parzen_dd.m

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%PARZEN_DD Parzen data description.
% 
%       W = parzen_dd(A,fracrej)
% 
% Fit a Parzen density on dataset A. The threshold is put such that
% fracrej of the target objects is rejected.
% 
%       W = parzen_dd(A,fracrej,h)
% 
% If the width parameter is known, it can be given as third parameter.
% 
% See also datasets, mappings, dd_roc

% Copyright: D. Tax, R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
  
function [W,out,h] = parzen_dd(a,fracrej,h)

if nargin < 2 | isempty(fracrej), fracrej = 0.05; end
if nargin < 1 | isempty(a) 
  W = mapping(mfilename,{fracrej});
  return
end

if isa(fracrej,'double')           %training

  if isa(a,'mapping')
    error('I need a dataset to train');
  end
  a = target_class(a);     % make sure a is an OC dataset
  [nlab,lablist,m,k,c] = dataset(a);

  % train it:
  if nargin<3
    h = parzenml(a);
  end
  w = parzenc(a,h);

  % obtain the threshold:
  d = +p_map(a,w);
  thr = threshold(d,fracrej);

  %and save all useful data:
  W.w = w;
  W.threshold = thr;
  W = mapping(mfilename,W,str2mat('target','outlier'),k,c);
  out = h;

else                               %testing

  [W,classlist,type,k,c] = mapping(fracrej);  % unpack
  [nlab,lablist,m,k,c,p] = dataset(a);

  %compute:
  out = +p_map(a,W.w);
  newout = [out, ones(m,1)*W.threshold];

  W = dataset(newout,getlab(a),classlist,p,lablist);
end
return