som自组织神经聚类算法matlab.zip

%---------------------------------------------% % % % 工作室提供代做matlab仿真 % % % % 详情请访问：http://cn.mikecrm.com/5k6v1DP % % % %---------------------------------------------% 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) % 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, dim] = size(x); if options(6) % Batch algorithm H = zeros(ndata, net.num_nodes); end % Put weights into matrix form tempw = (reshape(net.map, net.nin, net.num_nodes))'; % 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.map = reshape(tempw', [net.nin net.map_size]); [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)); %R = similarity_pearsonC(tempw', x(pnum(k), :)'); %[temp, bnode] = min(R); <==no need to adjust ! %tempw = tempw + ((alpha*(neighbourhood(:)))*ones(1, net.nin)).*repmat(R',1,dim); % 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.map = reshape(tempw', [net.nin net.map_size]); %options(8) = sum(min(dist2(tempw, x))); function [d2, win_nodes] = somfwd(net, x) % Turn nodes into matrix of centres nodes = (reshape(net.map, net.nin, net.num_nodes))'; d2 = dist2(x, nodes); % Compute squared distance matrix [w, win_nodes] = min(d2, [], 2); % winning node for each pattern function n2 = dist2(x, c) [ndata, dimx] = size(x); [ncentres, dimc] = size(c); if dimx ~= dimc error('Data dimension does not match dimension of centres') end n2 = (ones(ncentres, 1) * sum((x.^2)', 1))' + ones(ndata, 1) ... * sum((c.^2)',1) - 2.*(x*(c')); % Rounding errors occasionally cause negative entries in n2 if any(any(n2<0)) n2(n2<0) = 0; end