function [a,I] = oc_set(a,clnr)% OC_SET  makes an one-class dataset%%     A = OC_SET(A,CLNR)%% Change a normal dataset A into an one-class dataset: the class% indicated by the classnr (CLNR) is made target class and all other% data becomes outliers class. The labels are changed to 'target' and% 'outlier'.%% It is also possible to use a class label:%%    A = OC_SET(A,LABEL)%% but then the type of the label should be a char (else you cannot% distinguish it from a class number, can you?:-))%%    [A,I] = OC_SET(A,LABEL)%% As a second output argument an index vector I is returned, indicating% which objects are target (I=1) or outlier (I=2).%%% See also: target_class, find_target, gendatoc, isocset% 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 Netherlandsif (nargin<2)	clnr = 1;end% I have to check Prtools somewhere...:checkprversion;% First check if there is more than one label indicating the target% class, then we will recursively call oc_set.if ~ischar(clnr) & size(clnr,2)>1,	clnr = clnr';endif size(clnr,1)>1 % we have more than 1 label	I = [];	% go along each class and extract the indices of the objects	% belonging	for i=1:size(clnr,1)		[tmpx,tmpI] = oc_set(a,clnr(i,:));		I = [I; find(tmpI==1)];	end	nlab = repmat(2,size(a,1),1);	nlab(I) = 1;	%a = dataset(a,nlab,'lablist',['target ';'outlier']);	a = dataset(a,nlab);	a = setlablist(a);	a = set(a,'lablist',['target ';'outlier']);	% and fix the new dataset name:	clname = sprintf('%d classes as target',size(clnr,1));	a = setname(a,[getname(a),' (',clname,')']);	%a = setprior(a,[]);	returnend% If a is just a Matlab array, everything becomes target data:if ~isa(a,'dataset')	m = size(a,1);	a = dataset(a,ones(m,1),'lablist','target');	a = setprior(a,1);	I = ones(m,1);	return;else	% first unpack the clnr, if it was just a single entry in a	% cell-array:	if isa(clnr,'cell')		clnr = clnr{1};	end	if is_ocset(a)				% If we are given an one-class dataset, the only possibility is		% that we have to switch the target-outlier labels.		if (isa(clnr,'char') & strcmp(clnr,'outlier')) | ...			(isa(clnr,'double') & clnr<0)		% That is the case, we swap the classes. We just swap		% the labels by changing nlab so we can later search for 'target'		% instead of 'outlier':			% Now we check if we are not dealing with strange situations			% that all data is of one of the classes:			if getsize(a,3)==2				% Ok, swap the labels				a = set(a,'nlab',3-getnlab(a));			else				% we only have one class				if strcmp(getlablist(a),'outlier')					% and this class is indeed outlier					a = setlablist(a,'target');				else					% we only have target data in the dataset, but we are					% requesting outliers: problem! Return an empty dataset:					a = setlablist(a,'outlier');				end			end		end		%else we have already a correct oneclass dataset		I = repmat(2,size(a,1),1);		I(find_target(a)) = 1;		return;	end	% Now we have to work, detect the label in the dataset:	[nlab,lablist] = getnlab(a);	if isempty(lablist)		warning('dd_tools:UnlabeledDataset',...			'This dataset is unlabeled: all data is considered target.');		[a,I] = oc_set(+a);		return	end		[m,k,c] = getsize(a,1);	% This is a hack:	% If the lablist contains characters, but all character values are	% smaller than 32 (smaller ASCI value), than something went probabily	% wrong, and then we assume it were just doubles. This happens for	% instance in the NIST dataset:-(	if isa(lablist,'char')		if length(find(lablist<32))==size(lablist,1)			a = set(a,'lablist',double(lablist));		end	end	% Depending on the type of label, we have to do other things:	if isa(clnr,'char')		% We have to string-match with the lablist		if isa(lablist,'double')			% If the lablist is a number, we'd better change the clnr also			% to a number			clnr = str2num(clnr);			if ~isempty(clnr)				clnr = find(lablist==clnr);			%else the clnr was a label-string and not a number, so it will			%not match anyway			end		else % Lablist is character			% Ok, we get a bit inconsistent behaviour here. It might be			% that when the dataset contains just target objects, the label			% becomes 'target' without trailing ' '. Then the strmatch has			% to be switched. When also outliers are present, there will be			% always this trailing ' ', so then we don't have a problem.			% (Thanks Piotr!:-)			if (c==1)				clnr = strmatch(lablist,clnr);			else				clnr = strmatch(clnr,lablist);			end		end	end	% Make the new labels, target objects get 1, outlier objects get 2:	if isempty(clnr)		% everything becomes outlier		I = repmat(2,m,1);	else		% Otherwise apply the hard fought for clnr:		I = 2-(nlab==clnr);	end		% Now construct the dataset:	labelnames = str2mat('target', 'outlier');	a = set(a,'labels',labelnames(I,:));	% and fix the new dataset name:	if (clnr<=size(lablist,1))		clname = lablist(clnr,:);	else		clname = clnr;	end	if isa(clname,'double')		clname = num2str(clname);	end	if ~isempty(clname)		a = setname(a,[getname(a),' (targetcl. ',deblank(clname),')']);	end	a = setprior(a,getprior(a,0));% hmmm, is this the right thing to do??	% Maybe we should give some message?	if isempty(clname)		fprintf('No target class found.\n');	else		%fprintf('Class %s is used as target class.\n',deblank(clname));	end	endreturn