% DD_EX1%% Example of the creation of a One-Class problem, and the solutions% obtained by the Nearest Neighbor Data Description and the Support% Vector Data Description. Furthermore, the ROC curve is plotted and% the AUC-error and F1-performance is computed.% 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% generate normal classification problem: train data:a = gendatb([30 30]);% make the second class the target class and change the labels:a = oc_set(a,'1');% only use target class:a = target_class(a);% generate test data:b = oc_set(gendatb(200),'1');% first show a 2D plot:figure(1); clf; hold on; H = [0;0];h = scatterd(a);V = axis; axis(1.5*V);% train the individual data descriptions and plot them% the error on the target class:fracrej = 0.2;% train the nndd:w1 = nndd(a,fracrej);% and plot the decision boundary:h = plotc(w1,'k-');H(1) = h(1);% second, train the svdd:w2 = svdd(a,fracrej,5);% and also plot this:h = plotc(w2,'r--');H(2) = h(1);legend(H,'NNdd','SVDD');axis equal;axis image;% second show the ROC curves:figure(2); clf;hold on; H=[0;0];% the ROC for the Nearest Neighbor method:e1 = dd_roc(b,w1);% plot the ROC curve:h = plotroc(e1,'k-'); H(1) = h(1);% the area under the ROC curve:auc_nn = dd_auc(e1)% and the f_1 performance:f1_nn = b*w1*dd_f1% the ROC for the SVDD:e2 = dd_roc(b,w2);% also plot this:h = plotroc(e2,'r--'); H(2) = h(1);% and compute the area under the ROC:auc_svdd = dd_auc(e2)% and the f1 measure:f1_svdd = b*w2*dd_f1;legend(H,'NNdd','SVDD');