%SVC Support Vector Classifier% % 	[W,J] = SVC(A,TYPE,PAR,C)%% INPUT%   A	    Dataset%   TYPE  Type of the kernel (optional; default: 'p')%   PAR   Kernel parameter (optional; default: 1)%   C     Regularization parameter (optional; default: 1)%% OUTPUT%   W     Mapping: Support Vector Classifier%   J     Object identifiers of support objects		%% DESCRIPTION% Optimizes a support vector classifier for the dataset A by % quadratic programming. The classifier can be of one of the types % as defined by PROXM. Default is linear (TYPE = 'p', PAR = 1). In J % the identifiers of the support objects in A are returned. C < 1 allows% for more class overlap. Default C = 1.% % See also MAPPINGS, DATASETS, PROXM% Copyright: D. de Ridder, D. Tax, R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands  % $Id: svc.m,v 1.12 2004/12/20 17:51:27 duin Exp $function [W,J] = svc(a,type,par,C)		prtrace(mfilename);if nargin < 4 | isempty(C)	C = 1;	prwarning(3,'Regularization parameter C set to 1\n');endif nargin < 3 | isempty(par)	par = 1;	prwarning(3,'Kernel parameter par set to 1\n');endif nargin < 2 | isempty(type)	type = 'p';	prwarning(3,'Polynomial kernel type is used\n');endif nargin < 1 | isempty(a)	W = mapping(mfilename,{type,par,C});	W = setname(W,'Support Vector Classifier');	return;endif ~isa(type,'mapping') % training		islabtype(a,'crisp');	isvaldset(a,1,2); % at least 1 object per class, 2 classes	[m,k,c] = getsize(a);	nlab = getnlab(a);		% The SVC is basically a 2-class classifier. More classes are	% handled by mclassc.	if c == 2   % two-class classifier			% Compute the parameters for the optimization:		y = 3 - 2*nlab;		u = mean(a);		a = a -ones(m,1)*u;		K = a*proxm(a,type,par);		% Perform the optimization:		K = min(K,K'); % make sure kernel is symmetric		[v,J] = svo(+K,y,C);		% Store the results:		W = mapping(mfilename,'trained',{u,a(J,:),v,type,par},getlablist(a),k,2);		%W = cnormc(W,a);		W = setname(W,'Support Vector Classifier');		W = setcost(W,a);		J = a.ident(J);			else   % multi-class classifier:				[W,J] = mclassc(a,mapping(mfilename,{type,par,C}));			endelse % execution	w = +type;	m = size(a,1);		% The first parameter w{1} stores the mean of the dataset. When it	% is supplied, remove it from the dataset to improve the numerical	% precision. Then compute the kernel matrix using proxm:	if isempty(w{1})		d = a*proxm(w{2},w{4},w{5});	else		d = (a-ones(m,1)*w{1})*proxm(w{2},w{4},w{5});	end	% Data is mapped by the kernel, now we just have a linear	% classifier  w*x+b:	d = [d ones(m,1)] * w{3};   d = sigm([d -d]);	W = setdat(a,d,type);	end	return;