default results from the CPAN

                                 
    require Exporter;
	@ISA         = qw(Exporter);
	@EXPORT      = qw(range intr pdiff);
	%EXPORT_TAGS = ( 
		'default'    => [ qw ( range intr pdiff )],
		'all'        => [ qw ( p low high ramp and_gate or_gate range intr pdiff ) ],
		'p'          => [ qw ( p low high intr pdiff ) ],
		'acts'       => [ qw ( ramp and_gate or_gate range ) ],
	);
    @EXPORT_OK   = ( @{ $EXPORT_TAGS{'all'} }, qw( p low high ramp and_gate or_gate ) );

	#	my $set = $set->load_set($file, $column, $seperator);
	#
	# Returns a data set of the same format as required by the
	# learn_set() method. $file is the disk file to load set from.
	# $column an optional variable specifying the column in the 
	# data set to use as the class attribute. $class defaults to 0.
	# $seperator is an optional variable specifying the seperator
	# character between values. $seperator defaults to ',' (a single comma). 
	# NOTE: This does not handle quoted fields, or any other record
	# seperator other than "\n".
	#
	sub load_set {
		my $self	=	shift;

		for my $x (0..$len-1) { $tmp = $x if($ref1->[$x] < $ref1->[$tmp]) }
		return $tmp;
	}  
	
	# Following is a collection of a few nifty custom activation functions.
	# range() is exported by default, the rest you can get with:
	#	use AI::NeuralNet::Mesh ':acts'
	# The ':all' tag also gets these into your namespace.
	 
	#
	# range() returns a closure limiting the output

		sub{$_[1]->{t}=$_[0]if($_[0]>$_[1]->{t});$r[intr($_[0]/$_[1]->{t}*$#r)]}
	}
	
	#
	# ramp() preforms smooth ramp activation between 0 and 1 if $r is 1, 
	# or between -1 and 1 if $r is 2. $r defaults to 1, as you can see.	
	#
	# Note: when using a ramp() activatior, train the
	# net at least TWICE on the data set, because the first 
	# time the ramp() function searches for the top value in
	# the inputs, and therefore, results could flucuate.

uses a simpler recursion technique which, suprisingly, is more accurate than
the original technique that I've used in other ANNs.

=head1 EXPORTS

This module exports three functions by default:

	range
	intr
	pdiff


	use AI::NeuralNet::Mesh ':tag';
	
Tag sets are:

	:default 
	    - These functions are always exported.
		- Exports:
		range()
		intr()
		pdiff()

object. The network will have C<$layers> number of layers in it
and it will have C<$nodes> number of nodes per layer.

There is an optional parameter of $outputs, which specifies the number
of output neurons to provide. If $outputs is not specified, $outputs
defaults to equal $size. 


=item AI::NeuralNet::Mesh->new($file);

This will automatically create a new network from the file C<$file>. It will

the first element is the input layer, and the last is the output layer. The rest are
hidden layers.

Each hash refrence is expected to have AT LEAST the "nodes" key set to the number
of nodes (neurons) in that layer. The other two keys are optional. If "activation" is left
out, it defaults to "linear". If "threshold" is left out, it defaults to 0.50.

The "activation" key can be one of four values:

	linear                    ( simply use sum of inputs as output )
	sigmoid    [ sigmoid_1 ]  ( only positive sigmoid )




=item $net->learn($input_map_ref, $desired_result_ref [, options ]);

NOTE: learn_set() now has increment-degrading turned OFF by default. See note
on the degrade flag, below.

This will 'teach' a network to associate an new input map with a desired 
result. It will return a string containg benchmarking information.

	 error    =>    $maximum_allowable_percentage_of_error
	 degrade  =>    $degrade_increment_flag
	 

$learning_gradient is an optional value used to adjust the weights of the internal
connections. If $learning_gradient is ommitted, it defaults to 0.002.
 
$maximum_iterations is the maximum numbers of iteration the loop should do.
It defaults to 1024.  Set it to 0 if you never want the loop to quit before
the pattern is perfectly learned.

$maximum_allowable_percentage_of_error is the maximum allowable error to have. If 
this is set, then learn() will return when the perecentage difference between the
actual results and desired results falls below $maximum_allowable_percentage_of_error.

then learn() will not return until it gets an exact match for the desired result OR it
reaches $maximum_iterations.

$degrade_increment_flag is a simple flag used to allow/dissalow increment degrading
during learning based on a product of the error difference with several other factors.
$degrade_increment_flag is off by default. Setting $degrade_increment_flag to a true
value turns increment degrading on. 

In previous module releases $degrade_increment_flag was not used, as increment degrading
was always on. In this release I have looked at several other network types as well
as several texts and decided that it would be better to not use increment degrading. The

is not set, then it will return a percentage represting the amount of forgetfullness. Otherwise,
learn_set() will return an integer specifying the amount of forgetfulness when all the patterns 
are learned. 

If "pattern" is set, then learn_set() will use that pattern in the data set to measure forgetfulness by.
If "pattern" is omitted, it defaults to the first pattern in the set. Example:

	my @set = (
		[ 0,1,0,1 ],  [ 0 ],
		[ 0,0,1,0 ],  [ 1 ],
		[ 1,1,0,1 ],  [ 2 ],  #  <---

Loads a CSV-like dataset from disk

Returns a data set of the same structure as required by the
learn_set() method. $file is the disk file to load set from.
$column an optional variable specifying the column in the 
data set to use as the class attribute. $class defaults to 0.
$seperator is an optional variable specifying the seperator
character between values. $seperator defaults to ',' (a single comma). 
NOTE: This does not handle quoted fields, or any other record
seperator other than "\n".

The returned array ref is suitable for passing directly to
learn_set() or get_outs().

number of elements has passed. Additionally, if you include a $high_state_character and a $low_state_character,
it will print the $high_state_character (can be more than one character) for every element that
has a true value, and the $low_state_character for every element that has a false value. 
If you do not supply a $high_state_character, or the $high_state_character is a null or empty or 
undefined string, it join_cols() will just print the numerical value of each element seperated
by a null character (\0). join_cols() defaults to the latter behaviour.



=item $net->extend(\@array_of_hashes);

=item $net->const($const);

This sets the run const. for the network. The run const. is a value that is added
to every input line when a set of inputs are run() or learn() -ed, to prevent the
network from hanging on a 0 value. When called with no arguments, it returns the current
const. value. It defaults to 0.0001 on a newly-created network. The run const. value
is preserved across load() and save() calls.


=item $net->error();


=head1 CUSTOM ACTIVATION FUNCTIONS 

Included in this package are four custom activation functions meant to be used
as a guide to create your own, as well as to be useful to you in normal use of the
module. There is only one function exported by default into your namespace, which
is the range() functions. These are not meant to be used as methods, but as functions.
These functions return code refs to a Perl closure which does the actual work when
the time comes.



=item ramp($r);

ramp() preforms smooth ramp activation between 0 and 1 if $r is 1, 
or between -1 and 1 if $r is 2. $r defaults to 1.	

You can get this into your namespace with the ':acts' export 
tag as so:
	
	use AI::NeuralNet::Mesh ':acts';


=item and_gate($threshold);

Self explanitory, pretty much. This turns the node into a basic AND gate.
$threshold is used to decide if an input is true or false (1 or 0). If 
an input is below $threshold, it is false. $threshold defaults to 0.5.

You can get this into your namespace with the ':acts' export 
tag as so:
	
	use AI::NeuralNet::Mesh ':acts';


=item $AI::NeuralNet::Mesh::Connector

This is an option is step up from average use of this module. This variable 
should hold the fully qualified name of the function used to make the actual connections
between the nodes in the network. This contains '_c' by default, but if you use
this variable, be sure to add the fully qualified name of the method. For example,
in the ALN example, I use a connector in the main package called tree() instead of
the default connector. Before I call the new() constructor, I use this line of code:

	$AI::NeuralNet::Mesh::Connector = 'main::tree'
	
The tree() function is called as a blessed method when it is used internally, providing
access to the bless refrence in the first argument. See notes on CUSTOM NETWORK CONNECTORS,


=head1 CUSTOM NETWORK CONNECTORS

Creating custom network connectors is step up from average use of this module. 
However, it can be very useful in creating other styles of neural networks, other
than the default fully-connected feed-foward network. 

You create a custom connector by setting the variable $AI::NeuralNet::Mesh::Connector
to the fully qualified name of the function used to make the actual connections
between the nodes in the network. This variable contains '_c' by default, but if you use
this variable, be sure to add the fully qualified name of the method. For example,
in the ALN example, I use a connector in the main package called tree() instead of
the default connector. Before I call the new() constructor, I use this line of code:

	$AI::NeuralNet::Mesh::Connector = 'main::tree'
	
The tree() function is called as a blessed method when it is used internally, providing
access to the bless refrence in the first argument.

Using $y+$r2a we get the index of the node to pass to add_output_node() on the first node at
$y+B<$r1a>. 

And that's all there is to it!

For the fun of it, we'll take a quick look at the default connector.
Below is the actual default connector code, albeit a bit cleaned up, as well as
line numbers added.

	= line 1  =	sub _c {
	= line 2  =    	my $self	=	shift;
	= line 3  =    	my $r1a		=	shift;

view all matches for this distribution

AI-NeuralNet-SOM

3 results

lib/AI/NeuralNet/SOM.pm view on Meta::CPAN


The constructor takes arguments:

=over

=item C<input_dim> : (mandatory, no default)

A positive integer specifying the dimension of the sample vectors (and hence that of the vectors in
the grid).

=item C<learning_rate>: (optional, default C<0.1>)

This is a magic number which controls how strongly the vectors in the grid can be influenced. Stronger
movement can mean faster learning if the clusters are very pronounced. If not, then the movement is
like noise and the convergence is not good. To mediate that effect, the learning rate is reduced
over the iterations.

=item C<sigma0>: (optional, defaults to radius)

A non-negative number representing the start value for the learning radius. Practically, the value
should be chosen in such a way to cover a larger part of the map. During the learning process this
value will be narrowed down, so that the learning radius impacts less and less neurons.

view all matches for this distribution

AI-NeuralNet-Simple

1 match

        input  => $args[0],
        hidden => $args[1],
        output => $args[2],
        handle => $handle,
    }, $class;
    $self->iterations(10000);    # set a reasonable default
}

sub train {
    my ( $self, $inputref, $outputref ) = @_;
    return c_train( $self->handle, $inputref, $outputref );

the input, hidden, and output layers, respectively.  To create the "logical or"
network described earlier:

  my $net = AI::NeuralNet::Simple->new(2,1,2);

By default, the activation function for the neurons is the sigmoid function
S() with delta = 1:

	S(x) = 1 / (1 + exp(-delta * x))

but you can change the delta after creation.  You can also use a bipolar

argument is expected to be an array ref of pairs of input and output array
refs.

The second argument is the number of iterations to train the set.  If
this argument is not provided here, you may use the C<iterations()> method to
set it (prior to calling C<train_set()>, of course).  A default of 10,000 will
be provided if not set.

The third argument is the targeted Mean Square Error (MSE). When provided,
the traning sequence will compute the maximum MSE seen during an iteration
over the training set, and if it is less than the supplied target, the

      ->train_set(\@training_data);
  
=head2 C<learn_rate($rate)>)

This method, if called without an argument, will return the current learning
rate.  .20 is the default learning rate.

If called with an argument, this argument must be greater than zero and less
than one.  This will set the learning rate and return the object.
  
  $net->learn_rate; #returns the learning rate

For a more comprehensive example of how this is used, see the 
"examples/game_ai.pl" program.

=head1 EXPORT

None by default.

=head1 CAVEATS

This is B<alpha> code.  Very alpha.  Not even close to ready for production,
don't even think about it.  I'm putting it on the CPAN lest it languish on my

view all matches for this distribution

AI-Ollama-Client

15 results


=cut

has 'schema_file' => (
    is => 'lazy',
    default => sub { require AI::Ollama::Client::Impl; module_file('AI::Ollama::Client::Impl', 'ollama-curated.yaml') },
);

has 'schema' => (
    is => 'lazy',
    default => sub {
        if( my $fn = $_[0]->schema_file ) {
            YAML::PP->new( boolean => 'JSON::PP' )->load_file($fn);
        }
    },
);

has 'validate_requests' => (
    is => 'rw',
    default => 1,
);

has 'validate_responses' => (
    is => 'rw',
    default => 1,
);

has 'openapi' => (
    is => 'lazy',
    default => sub {
        if( my $schema = $_[0]->schema ) {
            OpenAPI::Modern->new( openapi_schema => $schema, openapi_uri => '' )
        }
    },
);

# The HTTP stuff should go into a ::Role I guess
has 'ua' => (
    is => 'lazy',
    default => sub { Mojo::UserAgent->new },
);

has 'server' => (
    is => 'ro',
);

If set to 0, the model will be unloaded immediately once finished.


=item -

If not set, the model will stay loaded for 5 minutes by default


=back

=item C<< messages >>


=item C<< model >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=item C<< options >>

Additional model parameters listed in the documentation for the Modelfile such as C<temperature>.


=item C<< name >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=item C<< stream >>

If C<false> the response will be returned as a single response object, otherwise the response will be streamed as a series of objects.


=item C<< name >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=back


=cut


=item C<< model >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=item C<< options >>

Additional model parameters listed in the documentation for the Modelfile such as C<temperature>.

If set to 0, the model will be unloaded immediately once finished.


=item -

If not set, the model will stay loaded for 5 minutes by default


=back

=item C<< model >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=item C<< options >>

Additional model parameters listed in the documentation for the Modelfile such as C<temperature>.


=item C<< name >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=item C<< stream >>

If C<false> the response will be returned as a single response object, otherwise the response will be streamed as a series of objects.


=item C<< name >>

The model name.

Model names follow a C<model:tag> format. Some examples are C<orca-mini:3b-q4_1> and C<llama2:70b>. The tag is optional and, if not provided, will default to C<latest>. The tag is used to identify a specific version.

=back

Returns a L<< AI::Ollama::ModelInfo >> on success.

view all matches for this distribution

AI-PBDD

2 results

XS.xs view on Meta::CPAN

  if (bddnodes[bdd].refcou == 0)
    {
      fprintf (stderr, "ERROR: refcount is zero\n");
      return 0;
    }
  // refcount for TRUE and FALSE is saturated by default, ignore them!
  if (bdd > 2 && bddnodes[bdd].refcou == MAXREF)
    {
      fprintf (stderr, "ERROR: refcount is saturated\n");
      return 0;
    }

view all matches for this distribution

AI-PSO

2 results

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


my $psoRandomRange = 'null';           # PSO::.86 new variable to support original unmodified algorithm
my $useModifiedAlgorithm = 'null';

#-#-# user/debug parameters #-#-#
my $verbose    = 0;                    # This one defaults for obvious reasons...

#NOTE: $meWeight and $themWeight should really add up to a constant value.  
#      Swarm Intelligence defines a 'pso random range' constant and then computes two random numbers
#      within this range by first getting a random number and then subtracting it from the range.
#      e.g.

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

# initialize_neighbors
# NOTE: I made this a separate subroutine so that different topologies of neighbors can be created and used instead of this.
# NOTE: This subroutine is currently not used because we access neighbors by index to the particle array rather than storing their references
# 
#  - adds a neighbor array to the particle hash data structure
#  - sets the neighbor based on the default neighbor hash function
#
sub initialize_neighbors() {
    for(my $p = 0; $p < $numParticles; $p++) {
        for(my $n = 0; $n < $numNeighbors; $n++) {
            $particles[$p]{neighbor}[$n] = $particles[&get_index_of_neighbor($p, $n)];

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

  running iterations of search and many calls to the fitness function 
  you registered with pso_register_fitness_function()

=item pso_get_solution_array()

  By default, pso_optimize() will print out to STDERR the first 
  solution, or the best solution so far if the max iterations were 
  reached.  This function will simply return an array of the winning 
  (or best so far) position of the entire swarm system.  It is an 
  array of floats to be used how you wish (like weights in a 
  neural network!).

view all matches for this distribution

AI-ParticleSwarmOptimization-MCE

4 results

lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN

=item I<-stallSpeed>: positive number, optional

Speed below which a particle is considered to be stalled and is repositioned to
a new random location with a new initial speed.

By default I<-stallSpeed> is undefined but particles with a speed of 0 will be
repositioned.

=item I<-themWeight>: number, optional

Coefficient determining the influence of the neighbourhod best position on the

view all matches for this distribution

AI-ParticleSwarmOptimization-Pmap

4 results

lib/AI/ParticleSwarmOptimization/Pmap.pm view on Meta::CPAN

=item I<-stallSpeed>: positive number, optional

Speed below which a particle is considered to be stalled and is repositioned to
a new random location with a new initial speed.

By default I<-stallSpeed> is undefined but particles with a speed of 0 will be
repositioned.

=item I<-themWeight>: number, optional

Coefficient determining the influence of the neighbourhod best position on the

view all matches for this distribution

AI-ParticleSwarmOptimization

3 results

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

=item I<-stallSpeed>: positive number, optional

Speed below which a particle is considered to be stalled and is repositioned to
a new random location with a new initial speed.

By default I<-stallSpeed> is undefined but particles with a speed of 0 will be
repositioned.

=item I<-themWeight>: number, optional

Coefficient determining the influence of the neighbourhod best position on the

view all matches for this distribution

AI-Pathfinding-AStar-Rectangle

2 results

lib/AI/Pathfinding/AStar/Rectangle.pm view on Meta::CPAN


require Exporter;

our @ISA = qw(Exporter);

# Items to export into callers namespace by default. Note: do not export
# names by default without a very good reason. Use EXPORT_OK instead.
# Do not simply export all your public functions/methods/constants.

# This allows declaration	use AI::Pathfinding::AStar::Rectangle ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.

lib/AI/Pathfinding/AStar/Rectangle.pm view on Meta::CPAN


See ./examples 

=head2 EXPORT

None by default.

=head1 SEE ALSO

=head1 AUTHOR

view all matches for this distribution

AI-Pathfinding-OptimizeMultiple

11 results

lib/AI/Pathfinding/OptimizeMultiple.pm view on Meta::CPAN


use PDL;
use Scalar::Util qw/ blessed /;

has chosen_scans     => ( isa => 'ArrayRef', is => 'rw' );
has _iter_idx        => ( isa => 'Int', is => 'rw', default  => sub { 0; }, );
has _num_boards      => ( isa => 'Int', is => 'ro', init_arg => 'num_boards', );
has _orig_scans_data => ( isa => 'PDL', is => 'rw' );
has _optimize_for => ( isa => 'Str', is => 'ro', init_arg => 'optimize_for', );
has _scans_data   => ( isa => 'PDL', is => 'rw' );
has _selected_scans =>

lib/AI/Pathfinding/OptimizeMultiple.pm view on Meta::CPAN

    ( isa => 'HashRef', is => 'rw', init_arg => 'scans_iters_pdls' );
has _stats_factors => (
    isa      => 'HashRef',
    is       => 'ro',
    init_arg => 'stats_factors',
    default  => sub { return +{}; },
);

sub BUILD
{
    my $self = shift;

lib/AI/Pathfinding/OptimizeMultiple.pm view on Meta::CPAN


=item *

RT: CPAN's Bug Tracker

The RT ( Request Tracker ) website is the default bug/issue tracking system for CPAN.

L<https://rt.cpan.org/Public/Dist/Display.html?Name=AI-Pathfinding-OptimizeMultiple>

=item *

view all matches for this distribution

AI-Pathfinding-SMAstar

2 results

lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN


require Exporter;

our @ISA = qw(Exporter);

# Items to export into callers namespace by default. Note: do not export
# names by default without a very good reason. Use EXPORT_OK instead.
# Do not simply export all your public functions/methods/constants.

# This allows declaration	use AI::Pathfinding::SMAstar ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.

lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN




=head2 EXPORT

None by default.



=head1 SEE ALSO

view all matches for this distribution

AI-Perceptron-Simple

3 results


=cut

our $VERSION = '1.04';

# default values
use constant LEARNING_RATE => 0.05;
use constant THRESHOLD => 0.5;
use constant TUNE_UP => 1;
use constant TUNE_DOWN => 0;

    shuffle_data ( ORIGINAL_STIMULI, $new_file_1, $new_file_2, ... );
    shuffle_data ( $original_stimuli => $new_file_1, $new_file_2, ... );

=head1 EXPORT

None by default.

All the subroutines from C<DATA PROCESSING RELATED SUBROUTINES>, C<NERVE DATA RELATED SUBROUTINES> and C<NERVE PORTABILITY RELATED SUBROUTINES> sections are importable through tags or manually specifying them.

The tags available include the following:


An array reference containing all the attributes / dendrites names. Yes, give them some names :)

=item learning_rate => $decimal

Optional. The default is C<0.05>.

The learning rate of the perceptron for the fine-tuning process.

This value is usually between 0 and 1. However, it all depends on your combination of numbers for the other options.

=item threshold => $decimal

Optional. The default is C<0.5>

This is the passing rate to determine the neuron output (C<0> or C<1>).

Generally speaking, this value is usually between C<0> and C<1>. However, it all depend on your combination of numbers for the other options.

the filename that will be generate each time the perceptron finishes the training process. This data file is the data of the C<AI::Perceptron::Simple> 
object and it is used in the C<validate> method.

C<$expected_output_header> is the header name of the columns in the csv file with the actual category or the exepcted values. This is used to determine to tune the nerve up or down. This value should only be 0 or 1 for the sake of simplicity.

C<$display_stats> is B<optional> and the default is 0. It will display more output about the tuning process. It will show the followings:

=over 4

=item tuning status


=item results_write_to => $new_csv_file

Optional.

The default behaviour will write the predicted output back into C<stimuli_validate> ie the original data. The sequence of the data will be maintained.

=back

I<*This method will call C<_real_validate_or_test> to do the actual work.>


The parameters are the same as C<display_confusion_matrix>. See the next method.

=head2 display_confusion_matrix ( \%confusion_matrix, \%labels ) 

Display the confusion matrix. If C<%confusion_matrix> has C<more_stats> elements, it will display them if they exists. The default elements ie C<accuracy> and C<sensitivity> must be present, while the rest can be absent.

C<%confusion_matrix> is the same confusion matrix returned by the C<get_confusion_matrix> method.

For C<%labels>, since C<0>'s and C<1>'s won't make much sense as the output labels in most cases, therefore, the following keys must be specified:


=back

Please take note that non-ascii characters ie. non-English alphabets B<might> cause the output to go off :)

For the C<%labels>, there is no need to enter "actual X", "predicted X" etc. It will be prefixed with C<A: > for actual and C<P: > for the predicted values by default.

=cut

sub display_exam_results {

view all matches for this distribution

AI-Perceptron

2 results

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


=over 4

=item new( [%args] )

Creates a new perceptron with the following default properties:

    num_inputs    = 1
    learning_rate = 0.01
    threshold     = 0.0
    weights       = empty list

view all matches for this distribution

AI-PredictionClient

4 results

bin/Inception.pl view on Meta::CPAN

use MooX::Options;
use 5.010;
use Data::Dumper qw(Dumper);
use Perl6::Form;

my $default_host            = '127.0.0.1';
my $default_port            = '9000';
my $default_model           = 'inception';
my $default_model_signature = 'predict_images';

option image_file => (
  is       => 'ro',
  required => 1,
  format   => 's',

bin/Inception.pl view on Meta::CPAN

);
option host => (
  is       => 'ro',
  required => 0,
  format   => 's',
  default  => $default_host,
  doc      => "IP address of the server [Default: $default_host]"
);
option port => (
  is       => 'ro',
  required => 0,
  format   => 's',
  default  => $default_port,
  doc      => "Port number of the server [Default: $default_port]"
);
option model_name => (
  is       => 'ro',
  required => 0,
  format   => 's',
  default  => $default_model,
  doc      => "Model to process image [Default: $default_model]"
);
option model_signature => (
  is       => 'ro',
  required => 0,
  format   => 's',
  default  => $default_model_signature,
  doc      => "API signature for model [Default: $default_model_signature]"
);
option debug_verbose => (is => 'ro', doc => 'Verbose output');
option debug_loopback_interface => (
  is       => 'ro',
  required => 0,

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AI-Prolog

5 results

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


# they don't want pretty printed strings if they're using this interface
Engine->formatted(0);

# Until (and unless) we figure out the weird bug that prevents some values
# binding in the external interface, we need to stick with this as the default
Engine->raw_results(1);

sub new {
    my ( $class, $program ) = @_;
    my $self = bless {

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

 }

Calling C<raw_results> without an argument returns the current C<raw_results>
value.

This is the default behavior.

=head2 C<quote($string)>.

This method quotes a Perl string to allow C<AI::Prolog> to treat it as a proper
Prolog term (and not worry about it accidentally being treated as a variable if

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


=back

=head1 EXPORT

None by default.  However, for convenience, you can choose ":all" functions to
be exported.  That will provide you with C<Term>, C<Parser>, and C<Engine>
classes.  This is not recommended and most support and documentation will now
target the C<AI::Prolog> interface.

If you choose not to export the functions, you may use the fully qualified

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AI-SimulatedAnnealing

2 results

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

use Exporter;

# Version:
our $VERSION = '1.02';

# Specify default exports:
our @ISA = ("Exporter");
our @EXPORT = (
  "anneal",
  );

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AI-TensorFlow-Libtensorflow

26 results

lib/AI/TensorFlow/Libtensorflow.pm view on Meta::CPAN

use FFI::C;

my $ffi = AI::TensorFlow::Libtensorflow::Lib->ffi;
FFI::C->ffi($ffi);

$ffi->mangler(AI::TensorFlow::Libtensorflow::Lib->mangler_default);

sub new {
	my ($class) = @_;
	bless {}, $class;
}

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AI-Termites

1 match

samples/termites.pl view on Meta::CPAN


=item --truecolor

Generate TrueColor PNGs.

The default PNG format uses a 8bit indexed palette that is not
correctly handled by some programs as ffmpeg or mencoder.

=back

=head1 EXAMPLES

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AI-XGBoost

1 match

lib/AI/XGBoost/DMatrix.pm view on Meta::CPAN


Array with the labels of the rows of matrix. Optional

=item missing

Value to identify missing values. Optional, default `NaN`

=back

=head2 set_float_info

lib/AI/XGBoost/DMatrix.pm view on Meta::CPAN


Filename and path

=item silent

Don't show information messages, optional, default 1

=back

=head2 set_label

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AIIA-GMT

2 results

lib/AIIA/GMT.pm view on Meta::CPAN

require Exporter;
use AutoLoader qw(AUTOLOAD);

our @ISA = qw(Exporter);

# Items to export into callers namespace by default. Note: do not export
# names by default without a very good reason. Use EXPORT_OK instead.
# Do not simply export all your public functions/methods/constants.

# This allows declaration	use AIIA::GMT ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.

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AIS-client

2 results

client.pm view on Meta::CPAN


=head1 USE-LINE CONFIGURATION OPTIONS

=item aissri

By default, AIS::client will refer to the AIS service defined
at http://www.pay2send.com/cgi/ais/ but an alternate AIS service
can be specified with the C<aissri> parameter:

   use AIS::client aissri => 'http://www.cpan.org/service/ais/';

=item agent

By default, AIS::client will give the URL of the webpage being
requested as the agent string, but an alternate agent string
can be specified with the C<agent> parameter:

   use AIS::client aissri => "Bob's web services: account MZNXBCV";

client.pm view on Meta::CPAN

AIS service might issue expiring capability keys which would have to be
listed as part of the agent string.

=item prefix

By default, C<AIS>, which means that AIS::client will store session
data (incliding identity, which is also available as
C<$AIS_STASH{identity}>) in subdirectories under a directory
called C<AIS_sessions> under the current directory
your script runs in.  This can be changed with the C<prefix> parameter:

  use AIS::client prefix => '.AIS'; # hide session directory

The prefix is also used as the prefix for the session cookie name,
which defaults to C<AIS_session>.

=item freq

By default, AIS::client will examine the session directory for stale
sessions approximately once every 2000 invocations.  Adjust this
with the C<freq> parameter. C<0> will suppress housekeeping entirely.

=item maxage

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AIX-LPP

2 results

TODO view on Meta::CPAN

improve the parsing routine to handle multiple filesets per package
add validation()
add defaults() ?
add object classes for handling other package data: post_i, odmadd,
	copywrite, etc.
add methods to store/query *installed* package info

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AIX-Perfstat

6 results

inc/Devel/CheckLib.pm view on Meta::CPAN

built once for each library that you specify, and (without linking) once
for each header file.

=head1 FUNCTIONS

All of these take the same named parameters and are exported by default.
To avoid exporting them, C<use Devel::CheckLib ()>.

=head2 assert_lib

This takes several named parameters, all of which are optional, and dies

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ALBD

6 results

lib/ALBD.pm view on Meta::CPAN

    my $self = shift;
    my $optionsHashRef = shift; 

    #initialize UMLS::Interface
    my %tHash = ();
    $tHash{'t'} = 1; #default hash values are with t=1 (silence module output)
    my $componentOptions = \%tHash;
    if (${$optionsHashRef}{'interfaceConfig'} ne '') {
	#read configuration file if its defined
	$componentOptions = 
	    $self->_readConfigFile(${$optionsHashRef}{'interfaceConfig'});
    }
    #else use default configuration
    $umls_interface = UMLS::Interface->new($componentOptions) 
	or die "Error: Unable to create UMLS::Interface object.\n";

    #initialize UMLS::Association
    $componentOptions = \%tHash;
    if (${$optionsHashRef}{'assocConfig'} ne '') {
	#read configuration file if its defined
	$componentOptions = 
	    $self->_readConfigFile(${$optionsHashRef}{'assocConfig'});
    }
    #else use default configuation
    $umls_association = UMLS::Association->new($componentOptions) or 
	die "Error: Unable to create UMLS::Association object.\n";

    #initialize LBD parameters
    %lbdOptions = %{$self->_readConfigFile(${$optionsHashRef}{'lbdConfig'})};

lib/ALBD.pm view on Meta::CPAN


# Reads the config file in as an options hash
# input: the name of a configuration file that has key fields in '<>'s, 
#        The '>' is followed directly by the value for that key, no space.
#        Each line of the file contains a new key-value pair (e.g. <key>value)
#        If no value is provided, a default value of 1 is set
# output: a hash ref to a hash containing each key value pair
sub _readConfigFile {
    my $self = shift;
    my $configFileName = shift;

lib/ALBD.pm view on Meta::CPAN

		if ($2) {
		    #add key and value to the optionsHash
		    $optionsHash{$1} = $2;
		}
		else {
		    #add key and set default value to the optionsHash
		    $optionsHash{$1} = 1;
		}
	    }
	}
    }

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ALPM

10 results

lib/ALPM/Conf.pm view on Meta::CPAN

	for my $db (@$dbs){
		my($r, $sl, $mirs) = @{$db}{'name', 'siglvl', 'mirrors'};
		next if(!@$mirs);

		_expurls($mirs, $opts->{'arch'}, $r);
		$sl = 'default' if(!$usesl);
		my $x = $alpm->register($r, $sl)
			or die "Failed to register $r database: " . $alpm->strerror;
		$x->add_server($_) for(@$mirs);
	}
	return $alpm;

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AMF-Connection

2 results

		'endpoint' => $endpoint,
		'headers' => [],
		'http_headers' => {},
		'http_cookie_jar' => new HTTP::Cookies(),
		'response_counter' => 0,
		'encoding' => 0, # default is AMF0 encoding
		'ua'	=> new LWP::UserAgent(),
		'append_to_endpoint' => ''
		};

	$self->{'ua'}->cookie_jar( $self->{'http_cookie_jar'} );

        return bless($self, $class);
	};

# plus add paramters, referer, user agent, authentication/credentials ( see also SecureAMFChannel stuff ), 
# plus timezone on retunred dates to pass to de-serializer - see AMF3 spec saying "it is suggested that time zone be queried independnetly as needed" - unelss local DateTime default to right locale!

# we pass the string, and let Storable::AMF to parse the options into a scalar - see Input/OutputStream and Storable::AMF0 documentation

sub setInputAMFOptions {
	my ($class, $options) = @_;

	my ($class, $cookie_jar) = @_;

	croak "Not a valid cookies jar $cookie_jar"
		unless( ref($cookie_jar) and $cookie_jar->isa("HTTP::Cookies") );

	# TODO - copy/pass over the current cookies (in-memory by default) if any set
	$class->{'http_cookie_jar'}->scan( sub { $cookie_jar->set_cookie( @_ ); } );

	$class->{'http_cookie_jar'} = $cookie_jar;

	# tell user agent to use new cookie jar


	#use Data::Dumper;
	#print STDERR Dumper( $request );

	# set any extra HTTP header
	map { $class->{'ua'}->default_header( $_ => $class->{'http_headers'}->{$_} ); } keys %{ $class->{'http_headers'} };

	my $http_response = $class->{'ua'}->post(
		$class->{'endpoint'}.$class->{'append_to_endpoint'}, # TODO - check if append to URL this really work for HTTP POST
		Content_Type => "application/x-amf",
		Content => $request_stream->getStreamData()

  my $service = 'myService';
  my $method = 'myMethod';

  my $client = new AMF::Connection( $endpoint );

  $client->setEncoding(3); # use AMF3 default AMF0

  $client->setHTTPCookieJar( HTTP::Cookies->new(file => "/tmp/mycookies.txt", autosave => 1, ignore_discard => 1 ) );

  my @params = ( 'param1', { 'param2' => 'value2' } );
  my $response = $client->call( "$service.$method", \@params );


AMF::Connection is meant to provide a simple AMF library to write client applications for invocation of remote services as used by most flex/AIR RIAs. 

The module includes basic support for synchronous HTTP/S based RPC request-response access, where the client sends a request to the server to be processed and the server returns a response to the client containing the processing outcome. Data is sent...

AMF0 and AMF3 support is provided using the Storable::AMF module. While HTTP/S requestes to the AMF endpoint are carried out using the LWP::UserAgent module. The requests are sent using the HTTP POST method as AMF0 encoded data by default. AMF3 encod...

If encoding is set to AMF3 the Flex Messaging framework is used on returned responses content (I.e. objects casted to "flex.messaging.messages.AcknowledgeMessage" and "flex.messaging.messages.ErrorMessage" are returned).

Simple batch requests and responses is provided also.


Allow to specify an alternative LWP::UserAgent. The $ua must support the post() method, proxy() and cookie_jar() if necessary.

=head2 setHTTPCookieJar ($cookie_jar)

Allow to specify an alternative HTTP::Cookies jar. By default AMF::Connection keeps cookies into main-memory and the cookie jar is reset when a new connection is created. When a new cookies jar is set, any existing AMF::Connection cookie is copied ov...

=head2 getHTTPCookieJar ()

Return the current HTTP::Cookies jar in use.

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AMF-Perl

5 results

lib/AMF/Perl.pm view on Meta::CPAN


require Exporter;
use vars qw($VERSION @ISA @EXPORT @EXPORT_OK %EXPORT_TAGS);
@ISA = qw(Exporter);

# Items to export into callers namespace by default. Note: do not export
# names by default without a very good reason. Use EXPORT_OK instead.
# Do not simply export all your public functions/methods/constants.

# This allows declaration	use AMF::Perl ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.

lib/AMF/Perl.pm view on Meta::CPAN

    and serializes and returns the data back to the client.


=head2 EXPORT

None by default.

=head1 SEE ALSO

There is a mailing list for AMF::Perl. You can subscribe here:
http://lists.sourceforge.net/lists/listinfo/flaph-general

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AMPR-Rip44

1 match

bin/rip44d view on Meta::CPAN

		. "  $me [-v] [-d] [-i <tunnelif>] [-a <localaddrs>] [-p <password>]\n"
		. "Options:\n"
		. "  -v   increase verbosity slightly to print error messages on stderr\n"
		. "  -d   increase verbosity greatly (debug mode)\n"
		. "  -i <tunnelinterface>\n"
		. "       use the specified tunnel interface, defaults to tunl0\n"
		. "  -a <comma-separated-ip-list>\n"
		. "       ignore routes pointing to these (local) gateways\n"
		. "       (list contains system's local IP addresses by default)\n"
		. "  -p <password>\n"
		. "       use RIPv2 password 'authentication', defaults to none\n"
		;
}

sub VERSION_MESSAGE()
{

bin/rip44d view on Meta::CPAN

}

fill_local_ifs();

# Enable multicast on the tunnel interface, the flag is
# not set by default
system($ifconfig, $tunnel_if, 'multicast') == 0 or die "ifconfig $tunnel_if multicast failed: $?\n";

# Create the UDP multicast socket to receive RIP broadcasts
warn "opening UDP socket...\n" if ($verbose);
my $socket = IO::Socket::Multicast->new(

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AMQP

4 results