start results from the CPAN

lib/AI/Calibrate.pm view on Meta::CPAN

    }
}


sub flatten {
    my ( $vec, $start, $len ) = @_;
    if (DEBUG) {
        print "Flatten called on vec, $start, $len\n";
        print "Vector before: \n";
        print_vector($vec);
    }

    my $sum = 0;
    for my $i ( $start .. $start + $len-1 ) {
        $sum += $vec->[$i][PROB];
    }
    my $avg = $sum / $len;
    print "Sum = $sum, avg = $avg\n" if DEBUG;
    for my $i ( $start .. $start + $len -1) {
        $vec->[$i][PROB] = $avg;
    }
    if (DEBUG) {
        print "Vector after: \n";
        print_vector($vec);

view all matches for this distribution

AI-Categorizer

4 results

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

how their features (words) will be extracted and turned into
meaningful representations.  In this sense, a knowledge set represents
not only a collection of data, but a particular view on that data.

A knowledge set is encapsulated by the
C<AI::Categorizer::KnowledgeSet> class.  Before you can start playing
with categorizers, you will have to start playing with knowledge sets,
so that the categorizers have some data to train on.  See the
documentation for the C<AI::Categorizer::KnowledgeSet> module for
information on its interface.

=head3 Feature selection

view all matches for this distribution

AI-Chat

1 match

lib/AI/Chat.pm view on Meta::CPAN

=head1 MODELS

Although the API Key is free, each use incurs a cost.  This is dependent on the
number of tokens in the prompt and the reply.  Different models have different costs.
The default model C<gpt-4o-mini> is the lowest cost of the useful models and
is a good place to start using this module.  Previous versions of this module
defaulted to C<gpt-3.5-turbo-0125> but the current default is cheaper and
quicker. For most purposes, the default model should be used.

See also L<https://platform.openai.com/docs/models/overview>

view all matches for this distribution

AI-Classifier-Japanese

1 match

    third parties under the terms of this General Public License (except
    that you may choose to grant warranty protection to some or all
    third parties, at your option).

    c) If the modified program normally reads commands interactively when
    run, you must cause it, when started running for such interactive use
    in the simplest and most usual way, to print or display an
    announcement including an appropriate copyright notice and a notice
    that there is no warranty (or else, saying that you provide a
    warranty) and that users may redistribute the program under these
    conditions, and telling the user how to view a copy of this General

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
the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.

    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) 19yy  <name of author>



Also add information on how to contact you by electronic and paper mail.

If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:

    Gnomovision version 69, Copyright (C) 19xx name of author
    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

view all matches for this distribution

AI-Classifier

2 results

lib/AI/Classifier/Text/FileLearner.pm view on Meta::CPAN

sub _build_iterator {
    my $self = shift;
    my $rule = File::Find::Rule->new( );
    $rule->file;
    $rule->not_name('*.data');
    $rule->start( $self->training_dir );
    return $rule;
}

sub get_category {
    my( $self, $file ) = @_;

view all matches for this distribution

AI-ConfusionMatrix

1 match

    third parties under the terms of this General Public License (except
    that you may choose to grant warranty protection to some or all
    third parties, at your option).

    c) If the modified program normally reads commands interactively when
    run, you must cause it, when started running for such interactive use
    in the simplest and most usual way, to print or display an
    announcement including an appropriate copyright notice and a notice
    that there is no warranty (or else, saying that you provide a
    warranty) and that users may redistribute the program under these
    conditions, and telling the user how to view a copy of this General

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
the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.

    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) 19yy  <name of author>



Also add information on how to contact you by electronic and paper mail.

If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:

    Gnomovision version 69, Copyright (C) 19xx name of author
    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

view all matches for this distribution

AI-DecisionTree

1 match

    third parties under the terms of this General Public License (except
    that you may choose to grant warranty protection to some or all
    third parties, at your option).

    c) If the modified program normally reads commands interactively when
    run, you must cause it, when started running for such interactive use
    in the simplest and most usual way, to print or display an
    announcement including an appropriate copyright notice and a notice
    that there is no warranty (or else, saying that you provide a
    warranty) and that users may redistribute the program under these
    conditions, and telling the user how to view a copy of this General

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
the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.

    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) 19yy  <name of author>



Also add information on how to contact you by electronic and paper mail.

If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:

    Gnomovision version 69, Copyright (C) 19xx name of author
    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

view all matches for this distribution

AI-Evolve-Befunge

7 results

lib/AI/Evolve/Befunge.pm view on Meta::CPAN

that's what you're after, please see the documentation contained
within that script.  Otherwise, read on.

This particular file (AI/Evolve/Befunge.pm) does not contain any code;
it exists mainly to provide a version number to keep Build.PL happy.
The rest of this file acts as a quick-start guide to the rest of the
codebase.

The important bits from a user's standpoint are the Population object
(which drives the main process of evolving AI), and the Physics plugin
(which implements the rules of the universe those AI live in).  There

lib/AI/Evolve/Befunge.pm view on Meta::CPAN

result gets better and better.

The important thing to remember here is that the results take time -
it will probably take several weeks of solid processing time before you
begin to see any promising results at all.  It takes a lot of random
code generation before it starts to generate code that does what you
want it to do.

If you don't know anything about Befunge, I recommend you read up on
that first, before trying to understand how this works.

lib/AI/Evolve/Befunge.pm view on Meta::CPAN


After a population fights it out for a while, the winners are chosen
(who continue to live) and everyone else dies.  Then a new population
is generated from the winners, some random mutation (random
generation of code, as well as potentially resizing the codebase)
occurs, and the whole process starts over for the next generation.


=head1 PHYSICS

At the other end of all of this is the Physics plugin.  The Physics

view all matches for this distribution

AI-ExpertSystem-Advanced

4 results


=over 4

=item B<initial_facts>

A list/set of initial facts the algorithms start using.

During the forward algorithm the task is to find a list of goals caused
by these initial facts (the only data we have in that moment).

Lets imagine your knowledge database is about symptoms and diseases. You need

            initial_facts => ['F', 'J']);
    $ai->forward();
    $ai->summary();

The forward chaining algorithm is one of the main methods used in Expert
Systems. It starts with a set of variables (known as initial facts) and reads
the available rules.

It will be reading rule by rule and for each one it will compare its causes
with the initial, inference and asked facts. If all of these causes are in the
facts then the rule will be shoot and all of its goals will be copied/converted
to inference facts and will restart reading from the first rule.

=cut
sub forward {
    my ($self) = @_;

            if $self->{'verbose'};

        my $rule_causes = $self->get_causes_by_rule($current_rule);
        # any of our rule facts match with our facts to check?
        if ($self->compare_causes_with_facts($current_rule)) {
            # shoot and start again
            $self->shoot($current_rule, 'forward');
            # Undef to start reading from the first rule.
            $current_rule = undef;
            next;
        }
    }
    return 1;

            knowledge_db => $yaml_kdb,
            goals_to_check => ['J']);
    $ai->backward();
    $ai->summary();

The backward algorithm starts with a set of I<assumed> goals (facts). It will
start reading goal by goal. For each goal it will check if it exists in the
initial, inference and asked facts (see L<is_goal_in_our_facts()>) for more
information).

=over 4

If the goal doesn't exist in the mentioned facts then the goal will be searched
in the goals of every rule.

In case it finds the rule that has the goal, this rule will be marked (added)
to the list of visited rules (L<visited_rules>) and also all of its causes
will be added to the top of the L<goals_to_check_dict> and it will start
reading again all the goals.

If there's the case where the goal doesn't exist as a goal in the rules then
it will ask the user (via L<ask_about()>) for the existence of it. If user is
not sure about it then the algorithm ends.


The first thing it does is to run the L<forward()> algorithm (hence the need of
at least one initial fact). If the algorithm fails then the mixed algorithm
also ends unsuccessfully.

Once the first I<run> of L<forward()> algorithm happens it starts looking for
any positive inference fact, if only one is found then this ends the algorithm
with the assumption it knows what's happening.

In case no positive inference fact is found then it will start reading the
rules and creating a list of intuitive facts.

For each rule it will get a I<certainty factor> of its causes versus the
initial, inference and asked facts. In case the certainity factor is greater or
equal than L<found_factor> then all of its goals will be copied to the

converted to a goal). After each I<run> of the L<backward()> algorithm it will
verify for any positive inference fact, if just one is found then the algorithm
ends.

At the end (if there are still no positive inference facts) it will run the
L<forward()> algorithm and restart (by looking again for any positive inference
fact).

A good example to understand how this algorithm is useful is: imagine you are
a doctor and know some of the symptoms of a patient. Probably with the first
symptoms you have you can get to a positive conclusion (eg that a patient has

    # any facts we found via inference?
    if (scalar @{$self->{'inference_facts'}->{'stack'}} eq 0) {
        $self->{'viewer'}->print_error("No inference was possible");
    } else {
        my $summary = {};
        # How the rules started being shot?
        my $order = 1;
        # So, what rules we shot?
        foreach my $shot_rule (sort(keys %{$self->{'shot_rules'}})) {
            $summary->{'rules'}->{$shot_rule} = {
                order => $order,

view all matches for this distribution

AI-ExpertSystem-Simple

4 results

#!/bin/sh
# the next line restarts using wish \
exec wish "$0" "$@"

################################################################################
# To do
# -----

	status "This program is used to call the command line"
	status "expert system called simpleshell and allows"
	status "you to interact with it via the gui"
	status ""
	status "First select a knowledgebase to load with the Load button"
	status "Then start a consultation with the Run button"
	status "You can rerun the consultation as many times as you like"
	status ""
	status "The Save button allows you to save the contents of the"
	status "session to a file"
	status ""

	}
}

proc do_run {} {
	status "Run the file..."
	status "This may take a few moments to get started..."
	.save configure -state normal
	.run  configure -state disabled

	global filename
	global program

	.text see end
	update
}

################################################################################
# The program starts here
################################################################################

# Was there a filename on the command line

set p_count 0

view all matches for this distribution

AI-FANN-Evolving

2 results

lib/AI/FANN/Evolving/TrainData.pm view on Meta::CPAN

		$log->debug("counts: $key => $seen{$key}");
		$seen{$key} = int( $seen{$key} * $sample );
		$log->debug("rescaled: $key => $seen{$key}");
	}

	# start the sampling	
	my @dc = map { $self->{'header'}->{$_} } $self->dependent_columns;
	my @new_table; # we will populate this
	my @table = @{ $clone1->{'table'} }; # work on cloned instance
	
	# as long as there is still sampling to do

view all matches for this distribution

AI-FANN

1 match

ax|||n
bad_type|||
bind_match|||
block_end|||
block_gimme||5.004000|
block_start|||
boolSV|5.004000||p
boot_core_PerlIO|||
boot_core_UNIVERSAL|||
boot_core_mro|||
boot_core_xsutils|||

debop||5.005000|
debprofdump||5.005000|
debprof|||
debstackptrs||5.007003|
debstack||5.007003|
debug_start_match|||
deb||5.007003|v
del_sv|||
delete_eval_scope|||
delimcpy||5.004000|
deprecate_old|||

jmaybe|||
join_exact|||
keyword|||
leave_scope|||
lex_end|||
lex_start|||
linklist|||
listkids|||
list|||
load_module_nocontext|||vn
load_module|5.006000||pv

package|||
packlist||5.008001|
pad_add_anon|||
pad_add_name|||
pad_alloc|||
pad_block_start|||
pad_check_dup|||
pad_compname_type|||
pad_findlex|||
pad_findmy|||
pad_fixup_inner_anons|||

qerror|||
qsortsvu|||
re_compile||5.009005|
re_croak2|||
re_dup|||
re_intuit_start||5.009005|
re_intuit_string||5.006000|
readpipe_override|||
realloc||5.007002|n
reentrant_free|||
reentrant_init|||

sortsv_flags||5.009003|
sortsv||5.007003|
space_join_names_mortal|||
ss_dup|||
stack_grow|||
start_force|||
start_glob|||
start_subparse||5.004000|
stashpv_hvname_match||5.009005|
stdize_locale|||
strEQ|||
strGE|||
strGT|||

strnNE|||
study_chunk|||
sub_crush_depth|||
sublex_done|||
sublex_push|||
sublex_start|||
sv_2bool|||
sv_2cv|||
sv_2io|||
sv_2iuv_common|||
sv_2iuv_non_preserve|||

    next unless $f =~ /$match/;
    print "\n=== $f ===\n\n";
    my $info = 0;
    if ($API{$f}{base} || $API{$f}{todo}) {
      my $base = format_version($API{$f}{base} || $API{$f}{todo});
      print "Supported at least starting from perl-$base.\n";
      $info++;
    }
    if ($API{$f}{provided}) {
      my $todo = $API{$f}{todo} ? format_version($API{$f}{todo}) : "5.003";
      print "Support by $ppport provided back to perl-$todo.\n";

	const line_t ocopline = PL_copline;
	COP * const ocurcop = PL_curcop;
	const int oexpect = PL_expect;

#if (PERL_BCDVERSION >= 0x5004000)
	utilize(!(flags & PERL_LOADMOD_DENY), start_subparse(FALSE, 0),
		veop, modname, imop);
#else
	utilize(!(flags & PERL_LOADMOD_DENY), start_subparse(),
		modname, imop);
#endif
	PL_expect = oexpect;
	PL_copline = ocopline;
	PL_curcop = ocurcop;


void
DPPP_(my_load_module)(U32 flags, SV *name, SV *ver, ...)
{
    va_list args;
    va_start(args, ver);
    vload_module(flags, name, ver, &args);
    va_end(args);
}

#endif

/* Hint: newCONSTSUB
 * Returns a CV* as of perl-5.7.1. This return value is not supported
 * by Devel::PPPort.
 */

/* newCONSTSUB from IO.xs is in the core starting with 5.004_63 */
#if (PERL_BCDVERSION < 0x5004063) && (PERL_BCDVERSION != 0x5004005)
#if defined(NEED_newCONSTSUB)
static void DPPP_(my_newCONSTSUB)(HV *stash, const char *name, SV *sv);
static
#else

		PL_curstash = PL_curcop->cop_stash = stash;

	newSUB(

#if   (PERL_BCDVERSION < 0x5003022)
		start_subparse(),
#elif (PERL_BCDVERSION == 0x5003022)
     		start_subparse(0),
#else  /* 5.003_23  onwards */
     		start_subparse(FALSE, 0),
#endif

		newSVOP(OP_CONST, 0, newSVpv((char *) name, 0)),
		newSVOP(OP_CONST, 0, &PL_sv_no),   /* SvPV(&PL_sv_no) == "" -- GMB */
		newSTATEOP(0, Nullch, newSVOP(OP_CONST, 0, sv))


void
DPPP_(my_sv_catpvf_mg)(pTHX_ SV *sv, const char *pat, ...)
{
  va_list args;
  va_start(args, pat);
  sv_vcatpvfn(sv, pat, strlen(pat), &args, Null(SV**), 0, Null(bool*));
  SvSETMAGIC(sv);
  va_end(args);
}

void
DPPP_(my_sv_catpvf_mg_nocontext)(SV *sv, const char *pat, ...)
{
  dTHX;
  va_list args;
  va_start(args, pat);
  sv_vcatpvfn(sv, pat, strlen(pat), &args, Null(SV**), 0, Null(bool*));
  SvSETMAGIC(sv);
  va_end(args);
}


void
DPPP_(my_sv_setpvf_mg)(pTHX_ SV *sv, const char *pat, ...)
{
  va_list args;
  va_start(args, pat);
  sv_vsetpvfn(sv, pat, strlen(pat), &args, Null(SV**), 0, Null(bool*));
  SvSETMAGIC(sv);
  va_end(args);
}

void
DPPP_(my_sv_setpvf_mg_nocontext)(SV *sv, const char *pat, ...)
{
  dTHX;
  va_list args;
  va_start(args, pat);
  sv_vsetpvfn(sv, pat, strlen(pat), &args, Null(SV**), 0, Null(bool*));
  SvSETMAGIC(sv);
  va_end(args);
}

  SV *sv;
  va_list args;

  PERL_UNUSED_ARG(err);

  va_start(args, pat);
  sv = vnewSVpvf(pat, &args);
  va_end(args);
  sv_2mortal(sv);
  warn("%s", SvPV_nolen(sv));
}

 * which is why the stack variable has been renamed to 'xdigit'.
 */

#ifndef grok_bin
#if defined(NEED_grok_bin)
static UV DPPP_(my_grok_bin)(pTHX_ const char * start, STRLEN * len_p, I32 * flags, NV * result);
static
#else
extern UV DPPP_(my_grok_bin)(pTHX_ const char * start, STRLEN * len_p, I32 * flags, NV * result);
#endif

#ifdef grok_bin
#  undef grok_bin
#endif
#define grok_bin(a,b,c,d) DPPP_(my_grok_bin)(aTHX_ a,b,c,d)
#define Perl_grok_bin DPPP_(my_grok_bin)

#if defined(NEED_grok_bin) || defined(NEED_grok_bin_GLOBAL)
UV
DPPP_(my_grok_bin)(pTHX_ const char *start, STRLEN *len_p, I32 *flags, NV *result)
{
    const char *s = start;
    STRLEN len = *len_p;
    UV value = 0;
    NV value_nv = 0;

    const UV max_div_2 = UV_MAX / 2;

	|| (!overflowed && value > 0xffffffff  )
#endif
	) {
	warn("Binary number > 0b11111111111111111111111111111111 non-portable");
    }
    *len_p = s - start;
    if (!overflowed) {
        *flags = 0;
        return value;
    }
    *flags = PERL_SCAN_GREATER_THAN_UV_MAX;

#endif
#endif

#ifndef grok_hex
#if defined(NEED_grok_hex)
static UV DPPP_(my_grok_hex)(pTHX_ const char * start, STRLEN * len_p, I32 * flags, NV * result);
static
#else
extern UV DPPP_(my_grok_hex)(pTHX_ const char * start, STRLEN * len_p, I32 * flags, NV * result);
#endif

#ifdef grok_hex
#  undef grok_hex
#endif
#define grok_hex(a,b,c,d) DPPP_(my_grok_hex)(aTHX_ a,b,c,d)
#define Perl_grok_hex DPPP_(my_grok_hex)

#if defined(NEED_grok_hex) || defined(NEED_grok_hex_GLOBAL)
UV
DPPP_(my_grok_hex)(pTHX_ const char *start, STRLEN *len_p, I32 *flags, NV *result)
{
    const char *s = start;
    STRLEN len = *len_p;
    UV value = 0;
    NV value_nv = 0;

    const UV max_div_16 = UV_MAX / 16;

	|| (!overflowed && value > 0xffffffff  )
#endif
	) {
	warn("Hexadecimal number > 0xffffffff non-portable");
    }
    *len_p = s - start;
    if (!overflowed) {
        *flags = 0;
        return value;
    }
    *flags = PERL_SCAN_GREATER_THAN_UV_MAX;

#endif
#endif

#ifndef grok_oct
#if defined(NEED_grok_oct)
static UV DPPP_(my_grok_oct)(pTHX_ const char * start, STRLEN * len_p, I32 * flags, NV * result);
static
#else
extern UV DPPP_(my_grok_oct)(pTHX_ const char * start, STRLEN * len_p, I32 * flags, NV * result);
#endif

#ifdef grok_oct
#  undef grok_oct
#endif
#define grok_oct(a,b,c,d) DPPP_(my_grok_oct)(aTHX_ a,b,c,d)
#define Perl_grok_oct DPPP_(my_grok_oct)

#if defined(NEED_grok_oct) || defined(NEED_grok_oct_GLOBAL)
UV
DPPP_(my_grok_oct)(pTHX_ const char *start, STRLEN *len_p, I32 *flags, NV *result)
{
    const char *s = start;
    STRLEN len = *len_p;
    UV value = 0;
    NV value_nv = 0;

    const UV max_div_8 = UV_MAX / 8;

	|| (!overflowed && value > 0xffffffff  )
#endif
	) {
	warn("Octal number > 037777777777 non-portable");
    }
    *len_p = s - start;
    if (!overflowed) {
        *flags = 0;
        return value;
    }
    *flags = PERL_SCAN_GREATER_THAN_UV_MAX;

DPPP_(my_my_snprintf)(char *buffer, const Size_t len, const char *format, ...)
{
    dTHX;
    int retval;
    va_list ap;
    va_start(ap, format);
#ifdef HAS_VSNPRINTF
    retval = vsnprintf(buffer, len, format, ap);
#else
    retval = vsprintf(buffer, format, ap);
#endif

view all matches for this distribution

AI-FuzzyEngine

1 match

t/01-fuzzyEngine.t view on Meta::CPAN

    ok( 1, 'POD synopsis' );
};

subtest 'PDL may not be loaded' => sub {
    if ($PDL_is_loaded) {
        diag "PDL was loaded at start of test - check not possible";
    }
    else {
        ok( (not exists $INC{PDL}), 'Module does not load PDL' );
    };
};

view all matches for this distribution

AI-Gene-Sequence

8 results


##
# Duplicates a sequence and writes it on top of some other position
# 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;

}

##
# 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;


##
# swaps over two sequences within the gene
# any sort of oddness can occur if regions overlap
# 0: number to perform
# 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;

  $new->[1] = [@{$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";

The token will be randomly generated by the calling object's 
C<generate_token> method.

=item C<mutate_overwrite([num, pos1, pos2, len])>

Copies a section of the gene (starting at I<pos1>, length I<len>)
and writes it back into the gene, overwriting current elements,
starting at I<pos2>.

=item C<mutate_reverse([num, pos, len])>

Takes a sequence within the gene and reverses the ordering of the
elements within that sequence.  Starts at position I<pos> for
length I<len>.

=item C<mutate_shuffle([num, pos1, pos2, len])>

This takes a sequence (starting at I<pos1> length I<len>)
 from within a gene and moves
it to another position (starting at I<pos2>).  Odd things might occur if the
position to move the sequence into lies within the
section to be moved, but the module will try its hardest
to cause a mutation.

=item C<mutate_duplicate([num, pos1, pos2, length])>

This copies a portion of the gene starting at I<pos1> of length
I<length> and then splices it into the gene before I<pos2>.

=item C<mutate_remove([num, pos, length]))>

Deletes I<length> tokens from the gene, starting at I<pos>. Repeats
I<num> times.

=item C<mutate_minor([num, pos])>

This will mutate a single token at position I<pos> in the gene

C<generate_token> method.

=item C<mutate_switch([num, pos1, pos2, len1, len2])>

This takes two sequences within the gene and swaps them
into each other's position.  The first starts at I<pos1>
with length I<len1> and the second at I<pos2> with length
I<len2>.  If the two sequences overlap, then no mutation will
be attempted.

=back

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AI-Genetic-Pro

1 match

t/02_cache.t view on Meta::CPAN


# init population of 32-bit vectors
$ga->init(BITS);
$ga->chromosomes( [ ] );
$ga->inject( [ [ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1) ] ] );
my $start = [Time::HiRes::gettimeofday()];
$ga->as_value($ga->chromosomes->[0]) for 0..10000;
my $time0 =Time::HiRes::tv_interval($start);


$ga = AI::Genetic::Pro->new(        
        -fitness         => \&fitness,        # fitness function
        -terminate       => sub { return; },  # terminate function

t/02_cache.t view on Meta::CPAN


# init population of 32-bit vectors
$ga->init(BITS);
$ga->chromosomes( [ ] );
$ga->inject( [ [ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1) ] ] );
$start = [Time::HiRes::gettimeofday()];
$ga->as_value($ga->chromosomes->[0]) for 0..10000;
my $time1 =Time::HiRes::tv_interval($start);

ok( $time0 >= $time1 );

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AI-Image

1 match

lib/AI/Image.pm view on Meta::CPAN


Although the API Key is free, each use incurs a cost.  This is dependent on the
model chosen and the size.  The 'dall-e-3' model produces better images but at a
higher cost.  Likewise, bigger images cost more.
The default model C<dall-e-2> with the default size of C<512x512> produces resonable
results at a low cost and is a good place to start using this module.

See also L<https://platform.openai.com/docs/models/overview>

=head1 METHODS

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AI-LibNeural

1 match

everyone can redistribute and change.  You can do so by permitting
redistribution under these terms (or, alternatively, under the terms of the
ordinary General Public License).

  To apply these terms, attach the following notices to the library.  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 the
"copyright" line and a pointer to where the full notice is found.

    <one line to give the library's name and a brief idea of what it does.>
    Copyright (C) <year>  <name of author>

view all matches for this distribution

AI-Logic-AnswerSet

2 results

lib/AI/Logic/AnswerSet.pm view on Meta::CPAN

This extension allows to interact with DLV, an Artificial Intelligence system
for Answer Set Programming (ASP).
Please note that the DLV system must appear in the same folder of the perl program
and it must be renamed as "dlv";
DLV can be freely obtained at www.dlvsystem.com.
For further info about DLV and Answer Set Programming please start from www.dlvsystem.com.

The module was originally published as "ASPerl", but suffered from
some problems with the namespace, now changed. The module has been
also significantly rearranged according to the advices coming from the
community. Thank you all!

lib/AI/Logic/AnswerSet.pm view on Meta::CPAN

=head1 AUTHOR

Ferdinando Primerano, E<lt>levia@cpan.orgE<gt>
Francesco Calimeri, E<lt>calimeri@mat.unical.itE<gt>

This work started within the bachelor degree thesis program of the
Computer Science course at Department of Mathematics of the University
of Calabria.

=head1 COPYRIGHT AND LICENSE

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AI-ML

6 results

lib/AI/ML/Expr.pm view on Meta::CPAN


=head2 mini-batch

=cut
sub mini_batch {
    my ($self, $start, $size, $axis) = @_;
    $axis = 0 unless defined $axis; #default
    return _bless _mini_batch($self->matrix_id, $start, $size, $axis);
}


=head2 prediction

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AI-MXNet

12 results

examples/cudnn_lstm_bucketing.pl view on Meta::CPAN

=cut

$bidirectional = $bidirectional ? 1 : 0;
$stack_rnn     = $stack_rnn     ? 1 : 0;

func tokenize_text($fname, :$vocab=, :$invalid_label=-1, :$start_label=0)
{
    open(F, $fname) or die "Can't open $fname: $!";
    my @lines = map { my $l = [split(/ /)]; shift(@$l); $l } (<F>);
    my $sentences;
    ($sentences, $vocab) = mx->rnn->encode_sentences(
        \@lines,
        vocab         => $vocab,
        invalid_label => $invalid_label,
        start_label   => $start_label
    );
    return ($sentences, $vocab);
}

my $buckets = [10, 20, 30, 40, 50, 60];
my $start_label   = 1;
my $invalid_label = 0;

func get_data($layout)
{
    my ($train_sentences, $vocabulary) = tokenize_text(
        './data/ptb.train.txt', start_label => $start_label,
        invalid_label => $invalid_label
    );
    my ($validation_sentences) = tokenize_text(
        './data/ptb.test.txt', vocab => $vocabulary,
        start_label => $start_label, invalid_label => $invalid_label
    );
    my $data_train  = mx->rnn->BucketSentenceIter(
        $train_sentences, $batch_size, buckets => $buckets,
        invalid_label => $invalid_label,
        layout        => $layout

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AI-MaxEntropy

4 results

inc/Module/AutoInstall.pm view on Meta::CPAN

    my $makeflags = $CPAN::Config->{make_install_arg} || '';
    $CPAN::Config->{make_install_arg} =
      join( ' ', split( ' ', $makeflags ), 'UNINST=1' )
      if ( $makeflags !~ /\bUNINST\b/ and eval qq{ $> eq '0' } );

    # don't show start-up info
    $CPAN::Config->{inhibit_startup_message} = 1;

    # set additional options
    while ( my ( $opt, $arg ) = splice( @config, 0, 2 ) ) {
        ( $args{$opt} = $arg, next )
          if $opt =~ /^force$/;    # pseudo-option

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AI-MegaHAL

3 results

lib/AI/MegaHAL.pm view on Meta::CPAN


=over 4

=item B<Path> - The path to MegaHALs brain or training file (megahal.brn and megahal.trn respectively).  If 'Path' is not specified the current working directory is assumed.

=item B<Banner> - A flag which enables/disables the banner which is displayed when MegaHAL starts up.  The default is to disable the banner.

=item B<Prompt> - A flag which enables/disables the prompt. This flag is only useful when MegaHAL is run interactively and is disabled by default.

=item B<Wrap> - A flag which enables/disables word wrapping of MegaHALs responses when the lines exceed 80 characters in length.  The default is to disable word wrapping.

lib/AI/MegaHAL.pm view on Meta::CPAN


=head2 initial_greeting

$text = $megahal->initial_greeting();

Returns a string containing the initial greeting which is created by MegaHAL at startup.

=head2 do_reply

$text = $megahal->do_reply($message);

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AI-MicroStructure

11 results

lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN

diesseitiges diesseits dinge dir direkt direkte direkten direkter doch doppelt dort dorther dorthin dran drauf drei dreißig drin dritte drueber drum drunter drüber du dunklen durch durchaus durchweg durchwegs durfte durften dürfen dürfte eben ebenfal...
entsprechender entsprechendes entweder er ergo ergänze ergänzen ergänzte ergänzten erhalten erhielt erhielten erhält erneut erst erste erstem ersten erster erstere ersterem ersteren ersterer ersteres erstes eröffne eröffnen eröffnet eröffnete eröffne...
häufige häufigem häufigen häufiger häufigere häufigeren häufigerer häufigeres höchst höchstens ich igitt ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres ihretwegen im immer immerhin immerwaehrend immerwaehrende immerwaehrendem immerwaehrenden immerwae...
jeglichen jeglicher jegliches jemals jemand jene jenem jenen jener jenes jenseitig jenseitigem jenseitiger jenseits jetzt jährig jährige jährigem jährigen jähriges kaeumlich kam kann kannst kaum kein keine keinem keinen keiner keinerlei keines keines...
naechste naemlich nahm naturgemaess naturgemaeß naturgemäss naturgemäß natürlich neben nebenan nehmen nein neu neue neuem neuen neuer neuerdings neuerlich neuerliche neuerlichem neuerlicher neuerliches neues neulich neun nicht nichts nichtsdestotrotz...
seine seinem seinen seiner seines seit seitdem seite seiten seither selbe selben selber selbst selbstredend selbstredende selbstredendem selbstredenden selbstredender selbstredendes seltsamerweise senke senken senkt senkte senkten setzen setzt setzte...
unmaßgeblichem unmaßgeblichen unmaßgeblicher unmaßgebliches unmoeglich unmoegliche unmoeglichem unmoeglichen unmoeglicher unmoegliches unmöglich unmögliche unmöglichen unmöglicher unnötig uns unsaeglich unsaegliche unsaeglichem unsaeglichen unsaeglic...
vollends vollstaendig vollstaendige vollstaendigem vollstaendigen vollstaendiger vollstaendiges vollständig vollständige vollständigem vollständigen vollständiger vollständiges vom von vor voran vorbei vorgestern vorher vorherig vorherige vorherigem ...
würde würden während währenddessen wär wäre wären x übel über überall überallhin überaus überdies überhaupt übermorgen üblicherweise übrig übrigens z.B. zahlreich zahlreichem zahlreicher zB zb. zehn zeitweise zeitweisem zeitweisen zeitweiser ziehen z...
return \%stoplist;
}

lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN

diesseitiges diesseits dinge dir direkt direkte direkten direkter doch doppelt dort dorther dorthin dran drauf drei dreißig drin dritte drueber drum drunter drüber du dunklen durch durchaus durchweg durchwegs durfte durften dürfen dürfte eben ebenfal...
entsprechender entsprechendes entweder er ergo ergänze ergänzen ergänzte ergänzten erhalten erhielt erhielten erhält erneut erst erste erstem ersten erster erstere ersterem ersteren ersterer ersteres erstes eröffne eröffnen eröffnet eröffnete eröffne...
häufige häufigem häufigen häufiger häufigere häufigeren häufigerer häufigeres höchst höchstens ich igitt ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres ihretwegen im immer immerhin immerwaehrend immerwaehrende immerwaehrendem immerwaehrenden immerwae...
jeglichen jeglicher jegliches jemals jemand jene jenem jenen jener jenes jenseitig jenseitigem jenseitiger jenseits jetzt jährig jährige jährigem jährigen jähriges kaeumlich kam kann kannst kaum kein keine keinem keinen keiner keinerlei keines keines...
naechste naemlich nahm naturgemaess naturgemaeß naturgemäss naturgemäß natürlich neben nebenan nehmen nein neu neue neuem neuen neuer neuerdings neuerlich neuerliche neuerlichem neuerlicher neuerliches neues neulich neun nicht nichts nichtsdestotrotz...
seine seinem seinen seiner seines seit seitdem seite seiten seither selbe selben selber selbst selbstredend selbstredende selbstredendem selbstredenden selbstredender selbstredendes seltsamerweise senke senken senkt senkte senkten setzen setzt setzte...
unmaßgeblichem unmaßgeblichen unmaßgeblicher unmaßgebliches unmoeglich unmoegliche unmoeglichem unmoeglichen unmoeglicher unmoegliches unmöglich unmögliche unmöglichen unmöglicher unnötig uns unsaeglich unsaegliche unsaeglichem unsaeglichen unsaeglic...
vollends vollstaendig vollstaendige vollstaendigem vollstaendigen vollstaendiger vollstaendiges vollständig vollständige vollständigem vollständigen vollständiger vollständiges vom von vor voran vorbei vorgestern vorher vorherig vorherige vorherigem ...
würde würden während währenddessen wär wäre wären x übel über überall überallhin überaus überdies überhaupt übermorgen üblicherweise übrig übrigens z.B. zahlreich zahlreichem zahlreicher zB zb. zehn zeitweise zeitweisem zeitweisen zeitweiser ziehen z...
return \%stoplist;
}

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AI-NNEasy

4 results

lib/AI/NNEasy.pm view on Meta::CPAN

Graciliano M. P. <gmpassos@cpan.org>

I will appreciate any type of feedback (include your opinions and/or suggestions). ;-P

Thanks a lot to I<Charles Colbourn <charlesc at nnflex.g0n.net>>, that is the
author of L<AI::NNFlex>, that 1st wrote it, since NNFlex was my starting point to
do this NN work, and 2nd to be in touch with the development of L<AI::NNEasy>.

=head1 COPYRIGHT

This program is free software; you can redistribute it and/or

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AI-NNFlex

4 results

lib/AI/NNFlex.pm view on Meta::CPAN

 my $outputsRef = $network->output(layer=>2,round=>1);


=head1 DESCRIPTION

AI::NNFlex is a base class for constructing your own neural network modules. To implement a neural network, start with the documentation for AI::NNFlex::Backprop, included in this distribution

=head1 CONSTRUCTOR 

=head2 AI::NNFlex->new ( parameter => value );

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AI-NaiveBayes

4 results

lib/AI/NaiveBayes.pm view on Meta::CPAN

version 0.04

=head1 SYNOPSIS

    # AI::NaiveBayes objects are created by AI::NaiveBayes::Learner
    # but for quick start you can use the 'train' class method
    # that is a shortcut using default AI::NaiveBayes::Learner settings

    my $classifier = AI::NaiveBayes->train( 
        {
            attributes => {

lib/AI/NaiveBayes.pm view on Meta::CPAN

algorithm.  This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.  

Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start 
you can use the limited C<train> class method that trains the 
classifier in a default way.

The classifier object is immutable.

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AI-Nerl

1 match

    third parties under the terms of this General Public License (except
    that you may choose to grant warranty protection to some or all
    third parties, at your option).

    c) If the modified program normally reads commands interactively when
    run, you must cause it, when started running for such interactive use
    in the simplest and most usual way, to print or display an
    announcement including an appropriate copyright notice and a notice
    that there is no warranty (or else, saying that you provide a
    warranty) and that users may redistribute the program under these
    conditions, and telling the user how to view a copy of this General

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
the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.

    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) 19yy  <name of author>



Also add information on how to contact you by electronic and paper mail.

If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:

    Gnomovision version 69, Copyright (C) 19xx name of author
    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

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AI-NeuralNet-BackProp

7 results

# author: Josiah Bryan 
# date:   Tuesday August 15 2000
# desc:   A simple back-propagation, feed-foward neural network with
#		  learning implemented via a generalization of Dobbs rule and
#		  several principals of Hoppfield networks. 
# online: http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl
#

package AI::NeuralNet::BackProp::neuron;
	
	use strict;

	    my ($fa,$fb);
		for my $x (0..$len) {
			print "\nLearning index $x...\n" if($AI::NeuralNet::BackProp::DEBUG);
			my $str = $self->learn( $data->[$x*2],			# The list of data to input to the net
					  		  		$data->[$x*2+1], 		# The output desired
					    			inc=>$inc,				# The starting learning gradient
					    			max=>$max,				# The maximum num of loops allowed
					    			error=>$error);			# The maximum (%) error allowed
			print $str if($AI::NeuralNet::BackProp::DEBUG); 
		}

			goto _GET_X if($learned[$x]);
			$learned[$x]=1;
			print "\nLearning index $x...\n" if($AI::NeuralNet::BackProp::DEBUG); 
			my $str =  $self->learn($data->[$x*2],			# The list of data to input to the net
					  		  		$data->[$x*2+1], 		# The output desired
					    			inc=>$inc,				# The starting learning gradient
			 		    			max=>$max,				# The maximum num of loops allowed
					    			error=>$error);			# The maximum (%) error allowed
			print $str if($AI::NeuralNet::BackProp::DEBUG); 
		}

			}
			AI::NeuralNet::BackProp::out1 "\n";             
		}
		
		# These next two loops connect the _run and _map packages (the IO interface) to 
		# the start and end 'layers', respectively. These are how we insert data into
		# the network and how we get data from the network. The _run and _map packages 
		# are connected to the neurons so that the neurons think that the IO packages are
		# just another neuron, sending data on. But the IO packs. are special packages designed
		# with the same methods as neurons, just meant for specific IO purposes. You will
		# never need to call any of the IO packs. directly. Instead, they are called whenever



=head1 DOWNLOAD

You can always download the latest copy of AI::NeuralNet::BackProp
from http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl


=head1 MAILING LIST

A mailing list has been setup for AI::NeuralNet::BackProp for discussion of AI and

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AI-NeuralNet-FastSOM

2 results