%FEATSELI Individual feature selection for classification% % [W,R] = FEATSELI(A,CRIT,K,T)% % INPUT	%   A    Training dataset%   CRIT Name of the criterion or untrained mapping%        (default: 'NN', i.e. the 1-Nearest Neighbor error)%   K    Number of features to select (default: sort all features)%   T    Tuning dataset (optional)%% OUTPUT%   W    Feature selection mapping%   R    Matrix with criterion values%% DESCRIPTION% Individual selection of K features using the dataset A. CRIT sets the% criterion used by the feature evaluation routine FEATEVAL. If the dataset% T is given, it is used as test set for FEATEVAL. For K = 0 all features are% selected, but reordered according to the criterion. The result W can be% used for selecting features using B*W. % The selected features are stored in W.DATA and can be found by +W.% In R, the search is reported step by step as:% % 	R(:,1) : number of features% 	R(:,2) : criterion value% 	R(:,3) : added / deleted feature% % SEE ALSO% MAPPINGS, DATASETS, FEATEVAL, FEATSELO, FEATSELB, FEATSELF,% FEATSEL,  FEATSELP, FEATSELM% 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: featseli.m,v 1.4 2008/07/03 09:08:43 duin Exp $function [w,r] = featseli(a,crit,ksel,t)	prtrace(mfilename);	if (nargin < 2) | isempty(crit)    prwarning(2,'no criterion specified, assuming NN');		crit = 'NN'; 	end	if (nargin < 3 | isempty(ksel))		ksel = 0; 	end	if (nargin < 4)    prwarning(3,'no tuning set supplied (risk of overfit)');		t = [];     	end  % If no arguments are supplied, return an untrained mapping.	if (nargin == 0) | (isempty(a))		w = mapping('featseli',{crit,ksel,t});		w = setname(w,'Individual FeatSel');		return	end	[m,k,c] = getsize(a); featlist = getfeatlab(a);	% If KSEL is not given, return all features.	if (ksel == 0), ksel = k; end		isvaldfile(a,1,2); % at least 1 object per class, 2 classes	a = testdatasize(a);	iscomdset(a,t);		critval = zeros(k,1);		% Evaluate each feature in turn.	s = sprintf('Evaluation of %i features: ',k);	prwaitbar(k,s);	if (isempty(t))		for j = 1:k			prwaitbar(k,j,[s int2str(j)]);			critval(j) = feateval(a(:,j),crit);		end	else		for j = 1:k			prwaitbar(k,j,[s int2str(j)]);			critval(j) = feateval(a(:,j),crit,t(:,j));		end	end	prwaitbar(0);	% Sort the features by criterion value (maximum first).	[critval_sorted,J] = sort(-critval);	r = [[1:k]', -critval_sorted, J];	J = J(1:ksel)'; 	% Return the mapping found.	w = featsel(k,J);	w = setlabels(w,featlist(J,:));	w = setname(w,'Individual FeatSel');return