%SUBSC Subspace Classifier%%	W = subsc(A,n)%% n-dimensional subspace maps are computed for each class of the dataset A% using PCA, such that they contain the origin. All object in A are normalized% first on unit length.%%	W = subsc(A,alf)%% Subspaces of different dimensionality are determined, each explaining at% least a fraction alf of the class variance.%% See datasets, mappings, fisherc, fisherm, klm, subsm% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlandsfunction W = subsc(a,n);if nargin < 2, n = 1; endif nargin < 1 | isempty(a) % handle untrained calls	W = mapping('subsc',n);	returnend[nlab,lablist,m,k,c,p] = dataset(a);if isa(n,'double') % training	W = {};	N = zeros(1,c);	for j = 1:c		J = find(nlab==j);		[w,nn] = subsm(+a(J,:),n);		W = [W,{w}];		N(j) = nn;	end	W = mapping('subsc',W,lablist,k,c,1);	W = cnormc(W,a);elseif isa(n,'mapping')			% testing	[W,classlist,type,k,c] = mapping(n);	b = zeros(size(a,1),c);	for j = 1:c		d = a*normm(2)*W{j};		b(:,j) = sqrt(sum(d.*d,2));	end	b = b ./ repmat(sum(b,2),1,c);	W = dataset(invsig(b),getlab(a),classlist,p,lablist);else	error('error')end