedding into a hashref
sub _make_vector {
my ($self, $embed_string) = @_;
if (!defined $embed_string) {
$self->{'error'} = 'Nothing to compare!';
return;
lib/AI/Embedding.pm view on Meta::CPAN
}
return \%vector;
}
# Return a comparator to compare to a set vector
sub comparator {
my($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) {
lib/AI/Embedding.pm view on Meta::CPAN
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;
AI-Evolve-Befunge
view release on metacpan or search on metacpan
lib/AI/Evolve/Befunge/Blueprint.pm view on Meta::CPAN
id - assign it an id, default is to call new_popid() (see below).
host - the hostname, default is $ENV{HOST}.
=cut
sub new {
my $self = bless({}, shift);
my %args = @_;
my $usage = 'Usage: AI::Evolve::Befunge::Blueprint->new(code => "whatever", dimensions => 4, [, id => 2, host => "localhost", fitness => 5]);\n';
croak $usage unless exists $args{code};
croak $usage unless exists $args{dimensions};
lib/AI/Evolve/Befunge/Blueprint.pm view on Meta::CPAN
object. The text representation was likely created by L</as_string>,
below.
=cut
sub new_from_string {
my ($package, $line) = @_;
return undef unless defined $line;
chomp $line;
if($line =~ /^\[I(-?\d+) D(\d+) F(\d+) H([^\]]+)\](.+)/) {
my ($id, $dimensions, $fitness, $host, $code) = ($1, $2, $3, $4, $5);
lib/AI/Evolve/Befunge/Blueprint.pm view on Meta::CPAN
a results file (or a migration file), returns a Blueprint object.
Calls L</new_from_string> to do the dirty work.
=cut
sub new_from_file {
my ($package, $file) = @_;
return $package->new_from_string($file->getline);
}
lib/AI/Evolve/Befunge/Blueprint.pm view on Meta::CPAN
sticking into a results file, or migrating to another node. See
L</new_from_string> above.
=cut
sub as_string {
my $self = shift;
my $rv =
"[I$$self{id} D$$self{dims} F$$self{fitness} H$$self{host}]";
$rv .= $$self{code};
$rv .= "\n";
lib/AI/Evolve/Befunge/Blueprint.pm view on Meta::CPAN
Return a unique identifier.
=cut
sub new_popid :Export(:DEFAULT) {
$_popid = 0 unless defined $_popid;
return $_popid++;
}
lib/AI/Evolve/Befunge/Blueprint.pm view on Meta::CPAN
when a new process reads a results file, to keep node identifiers
unique across runs.
=cut
sub set_popid :Export(:DEFAULT) {
$_popid = shift;
}
}
new_popid();
AI-ExpertSystem-Advanced
view release on metacpan or search on metacpan
inc/Module/Install.pm view on Meta::CPAN
use Cwd ();
use File::Find ();
use File::Path ();
use FindBin;
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;
}
inc/Module/Install.pm view on Meta::CPAN
unshift @_, ( $self, $1 );
goto &{$self->can('call')};
};
}
sub import {
my $class = shift;
my $self = $class->new(@_);
my $who = $self->_caller;
unless ( -f $self->{file} ) {
inc/Module/Install.pm view on Meta::CPAN
$MAIN = $self;
return 1;
}
sub preload {
my $self = shift;
unless ( $self->{extensions} ) {
$self->load_extensions(
"$self->{prefix}/$self->{path}", $self
);
inc/Module/Install.pm view on Meta::CPAN
}
}
my $who = $self->_caller;
foreach my $name ( sort keys %seen ) {
*{"${who}::$name"} = sub {
${"${who}::AUTOLOAD"} = "${who}::$name";
goto &{"${who}::AUTOLOAD"};
};
}
}
sub new {
my ($class, %args) = @_;
# 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 ) {
inc/Module/Install.pm view on Meta::CPAN
$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};
inc/Module/Install.pm view on Meta::CPAN
push @{$self->{extensions}}, $obj;
$obj;
}
sub load_extensions {
my ($self, $path, $top) = @_;
unless ( grep { ! ref $_ and lc $_ eq lc $self->{prefix} } @INC ) {
unshift @INC, $self->{prefix};
}
inc/Module/Install.pm view on Meta::CPAN
}
$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});
inc/Module/Install.pm view on Meta::CPAN
#####################################################################
# Common Utility Functions
sub _caller {
my $depth = 0;
my $call = caller($depth);
while ( $call eq __PACKAGE__ ) {
$depth++;
$call = caller($depth);
}
return $call;
}
sub _read {
local *FH;
if ( $] >= 5.006 ) {
open( FH, '<', $_[0] ) or die "open($_[0]): $!";
} else {
open( FH, "< $_[0]" ) or die "open($_[0]): $!";
inc/Module/Install.pm view on Meta::CPAN
my $string = do { local $/; <FH> };
close FH or die "close($_[0]): $!";
return $string;
}
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;
}
sub _write {
local *FH;
if ( $] >= 5.006 ) {
open( FH, '>', $_[0] ) or die "open($_[0]): $!";
} else {
open( FH, "> $_[0]" ) or die "open($_[0]): $!";
inc/Module/Install.pm view on Meta::CPAN
close FH or die "close($_[0]): $!";
}
# _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;
inc/Module/Install.pm view on Meta::CPAN
} $s =~ /(\d{1,3})\D?/g;
$l = $l . '.' . join '', @v if @v;
return $l + 0;
}
sub _cmp ($$) {
_version($_[0]) <=> _version($_[1]);
}
# Cloned from Params::Util::_CLASS
sub _CLASS ($) {
(
defined $_[0]
and
! ref $_[0]
and
AI-ExpertSystem-Simple
view release on metacpan or search on metacpan
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
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 = {};
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
$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}}) {
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
$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 $@;
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
} else {
die "Simple->load() unable to use file";
}
}
sub _goal {
my ($self, $t, $node) = @_;
my $attribute = undef;
my $text = undef;
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
$self->{_goal} = AI::ExpertSystem::Simple::Goal->new($attribute, $text);
eval { $t->purge(); }
}
sub _rule {
my ($self, $t, $node) = @_;
my $name = undef;
my $x = ($node->children('name'))[0];
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
}
eval { $t->purge(); }
}
sub _question {
my ($self, $t, $node) = @_;
my $attribute = undef;
my $text = undef;
my @responses = ();
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
$self->{_knowledge}->{$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();
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
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 = ();
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
$self->{_log} = ();
return @return;
}
sub _add_to_log {
my ($self, $message) = @_;
push( @{$self->{_log}}, $message );
}
sub explain {
my ($self) = @_;
die "Simple->explain() takes no arguments" if scalar(@_) != 1;
my $name = $self->{_goal}->name();
lib/AI/ExpertSystem/Simple.pm view on Meta::CPAN
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;
AI-FANN-Evolving
view release on metacpan or search on metacpan
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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);
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
$ann1->recombine($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 ) {
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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");
lib/AI/FANN/Evolving.pm view on Meta::CPAN
}
}
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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,
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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' ) {
lib/AI/FANN/Evolving.pm view on Meta::CPAN
$default{$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) ) {
$self->{$_} = $args{$_} // $default{$_};
}
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
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;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
=back
=cut
sub activation_function {
my $self = shift;
if ( @_ ) {
my $value = shift;
$log->debug("setting activation function to $value");
return $self->{'activation_function'} = $value;
lib/AI/FANN/Evolving.pm view on Meta::CPAN
# 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
AI-FANN
view release on metacpan or search on metacpan
lib/AI/FANN.pm view on Meta::CPAN
for my $constant (@constants) {
constant->import($constant, $constant);
}
}
sub num_neurons {
@_ == 1 or croak "Usage: AI::FANN::get_neurons(self)";
my $self = shift;
if (wantarray) {
AI-Fuzzy
view release on metacpan or search on metacpan
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
package AI::Fuzzy::Axis;
use AI::Fuzzy::Label;
## Container for Fuzzy Labels ####
sub new {
my ($class) = @_;
my $self = {};
$self->{labels} = {};
bless $self, $class;
return $self;
}
sub addlabel {
# adds a label for a range of values..
my ($self, $label, $low, $mid, $high) = @_;
if ($label->can("name") ) {
$self->{labels}->{$label->name} = $label;
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
return $self->{labels}->{$label};
}
sub applicability {
# this function should be called something else..
# calculates to what degree $label applies to a $value
my ($self, $value, $label) = @_;
my $membership = 0;
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
return $label->applicability($value) if ($label->can("applicability"));
return undef unless ( exists $self->{labels}->{$label} );
return $self->{labels}->{$label}->applicability($value);
}
sub label {
# returns a label associated with this text
my ($self, $name) = @_;
return $self->{labels}->{$name};
}
sub labelvalue {
# returns a label associated with this value
my ($self, $value) = @_;
my $label;
my %weight;
my $total_weight = 0;
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
return 0;
}
sub range {
# returns a list of sorted labels
my ($self) = @_;
my $l = $self->{labels};
return sort { $a <=> $b } values %{$l};
}
sub lessthan {
my ($self, $labela, $labelb) = @_;
if ( exists $self->{labels}->{$labela} and exists $self->{labels}->{$labelb} ) {
my $la = $self->{labels}->{$labela};
my $lb = $self->{labels}->{$labelb};
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
} else {
return undef;
}
}
sub lessequal {
my ($self, $labela, $labelb) = @_;
if ( exists $self->{labels}->{$labela} and exists $self->{labels}->{$labelb} ) {
my $la = $self->{labels}->{$labela};
my $lb = $self->{labels}->{$labelb};
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
} else {
return undef;
}
}
sub greaterthan {
my ($self, $labela, $labelb) = @_;
if ( exists $self->{labels}->{$labela} and exists $self->{labels}->{$labelb} ) {
my $la = $self->{labels}->{$labela};
my $lb = $self->{labels}->{$labelb};
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
return $la->greaterthan($lb);
} else {
return undef;
}
}
sub greaterequal {
my ($self, $labela, $labelb) = @_;
if ( exists $self->{labels}->{$labela} and exists $self->{labels}->{$labelb} ) {
my $la = $self->{labels}->{$labela};
my $lb = $self->{labels}->{$labelb};
lib/AI/Fuzzy/Axis.pm view on Meta::CPAN
} else {
return undef;
}
}
sub between {
my ($self, $labela, $labelb, $labelc) = @_;
if ( exists $self->{labels}->{$labela} and exists $self->{labels}->{$labelb}
and exists $self->{labels}->{$labelc} ) {
my $la = $self->{labels}->{$labela};
AI-FuzzyEngine
view release on metacpan or search on metacpan
lib/AI/FuzzyEngine.pm view on Meta::CPAN
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.
AI-FuzzyInference
view release on metacpan or search on metacpan
FuzzyInference.pm view on Meta::CPAN
# this hash defines the possible interpretations of the
# standard fuzzy logic operations.
my %_operations = (
'&' => {
min => sub { (sort {$a <=> $b} @_)[0] },
product => sub { my $p = 1; $p *= $_ for @_; $p },
default => 'min',
},
'|' => {
max => sub { (sort {$a <=> $b} @_)[-1] },
sum => sub { my $s = 0; $s += $_ for @_; $s > 1 ? 1 : $s },
default => 'max',
},
'!' => {
complement => sub { 1 - $_[0] },
custom => sub {},
default => 'complement',
},
);
# this hash defines the currently implemented implication methods.
FuzzyInference.pm view on Meta::CPAN
my %_defuzzification = qw(
centroid 1
default centroid
);
# sub new() - constructor.
#
# doesn't take any arguments. Returns an initialized AI::FuzzyInference object.
sub new {
my $self = shift;
my $class = ref($self) || $self;
my $obj = bless {} => $class;
$obj->_init;
return $obj;
}
# sub _init() - private method.
#
# no arguments. Initializes the data structures we will need.
# It also defines the default logic operations we might need.
sub _init {
my $self = shift;
$self->{SET} = new AI::FuzzyInference::Set;
$self->{INVARS} = {};
$self->{OUTVARS} = {};
FuzzyInference.pm view on Meta::CPAN
for my $op (qw/& | !/) {
$self->{OPERATIONS}{$op} = $_operations{$op}{default};
}
}
# sub implication() - public method.
#
# one optional argument: has to match one of the keys of the %_implication hash.
# used to query/set the implication method.
sub implication {
my ($self,
$new,
) = @_;
if (defined $new and exists $_implication{$new}) {
FuzzyInference.pm view on Meta::CPAN
}
return $self->{IMPLICATION};
}
# sub aggregation() - public method.
#
# one optional argument: has to match one of the keys of the %_aggregation hash.
# used to query/set the aggregation method.
sub aggregation {
my ($self,
$new,
) = @_;
if (defined $new and exists $_aggregation{$new}) {
FuzzyInference.pm view on Meta::CPAN
}
return $self->{AGGREGATION};
}
# sub defuzzification() - public method.
#
# one optional argument: has to match one of the keys of the %_defuzzification hash.
# used to query/set the defuzzification method.
sub defuzzification {
my ($self,
$new,
) = @_;
if (defined $new and exists $_defuzzification{$new}) {
FuzzyInference.pm view on Meta::CPAN
}
return $self->{DEFUZZIFICATION};
}
# sub operation() - public method.
#
# two arguments: first one mandatory and specifies the logic operation
# in question. Second one is optional and has to match one of the keys
# of the %{$_operations{$first_arg}} hash.
# Used to query/set the logic operations method.
sub operation {
my ($self,
$op,
$new,
) = @_;
FuzzyInference.pm view on Meta::CPAN
}
return $self->{OPERATIONS}{$op};
}
# sub inVar() - public method.
#
# 4 arguments or more : First is a name of a new input variable.
# Second and third are the min and max values of that variable.
# These define the universe of discourse for that variable.
# Additional argumets constitute a hash. The keys of the hash
FuzzyInference.pm view on Meta::CPAN
# 'tall' => [0, 0,
# 5, 1,
# 10,0],
# ....);
sub inVar {
my ($self,
$var,
$xmin,
$xmax,
@sets,
FuzzyInference.pm view on Meta::CPAN
$self->{SET}->add("$var:$s", $xmin, $xmax, @$c);
}
}
# sub outVar() - public method.
#
# 4 arguments or more : First is a name of a new output variable.
# Second and third are the min and max values of that variable.
# These define the universe of discourse for that variable.
# Additional argumets constitute a hash. The keys of the hash
# are term set names defined for the given variable. The values
# are the coordinates of the vertices of the term sets.
sub outVar {
my ($self,
$var,
$xmin,
$xmax,
@sets,
FuzzyInference.pm view on Meta::CPAN
$self->{SET}->add("$var:$s", $xmin, $xmax, @$c);
}
}
# sub addRule() - public method.
#
# Adds fuzzy if-then inference rules.
#
# $obj->addRule('x=medium' => 'z = slow',
# 'x=low & y=small' => 'z = fast',
# 'x=high & y=tiny' => 'z=veryfast');
# spaces are optional. The characters [&=|] are special.
sub addRule {
my ($self, %rules) = @_;
for my $k (keys %rules) {
my $v = $rules{$k};
s/\s+//g for $v, $k;
FuzzyInference.pm view on Meta::CPAN
}
return 1;
}
# sub show() - public method.
#
# This method displays the computed values of all
# output variables.
# It is ugly, and will be removed. Here for debugging.
sub show {
my $self = shift;
for my $var (keys %{$self->{RESULTS}}) {
print "Var $var = $self->{RESULTS}{$var}.\n";
}
}
# sub value() - public method.
#
# one argument: the name of an output variable.
# This method returns the computed value of a given output var.
sub value {
my ($self,
$var,
) = @_;
return undef unless exists $self->{RESULTS}{$var};
return $self->{RESULTS}{$var};
}
# sub reset() - public method
#
# cleans the data structures used.
sub reset {
my $self = shift;
my @list = $self->{SET}->listMatching(q|:implicated$|);
push @list => $self->{SET}->listMatching(q|:aggregated$|);
$self->{SET}->delete($_) for @list;
$self->{RESULTS} = {};
}
# sub compute() - public method
#
# This method takes as input crisp values for each
# of the input vars, and produces a crisp output value
# based on the application of the fuzzy if-then rules.
# ex.
# $z = $obj->compute(x => 5,
# y => 24);
sub compute {
my ($self,
%vars,
) = @_;
$self->reset();
FuzzyInference.pm view on Meta::CPAN
$self->_defuzzify;
return 1;
}
# sub _defuzzify() - private method.
#
# no arguments. This method applies the defuzzification technique
# to get a crisp value out of the aggregated set of each output
# var.
sub _defuzzify {
my $self = shift;
my $_defuzzification = $self->{DEFUZZIFICATION};
# iterate through all output vars.
FuzzyInference.pm view on Meta::CPAN
$self->{RESULTS}{$var} = $result;
}
}
# sub _aggregate() - private method.
#
# no arguments. This method applies the aggregation technique to get
# one fuzzy set out of the implicated sets of each output var.
sub _aggregate {
my $self = shift;
my $_aggregation = $self->{AGGREGATION};
# iterate through all output vars.
FuzzyInference.pm view on Meta::CPAN
$self->{SET}->delete("temp$j");
}
}
}
# sub _implicate() - private method.
#
# no arguments. This method applies the implication technique
# to all the fired rules to find a support value for each
# output variable.
sub _implicate {
my $self = shift;
my $_implication = $self->{IMPLICATION};
my %ind;
FuzzyInference.pm view on Meta::CPAN
$self->{SET}->add("$var:$ts:$ind{$var}{$ts}:implicated", @u, @c);
}
}
}
# sub _fuzzify() - private method.
#
# one argument: a hash. The keys are input variables. The
# values are the crisp values of the input variables (same arguments
# as compute()). It finds the degree of membership of each input
# variable in each of its term sets.
sub _fuzzify {
my ($self, %vars) = @_;
my %terms;
for my $var (keys %vars) {
FuzzyInference.pm view on Meta::CPAN
}
$self->{FUZZIFY} = \%terms;
}
# sub _infer() - private method.
#
# no arguments. This method applies the logic operations to combine
# multiple parts of the antecedent of a rule to get one crisp value
# that is the degree of support of that rule.
# Rules with positive support "fire".
sub _infer {
my $self = shift;
my @fired; # keep list of fired rules.
for my $i (0 .. $#{$self->{RULES}}) {
AI-Gene-Sequence
view release on metacpan or search on metacpan
AI/Gene/Sequence.pm view on Meta::CPAN
##
# calls mutation method at random
# 0: number of mutations to perform
# 1: ref to hash of probs to use (otherwise uses default mutations and probs)
sub mutate {
my $self = shift;
my $num_mutates = +$_[0] || 1;
my $rt = 0;
my ($hr_probs, $muts);
if (ref $_[1] eq 'HASH') { # use non standard mutations or probs
AI/Gene/Sequence.pm view on Meta::CPAN
##
# creates a normalised and cumulative prob distribution for the
# keys of the referenced hash
sub _normalise {
my $hr = $_[0];
my $h2 = {};
my $muts = [keys %{$hr}];
my $sum = 0;
foreach (values %{$hr}) {
AI/Gene/Sequence.pm view on Meta::CPAN
##
# inserts one element into the sequence
# 0: number to perform ( or 1)
# 1: position to mutate (undef for random)
sub mutate_insert {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
my $length = length $self->[0];
AI/Gene/Sequence.pm view on Meta::CPAN
# removes element(s) from sequence
# 0: number of times to perform
# 1: position to affect (undef for rand)
# 2: length to affect, undef => 1, 0 => random length
sub mutate_remove {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
my $length = length $self->[0];
AI/Gene/Sequence.pm view on Meta::CPAN
# 0: number to perform (or 1)
# 1: posn to copy from (undef for rand)
# 2: posn to splice in (undef for rand)
# 3: length (undef for 1, 0 for random)
sub mutate_duplicate {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
my $length = length $self->[0];
AI/Gene/Sequence.pm view on Meta::CPAN
# 0: num to perform (or 1)
# 1: pos to get from (undef for rand)
# 2: pos to start replacement (undef for rand)
# 3: length to operate on (undef => 1, 0 => rand)
sub mutate_overwrite {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
AI/Gene/Sequence.pm view on Meta::CPAN
# Takes a run of tokens and reverses their order, is a noop with 1 item
# 0: number to perform
# 1: posn to start from (undef for rand)
# 2: length (undef=>1, 0=>rand)
sub mutate_reverse {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
AI/Gene/Sequence.pm view on Meta::CPAN
##
# Changes token into one of same type (ie. passes type to generate..)
# 0: number to perform
# 1: position to affect (undef for rand)
sub mutate_minor {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
my $pos = defined $_[1] ? $_[1] : int rand length $self->[0];
AI/Gene/Sequence.pm view on Meta::CPAN
##
# Changes one token into some other token
# 0: number to perform
# 1: position to affect (undef for random)
sub mutate_major {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
my $pos = defined $_[1] ? $_[1] : int rand length $self->[0];
AI/Gene/Sequence.pm view on Meta::CPAN
# 1: start of first sequence (undef for rand)
# 2: start of second sequence (undef for rand)
# 3: length of first sequence (undef for 1, 0 for rand)
# 4: length of second sequence (undef for 1, 0 for rand)
sub mutate_switch {
my $self = shift;
my $num = $_[0] || 1;
my $rt = 0;
for (1..$num) {
my $length = length $self->[0];
AI/Gene/Sequence.pm view on Meta::CPAN
# 0: number to perform
# 1: posn to get from (undef for rand)
# 2: posn to put (undef for rand)
# 3: length of sequence (undef for 1, 0 for rand)
sub mutate_shuffle {
my $self = shift;
my $num = +$_[0] || 1;
my $rt = 0;
for (1..$num) {
AI/Gene/Sequence.pm view on Meta::CPAN
# can be called with a token type to produce, or with none.
# if called with a token type, it will also be passed the original
# token as the second argument.
# should return a two element list of the token type followed by the token itself.
sub generate_token {
my $self = shift;
my $token_type = $_[0];
my $letter = ('a'..'z')[rand 25];
unless ($token_type) {
return ($letter) x2;
AI/Gene/Sequence.pm view on Meta::CPAN
}
# takes sting of token types to be checked for validity.
# If a mutation affects only one place, then the position of the
# mutation can be passed as a second argument.
sub valid_gene {1}
## You might also want to have methods like the following,
# they will not be called by the 'sequence' methods.
# Default constructor
sub new {
my $gene = ['',[]];
return bless $gene, ref $_[0] || $_[0];
}
# remember that clone method may require deep copying depending on
# your specific needs
sub clone {
my $self = shift;
my $new = [$self->[0]];
$new->[1] = [@{$self->[1]}];
return bless $new, ref $self;
}
# You need some way to use the gene you've made and mutated, but
# this will let you have a look, if it starts being odd.
sub render_gene {
my $self = shift;
my $return = "$self\n";
$return .= $self->[0] . "\n";
$return .= (join ',', @{$self->[1]}). "\n";
return $return;
}
# used for testing
sub _test_dump {
my $self = shift;
my @rt = ($self->[0], join('',@{$self->[1]}));
return @rt;
}
1;
AI/Gene/Sequence.pm view on Meta::CPAN
our @ISA = qw(AI::Gene::Sequence);
my %things = ( a => [qw(a1 a2 a3 a4 a5)],
b => [qw(b1 b2 b3 b4 b5)],);
sub generate_token {
my $self = shift;
my ($type, $prev) = @_;
if ($type) {
$prev = ${ $things{$type} }[rand @{ $things{$type} }];
}
AI/Gene/Sequence.pm view on Meta::CPAN
$prev = ${$things{$type}}[rand @{$things{$type}}];
}
return ($type, $prev);
}
sub valid_gene {
my $self = shift;
return 0 if $_[0] =~ /(.)\1/;
return 1;
}
sub seed {
my $self = shift;
$self->[0] = 'ababab';
@{$self->[1]} = qw(A1 B1 A2 B2 A3 B3);
}
sub render {
my $self = shift;
return join(' ', @{$self->[1]});
}
# elsewhere
AI-General
view release on metacpan or search on metacpan
our $VERSION = '0.01';
sub new {
my ( $class ) = @_;
my $self = bless [], $class;
$self->dwim( "Implement self" );
return $self;
}
sub dwim {
my ( $self, $args ) = @_;
#... TO DO
}
AI-Genetic
view release on metacpan or search on metacpan
listvector => 'AI::Genetic::IndListVector',
);
##################
# sub new():
# This is the constructor. It creates a new AI::Genetic
# object. Options are:
# -population: set the population size
# -crossover: set the crossover probability
# -mutation: set the mutation probability
# -fitness: set the fitness function
# -type: set the genome type. See docs.
# -terminate: set termination sub.
sub new {
my ($class, %args) = @_;
my $self = bless {
ADDSEL => {}, # user-defined selections
ADDCRS => {}, # user-defined crossovers
ADDMUT => {}, # user-defined mutations
ADDSTR => {}, # user-defined strategies
} => $class;
$self->{FITFUNC} = $args{-fitness} || sub { 1 };
$self->{CROSSRATE} = $args{-crossover} || 0.95;
$self->{MUTPROB} = $args{-mutation} || 0.05;
$self->{POPSIZE} = $args{-population} || 100;
$self->{TYPE} = $args{-type} || 'bitvector';
$self->{TERM} = $args{-terminate} || sub { 0 };
$self->{PEOPLE} = []; # list of individuals
$self->{GENERATION} = 0; # current gen.
$self->{INIT} = 0; # whether pop is initialized or not.
$self->{INDIVIDUAL} = ''; # name of individual class to use().
return $self;
}
# sub createStrategy():
# This method creates a new strategy.
# It takes two arguments: name of strategy, and
# anon sub that implements it.
sub createStrategy {
my ($self, $name, $sub) = @_;
if (ref($sub) eq 'CODE') {
$self->{ADDSTR}{$name} = $sub;
} else {
}
return $name;
}
# sub evolve():
# This method evolves the population using a specific strategy
# for a specific number of generations.
sub evolve {
my ($self, $strategy, $gens) = @_;
unless ($self->{INIT}) {
carp "can't evolve() before init()";
return undef;
# print STDERR " Genes are: @{$f->genes}.\n";
# }
}
}
# sub sortIndividuals():
# This method takes as input an anon list of individuals, and returns
# another anon list of the same individuals but sorted in decreasing
# score.
sub sortIndividuals {
my ($self, $list) = @_;
# make sure all score's are calculated.
# This is to avoid a bug in Perl where a sort is called from whithin another
# sort, and they are in different packages, then you get a use of uninit value
$_->score for @$list;
return [sort {$b->score <=> $a->score} @$list];
}
# sub sortPopulation():
# This method sorts the population of individuals.
sub sortPopulation {
my $self = shift;
return if $self->{SORTED};
$self->{PEOPLE} = $self->sortIndividuals($self->{PEOPLE});
$self->{SORTED} = 1;
}
# sub getFittest():
# This method returns the fittest individuals.
sub getFittest {
my ($self, $N) = @_;
$N ||= 1;
$N = 1 if $N < 1;
return $r[0] if $N == 1 && not wantarray;
return @r;
}
# sub init():
# This method initializes the population to completely
# random individuals. It deletes all current individuals!!!
# It also examines the type of individuals we want, and
# require()s the proper class. Throws an error if it can't.
# Must pass to it an anon list that will be passed to the
# In case of rangevector, $newArgs is anon list of anon lists.
# each sub-anon list has two elements, min number and max number.
# In case of listvector, $newArgs is anon list of anon lists.
# Each sub-anon list contains possible values of gene.
sub init {
my ($self, $newArgs) = @_;
$self->{INIT} = 0;
my $ind;
$_->fitness($self->{FITFUNC}) for @{$self->{PEOPLE}};
$self->{INIT} = 1;
}
# sub people():
# returns the current list of individuals in the population.
# note: this returns the actual array ref, so any changes
# made to it (ex, shift/pop/etc) will be reflected in the
# population.
sub people {
my $self = shift;
if (@_) {
$self->{PEOPLE} = shift;
$self->{SORTED} = 0;
$self->{PEOPLE};
}
# useful little methods to set/query parameters.
sub size { $_[0]{POPSIZE} = $_[1] if defined $_[1]; $_[0]{POPSIZE} }
sub crossProb { $_[0]{CROSSRATE} = $_[1] if defined $_[1]; $_[0]{CROSSRATE} }
sub mutProb { $_[0]{MUTPROB} = $_[1] if defined $_[1]; $_[0]{MUTPROB} }
sub indType { $_[0]{INDIVIDUAL} }
sub generation { $_[0]{GENERATION} }
# sub inject():
# This method is used to add individuals to the current population.
# The point of it is that sometimes the population gets stagnant,
# so it could be useful add "fresh blood".
# Takes a variable number of arguments. The first argument is the
# total number, N, of new individuals to add. The remaining arguments
# are genomes to inject. There must be at most N genomes to inject.
# If the number, n, of genomes to inject is less than N, N - n random
# genomes are added. Perhaps an example will help?
# returns 1 on success and undef on error.
sub inject {
my ($self, $count, @genomes) = @_;
unless ($self->{INIT}) {
carp "can't inject() before init()";
return undef;
$ga->init(10);
$ga->evolve('rouletteTwoPoint', 100);
print "Best score = ", $ga->getFittest->score, ".\n";
sub fitnessFunc {
my $genes = shift;
my $fitness;
# assign a number to $fitness based on the @$genes
# ...
return $fitness;
}
sub terminateFunc {
my $ga = shift;
# terminate if reached some threshold.
return 1 if $ga->getFittest->score > $THRESHOLD;
return 0;
Defaults to I<bitvector>.
=item I<-terminate>
This option allows the definition of a termination subroutine.
It expects a subroutine reference. This sub will be called at
the end of each generation with one argument: the AI::Genetic
object. Evolution terminates if the sub returns a true value.
=back
=item I<$ga>-E<gt>B<createStrategy>(I<strategy_name>, I<sub_ref>)
the AI::Genetic object. This subroutine is expected to alter the object itself.
=item o
If a termination subroutine is given, it is executed and the return value is
checked. Evolution terminates if this sub returns a true value.
=back
=item I<$ga>-E<gt>B<getFittest>(?I<N>?)
own custom-made strategy. Consult their manpages for more info.
A custom-made strategy can be defined using the I<strategy()>
method and is called at the beginning of each generation. The only
argument to it is the AI::Genetic object itself. Note that the
population at this point is sorted accoring to each individual's
fitness score. It is expected that the strategy sub will modify
the population stored in the AI::Genetic object. Here's the
pseudo-code of events:
for (1 .. num_generations) {
sort population;
call strategy_sub;
if (termination_sub exists) {
call termination_sub;
last if returned true value;
}
}
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