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

Technically, you have reduced your dimension from N to 2.

=head1 INTERFACE

=head2 Constructor

The constructor takes arguments:

=over

=item C<input_dim> : (mandatory, no default)

A positive integer specifying the dimension of the sample vectors (and hence that of the vectors in
the grid).

=item C<learning_rate>: (optional, default C<0.1>)

This is a magic number which controls how strongly the vectors in the grid can be influenced. Stronger
movement can mean faster learning if the clusters are very pronounced. If not, then the movement is
like noise and the convergence is not good. To mediate that effect, the learning rate is reduced
over the iterations.

=item C<sigma0>: (optional, defaults to radius)

A non-negative number representing the start value for the learning radius. Practically, the value
should be chosen in such a way to cover a larger part of the map. During the learning process this
value will be narrowed down, so that the learning radius impacts less and less neurons.

B<NOTE>: Do not choose C<1> as the C<log> function is used on this value.

=back

Subclasses will (re)define some of these parameters and add others:

lib/AI/NeuralNet/SOM/Hexa.pm  view on Meta::CPAN

  # ... see also base class AI::NeuralNet::SOM

=head1 INTERFACE

=head2 Constructor

The constructor takes the following arguments (additionally to those in the base class):

=over

=item C<output_dim> : (mandatory, no default)

A positive, non-zero number specifying the diameter of the hexagonal. C<1> creates one with a single
hexagon, C<2> one with 4, C<3> one with 9. The number plays the role of a diameter.

=back

Example:

    my $nn = new AI::NeuralNet::SOM::Hexa (output_dim => 6,
                                           input_dim  => 3);

lib/AI/NeuralNet/SOM/Rect.pm  view on Meta::CPAN

  print $nn->as_data;

=head1 INTERFACE

=head2 Constructor

The constructor takes the following arguments (additionally to those in the base class):

=over

=item C<output_dim> : (mandatory, no default)

A string of the form "3x4" defining the X and the Y dimensions.

=back

Example:

    my $nn = new AI::NeuralNet::SOM::Rect (output_dim => "5x6",
                                           input_dim  => 3);