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I<$label> = I<$nn>->label (I<$x>, I<$y>)

I<$nn>->label (I<$x>, I<$y>, I<$label>)

Set or get the label for a particular neuron. The neuron is addressed via its coordinates.
The label can be anything, it is just attached to the position.

=cut

sub label {
    my $self    = shift;
    my ($x, $y) = (shift, shift);
    my $l       = shift;
    return defined $l ? $self->{labels}->[$x]->[$y] = $l : $self->{labels}->[$x]->[$y];
}

=pod

=item I<as_string>

print I<$nn>->as_string

This methods creates a pretty-print version of the current vectors.

=cut

sub as_string { die; }

=pod

=item I<as_data>

print I<$nn>->as_data

This methods creates a string containing the raw vector data, row by
row. This can be fed into gnuplot, for instance.

=cut

sub as_data { die; }

=pod

=back

=head1 HOWTOs

=over

=item I<using Eigenvectors to initialize the SOM>

See the example script in the directory C<examples> provided in the
distribution. It uses L<PDL> (for speed and scalability, but the
results are not as good as I had thought).

=item I<loading and saving a SOM>

See the example script in the directory C<examples>. It uses
C<Storable> to directly dump the data structure onto disk. Storage and
retrieval is quite fast.

=back

=head1 FAQs

=over

=item I<I get 'uninitialized value ...' warnings, many of them>

There is most likely something wrong with the C<input_dim> you
specified and your vectors should be having.

=back

=head1 TODOs

=over

=item maybe implement the SOM on top of PDL?

=item provide a ::SOM::Compat to have compatibility with the original AI::NeuralNet::SOM?

=item implement different window forms (bubble/gaussian), linear/random

=item implement the format mentioned in the original AI::NeuralNet::SOM

=item add methods as_html to individual topologies

=item add iterators through vector lists for I<initialize> and I<train>

=back

=head1 SUPPORT

Bugs should always be submitted via the CPAN bug tracker 
L<https://rt.cpan.org/Dist/Display.html?Status=Active&Queue=AI-NeuralNet-SOM>

=head1 SEE ALSO

Explanation of the algorithm:

L<http://www.ai-junkie.com/ann/som/som1.html>

Old version of AI::NeuralNet::SOM from Alexander Voischev:

L<http://backpan.perl.org/authors/id/V/VO/VOISCHEV/>

Subclasses:

L<AI::NeuralNet::Hexa>
L<AI::NeuralNet::Rect>
L<AI::NeuralNet::Torus>


=head1 AUTHOR

Robert Barta, E<lt>rho@devc.atE<gt>

=head1 COPYRIGHT AND LICENSE