function [X,Y1]=experiment_moon(X,Y,XT,YT,method,q,s);% 2 Moons Experiment% Author: Vikas Sindhwani (vikass@cs.uchicago.edu)options=ml_options('gamma_A',0.1, 'NN',6, 'Kernel','rbf','KernelParam',0.35); %q=-1:5;if nargin==6 % perform a search over lambda1 and lambda2    lambda1=2.^q;    lambda2=2.^q;else % interpret q as lambda1 and s as lambda2    lambda1=q;    lambda2=s;    end% best SVMmin_err=100;if strcmp(method,'svm') | strcmp(method,'tsvm')  strcmp(method,'rlsc') % optimize over just 1 parameterfor i=1:length(q)    options.gamma_A=lambda1(i);    options.gamma_I=0;    classifier=ml_train(X,Y,options, method);    [f,labels,error]=ml_test(classifier,XT,YT);    [lambda1(i) error min_err]    if error < min_err         min_err=error;         best_classifier=classifier;    end    end   else % optimize over both       for i=1:length(q)      for j=1:length(q)            options.gamma_A=lambda1(i);            options.gamma_I=lambda2(j);            classifier=ml_train(X,Y,options, method);            [f,labels,error]=ml_test(classifier,XT,YT);            [lambda1(i) error min_err]    if error < min_err         min_err=error;         best_classifier=classifier;    endend endendlab=find(Y); xmin=min(X(:,1)); ymin=min(X(:,2)); rmin=min(xmin,ymin)-0.2;xmax=max(X(:,1)); ymax=max(X(:,2)); rmax=max(xmax,ymax)+0.2;steps=(rmax-rmin)/100;xrange=rmin:steps:rmax;yrange=rmin:steps:rmax;plotclassifiers(best_classifier, xrange, yrange);               % i=0; j=0; % % for lgl2=1:nq%  i=i+1; j=0;%  %     for lgl1=1:nq%  j=j+1;%  p=p+1;       % options.lambda1=10^(q(lgl1)); % options.lambda2=10^(q(lgl2)); % classifier=Train(X,Y1,options,method);% [fT,labels,error_rate]=Test(classifier, XT,YT);% subplot(nq,nq,p); plotclassifiers(XT,YT,classifier);plot2D(X(lab,:),Y(lab),6);% e(i,j)=error_rate;% % end% end