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

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.

=item t

The current epoch, or moment in time.

=item l

The current learning rate.

=item map_dim_a

Average of the map dimensions.

=back

=cut

sub new {
	my $class					= shift;
	my %args					= @_;
	my $self 					= bless \%args,$class;

	$self->{missing_mask}		= 'x' unless defined $self->{missing_mask};
	$self->_process_table if defined $self->{table};	# Creates {input}
	$self->load_input($self->{input_file}) if defined $self->{input_file};	# Creates {input}
	if (not defined $self->{input}){
		cluck "No {input} supplied!";
		return undef;
	}

	$self->{map_dim_x}			= 19 unless defined $self->{map_dim_x};
	$self->{map_dim_y}			= 19 unless defined $self->{map_dim_y};
	# Legacy from...yesterday
	if ($self->{map_dim}){
		$self->{map_dim_x} 		= $self->{map_dim_y} = $self->{map_dim}
	}
	if (not defined $self->{map_dim_x} or $self->{map_dim_x}==0
	 or not defined $self->{map_dim_y} or $self->{map_dim_y}==0){
		 confess "No map dimensions in the input!";
	 }
	if ($self->{map_dim_x}>$self->{map_dim_y}){
		$self->{map_dim_a} 		= $self->{map_dim_y} + (($self->{map_dim_x}-$self->{map_dim_y})/2)
	} else {
		$self->{map_dim_a} 		= $self->{map_dim_x} + (($self->{map_dim_y}-$self->{map_dim_x})/2)
	}
	$self->{neighbour_factor}	= 2.5 unless $self->{neighbour_factor};
	$self->{epochs}				= 99 unless defined $self->{epochs};
	$self->{epochs}				= 1 if $self->{epochs}<1;
	$self->{time_constant}		= $self->{epochs} / log($self->{map_dim_a}) unless $self->{time_constant};	# to base 10?
	$self->{learning_rate}		= 0.5 unless $self->{learning_rate};
	$self->{l}					= $self->{learning_rate};
	if (not $self->{weight_dim}){
		cluck "{weight_dim} not set";
		return undef;
	}
	$self->randomise_map;
	return $self;
}