%EMC EM Classifier using semi-supervised data
%
%   W = EMC(A,B,CLASSF,LABTYPE,FID)
%   W = A*EMC([],B,CLASSF,LABTYPE,FID)
%
% INPUT
%   A       Labeled dataset used for training
%   B       Additional unlabeled dataset
%   CLASSF  Untrained classifier (default QDC)
%   LABTYPE Label type to be used (crisp (default) or soft)
%   FID     File ID to write progress to (default [], see PRPROGRESS)
%
% OUTPUT
%   W      Trained classifier
%
% DESCRIPTION
% Using the EM algorithm the classifier CLASSF is used iteratively
% on the joint dataset [A;B]. In each step the labels of A are reset
% to their initial values. Initial labels in B are neglected.
% Labels of LABTYPE 'soft' are not supported by all classifiers. 
%
% SEE ALSO
% DATASETS, MAPPINGS, EMCLUST, PRPROGRESS

% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands

function w = emc(a,b,classf,labtype)
	if nargin < 4 | isempty(labtype), labtype = 'crisp'; end
	if nargin < 3 | isempty(classf), classf = qdc; end	
	if nargin < 2, b = []; end
	if nargin < 1 | isempty(a)
		w = mapping(mfilename,'untrained',{b,classf,labtype,fid});
		w = setname(w,'EM CLassifier');
		return
	end

	islabtype(a,'crisp','soft');
	isvaldset(a,1,2); % at least 2 object per class, 2 classes
	if isempty(b)
		w = a*classf;
		return
	end
	if size(a,2) ~= size(b,2)
		error('Datasets should have same number of features')
  end
	
  %borg = setlabels(b,getnlab(b));
	c = getsize(a,3);
	epsilon = 1e-6;
	change = 1;
	nlab = getnlab(a);
	lablist = getlablist(a);
  p = getprior(a);
	a = setlabels(a,nlab); 
  a = setprior(a,p);
	a = setlabtype(a,labtype);
	switch labtype
		case 'crisp'
			lab = zeros(size(b,1),1);
		case 'soft'
			lab = zeros(size(b,1),c);
	end
	b = prdataset(+b);
	w = a*classf;
	
  starttime = clock;
  runtime = 0;
  iter = 0;
	while change > epsilon
    %disp(borg*w*testd)
    if runtime > prtime
      prwarning(2,['EM algorithme stopped by PRTIME after ' num2str(iter) ' iterations']);
      break
    end
		d = b*w;
		switch labtype
			case 'crisp'
				labb = d*labeld;
				change = mean(lab ~= labb); 
			case 'soft'
				labb = d*classc;
				change = mean(mean((+(labb-lab)).^2));
			otherwise
				error('Wrong LABTYPE given')
		end
		lab = labb;
		b = setlabtype(b,labtype,lab);
		c = [setlabtype(a,labtype); b];
		w = c*classf;
    runtime = etime(clock,starttime);
    iter = iter+1;
	end
	
	J = getlabels(w);
	w = setlabels(w,lablist(J,:));