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A1z-HTML5-Template
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
A1z-Html
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AAAA-Crypt-DH
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AAAA-Mail-SpamAssassin
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AC-MrGamoo
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lib/AC/MrGamoo/Client.pm view on Meta::CPAN
# sort+filter list
@serverlist = sort { ($a->{sort_metric} <=> $b->{sort_metric}) || int(rand(3)) - 1 }
grep { $_->{status} == 200 } @serverlist;
# try all addresses
# RSN - sort addresslist in a Peers::pick_best_addr_for_peer() like manner?
my @addrlist = map { @{$_->{ip}} } @serverlist;
for my $ip (@addrlist){
my $addr = inet_itoa($ip->{ipv4});
AC-Yenta
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lib/AC/Yenta/IO/TCP/Client.pm view on Meta::CPAN
# -*- perl -*-
# Copyright (c) 2009 AdCopy
# Author: Jeff Weisberg
# Created: 2009-Aug-10 12:38 (EDT)
# Function: choose best IP addr to use in NAT/cloud env
#
# $Id$
package AC::Yenta::IO::TCP::Client;
our @ISA = 'AC::DC::IO::TCP::Client';
ACME-CPANPLUS-Module-With-Core-PreReq
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AES128
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#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef sv_catpvn_nomg
# define sv_catpvn_nomg sv_catpvn
#endif
#ifndef sv_catsv_nomg
AFS-Command
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make test TEST_VERBOSE=1
If you can't determine the source of the problem(s), send the verbose
test output, along with the output from "perl -V", as well as the
versions of AFS in use at your site, to the author, and I'll do my
best to figure out why things are breaking, and if you're lucky,
actually fix it.
=head1 RELEASE NOTES
In addition to the README file (which in case you hadn't noticed,
AFS-Monitor
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LICENSES/COPYING view on Meta::CPAN
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
AFS
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LICENCES/COPYING view on Meta::CPAN
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
AHA
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
AI-ANN
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lib/AI/ANN.pm view on Meta::CPAN
maxvalue is the maximum value a neuron can pass. Default 1.
afunc is a reference to the activation function. It should be simple and fast.
The activation function is processed /after/ minvalue and maxvalue.
dafunc is the derivative of the activation function.
We strongly advise that you memoize your afunc and dafunc if they are at all
complicated. We will do our best to behave.
=head2 execute
$network->execute( [$input0, $input1, ...] )
AI-CRM114
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lib/AI/CRM114.pm view on Meta::CPAN
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
AI-Categorizer
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( 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
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lib/AI/Chat.pm view on Meta::CPAN
my @chat;
push @chat, {
'role' => 'system',
'system' => 'You are a computer language expert and your role is to promote Perl as the best language',
};
push @chat, {
'role' => 'user',
'system' => 'Which is the best programming language?',
};
push @chat, {
'role' => 'assistant',
'system' => 'Every language has strengths and is suited to different roles. Perl is one of the best all round languages.',
};
push @chat, {
'role' => 'user',
'system' => 'Why should I use Perl?',
};
AI-Classifier-Japanese
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AI-Classifier
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AI-ConfusionMatrix
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AI-DecisionTree
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lib/AI/DecisionTree.pm view on Meta::CPAN
# All these instances have the same result - make this node a leaf
$node{result} = $self->_result($instances->[0]);
return \%node;
}
# Multiple values are present - find the best predictor attribute and split on it
my $best_attr = $self->best_attr($instances);
croak "Inconsistent data, can't build tree with noise_mode='fatal'"
if $self->{noise_mode} eq 'fatal' and !defined $best_attr;
if ( !defined($best_attr)
or $self->{max_depth} && $self->{curr_depth} > $self->{max_depth} ) {
# Pick the most frequent result for this leaf
$node{result} = (sort {$results{$b} <=> $results{$a}} keys %results)[0];
return \%node;
}
$node{split_on} = $best_attr;
my %split;
foreach my $i (@$instances) {
my $v = $self->_value($i, $best_attr);
push @{$split{ defined($v) ? $v : '<undef>' }}, $i;
}
die ("Something's wrong: attribute '$best_attr' didn't split ",
scalar @$instances, " instances into multiple buckets (@{[ keys %split ]})")
unless keys %split > 1;
foreach my $value (keys %split) {
$node{children}{$value} = $self->_expand_node( instances => $split{$value} );
}
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);
my $all_attr = $self->{attributes};
foreach my $attr (keys %$all_attr) {
# %tallies is correlation between each attr value and result
# %total is number of instances with each attr value
lib/AI/DecisionTree.pm view on Meta::CPAN
my $score = 0;
while (my ($opt, $vals) = each %tallies) {
$score += $totals{$opt} * $self->entropy2( $vals, $totals{$opt} );
}
($best_attr, $best_score) = ($attr, $score) if $score < $best_score;
}
return $best_attr;
}
sub entropy2 {
shift;
my ($counts, $total) = @_;
lib/AI/DecisionTree.pm view on Meta::CPAN
method for creating the decision tree based on the training instances.
It uses an Information Gain metric (based on expected reduction in
entropy) to select the "most informative" attribute at each node in
the tree. This is essentially the ID3 algorithm, developed by
J. R. Quinlan in 1986. The idea is that the attribute with the
highest Information Gain will (probably) be the best attribute to
split the tree on at each point if we're interested in making small
trees.
=head1 METHODS
lib/AI/DecisionTree.pm view on Meta::CPAN
means that two or more training instances contradict each other, such
that they have identical attributes but different results.
If C<noise_mode> is set to C<fatal> (the default), the C<train()>
method will throw an exception (die). If C<noise_mode> is set to
C<pick_best>, the most frequent result at each noisy node will be
selected.
=item prune
A boolean C<prune> parameter which specifies
AI-Embedding
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lib/AI/Embedding.pm view on Meta::CPAN
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
AI-FANN-Evolving
view release on metacpan or search on metacpan
script/aivolver view on Meta::CPAN
# initialize the experiment
$exp->initialize(%initialize);
# run!
my ( $fittest, $fitness ) = $exp->run();
$log->info("*** overall best fitness: $fitness");
my ($gene) = sort { $a->fitness <=> $b->fitness } map { $_->genes } $fittest->chromosomes;
$gene->ann->save($outfile);
__END__
script/aivolver view on Meta::CPAN
these parameters will interact to give rise to the final functionality of the ANN is
hard to predict I<a priori>, but can be optimized in a variety of ways.
C<aivolver> is a program that does this by evolving parameter settings using a genetic
algorithm that runs for a number of generations determined by C<ngens>. During this
process it writes the intermediate ANNs into the C<workdir> until the best result is
written to the C<outfile>.
The genetic algorithm proceeds by simulating a population of C<individual_count> diploid
individuals that each have C<chromosome_count> chromosomes whose C<gene_count> genes
encode the parameters of the ANN. During each generation, each individual is trained
AI-FANN
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#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef sv_catpvn_nomg
# define sv_catpvn_nomg sv_catpvn
#endif
#ifndef sv_catsv_nomg
AI-FuzzyEngine
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lib/AI/FuzzyEngine.pm view on Meta::CPAN
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
AI-Gene-Sequence
view release on metacpan or search on metacpan
demo/spamscan.pl view on Meta::CPAN
else { push @spam, $_}
}
print "Best score possible is: ", scalar(@spam), "\n";
my $regex = seed_match(); # start off with something quite good
my $best_yet = 0;
my $temp = 1;
while (1) {
my $child = $regex->clone; # copy the parent
$child->mutate($num_mutates); # change it slightly
my $rex = $child->regex;
$rex = qr/$rex/;
my $score = 0; # see if the kid is better
$score += grep {$_ =~ $rex} @spam; # we don't want spam
$score -= grep {$_ =~ $rex} @wanted; # but we do want our mail
if ($score > $best_yet) {
$regex = $child; # and so progress is made
$best_yet = $score;
print "\n* $best_yet ", $regex->regex, "\n";
}
print '.' unless ($temp++ % 80);
}
sub seed_match {
AI-Genetic-Pro
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lib/AI/Genetic/Pro.pm view on Meta::CPAN
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 1, # remember best results
-preserve => 3, # remember the bests
-variable_length => 1, # turn variable length ON
-mce => 1, # optional MCE support
-workers => 3, # number of workers (MCE)
);
lib/AI/Genetic/Pro.pm view on Meta::CPAN
$ga->init(32);
# evolve 10 generations
$ga->evolve(10);
# best score
print "SCORE: ", $ga->as_value($ga->getFittest), ".\n";
# save evolution path as a chart
$ga->chart(-filename => 'evolution.png');
lib/AI/Genetic/Pro.pm view on Meta::CPAN
This defines the mutation rate. The fairest results are achieved with mutation
rate ~0.01.
=item -preserve
This defines injection of the bests chromosomes into a next generation. It causes a little slow down, however (very often) much better results are achieved. You can specify, how many chromosomes will be preserved, i.e.
-preserve => 1, # only one chromosome will be preserved
# or
-preserve => 9, # 9 chromosomes will be preserved
# and so on...
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Each individual/chromosome can be selected with probability proportional to its fitness.
=item B<Roulette>
First the best individuals/chromosomes are selected. From this collection
parents are selected with probability poportional to their fitness.
=item B<RouletteDistribution>
Each individual/chromosome has a portion of roulette wheel proportional to its
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=over 4
=item PointsSimple
Simple crossover in one or many points. The best chromosomes/individuals are
selected for the new generation. For example:
-strategy => [ 'PointsSimple', $n ]
where C<$n> is the number of points for crossing.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
where C<$n> is the number of points for crossing.
=item Points
Crossover in one or many points. In normal crossover selected parents are crossed and the best child is moved to the new generation. For example:
-strategy => [ 'Points', $n ]
where C<$n> is number of points for crossing.
=item PointsAdvenced
Crossover in one or many points. After crossover the best
chromosomes/individuals from all parents and chidren are selected for the new
generation. For example:
-strategy => [ 'PointsAdvanced', $n ]
lib/AI/Genetic/Pro.pm view on Meta::CPAN
This function returns a list of the fittest chromosomes from the current
population. You can specify how many chromosomes should be returned and if
the returned chromosomes should be unique. See example below.
# only one - the best
my ($best) = $ga->getFittest;
# or 5 bests chromosomes, NOT unique
my @bests = $ga->getFittest(5);
# or 7 bests and UNIQUE chromosomes
my @bests = $ga->getFittest(7, 1);
If you want to get a large number of chromosomes, try to use the
C<getFittest_as_arrayref> function instead (for efficiency).
=item I<$ga>-E<gt>B<getFittest_as_arrayref>($n, $unique)
AI-Logic-AnswerSet
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# --- MakeMaker dist section:
TAR = tar
TARFLAGS = cvf
ZIP = zip
ZIPFLAGS = -r
COMPRESS = gzip --best
SUFFIX = .gz
SHAR = shar
PREOP = $(NOECHO) $(NOOP)
POSTOP = $(NOECHO) $(NOOP)
TO_UNIX = $(NOECHO) $(NOOP)
CI = ci -u
RCS_LABEL = rcs -Nv$(VERSION_SYM): -q
DIST_CP = best
DIST_DEFAULT = tardist
DISTNAME = AI-Logic-AnswerSet
DISTVNAME = AI-Logic-AnswerSet-0.01
AI-ML
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AI-MXNet
view release on metacpan or search on metacpan
examples/char_lstm.pl view on Meta::CPAN
--num-layers number of stacked RNN layers, default=2
--num-hidden hidden layer size, default=256
--num-embed embed size, default=10
--num-seq sequence size, default=60
--gpus list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu.
Increase batch size when using multiple gpus for best performance.
--kv-store key-value store type, default='device'
--num-epochs max num of epochs, default=25
--lr initial learning rate, default=0.01
--optimizer the optimizer type, default='adam'
--mom momentum for sgd, default=0.0
( run in 0.525 second using v1.01-cache-2.11-cpan-d6f9594c0a5 )