%FEATSELF Forward feature selection for classification% % [W,R] = FEATSELF(A,CRIT,K,T,FID)% [W,R] = FEATSELF(A,CRIT,K,N,FID)%% 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: K = 0, return optimal set)%   T    Tuning dataset (optional)%   N    Number of cross-validations (optional)%   FID  File ID to write progress to (default [], see PRPROGRESS)%% OUTPUT%   W    Output feature selection mapping%   R    Matrix with step-by-step results%% DESCRIPTION% Forward 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. Alternatvely a% a number of cross-validation N may be supplied. For K = 0, the optimal % feature set (corresponding to the maximum value of FEATEVAL) is returned.  % 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, FEATSELLR, FEATSEL,% FEATSELO, FEATSELB, FEATSELI, FEATSELP, 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: featself.m,v 1.3 2007/04/21 23:06:46 duin Exp $function [w,r] = featself(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)    prwarning(3,'no tuning set supplied (risk of overfit)');    t = [];  end	if (nargin < 5)		fid = [];	end		if nargin == 0 | isempty(a)		% Create an empty mapping:		w = mapping(mfilename,{crit,ksel,t});	else		prprogress(fid,'\nfeatself : Forward Feature Selection')		[w,r] = featsellr(a,crit,ksel,1,0,t,fid);		prprogress(fid,'featself  finished\n')	end	w = setname(w,'Forward FeatSel');return