%PKLIBSVC Automatic radial basis SVM, using nu_libsvc and the Parzen kernel
%
% W = PKLIBSVC(A,ALF)
% W = A*PKLIBSVC([],A)
% W = A*PKLIBSVC(A)
%
% INPUT
% A Dataset
% ALF Parameter, default 1
%
% OUTPUT
% W Mapping: radial basis support vector classiifer
%
% DESCRIPTION
% This routine provides a radial basis support vector classifier based on
% NULIBSVC (which estimates NU using the leave-one-out 1NN error) and
% estimates the kernel width SIGMA by the the value found by PARZENC. The
% kernel width used is ALF*3*SQRT(2)*SIGMA. This is much faster than the
% gridsearch used by RBLIBSVC and performs about equal.
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, MAPPINGS, LIBSVC, NULIBSVC, RBLIBSVC, PARZENC
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
function w = pklibsvc(varargin)
checktoolbox('libsvm');
mapname = 'PK-LIBSVM';
argin = shiftargin(varargin,'scalar');
argin = setdefaults(argin,[],1);
if mapping_task(argin,'definition')
w = define_mapping(argin,'untrained',mapname);
elseif mapping_task(argin,'training') % Train a mapping.
[a,alf] = deal(argin{:});
if size(a,1) <= 1000
[v,sig] = parzenc(a);
else
[v,sig] = parzenc(gendat(a,1000));
end
w = nulibsvc(a,proxm([],'r',alf*3*sig*sqrt(2)));
end