function net = ecoctrain(net, learner1, X, Y, varargin)% ECOCTRAIN - Train multi class problem with ECOC% %   NET = ECOCTRAIN(NET, LEARNER, X, Y)%   For an error correcting output code wrapper NET, the bit learners%   are trained given training data X (one example per row) with class%   labels Y. Y(I) is the class label for X(I,:), Y(I) is a number in the%   range 1...NET.NCLASSES%   If LEARNER has size [1 1], it is assumed to be an initialised%   template learning algorithm, for example SVM or MLP. This template%   with its parameter setting is used to train all NET.NBITS bit%   learners. If LEARNER is a cell array of length NET.NBITS, LEARNER{I}%   is used as the template for bit learner I.%   LEARNER is assumed to be standard Netlab structure. The name of the%   training procedure is [LEARNER{I}.TYPE 'FWD']. Any additional%   parameters ECOCTRAIN(NET, LEARNER, X, Y, VARARGIN) are passed on%   directly to  the training procedure.%   %   See also ECOC, ECOCLOAD, ECOCFWD.%% % Copyright (c) by Anton Schwaighofer (2001)% $Revision: 1.2 $ $Date: 2002/01/07 17:58:21 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% error(nargchk(4, Inf, nargin));error(consist(net, 'ecoc'));if all(size(learner1)==[1 1]) & isstruct(learner1),  [learner{1:net.nbits}] = deal(learner1);elseif length(learner1)==net.nbits & iscell(learner1),  learner = learner1;else  error(['Input parameter LEARNER must be a cell array of length NET.NBITS' ...         ' or a structure array']);endif any((Y<1) | (Y>net.nclasses))  error('Invalid class labels');endif any((net.code~=1) & (net.code~=-1) & (net.code~=0)),  error('Invalid code matrix. Entries must be +1, -1 or 0.');endif ~all(size(net.code)==[net.nclasses, net.nbits]),  error('Code matrix must be of size [number of classes, number of bits]');endfor i = 1:net.nbits,  if net.verbosity>0,     fprintf('Training for code bit %i (out of %i)\n', i, net.nbits);  end  % Code column i gives the targets for learner i  codei = net.code(:,i);  Yi = Y;  for j = 1:net.nclasses,    Yi(Y==j) = codei(j);  end  % Squash out examples of those classes that have a 0 entry in the  % current code bit  Yi0 = Yi(Yi~=0);  % Code trivial hypothesis directly  if all(Yi0==-1),    learner{i} = -1;  elseif all(Yi0==1),    learner{i} = 1;  else    % If necessary, convert the -1/+1 targets to 0/+1.    if net.use01targets,      Yi0 = (Yi0>0);    end    trainFunc = [learner{i}.type 'train'];    learner{i} = feval(trainFunc, learner{i}, X(Yi~=0,:), Yi0, varargin{:});  endendnet.learner = learner;