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

See also L<_decay_learning_rate>.

=cut

sub _gauss_weight { my ($r, $sigma) = (shift,shift);
	return exp( -($r**2) / (2 * $sigma**2) );
}


=head1 PUBLIC METHOD quantise_error

Returns the quantise error for either the supplied points,
or those in the C<input> field.

=cut


sub quantise_error { my ($self,$targets) = (shift,shift);
	my $qerror=0;
	if (not defined $targets){
		$targets = $self->{input};
	} else {
		foreach (@$targets){
			if (not ref $_ or ref $_ ne 'ARRAY'){
				croak "Supplied target parameter should be an array of arrays!"
			}
			$_ = new AI::NeuralNet::Kohonen::Input(values=>$_);
		}
	}

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

	# should be an array of an array of elements
	my @bmu = $self->get_results($targets);

	# Check input and output dims are the same
	if ($#{$self->{map}->[0]->[1]->{weight}} != $targets->[0]->{dim}){
		confess "target input and map dimensions differ";
	}

	for my $i (0..$#bmu){
		foreach my $w (0..$self->{weight_dim}){
			$qerror += $targets->[$i]->{values}->[$w]
			- $self->{map}->[$bmu[$i]->[1]]->[$bmu[$i]->[2]]->{weight}->[$w];
		}
	}
	$qerror /= scalar @$targets;
	return $qerror;
}


=head1 PRIVATE METHOD _add_input_from_str

Adds to the C<input> field an input vector in SOM_PAK-format
whitespace-delimited ASCII.

Returns C<undef> on failure to add an item (perhaps because
the data passed was a comment, or the C<weight_dim> flag was

t/AI-NeuralNet-Kohonen.t  view on Meta::CPAN

isa_ok($net->{map}->[ $bmu[0]->[1] ]->[ $bmu[0]->[2] ],
	"AI::NeuralNet::Kohonen::Node"
);
# warn $net->{map}->[ $bmu[1] ]->[ $bmu[2] ];#->get_class;
# Get the nearest class?

{
	my $i=0;
	my $targets = [[1, 0, 0],[0,1,0],[0,0,1]];
	my @bmu = $net->get_results($targets);
	# qerror
	my $qerror=0;
	foreach my $j (0..$net->{weight_dim}){ # loop over weights
		$qerror += $targets->[0]->{values}->[$j]
		- $net->{map}->[$bmu[$i]->[1]]->[$bmu[$i]->[2]]->{weight}->[$j];
	}
	is( $qerror, $net->quantise_error([ [1,0,0] ]));
}


SKIP: {
	skip 'Lost the input file',9;

	# Input file tests\n";
	$net = AI::NeuralNet::Kohonen->new(
		epochs	=> 0,
		input_file => $dir.'ex.dat',