function sMap = som_make(D, varargin)%SOM_MAKE Create, initialize and train Self-Organizing Map.%% sMap = som_make(D, [[argID,] value, ...])%%  sMap = som_make(D);%  sMap = som_make(D, 'munits', 20);%  sMap = som_make(D, 'munits', 20, 'hexa', 'sheet');%  sMap = som_make(D, 'msize', [4 6 7], 'lattice', 'rect');%%  Input and output arguments ([]'s are optional): %   D        (matrix) training data, size dlen x dim%            (struct) data struct%   [argID,  (string) See below. The values which are unambiguous can %    value]  (varies) be given without the preceeding argID.%%   sMap     (struct) map struct%% Here are the valid argument IDs and corresponding values. The values % which are unambiguous (marked with '*') can be given without the% preceeding argID.%   'init'       *(string) initialization: 'randinit' or 'lininit' (default)%   'algorithm'  *(string) training: 'seq' or 'batch' (default) or 'sompak'%   'munits'      (scalar) the preferred number of map units%   'msize'       (vector) map grid size%   'mapsize'    *(string) do you want a 'small', 'normal' or 'big' map%                          Any explicit settings of munits or msize override this.%   'lattice'    *(string) map lattice, 'hexa' or 'rect'%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',%                          'ep' or 'bubble'%   'topol'      *(struct) topology struct%   'som_topol','sTopol' = 'topol'%   'mask'        (vector) BMU search mask, size dim x 1%   'name'        (string) map name%   'comp_names'  (string array / cellstr) component names, size dim x 1%   'tracking'    (scalar) how much to report, default = 1%   'training'    (string) 'short', 'default', 'long'%                 (vector) size 1 x 2, first length of rough training in epochs, %                          and then length of finetuning in epochs%% For more help, try 'type som_make' or check out online documentation.% See also SOM_MAP_STRUCT, SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT,%          SOM_RANDINIT, SOM_LININIT, SOM_SEQTRAIN, SOM_BATCHTRAIN.          %%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% som_make%% PURPOSE%% Creates, initializes and trains a SOM using default parameters.%% SYNTAX%%  sMap = som_make(D);%  sMap = som_make(...,'argID',value,...);%  sMap = som_make(...,value,...);%% DESCRIPTION%% Creates, initializes and trains a SOM with default parameters. Uses functions% SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT, SOM_DATA_STRUCT and SOM_MAP_STRUCT to come% up with the default values.%% First, the number of map units is determined. Unless they are% explicitly defined, function SOM_TOPOL_STRUCT is used to determine this.% It uses a heuristic formula of 'munits = 5*dlen^0.54321'. The 'mapsize'% argument influences the final number of map units: a 'big' map has % x4 the default number of map units and a 'small' map has x0.25 the% default number of map units. %% After the number of map units has been determined, the map size is % determined. Basically, the two biggest eigenvalues of the training% data are calculated and the ratio between sidelengths of the map grid% is set to this ratio. The actual sidelengths are then set so that % their product is as close to the desired number of map units as% possible.%% Then the SOM is initialized. First, linear initialization along two% greatest eigenvectors is tried, but if this can't be done (the% eigenvectors cannot be calculated), random initialization is used% instead.  After initialization, the SOM is trained in two phases:% first rough training and then fine-tuning. If the 'tracking'% argument is greater than zero, the average quantization error and% topographic error of the final map are calculated.%% REQUIRED INPUT ARGUMENTS%%  D           The data to use in the training.%     (struct) A data struct. If a struct is given, '.comp_names' field as %              well as '.comp_norm' field is copied to the map struct.%     (matrix) A data matrix, size dlen x dim. The data matrix may%              contain unknown values, indicated by NaNs. %  % OPTIONAL INPUT ARGUMENTS %%  argID (string) Argument identifier string (see below).%  value (varies) Value for the argument (see below).%% Here are the valid argument IDs and corresponding values. The values % which are unambiguous (marked with '*') can be given without the% preceeding argID.%   'init'       *(string) initialization: 'randinit' or 'lininit' (default)%   'algorithm'  *(string) training: 'seq' or 'batch' (default) or 'sompak'%   'munits'      (scalar) the preferred number of map units%   'msize'       (vector) map grid size%   'mapsize'    *(string) do you want a 'small', 'normal' or 'big' map%                          Any explicit settings of munits or msize override this.%   'lattice'    *(string) map lattice, 'hexa' or 'rect'%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',%                          'ep' or 'bubble'%   'topol'      *(struct) topology struct%   'som_topol','sTopol' = 'topol'%   'mask'        (vector) BMU search mask, size dim x 1%   'name'        (string) map name%   'comp_names'  (string array / cellstr) component names, size dim x 1%   'tracking'    (scalar) how much to report, default = 1%   'training'    (string) 'short', 'default' or 'long'%                 (vector) size 1 x 2, first length of rough training in epochs, %                          and then length of finetuning in epochs%% OUTPUT ARGUMENTS% %  sMap (struct) the trained map struct%% EXAMPLES%%  To simply train a map with default parameters: %%   sMap = som_make(D); %  %  With the optional arguments, the initialization and training can be%  influenced. To change map size, use 'msize', 'munits' or 'mapsize'%  arguments:  %%   sMap = som_make(D,'mapsize','big'); or sMap=som_make(D,'big');%   sMap = som_make(D,'munits', 100);%   sMap = som_make(D,'msize', [20 10]); %%  Argument 'algorithm' can be used to switch between 'seq' and 'batch'%  algorithms. 'batch' is the default, so to use 'seq' algorithm: %%   sMap = som_make(D,'algorithm','seq'); or sMap = som_make(D,'seq'); %%  The 'tracking' argument can be used to control the amout of reporting%  during training. The argument is used in this function, and it is%  passed to the training functions. To make the function work silently%  set it to 0.%%   sMap = som_make(D,'tracking',0); %% SEE ALSO% %  som_map_struct   Create a map struct.%  som_topol_struct Default values for SOM topology.%  som_train_struct Default values for SOM training parameters.%  som_randinint    Random initialization algorithm.%  som_lininit      Linear initialization algorithm.%  som_seqtrain     Sequential training algorithm.%  som_batchtrain   Batch training algorithm.% Copyright (c) 1999-2000 by the SOM toolbox programming team.% http://www.cis.hut.fi/projects/somtoolbox/% Version 2.0beta juuso 111199%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% check arguments% Dif isstruct(D)   data_name = D.name;   comp_names = D.comp_names;  comp_norm = D.comp_norm;  D = D.data;else   data_name = inputname(1);  sDummy = som_data_struct(D(1,:));   comp_names = sDummy.comp_names;  comp_norm = sDummy.comp_norm;end[dlen dim] = size(D);% defaultsmapsize = '';sM = som_map_struct(dim); sTopol = sM.topol;munits = prod(sTopol.msize); % should be zeromask = sM.mask; name = sM.name; neigh = sM.neigh; tracking = 1;algorithm = 'batch'; initalg = 'lininit';training = 'default'; % varargini=1; while i<=length(varargin),   argok = 1;   if ischar(varargin{i}),     switch varargin{i},       % argument IDs     case 'mask',       i=i+1; mask = varargin{i};      case 'munits',     i=i+1; munits = varargin{i};      case 'msize',      i=i+1; sTopol.msize = varargin{i};                         munits = prod(sTopol.msize);      case 'mapsize',    i=i+1; mapsize = varargin{i};      case 'name',       i=i+1; name = varargin{i};     case 'comp_names', i=i+1; comp_names = varargin{i};      case 'lattice',    i=i+1; sTopol.lattice = varargin{i};     case 'shape',      i=i+1; sTopol.shape = varargin{i};      case {'topol','som_topol','sTopol'},                         i=i+1; sTopol = varargin{i}; munits = prod(sTopol.msize);      case 'neigh',      i=i+1; neigh = varargin{i};     case 'tracking',   i=i+1; tracking = varargin{i};     case 'algorithm',  i=i+1; algorithm = varargin{i};      case 'init',       i=i+1; initalg = varargin{i};     case 'training',   i=i+1; training = varargin{i};       % unambiguous values     case {'hexa','rect'}, sTopol.lattice = varargin{i};     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};      case {'gaussian','cutgauss','ep','bubble'}, neigh = varargin{i};     case {'seq','batch','sompak'}, algorithm = varargin{i};      case {'small','normal','big'}, mapsize = varargin{i};      case {'randinit','lininit'}, initalg = varargin{i};     case {'short','default','long'}, training = 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};      otherwise argok=0;     end  else    argok = 0;   end  if ~argok,     disp(['(som_make) Ignoring invalid argument #' num2str(i+1)]);   end  i = i+1; end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% make the map struct%% map sizeif isempty(sTopol.msize) | ~prod(sTopol.msize),   if tracking>0, fprintf(1,'Determining map size...\n'); end  if ~munits,         sTemp = som_topol_struct('dlen',dlen);    munits = prod(sTemp.msize);    switch mapsize,     case 'small', munits = max(9,ceil(munits/4));     case 'big',   munits = munits*4;     otherwise % nil    end  end  sTemp = som_topol_struct('data',D,'munits',munits);  sTopol.msize = sTemp.msize;  if tracking>0,     fprintf(1,' map size [%d, %d]\n',sTopol.msize(1), sTopol.msize(2));     endend% map structsMap = som_map_struct(dim,sTopol,neigh,'mask',mask,'name',name, ...                      'comp_names', comp_names, 'comp_norm', comp_norm);        % functionif strcmp(algorithm,'sompak'),   algorithm = 'seq';  func = 'sompak';else  func = algorithm;end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% initializationif tracking>0, fprintf(1,'Initialization...\n'); endswitch initalg,  case 'randinit', sMap = som_randinit(D, sMap); case 'lininit', sMap = som_lininit(D, sMap); endsMap.trainhist(1) = som_set(sMap.trainhist(1),'data_name',data_name);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% trainingif tracking>0, fprintf(1,'Training using %s algorithm...\n',algorithm); end% rough trainif tracking>0, fprintf(1,'Rough training phase...\n'); endsTrain = som_train_struct(sMap,'dlen',dlen,'algorithm',algorithm,'phase','rough');sTrain = som_set(sTrain,'data_name',data_name);if isnumeric(training), sTrain.trainlen = training(1); else  switch training,    case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4);   case 'long',  sTrain.trainlen = sTrain.trainlen*4;  endendswitch func, case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); case 'batch',  sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);end% finetuneif tracking>0, fprintf(1,'Finetuning phase...\n'); endsTrain = som_train_struct(sMap,'dlen',dlen,'phase','finetune');sTrain = som_set(sTrain,'data_name',data_name,'algorithm',algorithm);if isnumeric(training), sTrain.trainlen = training(2); else  switch training,    case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4);   case 'long',  sTrain.trainlen = sTrain.trainlen*4;  endendswitch func, case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); case 'batch',  sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);end% qualityif tracking>0,   [mqe,tge] = som_quality(sMap,D);  fprintf(1,'Final quantization error: %5.3f\n',mqe)  fprintf(1,'Final topographic error:  %5.3f\n',tge)end  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%