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lib/AI/Genetic/Pro.pm  view on Meta::CPAN

use constant GD 		=> 'GD::Graph::linespoints'; 
#=======================================================================
my $_Cache = { };
my $_temp_chromosome;
#=======================================================================
sub new {
	my ( $class, %args ) = ( shift, @_ );
	
	#-------------------------------------------------------------------
	my %opts = map { if(ref $_){$_}else{ /^-?(.*)$/o; $1 }} @_;
	my $self = bless \%opts, $class;

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examples/image_classification.pl  view on Meta::CPAN

GetOptions(
    ## my Pembroke Welsh Corgi Kyuubi, enjoing Solar eclipse of August 21, 2017
    'image=s' => \(my $image = 'http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'.
                               'gluon/dataset/kyuubi.jpg'),
    'model=s' => \(my $model = 'resnet152_v2'),
    'help'    => sub { HelpMessage(0) },
) or HelpMessage(1);

## get a pretrained model (download parameters file if necessary)
my $net = get_model($model, pretrained => 1);

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examples/calculator.pl  view on Meta::CPAN

use warnings;
use AI::MXNet ('mx');

## preparing the samples
## to train our network
sub samples {
    my($batch_size, $func) = @_;
    # get samples
    my $n = 16384;
    ## creates a pdl with $n rows and two columns with random
    ## floats in the range between 0 and 1

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bin/from-folder.pl  view on Meta::CPAN

GetOptions (\%opts, "cache_file=s");



sub translate
{
  return unless -f;
  (my $rel_name = $File::Find::name) =~ s{.*/}{}xs;
   my $name = md5_hex($rel_name);
    my $go = 0;

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examples/bp.pl  view on Meta::CPAN

#==============================================================
#********** THIS IS THE MAIN PROGRAM **************************
#==============================================================

sub main
 {

 # initiate the weights
  initWeights();

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

with Storage(format => 'Storable', io => 'File');

has model   => (is => 'ro', isa => 'HashRef[HashRef]', required => 1);

sub train {
    my $self = shift;
    my $learner = AI::NaiveBayes::Learner->new();
    for my $example ( @_ ){
        $learner->add_example( %$example );
    }
    return $learner->classifier;
}


sub classify {
    my ($self, $newattrs) = @_;
    $newattrs or die "Missing parameter for classify()";

    my $m = $self->model;

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examples/digits/digits.pl  view on Meta::CPAN

   show784($delta(:,6));
   show784($delta(:,4));
}
#die join (',',$nncost->dims);
use PDL::Graphics2D;
sub show784{
   my $w = shift;
   $w = $w->squeeze;
   my $min = $w->minimum;
   $w -= $min;
   my $max = $w->maximum;
   $w /= $max;
   $w = $w->reshape(28,28);
   imag2d $w;
}
sub sigmoid{
   my $foo = shift;
   return 1/(1+E**-$foo);
}

sub logistic{
   #find sigmoid before calling this.
   #grad=logistic(sigmoid(foo))
   my $foo = shift;
   return $foo * (1-$foo);
}

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BackProp.pm  view on Meta::CPAN

package AI::NeuralNet::BackProp::neuron;
	
	use strict;
	
	# Dummy constructor
    sub new {
    	bless {}, shift
	}	
	
	# Rounds floats to ints
	sub intr  {
    	shift if(substr($_[0],0,4) eq 'AI::');
      	try   { return int(sprintf("%.0f",shift)) }
      	catch { return 0 }
	}
    
	
	# Receives input from other neurons. They must
	# be registered as a synapse of this neuron to effectively
	# input.
	sub input {
		my $self 	 =	shift;
		my $sid		 =	shift;
		my $value	 =	shift;
		
		# We simply weight the value sent by the neuron. The neuron identifies itself to us

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

has 'matrix_rows'   => ( is => 'rw', isa => 'Int');

has 'matrix_cols'   => ( is => 'rw', isa => 'Int');

sub BUILD {
	my $self = shift;
	my $args = shift;
	my $matrix = Math::SparseMatrix->new($args->{row}, $args->{col});
	$self->matrix($matrix);	
	$self->matrix_rows($args->{row});
	$self->matrix_cols($args->{col});
}

sub train() {	
	my $self = shift;
	my @pattern = @_;	

	if ( ($#pattern + 1) != $self->matrix_rows) {
		die "Can't train a pattern of size " . ($#pattern + 1) . " on a hopfield network of size " , $self->matrix_rows;

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examples/game_ai.pl  view on Meta::CPAN

        $knife, 
        $gun, 
        $enemies])];
}

sub prompt 
{
    my ($message,$domain) = @_;
    my $valid_response = 0;
    my $response;
    do {

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examples/NeuralNet/pso_ann.pl  view on Meta::CPAN

my $annInputs = "pso.dat";

my $expectedValue = 3.5;	# this is the value that we want to train the ANN to produce (just like the example in t/PTO.t)


sub test_fitness_function(@) {
    my (@arr) = (@_);
	&writeAnnConfig($annConfig, $numInputs, $numHidden, $xferFunc, @arr);
	my $netValue = &runANN($annConfig, $annInputs);
	print "network value = $netValue\n";

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example/PSOTest-MultiCore.pl  view on Meta::CPAN

#use AI::ParticleSwarmOptimization;
use AI::ParticleSwarmOptimization::MCE;
#use AI::ParticleSwarmOptimization::Pmap;
use Data::Dumper; $::Data::Dumper::Sortkeys = 1;
#=======================================================================
sub calcFit {
    my @values = @_;
    my $offset = int (-@values / 2);
    my $sum;

	select( undef, undef, undef, 0.01 );	# Simulation of heavy processing...

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Samples/PSOPlatTest.pl  view on Meta::CPAN

printf ",# Fit %.5f at (%s) after %d iterations\n",
    $fit, join (', ', map {sprintf '%.4f', $_} @values), $iters;


sub calcFit {
    my @values = @_;
    my $offset = int (-@values / 2);
    my $sum;

    $sum += ($_ - $offset++)**2 for @values;