%PARZEN_MAP Map a dataset on a Parzen densities based classifier% % 	F = PARZEN_MAP(A,W)%% INPUT%   A   Dataset%   W   Trained Parzen classifier mapping (default: PARZENC(A))%% OUTPUT%   F   Mapped dataset%% DESCRIPTION % Maps the dataset A by the Parzen density based classfier W. F*sigm are the% posterior probabilities. W should be trained by a classifier like PARZENC.% This routine is called automatically to solve A*W, if W is trained by% PARZENC.% % The global PRMEMORY is read for the maximum size of the internally declared % matrix, default inf.% % SEE ALSO% MAPPINGS, DATASETS, PARZENC, TESTP% 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: parzen_map.m,v 1.14 2006/03/07 16:15:54 duin Exp $function f = parzen_map(b,w)	prtrace(mfilename);	% If no mapping is supplied, train one.	if (nargin < 2)		w = parzenc(b); 	end	pars = getdata(w);	a = pars{1}; 								% Stored training dataset.	h = pars{2}; 								% Smoothing paramater.	[m,k,c] = getsize(a); nlab = getnlab(a); p = getprior(a);	% do we have priors set in the mapping?	if length(pars)==3	   p=pars{3};	end		% Prepare a matrix with smoothing parameters for each class and feature.	if (size(h,1) == 1)		h = repmat(h,c,1);	end	if (size(h,2) == 1)		h = repmat(h,1,k);	end	if (any(size(h) ~= [c,k]))		error('The size of the array with smoothing parameters does not match that of the training set of W.');	end	[mt,kt] = size(b);	if (kt ~= k)		error('The size of the set A does not match that of the training set of W.'); 	end	[num,n] = prmem(mt,m);	f = ones(mt,c); 					% Prob. densities for each test sample and class.	for j = 0:num-1						% Process each chunk of the test set separately.		if (j == num-1)			nn = mt - num*n + n;	% Last chunk may have smaller size.		else			nn = n;		end		range = [j*n+1:j*n+nn];		% Estimate the class probabilities.		for i = 1:c						if islabtype(a,'crisp')				I = findnlab(a,i); hh = h(i,:);		% Parameters for this class.				% Calculate squared distances to kernel centers.				D = +distm(a(I,:)./repmat(hh,length(I),1), ...                 b(range,:)./repmat(hh,length(range),1));				if (length(I) > 0)					f(range,i) = mean(exp(-D*0.5),1)';			% Apply kernel.				end				const = repmat(-log(length(I)),length(I),1);			else								hh = h(i,:);				I = find(a.targets(:,i) > 0); % Avoid objects with zero weight				v = a.targets(I,i);				D = distm(+a(I,:)./repmat(hh,length(I),1), ...                 +b(range,:)./repmat(hh,length(range),1));				f(range,i) = exp(-D'*0.5) * v / sum(v);				const = log(v/sum(v));			end			% Normalize and multiply by class prior to get a density. Add REALMIN			% to prevent division-by-zero errors.			f(range,i) = p(i)*f(range,i)/((sqrt(2*pi).^k)*prod(hh)+realmin);			if (getout_conv(w) == 2) % take log of density to preserve tails				J = find(f(range,i) <= 1e-303);				N = find(f(range,i) >  1e-303);				f(range(N),i) = log(f(range(N),i));				[dm,R] = min(D(:,J)); % for zero densities use nearest neighbor only				f(range(J),i) = log(p(i)/((sqrt(2*pi).^k)*prod(hh)+realmin)) - dm'*0.5 + const(R);				% in case of soft labels this is tricki. We just hope that the				% weight of the nearest neighbor is sufficiently large			end		end	end	if (getout_conv(w) == 2) % scale to gain accuracy in the tails		fmax = max(f,[],2);		f = f - repmat(fmax,1,c);		f = exp(f);	else		f = f + realmin; % avoid devision by 0 in computing posterios later	end		f = setdata(b,f,getlabels(w));return