undef results from the CPAN

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


=cut

sub new_from_string {
    my ($package, $line) = @_;
    return undef unless defined $line;
    chomp $line;
    if($line =~ /^\[I(-?\d+) D(\d+) F(\d+) H([^\]]+)\](.+)/) {
        my ($id, $dimensions, $fitness, $host, $code) = ($1, $2, $3, $4, $5);
        return AI::Evolve::Befunge::Blueprint->new(
            id         => $id,

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

            fitness    => $fitness,
            host       => $host,
            code       => $code,
        );
    }
    return undef;
}


=head2 new_from_file

view all matches for this distribution

AI-ExpertSystem-Advanced

8 results

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

	# releases once we can make sure it won't clash with custom
	# Module::Install extensions.
	$VERSION = '0.91';

	# Storage for the pseudo-singleton
	$MAIN    = undef;

	*inc::Module::Install::VERSION = *VERSION;
	@inc::Module::Install::ISA     = __PACKAGE__;

}

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

		defined $_[0]
		and
		! ref $_[0]
		and
		$_[0] =~ m/^[^\W\d]\w*(?:::\w+)*\z/s
	) ? $_[0] : undef;
}

1;

# Copyright 2008 - 2009 Adam Kennedy.

view all matches for this distribution

AI-ExpertSystem-Simple

9 results


	my $self = {};

	$self->{_rules} = ();
	$self->{_knowledge} = ();
	$self->{_goal} = undef;
	$self->{_filename} = undef;

	$self->{_ask_about} = undef;
	$self->{_told_about} = undef;

	$self->{_log} = ();

	$self->{_number_of_rules} = 0;
	$self->{_number_of_attributes} = 0;


	foreach my $name (keys %{$self->{_knowledge}}) {
		$self->{_knowledge}->{$name}->reset();
	}

	$self->{_ask_about} = undef;
	$self->{_told_about} = undef;
	$self->{_log} = ();
}

sub load {
	my ($self, $filename) = @_;

	die "Simple->load() takes 1 argument" if scalar(@_) != 2;
	die "Simple->load() argument 1 (FILENAME) is undefined" if !defined($filename);

	if(-f $filename and -r $filename) {
		my $twig = XML::Twig->new(
			twig_handlers => { goal => sub { $self->_goal(@_) },
					   rule => sub { $self->_rule(@_) },

}

sub _goal {
	my ($self, $t, $node) = @_;

	my $attribute = undef;
	my $text = undef;

	my $x = ($node->children('attribute'))[0];
	$attribute = $x->text();

	$x = ($node->children('text'))[0];

}

sub _rule {
	my ($self, $t, $node) = @_;

	my $name = undef;

	my $x = ($node->children('name'))[0];
	$name = $x->text();

	if(!defined($self->{_rules}->{$name})) {
		$self->{_rules}->{$name} = AI::ExpertSystem::Simple::Rule->new($name);
		$self->{_number_of_rules}++;
	}

	foreach $x ($node->get_xpath('//condition')) {
		my $attribute = undef;
		my $value = undef;

		my $y = ($x->children('attribute'))[0];
		$attribute = $y->text();

		$y = ($x->children('value'))[0];

			$self->{_knowledge}->{$attribute} = AI::ExpertSystem::Simple::Knowledge->new($attribute);
		}
	}

	foreach $x ($node->get_xpath('//action')) {
		my $attribute = undef;
		my $value = undef;

		my $y = ($x->children('attribute'))[0];
		$attribute = $y->text();

		$y = ($x->children('value'))[0];

}

sub _question {
	my ($self, $t, $node) = @_;

	my $attribute = undef;
	my $text = undef;
	my @responses = ();

	$self->{_number_of_questions}++;

	my $x = ($node->children('attribute'))[0];

		my %answers = ();

		$answers{$self->{_ask_about}}->{value} = $self->{_told_about};
		$answers{$self->{_ask_about}}->{setter} = '';

		$self->{_ask_about} = undef;
		$self->{_told_about} = undef;

		while(%answers) {
			my %old_answers = %answers;
			%answers = ();


sub answer {
	my ($self, $value) = @_;

	die "Simple->answer() takes 1 argument" if scalar(@_) != 2;
	die "Simple->answer() argument 1 (VALUE) is undefined" if ! defined($value);

	$self->{_told_about} = $value;
}

sub get_answer {

=item Simple->load() takes 1 argument

When the method is called it requires one argument. This message is given if more or 
less arguments were supplied.

=item Simple->load() argument 1 (FILENAME) is undefined

The corrct number of arguments were supplied with the method call, however the first 
argument, FILENAME, was undefined.

=item Simple->load() XML parse failed

XML Twig encountered some errors when trying to parse the XML knowledgebase.

=item Simple->answer() takes 1 argument

When the method is called it requires one argument. This message is given if more or 
less arguments were supplied.

=item Simple->answer() argument 1 (VALUE) is undefined

The corrct number of arguments were supplied with the method call, however the first 
argument, VALUE, was undefined.

=item Simple->get_answer() takes no arguments

When the method is called it requires no arguments. This message is given if 
some arguments were supplied.

view all matches for this distribution

AI-FANN

3 results

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

  fann_set_activation_function_hidden => hidden_activation_function
  fann_set_activation_function_output => output_activation_function

=item *

Boolean methods return true on success and undef on failure.

=item *

Any error reported from the C side is automaticaly converter to a Perl
exception. No manual error checking is required after calling FANN

view all matches for this distribution

AI-Fuzzy

2 results


    my ($self, $value, $label) = @_;
    my $membership = 0;

    return $label->applicability($value) if ($label->can("applicability"));
    return undef unless ( exists $self->{labels}->{$label} );
    return $self->{labels}->{$label}->applicability($value);
}

sub label {
    # returns a label associated with this text

	my $lb = $self->{labels}->{$labelb};

	return $la->lessthan($lb);

    } else {
	return undef;
    }
}
sub lessequal {
    my ($self, $labela, $labelb) = @_;

	my $la = $self->{labels}->{$labela};
	my $lb = $self->{labels}->{$labelb};
	
	return $la->lessequal($lb);
    } else {
	return undef;
    }
}

sub greaterthan {
    my ($self, $labela, $labelb) = @_;

	my $la = $self->{labels}->{$labela};
	my $lb = $self->{labels}->{$labelb};
	
	return $la->greaterthan($lb);
    } else {
	return undef;
    }
}
sub greaterequal {
    my ($self, $labela, $labelb) = @_;

	my $la = $self->{labels}->{$labela};
	my $lb = $self->{labels}->{$labelb};
	
	return $la->greaterequal($lb);
    } else {
	return undef;
    }
}

sub between {
    my ($self, $labela, $labelb, $labelc) = @_;

	my $lb = $self->{labels}->{$labelb};
	my $lc = $self->{labels}->{$labelc};
	
	return $la->between($lb, $lc);
    } else {
	return undef;
    }
}
1;

view all matches for this distribution

AI-FuzzyEngine

4 results

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


this implements the fuzzy implication

    if $var_1->xxx and $var_2->yyy and ... then $var_3->zzz

The membership degrees of a variable's sets can be reset to undef:

    $var->reset(); # resets a variable
    $fe->reset();  # resets all variables

The fuzzy engine C<$fe> has all variables registered

view all matches for this distribution

AI-FuzzyInference

2 results

FuzzyInference.pm view on Meta::CPAN

sub value {
    my ($self,
	$var,
	) = @_;

    return undef unless exists $self->{RESULTS}{$var};
    return $self->{RESULTS}{$var};
}

# sub reset() - public method
#

view all matches for this distribution

AI-Gene-Sequence

6 results

}

##
# inserts one element into the sequence
# 0: number to perform ( or 1)
# 1: position to mutate (undef for random)

sub mutate_insert {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;

}

##
# removes element(s) from sequence
# 0: number of times to perform
# 1: position to affect (undef for rand)
# 2: length to affect, undef => 1, 0 => random length

sub mutate_remove {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;

}

##
# copies an element or run of elements into a random place in the gene
# 0: number to perform (or 1)
# 1: posn to copy from (undef for rand)
# 2: posn to splice in (undef for rand)
# 3: length            (undef for 1, 0 for random)

sub mutate_duplicate {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;

}

##
# 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;
  my $rt = 0;

}

##
# Takes a run of tokens and reverses their order, is a noop with 1 item
# 0: number to perform
# 1: posn to start from (undef for rand)
# 2: length             (undef=>1, 0=>rand)

sub mutate_reverse {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;

}

##
# Changes token into one of same type (ie. passes type to generate..)
# 0: number to perform
# 1: position to affect (undef for rand)

sub mutate_minor {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;

}

##
# Changes one token into some other token
# 0: number to perform
# 1: position to affect (undef for random)

sub mutate_major {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;


##
# 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;
  my $num = $_[0] || 1;
  my $rt = 0;


##
# takes a sequence, removes it, then inserts it at another position
# odd things might occur if posn to replace to lies within area taken from
# 0: number to perform
# 1: posn to get from   (undef for rand)
# 2: posn to put        (undef for rand)
# 3: length of sequence (undef for 1, 0 for rand)

sub mutate_shuffle {
  my $self = shift;
  my $num = +$_[0] || 1;
  my $rt = 0;

If a mutation is attempted which could corrupt your gene (copying
from a region beyond the end of the gene for instance) then it
will be silently skipped.  Mutation methods all return the number
of mutations carried out (not the number of tokens affected).

These methods all expect to be passed positive integers, undef or zero,
other values could (and likely will) do something unpredictable.

=over 4

=item C<mutate([num, ref to hash of probs & methods])>

create valid genes, but fast if you do, thems the breaks.  There
is a AI::Gene::Simple class instead if this bothers you.

Some methods will do odd things if you pass them weird values,
so try not to do that.  So long as you stick to passing
positive integers or C<undef> to the methods then they should
recover gracefully.

While it is easy and fun to write genetic and evolutionary
algorithms in perl, for most purposes, it will be much slower
than if they were implemented in another more suitable language.

view all matches for this distribution

AI-Genetic-Pro

4 results

	my $data = clone($data_org);
	my $ars;
	for(0..$#$data){
		next if $ars->{$data->[$_]};
		$ars->{$data->[$_]} = 1;
		unshift @{$data->[$_]}, undef;
	}
	return $data;
}
#=======================================================================
# we have to find C to (in some cases) incrase value of range

	$Storable::Eval = 1;
	#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
	my ( $self ) = @_;
 	
	my $clone = { 
		_selector	=> undef,
		_strategist	=> undef,
		_mutator	=> undef,
	};
	
	$clone->{ chromosomes } = [ map { ${ tied( @$_ ) } } @{ $self->chromosomes } ] 
		if $self->_package;

		return \@chr;
		
	}elsif($self->type eq q/rangevector/){
		my $fix_range = $self->_fix_range;
		my $c = -1;
		#my @array = map { $c++; warn "WARN: $c | ",scalar @$chromosome,"\n" if not defined $fix_range->[$c]; $_ ? $_ - $fix_range->[$c] : undef } @$chromosome;
		my @array = map { $c++; $_ ? $_ - $fix_range->[$c] : undef } @$chromosome;
		
		return @array if wantarray;
		return \@array;
	}else{
		my $cnt = 0;

}
#=======================================================================
sub _save_history {
	my @tmp;
	if($_[0]->history){ @tmp = $_[0]->getAvgFitness; }
	else { @tmp = (undef, undef, undef); }
	
	push @{$_[0]->_history->[0]}, $tmp[0]; 
	push @{$_[0]->_history->[1]}, $tmp[1];
	push @{$_[0]->_history->[2]}, $tmp[2];
	return 1;

    # ...and so on
	
=item level 2 

Feature is active and chromosomes can varies B<on the left side and on 
the right side>; unwanted values/genes on the left side are replaced with C<undef>, ie.
 
	-variable_length => 2
 
    # chromosomes (i.e. bitvectors)
    x x x 0 1 1 0 1 1 1 
    x x x x 0 1 1 1 1
    x 1 1 1 0 1 0 0 1 1 0 1 1 1
    0 1 0 0 1 1 0 1 1 1
    # where 'x' means 'undef'
    # ...and so on

In this situation returned chromosomes in an array context ($ga-E<gt>as_array($chromosome)) 
can have B<undef> values on the left side (only). In a scalar context each 
undefined value is replaced with a single space. If You don't want to see
any C<undef> or space, just use C<as_array_def_only> and C<as_string_def_only> 
instead of C<as_array> and C<as_string>.

=back

=item -parents

=back

=item I<$ga>-E<gt>B<evolve>($n)

This method causes the GA to evolve the population for the specified number of
generations. If its argument is 0 or C<undef> GA will evolve the population to
infinity unless a C<terminate> function is specified.

=item I<$ga>-E<gt>B<getHistory>()

Get history of the evolution. It is in a format listed below:

=item I<$ga>-E<gt>B<as_array>($chromosome)

In list context return an array representing the specified chromosome. 
In scalar context return an reference to an array representing the specified 
chromosome. If I<variable_length> is turned on and is set to level 2, an array 
can have some C<undef> values. To get only C<not undef> values use 
C<as_array_def_only> instead of C<as_array>.

=item I<$ga>-E<gt>B<as_array_def_only>($chromosome)

In list context return an array representing the specified chromosome. 
In scalar context return an reference to an array representing the specified 
chromosome. If I<variable_length> is turned off, this function is just an
alias for C<as_array>. If I<variable_length> is turned on and is set to 
level 2, this function will return only C<not undef> values from chromosome. 
See example below:

    # -variable_length => 2, -type => 'bitvector'
	
    my @chromosome = $ga->as_array($chromosome)
    # @chromosome looks something like that
    # ( undef, undef, undef, 1, 0, 1, 1, 1, 0 )
	
    @chromosome = $ga->as_array_def_only($chromosome)
    # @chromosome looks something like that
    # ( 1, 0, 1, 1, 1, 0 )

	$string = $ga->as_string($chromosome);
	# $string looks something like that
	# element0___element1___element2___element3...

Attention! If I<variable_length> is turned on and is set to level 2, it is 
possible to get C<undef> values on the left side of the vector. In the returned
string C<undef> values will be replaced with B<spaces>. If you don't want
to see any I<spaces>, use C<as_string_def_only> instead of C<as_string>.

=item I<$ga>-E<gt>B<as_string_def_only>($chromosome)

Return a string representation of specified chromosome. If I<variable_length> 
is turned off, this function is just alias for C<as_string>. If I<variable_length> 
is turned on and is set to level 2, this function will return a string without
C<undef> values. See example below:

	# -variable_length => 2, -type => 'bitvector'
	
	my $string = $ga->as_string($chromosome);
	# $string looks something like that

view all matches for this distribution

AI-Genetic

1 match

ERROR: Must specify anonymous subroutine for strategy.
       Strategy '$name' will be deleted.
EOC
    ;
    delete $self->{ADDSTR}{$name};
    return undef;
  }

  return $name;
}

sub evolve {
  my ($self, $strategy, $gens) = @_;

  unless ($self->{INIT}) {
    carp "can't evolve() before init()";
    return undef;
  }

  my $strSub;
  if      (exists $self->{ADDSTR}{$strategy}) {
    $strSub = $self->{ADDSTR}{$strategy};
  } elsif (exists $_strategy{$strategy}) {
    $strSub = $_strategy{$strategy};
  } else {
    carp "ERROR: Do not know what strategy '$strategy' is,";
    return undef;
  }

  $gens ||= 1;

  for my $i (1 .. $gens) {

  }

  eval "use $ind";  # does this work if package is in same file?
  if ($@) {
    carp "ERROR: Init failed. Can't require '$ind': $@,";
    return undef;
  }

  $self->{INDIVIDUAL} = $ind;
  $self->{PEOPLE}     = [];
  $self->{SORTED}     = 0;

# Takes a variable number of arguments. The first argument is the
# total number, N, of new individuals to add. The remaining arguments
# are genomes to inject. There must be at most N genomes to inject.
# If the number, n, of genomes to inject is less than N, N - n random
# genomes are added. Perhaps an example will help?
# returns 1 on success and undef on error.

sub inject {
  my ($self, $count, @genomes) = @_;

  unless ($self->{INIT}) {
    carp "can't inject() before init()";
    return undef;
  }

  my $ind = $self->{INDIVIDUAL};

  my @newInds;

view all matches for this distribution

AI-Image

2 results

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

The size for the generated image (default: '512x512').

=item debug

Used for testing.  If set to any true value, the image method
will return details of the error encountered instead of C<undef>

=back

=head2 image

view all matches for this distribution

AI-LibNeural

2 results

t/00.AILibNeural.t view on Meta::CPAN

ok( $nn->run( [ 0, 1 ] ) < 0.5 );
ok( $nn->run( [ 1, 0 ] ) < 0.5 );
ok( $nn->run( [ 1, 1 ] ) > 0.5 );
ok( $nn->save('test.mem') );

$nn = undef;
ok( $nn = AI::LibNeural->new() );
ok( $nn =~ m/AI::LibNeural=SCALAR(.*)/ );
ok( $nn->load('test.mem') );
ok( $nn->run( [ 0, 0 ] ) < 0.5 );
ok( $nn->run( [ 0, 1 ] ) < 0.5 );
ok( $nn->run( [ 1, 0 ] ) < 0.5 );
ok( $nn->run( [ 1, 1 ] ) > 0.5 );

$nn = undef;
ok( $nn = AI::LibNeural->new('test.mem') );
ok( $nn =~ m/AI::LibNeural=SCALAR(.*)/ );
ok( $nn->run( [ 0, 0 ] ) < 0.5 );
ok( $nn->run( [ 0, 1 ] ) < 0.5 );
ok( $nn->run( [ 1, 0 ] ) < 0.5 );

view all matches for this distribution

AI-ML

4 results

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

    my ($self, $x, $y) = @_;
    my ($lambda, $thetas, $h, $cost, $reg, $reg_thetas, $grad);

    my $iters     = $self->{n};
    my $alpha     = $self->{alpha};
		#my $cost_file = exists $opts{cost}  ? $opts{cost}  : undef;

    $x = Math::Lapack::Matrix::concatenate(
        M->ones($x->rows,1),
        $x
    );

view all matches for this distribution

AI-MXNet-Gluon-ModelZoo

2 results

lib/AI/MXNet/Gluon/ModelZoo/Vision/Inception.pm view on Meta::CPAN


func _make_A($pool_features, $prefix)
{
    my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
    $out->name_scope(sub {
        $out->add(_make_branch('', [64, 1, undef, undef]));
        $out->add(_make_branch(
            '',
            [48, 1, undef, undef],
            [64, 5, undef, 2]
        ));
        $out->add(_make_branch(
            '',
            [64, 1, undef, undef],
            [96, 3, undef, 1],
            [96, 3, undef, 1]
        ));
        $out->add(_make_branch('avg', [$pool_features, 1, undef, undef]));
    });
    return $out;
}

func _make_B($prefix)
{
    my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
    $out->name_scope(sub {
        $out->add(_make_branch('', [384, 3, 2, undef]));
        $out->add(_make_branch(
            '',
            [64, 1, undef, undef],
            [96, 3, undef, 1],
            [96, 3, 2, undef]
        ));
        $out->add(_make_branch('max'));
    });
    return $out;
}

func _make_C($channels_7x7, $prefix)
{
    my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
    $out->name_scope(sub {
        $out->add(_make_branch('', [192, 1, undef, undef]));
        $out->add(_make_branch(
            '',
            [$channels_7x7, 1, undef, undef],
            [$channels_7x7, [1, 7], undef, [0, 3]],
            [192, [7, 1], undef, [3, 0]]
        ));
        $out->add(_make_branch(
            '',
            [$channels_7x7, 1, undef, undef],
            [$channels_7x7, [7, 1], undef, [3, 0]],
            [$channels_7x7, [1, 7], undef, [0, 3]],
            [$channels_7x7, [7, 1], undef, [3, 0]],
            [192, [1, 7], undef, [0, 3]]
        ));
        $out->add(_make_branch(
            'avg',
            [192, 1, undef, undef]
        ));
    });
    return $out;
}

lib/AI/MXNet/Gluon/ModelZoo/Vision/Inception.pm view on Meta::CPAN

{
    my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
    $out->name_scope(sub {
        $out->add(_make_branch(
            '',
            [192, 1, undef, undef],
            [320, 3, 2, undef]
        ));
        $out->add(_make_branch(
            '',
            [192, 1, undef, undef],
            [192, [1, 7], undef, [0, 3]],
            [192, [7, 1], undef, [3, 0]],
            [192, 3, 2, undef]
        ));
        $out->add(_make_branch('max'));
    });
    return $out;
}

func _make_E($prefix)
{
    my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
    $out->name_scope(sub {
        $out->add(_make_branch('', [320, 1, undef, undef]));

        my $branch_3x3 = nn->HybridSequential(prefix=>'');
        $out->add($branch_3x3);
        $branch_3x3->add(_make_branch(
            '',
            [384, 1, undef, undef]
        ));
        my $branch_3x3_split = nn->HybridConcurrent(axis=>1, prefix=>'');
        $branch_3x3_split->add(_make_branch('', [384, [1, 3], undef, [0, 1]]));
        $branch_3x3_split->add(_make_branch('', [384, [3, 1], undef, [1, 0]]));
        $branch_3x3->add($branch_3x3_split);

        my $branch_3x3dbl = nn->HybridSequential(prefix=>'');
        $out->add($branch_3x3dbl);
        $branch_3x3dbl->add(_make_branch(
            '',
            [448, 1, undef, undef],
            [384, 3, undef, 1]
        ));
        my $branch_3x3dbl_split = nn->HybridConcurrent(axis=>1, prefix=>'');
        $branch_3x3dbl->add($branch_3x3dbl_split);
        $branch_3x3dbl_split->add(_make_branch('', [384, [1, 3], undef, [0, 1]]));
        $branch_3x3dbl_split->add(_make_branch('', [384, [3, 1], undef, [1, 0]]));

        $out->add(_make_branch('avg', [192, 1, undef, undef]));
    });
    return $out;
}

func make_aux($classes)

view all matches for this distribution

AI-MXNet

29 results

examples/char_lstm.pl view on Meta::CPAN

has '+batch_size'   => (is => 'ro',  isa => 'Int',   required => 1);
has 'data_name'     => (is => 'ro',  isa => 'Str',   default => 'data');
has 'label_name'    => (is => 'ro',  isa => 'Str',   default => 'softmax_label');
has 'dtype'         => (is => 'ro',  isa => 'Dtype', default => 'float32');
has [qw/nd counter seq_counter vocab_size
    data_size provide_data provide_label idx/] => (is => 'rw', init_arg => undef);

sub BUILD
{
    my $self = shift;
    $self->data_size($self->data->nelem);

examples/char_lstm.pl view on Meta::CPAN

    @{ $self->idx } = List::Util::shuffle(@{ $self->idx });
}

method next()
{
    return undef if $self->counter == @{$self->idx};
    my $offset = $self->idx->[$self->counter]*$self->batch_size*$self->seq_size + $self->seq_counter;
    my $data = $self->nd->slice(
        [$offset, $offset + $self->batch_size*$self->seq_size-1]
    )->reshape([$self->batch_size, $self->seq_size]);
    my $label = $self->nd->slice(

examples/char_lstm.pl view on Meta::CPAN


package main;
my $file = "data/input.txt";
open(F, $file) or die "can't open $file: $!";
my $fdata;
{ local($/) = undef; $fdata = <F>; close(F) };
my %vocabulary; my $i = 0;
$fdata = pdl(map{ exists $vocabulary{$_} ? $vocabulary{$_} : ($vocabulary{$_} = $i++) } split(//, $fdata));
my $data_iter = AI::MXNet::RNN::IO::ASCIIIterator->new(
    batch_size => $batch_size,
    data       => $fdata,

view all matches for this distribution

AI-MaxEntropy

9 results

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


    $file =~ s|::|/|g;
    $file .= '.pm';

    local $@;
    return eval { require $file; $mod->VERSION } || ( $@ ? undef: 0 );
}

# Load CPAN.pm and it's configuration
sub _load_cpan {
    return if $CPAN::VERSION;

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

             version->can('new') 
            ) {

            # use version.pm if it is installed.
            return (
                ( version->new($cur) >= version->new($min) ) ? $cur : undef );
        }
        elsif ( $Sort::Versions::VERSION or defined( _load('Sort::Versions') ) )
        {

            # use Sort::Versions as the sorting algorithm for a.b.c versions
            return ( ( Sort::Versions::versioncmp( $cur, $min ) != -1 )
                ? $cur
                : undef );
        }

        warn "Cannot reliably compare non-decimal formatted versions.\n"
          . "Please install version.pm or Sort::Versions.\n";
    }

    # plain comparison
    local $^W = 0;    # shuts off 'not numeric' bugs
    return ( $cur >= $min ? $cur : undef );
}

# nothing; this usage is deprecated.
sub main::PREREQ_PM { return {}; }

view all matches for this distribution

AI-MegaHAL

2 results

libmegahal.c view on Meta::CPAN

#else
#define SEP "/"
#endif

#ifdef AMIGA
#undef toupper
#define toupper(x) ToUpper(x)
#undef tolower
#define tolower(x) ToLower(x)
#undef isalpha
#define isalpha(x) IsAlpha(_AmigaLocale,x)
#undef isalnum
#define isalnum(x) IsAlNum(_AmigaLocale,x)
#undef isdigit
#define isdigit(x) IsDigit(_AmigaLocale,x)
#undef isspace
#define isspace(x) IsSpace(_AmigaLocale,x)
#endif

#ifndef __mac_os
#undef FALSE
#undef TRUE
typedef enum { FALSE, TRUE } bool;
#endif

typedef struct {
    BYTE1 length;

view all matches for this distribution

AI-MicroStructure

1 match

t/t/007.t view on Meta::CPAN


  use_ok('LWP');
  use_ok('Storable::CouchDB');
  my $s = Storable::CouchDB->new;

  ok("sprintf $s->retrieve('doc')"); #undef if not exists
  ok("sprintf $s->store('doc1' => 'data');");

  ok('sprintf $s->store("doc2" => {"my" => "data"});');
  ok('sprintf $s->store("doc3" => ["my", "data"]);');

  ok("sprintf $s->store('doc4' => undef);");
  ok("sprintf $s->delete('doc');");


  my $browser = LWP::UserAgent->new();
  my $seite = "http://127.0.0.1:5984";

view all matches for this distribution

AI-NNEasy

9 results

  use Data::Dumper ;
  


  sub NNEasy { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $file = shift(@_) ;
    my @out_types = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $error_ok = shift(@_) ;
    my $in = shift(@_) ;

    
    return $this ;
  }
  
  sub _layer_conf { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $def = shift(@_) ;
    my $conf = shift(@_) ;
    
    $def ||= {} ;


    return $layer_conf ;
  }
  
  sub reset_nn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    $this->{NN} = AI::NNEasy::NN->new( @{ $this->{NN_ARGS} } ) ;
  }
  
  sub load { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $file = shift(@_) ;
    
    $file ||= $this->{FILE} ;
    if ( -s $file ) {

    }
    return ;
  }
  
  sub save { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $file = shift(@_) ;
    
    $file ||= $this->{FILE} ;

    print $fh $dump ;
    close ($fh) ;
  }
  
  sub learn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $in = shift(@_) ;
    my $out = shift(@_) ;
    my $n = shift(@_) ;

  }
  
  *_learn_set_get_output_error = \&_learn_set_get_output_error_c ;
  
  sub _learn_set_get_output_error_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $set = shift(@_) ;
    my $error_ok = shift(@_) ;
    my $ins_ok = shift(@_) ;
    my $verbose = shift(@_) ;

  
  
  
    
  sub learn_set { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my @set = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $ins_ok = shift(@_) ;
    my $limit = shift(@_) ;
    my $verbose = shift(@_) ;

    }
    
  }
  
  sub get_set_error { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my @set = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $ins_ok = shift(@_) ;
    
    my $ins_sz = @set / 2 ;

    $err /= $ins_ok ;
    return $err ;
  }
  
  sub run { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $in = shift(@_) ;
    
    $this->{NN}->run($in) ;
    my $out = $this->{NN}->output() ;
    return $out ;
  }
  
  sub run_get_winner { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    my $out = $this->run(@_) ;
    
    foreach my $out_i ( @$out ) {

    
    return $out ;
  }
  
  sub out_type_winner { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $val = shift(@_) ;
    
    my ($out_type , %err) ;

view all matches for this distribution

AI-NNFlex

1 match

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

		{
			my $probability = rand(1);
			if ($probability < $connectionLesion)
			{
				my $reverseNodeCounter=0; # maybe should have done this differntly in init, but 2 late now!
				${$node->{'connectedNodesEast'}->{'nodes'}}[$nodeCounter] = undef;
				foreach my $reverseConnection (@{$connectedNode->{'connectedNodesWest'}->{'nodes'}})
				{
					if ($reverseConnection == $node)
					{
						${$connectedNode->{'connectedNodesEast'}->{'nodes'}}[$reverseNodeCounter] = undef;
					}
					$reverseNodeCounter++;
				}

			}

view all matches for this distribution

AI-NeuralNet-BackProp

3 results

package AI::NeuralNet::BackProp;
	
	use Benchmark;          
	use strict;
	
	# Returns the number of elements in an array ref, undef on error
	sub _FETCHSIZE {
		my $a=$_[0];
		my ($b,$x);
		return undef if(substr($a,0,5) ne "ARRAY");
		foreach $b (@{$a}) { $x++ };
		return $x;
	}

	# Debugging subs

		my $a2	=	shift;
		my $a1s	=	$#{$a1}; #AI::NeuralNet::BackProp::_FETCHSIZE($a1);
		my $a2s	=	$#{$a2}; #AI::NeuralNet::BackProp::_FETCHSIZE($a2);
		my ($a,$b,$diff,$t);
		$diff=0;
		#return undef if($a1s ne $a2s);	# must be same length
		for my $x (0..$a1s) {
			$a = $a1->[$x];
			$b = $a2->[$x];
			if($a!=$b) {
				if($a<$b){$t=$a;$a=$b;$b=$t;}

		}
		return \@map;
	}
	
	# Finds if a word has been crunched.
	# Returns undef on failure, word index for success.
	sub crunched {
		my $self	=	shift;
		for my $a (0..$self->{_CRUNCHED}->{_LENGTH}-1) {
			return $a+1 if($self->{_CRUNCHED}->{LIST}->[$a] eq $_[0]);
		}
		return undef;
	}
	
	# Alias for crunched(), above
	sub word { crunched(@_) }

		my $rA		=	0;
		my $rB		=	$#{$ref};
		my $rS		=	0; #shift;
		if(!$rA && !$rB) {
			$self->{rA}=$self->{rB}=-1;
			return undef;
		}
		if($rB<$rA){my $t=$rA;$rA=$rB;$rB=$t};
		$self->{rA}=$rA;
		$self->{rB}=$rB;
		$self->{rS}=$rS if($rS);

		#print "Creating $size neurons in each layer for $layers layer(s)...\n";
		
		AI::NeuralNet::BackProp::out2 "Creating $size neurons in each layer for $layers layer(s)...\n";
		
		# Error checking
		return undef if($out>$size);
		
		# When this is called, they tell us howmany layers and neurons in each layer.
		# But really what we store is a long line of neurons that are only divided in theory
		# when connecting the outputs and inputs.
		my $div = $size;

	sub load {
		my $self	=	shift;
		my $file	=	shift;
		my $load_flag = shift || 0;
	    
	    return undef if(!(-f $file));
	    
	    open(FILE,"$file");
	    my @lines=<FILE>;
	    close(FILE);

	    	chomp($line);
	    	my ($a,$b) = split /=/, $line;
	    	$db{$a}=$b;
	    }
	    
	    return undef if(!$db{"size"});
	    
	    if($load_flag) {
		    undef $self;
	
			# Create new network
		    $self = AI::NeuralNet::BackProp->new(intr($db{"size"}/$db{"div"}),
		    								     $db{"div"},
		    								     $db{"out"},

	sub run {
		my $self	=	shift;
		my $map		=	shift;
		my $x		=	0;
		$map = $self->{PARENT}->crunch($map) if($map == 0);
		return undef if(substr($map,0,5) ne "ARRAY");
		foreach my $el (@{$map}) {
			# Catch ourself if we try to run more inputs than neurons
			return $x if($x>$self->{PARENT}->{DIV}-1);
			
			# Here we add a small ammount of randomness to the network.

and each layer will have C<$size> number of neurons in that 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. $outputs may not exceed $size. If $outputs
exceeds $size, the new() constructor will return undef.

The optional parameter, $topology_flag, defaults to 0 when not used. There are
three valid topology flag values:

B<0> I<default>

it prints the elements of $array_ref to STDIO, adding a newline (\n) after every $row_length_in_elements
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 $net->pdiff($array_ref_A, $array_ref_B);




=item $net->crunched($word);

This will return undef if the word is not in the internal crunch list, or it will return the
index of the word if it exists in the crunch list. 


=item $net->col_width($width);

This is useful for formating the debugging output of Level 4 if you are learning simple 
bitmaps. This will set the debugger to automatically insert a line break after that many
elements in the map output when dumping the currently run map during a learn loop.

It will return the current width when called with a 0 or undef value.


=item $net->random($rand);

This will set the randomness factor from the network. Default is 0.001. When called 
with no arguments, or an undef value, it will return current randomness value. When
called with a 0 value, it will disable randomness in the network. See NOTES on learning 
a 0 value in the input map with randomness disabled.

view all matches for this distribution

AI-NeuralNet-FastSOM

7 results

examples/eigenvector_initialization.pl view on Meta::CPAN

    my $v = shift;
    my $l = shift;
    for my $i (0..$#$l) {
	return $i if $v == $l->[$i];
    }
    return undef;
}

	for (@es_idx) {                                                  # from the highest values downwards, take the index
	    push @training_vectors, [ list $E->dice($_) ] ;              # get the corresponding vector
	}

view all matches for this distribution

AI-NeuralNet-Kohonen-Demo-RGB

1 match

test.pl view on Meta::CPAN


use AI::NeuralNet::Kohonen::Demo::RGB;
ok(1,1);

$_ = new AI::NeuralNet::Kohonen;
ok ($_,undef);

$_ = new AI::NeuralNet::Kohonen::Demo::RGB(
	input => [
		[1,2,3]
	],

view all matches for this distribution

AI-NeuralNet-Kohonen-Visual

1 match

lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN


	# Replaces Tk's MainLoop
    for (0..$self->{epochs}) {
		if ($self->{_quit_flag}) {
			$self->{_mw}->destroy;
			$self->{_mw} = undef;
			return;
		}
		$self->{t}++;				# Measure epoch
		&{$self->{epoch_start}} if exists $self->{epoch_start};

lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN

		-relief	=> 'raised',
		-border => 2,
	);
	$self->{_canvas}->pack(-side=>'top');
	$self->{_label} = $self->{_mw}->Button(
		-command      => sub { $self->{_mw}->destroy;$self->{_mw} = undef; },
		-relief       => 'groove',
		-text         => ' ',
		-wraplength   => $w,
		-textvariable => \$self->{_label_txt}
	);

view all matches for this distribution

AI-NeuralNet-Kohonen

4 results


Reference to code to call at the end of training.

=item targeting

If undefined, random targets are chosen; otherwise
they're iterated over. Just for experimental purposes.

=item smoothing

The amount of smoothing to apply by default when C<smooth>

	$self->{missing_mask}		= 'x' unless defined $self->{missing_mask};
	$self->_process_table if defined $self->{table};	# Creates {input}
	$self->load_input($self->{input_file}) if defined $self->{input_file};	# Creates {input}
	if (not defined $self->{input}){
		cluck "No {input} supplied!";
		return undef;
	}

	$self->{map_dim_x}			= 19 unless defined $self->{map_dim_x};
	$self->{map_dim_y}			= 19 unless defined $self->{map_dim_y};
	# Legacy from...yesterday

	$self->{time_constant}		= $self->{epochs} / log($self->{map_dim_a}) unless $self->{time_constant};	# to base 10?
	$self->{learning_rate}		= 0.5 unless $self->{learning_rate};
	$self->{l}					= $self->{learning_rate};
	if (not $self->{weight_dim}){
		cluck "{weight_dim} not set";
		return undef;
	}
	$self->randomise_map;
	return $self;
}



=head1 METHOD clear_map

As L<METHOD randomise_map> but sets all C<map> nodes to
either the value supplied as the only paramter, or C<undef>.

=cut

sub clear_map { my $self=shift;
	confess "{weight_dim} not set" unless $self->{weight_dim};

co-ordinates.

Accepts: I<x>,I<y> co-ordinates, each a scalar.

Returns: reference to an array that is the weight of the node, or
C<undef> on failure.

=cut

sub get_weight_at { my ($self,$x,$y) = (shift,shift,shift);
	return undef if $x<0 or $y<0 or $x>$self->{map_dim_x} or $y>$self->{map_dim_y};
	return $self->{map}->[$x]->[$y]->{weight};
}

This method is automatically accessed if the constructor is supplied
with an C<input_file> field.

Requires: a path to a file.

Returns C<undef> on failure.

See L</FILE FORMAT>.

=cut

sub load_input { my ($self,$path) = (shift,shift);
	local *IN;
	if (not open IN,$path){
		warn "Could not open file <$path>: $!";
		return undef;
	}
	@_ = <IN>;
	close IN;
	$self->_process_input_text(\@_);
	return 1;

=head1 METHOD save_file

Saves the map file in I<SOM_PAK> format (see L<METHOD load_input>)
at the path specified in the first argument.

Return C<undef> on failure, a true value on success.

=cut

sub save_file { my ($self,$path) = (shift,shift);
	local *OUT;
	if (not open OUT,">$path"){
		warn "Could not open file for writing <$path>: $!";
		return undef;
	}
	#- Dimensionality of the vectors (integer, compulsory).
	print OUT ($self->{weight_dim}+1)," ";	# Perl indexing
	#- Topology type, either hexa or rect (string, optional, case-sensitive).
	if (not defined $self->{display}){

	#- Topology type, either hexa or rect (string, optional, case-sensitive).
	my $display		    = shift @specs;
	if (not defined $display and exists $self->{display}){
		# Intentionally blank
	} elsif (not defined $display){
		$self->{display} = undef;
	} elsif ($display eq 'hexa'){
		$self->{display} = 'hex'
	} elsif ($display eq 'rect'){
		$self->{display} = undef;
	}
	#- Map dimension in x-direction (integer, optional).
	$_				      = shift @specs;
	$self->{map_dim_x}    = $_ if defined $_;
	#- Map dimension in y-direction (integer, optional).

=head1 PRIVATE METHOD _add_input_from_str

Adds to the C<input> field an input vector in SOM_PAK-format
whitespace-delimited ASCII.

Returns C<undef> on failure to add an item (perhaps because
the data passed was a comment, or the C<weight_dim> flag was
not set); a true value on success.

=cut

sub _add_input_from_str { my ($self) = (shift);
	$_ = shift;
	s/#.*$//g;
	return undef if /^$/ or not defined $self->{weight_dim};
	my @i = split /\s+/,$_;
	return undef if $#i < $self->{weight_dim}; # catch bad lines
	# 'x' in files signifies unknown: we prefer undef?
#	@i[0..$self->{weight_dim}] = map{
#		$_ eq 'x'? undef:$_
#	} @i[0..$self->{weight_dim}];
	my %args = (
		dim 	=> $self->{weight_dim},
		values	=> [ @i[0..$self->{weight_dim}] ],
	);

#
# Processes the 'table' paramter to the constructor
#
sub _process_table { my $self = shift;
	$_ = $self->_process_input_text( $self->{table} );
	undef $self->{table};
	return $_;
}


__END__

view all matches for this distribution

AI-NeuralNet-Mesh

4 results

    
    	# Looks like we got ourselves a layer specs array
		if(ref($layers) eq "ARRAY") { 
			if($self->{total_layers}!=$#{$layers}) {
				$self->{error} = "extend(): Cannot add new layers. Create a new network to add layers.\n";
				return undef;
			}
			if(ref($layers->[0]) eq "HASH") {
				$self->{total_nodes}	=	0;
				$self->{inputs}			=	$layers->[0]->{nodes};
				$self->{nodes}			=	$layers->[0]->{nodes};

				$self->{layers} 		= $layers;
				for (0..$self->{total_layers}){$self->{total_nodes}+= $self->{layers}->[$_]}
			}
		} else {
			$self->{error} = "extend(): Invalid argument type.\n";
			return undef;
		}
		return 1;
	}
    
    # See POD for usage

    	my $self	=	shift;
    	my $layer	=	shift || 0;
    	my $specs	=	shift;
    	if(!$specs) {
    		$self->{error} = "extend_layer(): You must provide specs to extend layer $layer with.\n";
    		return undef;
    	}
    	if(ref($specs) eq "HASH") {
    		$self->activation($layer,$specs->{activation}) if($specs->{activation});
    		$self->threshold($layer,$specs->{threshold})   if($specs->{threshold});
    		$self->mean($layer,$specs->{mean})             if($specs->{mean});

		my $more	=	$nodes - $self->{layers}->[$layer] - 1;
        d("Checking on extending layer $layer to $nodes nodes (check:$self->{layers}->[$layer]).\n",9);
        return 1 if ($nodes == $self->{layers}->[$layer]);
        if ($self->{layers}->[$layer]>$nodes) {
        	$self->{error} = "add_nodes(): I cannot remove nodes from the network with this version of my module. You must create a new network to remove nodes.\n";
        	return undef;
        }
        d("Extending layer $layer by $more.\n",9);
        for (0..$more){$self->{mesh}->[$#{$self->{mesh}}+1]=AI::NeuralNet::Mesh::node->new($self)}
        for(0..$layer-2){$n+=$self->{layers}->[$_]}
		$self->_c($n,$n+$self->{layers}->[$layer-1],$#{$self->{mesh}}-$more+1,$#{$self->{mesh}});

		
	    close(FILE);
	    
	    if(!(-f $file)) {
	    	$self->{error} = "Error writing to \"$file\".";
	    	return undef;
	    }
	    
	    return $self;
	}

	    	$db{$a}=$b;
	    }
	    
	    if(!$db{"header"}) {
	    	$self->{error} = "Invalid format.";
	    	return undef;
	    }
	    
	    return $self->load_old($file) if($self->version($db{"header"})<0.21);
	    
	    if($load_flag) {
		    undef $self;
	        $self = AI::NeuralNet::Mesh->new([split(',',$db{layers})]);
		} else {
			$self->{inputs}			= $db{inputs};
		    $self->{nodes}			= $db{nodes};
			$self->{outputs}		= $db{outputs};

		my $file		=	shift;  
		my $load_flag   =	shift;
		
	    if(!(-f $file)) {
	    	$self->{error} = "File \"$file\" does not exist.";
	    	return undef;
	    }
	    
	    open(FILE,"$file");
	    my @lines=<FILE>;
	    close(FILE);

	    	$db{$a}=$b;
	    }
	    
	    if(!$db{"header"}) {
	    	$self->{error} = "Invalid format.";
	    	return undef;
	    }
	    
	    if($load_flag) {
		    undef $self;
	
			# Create new network
			$self = AI::NeuralNet::Mesh->new($db{"layers"},
		    			 				 	 $db{"nodes"},
		    						      	 $db{"outputs"});

		my $self	=	shift;
		my $file	=	shift;
		eval('use PCX::Loader');
		if(@_) {
			$self->{error}="Cannot load PCX::Loader module: @_";
			return undef;
		}
		return PCX::Loader->new($self,$file);
	}	
	
	# Crunch a string of words into a map

		}
		return \@map;
	}
	
	# Finds if a word has been crunched.
	# Returns undef on failure, word index for success.
	sub crunched {
		my $self	=	shift;
		for my $a (0..$self->{_crunched}->{_length}-1) {
			return $a+1 if($self->{_crunched}->{list}->[$a] eq $_[0]);
		}
		$self->{error} = "Word \"$_[0]\" not found.";
		return undef;
	}
	
	# Alias for crunched(), above
	sub word { crunched(@_) }

	# Same as benchmark()
	sub benchmarked {
		benchmark(shift);
	}
	
	# Return the last error in the mesh, or undef if no error.
	sub error {
		my $self = shift;
		return undef if !$self->{error};
		chomp($self->{error});
		return $self->{error}."\n";
	}
	
	# Used to format array ref into columns



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

This will automatically create a new network from the file C<$file>. It will
return undef if the file was of an incorrect format or non-existant. Otherwise,
it will return a blessed refrence to a network completly restored from C<$file>.

=item AI::NeuralNet::Mesh->new(\@layer_sizes);

This constructor will make a network with the number of layers corresponding to the length

after loading.

This uses a simple flat-file text storage format, and therefore the network files should
be fairly portable.

This method will return undef if there was a problem with writing the file. If there is an
error, it will set the internal error message, which you can retrive with the error() method,
below.

If there were no errors, it will return a refrence to $net.


This will load from disk any network saved by save() and completly restore the internal
state at the point it was save() was called at.

If the file is of an invalid file type, then load() will
return undef. Use the error() method, below, to print the error message.

If there were no errors, it will return a refrence to $net.

UPDATE: $filename can now be a newline-seperated set of mesh data. This enables you
to do $net->load(join("\n",<DATA>)) and other fun things. I added this mainly

it prints the elements of $array_ref to STDIO, adding a newline (\n) after every $row_length_in_elements
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 $net->crunched($word);

This will return undef if the word is not in the internal crunch list, or it will return the
index of the word if it exists in the crunch list. 

If the word is not in the list, it will set the internal error value with a text message
that you can retrive with the error() method, below.


This is useful for formating the debugging output of Level 4 if you are learning simple 
bitmaps. This will set the debugger to automatically insert a line break after that many
elements in the map output when dumping the currently run map during a learn loop.

It will return the current width when called with a 0 or undef value.

The column width is preserved across load() and save() calls.


=item $net->random($rand);

This will set the randomness factor from the network. Default is 0. When called 
with no arguments, or an undef value, it will return current randomness value. When
called with a 0 value, it will disable randomness in the network. The randomness factor
is preserved across load() and save() calls. 


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

is preserved across load() and save() calls.


=item $net->error();

Returns the last error message which occured in the mesh, or undef if no errors have
occured.


=item $net->load_pcx($filename);

NOTE: To use this function, you must have PCX::Loader installed. If you do not have
PCX::Loader installed, it will return undef and store an error for you to retrive with 
the error() method, below.

This is a treat... this routine will load a PCX-format file (yah, I know ... ancient 
format ... but it is the only one I could find specs for to write it in Perl. If 
anyone can get specs for any other formats, or could write a loader for them, I

view all matches for this distribution

AI-NeuralNet-SOM

5 results

examples/eigenvector_initialization.pl view on Meta::CPAN

    my $v = shift;
    my $l = shift;
    for my $i (0..$#$l) {
	return $i if $v == $l->[$i];
    }
    return undef;
}

	for (@es_idx) {                                                  # from the highest values downwards, take the index
	    push @training_vectors, [ list $E->dice($_) ] ;              # get the corresponding vector
	}

view all matches for this distribution

AI-NeuralNet-Simple

2 results

Simple.xs view on Meta::CPAN

        int j;
        AV *subav;

        sav = av_fetch(av, i, 0);
        if (sav == NULL)
            croak("serialized item %d has undefined row %d", idx, i);
        rv = *sav;
        if (!is_array_ref(rv))
            croak("row %d of serialized item %d is not an array ref", i, idx);

        subav = get_array(rv);

Simple.xs view on Meta::CPAN

    handle = c_new_handle();
    n = c_get_network(handle);

    sav = av_fetch(av, i++, 0);
    if (sav == NULL)
        croak("undefined input size (item %d)", i - 1);
    n->size.input  = SvIVx(*sav);

    sav = av_fetch(av, i++, 0);
    if (sav == NULL)
        croak("undefined hidden size (item %d), i - 1");
    n->size.hidden = SvIVx(*sav);

    sav = av_fetch(av, i++, 0);
    if (sav == NULL)
        croak("undefined output size (item %d)", i - 1);
    n->size.output = SvIVx(*sav);

    if (!c_create_network(n))
        return -1;

    sav = av_fetch(av, i++, 0);
    if (sav == NULL)
        croak("undefined learn_rate (item %d)", i - 1);
    n->learn_rate = SvNVx(*sav);

    sav = av_fetch(av, i++, 0);
    if (sav == NULL)
        croak("undefined delta (item %d)", i - 1);
    n->delta = SvNVx(*sav);

    sav = av_fetch(av, i++, 0);
    if (sav == NULL)
        croak("undefined use_bipolar (item %d)", i - 1);
    n->use_bipolar = SvIVx(*sav);

    c_load_axa(av, i++, n->weight.input_to_hidden,
        n->size.input + 1, n->size.hidden + 1);
    c_load_axa(av, i++, n->weight.hidden_to_output,

view all matches for this distribution

AI-Ollama-Client

4 results


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}

    });

    my $_tx;
    $tx->res->once( progress => sub($msg, @) {
        $r1->resolve( $tx );
        undef $_tx;
        undef $r1;
    });
    $self->emit(request => $tx);
    $_tx = $self->ua->start_p($tx);

    return $res


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}

    });

    my $_tx;
    $tx->res->once( progress => sub($msg, @) {
        $r1->resolve( $tx );
        undef $_tx;
        undef $r1;
    });
    $self->emit(request => $tx);
    $_tx = $self->ua->start_p($tx);

    return $res


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}

    });

    my $_tx;
    $tx->res->once( progress => sub($msg, @) {
        $r1->resolve( $tx );
        undef $_tx;
        undef $r1;
    });
    $self->emit(request => $tx);
    $_tx = $self->ua->start_p($tx);

    return $res

    });

    my $_tx;
    $tx->res->once( progress => sub($msg, @) {
        $r1->resolve( $tx );
        undef $_tx;
        undef $r1;
    });
    $self->emit(request => $tx);
    $_tx = $self->ua->start_p($tx);

    return $res


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}


    # Start our transaction
    $self->emit(request => $tx);
    $tx = $self->ua->start_p($tx)->then(sub($tx) {
        $r1->resolve( $tx );
        undef $r1;
    })->catch(sub($err) {
        $self->emit(response => $tx, $err);
        $r1->fail( $err => $tx );
        undef $r1;
    });

    return $res
}