comparison toolboxes/FullBNT-1.0.7/netlab3.3/somtrain.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function net = somtrain(net, options, x)
+%SOMTRAIN Kohonen training algorithm for SOM.
+%
+%	Description
+%	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.
+%
+%	See also
+%	KMEANS, SOM, SOMFWD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'som', x);
+if ~isempty(errstring)
+    error(errstring);
+end
+
+% Set number of iterations in convergence phase
+if (~options(14))
+    options(14) = 100;
+end
+niters = options(14);
+
+% Learning rate must be positive
+if (options(18) > 0)
+    alpha_first = options(18);
+else
+    alpha_first = 0.9;
+end
+% Final learning rate must be no greater than initial learning rate
+if (options(16) > alpha_first | options(16) < 0)
+    alpha_last = alpha_first;
+else
+    alpha_last = options(16);
+end
+
+% Neighbourhood size
+if (options(17) >= 0)
+    nsize_first = options(17);
+else
+    nsize_first = max(net.map_dim)/2;
+end
+% Final neighbourhood size must be no greater than initial size
+if (options(15) > nsize_first | options(15) < 0)
+    nsize_last = nsize_first;
+else
+    nsize_last = options(15);
+end
+
+ndata = size(x, 1);
+
+if options(6)
+    % Batch algorithm
+    H = zeros(ndata, net.num_nodes);
+end
+% Put weights into matrix form
+tempw = sompak(net);
+
+% Then carry out training
+j = 1;
+while j <= niters
+    if options(6)
+	% Batch version of algorithm
+	alpha = 0.0;
+	frac_done = (niters - j)/niters;
+	% Compute neighbourhood
+	nsize = round((nsize_first - nsize_last)*frac_done + nsize_last);
+	
+	% Find winning node: put weights back into net so that we can
+	% call somunpak
+	net = somunpak(net, tempw);
+	[temp, bnode] = somfwd(net, x);
+	for k = 1:ndata
+	    H(k, :) = reshape(net.inode_dist(:, :, bnode(k))<=nsize, ...
+		1, net.num_nodes);
+	end
+	s = sum(H, 1);
+	for k = 1:net.num_nodes
+	    if s(k) > 0
+		tempw(k, :) = sum((H(:, k)*ones(1, net.nin)).*x, 1)/ ...
+		    s(k);
+	    end
+	end
+    else
+	% On-line version of algorithm
+	if options(5)
+	    % Randomise order of pattern presentation: with replacement
+	    pnum = ceil(rand(ndata, 1).*ndata);
+	else
+	    pnum = 1:ndata;
+	end
+	% Cycle through dataset
+	for k = 1:ndata
+	    % Fraction done
+	    frac_done = (((niters+1)*ndata)-(j*ndata + k))/((niters+1)*ndata);
+	    % Compute learning rate
+	    alpha = (alpha_first - alpha_last)*frac_done + alpha_last;
+	    % Compute neighbourhood
+	    nsize = round((nsize_first - nsize_last)*frac_done + nsize_last);
+	    % Find best node
+	    pat_diff = ones(net.num_nodes, 1)*x(pnum(k), :) - tempw;
+	    [temp, bnode] = min(sum(abs(pat_diff), 2));
+	
+	    % Now update neighbourhood
+	    neighbourhood = (net.inode_dist(:, :, bnode) <= nsize);
+	    tempw = tempw + ...
+		((alpha*(neighbourhood(:)))*ones(1, net.nin)).*pat_diff;
+	end
+    end
+    if options(1)
+	% Print iteration information
+	fprintf(1, 'Iteration %d; alpha = %f, nsize = %f. ', j, alpha, ...
+	nsize);
+	% Print sum squared error to nearest node
+	d2 = dist2(tempw, x);
+	fprintf(1, 'Error = %f\n', sum(min(d2)));
+    end
+    j = j + 1;
+end
+
+net = somunpak(net, tempw);
+options(8) = sum(min(dist2(tempw, x)));