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

C<undef> on failure.

=cut

sub get_weight_at { my ($self,$x,$y) = (shift,shift,shift);
	return undef if $x<0 or $y<0 or $x>$self->{map_dim_x} or $y>$self->{map_dim_y};
	return $self->{map}->[$x]->[$y]->{weight};
}



=head1 METHOD get_results

Finds and returns the results for all input vectors in the supplied
reference to an array of arrays,
placing the values in the C<results> field (array reference),
and, returning it either as an array or as it is, depending on
the calling context

If no array reference of input vectors is supplied, will use
the values in the C<input> field.

Individual results are in the array format as described in
L<METHOD find_bmu>.

See L<METHOD find_bmu>, and L</METHOD get_weight_at>.

=cut

sub get_results { my ($self,$targets)=(shift,shift);
	$self->{results} = [];
	if (not defined $targets){
		$targets = $self->{input};
	} elsif (not $targets eq $self->{input}){
		foreach (@$targets){
			next if ref $_ eq 'AI::NeuralNet::Kohonen::Input';
			$_ = new AI::NeuralNet::Kohonen::Input(values=>$_);
		}
	}
	foreach my $target (@{ $targets}){
		$_ = $self->find_bmu($target);
		push @$_, $target->{class}||"?";
		push @{$self->{results}}, $_;
	}
	# Make it a scalar if it's a scalar
#	if ($#{$self->{results}} == 0){
#		$self->{results} = @{$self->{results}}[0];
#	}
	return wantarray? @{$self->{results}} : $self->{results};
}


=head1 METHOD map_results

Clears the C<map> and fills it with the results.

The sole paramter is passed to the L<METHOD clear_map>.
L<METHOD get_results> is then called, and the results
returned fed into the object field C<map>.

This may change, as it seems misleading to re-use that field.

=cut

sub map_results { my $self=shift;

}


=head1 METHOD dump

Print the current weight values to the screen.

=cut

sub dump { my $self=shift;
	print "    ";
	for my $x (0..$self->{map_dim_x}){
		printf ("  %02d ",$x);
	}
	print"\n","-"x107,"\n";
	for my $x (0..$self->{map_dim_x}){
		for my $w (0..$self->{weight_dim}){
			printf ("%02d | ",$x);
			for my $y (0..$self->{map_dim_y}){
				printf("%.2f ", $self->{map}->[$x]->[$y]->{weight}->[$w]);
			}
			print "\n";
		}
		print "\n";
	}
}

=head1 METHOD smooth

Perform gaussian smoothing upon the map.

Accepts: the length of the side of the square gaussian mask to apply.
If not supplied, uses the value in the field C<smoothing>; if that is
empty, uses the square root of the average of the map dimensions
(C<map_dim_a>).

Returns: a true value.

=cut

sub smooth { my ($self,$smooth) = (shift,shift);
	$smooth = $self->{smoothing} if not $smooth and defined $self->{smoothing};
	return unless $smooth;
	$smooth = int( sqrt $self->{map_dim_a} );
	my $mask = _make_gaussian_mask($smooth);

	# For every weight at every point
	for my $x (0..$self->{map_dim_x}){
		for my $y (0..$self->{map_dim_y}){
			for my $w (0..$self->{weight_dim}){
				# Apply the mask
				for my $mx (0..$smooth){
					for my $my (0..$smooth){
						$self->{map}->[$x]->[$y]->{weight}->[$w] *= $mask->[$mx]->[$my];
					}