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

=head1 SYNOPSIS

Test the test file in this distribution, or:

	package YourClass;
	use base "AI::NeuralNet::Kohonen::Visual";

	sub get_colour_for { my ($self,$x,$y) = (shift,shift,shift);
		# From here you return a TK colour name.
		# Get it as you please; for example, values of a 3D map:
		return sprintf("#%02x%02x%02x",
			(int (255 * $self->{map}->[$x]->[$y]->{weight}->[0])),
			(int (255 * $self->{map}->[$x]->[$y]->{weight}->[1])),
			(int (255 * $self->{map}->[$x]->[$y]->{weight}->[2])),
		);
	}

	exit;
	1;

And then:

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

			my $bmu = $self->find_bmu($target);

			$self->_adjust_neighbours_of($bmu,$target);

			if (exists $self->{show_training}){
				if ($self->{show_bmu}){
					$self->plot_map(bmu_x=>$bmu->[1],bmu_y=>$bmu->[2]);
				} else {
					$self->plot_map;
				}
				$self->{_label_txt} = sprintf("Epoch: %04d",$self->{t})."  "
				. "Learning: $self->{l}  "
				. sprintf("BMU: %02d,%02d",$bmu->[1],$bmu->[2])."  "
				.( exists $target->{class}? "Target: [$target->{class}]  " : "")
				;
				$self->{_canvas}->update;
				$self->{_label}->update;
				DoOneEvent(DONT_WAIT);		# be kind and process XEvents if they arise
			}
		}

		$self->_decay_learning_rate;
 		&{$self->{epoch_end}} if exists $self->{epoch_end};

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

=cut

sub get_colour_for { my ($self,$x,$y) = (shift,shift,shift);
	my $_0 = $self->{map}->[$x]->[$y]->{weight}->[0];
	$_0 = $self->{missing_colour} || 0 if $_0 eq $self->{missing_mask};
	my $_1 = $self->{map}->[$x]->[$y]->{weight}->[1];
	$_1 = $self->{missing_colour} || 0 if $_1 eq $self->{missing_mask};
	my $_2 = $self->{map}->[$x]->[$y]->{weight}->[2];
	$_2 = $self->{missing_colour} || 0 if $_2 eq $self->{missing_mask};
	return sprintf("#%02x%02x%02x",
		(int (255 * $_0)),
		(int (255 * $_1)),
		(int (255 * $_2)),
	);
}


=head1 METHOD prepare_display

Depracated: see L<METHOD create_empty_map>.