function W = lpdd(x,nu,dtype,par1,par2)% W = lpdd(x,nu,dtype,p1,p2)% % One-class classifier put into a linear programming framework. From% the data x the distance matrix is computed, and on this the linear% machine is trained. The parameter nu gives the possible error on the% target class.%% Several distances dtype can be used: see ddistm to see the% possibilities and the definition of the parameters par1 and par2. %% Copyright: D. Tax, E. Pekalska, davidt@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% first set up the parametersif nargin < 5 | isempty(par2), par2 = 1; endif nargin < 4 | isempty(par1), par1 = 2; endif nargin < 3 | isempty(dtype), dtype = 'lp'; endif nargin < 2 | isempty(nu), nu = 0.05; endif nargin < 1 | isempty(x) % empty  W = mapping('lpdd',{nu,dtype,par1,par2});  returnend% trainingif isa(nu,'double')  % do some trick to compute the distances later:  W.x = x;  [N,d_org] = size(x);  % maybe we have example outliers...  if is_ocset(x)    labx = -ones(N,1);    labx(find_target(x)) = 1;  else    labx = ones(N,1);  end%  x = +x; % no dataset please.  x = ddistm(+x,+x,dtype,par1,par2);  [N,d] = size(x);  % set up the LP problem:  f = [ones(N,1)/(nu*N); 1; zeros(d,1)];  A = [-eye(N) -labx repmat(labx,1,d).*x];  b = zeros(N,1);  Aeq = [zeros(1,N+1) ones(1,d)];  beq = 1;  lb = [zeros(N,1); -inf; zeros(d,1)];  ub = inf*ones(N+d+1,1);  % optimize, use the mex-interface if available, else the Matlab  % optimizer:  if exist('lp_solve')>0%    if ~exist('lpenv') lpenv=cplex_init; end%    [alf,y_upd_,how_upd_,p_lp]=...%      lp_solve(lpenv, f, sparse([Aeq;A]), [beq;b], lb, ub, 1, 0);    newf = [f;-1];    newA = [A +labx];    newb = [b; 0];    newAeq = [Aeq 0];    e = [0; -ones(N,1)];    [v,alf] = lp_solve(-newf,sparse([newAeq;newA]),[beq;b],e);    alf(N+1) = -(alf(N+1) + alf(end));    alf(end) = [];    alf1 = linprog(f,A,b,Aeq,beq,lb,ub);  else    alf = linprog(f,A,b,Aeq,beq,lb,ub);  end  % store the results  W.dtype = dtype;  W.par1 = par1;  W.par2 = par2;  W.rho = alf(N+1);  W.w = alf((N+2):end);  W = mapping('lpdd',W,str2mat('target','outlier'),d_org,2);else                               %testing  [W,classlist,type,k,c] = mapping(nu);  % unpack  [nlab,lablist,m,k,c,p] = dataset(x);  % and here we go:  D = ddistm(+x,W.x,W.dtype,W.par1,W.par2);  % the official output:%  out = W.rho - D*W.w;%  newout = [out -out];  % but I choose for:  newout = [-D*W.w -repmat(W.rho,m,1)];  W = dataset(newout,getlab(x),classlist,p,lablist);endreturn