source: prextra/incsvc.m @ 110

function w = incsvc(a,ktype,par,C)
%INCSVC Incremental support vector classifier
%
%     W = INCSVC(A,KTYPE,PAR,C)
%
% INPUT
%   A      Dataset
%   KTYPE  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
%
%  DESCRIPTION
%  Optimizes a support vector classifier for the dataset A by an
%  incremental procedure to perform the quadratic programming. The
%  classifier can be of one of the types as defined by PROXM. Default is
%  linear (TYPE = 'p', PAR = 1). The kernel computation is done by
%  INCKERNEL which is more lightweight than PROXM.  C < 1 allows for
%  more class overlap. Default C = 1.
%
%  See also ADD_OBJ_CL, SVC, INCKERNEL

% 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 Netherlands

if nargin < 4 | isempty(C)
        C = 1;
        prwarning(3,'Regularization parameter C set to 1\n');
end
if nargin < 3 | isempty(par)
        par = 1;
        prwarning(3,'Kernel parameter par set to 1\n');
end
if nargin < 2 | isempty(ktype)
        ktype = 'p';
        prwarning(3,'Polynomial kernel type is used\n');
end
if nargin < 1 | isempty(a)
        w = mapping(mfilename,{ktype,par,C});
        w = setname(w,'Inc. Support Vector Classifier');
        return;
end

if ~isa(ktype,'mapping') %training
        %Basic sizes::
        [N,k,c] = getsize(a);
        %kernel definition:
        kernel = 'inckernel';
        kpar.type = ktype;
        kpar.s = par;
        %setting for displaying: (I know you can program it shorter, but this is
        %more clear):
        if N>=1000
                dodisplay = 1;
        else
                dodisplay = 0;
        end

        if c==2
                % randomize the data:
                %I = randperm(N); a = a(I,:);
                a = unrandomize(a);

                % settings for the program:
                global X_incremental;
                X_incremental = +a;
                y = 3 - 2*getnlab(a);

                % here we go:
                alf = zeros(N,1);   % weights
                grad = [];          % the gradient of all seen objects
                setR = [];          % 'rest' set
                Kr = [];            % kernel matrix of the rest objects(RxS)
                setD = [];
                setS = [];
                setE = [];
                Ke = [];
                Ks = 0;
                b = 0;
                R = inf;
                tol = 1e-8;

                % startup:
                for c=1:N
                        if dodisplay & mod(c,100)==0
                                fprintf('%d/%d ',c,N);
                        end
                        dd_message(6,'ADD obj %d\n',c);
                        add_obj_cl;
%fprintf('%d [%f',c,alf(setS(1)));
%for jj=2:length(setS)
%    fprintf(' %f',alf(setS(jj)));
%end
%fprintf(']\n');
                end

                % make the classifier
                W.K = kernel;
                W.par = kpar;
                J = [setS;setE];
                W.J = J;
                W.sv = X_incremental(J,:);
                W.alf = y(J).*alf(J,:);
                W.b = b;
                w = mapping(mfilename,'trained',W,getlablist(a),k,2);
                w = setname(w,'Inc. Support Vector Classifier');

        else   % multi-class classifier:
                
                w = mclassc(a,mapping(mfilename,{ktype,par,C}));
                
        end

else   %execution
        W = +ktype;
        [n,d] = size(a);
        laba = getlab(a);
        orga = a;
        a = +a;
        global X_incremental;
        X_incremental = [W.sv; zeros(1,d)];
        nra = size(W.sv,1)+1; I = 1:nra;
        out = repmat(W.b,n,1);
        for i=1:n
                X_incremental(nra,:) = a(i,:);
                Ka = feval(W.K,W.par,nra,I);
                out(i) = out(i) + Ka(1:(nra-1))*W.alf;
        end
        newout = [out -out];
        w = setdat(orga,newout,ktype);
end

return



function a= unrandomize(a);
% Unrandomize a dataset;

[n,k,c]=getsize(a);
if c~=2
        error('I assume 2 classes');
end

nlab = getnlab(a);
I1 = find(nlab==1);
I2 = find(nlab==2);

if length(I1)<length(I2)
        cmin = length(I1);
else
        cmin = length(I2);
        tmpI = I1;
        I1 = I2;
        I2 = tmpI;
end

J=[];
J(1:2:2*cmin) = I1;
J(2:2:2*cmin) = I2(1:cmin);
J = [J,I2((cmin+1):end)'];
a = a(J,:);

return