%SCALEM Compute scaling map% %   W = SCALEM(A,T)%% INPUT%   A  Dataset%   T  Type of s	caling (optional; default: the class priors weighted mean of A is shifted to the origin)%% OUTPUT%   W  Scaling mapping%% DESCRIPTION% Computes a scaling map W, whose type depends on the parameter T:%% [], 'c-mean'  - The mean of A is shifted to the origin. Class priors are taken %                 into account.% 'mean'        - The mean of A is shifted to the origin. This is computed for %                 the data as given in A, neglecting class priors.% 'c-variance'  - The mean of A  is shifted to the origin and the average class %                 variances (within-scatter) are normalized.  Class priors are %                 taken into account.% 'variance'    - The mean of A is shifted to the origin and the total variances %                 of all features are scaled to 1. This is computed for the%                 data as given in A, neglecting class priors.% '2-sigma'     - For each feature f, the 2-sigma interval, [f-2*std(f),f+2*std(f)] %                 is rescaled to [0,1]. Values outside this domain are clipped.%                 Class priors are taken into account.% 'domain'      - The domain for all features in the dataset A is set to [0,1].%                 This is computed for the data as given in A, neglecting class priors.%% The map W may be applied to a new dataset B by B*W.% % SEE ALSO% MAPPING, DATASET% 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% $Id: scalem.m,v 1.11 2005/10/29 19:16:43 duin Exp $function W = scalem(a,t)	prtrace(mfilename);	if nargin < 2		prwarning(4,'No mapping type specified. The class priors weighted mean of A is shifted to the origin.');		t = '';   elseif isempty(t)    t = '';  end	  % No data, return an untrained mapping.	if (nargin < 1) | isempty(a)		W = mapping('scalem',{t});		W = setname(W,'Scaling Mapping');		return;	end	[a,c,lablist,p] = cdats(a);	[m,k] = size(a);		switch t	case {'','c-mean'}    u = meancov(a);		u =p*(+u);		s = ones(1,k);		clip = 0;	case 'mean'		%u = meancov(+a);		u = mean(+a,1);		s = ones(1,k);		clip = 0;	case 'c-variance'		r = classsizes(a);		U = meancov(a);		u = p*(+U);		s = zeros(1,k);		for j=1:c			s = s + p(j)*var(+seldat(a,j)-repmat(+U(j,:),r(j),1));		end		s = sqrt(s);		clip = 0;	case 'variance'		%[u,G] = meancov(+a);		%s = sqrt(diag(G))';  	u = mean(+a,1);		s = std(+a,0,1);		clip = 0;	case '2-sigma'		U = meancov(a);		u = p*(+U);		s = zeros(1,k);		for j=1:c			s = s + p(j)*var(+seldat(a,j));		end		s = sqrt(s);		s = 4*s;		u = u-0.5*s;		clip = 1;	case 'domain'		mx = max(a,[],1)+eps;		mn = min(a,[],1)-eps;		u = mn;		s = (mx - mn)*(1+eps);		clip = 0;	otherwise		error('Unknown option')	end	J = find(s==0);	s(J) = ones(size(J)); 				% No scaling for zero variances.	W = cmapm({u,s,clip},'scale');		W.size_out = a.featsize;	W = setlabels(W,getfeatlab(a));	return;