AI-NeuralNet-FastSOM
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Makefile.PL view on Meta::CPAN
#
# everything below is a work-around for some sort of bug in ExtUtils::ParseXS
# not picking up typemap files unless named "typemap" in perl5.6.2
#
# note however that the TYPEMAPS entry above is still needed for 5.6.2's still
# using the old xsubpp
#
package MY;
sub xs_c {
my $t = shift->SUPER::xs_c(@_);
$t =~ s/:/:\n \$(MAKE) typemap/;
$t;
}
sub test {
my $t = shift->SUPER::test(@_);
$t =~ s/(PERL_DL_NONLAZY=)/HARNESS_OPTIONS=j1 $1/g;
$t;
}
sub postamble {
my $out = <<'README';
readme:
pod2text lib/AI/NeuralNet/FastSOM.pm README
perl -i -pe's{\\*(\\S+)\\*}{\\1}g' README
README
if ( $] < 5.008000 ) {
$out .= <<'EOP';
typemap:
examples/eigenvector_initialization.pl view on Meta::CPAN
# print $E;
# print $e;
my @es = list $e; # eigenvalues
# warn "es : ".Dumper \@es;
my @es_desc = sort { $b <=> $a } @es; # eigenvalues sorted desc
# warn "desc: ".Dumper \@es_desc;
my @es_idx = map { _find_num ($_, \@es) } @es_desc; # eigenvalue indices sorted by eigenvalue (desc)
# warn "idx: ".Dumper \@es_idx;
sub _find_num {
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
examples/load_save.pl view on Meta::CPAN
# print $E;
# print $e;
my @es = list $e; # eigenvalues
# warn "es : ".Dumper \@es;
my @es_desc = sort { $b <=> $a } @es; # eigenvalues sorted desc
# warn "desc: ".Dumper \@es_desc;
my @es_idx = map { _find_num ($_, \@es) } @es_desc; # eigenvalue indices sorted by eigenvalue (desc)
# warn "idx: ".Dumper \@es_idx;
sub _find_num {
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
lib/AI/NeuralNet/FastSOM.pm view on Meta::CPAN
package AI::NeuralNet::FastSOM;
use strict;
use warnings;
use XSLoader;
our $VERSION = '0.19';
sub new { die 'Dont use this class directly' }
sub label {
my ($self, $x, $y, $l) = @_;
return defined $l
? $self->{labels}->[$x]->[$y] = $l
: $self->{labels}->[$x]->[$y];
}
sub value {
my ($self, $x, $y, $v) = @_;
return defined $v
? $self->{map}[$x][$y] = $v
: $self->{map}[$x][$y];
}
sub mean_error {
my $self = shift;
my $error = 0;
map { $error += $_ } # then add them all up
map { ( $self->bmu($_) )[2] } # then find the distance
@_; # take all data vectors
return ($error / scalar @_); # return the mean value
}
XSLoader::load(__PACKAGE__);
lib/AI/NeuralNet/FastSOM/Hexa.pm view on Meta::CPAN
package AI::NeuralNet::FastSOM::Hexa;
use strict;
use warnings;
use AI::NeuralNet::FastSOM;
our @ISA = qw/AI::NeuralNet::FastSOM/;
our $VERSION = '0.19';
sub radius { shift->{_R} }
sub diameter { shift->{_X} }
sub as_data { die 'not implemented' }
sub as_string { die 'not implemented' }
sub initialize {
my $self = shift;
my @data = @_;
our $i = 0;
my $get_from_stream = sub {
$i = 0 if $i > $#data;
return [ @{ $data[$i++] } ]; # cloning !
} if @data;
$get_from_stream ||= sub {
return [ map { rand( 1 ) - 0.5 } 1..$self->{_Z} ];
};
for my $x (0 .. $self->{_X}-1) {
for my $y (0 .. $self->{_X}-1) {
$self->{map}->[$x]->[$y] = &$get_from_stream;
}
}
}
lib/AI/NeuralNet/FastSOM/Hexa.pm view on Meta::CPAN
=head1 COPYRIGHT AND LICENSE
Copyright (C) 2009-2016 by Rick Myers
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself, either Perl version 5.10.0 or,
at your option, any later version of Perl 5 you may have available.
=cut
sub _get_coordinates {
my $self = shift;
my $D1 = $self->{_D}-1;
my $t;
return map { $t = $_ ; map { [ $t, $_ ] } (0 .. $D1) } (0 .. $D1)
}
sqrt ( ($x - $X) ** 2 + ($y - $Y) ** 2 );
lib/AI/NeuralNet/FastSOM/Rect.pm view on Meta::CPAN
package AI::NeuralNet::FastSOM::Rect;
use strict;
use warnings;
use AI::NeuralNet::FastSOM;
our @ISA = qw/AI::NeuralNet::FastSOM/;
our $VERSION = '0.19';
sub _old_radius { shift->{_R} }
sub initialize {
my $self = shift;
my @data = @_;
my $i = 0;
my $get_from_stream = sub {
$i = 0 if $i > $#data;
return [ @{ $data[$i++] } ]; # cloning !
} if @data;
$get_from_stream ||= sub {
return [ map { rand( 1 ) - 0.5 } 1..$self->{_Z} ];
};
for my $x (0 .. $self->{_X}-1) {
for my $y (0 .. $self->{_Y}-1) {
$self->{map}->[$x]->[$y] = &$get_from_stream;
}
}
}
sub as_string {
my $self = shift;
my $s = '';
$s .= " ";
for my $y (0 .. $self->{_Y}-1){
$s .= sprintf(" %02d ",$y);
}
$s .= "\n" . "-"x107 . "\n";
my $dim = scalar @{ $self->{map}->[0]->[0] };
lib/AI/NeuralNet/FastSOM/Rect.pm view on Meta::CPAN
for my $y (0 .. $self->{_Y}-1){
$s .= sprintf("% 2.2f ", $self->{map}->[$x]->[$y]->[$w]);
}
$s .= "\n";
}
$s .= "\n";
}
return $s;
}
sub as_data {
my $self = shift;
my $s = '';
my $dim = scalar @{ $self->{map}->[0]->[0] };
for my $x (0 .. $self->{_X}-1) {
for my $y (0 .. $self->{_Y}-1){
for my $w ( 0 .. $dim-1 ){
$s .= sprintf("\t%f", $self->{map}->[$x]->[$y]->[$w]);
}
$s .= "\n";
t/orig/rect.t view on Meta::CPAN
# print Dumper $nn;
# exit;
my @vs = ([ 3, 2, 4 ], [ -1, -1, -1 ], [ 0, 4, -3]);
$nn->train (400, @vs);
foreach my $v (@vs) {
ok (_find ($v, $nn->map), 'found learned vector '. join (",", @$v));
}
sub _find {
my $v = shift;
my $m = shift;
use AI::NeuralNet::FastSOM::Utils;
foreach my $x ( 0 .. 4 ) {
foreach my $y ( 0 .. 5 ) {
return 1 if AI::NeuralNet::FastSOM::Utils::vector_distance ($m->[$x]->[$y], $v) < 0.01;
}
}
return 0;
t/orig/rect.t view on Meta::CPAN
foreach my $y (0 .. 6 -1 ) {
ok ( (!grep { $_ > 0.5 || $_ < -0.5 } @{ $nn->value ( $x, $y ) }) , "$x, $y: random vectors in [-0.5, 0.5]");
}
}
}
__END__
# randomized pick
@vectors = ...;
my $get = sub {
return @vectors [ int (rand (scalar @vectors) ) ];
}
$nn->train ($get);
# take exactly 500, round robin, in order
our $i = 0;
my $get = sub {
return undef unless $i < 500;
return @vectors [ $i++ % scalar @vectors ];
}
t/orig/som.t view on Meta::CPAN
my @mes = $nn->train (20, @vs);
is (scalar @mes, 3 * 20, 'errors while training, nr');
ok ((!grep { $_ > 10 * $me } @mes), 'errors while training, none significantly bigger');
}
}
__END__
# randomized pick
@vectors = ...;
my $get = sub {
return @vectors [ int (rand (scalar @vectors) ) ];
}
$nn->train ($get);
# take exactly 500, round robin, in order
our $i = 0;
my $get = sub {
return undef unless $i < 500;
return @vectors [ $i++ % scalar @vectors ];
}
t/orig/torus.t view on Meta::CPAN
# print Dumper $nn;
# exit;
my @vs = ([ 3, 2, 4 ], [ -1, -1, -1 ], [ 0, 4, -3]);
$nn->train (400, @vs);
foreach my $v (@vs) {
ok (_find ($v, $nn->map), 'found learned vector '. join (",", @$v));
}
sub _find {
my $v = shift;
my $m = shift;
use AI::NeuralNet::FastSOM::Utils;
foreach my $x ( 0 .. 4 ) {
foreach my $y ( 0 .. 5 ) {
return 1 if AI::NeuralNet::FastSOM::Utils::vector_distance ($m->[$x]->[$y], $v) < 0.01;
}
}
return 0;
t/orig/torus.t view on Meta::CPAN
ok ($nn->as_string, 'pretty print');
ok ($nn->as_data, 'raw format');
# print $nn->as_string;
}
__END__
# randomized pick
@vectors = ...;
my $get = sub {
return @vectors [ int (rand (scalar @vectors) ) ];
}
$nn->train ($get);
# take exactly 500, round robin, in order
our $i = 0;
my $get = sub {
return undef unless $i < 500;
return @vectors [ $i++ % scalar @vectors ];
}
input_dim => 3
);
ok ($nn->isa ('AI::NeuralNet::FastSOM::Rect'), 'class');
is ($nn->{_X}, 5, 'X');
is ($nn->{_Y}, 6, 'Y');
is ($nn->{_Z}, 3, 'Z');
is ($nn->radius, 2.5, 'radius');
is ($nn->output_dim, "5x6", 'output dim');
}
sub _find {
my $v = shift;
my $m = shift;
for my $x ( 0 .. 4 ) {
for my $y ( 0 .. 5 ) {
my $rv = AI::NeuralNet::FastSOM::Utils::vector_distance($m->[$x]->[$y], $v);
return 1 if $rv < 0.01;
}
}
return 0;
[ 3, 1, '1' ],
[ 3, 2, '0' ],
[ 3, 3, '1' ],
[ 4, 2, '1' ]
]
),
'neighbors 4+1'
);
}
sub _find {
my $v = shift;
my $m = shift;
for my $x ( 0 .. 4 ) {
for my $y ( 0 .. 5 ) {
return 1
if AI::NeuralNet::FastSOM::Utils::vector_distance( $m->[$x]->[$y], $v ) < 0.01;
}
}
return 0;
( run in 0.469 second using v1.01-cache-2.11-cpan-a5abf4f5562 )