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

    my @mes    = ();                                                                    # this will contain the errors during the epochs
    for my $epoch (1..$epochs)  {
	$self->{T} = $epoch;
	my $sigma = $self->{_Sigma0} * exp ( - $self->{T} / $self->{LAMBDA} );          # compute current radius
	my $l     = $self->{_L0}     * exp ( - $self->{T} / $epochs );                  # current learning rate

	my @veggies = @_;                                                               # make a local copy, that will be destroyed in the loop
	while (@veggies) {
	    my $sample = splice @veggies, int (rand (scalar @veggies) ), 1;             # find (and take out)

	    my @bmu = $self->bmu ($sample);                                             # find the best matching unit
	    push @mes, $bmu[2] if wantarray;
	    my $neighbors = $self->neighbors ($sigma, @bmu);                            # find its neighbors
	    map { _adjust ($self, $l, $sigma, $_, $sample) } @$neighbors;               # bend them like Beckham
	}
    }
    return @mes;
}

sub _adjust {                                                                           # http://www.ai-junkie.com/ann/som/som4.html
    my $self  = shift;

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

	$w->[$i] = $w->[$i] + $theta * $l * ( $v->[$i] - $w->[$i] );
    }
}

=pod

=item I<bmu>

(I<$x>, I<$y>, I<$distance>) = I<$nn>->bmu (I<$vector>)

This method finds the I<best matching unit>, i.e. that neuron which is closest to the vector passed
in. The method returns the coordinates and the actual distance.

=cut

sub bmu { die; }

=pod

=item I<mean_error>