%CLEVALF Classifier evaluation (feature size curve)% %   E = CLEVALF(A,CLASSF,FEATSIZES,LEARNSIZE,N,T,FID)% % INPUT%   A          Training dataset.%   CLASSF     The untrained classifier to be tested.%   FEATSIZES  Vector of feature sizes (default: all sizes)%   LEARNSIZE  Number of objects/fraction of training set size%   N          Number of repetitions (default: 1)%   T          Independent test dataset (optional)%   FID        File descriptor for progress report file (default: 0)%% OUTPUT%   E          Structure with results%              See PLOTR for a description%% DESCRIPTION% Generates at random for all feature sizes stored in FEATSIZES training% sets of the given LEARNSIZE out of the dataset A.  See GENDAT for the% interpretation of LEARNSIZE.  These are used for training the untrained% classifier CLASSF.  The result is tested by all unused ojects of A, or,% if given, by the test dataset T. This is repeated N times. If no testset% is given and if LEARNSIZE is not given or empty, the training set is% bootstrapped. If a testset is given, the default training set size is % the entire training set. Default FEATSIZES: all feature sizes.  % The mean erors are stored in E. The observed standard deviations are % stored in S.% % This function uses the rand random generator and thereby reproduces only% if its seed is saved and reset.% Progress is reported in file FID, default FID=0: no report.% Use FID=1 for report in the command windo% % See also MAPPINGS, DATASETS, CLEVAL, CLEVALB, TESTC, PLOTR, 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: clevalf.m,v 1.10 2005/02/01 16:00:58 duin Exp $function e = clevalf(a,classf,featsizes,learnsize,n,Tset,fid)		prtrace(mfilename);		[m,k] = size(a);	if nargin < 7, fid = 0; end;	if nargin < 6, Tset = []; end	if nargin < 5, n = 1; end;	if nargin < 4, learnsize = []; end	if nargin < 3 | isempty(featsizes), featsizes = [1:k]; end		if isdataset(classf) & ismapping(a) % correct for old order		dd = a; a = classf; classf = {dd};	end	if isdataset(classf) & iscell(a) & ismapping(a{1}) % correct for old order		dd = a; a = classf; classf = dd;	end		if ~iscell(classf), classf = {classf}; end	isdataset(a);	ismapping(classf{1});	if ~isempty(Tset), isdataset(Tset); T = Tset; end	[m,k,c] = getsize(a);	featsizes(find(featsizes > k)) = [];	featsizes = featsizes(:)';	if length(learnsize) > 1		error('Learnsize should be scalar')	end		r = length(classf(:));	e.error = zeros(r,length(featsizes));	e.std   = zeros(r,length(featsizes));	e.xvalues = featsizes;	e.n = n;	datname = getname(a);	if ~isempty(datname)		e.title = ['Feature curve for ' getname(a)];	end	e.xlabel= 'Feature size';	if n > 1		e.ylabel= ['Averaged error (' num2str(n) ' experiments)'];	else		e.ylabel = 'Error';	end	if featsizes(end)/featsizes(1) > 20		e.plot = 'semilogx';	end	e.names = [];	prprogress(fid,['\nclevalf: classifier evaluation (feature curve): \n' ...		     '    %i classifiers, %i repetitions, %i featuresizes ['],r,n,length(featsizes));	fprintf(fid,' %i ',featsizes)	fprintf(fid,']\n  ');	e1 = zeros(n,length(featsizes));	seed = rand('state');	% loop over all classifiers		for q = 1:r		isuntrained(classf{q});		name = getname(classf{q});		prprogress(fid,'classifier: %s\n  ',name);		e.names = char(e.names,name);		e1 = zeros(n,length(featsizes));		rand('state',seed);  % take care that classifiers use same training set		seed2 = rand('state');		for i = 1:n			rand('state',seed2);			if isempty(Tset)				[b,T] = gendat(a,learnsize);			elseif ~isempty(learnsize)				b = gendat(a,learnsize);			else				b = a;			end			seed2 = rand('state');			for j=1:length(featsizes)				f = featsizes(j);				e1(i,j) = T(:,1:f)*(b(:,1:f)*classf{q})*testc;				prprogress(fid,'.');			end			prprogress(fid,'\n  ');		end		e.error(q,:) = mean(e1,1);		if n == 1			e.std(q,:) = zeros(1,size(e.std,2));		else			e.std(q,:) = std(e1)/sqrt(n);		end	end	prprogress(fid,'\b\bclevalf finished\n')	e.names(1,:) = [];		return