%LOGDENS Force density based classifiers to use log-densities
%
% V = LOGDENS(W)
% V = W*LOGDENS
%
% INPUT
% W Density based trained classifier
%
% OUTPUT
% V Log-density based trained classifier
%
% DESCRIPTION
% Density based classifiers suffer from a low numeric accuracy in the tails
% of the distributions. Especially for overtrained or high dimensional
% classifiers this may cause zero-density estimates for many test samples,
% resulting in a bad performance. This can be avoided by computing
% log-densities offered by this routine. This works for all classifiers
% based on normal distributions (e.g. LDC, QDC, MOGC) and Parzen estimates
% (PARZENC, PARZENDC). The computation of log-densities is , in order to be
% effective, combined with a normalisation, resulting in posterior
% distributions. As a consequence, the possibility to output densities is
% lost.
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% MAPPINGS, LDC, UDC, QDC, MOGC, PARZENC, PARZENDC, NORMAL_MAP, PARZEN_MAP,
% CLASSC
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function v = logdens(w)
if (nargin < 1 | isempty(w))
v = prmapping(mfilename,'combiner');
return
else
mfile = getmapping_file(w);
if issequential(w)
wdata = getdata(w);
wdata{end} = feval(mfilename,wdata{end});
v = setdata(w,wdata);
elseif isuntrained(w)
v = prmapping('sequential','untrained',{w,logdens});
else
if (~strcmp(mfile,'normal_map') & ~strcmp(mfile,'parzen_map'))
error('LOGDENS can only be applied to normal densities and Parzen estimators')
end
v = classc(w);
end
end
return