%LPDD Linear programming distance data description%%         W = LPDD(X,NU,S,DTYPE,P)% % One-class classifier put into a linear programming framework. From% the data X the distance matrix is computed (using distance DTYPE,% see myproxm for the possibilities). The distances are then% transformed using a sigmoidal transformation (with parameter S,% see the function dissim) and on this the linear machine is% trained. The parameter NU gives the possible error on the target% class.%% This function is basically a wrapper around dlpdd. See dd_ex2 to% see how it works.%% See also: myproxm, dissim, dlpdd, dd_ex2%%@inproceedings{Pekalska2002,%	author = {Pekalska, E. and Tax, D.M.J. and Duin, R.P.W.},%	title = {One-class {LP} classifier for dissimilarity representations},%	booktitle = {Advances in Neural Information Processing Systems},%	year = {2003},%	pages = {},%  editor =       {S.~Becker and S.~Thrun and K.~Obermayer},%  volume =       {15},%  publisher = {MIT Press: Cambridge, MA}%}% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlandsfunction W = lpdd(x,nu,s,dtype,par)% first set up the parametersif nargin < 5 | isempty(par), par = 2; endif nargin < 4 | isempty(dtype), dtype = 'd'; endif nargin < 3 | isempty(s), s = 1; endif nargin < 2 | isempty(nu), nu = 0.05; endif nargin < 1 | isempty(x) % empty	W = mapping(mfilename,{nu,s,dtype,par});	W = setname(W,'Linear Programming Distance-data description');	returnend% trainingif ~ismapping(nu)	% Use all different methods:	% First define the distance mapping:	wd = myproxm(x,dtype,par);	% Second the distance transformation:	ws = dissim([],'d',s);	% And finally do the real work in dlpdd:	w = dlpdd(x*wd*ws,nu);	% store the results	W.wd = wd;	W.ws = ws;	W.w = w;  	% Also set the s explicitly, useful for inspection purposes:	ww = +ws;	W.s = +ww{2};	% Because I promised that all the OCCs have a threshold, it	% should be given here:	ww = +w;	W.threshold = ww.threshold; 	W = mapping(mfilename,'trained',W,str2mat('target','outlier'),size(x,2),2);	W = setname(W,'Linear Programming Distance-dd');else                               %testing	W = getdata(nu);  % unpack	% and here we go:	newout = x*W.wd*W.ws*W.w;	% Copy the output of the dlpdd:	W = setdat(x,newout,nu);	W = setfeatdom(W,{[-inf 0] [-inf 0]});endreturn