diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_prototrain.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_prototrain.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,155 @@
+function [sM,sTrain] = som_prototrain(sM, D)
+
+%SOM_PROTOTRAIN  Use sequential algorithm to train the Self-Organizing Map.
+%
+% [sM,sT] = som_prototrain(sM, D)
+% 
+%  sM = som_prototrain(sM,D);
+%
+%  Input and output arguments: 
+%   sM      (struct) map struct, the trained and updated map is returned
+%           (matrix) codebook matrix of a self-organizing map
+%                    size munits x dim or  msize(1) x ... x msize(k) x dim
+%                    The trained map codebook is returned.
+%   D       (struct) training data; data struct
+%           (matrix) training data, size dlen x dim
+%
+% This function is otherwise just like SOM_SEQTRAIN except that
+% the implementation of the sequential training algorithm is very 
+% straightforward (and slower). This should make it easy for you 
+% to modify the algorithm, if you want to. 
+%
+% For help on input and output parameters, try 
+% 'type som_prototrain' or check out the help for SOM_SEQTRAIN.
+% See also SOM_SEQTRAIN, SOM_BATCHTRAIN.
+
+% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta juuso 080200 130300
+ 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% Check input arguments
+
+% map 
+struct_mode = isstruct(sM);
+if struct_mode, 
+  M = sM.codebook; 
+  sTopol = sM.topol; 
+  mask = sM.mask; 
+  msize = sTopol.msize;
+  neigh = sM.neigh;
+else  
+  M = sM; orig_size = size(M);
+  if ndims(sM) > 2, 
+    si = size(sM); dim = si(end); msize = si(1:end-1);
+    M = reshape(sM,[prod(msize) dim]);
+  else
+    msize = [orig_size(1) 1]; dim = orig_size(2);
+  end
+  sM = som_map_struct(dim,'msize',msize); sTopol = sM.topol;
+  mask = ones(dim,1);
+  neigh = 'gaussian';
+end
+[munits dim] = size(M); 
+
+% data
+if isstruct(D), data_name = D.name; D = D.data; 
+else data_name = inputname(2); 
+end
+D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data
+[dlen ddim] = size(D);                % check input dimension
+if dim ~= ddim, error('Map and data input space dimensions disagree.'); end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% initialize (these are default values, change as you will)
+
+% training length
+trainlen = 20*dlen; % 20 epochs by default
+
+% neighborhood radius
+radius_type = 'linear';
+rini = max(msize)/2;
+rfin = 1;
+
+% learning rate
+alpha_type = 'inv'; 
+alpha_ini = 0.2;
+
+% initialize random number generator
+rand('state',sum(100*clock));
+
+% tracking 
+start = clock; trackstep = 100;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% Action
+
+Ud = som_unit_dists(sTopol); % distance between map units on the grid
+mu_x_1 = ones(munits,1);     % this is used pretty often
+
+for t = 1:trainlen, 
+
+  %% find BMU
+  ind = ceil(dlen*rand(1)+eps);       % select one vector
+  x = D(ind,:);                       % pick it up
+  known = ~isnan(x);                  % its known components
+  Dx = M(:,known) - x(mu_x_1,known);  % each map unit minus the vector
+  dist2 = (Dx.^2)*mask(known);        % squared distances  
+  [qerr bmu] = min(dist2);            % find BMU
+
+  %% neighborhood  
+  switch radius_type, % radius
+   case 'linear', r = rini+(rfin-rini)*(t-1)/(trainlen-1);
+  end
+  if ~r, r=eps; end % zero neighborhood radius may cause div-by-zero error  
+  switch neigh, % neighborhood function 
+  case 'bubble',   h = (Ud(:,bmu) <= r);
+  case 'gaussian', h = exp(-(Ud(:,bmu).^2)/(2*r*r)); 
+  case 'cutgauss', h = exp(-(Ud(:,bmu).^2)/(2*r*r)) .* (Ud(:,bmu) <= r);
+  case 'ep',       h = (1 - (Ud(:,bmu).^2)/(r*r)) .* (Ud(:,bmu) <= r);
+  end  
+
+  %% learning rate
+  switch alpha_type,
+   case 'linear', a = (1-t/trainlen)*alpha_ini;
+   case 'inv',    a = alpha_ini / (1 + 99*(t-1)/(trainlen-1));
+   case 'power',  a = alpha_ini * (0.005/alpha_ini)^((t-1)/trainlen); 
+  end
+  
+  %% update
+  M(:,known) = M(:,known) - a*h(:,ones(sum(known),1)).*Dx;
+			 
+  %% tracking
+  if t==1 | ~rem(t,trackstep),
+    elap_t = etime(clock,start); tot_t = elap_t*trainlen/t; 
+    fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)
+  end
+  
+end; % for t = 1:trainlen
+fprintf(1,'\n');
+
+% outputs
+sTrain = som_set('som_train','algorithm','proto',...
+		 'data_name',data_name,...
+		 'neigh',neigh,...
+		 'mask',mask,...
+		 'radius_ini',rini,...
+		 'radius_fin',rfin,...
+		 'alpha_ini',alpha_ini,...
+		 'alpha_type',alpha_type,...
+		 'trainlen',trainlen,...
+		 'time',datestr(now,0));
+
+if struct_mode, 
+  sM = som_set(sM,'codebook',M,'mask',mask,'neigh',neigh);
+  sM.trainhist(end+1) = sTrain;
+else
+  sM = reshape(M,orig_size);
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+
+