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	$_->train;
	$_->save_file('mydata.txt');
	exit;

=head1 DESCRIPTION

An illustrative implimentation of Kohonen's Self-organising Feature Maps (SOMs)
in Perl. It's not fast - it's illustrative. In fact, it's slow: but it is illustrative....

Have a look at L<AI::NeuralNet::Kohonen::Demo::RGB> for an example of
visualisation of the map.

I'll maybe add some more text here later.

=head1 DEPENDENCIES

	AI::NeuralNet::Kohonen::Node
	AI::NeuralNet::Kohonen::Input

=head1 EXPORTS

None

=head1 CONSTRUCTOR new

Instantiates object fields:

=over 4

=item input_file

A I<SOM_PAK> training file to load. This does not prevent
other input methods (C<input>, C<table>) being processed, but
it does over-ride any specifications (C<weight_dim>) which may
have been explicitly handed to the constructor.

See also L</FILE FORMAT> and L</METHOD load_input>.

=item input

A reference to an array of training vectors, within which each vector
is represented by an array:

	[ [v1a, v1b, v1c], [v2a,v2b,v2c], ..., [vNa,vNb,vNc] ]

See also C<table>.

=item table

The contents of a file of the format that could be supplied to
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.

=item train_start

Reference to code to call at the begining of training.

=item epoch_start

Reference to code to call at the begining of every epoch
(such as a colour calibration routine).

=item epoch_end

Reference to code to call at the end of every epoch
(such as a display routine).

=item train_end

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:

=over 4

=item time_constant

The number of iterations (epochs) to be completed, over the log of the map radius.