function [alpha,b]=lapsvm(K,Y,L,lambda1,lambda2)parameters =  [4 1 1 0 1 40.00 0.001 0 0.5 0.1 1] ;I=eye(size(K,1));lab=find(Y);l=length(lab);    if isempty(L) | lambda2==0        G=I/(2*lambda1);    else         G=(2*lambda1*I + 2*lambda2*L*K)\I;    end        Gram=K*G;    Gram=Gram(lab,lab);    Ylab=Y(find(Y==-1 | Y==1));    [betay, svs, b, nsv, nlab] = mexGramSVMTrain(Gram', Ylab', parameters);    if nlab(1)==-1    	betay=-betay;    end    Betay=zeros(l,1);    Betay(svs+1)=betay';    alpha=G(:,lab)*Betay;    %alpha=G*K(:,lab)*Betay;    %disp('lapsvm: b set to median K alpha (balanced datasets)');    %b=median(K*alpha); % for the time being we assume balanced datasets    b=0;