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

	} else {
		$self->{map_dim_a} 		= $self->{map_dim_x} + (($self->{map_dim_y}-$self->{map_dim_x})/2)
	}
	$self->{neighbour_factor}	= 2.5 unless $self->{neighbour_factor};
	$self->{epochs}				= 99 unless defined $self->{epochs};
	$self->{epochs}				= 1 if $self->{epochs}<1;
	$self->{time_constant}		= $self->{epochs} / log($self->{map_dim_a}) unless $self->{time_constant};	# to base 10?
	$self->{learning_rate}		= 0.5 unless $self->{learning_rate};
	$self->{l}					= $self->{learning_rate};
	if (not $self->{weight_dim}){
		cluck "{weight_dim} not set";
		return undef;
	}
	$self->randomise_map;
	return $self;
}




=head1 METHOD randomise_map

Populates the C<map> with nodes that contain random real nubmers.

See L<AI::NerualNet::Kohonen::Node/CONSTRUCTOR new>.

=cut

sub randomise_map { my $self=shift;
	confess "{weight_dim} not set" unless $self->{weight_dim};
	confess "{map_dim_x} not set" unless $self->{map_dim_x};
	confess "{map_dim_y} not set" unless $self->{map_dim_y};
	for my $x (0..$self->{map_dim_x}){
		$self->{map}->[$x] = [];
		for my $y (0..$self->{map_dim_y}){
			$self->{map}->[$x]->[$y] = new AI::NeuralNet::Kohonen::Node(
				dim => $self->{weight_dim},
				missing_mask => $self->{missing_mask},
			);
		}
	}
}


=head1 METHOD clear_map

As L<METHOD randomise_map> but sets all C<map> nodes to
either the value supplied as the only paramter, or C<undef>.

=cut

sub clear_map { my $self=shift;
	confess "{weight_dim} not set" unless $self->{weight_dim};
	confess "{map_dim_x} not set" unless $self->{map_dim_x};
	confess "{map_dim_y} not set" unless $self->{map_dim_y};
	my $val = shift || $self->{missing_mask};
	my $w = [];
	foreach (0..$self->{weight_dim}){
		push @$w, $val;
	}
	for my $x (0..$self->{map_dim_x}){
		$self->{map}->[$x] = [];
		for my $y (0..$self->{map_dim_y}){
			$self->{map}->[$x]->[$y] = new AI::NeuralNet::Kohonen::Node(
				weight		 => $w,

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

	# 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,
unless the C<targeting> field is defined, when the targets are
iterated over.

=cut

sub _select_target { my $self=shift;
	if (not $self->{targeting}){
		return $self->{input}->[
			(int rand(scalar @{$self->{input}}))
		];

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

}


=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
	# 'x' in files signifies unknown: we prefer undef?