gauss_dd.m

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%GAUSS_DD Gaussian data description.
% 
%       W = gauss_dd(A,fracrej,r)
% 
% Fit a Gaussian density on dataset A. If requested, the r can be
% given to add some regularization to the estimated covariance matrix:
% sig_new = (1-r)*sig + r*eye(dim). Default r = 0.1!!!!! (might be
% dangerous!)
% 
% 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] = gauss_dd(a,fracrej,r)

if nargin < 3, r = 0.1; end
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,'dataset')              %train on training set
    error('I need a dataset to train');
  end
  a = target_class(a);     % only use the target class
  [nlab,lablist,m,k,c,prob,featlist] = dataset(a);

  % train it:
  [mu,sig] = meancov(a);
  sig = (1-r)*sig + r*eye(k);

  % obtain the threshold:
  p = gausspdf(a,+mu,sig);
  thr = threshold(p,fracrej);

  %and save all useful data:
  W.m = +mu;
  W.s = sig;
  W.threshold = thr;
%  W = {+mu,sig,thr,mean(d)};
  W = mapping(mfilename,W,str2mat('target','outlier'),k,c);

else                               %testing

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

  %compute, target class > 0:
  newout = [gausspdf(a,W.m,W.s) ones(m,1)*W.threshold];

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