%MULTIC Make a multi-class classifier%%    W = MULTIC(A,V)%% Train the (untrained!) one-class classifier V on each of the classes% in A, and combine it to a multi-class classifier W. If an object is% rejected by all one-class classifiers, it will be classified% 'outlier'. If it is accepted by more than one one-class classifier, it% will be assigned to the class with the highest class posterior.%% Frustratingly, it can happen that the class labels are numbers. In% that case the outlier class label will be 0.%% Furthermore, in the current version the OCC's are only given the% target data, and do not use any example outlier data. This may change% in the future.%%    W = MULTIC(A,{V1 V2 ... VK})% % The trained one-class classifiers V1...VK are combined to a multiclass% classifier W.%%    W = MULTIC(A,W,EMPTYV)% %% See also dd_normc, myproxm% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands  function w = multic_dd(a,v,newv)if nargin<3	newv = [];endif nargin<2	v{1} = gauss_dd([],0.1);endif nargin<1 | isempty(a)	if ~isa(v,'cell')		tmp = v; v = {}; v{1} = tmp;	end	w = mapping(mfilename,{v});	% full name:	fname = ['Multiclass ',getname(v{1})];	w = setname(w,fname);	returnendif ~ismapping(v) | ~istrained(v)  % first training of the multic	[n,k,c] = getsize(a);	if ~isa(v,'cell') %check if we have a cell-array of mappings		%warning('Input v should be a (%dx1) cell array of mappings.',c);		tmp{1} = v;		v = tmp;	end	if length(v)~=c % check if the size of the cellarray is good enough.		if length(v)==1			v = repmat(v,c,1);		else			error('Input v should be a (%dx1) cell array of mappings.',c);		end	end	for i=1:c % check if we really have untrained mappings		if ~ismapping(v{i}) | istrained(v{i})			error('The supplied V''s should be untrained OC mappings.');		end	end	pr = getprior(a);	% train the individual OCCs	for i=1:c		v_tr{i} = target_class(a,i)*v{i};		if ~is_occ(v_tr{i})  % this test can only be applied to trained			               % mappings			error('Multic can only combine one-class classifiers.');		end		% find the average output on the training set and use that for the		% 'natural' scaling of the output		out = target_class(a,i)*v_tr{i};		out = (+out(:,'target')) - double(out(1,'outlier'));%		Iin = find(out>0); sc(i) = mean(out(Iin));		sc(i) = mean(out)/pr(i);		%sc(i) = 1;	end	% construct the lablist for the final mapping	labl = getlablist(a);	if ~isa(labl,'char')		warning('Labels are integers. Outlier class will be labeled 0.');		L = [labl; 0];	else		L = strvcat(labl,'outlier');	end	% store and pack	W.w = v_tr;	W.sc = sc;	w = mapping(mfilename,'trained',W,L,k,size(L,1));	fname = ['Multiclass ',getname(W.w{1})];	w = setname(w,fname);elseif ismapping(v) & istrained(v) & ~isempty(newv) % add an extra OCC	if isempty(newv)		error('An untrained OC mapping should be supplied for the new class.');	end	if ~ismapping(newv) | istrained(newv)		error('An untrained OC mapping should be supplied for the new class.');	end	% now we have to check the class label	if ~isdataset(a)		error('Please supply a dataset A with one class.');	end	c = getsize(a,3);	if length(c)>1		error('Only a single class should be supplied.');	end	% are we really updating a multic?	if ~strcmp(getmapping_file(v),mfilename)		error('Only a Multic classifier can be updated.');	end	% check if this class already exist, if not, add it, otherwise	% complain.	newlab = getlablist(a);	oldll = getlabels(v);	if ~strcmp(class(newlab),class(oldll))		error('The labels in the old and new datasets are not of the same type.');	end	%     (comparing labels is different for character and numeric	%     labels)	if isa(newlab,'double')		if any(oldll==newlab)			error('Label %d already exist.',newlab);		end		newll = [oldll(1:end-1); newlab; oldll(end)];	else		if ~isempty(strmatch(newlab,oldll))			error('Label %s already exist.',newlab);		end		newll = strvcat(oldll(1:end-1,:), newlab, oldll(end,:));	end	% Finally, train the new OCC and extend our multic:	W = v.data;	W.w{end+1} = target_class(+a)*newv;	if ~is_occ(W.w{end})  % this test can only be applied to trained			               % mappings		error('Multic can only add a one-class classifier.');	end	% find the average output on the training set and use that for the	% 'natural' scaling of the output	out = target_class(+a)*W.w{end};	out = (+out(:,'target')) - double(out(1,'outlier'));	Iin = find(out>0);	W.sc(end+1) = mean(+out(Iin));	% store the mapping and update labels/sizes:	w = setdata(v,W);	w = setlabels(w,newll);	w = setsize_out(w,getsize_out(w)+1);else	% evaluation!	W = getdata(v);	[m,k] = size(a);	% apply all mappings sequentially:	% (the first mapping is a special case)	out = a*W.w{1};	% fix the outputs such that all the threshold is always at 0:	out = +(out(:,'target')-out(:,'outlier'));	% and such that the output was always 1:	out = out/W.sc(1);	for i=2:length(W.w)		newout = a*W.w{i};		newout = +(newout(:,'target')-newout(:,'outlier'));		out = [out newout/W.sc(i)];	end	% add the 'outlier'-class output as the last column and return	w = setdat(a,[out zeros(m,1)],v);endreturn