mog_dd.m

数据挖掘的工具箱,最新版的,希望对做这方面研究的人有用

%MOG_DD Mixture of Gaussians data description
%
%       W = MOG_DD(A,FRACREJ,N)
%
% Training of a mixture of Gaussians, with N Gaussians.
%
% The algorithm was inspired from NetLab, Bishop but changed very much.
% For a given number of gaussians, the EM procedure optimizes the size and
% the place of the gaussians.
%
%       W = MOG_DD(A,FRACREJ,N,CTYPE)
%
% By setting ctype, the covariance structure of the covariance can be
% set. There are three possibilities:
%      CTYPE = 'sphr'  : diagonal cov. matrix with equal values
%      CTYPE = 'diag'  : diagonal cov. matrix
%      CTYPE = 'full'  : full cov. matrix
%
%
% Required functions: mogEM and mogP

% Copyright: D.M.J. 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 = mog_dd(a,fracrej,n,ctype,reg,numiters)

if (nargin<6)
	numiters = 25;
end
if (nargin<5)
	reg = 0.1;
end
if (nargin<4)
	ctype = 'sphr';
end
if (nargin<3)
	n = 5;
end
if (nargin<2)
	fracrej = 0.05;
end
if (nargin<1)|isempty(a)
	W = mapping(mfilename,{fracrej,n,ctype,reg,numiters});
	W = setname(W,'Mixture of Gaussians');
	return
end

if isa(fracrej,'double')           %training

	a = +target_class(a);     % only use the target class
	[m,k] = size(a);

	% Train it
	[means, covs, priors] = mogEM(a, n, ctype, reg, numiters);

	% Obtain the threshold on the training set:
	d = sum(mogP(a,means,covs,priors),2);
	thr = dd_threshold(d,fracrej);

	%  and save all useful data:
	W.m = means;
	W.c = covs;
	W.p = priors;
	W.threshold = thr;
	W = mapping(mfilename,'trained',W,str2mat('target','outlier'),k,2);
	W = setname(W,'Mixture of Gaussians');

else                               %testing

	W = getdata(fracrej);  % unpack
	m = size(a,1);

	%compute:
	out = sum(mogP(+a,W.m,W.c,W.p),2);
	newout = [out repmat(W.threshold,m,1)];

	W = setdat(a,newout,fracrej);

end

return