%FEATSELF Trainable mapping for forward feature selection
%
% [W,R] = FEATSELF(A,CRIT,K,T)
% [W,R] = A*FEATSELF([],CRIT,K,T)
% [W,R] = A*FEATSELF(CRIT,K,T)
% [W,R] = FEATSELF(A,CRIT,K,N)
% [W,R] = A*FEATSELF([],CRIT,K,N)
% [W,R] = A*FEATSELF(CRIT,K,N)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', i.e. the LOO 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)
%
% 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 (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% MAPPINGS, DATASETS, FEATEVAL, FEATSELLR, FEATSEL,
% FEATSELO, FEATSELB, FEATSELI, 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: featself.m,v 1.3 2007/04/21 23:06:46 duin Exp $
function [w,r] = featself(varargin)
varargin = shiftargin(varargin,{'char','prmapping'});
argin = setdefaults(varargin,[],'NN',0,[],[]);
if mapping_task(argin,'definition')
w = define_mapping(argin,'untrained','Forward FeatSel');
return
end
[a,crit,ksel,t,fid] = deal(argin{:});
[w,r] = featsellr(a,crit,ksel,1,0,t);
return