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diff --git a/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m b/sourcecodes/bnt-master/SLP/misc/classification_evaluation.m
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+function [ratio, ratiominus, ratioplus, proba_post, yt] = classification_evaluation(bnet, BDT, class)
+% Computes the classification ratio of a bnet structure on a test dataset BDT
+% [ratio ratiominus ratioplus] = classification_evaluation(bnet, BDT, class)
+% 
+% [ratiominus rationplus] is the 95 percent confident interval.
+% results are in percentage [0 100].
+%
+%  francois.olivier.c.h@gmail.com
+%
+
+    [proba_post,engine] = inference(bnet, mat_to_bnt(BDT), class);
+               [tmp yt] = max(proba_post, [],2);
+                  [N L] = size(BDT);
+                  count = length(find(BDT(class,:)==yt'));
+                  ratio = 100*count/L;
+[ratiominus, ratioplus] = confiance(ratio,L);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function [I, J] = confiance(t, N)
+% Compute the 95 percent confident interval
+%
+% see Y. Bennani and F. Bossaert, 
+%     Predictive neural networks for traffic disturbance detection in the telephone network
+%     In Proceedings of IMACS-CESA 1996, Lille, France.
+
+Z    = 1.96; % this value for the 95 percent confident interval
+T    = t/100;
+tmp  = (Z*Z)/N;
+D    = 1+tmp;
+N1   = T+tmp/2;
+tmp2 = T*(1-T)/N + tmp/(4*N);
+N2   = Z*sqrt(tmp2);
+I    = 100*(N1-N2)/D;
+J    = 100*(N1+N2)/D;