som_sompaktrain.m

很全的som工具箱 四个demo可为初学者提够帮助

    M = reshape(sMap,[prod(msize) dim]);
  else
    msize = [orig_size(1) 1]; 
    dim = orig_size(2);
  end
  sMap   = som_map_struct(dim,'msize',msize);
  sTopol = sMap.topol;
end
[munits dim] = size(sMap.codebook);

% data
givendatafile = '';
if ischar(D), 
  data_name = D; 
  givendatafile = D;
  D = [];
  dlen = NaN;
else
  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 ddim ~= dim, error('Map and data dimensions must agree.'); end
end

% varargin
sTrain = som_set('som_train','algorithm','seq',...
			     'neigh',sMap.neigh,...
			     'mask',ones(dim,1),...
			     'data_name',data_name);
tlen_type  = 'epochs';
random_seed = 0; 
snapshotname = ''; 
snapshotinterval = 0;

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 'neigh',       i=i+1; sTrain.neigh = varargin{i};
     case 'trainlen',    i=i+1; sTrain.trainlen = varargin{i};
     case 'tlen_type',   i=i+1; tlen_type = varargin{i}; 
     case 'radius_ini',  i=i+1; sTrain.radius_ini = varargin{i};
     case 'radius',      i=i+1; sTrain.radius_ini = varargin{i}(1);
     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);
     case 'seed',        i=i+1; random_seed = varargin{i};
     case 'snapshotname',i=i+1; snapshotname = varargin{i};
     case 'snapshotinterval',i=i+1; snapshotinterval = varargin{i};
     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'}, sTrain.alpha_type = varargin{i}; 
     case {'hexa','rect'}, sTopol.lattice = varargin{i};
     case {'gaussian','bubble'}, sTrain.neigh = varargin{i};
     case {'epochs','samples'}, tlen_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_sompaktrain) Ignoring invalid argument #' num2str(i+2)]); 
  end
  i = i+1; 
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
if ~isnan(dlen), 
  sTrain = som_train_struct(sTrain,sMap,'dlen',dlen);
else
  sTrain = som_train_struct(sTrain,sMap); 
end
if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end

% training length
if strcmp(tlen_type,'epochs'), 
  if isnan(dlen),   
    error('Training length given as epochs, but data length is not known.\n');
  else
    rlen = sTrain.trainlen*dlen;
  end
else
  rlen = sTrain.trainlen;
  sTrain.trainlen = sTrain.trainlen/dlen;   
end 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% check arguments

% mask
if any(sTrain.mask~=1), 
  sTrain.mask = ones(dim,1); 
  fprintf(1,'Ignoring given mask.\n');
end

% learning rate
if strcmp(sTrain.alpha_type,'power'), 
  sTrain.alpha_type = 'inv';
  fprintf(1,'Using ''inv'' learning rate type instead of ''power''\n');
end
  
% neighborhood
if any(strcmp(sTrain.neigh,{'cutgauss','ep'})), 
  fprintf(1,'Using ''gaussian'' neighborhood function instead of %s.\n',sTrain.neigh);
  sTrain.neigh = 'gaussian'; 
end

% map shape
if ~strcmp(sMap.topol.shape,'sheet'), 
  fprintf(1,'Using ''sheet'' map shape of %s.\n',sMap.topol.shape);
  sMap.topol.shape = 'sheet'; 
end

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

% write files
if ~isempty(givendatafile), 
  temp_din = givendatafile; 
else 
  temp_din = tempname; 
  som_write_data(D, temp_din, 'x')
end
temp_cin  = tempname;
som_write_cod(sMap, temp_cin)
temp_cout = tempname;

% check if the environment variable 'SOM_PAKDIR' has been defined
if any(strcmp('SOM_PAKDIR', evalin('base', 'who')))
  som_pak_dir = evalin('base', 'SOM_PAKDIR');
else
  som_pak_dir = '';
end
if ~isempty(som_pak_dir) & ~strncmp(som_pak_dir(end), '/', 1)
  som_pak_dir(end + 1) = '/';
end

aini  = sTrain.alpha_ini; 
atype = sTrain.alpha_type;
if strcmp(atype,'inv'), atype = 'inverse_t'; end
rad   = sTrain.radius_ini;
str = [som_pak_dir 'vsom ' ...
       sprintf('-cin %s -din %s -cout %s', temp_cin, temp_din, temp_cout) ...
       sprintf(' -rlen %d -alpha %g -alpha_type %s', rlen, aini, atype) ...
       sprintf(' -radius %g -rand %g ',rad,random_seed)];
if ~isempty(snapshotname) & snapinterval>0, 
  str = [str, sprintf(' -snapfile %s -snapinterval %d',snapshotname,snapshotinterval)];
end

fprintf(1,'Execute: %s\n',str);
if isunix, 
  [status,w] = unix(str); 
  if status, fprintf(1,'Execution failed.\n'); end
  if ~isempty(w), fprintf(1,'%s\n',w); end
else 
  [status,w] = dos(str); 
  if status, fprintf(1,'Execution failed.\n'); end
  if ~isempty(w), fprintf(1,'%s\n',w); end
end

sMap_temp = som_read_cod(temp_cout);
M = sMap_temp.codebook;

if isunix
  unix(['/bin/rm -f ' temp_din ' ' temp_cin ' ' temp_cout]);
else
  dos(['del ' temp_din ' ' temp_cin ' ' temp_cout]);
end

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

% 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;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%