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

the C<input_file> field.

=item input_names

A name for each dimension of the input vectors.

=item map_dim_x

=item map_dim_y

The dimensions of the feature map to create - defaults to a toy 19.
(note: this is Perl indexing, starting at zero).

=item epochs

Number of epochs to run for (see L<METHOD train>).
Minimum number is C<1>.

=item learning_rate

The initial learning rate.

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

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>
is applied (see L</METHOD smooth>).

=item neighbour_factor

When working out the size of the neighbourhood of influence,
the average of the dimensions of the map are divided by this variable,
before the exponential function is applied: the default value is 2.5,
but you may with to use 2 or 4.

=item missing_mask

Used to signify data is missing in an input vector. Defaults
to C<x>.

=back

Private fields:

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

		}
	}
}




=head1 METHOD train

Optionally accepts a parameter that is the number of epochs
for which to train: the default is the value in the C<epochs> field.

An epoch is composed of A number of generations, the number being
the total number of input vectors.

For every generation, iterates:

=over 4

=item 1