net = somtrain{net, options, x)
net = somtrain{net, options, x) uses Kohonen's algorithm to
train a SOM. Both on-line and batch algorithms are implemented.
The learning rate (for on-line) and neighbourhood size decay linearly.
There is no error function minimised during training (so there is
no termination criterion other than the number of epochs), but the
sum-of-squares is computed and returned in options(8).
The optional parameters have the following interpretations.
options(1) is set to 1 to display error values; also logs learning
rate alpha and neighbourhood size nsize.
Otherwise nothing is displayed.
options(5) determines whether the patterns are sampled randomly
with replacement. If it is 0 (the default), then patterns are sampled
in order. This is only relevant to the on-line algorithm.
options(6) determines if the on-line or batch algorithm is
used. If it is 1
then the batch algorithm is used. If it is 0
(the default) then the on-line algorithm is used.
options(14) is the maximum number of iterations (passes through
the complete pattern set); default 100.
options(15) is the final neighbourhood size; default value is the
same as the initial neighbourhood size.
options(16) is the final learning rate; default value is the same
as the initial learning rate.
options(17) is the initial neighbourhood size; default 0.5*maximum
map size.
options(18) is the initial learning rate; default 0.9. This parameter
must be positive.
net = som(nin, [8, 7]); options = foptions;% Ordering phase options(1) = 1; options(14) = 50; options(18) = 0.9; % Initial learning rate options(16) = 0.05; % Final learning rate options(17) = 8; % Initial neighbourhood size options(15) = 1; % Final neighbourhood size net2 = somtrain(net, options, x);
% Convergence phase options(14) = 400; options(18) = 0.05; options(16) = 0.01; options(17) = 0; options(15) = 0; net3 = somtrain(net2, options, x);
kmeans, som, somfwdCopyright (c) Ian T Nabney (1996-9)