%MAP Map a dataset, train a mapping or classifier, or combine mappings%%	B = MAP(A,W) or B = A*W%% Maps a dataset A by a fixed or trained mapping (or classifier) W, generating% a new dataset B. This is done object by object. So B has as many objects% (rows) as A. The number of features of B is determined by W. All dataset% fields of A are copied to B, except the feature labels. These are defined% by the labels stored in W.%%	V = MAP(A,W) or B = A*W%% If W is an untrained mapping (or classifier), it is trained by the dataset A.% The resulting trained mapping (or classifier) is stored in V.%%	V = MAP(W1,W2) or V = W1*W2%% The two mappings W1 and W2 are combined sequentially. See SEQUENTIAL for% a description. The resulting combination is stored in V.%% See also DATASETS, MAPPINGS, SEQUENTIAL% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: map.m,v 1.19 2006/10/23 16:44:10 davidt Exp $function [d, varargout] = map(a,b)		prtrace(mfilename);varargout = repmat({[]},[1, max((nargout-1),0)]);[ma,ka] = size(a);[mb,kb] = size(b);if iscell(a) | iscell(b)	if (iscell(a) & min([ma,ka]) ~= 1) | (iscell(b)& min([mb,kb]) ~= 1)		error('Only one-dimensional cell arrays are supported')	end	if iscell(a) & ~iscell(b)		d = cell(size(a));		for j=1:length(a(:))			d{j} = map(a{j},b);		end	elseif ~iscell(a) & iscell(b)		d = cell(size(b));		for j=1:length(b(:))			d{j} = map(a,b{j});		end	else		d = cell(length(a),length(b));		for i = 1:length(a)		for j = 1:length(b)			d{i,j} = map(a{i},b{j});		end		end	end	returnend		if all([ma,mb,ka,kb] ~= 0) & ka ~= mb	error('Output size of first argument should match input size of second')endif isa(a,'mapping') & isa(b,'mapping')		if istrained(a) & isaffine(a) & istrained(b) & isaffine(b)						% combine affine mappings		d = affine(a,b);%		d = b;%		data.rot = a.data.rot * b.data.rot;%		data.offset = a.data.offset*b.data.rot + b.data.offset;%		data.lablist_in = a.data.lablist_in;%		d = setdata(d,data);%		d = setsize_in(d,a.size_in);%		d = setscale(d,a.scale*b.scale);			else	  d = sequential(a,b);	end	elseif isa(a,'dataset') | isa(a,'double')			% don't know what to do with empty dataset 			% produce empty mapping, unity mapping ??			% for the moment we don't support it	if isempty(a)		error('Empty dataset * mapping is not supported'); 	end			% handle scalar * mapping by .* (see times.m)	if isa(a,'double') & ka == 1 & ma == 1		d = a.*b;		return; 	end	if ~isa(b,'mapping')		error('Second argument should be mapping or classifier')	end	mapp = getmapping_file(b);	if isuntrained(b)		pars = +b;		if iscombiner(feval(mapp)) % sequentiall, parallel and stacked need;      % special treatment as untrained combiners            % matlab 5 cannot handle the case [d, varargout{:}] = feval when varargout is empty      % because of this we have next piece of code			if isempty(varargout)         d = feval(mapp,a,b);         else            [d, varargout{:}] = feval(mapp,a,b);       end 		else			if isempty(varargout)        d = feval(mapp,a,pars{:});      else            [d, varargout{:}] = feval(mapp,a,pars{:});      end 		end		if ~isa(d,'mapping')			error('Training an untrained classifier should produce a mapping')		end		if getout_conv(b) > 1			d = d*classc;		end		name = getname(b);		if ~isempty(name)			d = setname(d,name);		end	elseif isfixed(b) | iscombiner(b)		pars = getdata(b); % parameters supplied in fixed mapping definition		if nargout > 0			if isempty(varargout)        d = feval(mapp,a,pars{:});      else            [d, varargout{:}] = feval(mapp,a,pars{:});      end 		else			feval(mapp,a,pars{:})			return		end	elseif istrained(b)		if ~isdataset(a)			a = dataset(a);		end		if isempty(varargout)      d = feval(mapp,a,b);    else          [d, varargout{:}] = feval(mapp,a,b);    end 		if ~isreal(+d)			prwarning(2,'Complex values appeared in dataset');		end		if isdataset(d)			d = setcost(d,b.cost);			% see if we have reasonable data in the dataset			end	else		error(['Unknown mapping type: ' getmapping_type(b)])	end    if isa(d,'dataset') 				% we assume that just a basic dataset is returned, 			% including setting of feature labels, but that scaling			% and outputconversion still have to be done.			% scaling		v = getscale(b);		if length(v) > 1, v = repmat(v(:)',ma,1); end		d = v.*d;			% outputconversion		switch 	getout_conv(b);		case 1  % SIGM output			if size(d,2) == 1				d = [d -d]; % obviously still single output discriminant				d = setfeatlab(d,d.featlab(1:2,:));			end             			d = sigm(d);		case 2  % NORMM output			if size(d,2) == 1				d = [d 1-d]; % obviously still single output discriminant				d = setfeatlab(d,d.featlab(1:2,:));			end             % needs conversion to two-classes before normm			d = normm(d);		case 3  % SIGM and NORMM output			if size(d,2) == 1				d = [d -d]; % obviously still single output discriminant				d = setfeatlab(d,d.featlab(1:2,:));			end             % needs conversion to two-classes before sigmoid			d = sigm(d);			d = normm(d);		end		%DXD finally, apply the cost matrix when it is defined in the		%mapping/dataset:		d = costm(d);	endelseif isa(a,'mapping') & isa(b,'dataset')	error('Datasets should be given as first argument')else	error('Data type not supported')end