%TESTC Test classifier, simple test routine%%   [E,F] = TESTC(A*W)%	  [E,F] = TESTC(A,W)%	  E     = A*W*TESTC%% INPUT%   A  Dataset%   W  Trained classifier mapping% % OUTPUT%   E  Estimated error rate%   F  Number of erroneously classified objects per class% % DESCRIPTION% An error estimate E is calculated for the classifier W using dataset A,% by counting the number of errors. Prior probabilities in A are used to% weight the class error contributions. Numbers of erroneously classified% objects per class are returned in F. It is also possible to supply cell% array of datasets {A*W}, an N x 1 cell array of datasets {A} and an N x M% cell array of mappings {W}.%% For soft labelled test sets A the error contribution of a single object is% the difference between one and the soft label value for the assigned class.% Soft label values of test objects for other classes are not taken into% account.%% EXAMPLES% See PREX_PLOTC.%% SEE ALSO% MAPPINGS, DATASETS% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands% $Id: testc.m,v 1.15 2007/02/28 12:16:23 duin Exp $function [errors,class_errors] = testc(a,w)	prtrace(mfilename);	if (nargin == 0) | (isempty(a))		% No input arguments given: return mapping information.		errs       = mapping(mfilename,'fixed');		class_errs = [];	elseif (nargin == 1)		% If there's a single output argument, assume it's a (cell array of) 		% dataset which has already been mapped by the classifier (A*W), so just		% apply LABELD.		if (iscell(a))  		% If this single argument is a cell array, recursively call this  		% function to get errors for all elements in the cell array.			errs       = zeros(size(a));			class_errs = cell(size(a));			for j1 = 1:size(a,1)				for j2 = 1:size(a,2)					[errs(j1,j2),class_errs{j1,j2}] = feval(mfilename,a{j1,j2});				end			end		else					% Assert that A is a dataset with the right kind of labels.			isdataset(a); islabtype(a,'crisp','soft');			[m,k,c] = getsize(a); p = getprior(a); labtype = getlabtype(a);			% Find the labels.			labout= labeld(a);			class_errs = zeros(1,c); errs = 0;			switch (labtype)			 	case 'crisp'  				% Crisp labels: count number of different labels to find error.			  	labin = getlabels(a);			  	for j=1:c				  	J = findnlab(a,j);						if (~isempty(J))					  	class_errs(j) = nlabcmp(labin(J,:),labout(J,:));						elseif ~isempty(a.prior)							prwarning(2,'Empty class in testset found')							class_errs(j) = 0;						end						if isempty(a.prior)							errs = errs + class_errs(j);						elseif (~isempty(J))					  	errs = errs + p(j) * class_errs(j) / length(J);						end				  end					if isempty(a.prior)						errs = errs / m;					end									case 'soft'					 % Soft labels: measure errors w.r.t. the targets.			  	targets = getlabels(a);				  lablist = getlablist(a);				  nlab = renumlab(labout,lablist);				  K = (nlab-1)*m+[1:m]';			  	for j = 1:c					  J = findnlab(a,j);						if (~isempty(J))							class_errs(j) = (1-mean(targets(K(J))));						elseif ~isempty(a.prior)							error('Empty class in testset found')						end						if isempty(a.prior)							errs = errs + class_errs(j);						else					  	errs = errs + p(j) * class_errs(j);						end				  end					if isempty(a.prior)						errs = errs / m;					end				otherwise					error(['Wrong label type for testing classifiers: ' labtype])			end		end	elseif (iscell(a)) | (iscell(w))				% If there are two input arguments and either of them is a cell array,		% recursively call this function on each of the cells.		% Non-cell array inputs are turned into 1 x 1 cell arrays.		if (~iscell(a)), a = {a}; end		if (~iscell(w)), w = {w}; end		if (min(size(a) > 1))			error('2D cell arrays of datasets not supported')		end		% Check whether the number of datasets matches the number of mappings.		if (length(a) == 1)			a = repmat(a,size(w,1));		elseif (min(size(w)) == 1 & length(a) ~= length(w)) | ...			     (min(size(w))  > 1 & length(a) ~= size(w,1))			error('Number of datasets does not match cell array size of classifiers.')		end		% Now recursively call this function for each combination of dataset		% A{I} and mapping W{I,J}.		errs = zeros(size(w)); 		class_errs = cell(size(w));		for i=1:size(w,1)			for j=1:size(w,2)				[errs(i,j),class_errs{i,j}] = feval(mfilename,a{i}*w{i,j});			end		end	else		% Assert that the second argument is a trained mapping, and call		% this function on the mapped data.		ismapping(w); istrained(w);		[errs,class_errs]= feval(mfilename,a*w);	end	% If there are no output arguments, display the error(s) calculated.	% Otherwise, copy the calculated errors to the output arguments.	if (nargout == 0) & (nargin > 0)		if (iscell(a))			if (nargin == 1)				for j1 = 1:size(a,1)					for j2 = 1:size(a,2)						disp(['Mean classification error on ' ...						      num2str(size(a{j1,j2},1)) ' test objects: ' num2str(errs(j1,j2))]);					end				end			else				fprintf('\n  Test results result for');				if (size(errs,2) > 1)					disperror(a,w(1,:),errs);				else					disperror(a,w,errs);				end			end		else			if (nargin == 1)				disp(['Mean classification error on ' num2str(size(a,1)) ' test objects: ' num2str(errs)])			else				fprintf(' %s',getname(w,20));				fprintf(' %5.3f',errs);				fprintf('\n');			end		end	else		errors       = errs;		class_errors = class_errs;	endreturn