som_seqtrain.m

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radius     = [];
alpha      = [];
tracking   = 1;
sample_order_type = 'random';
tlen_type  = 'epochs';

i=1; 
while i<=length(varargin), 
  argok = 1; 
  if ischar(varargin{i}), 
    switch varargin{i}, 
     % argument IDs
     case 'msize', i=i+1; sTopol.msize = varargin{i}; 
     case 'lattice', i=i+1; sTopol.lattice = varargin{i};
     case 'shape', i=i+1; sTopol.shape = varargin{i};
     case 'mask', i=i+1; sTrain.mask = varargin{i};
     case 'neigh', i=i+1; sTrain.neigh = varargin{i};
     case 'trainlen', i=i+1; sTrain.trainlen = varargin{i};
     case 'trainlen_type', i=i+1; tlen_type = varargin{i}; 
     case 'tracking', i=i+1; tracking = varargin{i};
     case 'sample_order', i=i+1; sample_order_type = varargin{i};
     case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i};
     case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i};
     case 'radius', 
      i=i+1; 
      l = length(varargin{i}); 
      if l==1, 
        sTrain.radius_ini = varargin{i}; 
      else 
        sTrain.radius_ini = varargin{i}(1); 
        sTrain.radius_fin = varargin{i}(end);
        if l>2, radius = varargin{i}; tlen_type = 'samples'; end
      end 
     case 'alpha_type', i=i+1; sTrain.alpha_type = varargin{i};
     case 'alpha_ini', i=i+1; sTrain.alpha_ini = varargin{i};
     case 'alpha',     
      i=i+1; 
      sTrain.alpha_ini = varargin{i}(1);
      if length(varargin{i})>1, 
        alpha = varargin{i}; tlen_type = 'samples'; 
        sTrain.alpha_type = 'user defined'; 
      end
     case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i};
     case {'topol','sTopol','som_topol'}, 
      i=i+1; 
      sTopol = varargin{i};
      if prod(sTopol.msize) ~= munits, 
        error('Given map grid size does not match the codebook size.');
      end
      % unambiguous values
     case {'inv','linear','power'}, sTrain.alpha_type = varargin{i}; 
     case {'hexa','rect'}, sTopol.lattice = varargin{i};
     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; 
     case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i};
     case {'epochs','samples'}, tlen_type = varargin{i};
     case {'random', 'ordered'}, sample_order_type = varargin{i}; 
     otherwise argok=0; 
    end
  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), 
    switch varargin{i}(1).type, 
     case 'som_topol', 
      sTopol = varargin{i}; 
      if prod(sTopol.msize) ~= munits, 
        error('Given map grid size does not match the codebook size.');
      end
     case 'som_train', sTrain = varargin{i};
     otherwise argok=0; 
    end
  else
    argok = 0; 
  end
  if ~argok, 
    disp(['(som_seqtrain) Ignoring invalid argument #' num2str(i+2)]); 
  end
  i = i+1; 
end

% training length
if ~isempty(radius) | ~isempty(alpha), 
  lr = length(radius);
  la = length(alpha);
  if lr>2 | la>1,
    tlen_type = 'samples';
    if     lr> 2 & la<=1, sTrain.trainlen = lr;
    elseif lr<=2 & la> 1, sTrain.trainlen = la;
    elseif lr==la,        sTrain.trainlen = la;
    else
      error('Mismatch between radius and learning rate vector lengths.')
    end
  end
end
if strcmp(tlen_type,'samples'), sTrain.trainlen = sTrain.trainlen/dlen; end 

% check topology
if struct_mode, 
  if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ...
	~strcmp(sTopol.shape,sMap.topol.shape) | ...
	any(sTopol.msize ~= sMap.topol.msize), 
    warning('Changing the original map topology.');
  end
end
sMap.topol = sTopol; 

% complement the training struct
sTrain = som_train_struct(sTrain,sMap,'dlen',dlen);
if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% initialize

M        = sMap.codebook;
mask     = sTrain.mask;
trainlen = sTrain.trainlen*dlen;

% neighborhood radius
if length(radius)>2,
  radius_type = 'user defined';
else
  radius = [sTrain.radius_ini sTrain.radius_fin];    
  rini = radius(1); 
  rstep = (radius(end)-radius(1))/(trainlen-1);
  radius_type = 'linear';
end    

% learning rate
if length(alpha)>1, 
  sTrain.alpha_type ='user defined';
  if length(alpha) ~= trainlen, 
    error('Trainlen and length of neighborhood radius vector do not match.')
  end
  if any(isnan(alpha)), 
    error('NaN is an illegal learning rate.')
  end
else
  if isempty(alpha), alpha = sTrain.alpha_ini; end
  if strcmp(sTrain.alpha_type,'inv'), 
    % alpha(t) = a / (t+b), where a and b are chosen suitably
    % below, they are chosen so that alpha_fin = alpha_ini/100
    b = (trainlen - 1) / (100 - 1);
    a = b * alpha;
  end
end
                                   
% initialize random number generator
rand('state',sum(100*clock));

% distance between map units in the output space
%  Since in the case of gaussian and ep neighborhood functions, the 
%  equations utilize squares of the unit distances and in bubble case
%  it doesn't matter which is used, the unitdistances and neighborhood
%  radiuses are squared.
Ud = som_unit_dists(sTopol).^2;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Action

update_step = 100; 
mu_x_1 = ones(munits,1);
samples = ones(update_step,1);
r = samples; 
alfa = samples;

qe = 0;
start = clock;
if tracking >  0, % initialize tracking
  track_table = zeros(update_step,1);
  qe = zeros(floor(trainlen/update_step),1);  
end

for t = 1:trainlen, 

  % Every update_step, new values for sample indeces, neighborhood
  % radius and learning rate are calculated. This could be done
  % every step, but this way it is more efficient. Or this could 
  % be done all at once outside the loop, but it would require much
  % more memory.
  ind = rem(t,update_step); if ind==0, ind = update_step; end
  if ind==1, 
    steps = [t:min(trainlen,t+update_step-1)];
    % sample order    
    switch sample_order_type, 
     case 'ordered', samples = rem(steps,dlen)+1;
     case 'random',  samples = ceil(dlen*rand(update_step,1)+eps);
    end

    % neighborhood radius
    switch radius_type, 
     case 'linear',       r = rini+(steps-1)*rstep;
     case 'user defined', r = radius(steps); 
    end    
    r=r.^2;        % squared radius (see notes about Ud above)
    r(r==0) = eps; % zero radius might cause div-by-zero error
    
    % learning rate
    switch sTrain.alpha_type,
     case 'linear',       alfa = (1-steps/trainlen)*alpha;
     case 'inv',          alfa = a ./ (b + steps-1);
     case 'power',        alfa = alpha * (0.005/alpha).^((steps-1)/trainlen); 
     case 'user defined', alfa = alpha(steps);
    end    
  end
  
  % find BMU
  x = D(samples(ind),:);                 % pick one sample vector
  known = ~isnan(x);                     % its known components
  Dx = M(:,known) - x(mu_x_1,known);     % each map unit minus the vector
  [qerr bmu] = min((Dx.^2)*mask(known)); % minimum distance(^2) and the BMU

  % tracking
  if tracking>0, 
    track_table(ind) = sqrt(qerr);
    if ind==update_step, 
      n = ceil(t/update_step); 
      qe(n) = mean(track_table);
      trackplot(M,D,tracking,start,n,qe);
    end
  end
  
  % neighborhood & learning rate
  % notice that the elements Ud and radius have been squared!
  % (see notes about Ud above)
  switch sTrain.neigh, 
  case 'bubble',   h = (Ud(:,bmu)<=r(ind));
  case 'gaussian', h = exp(-Ud(:,bmu)/(2*r(ind))); 
  case 'cutgauss', h = exp(-Ud(:,bmu)/(2*r(ind))) .* (Ud(:,bmu)<=r(ind));
  case 'ep',       h = (1-Ud(:,bmu)/r(ind)) .* (Ud(:,bmu)<=r(ind));
  end  
  h = h*alfa(ind);  
  
  % update M
  M(:,known) = M(:,known) - h(:,ones(sum(known),1)).*Dx;

end; % for t = 1:trainlen

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Build / clean up the return arguments

if tracking, fprintf(1,'\n'); end

% update structures
sTrain = som_set(sTrain,'time',datestr(now,0));
if struct_mode, 
  sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh);
  tl = length(sMap.trainhist);
  sMap.trainhist(tl+1) = sTrain;
else
  sMap = reshape(M,orig_size);
end

return;



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions

%%%%%%%%
function [] = trackplot(M,D,tracking,start,n,qe)

  l = length(qe);
  elap_t = etime(clock,start); 
  tot_t = elap_t*l/n;
  fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)  
  switch tracking
   case 1, 
   case 2,       
    plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
    title('Quantization errors for latest samples')    
    drawnow
   otherwise,
    subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l))
    title('Quantization error for latest samples');
    subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b.'); 
    title('First two components of map units (o) and data vectors (+)');
    drawnow
  end  
  % end of trackplot