AI-NeuralNet-Kohonen

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

}


=head1 METHOD dump

Print the current weight values to the screen.

=cut

sub dump { my $self=shift;
	print "    ";
	for my $x (0..$self->{map_dim_x}){
		printf ("  %02d ",$x);
	}
	print"\n","-"x107,"\n";
	for my $x (0..$self->{map_dim_x}){
		for my $w (0..$self->{weight_dim}){
			printf ("%02d | ",$x);
			for my $y (0..$self->{map_dim_y}){
				printf("%.2f ", $self->{map}->[$x]->[$y]->{weight}->[$w]);
			}
			print "\n";
		}
		print "\n";
	}
}

=head1 METHOD smooth

Perform gaussian smoothing upon the map.

Accepts: the length of the side of the square gaussian mask to apply.
If not supplied, uses the value in the field C<smoothing>; if that is
empty, uses the square root of the average of the map dimensions

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

=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}){
		print OUT "rect ";
	} else { # $self->{display} eq 'hex'
		print OUT "hexa ";
	}
	#- Map dimension in x-direction (integer, optional).
	print OUT $self->{map_dim_x}." ";
	#- Map dimension in y-direction (integer, optional).
	print OUT $self->{map_dim_y}." ";
	#- Neighborhood type, either bubble or gaussian (string, optional, case-sen- sitive).
	print OUT "gaussian ";
	# End of header
	print OUT "\n";

	# Format input data
	foreach (@{$self->{input}}){
		print OUT join("\t",@{$_->{values}});
		if ($_->{class}){
			print OUT " $_->{class} " ;
		}
		print OUT "\n";
	}
	# EOF
	print OUT chr 26;
	close OUT;
	return 1;
}


#
# Process ASCII from table field or input file
# Accepts: ASCII as array or array ref
#
sub _process_input_text { my ($self) = (shift);

t/AI-NeuralNet-Kohonen.t  view on Meta::CPAN

$node = new AI::NeuralNet::Kohonen::Node(
	weight => [0.1, 0.6, 0.5],
);
isa_ok( $node, 'AI::NeuralNet::Kohonen::Node');
is( $node->{dim}, 2);
my $input = new AI::NeuralNet::Kohonen::Input(
	dim		=> 2,
	values	=> [1,0,0],
);

is( sprintf("%.2f",$node->distance_from($input)), 1.19);

$net = AI::NeuralNet::Kohonen->new(
	map_dim_x	=> 14,
	map_dim_y	=> 10,
	epoch_end	=> sub {print"."},
	train_end	=> sub {print"\n"},
	epochs		=> 2,
	table		=>
"3
1 0 0 red
0 1 0 green
0 0 1 blue
",
);
isa_ok( $net->{input}, 'ARRAY');
isa_ok( $net->{input}->[0],'AI::NeuralNet::Kohonen::Input');

t/AI-NeuralNet-Kohonen.t  view on Meta::CPAN

}


SKIP: {
	skip 'Lost the input file',9;

	# Input file tests\n";
	$net = AI::NeuralNet::Kohonen->new(
		epochs	=> 0,
		input_file => $dir.'ex.dat',
		epoch_end	=> sub {print"."},
		train_end	=> sub {print"\n"},
	);
	isa_ok( $net,'AI::NeuralNet::Kohonen');
	isa_ok( $net->{input}, 'ARRAY');
	is( scalar @{$net->{input}}, 3840);
	is( $net->{map_dim_x}, 19);
	is ($net->{input}->[$#{$net->{input}}]->{values}->[4], 406.918518);
	is( ref $net->{input}->[$#{$net->{input}}]->{values}, 'ARRAY');
	diag "Training on a big file: this is SLOW, sorry\n";
	is($net->train,1);
	my $filename = substr(time,0,8);