%FEATSELP Pudil's floating feature selection (forward)% % [W,R] = FEATSELP(A,CRIT,K,T,FID)%% INPUT	% 	A    Training dataset%   CRIT Name of the criterion or untrained mapping %        	(default: 'NN', 1-Nearest Neighbor error)%   K    Number of features to select (default: K = 0, select optimal set)%   T    Tuning dataset (optional)%   N    Number of cross-validations (optional)%   FID  File ID to write progress to (default [], see PRPROGRESS)%% OUTPUT%   W    Feature selection mapping%   R    Matrix with step-by-step results% % DESCRIPTION% Forward floating 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. Alternatively% a number of cross-validations N may be supplied. For K = 0, the optimal% feature set (maximum value of FEATEVAL) is returned. The result W can% be used for selecting features in a dataset B using B*W.%% Note: this routine is highly time consuming.%% In R the search is reported step by step:% % 	R(:,1) : number of features% 	R(:,2) : criterion value% 	R(:,3) : added / deleted feature% % SEE ALSO% MAPPINGS, DATASETS, FEATEVAL, FEATSELO, FEATSELB, FEATSELI,% FEATSEL,  FEATSELF, FEATSELM, PRPROGRESS% 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: featselp.m,v 1.8 2004/05/16 14:48:08 duin Exp $function [w,r] = featselp(a,crit,ksel,t,fid)	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) | isempty(t)    prwarning(3,'no tuning set supplied (risk of overfit)');		t = [];	end	if (nargin < 5)		fid = [];	end  % If no arguments are supplied, return an untrained mapping.	if (nargin == 0) | (isempty(a))		w = mapping('featselp',{crit,ksel,t});		w = setname(w,'Floating FeatSel');		return	end	isvaldset(a,1,2); % at least 1 object per class, 2 classes	iscomdset(a,t);		[m,k,c] = getsize(a); featlist = getfeatlab(a);	% If KSEL is not given, return all features.	if (ksel == 0)		peak = 1; ksel = k; 	else 		peak = 0; 	end	if (~isempty(t))		if (k ~= size(t,2))      error('The sizes of the training and tuning dataset do not match.')		end	end	critval_opt = zeros(1,k);	% Maximum criterion value for sets of all sizes.  critval_max = 0;    			% Maximum criterion value found so far.  I = [1:k];      			    % Pool of remaining feature indices.  J = [];      			      	% Pool of selected feature indices.  r = [];           			  % Result matrix with selection history.	Iopt = J; 	n = 0;	prprogress(fid,'\nfeatselp: Pudils Floating Search\n')	while (n < k)		critval = zeros(1,length(I));		% Add the best feature.		for j = 1:length(I)			L = [J,I(j)];					% Add one feature to the already selected ones.			if (isempty(t))				% Evaluate the criterion function.				critval(j) = feateval(a(:,L),crit);			else				critval(j) = feateval(a(:,L),crit,t(:,L));			end      % If this feature is the best so far and we have not yet selected			% KSEL features, store it.			if (critval(j) > critval_max) & (n < ksel)				n_max = length(L);				critval_max = critval(j);				Iopt = L;			end		end    [mx,j] = max(critval);  % Find best feature of the remaining ones,    J = [J, I(j)];          %   add it to the set of selected features    I(j) = [];              %   and remove it from the pool.		% Store the best criterion value found for any set of n features.		n = n + 1; critval_opt(n) = mx;		r = [r; [n, mx, J(end)]];		prprogress(fid,'  %d %f',r(end,1:2));		prprogress(fid,' %i',J);		prprogress(fid,'\n')				% Now keep removing features until the criterion gets worse.		while (n > 2)			critval = zeros(1,n);			for j = 1:n				L = J; L(j) = [];		% Remove one feature from the selected ones.						if (isempty(t))			% Evaluate the criterion function.					critval(j) = feateval(a(:,L),crit);				else					critval(j) = feateval(a(:,L),crit,t(:,L));				end						% If removing this feature gives the best result so far (or				% the same result using less features), and we have not yet				% removed all KSEL features, store it.				if ((critval(j) > critval_max) | ((critval(j) == critval_max) & ...																					(length(L) < n_max))) & ...					 (n <= ksel + 1)					n_max = length(L);					critval_max = critval(j);					Iopt = L;				end			end			% If this subset is better than any found before, store and report it.			% Otherwise, stop removing features.			[mx,j] = max(critval);			if (mx > critval_opt(n-1))				n = n - 1; critval_opt(n) = mx;				I = [I,J(j)]; J(j) = [];				r = [r; [n, mx, -I(end)]];		    prprogress(fid,'  %d %f',r(end,1:2));				prprogress(fid,' %i',J);				prprogress(fid,'\n')			else				break;			end		end		% If we have found more than KSEL features, return the mapping using		% the best KSEL features.		if (n > ksel)			if (ksel < length(Iopt))				J = Iopt(1:ksel);			else				J = Iopt;			end			prprogress(fid,'featselp finished\n')			w = cmapm(k,J);			w = setlabels(w,featlist(J,:));			w = setname(w,'Floating FeatSel');			return		end	end	prprogress(fid,'featselp finished\n')		% Return all features, sorted by their criterion value.	w = featsel(k,Iopt);	w = setlabels(w,featlist(Iopt,:));	w = setname(w,'Floating FeatSel');return