 NAME
 DESCRIPTION
 CONSTRUCTOR (new)
 METHOD distance_from
 METHOD distance_effect
 SEE ALSO
 AUTHOR AND COYRIGHT
NAME
AI::NeuralNet::Kohonen::Node  a node for AI::NeuralNet::Kohonen
DESCRIPTION
Implimentation of a node in a SOM  see AI::NeuralNet::Kohonen.
CONSTRUCTOR (new)
Returns a new Node
object. If no wieghts are supplied, the node's weights are randomized with real nubmers.
 dim

The number of dimensions of this node's weights. Do not supply if you are supplying
weight
.  weight

Optional: a reference to an array containing the weight for this node. Supplying this allows the constructor to work out
dim
, above.  values

The values of the vector. Use
x
for unknown values.  missing_mask

Used to donate missing input in the node. Default is
x
.
METHOD distance_from
Find the distance of this node from the target.
Accepts: the target vector as an array reference.
Returns: the distance.
__________________
/ i=n 2
Distance = / E ( V  W )
\/ i=0 i i
Where V
is the current input vector, and W
is this node's weight vector.
METHOD distance_effect
Calculates the effect on learning of distance from a given point (intended to be the BMU).
Accepts: the distance of this node from the given point; the radius of the neighbourhood of affect around the given point.
Returns:
( 2 )
( distance )
THETA(t) = exp (   )
( 2 )
( 2 sigma (t) )
Where distance
is the distance of the node from the BMU, and sigma
is the width of the neighbourhood as calculated elsewhere (see "FINDING THE NEIGHBOURS OF THE BMU" in AI::NeuralNet::Kohonen). THETA also decays over time.
The time t
is always that of the calling object, and is not referenced here.
SEE ALSO
AUTHOR AND COYRIGHT
This implimentation Copyright (C) Lee Goddard, 2003. All Rights Reserved.
Available under the same terms as Perl itself.