%FEATEVAL Evaluation of feature set for classification% % 	J = FEATEVAL(A,CRIT,T)% 	J = FEATEVAL(A,CRIT,N)% % INPUT%       A      input dataset%       CRIT   string name of a method or untrained mapping%       T      validation dataset (optional)%       N      number of cross-validations (optional)%% OUTPUT%       J      scalar criterion value%% DESCRIPTION%  Evaluation of features by the criterion CRIT for classification,%  using objects in the dataset A. The larger J, the better. Resulting%  J-values are incomparable over the various methods.%  The following methods are supported:%  %   crit='in-in' : inter-intra distance.%   crit='maha-s': sum of estimated Mahalanobis distances.%   crit='maha-m': minimum of estimated Mahalanobis distances.%   crit='eucl-s': sum of squared Euclidean distances.%   crit='eucl-m': minimum of squared Euclidean distances.%   crit='NN'    : 1-Nearest Neighbour leave-one-out%                  classification performance (default).%                  (performance = 1 - error). %  %  CRIT can also be any untrained classifier, e.g. LDC([],1e-6,1e-6). %  The classification error is used for a performance estimate. If %  supplied, the dataset T is used for obtaining an unbiased estimate %  of the performance of classifiers trained with the dataset A. %  If a number of cross-validations N is supplied, the routine is%  run for N times with different training and test sets generated%  from A by cross-validation. Results are averaged. If T nor N are %  given, the apparent performance on A is used. % % SEE ALSO% DATASETS, FEATSELO, FEATSELB, FEATSELF, FEATSELP, FEATSELM, FEATRANK% 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 Netherlands% REVISIONS% DXD1: David Tax, 08-05-2003%       I added the inter/intra distance criterion.% $Id: feateval.m,v 1.8 2005/12/15 20:43:25 duin Exp $function J = feateval(a,crit,t)	prtrace(mfilename);		[ma,k,c] = getsize(a);	if nargin < 2		crit = 'NN';	end	if nargin < 3		t =[]; 		prwarning(4,'Where needed, input dataset is used for validation')	end	if is_scalar(t) & ~isdataset(t) % cross-validation desired, t rotations		K = crossval(a,nmc,t,0); % trick to get rotation set from crossval		J = 0;		JALL = [1:size(a,1)];		for j=1:t			JIN = JALL;			JOUT = find(K==j);			JIN(JOUT) = [];			JOUT = JALL(JOUT);			train = a(JIN,:);			test  = a(JOUT,:);			J = J + feval(mfilename,train,crit,test);		end		J = J/t;		return	end		%	islabtype(a,'crisp');	isvaldset(a,1,2); % at least 1 object per class, 2 classes	iscomdset(a,t);	if isstr(crit)		%DXD1		if strcmp(crit,'in-in')     % inter/intra distances			islabtype(a,'crisp','soft');			if isempty(t)				[U,G] = meancov(a);			else				[U,G] = meancov(t);			end			S_b = cov(+U); % between scatter			prior = getprior(a);			S_w = reshape(sum(reshape(G,k*k,c)*prior',2),k,k); % within scatter			J = trace(inv(S_w)*S_b);		elseif strcmp(crit,'maha-s') | strcmp(crit,'maha-m') % Mahalanobis distances			islabtype(a,'crisp','soft');			if isempty(t)				D = distmaha(a);			else				[U,G] = meancov(a);				D = distmaha(t,U,G);				D = meancov(D);			end			if strcmp(crit,'maha-m')				D = D + realmax*eye(c);				J = min(min(D));			else				J = sum(sum(D))/2; 			end		elseif strcmp(crit,'eucl-s') | strcmp(crit,'eucl-m') % Euclidean distances			islabtype(a,'crisp','soft');			U = meancov(a);			if isempty(t)				D = distm(U);			else				D = distm(t,U);				D = meancov(D);			end			if strcmp(crit,'eucl-m')				D = D + realmax*eye(c);				J = min(min(D));			else				J = sum(sum(D))/2; 			end		elseif strcmp(crit,'NN')	% 1-NN performance			islabtype(a,'crisp','soft');			if isempty(t)				J = 1 - testk(a,1);			else				J = 1 - testk(a,1,t);			end		else			error('Criterion undefined');		end	else		ismapping(crit);		isuntrained(crit);		if isempty(t)			J = 1 - (a * (a * crit) * testc);		else			J = 1 - (t * (a * crit) * testc);		end	endreturn