AI-NeuralNet-Kohonen

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MANIFEST  view on Meta::CPAN

Changes
Makefile.PL
MANIFEST
README
t/AI-NeuralNet-Kohonen.t
lib/AI/NeuralNet/Kohonen.pm
lib/AI/NeuralNet/Kohonen/Input.pm
lib/AI/NeuralNet/Kohonen/Node.pm
META.yml                                 Module meta-data (added by MakeMaker)

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

	1 0 0 red
	0 1 0 yellow
	0 0 1 blue
	0 1 1 cyan
	1 1 0 yellow
	1 .5 0 orange
	1 .5 1 pink"
	);

	$_->train;
	$_->save_file('mydata.txt');
	exit;

=head1 DESCRIPTION

An illustrative implimentation of Kohonen's Self-organising Feature Maps (SOMs)
in Perl. It's not fast - it's illustrative. In fact, it's slow: but it is illustrative....

Have a look at L<AI::NeuralNet::Kohonen::Demo::RGB> for an example of
visualisation of the map.

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

=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

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

	}
	#- Map dimension in x-direction (integer, optional).
	print OUT $self->{map_dim_x}." ";
	#- Map dimension in y-direction (integer, optional).
	print OUT $self->{map_dim_y}." ";
	#- Neighborhood type, either bubble or gaussian (string, optional, case-sen- sitive).
	print OUT "gaussian ";
	# End of header
	print OUT "\n";

	# Format input data
	foreach (@{$self->{input}}){
		print OUT join("\t",@{$_->{values}});
		if ($_->{class}){
			print OUT " $_->{class} " ;
		}
		print OUT "\n";
	}
	# EOF
	print OUT chr 26;
	close OUT;

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

	}
	#- Map dimension in x-direction (integer, optional).
	$_				      = shift @specs;
	$self->{map_dim_x}    = $_ if defined $_;
	#- Map dimension in y-direction (integer, optional).
	$_				      = shift @specs;
	$self->{map_dim_y}    = $_ if defined $_;
	#- Neighborhood type, either bubble or gaussian (string, optional, case-sen- sitive).
	# not implimented

	# Format input data
	foreach (@_){
		$self->_add_input_from_str($_);
	}
	return 1;
}


=head1 PRIVATE METHOD _select_target

Return a random target from the training set in the C<input> field,

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

	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
not set); a true value on success.

=cut

sub _add_input_from_str { my ($self) = (shift);
	$_ = shift;
	s/#.*$//g;
	return undef if /^$/ or not defined $self->{weight_dim};
	my @i = split /\s+/,$_;
	return undef if $#i < $self->{weight_dim}; # catch bad lines

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

1;

=head1 FILE FORMAT

This module has begun to attempt the I<SOM_PAK> format:
I<SOM_PAK> file format version 3.1 (April 7, 1995),
Helsinki University of Technology, Espoo:

=over 4

The input data is stored in ASCII-form as a list of entries, one line
...for each vectorial sample.

The first line of the file is reserved for status knowledge of the
entries; in the present version it is used to define the following
items (these items MUST occur in the indicated order):

   - Dimensionality of the vectors (integer, compulsory).
   - Topology type, either hexa or rect (string, optional, case-sensitive).
   - Map dimension in x-direction (integer, optional).
   - Map dimension in y-direction (integer, optional).
   - Neighborhood type, either bubble or gaussian (string, optional, case-sen-
      sitive).

...

Subsequent lines consist of n floating-point numbers followed by an
optional class label (that can be any string) and two optional
qualifiers (see below) that determine the usage of the corresponding
data entry in training programs.  The data files can also contain an
arbitrary number of comment lines that begin with '#', and are
ignored. (One '#' for each comment line is needed.)

If some components of some data vectors are missing (due to data
collection failures or any other reason) those components should be
marked with 'x'...[in processing, these] are ignored.

...

Each data line may have two optional qualifiers that determine the
usage of the data entry during training. The qualifiers are of the
form codeword=value, where spaces are not allowed between the parts of
the qualifier. The optional qualifiers are the following:

=over 4

=item -

Enhancement factor: e.g. weight=3.  The training rate for the
corresponding input pattern vector is multiplied by this
parameter so that the reference vectors are updated as if this
input vector were repeated 3 times during training (i.e., as if
the same vector had been stored 2 extra times in the data file).

=item -

Fixed-point qualifier: e.g. fixed=2,5.  The map unit defined by
the fixed-point coordinates (x = 2; y = 5) is selected instead of
the best-matching unit for training. (See below for the definition
of coordinates over the map.) If several inputs are forced to
known locations, a wanted orientation results in the map.

=back

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

=item *

hexa/rect is only visual, and only in the ::Demo::RGB package atm

=item *

I<neighbourhood type> is always gaussian.

=item *

i<x> for missing data.

=item *

the two optional qualifiers

=back

=cut

=head1 SEE ALSO